Delivery of IL2RG mRNA via nanoparticles to enhance CD8+ T cell promotes anti-tumor effects against late-stage triple-negative breast cancer

Li, Ying; Huang, Fang; Deng, Ruoying; Jiang, Da

doi:10.1186/s12645-024-00290-2

Research
Open access
Published: 22 October 2024

Delivery of IL2RG mRNA via nanoparticles to enhance CD8⁺ T cell promotes anti-tumor effects against late-stage triple-negative breast cancer

Ying Li¹,
Fang Huang¹,
Ruoying Deng¹ &
…
Da Jiang¹

Cancer Nanotechnology volume 15, Article number: 54 (2024) Cite this article

950 Accesses
Metrics details

Abstract

Background

The manipulation of CD8⁺ T cell functions through genetic modifications presents a novel approach to cancer immunotherapy. This study aimed to explore the effects of IL2RG-overexpressing CD8⁺ T cells and assess the efficacy of delivering IL2RG mRNA via nanoparticles (NPs) to the tumor microenvironment in triple-negative breast cancer (TNBC).

Methods

Single-cell RNA sequencing (scRNA-seq) was conducted on tissue from early and late-stage TNBC, followed by a meta-analysis of six breast cancer arrays from the GEO database to identify candidate genes. CRISPR/Cas9 was employed for gene knockout and overexpression in CD8⁺ T cells, which were then analyzed using flow cytometry, ELISA, immunofluorescence, and metabolic assays. A 3D cancer cell spheroid model was used for co-culture experiments. Lipid-coated calcium-phosphate (LCP)@IL2RG NPs were synthesized and injected into TNBC mouse models.

Results

IL2RG was identified as significantly associated with late-stage TNBC. Overexpression of IL2RG in CD8⁺ T cells led to increased cell activation, cytotoxicity, and glycolysis, while knockout reduced these effects. Overexpressing IL2RG in CD8⁺ T cells co-cultured with 3D cancer spheroids resulted in enhanced apoptosis and reduced cancer cell invasion. Injection of LCP@IL2RG NPs into TNBC mouse models significantly mitigated tumor growth.

Conclusions

Overexpressing IL2RG in CD8⁺ T cells enhances tumor immunotherapy. Delivering IL2RG mRNA via NPs further amplifies this effect, offering a promising strategy for the treatment of late-stage TNBC.

Background

Triple-negative breast cancer (TNBC) is a specific subtype of breast cancer distinguished by the absence of estrogen receptor (ER), progesterone receptor (PR), and overexpression or amplification of human epidermal growth factor receptor 2 (HER2). Therefore, it is commonly called “triple negative” (Remon et al. 2023). This particular subtype of breast cancer is distinguished by its aggressive biological behavior, high likelihood of recurrence and metastasis, and a shortage of well-defined molecular targets, rendering its treatment notably challenging (VandenBussche et al. 2017). In recent years, immunotherapy has emerged as a promising treatment option in cancer therapy, demonstrating encouraging efficacy across various tumor types. Nevertheless, the effectiveness of immunotherapy and the specific treatment targets for late-stage TNBC are still poorly understood (Wu et al. 2022a; Borgers et al. 2021; Shang et al. 2020).

Immune cells, particularly T cells, are widely regarded as contributors to the tumor microenvironment (Liang et al. 2019; Park et al. 2023; Downs-Canner et al. 2022). CD8⁺ T cells have garnered considerable attention because of their capacity to directly eliminate tumor cells (Ren et al. 2023; van der Leun et al. 2020; Jiang et al. 2021). However, the tumor microenvironment frequently inhibits the activity of T cells, leading to a state of T cell exhaustion and subsequently impairing their capacity to target the tumor (Bahrambeigi et al. 2019). Thus, regulating the function and status of immune cells, particularly CD8⁺ T cells, to enhance their anti-tumor efficacy is a prominent area of research in immunotherapy.

The advancement of bioinformatics technology has enabled single-cell RNA sequencing (scRNA-seq), allowing us to interpret cellular states and functions at the individual cell level (Wang et al. 2023). scRNA-seq enables a comprehensive understanding of the heterogeneity and functional status of immune cells in the tumor microenvironment, offering precise targets for immunotherapy. Additionally, meta-analysis can identify the most promising therapeutic targets from extensive biological data, thus offering valuable guidance for subsequent experimental research (Brito et al. 2020).

Nanoparticle (NP) technology, particularly in the field of medicine, has revolutionized the way drugs are delivered and diseases are treated. NPs, due to their small size and large surface area, offer unique interactions at the molecular level (Zhang et al. 2015). Their ability to encapsulate drugs and protect them from degradation while in circulation in the body makes them especially useful for targeted therapy (Zhang et al. 2016). In cancer treatment, NPs are designed to deliver drugs directly to the tumor site, thereby minimizing the impact on healthy tissues and enhancing the efficacy of the treatment (Hoshyar et al. 2016). Lipid NPs (LNPs) have emerged as one of the most effective carriers for gene therapies, including mRNA-based therapies (Zong et al. 2023). The choice of LNPs in the context of this manuscript is likely due to several advantageous properties they possess. LNPs can encapsulate mRNA molecules, protecting them from enzymatic degradation and facilitating their delivery into target cells (Wang et al. 2021). They are known for their biocompatibility and low toxicity, making them suitable for in vivo applications. Lastly, their surface can be modified to target specific cells or tissues, which is crucial in cancer therapy, where targeting tumor cells without affecting healthy cells is paramount.

The IL2RG gene encodes the common gamma chain, a component of several interleukin receptors. This gene is essential in immunotherapy due to its role in the immune system. IL2RG is involved in the signaling pathways of various interleukins critical for the growth, development, and differentiation of T cells, B cells, and natural killer cells (Walsh et al. 2017). Mutations in IL2RG can lead to severe combined immunodeficiency, highlighting its vital role in immune response (Ren et al. 2020). The focus on IL2RG in the manuscript suggests an interest in harnessing its role in immune cell function to enhance the efficacy of tumor-targeting T cells. By overexpressing IL2RG, the researchers aim to boost the immune response against cancer cells, particularly in the challenging context of TNBC (Chen et al. 2021). The use of LNPs to deliver IL2RG mRNA is a strategic choice, combining the advanced drug delivery system of NPs with the specific targeting and therapeutic potential of the IL2RG gene. This approach represents a cutting-edge integration of nanotechnology and genetic manipulation for cancer immunotherapy (Wang et al. 2018).

Considering the background above and the advantages of this technology, our study aims to investigate potential targets associated with immunotherapy in late-stage TNBC using a combination of scRNA-seq and meta-analysis. Our research focuses on CD8⁺ T cells, specifically investigating their functional alterations and potential regulatory mechanisms in late-stage TNBC. The aim is to offer novel strategies and insights for immunotherapy. This study presents novel targets for treating late-stage TNBC and shows potential for immunotherapy in other tumor types, which has scientific and clinical implications.

Results

scRNA-seq reveals seven distinct cell types in TNBC samples

We obtained clinical samples from patients with TNBC for scRNA-seq, which comprised three cases of early-stage TNBC, three cases of late-stage TNBC, and three cases of normal adjacent tissue. Seurat package was used to integrate the data and initially assessed the number of genes (nFeature_RNA), the count of mRNA molecules (nCount_RNA), and the proportion of mitochondrial genes (percent.mt) in the scRNA-seq data from all cells. The findings demonstrated that most cells exhibited nFeature_RNA values less than 5000, nCount_RNA values less than 20,000, and percent.mt values less than 20% (Figure S1A). Cells of low quality were excluded, setting criteria of 200 < nFeature_RNA < 5000 and percent.mt < 20%, resulting in an expression matrix containing 20,989 genes across 48,874 cells. The correlation analysis of sequencing depth revealed that the filtered data exhibited a correlation coefficient (r) of − 0.02 between nCount_RNA and percent.mt, as well as a correlation coefficient of 0.90 between nCount_RNA and nFeature_RNA (Figure S1B). These findings suggest that the filtered cell data is high quality and suitable for subsequent analysis.

Next, we deepen our analysis of the filtered cells by selecting genes with higher expression variance. We then choose the top 2000 genes with the highest variance for subsequent downstream analysis (Figure S1C). The CellCycleScoring function was utilized to calculate the cell cycle scores of the cells in the samples (Figure S1D). Subsequently, the data was preliminarily normalized. Next, principal component analysis (PCA) was utilized to reduce the linear dimensionality of the data, using the selected highly variable genes. This study presents the heatmap illustrating the principal components (PC, PC_1–PC_6) and their correlation with gene expression (Figure S1E). Additionally, we analyze the spatial distribution of cells in PC_1 and PC_2 (Figure S1F). The findings demonstrate the presence of a distinct batch effect among the samples.

To mitigate batch effects and enhance the accuracy of cell clustering, we conducted batch correction using the harmony package (Figure S1G). This study used ElbowPlot to conduct standard deviation sorting on the PCs. The findings revealed that PC_1–PC_20 effectively captured the information from the selected highly variable genes and possessed notable analytical implications, as shown in Figure S1H. After applying the correction method, the analysis revealed the successful elimination of batch effects among samples (Fig. 1A).

Additionally, we utilized the UMAP algorithm to nonlinearly reduce the dimensionality of the top 20 PCs. Using UMAP clustering analysis, we grouped all the cells into 22 cellular clusters (Fig. 1B, C). We utilized the Bioconductor/R package “SingleR” to automatically annotate the 22 cell clusters, resulting in the identification of 7 cell types: Epithelial cells (Cancer cells), T cells, Fibroblasts, Endothelial cells, Macrophages, Tissue stem cells, and B cells (Fig. 1D, E). Additionally, we presented the UMAP expression maps for these seven cell types, with EPCAM serving as the marker gene for Epithelial cells (including Cancer cells), CD3E as the marker gene for T cells, COL1A2 as the marker gene for Fibroblasts, KDR as the marker gene for Endothelial cells, CD68 as the marker gene for Macrophages, ITGA6 as the marker gene for Tissue stem cells, and CD79A as the marker gene for B cells (Fig. 1F).

Thus, using scRNA-seq analysis, we successfully identified seven distinct cell types.

Differential T-Cell interactions and potential immunotherapy targets identified across TNBC stages

We thoroughly analyzed the quantity and functional disparities among seven cell types in various TNBC samples. Notably, there are variations in the interactions between T cells and epithelial cells across different stages of TNBC. By conducting an intersection analysis with the immune gene database, we have identified additional differentially expressed genes (DEGs) potentially associated with late-stage TNBC treatment.

This study presents a comprehensive examination of the distribution of cellular composition among 7 different cell types across 9 individual samples. Through the application of t-tests, we conducted a comparative analysis of the variations in cell quantity between samples from normal, early-stage TNBC, and late-stage TNBC. The analysis indicates that early-stage TNBC samples exhibit a reduction in epithelial cells (cancer cells) compared to normal samples, along with a notable increase in T and B cells. In contrast to early-stage TNBC, late-stage TNBC samples exhibited a notable rise in epithelial cells and decreased T and B cell counts (Fig. 2A, B).

Moreover, to comprehend the functional variations underlying these quantitative differences, we employed the “CellChat” package in the R language to analyze the pathway activity among distinct cells. In early-stage TNBC, the communication pathway between T cells and epithelial cells (cancer cells) is enhanced compared to normal samples. However, in late-stage TNBC, this communication is weakened compared to early-stage TNBC (Fig. 2C–E).

T cells are recognized to confer a crucial role in initiating immune responses against tumor cells during the early stages of breast cancer. T cells direct immune responses through the production of cytotoxic cytokines and chemokines. Additionally, they can identify and eliminate tumor cells (Olivera et al. 2021). However, as cancer progresses, tumor cells can evade or suppress immune attacks through various mechanisms, ultimately reducing the quantity and function of T cells (Baldominos et al. 2022).

Given that our primary research goal is to identify immunotherapy targets for late-stage TNBC, we conducted a screening to identify genes with differential expression in T cells. Three hundred seventy-two genes were differentially expressed between normal samples and early-stage TNBC. Among these, 120 genes were downregulated, while 252 were upregulated (Fig. 2F). Similarly, between early and late-stage TNBC, 630 genes showed differential expression, with 210 genes being downregulated and 420 genes being upregulated (Fig. 2G).

Furthermore, we compared the two sets of DEGs mentioned earlier with the immune genes contained in the IMPORT (https://www.immport.org) and innate (https://www.innatedb.com) databases, resulting in a total of 18 genes that overlap (Fig. 2H). There are 16 genes whose expression trends are opposite in late-stage and early-stage TNBC. T cells are increased in early-stage TNBC and decreased in late-stage TNBC. These 16 genes may regulate T-cell proliferation or apoptosis (Figure S2).

We thoroughly analyzed the quantity and functional disparities among seven cell types in various TNBC samples. Notably, there are variations in the interactions between T cells and epithelial cells across different stages of TNBC. We have identified DEGs potentially relevant for treating late-stage TNBC by analyzing the immune gene database intersection.

Identification of IL2RG as a potential immunotherapy target in late-stage TNBC through comprehensive analysis and validation

To conduct a more comprehensive analysis of immune-related genes in TNBC, we incorporated six GEO datasets (GSE46581, GSE76124, GSE157284, GSE45725, GSE53752, GSE59595) and specifically identified TNBC samples that exhibited negative expression of ER, PR, and HER-2 receptors while differentiating between early and late stages. Based on the meta-analysis, a subgroup analysis was conducted on those above 16 intersecting genes in TNBC. The results revealed that seven genes exhibited expression trends different from zero, as indicated by excluding zero from their 95% confidence intervals (Table S1; Fig. 3). All of these genes were down-regulated in late-stage TNBC (Fig. 3). On the other hand, the analysis of the remaining nine genes did not yield results, as their 95% confidence intervals included zero (Figure S3).

It was determined that the between-group heterogeneity of the data for 7 genes was low, as indicated by the I2 values, all of which were less than 50%. Furthermore, we performed a sensitivity analysis on these seven genes and evaluated the data sources. The analysis results of the genes revealed no changes in the I2 values when each dataset was individually excluded (Figure S4). Thus, we deemed the meta-analysis results of these seven genes reliable.

The ROC curve is a graphical representation that illustrates the relationship between sensitivity and specificity. To some extent, it can reflect the discriminative ability of gene expression in the context of diseases. Based on our analysis results, most of the areas under the curve (AUC) for 7 genes in the ROC curves of various datasets are greater than 0.5 (Fig. 4A).

Subsequently, we utilized the String website to establish the protein-protein interaction (PPI) network for these seven genes, followed by graph generation through the employment of Cytoscape software. Interactions among these seven genes could be observed, with CD8A, IL2RG, and ITGB2 exhibiting the highest correlation (Fig. 4B).

Next, we employed RT-qPCR and Western blot techniques to analyze the expression levels of CD8A, IL2RG, and ITGB2 in both early- and late-stage TNBC tissue and in normal adjacent tissue obtained from patients with TNBC. The results indicated that CD8A, IL2RG, and ITGB2 expression levels were higher in early-stage TNBC tissue compared to normal tissue. Additionally, in late-stage TNBC tissue, only the expression level of IL2RG was lower when compared to early-stage TNBC tissue (Fig. 4C, D). The CD8A, IL2RG, and ITGB2 expression levels were determined using immunohistochemistry. The findings revealed an upregulation of CD8A, IL2RG, and ITGB2 in early-stage TNBC, while late-stage TNBC demonstrated a downregulation. Notably, IL2RG exhibited the most substantial variation (Fig. 4E, F). Additionally, the distribution of IL2RG in other cell types was examined, revealing that IL2RG was predominantly expressed in T cells and, to a lesser extent, distributed among other immune cells, such as B cells and macrophages in early TNBC (Figure S5).

Considering the information above, IL2RG has been selected as the definitive focal gene for our study. Multiple studies have demonstrated that IL-2 is a crucial factor produced following T-cell activation (Yuan et al. 2022). By binding with IL2RG, IL-2 could stimulate the proliferation and activation of T cells, thus enhancing the immune response (Kumari et al. 2023). However, the immunological effects of this treatment in late-stage TNBC are still poorly understood. Hence, IL2RG was selected as a potential immunotherapy target for late-stage TNBC in our study.

The potential immunotherapy target IL2RG for late-stage TNBC was initially identified by combining scRNA-seq with meta-analysis.

IL2RG modulation influences the activity and cytotoxicity of CD8+ T cells in TNBC

We identified the potential immunotherapy target IL2RG for late-stage TNBC through bioinformatics analysis. To examine the expression profile of IL2RG in tumor-infiltrating T cells, we employed flow cytometry to assess the abundance of IL2RG⁺ CD4⁺ T cells and IL2RG⁺ CD8⁺ T cells in both early and late-stage TNBC tumor tissues. The results demonstrated an increase in IL2RG⁺ CD4⁺ and IL2RG⁺ CD8⁺ T cells in early-stage TNBC tumor tissues compared to late-stage TNBC tumor tissues (Fig. 5A, B). In the meantime, we isolated immune cells from tumor tissues and purified them using CD4 and CD8 microbeads. We then measured the levels of IL2RG in these two types of cells using Western blot analysis. The results revealed substantial changes in the expression of IL2RG in both cell types (Fig. 5C). However, the magnitude of IL2RG expression changes was greater in CD8⁺ T cells than in CD4⁺ T cells. Hence, CD8⁺ T cells were selected as the focus for subsequent analysis.

To further elucidate the role of IL2RG in CD8⁺ T cells, we initially isolated CD8⁺ T cells from the spleen and lymph nodes of healthy mice. Subsequently, we employed CRISPR/Cas9 gene editing technology to create IL2RG-deleted CD8⁺ T cells, with IL2RG-WT as the wild-type control (Fig. 5D). The expression level of IL2RG in monoclonal cells was determined using RT-qPCR and Western blot techniques. Monoclonal cells with an expression level of 0 were chosen for further expansion in culture (Figure S6A-B). This study used lentiviruses to generate CD8⁺ T cells derived from mice that overexpressed IL2RG, which was referred to as IL2RG-UP. We assessed the transfection efficiency of these cells using RT-qPCR and Western blot techniques, as shown in Figure S6C-D.

We supplemented the culture with CD3/CD28 to induce activation of CD8⁺ T cells with varying genotypes, as this step is essential for all subsequent experiments. After 48 h, cell viability was assessed using the MTT assay, and the analysis showed a decrease in the viability of IL2RG knockout (KO) cells, whereas the viability of IL2RG overexpressing cells was enhanced (Fig. 5E).

CD8⁺ T cells can secrete granzyme B (GZMB) and interferon-gamma (IFN-gamma), thereby enhancing their cytotoxicity against tumor cells (McKenzie et al. 2006) (Fig. 5F). We detected the secretion of IFN-γ in the supernatant of different cell groups through ELISA experiments. Furthermore, we observed a decrease in the level of IFN-γ in IL2RG KO cells, while IL2RG overexpressing cells showed an increase in IFN-γ secretion (Fig. 5G). Western blot analysis was employed to quantify the expression of GZMB in CD8⁺ T cells. Furthermore, a noteworthy decrease in GZMB protein levels was observed in IL2RG KO cells, whereas an increase in GZMB expression was observed in IL2RG overexpression cells (Fig. 5H).

Flow cytometry analysis demonstrated a reduction in the IFN-γ and GZMB levels in the IL2RG KO group, whereas a substantial increase in the IFN-γ and GZMB levels was observed in the IL2RG overexpression group (Fig. 5I–K). Immunofluorescence analysis of CD8⁺ T cells revealed a decrease in the number of CD8/GZMB double-positive cells in IL2RG KO cells, whereas the number of CD8/GZMB double-positive cells increased in IL2RG overexpression cells (Fig. 5L).

In conclusion, our initial research suggests that the ablation of IL2RG could diminish the activity and cytotoxicity of CD8⁺ T cells, whereas overexpression of IL2RG could augment the activity and cytotoxicity of CD8⁺ T cells.

IL2RG drives metabolic reprogramming and enhances glycolysis in activated CD8+ T cells

The role of IL2RG in promoting glycolytic reprogramming in CD8⁺ T cells has been elucidated through comprehensive research. We have observed that IL2RG plays a crucial role in regulating the activity and cytotoxicity of CD8⁺ T cells. Metabolic reprogramming induces enduring immune activity, longevity, and functionality in T cells (Patsoukis et al. 2015). Energy supply is the predominant determinant of T-cell activity, particularly in tumor microenvironments characterized by glucose scarcity (Chang et al. 2015). To elucidate the specific effects of IL2RG on the internal composition of CD8⁺ T cells, we selected JC-1, a lipophilic cationic dye designed for the detection of mitochondrial membrane potential, as a means to quantify the mitochondrial membrane potential difference (Δψm) within CD8⁺ T cells following CD3/CD28 stimulation. The experimental results demonstrated a reduction in Δψm in the IL2RG KO group, whereas the IL2RG overexpression group exhibited an increase in Δψm (Fig. 6A, B).

Furthermore, in line with the changes observed in Δψm, the IL2RG-KO cell group exhibited a reduction in ATP levels, whereas the IL2RG-UP cell group showed a slight increase in ATP levels (Fig. 6C). We further evaluate mitochondrial quality using MitoTracker staining technology. The data indicate that the KO of IL2RG results in a notable reduction in mitochondrial quality, whereas overexpression of IL2RG improves mitochondrial quality (Fig. 6D, E). Given the importance of maintaining an appropriate reactive oxygen species (ROS) level for T cell activation, proliferation, and function, we employed the fluorescent probe DCFH-DA to measure intracellular ROS levels. The results indicate that the IL2RG-KO cell group demonstrated a reduction in ROS compared to the wild-type cells, whereas the IL2RG-UP cell group exhibited a noticeable increase in ROS (Fig. 6F-G). These data suggest that IL2RG influences the metabolic reprogramming of CD8⁺ T cells.

Glucose is the primary fuel source for ATP production in CD8⁺ T cells, facilitating this process through glycolysis and OXPHOS pathways (Zhong et al. 2022). Using the Seahorse device, we measured the oxygen consumption rate (OCR) of CD8⁺ T cells and the extracellular acidification rate (ECAR) for aerobic glycolysis in an activated state. The findings indicate that the deletion of IL2RG markedly decreases the glycolysis and OXPHOS in CD8⁺ T cells, whereas the overexpression of IL2RG enhances the glycolysis and OXPHOS in CD8⁺ T cells (Fig. 6H, I).

To investigate the energy metabolism of CD8⁺ T cells more comprehensively, we employed liquid chromatography-mass spectrometry (LC-MS) for quantitative measurements of essential metabolites in the glycolysis pathway, oxidative phosphorylation (OXPHOS), and pentose phosphate pathway. The results demonstrated that IL2RG-KO cells exhibited a reduction in metabolic activity in the mentioned pathways compared to the control group. The levels of specific metabolites decreased, including D-glucose 1-phosphate, D-glucose 6-phosphate, and α-D-ribose 5-phosphate. Conversely, the IL2RG-UP cell group exhibited enhanced metabolic activity, as shown in Fig. 6J.

In conclusion, the research has provided further confirmation of the role of IL2RG in promoting glycolytic reprogramming in CD8⁺ T cells, thereby emphasizing its significance in the activation process of these cells.

IL2RG Modulation in CD8⁺T cells impacts tumor cell proliferation, invasion, and survival in TNBC models

IL2RG regulates the function and interaction of CD8⁺ T cells with tumor cells. Our research has discovered that IL2RG can augment the activity and cytotoxicity of CD8⁺ T cells, along with promoting intracellular glycolysis. Furthermore, we proceeded to validate the influence of IL2RG on TNBC cells.

We conducted separate co-cultures of distinct groups of activated CD8⁺ T cells with 4T1 and EO771 breast cancer cell lines. Subsequently, tumor cells were isolated through flow cytometry to assess their apoptosis status and cell cycle. The results revealed that the tumor cells in the IL2RG-KO group exhibited enhanced proliferation during the cell cycle and decreased apoptosis. Conversely, the tumor cells in the IL2RG-UP group demonstrated reduced proliferation in the cell cycle and increased apoptosis (Fig. 7A–C). The proliferation ability of tumor cells was assessed using the MTT kit. The results demonstrated that IL2RG KO augmented the proliferative effect of tumor cells, whereas IL2RG overexpression suppressed this effect (Fig. 7D).

To simulate the in vivo environment more accurately, we initially constructed 3D tumor spheroids using the 4T1 and EO771 cell lines. Subsequently, we co-cultured these spheroids with CD8⁺ T cells labeled with carboxyfluorescein succinimidyl ester (CFSE). The results indicate a reduction in the infiltration rate in the IL2RG-KO group, while an increase is observed in the IL2RG-UP group (Fig. 7E), suggesting that IL2RG influences the infiltration ability of CD8⁺ T cells.

Further investigation was conducted to examine the influence of different types of CD8⁺ T cells on the invasive ability of TNBC cells. Results from the co-culture for 4 days showed an increase in the invasive spread of spheroids when IL2RG-KO CD8⁺ T cells were present. Conversely, the IL2RG-UP group reduced the invasive spread of spheroids (Fig. 7F).

To accurately evaluate the cytotoxic effects of CD8⁺ T cells in multicellular structures, we stained viable cells with calcein AM and non-viable cells with EthD-1. Microscopic observation reveals that the overexpression of IL2RG results in the death of most tumor cells. Conversely, in the IL2RG KO group, most tumor cells remain alive, further confirming the crucial role of IL2RG in regulating the killing function of CD8⁺ T cells (Fig. 7G).

In conclusion, these experimental results provide evidence that IL2RG plays a crucial regulatory role in CD8⁺ T cells, exerting an influence on the growth, invasion, and survival of tumor cells during their interaction with the tumor microenvironment.

IL2RG expression influences CD8+ T cell infiltration and distribution in TNBC Mouse models

Subsequently, we injected 4T1 and EO771 cells into the mammary fat pad of female mice. We then utilized bioluminescent imaging to observe tumor bioluminescent images 7, 12, 24, and 36 days after cell injection. The results indicate that on day 12, the tumor was primarily localized in the breast area. However, by day 36, the tumor had started spreading to other sites (Fig. 8A). Hence, we chose 12-day-old mice in early-stage TNBC and 36-day-old mice as those in late-stage TNBC. Tumor tissues were isolated from mice with early- and late-stage TNBC. The expression levels of IL2RG were evaluated using Western blot and immunohistochemistry techniques. The results demonstrated a reduction in the expression of IL2RG within the tumor tissue of late-stage TNBC tumor-bearing mice (Fig. 8B–D). Flow cytometry was utilized to analyze the presence of IL2RG⁺ CD4⁺ T cells and IL2RG⁺ CD8⁺ T cells in early- and late-stage TNBC tumor tissues, which uncovered a noteworthy reduction in IL2RG⁺ CD8⁺ T cell count in late-stage TNBC tumor-bearing mice (Fig. 8E–F). These results are consistent with our clinical sample results.

We observed the accumulation of CD8⁺ T cells within the tumor using near-infrared imaging. The results indicated a lower accumulation of cells in the IL2RG-KO group, whereas there was a substantial increase in the accumulation of CD8⁺ T cells in the IL2RG-UP group (Fig. 9A, B). We administered different groups of CD8⁺ T cells via tail vein injection. After 24 h, we removed the tumor for ex vivo measurement to observe the infiltration capability of CD8⁺ T cells in the tumor model. The results show that the IL2RG-UP group had the highest accumulation of CD8⁺ T cells, whereas the IL2RG-KO group demonstrated the lowest accumulation of CD8⁺ T cells (Fig. 9C, D).

We conducted immunofluorescence analysis to further assess the infiltration of CD8⁺ T cells. The results indicate that CD8⁺ T cells in the IL2RG-UP group are extensively dispersed in areas distant from tumor blood vessels, whereas CD8⁺ T cells in the IL2RG-KO group are sequestered around the blood vessels (Fig. 9E). The results of this study demonstrate that IL2RG enhances the infiltration capacity of CD8⁺ T cells in tumor tissue.

LCP@IL2RG enhances CD8+ T cell effects against late-stage TNBC

This study demonstrated that CD8⁺ T cells overexpressing IL2RG exhibited significant enhancements in cellular activity, cytotoxicity, and glycolysis, thereby intensifying their tumoricidal effects. To further explore the potential of IL2RG in immunotherapy, NPs were engineered as carriers (Figure S7A) to specifically deliver mRNA encoding IL2RG to CD8⁺ T cells within the tumor, confirming the NPs’ capacity to activate CD8⁺ T cells and their effectiveness in killing tumor cells. Lipid-coated calcium-phosphate (LCP) NPs were successfully utilized to encapsulate nucleic acids, and the encapsulated mRNA was prepared in oil-in-water microemulsions. The high density of polyethylene glycol (PEG) on the surface of LCP significantly enhanced the colloidal stability in vivo, thereby improving the pharmacokinetics and pharmacodynamics of the therapeutic agents (Liu et al. 2018). The aldehyde-functionalized LCP conjugated with anti-CD8 antibodies facilitated uptake by CD8⁺ T cells. Transmission electron microscopy (TEM) revealed that LCP NPs were spherical with a CaP core, displaying diameters of approximately 15 nm and 25 nm, respectively (Figure S7B-C). The hydrodynamic diameter of LCP was determined to be 58 nm with a surface charge of 38 mV (Figure S7D-E), which confirmed their stability and dispersibility. The uptake capability of LCP-NP was further confirmed using inverted fluorescence microscopy, where LCP-NPs (labeled with DiI, a red dye) were co-cultured with CD8⁺ T cells (nuclei stained with DAPI, blue fluorescence), and effective uptake by CD8⁺ T cells was observed (Figure S7F). These results substantiate the potential of LCP to enhance the cytotoxic capabilities of CD8⁺ T cells against tumor cells.

To evaluate the effect of LCP@IL2RG on late-stage TNBC tumors, the researchers injected it directly into the tumors of mice and conducted bioluminescence imaging over 14 days. The findings indicated that LCP@IL2RG effectively suppressed tumor growth in the late-stage TNBC tumor-bearing mice (Fig. 10A). On the 14th day, tumor tissues were isolated from mice, and their sizes were measured. The results demonstrated a reduction in tumor size in the LCP@IL2RG group compared to the control group (Fig. 10B-C). Tumor tissue sections from each group were subjected to H&E staining. The phosphate-buffered saline (PBS) and LCP groups displayed larger nuclei with deeper staining, clear contours, minimal tumor necrosis, and reduced intercellular material. In contrast, the LCP@IL2RG group exhibited obvious cell apoptosis (Fig. 10D).

Flow cytometry was employed to determine the quantity of IL2RG⁺ CD8⁺ T cells in tumor tissues. The results showed an increase in the count of IL2RG⁺ CD8⁺ T cells in mice tumors injected with LCP@IL2RG (Fig. 10E, F). Additionally, the production of IFN-γ and GZMB by CD8⁺ T cells was assessed, revealing a rise in the numbers of IFN-γ⁺ CD8⁺ T cells and GZMB + CD8⁺ T cells in the LCP@IL2RG group (Fig. 10G–J).

The experiments above revealed that the processing of LCP@IL2RG resulted in an enhanced immune response activity. In summary, IL2RG enhances the glycolysis of CD8⁺ T cells, thus impacting their cellular activity and cytotoxicity. This augmentation results in an increased efficacy in killing tumor cells. Consequently, IL2RG may be considered a potential immunotherapy target for late-stage TNBC.

Discussion

Immunotherapy has emerged as a significant breakthrough in modern cancer treatment, leveraging the regulation and activation of the patient’s immune system to combat TNBC with notable efficacy (Arimoto et al. 2023; Borgers et al. 2021; Shang et al. 2020). Within this domain, CD8⁺ T cells play a crucial role in the immune response, exerting cytotoxic effects on tumor cells (Zheng et al. 2021; van der Leun et al. 2020; Jiang et al. 2021). This study delves into the role and potential applications of CD8⁺ T cells overexpressing IL2RG in the immunotherapy of late-stage TNBC, laying a promising foundation for further advancements in this field (Fig. 11).

Regulating the activity of immune cells is pivotal in tumor immunotherapy (Yuan 2020). Our findings underscore the critical role of the IL2RG gene in the treatment of late-stage TNBC. Overexpression of IL2RG significantly enhances the activity and cytotoxicity of CD8⁺ T cells, rendering them more aggressive. Although the function of IL2RG in other tumor types has been documented, its role and mechanisms in late-stage TNBC represent a novel discovery of this study (Chen et al. 2021). In recent years, nanotechnology has increasingly become a new direction in cancer treatment. This research successfully utilized NPs to deliver IL2RG mRNA to CD8⁺ T cells in the tumor microenvironment, thereby enhancing their antitumor activity. Compared to traditional gene editing and delivery methods, NPs offer improved uptake and delivery efficiency while reducing side effects associated with treatment (Bai et al. 2022).

This study combined scRNA-seq with meta-analysis to identify key genes in tumor immunotherapy. This approach enhances the accuracy of gene selection and allows for a deeper exploration of intracellular gene regulatory networks, opening up new possibilities for future treatments. Beyond focusing on the role of CD8⁺ T cells in tumor therapy, this research also extensively investigated their energy metabolism pathways. It was found that overexpression and KO of IL2RG significantly impact the glycolysis and OXPHOS pathways in CD8⁺ T cells, echoing previous studies on T cell energy metabolism and offering new strategies for modulating cell function (Correnti et al. 2022). Unlike traditional two-dimensional cell culture models, the 3D cancer cell spheroid model more accurately mimics the tumor microenvironment, enhancing the biological relevance of the findings. Through this model, the study revealed significant effects of IL2RG overexpression and KO on cancer cell invasion and apoptosis.

Late-stage TNBC, a subtype of breast cancer with an unfavorable prognosis, currently lacks effective treatment strategies. This research not only demonstrated the enhanced cellular activity, cytotoxicity, and glycolysis of CD8⁺ T cells overexpressing IL2RG but also designed NPs as specific delivery systems to efficiently transport mRNA encoding IL2RG to CD8⁺ T cells in the tumor microenvironment. The application of LCP@IL2RG significantly inhibited tumor growth, providing important experimental evidence for the use of IL2RG in the treatment of TNBC.

This study combined scRNA-seq with meta-analysis to perform an in-depth analysis of late-stage TNBC and successfully identified the immune gene IL2RG associated with TNBC. This finding provides a new mechanism for the immunotherapy of TNBC and contributes valuable insights to the field of tumor immunology. The technique of delivering IL2RG mRNA via NPs addresses the low efficiency associated with traditional mRNA delivery methods and enables precise targeting of specific cells within the tumor microenvironment, offering an effective tool for cancer treatment. Advanced TNBC, characterized by a lack of clear therapeutic targets, is difficult to treat, but the methods demonstrated in this study not only prove their therapeutic efficacy but also validate their practical application in animal models, showing the potential to extend patient survival.

Despite positive results in mouse models, the applicability of this technology to humans requires further validation. The scale, complexity, and uncertainties of clinical trials far exceed those of preliminary laboratory studies. While this research primarily focused on the short-term effects of the treatment, the long-term impacts and potential side effects of delivering mRNA encoding IL2RG to CD8⁺ T cells via NPs have yet to be thoroughly explored. Additionally, the infiltration of immune cells in both early and late-stage TNBC requires more in-depth investigation. Although IL2RG showed significant relevance in this study, its applicability to all TNBC patients or patients with other types of tumors needs to be confirmed through more extensive research. Based on current findings, there is anticipation to expand this technology into larger-scale clinical trials to verify its real-world effects and safety in humans. Combining single-cell transcriptomic sequencing with other high-throughput technologies may further explore potential therapeutic targets for advanced TNBC or other tumors. Due to time and budget constraints, the factors controlling the upregulation of IL2RG in early TNBC and its subsequent decline in later stages have not been identified; further investigation into the upstream regulatory mechanisms of IL2RG will continue. The functional validation of IL2RG provides new directions for tumor immunotherapy, and future strategies could consider combining this approach with existing immunotherapeutic strategies, such as immune checkpoint inhibitors, to provide more comprehensive and effective treatment options.

Conclusion

In conclusion, this study not only provides a detailed mechanistic explanation of the role of IL2RG in immunotherapy but also offers new strategies for the delivery of nucleic acid medicines. These findings are expected to drive further advancements in the field of immunotherapy, bringing more precise and efficient treatment options to patients. While many issues remain to be addressed, the scientific and clinical significance of these discoveries is undeniable.

Methods

Clinical sample collection

TNBC7, TNBC8, and TNBC9 were derived from samples of normal adjacent breast tissue, whereas TNBC1, TNBC2, and TNBC3 were obtained from early-stage TNBC tumors, and TNBC4, TNBC5, and TNBC6 were obtained from late-stage TNBC tumors. For detailed clinical information, please refer to Table S2. The tumors and the adjacent normal breast tissues located at a minimum distance of 5 cm from the tumor were swiftly frozen in liquid nitrogen and subsequently stored in a freezer set at -80 °C. Before utilizing these clinical resources for investigation, informed consent forms were signed by all patients with TNBC. Data was collected from nine patients with TNBC who underwent surgery in The Fourth Hospital of Hebei Medical University between January 2021 and December 2022. The study was conducted under the approval of the Ethics Committee of The Fourth Hospital of Hebei Medical University and in compliance with the guidelines outlined in the Helsinki Declaration. All participating patients signed informed consent documentation.

The age range of the 9 patients included in the study was 30 to 50 years old, with a mean age of 42.1 ± 6.1. They were diagnosed with TNBC based on negative results for ER, PR, and HER2 testing (Vagia et al. 2020). These TNBC tumors are classified into early and late stages according to the tumor-node-metastasis (TNM) staging system of the American Joint Committee on Cancer (AJCC) (Edge and Compton 2010).

Tumor size and invasion (T) were characterized as follows: Tx denoted that the primary tumor was unassessable, T0 indicated an absence of primary tumor, Tis referred to carcinoma in situ, T1 represented tumors with a diameter not exceeding 2 cm, T1a corresponded to tumors greater than 0.1 cm but not exceeding 0.5 cm, T1b denoted tumors larger than 0.5 cm but not exceeding 1 cm, T1c signified tumors greater than 1 cm but not exceeding 2 cm, T2 indicated tumors larger than 2 cm but not exceeding 5 cm, T3 described tumors larger than 5 cm, and T4 represented tumors of any size with invasion of the chest wall or skin.

Lymph node involvement (N) was assessed with the following criteria: Nx signified that lymph nodes could not be detected, N0 indicated the absence of lymph node metastasis, and Grades N1, N2, and N3 were assigned based on the number, size, and location of lymph node metastasis, with higher grades indicative of more extensive metastasis.

Distant metastasis (M) was evaluated as follows: Mx suggested that distant metastasis could not be assessed, M0 indicated the absence of distant metastasis, and M1 indicated the presence of distant metastasis.

scRNA-seq

Three samples per group were collected from the aforementioned patients to obtain samples of both early- and late-stage TNBC tumors and adjacent normal tissues. Subsequently, a single-cell suspension was prepared utilizing trypsin (Sigma Aldrich, USA, CAS: 9002-07-7). The C1 Single-Cell Auto Prep System, developed by Fluidigm, Inc., South San Francisco, CA, USA, was employed for the isolation of individual cells. Upon isolation within the microfluidic chip, each encapsulated cell underwent lysis, releasing messenger RNA (mRNA) subjected to reverse transcription, yielding complementary DNA (cDNA). Following fragmentation and reverse transcription, the cDNA was subjected to pre-amplification within the microfluidic chip to facilitate subsequent sequencing. The library construction process utilized the amplified cDNA, and single-cell sequencing was conducted on the HiSeq 4000 Illumina platform. Sequencing was executed with paired-end reading, featuring a read length of 2 × 75 bp and generating an approximate count of 20,000 reads per cell (Keefe et al. 2023).

Subsequent data analysis was performed using the Seurat package within the R software. Quality control was executed based on the criteria of 200 < nFeature_RNA < 5000 and a percent of mitochondria genes (percent.mt) lower than 20. Following quality control, the top 2000 genes with the highest expression variability were selected based on variance (Hao et al. 2021).

PCA was employed to reduce the scRNA-seq dataset’s dimensionality, utilizing the top 2000 variably expressed genes selected based on variance. Subsequently, the top 20 PCs were determined using the Elbowplot function in the Seurat package. The main cell subpopulations were identified using the FindClusters function within Seurat, with the resolution set to the default value (res = 1). Subsequently, the t-SNE algorithm was applied to reduce non-linear dimensionality within the scRNA-seq data. Gene markers indicative of different cell subtypes were filtered utilizing the Seurat package, and cell annotation was carried out utilizing the “Singel R” package (Ma et al. 2019). Additionally, cellular communication analysis was conducted using the “CellChat” package in the R language.

The “Limma” package within the R software was employed to identify DEGs within the scRNA-Seq dataset. DEGs between normal samples and early-stage TNBC samples and between early-stage TNBC samples and late-stage TNBC samples were filtered based on the criteria of |logFC| > 0.5 and P.adjust < 0.05 (Ritchie et al. 2015).

Meta-analysis and ROC curve analysis

All six GEO datasets related to breast cancer (namely, GSE46581, GSE76124, GSE157284, GSE45725, GSE53752, and GSE59595) retrieved from the GEO database at https://www.ncbi.nlm.nih.gov/geo/ have been included in the analysis Table S3. The samples were categorized based on their stage classification into two groups: early-stage TNBC (stage < 2) and late-stage TNBC (stage > 2). A meta-analysis was conducted utilizing the ‘meta’ package within the R software, with continuous variables serving as the data type. Given the inconsistency in data detection methods and units across these studies, the combined effect was assessed using the standard mean difference (SMD) and the 95% confidence interval (95% CI). The assessment of heterogeneity also referred to as the statistical homogeneity test, was performed by employing the Q-test and I2 to evaluate heterogeneity. Assessments were conducted using the χ2 value, P-value, and I² to evaluate the presence and extent of heterogeneity. If no statistical heterogeneity was observed among the studies (indicated by P > 0.05 and I² < 50%), it signified no statistical significance, permitting the combination of results. In such instances, variations among the studies were attributed to sampling errors, warranting the use of a fixed-effects model. Conversely, if statistical heterogeneity was detected among the studies (indicated by P < 0.05 and I2 > 50%), it suggested adopting a random-effects model for the meta-analysis. Subgroup analysis was employed to explore data heterogeneity, while sensitivity analysis was conducted to assess result stability using a successive elimination method (Balduzzi et al. 2019).

Furthermore, ROC curves were constructed using the R package pROC based on the expression values of candidate genes extracted from the six datasets. These curves were designed to assess the accuracy of disease status determination in samples through gene expression analysis.

RT-qPCR

Cell lysis was performed to extract total RNA, employing the Trizol reagent (Invitrogen, Thermo Fisher, USA, Catalog Number: 10296010). Subsequently, the quality and concentration of the RNA were determined utilizing an ultraviolet-visible spectrophotometry device (Nanodrop, Thermo Fisher, USA, Model: ND-1000). Reverse transcription was conducted using the PrimeScript™ RT-qPCR kit (TaKaRa, Mountain View, CA, USA, Catalog Number: RR086A). RT-qPCR was performed employing SYBR Premix Ex TaqTM (TaKaRa, Catalog Number: DRR820A) on the LightCycler 480 system (Roche Diagnostics, Pleasanton, CA, USA). GAPDH was utilized as a reference gene for mRNA quantification. The amplification primers were designed and provided by Shanghai General Bioscience Co., Ltd, with the primer sequences detailed in Table S4. The 2^−ΔΔCt method represents the fold change in the target gene expression (Wang et al., 2019a).

Western blot

Total protein was extracted from CD8⁺ T cells, CD4⁺ T cells, and tumor tissues. The cultured cells and tissues were subjected to digestion and subsequent collection using trypsin (Sigma-Aldrich, USA, Catalog Number: T4799-5G). Cell lysis was performed utilizing an enhanced RIPA lysis buffer supplemented with a protease inhibitor (Wuhan Boshida Co., Ltd, Wuhan, China, Catalog Number: AR0108). A BCA protein quantification assay kit was employed to quantify the protein concentration (Wuhan Boshida Co., Ltd, Wuhan, China, Catalog Number: AR1189). Protein separation was achieved through the utilization of SDS-PAGE. Subsequently, the resultant proteins were transferred onto a PVDF membrane. The membrane underwent a blocking step at room temperature, lasting for 1 h, utilizing 5% BSA (Solarbio, Beijing, China, CAS Number: 9048-46-8). The primary antibody, diluted in accordance with the details specified in Table S5, was then added and subjected to overnight incubation at 4 °C. The membrane underwent three washes with PBST (each wash lasting 5 min). Following this, it was incubated at room temperature for 1 h with either the Anti-Mouse-HRP secondary antibody (CST, USA, Catalog Number: 7076, 1/5000) or the Anti-Rabbit-HRP secondary antibody (CST, USA, Catalog Number: 7074, 1/5000). Afterward, blots were visualized using the ECL working solution (Beijing Omegagic Pharmaceutical Technology Co., Ltd., Beijing, China, Catalog Number: Omt-01). The grayscale intensities of the bands in various groups of Western blot images were quantified using ImageJ analysis software. GAPDH served as the internal reference (Luo et al. 2019; Lu et al. 2018).

Immunohistochemistry

The process commenced with the acquisition of the desired tissue or cells for testing, followed by fixation processing and embedding. The deeply embedded tissues were subsequently sectioned into thin slices, and a dewaxing treatment was applied. Dewaxing, an essential step, effectively removed wax from the sections, thereby rendering them hydrophilic and ready for subsequent immunostaining procedures. Following dewaxing, specific antibodies were employed to immunostain the tissue sections. These antibodies included CD8A antibody (Human, HPA037756, 1:50; Sigma Aldrich, USA), IL2RG antibody (Human and Mouse, PA5-115413, 1:50; Thermo Fisher, USA), and ITGB2 antibody (Human, HPA008877, 1:50; Sigma Aldrich, USA). Subsequently, the sections underwent treatment with an Anti-Rabbit-HRP secondary antibody (Sigma Aldrich, USA, Catalog Number: 12–348, 1:1000). To visualize the binding sites between the secondary and primary antibodies, the DAB dye (Abcam, USA, Catalog Number: ab64238) was employed. Following staining, the dewaxed tissue sections were carefully placed on slides for observation. These sections were meticulously examined under a microscope, and the expression patterns were diligently recorded. The criteria for determining the staining results involved the random selection of five lesion areas under the microscope. The number of positively stained cells was quantified in each of these selected areas. The density of positive cells was then graded semi-quantitatively based on the proportion of positive cells present: if less than 15% of the cells were positive, they were classified as negative (0); if between 15% and 25% were positive, they were categorized as positive (+); if between 25% and 50% were positive, they were designated as positive-positive (++); if between 50% and 75% were positive, they were labeled as positive-positive-positive (+++); and if over 75% of the cells were positive, they were denoted as positive-positive-positive-positive (++++). The criteria for classification were established based on the staining results: when the number of positive cells fell below 25%, they were categorized as negative; conversely, when the number of positive cells exceeded 25%, they were classified as positive (Alhabbal and Abou Khamis 2022).

Cell culture

Isolation of CD8⁺ and CD4⁺ T cells from tumor tissues was accomplished using the Human CD8⁺ T Cell Isolation Kit (Miltenyi Biotec, Beijing, China, Catalog Number: 130-045-201) and the Human CD4⁺ T Cell Isolation Kit (Miltenyi Biotec, Beijing, China, Catalog Number: 130-094-125), respectively. The isolated T cells were subjected to centrifugation at 3000×g for 10 min, leading to the separation of the supernatant, which was subsequently filtered through a 0.2 μm filter. Additionally, mouse CD8⁺ T cells were isolated from the spleen and lymph nodes using the Mouse CD8⁺ T Cell Isolation Kit (Beaverbio, Suzhou, China, Catalog Number: 70902-50). Similarly, the supernatant was separated via centrifugation at 3000×g for 10 min and then passed through a 0.2 μm filter (Wang et al. 2019b). Before the commencement of experiments, the isolated CD8⁺ T cells were subjected to stimulation with a CD3/CD28 mouse activator (Thermo Fisher, USA, Catalog Number: 11452D) for 24 h.

The mouse breast cancer cell lines EO771 (EO771-LUC, expressing luciferase, provided by Shanghai Chibiotech, Shanghai, China) and 4T1 (4T1-LUC, also provided by Shanghai Chibiotech, Shanghai, China) were cultured in DMEM medium (Gibco, USA, Catalog Number: 11965092), supplemented with 10% fetal bovine serum (FBS), 10 µg/mL streptomycin, and 100 U/mL penicillin. Cell cultures were maintained in a Heracell™ Vios 160i CR CO₂ Incubator (Thermo Scientific™, Germany) at 37 °C with 5% CO₂ in a humidified environment. Passage cultivation was conducted when cell confluence reached 80%~90% (Lyu et al. 2021; Zhu et al. 2021; Zhang et al. 2019).

Co-culture experiments were executed by introducing CD8⁺ T cells into EO771 or 4T1 culture flasks at a 5:1 ratio. The mixed cells were then incubated in a constant temperature incubator (Cheng et al. 2022).

Flow cytometry

The CD3 + T cells, procured from either breast cancer patients or mouse tumor tissues, underwent staining procedures using specific antibodies. These antibodies included FITC-conjugated anti-CD4 (Human: 11-0049-42; Mouse: 11-0041-82, Thermo Fisher, USA), APC-conjugated anti-CD8 (Human: 47-0088-42; Mouse: 47-0081-82, Thermo Fisher, USA), and PE-conjugated anti-IL2RG antibody (Human: 12-1329-42; Mouse: MA5-46786, Thermo Fisher, USA). Subsequently, IL2RG⁺ CD8⁺ or IL2RG⁺ CD4⁺ cells were isolated. These cells were incubated at 4 °C in the dark for 30 min, followed by the addition of 2 mL of PBS (Sigma-Aldrich, USA, Catalog Number: P4417) solution. The cells were then centrifuged at 1500×g for 10 min at 4 °C, and the supernatant was discarded. The samples were fixed using a 2% paraformaldehyde (Sigma-Aldrich, USA, CAS Number: 30525-89-4)/PBS solution and stored in the dark at 4 °C. Analysis was conducted utilizing the FACS Aria II flow cytometer (BD Bioscience, USA) within 24 h (He et al. 2017).

The Cytofix/Cytoperm Plus Reagent Kit (BD, USA, Catalog Number: 555028) was employed for fixing and permeabilizing CD8⁺ T cells. Subsequently, the fixed and permeabilized cells were stained with anti-CD8 antibodies conjugated with APC (Thermo Fisher, USA, Catalog Number: 47-0081-82), PE-GZMB antibodies (Thermo Fisher, USA, Catalog Number: 17-8898-82), and BV421-IFN-γ antibodies (BD, USA, Catalog Number: 563376). The collected stained cell data were analyzed using a flow cytometer and FlowJo CE software (Luo et al. 2021).

CD8⁺ T cells were cultured in high glucose RPMI-1640 medium (R8758, Sigma-Aldrich, USA, 4.5 g/L glucose), and JC-1 (ab113850, Abcam, UK) was added to the mitochondrial membrane potential assay kit. Flow cytometry was used to analyze the cells after 24 h of culture with CD3/CD28 stimulation. The ratio of red JC-1 to green JC-1 indicates the mitochondrial membrane potential (Δψm) in cells (Zhong et al. 2022).

Cultivate CD8⁺ T cells in a culture medium with a high glucose concentration. Mitochondrial quality was examined using CMXRos Red Mito-Tracker (C1049B, Beyotime, Shanghai, China). Additionally, ROS levels were measured using the DCFH-DA probe (S0033M, Beyotime, Shanghai, China) and flow cytometry (Zhong et al. 2022).

Cells were collected, fixed with 70% ethanol, washed with cold 1×PBS, and then incubated in the dark with FxCycle™ PI/RNase staining solution (Thermo Fisher, USA, Catalog Number: F10797) for 30 min. Subsequently, the samples were analyzed using a flow cytometer, and the percentages of cells in each phase of the cell cycle were determined using FCS Express software (De Novo Software) (Sun et al. 2023).

Cell death rates were detected using flow cytometry. Tumor cells, at a density of 1 × 10⁵ cells per well, were collected, washed with cold PBS, and stained in the dark using a detection assay kit (Sigma-Aldrich, USA, Catalog Number: APOAF-20TST) for 15 min. Subsequently, the residue was suspended in 400µL of binding buffer, and 5µL of Annexin-V from the kit was added for staining. Cell analysis was conducted using flow cytometry (Tong et al. 2021). Cells in the upper right quadrant exhibited the phenotypes Annexin V + PI + and corresponded to late apoptotic cells, while cells in the lower right quadrant exhibited Annexin V + PI phenotypes, indicating early apoptotic cells. Cells in the upper left quadrant displayed the phenotypes Annexin V-PI + and corresponded to necrotic cells, whereas cells in the lower left quadrant exhibited Annexin V-PI phenotypes corresponding to live cells.

CRISPR/Cas9 gene editing technology

IL2RG-sgRNA: F: 5’-TCCTTCAGCTGCTCCTGCTG-3’ (PAM: AGG), R: 5’-TGACAATAATAGTTTCAACA-3’ (PAM: TGG). The sgRNA was introduced into the Lenti-CRISPR v2 vector, which harbors the Streptococcus pyogenes Cas9 nuclease gene (HanBio, Shanghai, China). IL2RG-KO cells were generated by transducing cells with lentiviral Lenti-CRISPR v2 vector and employing the CRISPR/Cas9 editing system. Transfected cells were screened using puromycin (4 µg/mL, HY-K1057, MCE, USA) to select for sgRNA plasmid and donor sequence. The IL2RG-KO cells were screened using restricting dilution cloning and validated through RT-qPCR and Western blot techniques (Zeng et al. 2023; Nishiyama et al. 2022).

Lentivirus transduction

For the purpose of gene overexpression, the pCMV6-AC-GFP plasmid vector (LMAI Bio, Shanghai, China, Catalog Number: LM-2069) was selected and subsequently transformed with the IL2RG plasmid obtained from Sengong Biotech (Shanghai, China). To create lentiviral vectors, the IL2RG-UP lentivirus (IL2RG-UP-LTEP-s, hereinafter referred to as IL2RG-UP) and control lentivirus (NC-LTEP-s, hereinafter referred to as Mock) were meticulously constructed employing HEK293T cells (Nanjing Kebai Biotechnology Co., Ltd., Jiangsu, China, Catalog Number: CBP60661). Plasmid and lentivirus packaging services were generously provided by Shanghai Biotechnology Co., Ltd. In addition, plasmids incorporating luciferase reporter genes (Mock-luc, IL2RG-UP-luc) were co-transfected with a helper plasmid into HEK293T cells, utilizing Lipofectamine 2000 reagent (Thermo Fisher, USA, Catalog Number: 11668030). Following a series of verification, amplification, and purification steps, we successfully obtained packaged lentivirus.

To perform lentiviral-mediated cell transfection, 5 × 10⁵ cells were seeded into individual wells of a 6-well plate. When the confluence of CD8⁺ T cells reached the range of 70–90%, we introduced a medium containing an appropriate quantity of packaged lentivirus (MOI = 10, working titer approximately 5 × 10⁶ TU/mL) along with 5 µg/mL polybrene (Merck, USA, Catalog Number: TR-1003) for transfection. After a 4-hour incubation, an equivalent volume of medium was employed to dilute the polybrene. Subsequently, fresh medium was substituted after 24 h. Following an additional 48 h, the transfection status was evaluated using a luciferase reporter gene, and stable cell lines were established through selection for resistance using an appropriate concentration of puromycin (Gibco, Grand Island, NY, USA, Catalog Number: A1113803). Cell collection was carried out once the cells ceased to exhibit signs of mortality in the medium supplemented with the purine antimetabolite. Validation of the knockdown efficiency was subsequently performed using RT-qPCR (Yan et al. 2015).

The cell groups established encompassed IL2RG-WT CD8⁺ T cells (representing wild-type cells), IL2RG-KO CD8⁺ T cells (characterizing IL2RG KO cells), IL2RG-UP CD8⁺ T cells (depicting IL2RG overexpressing cells), and Mock CD8⁺ T cells (indicating cells transfected with an empty lentivirus).

MTT cell viability assay

Cells were seeded at a density of 1 × 10⁴ cells per well in a 96-well plate, utilizing a cell culture medium. Subsequently, the cells were incubated for 24 h. Following the incubation period, cells from various treatment groups were combined with 0.01 mL of MTT solution (Sigma Aldrich, USA, Catalog Number: CT02) and incubated within a CO₂ incubator for 4 hours. After the incubation, 0.1 mL of isopropanol containing 0.04 N hydrochloric acid was introduced into each well. Thorough mixing was achieved through repeated pipetting using a multichannel pipettor. The addition of hydrochloric acid served to convert phenol red in the tissue culture medium into a non-interfering yellow color, thus facilitating unimpeded MTT/formaldehyde measurement. Cell viability was assessed by quantifying the absorbance at 570 nm (Shen et al. 2019).

Enzyme-linked immunosorbent assay (ELISA)

To initiate the IFN-γ quantification process, it was imperative to collect the cell culture supernatant, following which the IFN-γ ELISA kit (Abcam, UK, Catalog Number: ab252363) was employed in strict accordance with established protocols. Initially, the antigens underwent dilution in an appropriate diluent, ensuring the attainment of a suitable concentration. Subsequently, these diluted antigens were incubated within sealed enzyme-labeled reaction wells at a temperature of 37 °C for a duration of 40 min. Furthermore, an addition of 5% FBS (MSK, Wuhan, China, Catalog Number: F8318) was executed. Subsequent steps encompassed the introduction of the diluted samples into the ELISA wells, followed by the sequential addition of the enzyme-labeled antibody and the substrate solution. The conclusion of the procedure was marked by the addition of 50 µL of stop solution to each well, thereby terminating the reaction. It is noteworthy that experimental results were swiftly obtained within a mere 20-minute timeframe. Subsequently, the absorbance levels of the samples were meticulously gauged at a wavelength of 450 nm, utilizing a microplate reader sourced from Bio-Rad (USA). These absorbance values were employed to construct a standard curve, facilitating subsequent data analysis (Zhang et al. 2021).

ATP content detection

CD8⁺ T cells were meticulously cultured at a density of 1 × 10⁵ cells per well, utilizing a 6-well culture plate. Subsequently, these cultured cells were subjected to various treatments to induce specific responses. To quantify the total cellular ATP, the cells underwent lysis employing the ATP detection kit (Solarbio, Beijing, China, Catalog Number: BC0300). The subsequent extraction of ATP from osteoblast cells was conducted, following which ATP levels were quantified utilizing a UV spectrophotometer (Beckman, USA, Model: DU720) in strict adherence to the manufacturer’s provided guidelines (Guo et al. 2021).

Immuno-fluorescence

The cells were fixed using a 4% paraformaldehyde solution (Macklin, Shanghai, China, Catalog Number: P885233) for 15 to 30 min. Following fixation, the cells underwent treatment with 0.1% Triton (Macklin, Shanghai, China, Catalog Number: L885651) for 15 min to facilitate cell membrane permeabilization. Cells were incubated in PBS supplemented with 15% FBS at 5 °C overnight. In the subsequent steps, the cells were subjected to immunolabeling. This process entailed incubation with anti-CD8 (Thermo Fisher, USA, Catalog Number: MA5-29682; 1:100) and anti-GZMB (Thermo Fisher, USA, Catalog Number: 701395; 1:100) antibodies overnight at 4 °C. Following this primary antibody incubation, the cells were further incubated with Cy3 or FITC-conjugated secondary antibodies. The immunostained cells were observed utilizing a fluorescence microscope (Zeiss Observer Z1, Germany) (Li et al. 2019). CD8⁺ T cells, labeled with carboxyfluorescein succinimidyl ester (CFSE) (Abcam, UK, Catalog Number: ab113853), were introduced into mice via the tail vein. Tumors were harvested, and immunostaining was conducted 24 h post-injection. Frozen sections of the tumors were subjected to immunostaining using an anti-CD31 antibody (BD Pharmingen, Catalog Number: 550274), and subsequent counterstaining was performed using a Cy3-conjugated secondary antibody (Life Technologies, Catalog Number: A10522). Following PBS washing, the sections were mounted with DAPI (Beyotime, Shanghai, China), and imaging was carried out (Zhou et al. 2021).

Metabolic measurement

The Seahorse XFe96 extracellular flux analyzer, manufactured by Agilent Technologies, played a pivotal role in our metabolic analysis. To assess cellular metabolic activities, the extracellular acidification rate (ECAR) and oxygen consumption rate (OCR) of each well were determined. This involved the precise utilization of designated concentrations of injected compounds for the XF glycolysis stress or XF cell Mito tests. The compounds employed in these tests encompassed 10 mM glucose (Sigma-Aldrich, USA, Catalog Number: 50-99-7), 2 µM oligomycin (Sigma-Aldrich, USA, Catalog Number: 1404-19-9), 50 mM 2-deoxy-D-glucose (2-DG) (Sigma-Aldrich, USA, Catalog Number: 154-17-6), 1 µM carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone (FCCP) (Sigma-Aldrich, USA, Catalog Number: 370-86-5), and 0.5 µM rotenone (Sigma-Aldrich, USA, Catalog Number: 83-79-4). Subtitle: Utilization of XF Sugar Fermentation Stress or XF Cell Mito Detection Test Kit For the seamless execution of these metabolic analyses, we acquired the XF sugar fermentation stress or XF cell Mito detection test kit from Agilent Technologies (USA) (Wu et al. 2022b).

Multicellular spheroid (MCS) formation and 3D co-culture with CD8+ T cells

Cancer cells/spheroids (1000 cells) were seeded into 35 or 81 wells of agarose tube sets (Sigma-Aldrich, USA, Catalog Number: A6013). These agarose tube sets had been generated using the 3D Petri dishes, a spheroid formation device developed by Microtissues^® Inc. (RI, USA). Following cell seeding, either 1 mL of cell culture medium (for 35-well plates) or 2 mL (for 81-well plates) was added. The cultures were incubated at 37 °C with 5% CO₂ to facilitate the formation of tumor spheres. On the second day of the experiment, the cancer spheroids were co-cultured with CD8⁺ T cells previously labeled with Carboxyfluorescein succinimidyl ester (CFSE) (Sigma-Aldrich, USA). The co-culture was maintained in RPMI medium (Sigma-Aldrich, USA, Catalog Number: R4130) supplemented with 10% FBS and 100 units/mL penicillin/streptomycin. After a 24-hour incubation at 37 °C, the spheres were washed and fixed using a 4% paraformaldehyde solution. Subsequently, the ZEN 710 confocal microscope (ZEISS, Germany) was employed for imaging. Images were captured at the intermediate height of the microsphere, and surface maps were generated using ImageJ software.

After a 2-day co-culture, the infiltration of type I collagen (Sigma-Aldrich, USA, Catalog Number: CC050) was initiated. The collagen was first neutralized to a pH range of 7.0–8.0. The neutralized collagen was then embedded within a 35-well agarose tube filled with the co-culture medium. After a 4-minute incubation, the agarose tube containing the collagen-embedded cells was inverted and incubated for an additional 1 h. Following this, the culture tube was inverted, and RPMI medium supplemented with 5% FBS and 1% penicillin-streptomycin (Pen/Strep) was added. Invasion assays were conducted over the next two days using an inverted microscope (Caikon Shanghai, China, Model: XDS-900) for imaging. Images were captured, and cell survival was evaluated after recovery from the collagen matrix.

To assess the cytotoxicity of CD8⁺ T cells against MCSs, the spheroids were washed and fixed after 24 h of co-culture. Subsequently, the cells were stained using the viability/cytotoxicity assay kit (Biotium, USA, Catalog Number: 30002). Images were captured using a confocal microscope, scanning a Z-stack from the top to the middle of the MCS with a 5 μm interval. The image was then presented as a maximum-intensity projection. Zeiss image processing software was employed to generate a 2.5D surface display. For quantifying live and dead cells, ImageJ software was used to measure the overall cellular area for each dye (Zhou et al. 2021; Lin et al. 2022).

Animal modeling

The 6-week-old female mice (16–20 g) were obtained from our Experimental Animal Research Center. Experimental animals were cared for in accordance with the principles of the Guide for the Care and Use of Laboratory Animals under a protocol approved by the Laboratory Animal Ethics Committee of The Fourth Hospital of Hebei Medical University.

For the establishment of a breast cancer orthotopic transplantation model, the EO771 and 4T1 cell lines, comprising 2 × 10⁶ cells each, were subcutaneously injected into the right lower mammary fat pad of the 6-week-old mice, as previously documented (Carpenter et al. 2019). The CRi Maestro in vivo imaging system, developed by CRi Inc. in the United States, was employed to analyze the bioluminescent signals emanating from firefly luciferase in both the EO771 and 4T1 cells (Zhao et al. 2015).

Near-infrared imaging was employed for the quantification and spatial localization of CD8⁺ T cells. In the initial step, CD8⁺ T cells were labeled with DiR, a near-infrared fluorescence probe obtained from Mokang Biotech in Shanghai, China (Catalog Number: MX4005). Following labeling, CD8⁺ T cells in a quantity of 1 × 10⁷ were administered intratumorally or intravenously to mice that already bore established tumors. This administration occurred at two distinct time points, specifically 12 or 36 days after transplantation. Subsequent imaging of anesthetized mice was conducted utilizing the CRi Maestro in vivo imaging system, an instrumental part of our experimental setup. For imaging of excised tissues, mice were humanely euthanized through cervical dislocation while under deep anesthesia achieved using isoflurane (Catalog Number: R510-22-10, Revival, Shenzhen, China). Surgical resection was executed to extract both tumors and major organs, including the heart, liver, spleen, lungs, kidneys, and intestines, to facilitate further analysis (Zhou et al. 2021).

Subsequent to euthanasia, the mice were anesthetized and then euthanized through cervical dislocation, after which the tumors were meticulously dissected. These dissected tumors were subsequently either fixed in Formalin-Fixed Paraffin-Embedded (FFPE) or rapidly frozen to be employed in subsequent experimental analyses. The experimental groups were systematically divided into three categories, namely LCP@IL2RG (NPs packaged with IL2RG mRNA, dosage: 500 µL, concentration: 10 µg/mL), LCP (NPs devoid of packaged mRNA), and PBS (serving as the blank control).

Construction of LCP@IL2RG

The cell lysis was carried out utilizing a Trizol reagent, followed by the extraction of total RNA. Subsequently, reverse transcription was performed utilizing the PrimeScript™ RT-qPCR Kit. The entire IL2RG gene was then amplified from the generated cDNA, employing specific oligonucleotide primers (forward: 5’-GAGAAAGAAGAGCAAGCACC-3’, reverse: 5’-TTCCATCAAAGGATTGATGT-3’). The resulting PCR product was cloned into the eukaryotic expression vector pcDNA3.1(+) obtained from Invitrogen, USA. The recombinant plasmids were then transfected into the 4T1 cell line using Lipofectamine 2000 reagent.

The template for in vitro transcription was prepared using the PCR amplification method and subsequently purified through agarose gel electrophoresis. RNA was synthesized in vitro using the MEGAscript T7 Transcription Kit (AM1333, Ambion, Thermo Fisher, USA). A Poly(A) Tailing Kit (R7075S, Biotech, Shanghai, China) was utilized to polyadenylate the obtained RNA. RNA purification was then executed using the MEGAclear Transcriptome Clean-up Kit (AM1908, Ambion, Thermo Fisher, USA).

The preparation of mRNA-loaded LCP in oil-in-water emulsion commenced by dispersing 600 µL of 2.5 M CaCl2 (Sigma-Aldrich, USA, 10043-52-4) and 50 µg of mRNA in a 20 mL oil phase containing cyclohexane (Sigma-Aldrich, USA, 110-82-7) and Igepal CO-520 (ChemeGen, USA, 68412-54-4) in a ratio of 71:29 (v/v). Simultaneously, 600 µL of 12.5 mM Na2HPO4 (7558-79-4, Sigma-Aldrich, USA) with a pH of 9.0 was dispersed into 20 mL oil phases. Each oil phase was stirred individually for 5 min at room temperature before being combined and stirred for an additional 20 min at the same temperature. Subsequently, 400 µL of 20 mM DOPA (852288-18-7, Sigma-Aldrich, USA) was introduced to the microemulsion and stirred for 15 min. Then, 40 mL of ethanol (64-17-5, Sigma-Aldrich, USA) was added to precipitate the calcium phosphate core. The subsequent step involved centrifugation at 10,000 × g for 20 min to isolate the calcium phosphate core containing the packaged mRNA. The collected core was then rinsed with 40 mL of ethanol, and the residue was dissolved in chloroform. Lastly, the final preparation of large core particles (LCP) involved the core mixture consisting of 140 µL of 20 mM DOTAP (132172-61-3, Sigma-Aldrich, USA), 140 µL of 20 mM cholesterol (57-88-5, Sigma-Aldrich, USA), 100 µL of 20 mM DSPE-PEG-2000 (892144-24-0, MCE, USA), and 80 µL of 5 mM DSPE-PEG-CHO-CD8 (DSPE-PEG-CHO(X-GF-0294-2k, Shenzhen Xinyanbo Bio-Tech, China) conjugated with anti-CD8 (11-0081-82, Thermo Fisher, USA). Chloroform was evaporated, and the LCP was rehydrated in 250 µL of a 5% glucose solution. The morphology of both the calcium phosphate core and the final LCP was visualized using Japan’s JEOL 100CX II TEM TEM, and particle size as well as zeta potential were measured utilizing the Malvern Zetasizer Nano ZS (Liu et al. 2018).

The self-assembled LCP@IL2RG was stained with DiI (MP6013, Mocan Biotech, Shanghai, China) for 30 min. Subsequently, CD8⁺ T cells (2 × 10⁵ cells/well) were seeded into a 6-well plate and cultured for 24 h. Following this, the cells were incubated with LCP@IL2RG at a 10 µg/mL concentration. Afterward, the cells were stained with DAPI (D1306, Thermo Fisher, USA) for 30 min. Finally, the cells were subjected to imaging utilizing an inverted microscope (Shi et al. 2021).

Histopathological staining

Hematoxylin and eosin (H&E) staining involved a series of meticulously executed steps. Initially, the tissue sample is procured for examination, subjected to fixation, and meticulously sectioned into slides. These slides are subsequently deparaffinized through immersion in xylene. In a systematic manner, the slides are then subjected to sequential dehydration in a graded ethanol series, comprising 100% ethanol, followed by 95% ethanol, and ultimately, 70% ethanol. Finally, the slides are either rinsed with distilled water or affixed onto slides. Subsequent to these preparatory stages, the tissue sections are immersed in a Sudan Black staining solution (H8070, Solarbio, Beijing, China). This staining procedure is conventionally conducted at room temperature and spans 5 to 10 min. Following the staining process, the slides are subjected to a thorough wash with distilled water, followed by a round of dehydration in 95% ethanol. Subsequently, they are immersed in the Yihong staining solution (G1100, Solarbio, Beijing, China) for 5 to 10 min. Upon completion of the staining procedures, the slides must undergo standard protocols for dehydration, clearing, and ultimately, mounting (Bian et al. 2021).

Statistical analysis

R language version 4.2.1 and the RStudio integrated development environment version 2022.12.0-353 were used in this study. The data were analyzed using GraphPad Prism 8.0. Continuous data were presented as mean ± standard deviation (Mean ± SD). A non-paired t-test was employed to compare data between two groups, while a one-way analysis of variance (ANOVA) was used to compare data among multiple groups. Levene’s test was employed to assess the homogeneity of variance. If the variance was homogeneous, pairwise comparisons were performed using Dunnett’s T3 and LSD-t tests. The Dunnett’s T3 test was employed when variances were heterogeneous. A significance level of P < 0.05 suggests a statistical difference in the comparison between the two groups of data.

Data availability

No datasets were generated or analysed during the current study.

References

Alhabbal A, Abou Khamis I (2022) Immunohistochemical analysis of intestinal biopsies in individuals with celiac disease. JGH Open 6(10):692–695 Published 2022 Aug 26. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/jgh3.12807
Article PubMed PubMed Central Google Scholar
Arimoto KI, Miyauchi S, Troutman TD et al (2023) Expansion of interferon inducible gene pool via USP18 inhibition promotes cancer cell pyroptosis. Nat Commun 14(1):251. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41467-022-35348-5
Article CAS PubMed PubMed Central Google Scholar
Bahrambeigi S, Sanajou D, Shafiei-Irannejad V (2019) Major fundamental factors hindering immune system in defense against tumor cells: the link between insufficiency of innate immune responses, metabolism, and neurotransmitters with effector immune cells disability. Immunol Lett 212:81–87. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.imlet.2019.06.008
Article CAS PubMed Google Scholar
Bai X, Zhao G, Chen Q et al (2022) Inhaled siRNA nanoparticles targeting IL11 inhibit lung fibrosis and improve pulmonary function post-bleomycin challenge. Sci Adv 8(25):eabn7162. https://doiorg.publicaciones.saludcastillayleon.es/10.1126/sciadv.abn7162
Article CAS PubMed PubMed Central Google Scholar
Baldominos P, Barbera-Mourelle A, Barreiro O et al (2022) Quiescent cancer cells resist T cell attack by forming an immunosuppressive niche. Cell 185(10):1694–1708e19. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.cell.2022.03.033
Article CAS PubMed PubMed Central Google Scholar
Balduzzi S, Rücker G, Schwarzer G (2019) How to perform a meta-analysis with R: a practical tutorial. Evid Based Ment Health 22(4):153–160. https://doiorg.publicaciones.saludcastillayleon.es/10.1136/ebmental-2019-300117
Article PubMed PubMed Central Google Scholar
Bian J, Cai F, Chen H et al (2021) Modulation of local overactive inflammation via injectable hydrogel microspheres. Nano Lett 21(6):2690–2698. https://doiorg.publicaciones.saludcastillayleon.es/10.1021/acs.nanolett.0c04713
Article CAS PubMed Google Scholar
Borgers JSW, Heimovaara JH, Cardonick E et al (2021) Immunotherapy for cancer treatment during pregnancy. Lancet Oncol 22(12):e550–e561. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S1470-2045(21)00525-8
Article CAS PubMed Google Scholar
Brito ACNL, Santos SEV, Martins WA et al (2020) Efficacy of tubing technique with biomaterials compared to direct coaptation technique after peripheral neurotmesis in nerve healing and return to functionality in young adult rats: a systematic review protocol. Syst Rev 9(1):118. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13643-020-01388-5
Article PubMed PubMed Central Google Scholar
Carpenter KJ, Valfort AC, Steinauer N et al (2019) LXR-inverse agonism stimulates immune-mediated tumor destruction by enhancing CD8 T-cell activity in triple negative breast cancer. Sci Rep 9(1):19530. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41598-019-56038-1
Article CAS PubMed PubMed Central Google Scholar
Chang CH, Qiu J, O’Sullivan D et al (2015) Metabolic competition in the tumor microenvironment is a driver of cancer progression. Cell 162(6):1229–1241. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.cell.2015.08.016
Article CAS PubMed PubMed Central Google Scholar
Chen Z, Wang M, De Wilde RL et al (2021) A machine learning model to predict the triple negative breast cancer immune subtype. Front Immunol 12:749459. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fimmu.2021.749459
Article CAS PubMed PubMed Central Google Scholar
Cheng CC, Ho AS, Peng CL et al (2022) Sorafenib suppresses radioresistance and synergizes radiotherapy-mediated CD8⁺ T cell activation to eradicate hepatocellular carcinoma. Int Immunopharmacol 112:109110. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.intimp.2022.109110
Article CAS PubMed Google Scholar
Correnti M, Cappon A, Pastore M et al (2022) The protease-inhibitor SerpinB3 as a critical modulator of the stem-like subset in human cholangiocarcinoma. Liver Int 42(1):233–248. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/liv.15049
Article CAS PubMed Google Scholar
Downs-Canner SM, Meier J, Vincent BG, Serody JS (2022) B cell function in the tumor microenvironment. Annu Rev Immunol 40:169–193. https://doiorg.publicaciones.saludcastillayleon.es/10.1146/annurev-immunol-101220-015603
Article CAS PubMed Google Scholar
Edge SB, Compton CC (2010) The American Joint Committee on Cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann Surg Oncol 17(6):1471–1474. https://doiorg.publicaciones.saludcastillayleon.es/10.1245/s10434-010-0985-4
Article PubMed Google Scholar
Guo Y, Jia X, Cui Y et al (2021) Sirt3-mediated mitophagy regulates AGEs-induced BMSCs senescence and senile osteoporosis. Redox Biol 41:101915. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.redox.2021.101915
Article CAS PubMed PubMed Central Google Scholar
Hao Y, Hao S, Andersen-Nissen E et al (2021) Integrated analysis of multimodal single-cell data. Cell 184(13):3573–3587e29. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.cell.2021.04.048
Article CAS PubMed PubMed Central Google Scholar
He W, Zhang H, Han F et al (2017) CD155T/TIGIT signaling regulates CD8⁺ T-cell metabolism and promotes tumor progression in human gastric cancer. Cancer Res 77(22):6375–6388. https://doiorg.publicaciones.saludcastillayleon.es/10.1158/0008-5472.CAN-17-0381
Article CAS PubMed Google Scholar
Hoshyar N, Gray S, Han H, Bao G (2016) The effect of nanoparticle size on in vivo pharmacokinetics and cellular interaction. Nanomed (Lond) 11(6):673–692. https://doiorg.publicaciones.saludcastillayleon.es/10.2217/nnm.16.5
Article CAS Google Scholar
Jiang W, He Y, He W et al (2021) Exhausted CD8 + T cells in the tumor immune microenvironment: new pathways to therapy. Front Immunol 11:622509. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fimmu.2020.622509
Article CAS PubMed PubMed Central Google Scholar
Keefe F, Monzón-Sandoval J, Rosser AE, Webber C, Li M (2023) Single-cell transcriptomics reveals conserved regulatory networks in human and mouse interneuron development. Int J Mol Sci 24(9):8122. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/ijms24098122
Article CAS PubMed PubMed Central Google Scholar
Kumari B, Sakode C, Lakshminarayanan R, Roy PK (2023) Computational systems biology approach for permanent tumor elimination and normal tissue protection using negative biasing: experimental validation in malignant melanoma as case study. Math Biosci Eng 20(5):9572–9606. https://doiorg.publicaciones.saludcastillayleon.es/10.3934/mbe.2023420
Article PubMed Google Scholar
Li W, Zhang S, Liu J, Liu Y, Liang Q (2019) Vitamin K2 stimulates MC3T3–E1 osteoblast differentiation and mineralization through autophagy induction. Mol Med Rep 19(5):3676–3684. https://doiorg.publicaciones.saludcastillayleon.es/10.3892/mmr.2019.10040
Article CAS PubMed PubMed Central Google Scholar
Liang J, Tian C, Zeng Y et al (2019) FOXA1⁺ regulatory T cells: a novel T cell subset that suppresses antitumor immunity in lung cancer. Biochem Biophys Res Commun 514(1):308–315. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.bbrc.2019.04.152
Article CAS PubMed Google Scholar
Lin YN, Schmidt MO, Sharif GM et al (2022) Impaired CXCL12 signaling contributes to resistance of pancreatic cancer subpopulations to T cell-mediated cytotoxicity. Oncoimmunology 11(1):2027136. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/2162402X.2022.2027136
Article PubMed PubMed Central Google Scholar
Liu L, Wang Y, Miao L et al (2018) Combination immunotherapy of MUC1 mRNA Nano-vaccine and CTLA-4 Blockade effectively inhibits growth of Triple negative breast Cancer. Mol Ther 26(1):45–55. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ymthe.2017.10.020
Article CAS PubMed Google Scholar
Lu J, Liu QH, Wang F et al (2018) Exosomal miR-9 inhibits angiogenesis by targeting MDK and regulating PDK/AKT pathway in nasopharyngeal carcinoma. J Exp Clin Cancer Res 37(1):147. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13046-018-0814-3
Article CAS PubMed PubMed Central Google Scholar
Luo A, Zhou X, Shi X et al (2019) Exosome-derived mir-339-5p mediates radiosensitivity by targeting Cdc25A in locally advanced esophageal squamous cell carcinoma. Oncogene 38(25):4990–5006. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41388-019-0771-0
Article CAS PubMed Google Scholar
Luo ZW, Xia K, Liu YW et al (2021) Extracellular vesicles from Akkermansia muciniphila Elicit Antitumor immunity against prostate cancer via modulation of CD8⁺ T cells and macrophages. Int J Nanomed 16:2949–2963. https://doiorg.publicaciones.saludcastillayleon.es/10.2147/IJN.S304515
Article Google Scholar
Lyu ZJ, Wang Y, Huang JL et al (2021) Recurrent ZNF83-E293V mutation promotes bladder cancer progression through the NF-κB pathway via transcriptional dysregulation of S100A8. Mol Ther 29(1):275–290. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ymthe.2020.09.004
Article CAS PubMed Google Scholar
Ma L, Hernandez MO, Zhao Y et al (2019) Tumor cell biodiversity drives microenvironmental reprogramming in liver cancer. Cancer Cell 36(4):418-430e6. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.ccell.2019.08.007
Article CAS PubMed PubMed Central Google Scholar
McKenzie MD, Dudek NL, Mariana L et al (2006) Perforin and Fas induced by IFNgamma and TNFalpha mediate beta cell death by OT-I CTL. Int Immunol 18(6):837–846. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/intimm/dxl020
Article CAS PubMed Google Scholar
Nishiyama T, Zhang Y, Cui M et al (2022) Precise genomic editing of pathogenic mutations in RBM20 rescues dilated cardiomyopathy. Sci Transl Med 14(672):eade1633. https://doiorg.publicaciones.saludcastillayleon.es/10.1126/scitranslmed.ade1633
Article CAS PubMed PubMed Central Google Scholar
Olivera GC, Vetter L, Tesoriero C et al (2021) Role of T cells during the cerebral infection with Trypanosoma Brucei. PLoS Negl Trop Dis 15(9):e0009764. https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pntd.0009764
Article CAS PubMed PubMed Central Google Scholar
Park J, Hsueh PC, Li Z, Ho PC (2023) Microenvironment-driven metabolic adaptations guiding CD8⁺ T cell anti-tumor immunity. Immunity 56(1):32–42. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.immuni.2022.12.008
Article CAS PubMed Google Scholar
Patsoukis N, Bardhan K, Chatterjee P et al (2015) PD-1 alters T-cell metabolic reprogramming by inhibiting glycolysis and promoting lipolysis and fatty acid oxidation. Nat Commun 6:6692. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/ncomms7692
Article CAS PubMed Google Scholar
Remon J, Hendriks LEL, Mountzios G et al (2023) MET alterations in NSCLC-Current perspectives and Future challenges. J Thorac Oncol 18(4):419–435. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jtho.2022.10.015
Article CAS PubMed Google Scholar
Ren J, Yu D, Fu R et al (2020) IL2RG-deficient minipigs generated via CRISPR/Cas9 technology support the growth of human melanoma-derived tumours. Cell Prolif 53(10):e12863. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/cpr.12863
Article CAS PubMed PubMed Central Google Scholar
Ren J, Han X, Lohner H et al (2023) P. gingivalis infection upregulates PD-L1 expression on dendritic cells, suppresses CD8 + T-cell responses, and aggravates oral Cancer. Cancer Immunol Res 11(3):290–305. https://doiorg.publicaciones.saludcastillayleon.es/10.1158/2326-6066.CIR-22-0541
Article CAS PubMed PubMed Central Google Scholar
Ritchie ME, Phipson B, Wu D et al (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43(7):e47. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/nar/gkv007
Article CAS PubMed PubMed Central Google Scholar
Shang T, Yu X, Han S, Yang B (2020) Nanomedicine-based tumor photothermal therapy synergized immunotherapy. Biomater Sci 8(19):5241–5259. https://doiorg.publicaciones.saludcastillayleon.es/10.1039/d0bm01158d
Article CAS PubMed Google Scholar
Shen J, Yang D, Zhou X et al (2019) Role of Autophagy in Zinc Oxide nanoparticles-Induced apoptosis of mouse LEYDIG cells. Int J Mol Sci 20(16):4042 Published 2019 Aug 19. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/ijms20164042
Article CAS PubMed PubMed Central Google Scholar
Shi W, Qiu Q, Feng Z et al (2021) Design, synthesis and immunological evaluation of self-assembled antigenic peptides from dual-antigen targets: a broad-spectrum candidate for an effective antibreast cancer therapy. J Immunother Cancer 9(6):e002523. https://doiorg.publicaciones.saludcastillayleon.es/10.1136/jitc-2021-002523
Article PubMed PubMed Central Google Scholar
Sun CC, Zhu W, Li SJ et al (2023) (2021) FOXC1-mediated LINC00301 facilitates tumor progression and triggers an immune-suppressing microenvironment in non-small cell lung cancer by regulating the HIF1α pathway. Genome Med 15(1):23. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13073-023-01172-9]
Article PubMed PubMed Central Google Scholar
Tong D, Zhao Y, Tang Y, Ma J, Wang Z, Li C (2021) Circ-Usp10 promotes microglial activation and induces neuronal death by targeting miRNA-152-5p/CD84. Bioengineered 12(2):10812–10822. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/21655979.2021.2004362
Article CAS PubMed PubMed Central Google Scholar
Vagia E, Mahalingam D, Cristofanilli M (2020) The Landscape of targeted therapies in TNBC. Cancers (Basel) 12(4):916 Published 2020 Apr 8. https://doiorg.publicaciones.saludcastillayleon.es/10.3390/cancers12040916
Article CAS PubMed Google Scholar
van der Leun AM, Thommen DS, Schumacher TN (2020) CD8⁺ T cell states in human cancer: insights from single-cell analysis. Nat Rev Cancer 20(4):218–232. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41568-019-0235-4
Article CAS PubMed PubMed Central Google Scholar
VandenBussche CJ, Adams CL, McDonald OG, Whitworth SA, Ali SZ (2017) The fine needle aspiration of translocation sarcomas. Cytopathology 28(5):356–363. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/cyt.12446
Article CAS PubMed Google Scholar
Walsh NC, Kenney LL, Jangalwe S et al (2017) Humanized Mouse models of clinical disease. Annu Rev Pathol 12:187–215. https://doiorg.publicaciones.saludcastillayleon.es/10.1146/annurev-pathol-052016-100332
Article CAS PubMed Google Scholar
Wang M, Yao LC, Cheng M et al (2018) Humanized mice in studying efficacy and mechanisms of PD-1-targeted cancer immunotherapy. FASEB J 32(3):1537–1549. https://doiorg.publicaciones.saludcastillayleon.es/10.1096/fj.201700740R
Article CAS PubMed Google Scholar
Wang W, Green M, Choi JE et al (2019b) CD8⁺ T cells regulate tumour ferroptosis during cancer immunotherapy. Nature 569(7755):270–274. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41586-019-1170-y
Article CAS PubMed PubMed Central Google Scholar
Wang C, Zhang Y, Dong Y (2021) Lipid Nanoparticle-mRNA formulations for therapeutic applications. Acc Chem Res 54(23):4283–4293. https://doiorg.publicaciones.saludcastillayleon.es/10.1021/acs.accounts.1c00550
Article CAS PubMed PubMed Central Google Scholar
Wang C, Ren YL, Zhai J, Zhou XY, Wu J (2019a) Down-regulated LAMA4 inhibits oxidative stress-induced apoptosis of retinal ganglion cells through the MAPK signaling pathway in rats with glaucoma. Cell Cycle 21(23):2551. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/15384101.2022.2097798
Article CAS Google Scholar
Wang W, Lin P, Wang S et al (2023) In-depth mining of single-cell transcriptome reveals the key immune-regulated loops in age-related macular degeneration. Front Mol Neurosci 16:1173123. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fnmol.2023.1173123
Article CAS PubMed PubMed Central Google Scholar
Wu X, Deng Z, Zhao Q (2022a) Immunotherapy improves disease prognosis by affecting the tumor microenvironment: a bibliometric study. Front Immunol 13:967076 Published 2022 Oct 6. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fimmu.2022.967076
Article CAS PubMed PubMed Central Google Scholar
Wu Z, Huang H, Han Q et al (2022b) SENP7 senses oxidative stress to sustain metabolic fitness and antitumor functions of CD8 + T cells. J Clin Invest 132(7):e155224. https://doiorg.publicaciones.saludcastillayleon.es/10.1172/JCI155224
Article CAS PubMed PubMed Central Google Scholar
Yan R, Zhang L, Xia N, Liu Q, Sun H, Guo H (2015) Knockdown of augmenter of liver regeneration in HK-2 cells inhibits inflammation response via the mitogen-activated protein kinase signaling pathway. Inflamm Res 64(6):453–462. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s00011-015-0825-x
Article CAS PubMed Google Scholar
Yuan Y (2020) Mechanisms inspired targeting peptides. Adv Exp Med Biol 1248:531–546. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/978-981-15-3266-5_21
Article CAS PubMed Google Scholar
Yuan Y, Kolios AGA, Liu Y et al (2022) Therapeutic potential of interleukin-2 in autoimmune diseases. Trends Mol Med 28(7):596–612. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.molmed.2022.04.010
Article CAS PubMed Google Scholar
Zeng X, Yao B, Liu J et al (2023) The SMARCA4^R1157W mutation facilitates chromatin remodeling and confers PRMT1/SMARCA4 inhibitors sensitivity in colorectal cancer. NPJ Precis Oncol 7(1):28. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/s41698-023-00367-y
Article CAS PubMed PubMed Central Google Scholar
Zhang S, Gao H, Bao G (2015) Physical principles of Nanoparticle Cellular endocytosis. ACS Nano 9(9):8655–8671. https://doiorg.publicaciones.saludcastillayleon.es/10.1021/acsnano.5b03184
Article CAS PubMed PubMed Central Google Scholar
Zhang YN, Poon W, Tavares AJ, McGilvray ID, Chan WCW (2016) Nanoparticle-liver interactions: cellular uptake and hepatobiliary elimination. J Control Release 240:332–348. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jconrel.2016.01.020
Article CAS PubMed Google Scholar
Zhang X, Zhao Y, Kong P, Han M, Li B (2019) Expression of circZNF609 is down-regulated in Colorectal Cancer tissue and promotes apoptosis in Colorectal Cancer cells by upregulating p53. Med Sci Monit 25:5977–5985 Published 2019 Aug 11. https://doiorg.publicaciones.saludcastillayleon.es/10.12659/MSM.915926
Article CAS PubMed PubMed Central Google Scholar
Zhang Y, Ning C, Zhou H, Yan Y, Liu F, Huang Y (2021) Interleukin-1β, interleukin-6, and interleukin-17A as indicators reflecting clinical response to celecoxib in ankylosing spondylitis patients. Ir J Med Sci 190(2):631–638. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11845-020-02366-5
Article CAS PubMed Google Scholar
Zhao F, Wang M, Li S et al (2015) DACH1 inhibits SNAI1-mediated epithelial-mesenchymal transition and represses breast carcinoma metastasis. Oncogenesis 4(3):e143 Published 2015 Mar 16. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/oncsis.2015.3
Article CAS PubMed PubMed Central Google Scholar
Zheng L, Qin S, Si W et al (2021) Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science 374(6574):abe6474. https://doiorg.publicaciones.saludcastillayleon.es/10.1126/science.abe6474
Article CAS PubMed Google Scholar
Zhong G, Wang Q, Wang Y et al (2022) scRNA-seq reveals ATPIF1 activity in control of T cell antitumor activity. Oncoimmunology 11(1):2114740. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/2162402X.2022.2114740
Article PubMed PubMed Central Google Scholar
Zhou S, Meng F, Du S et al (2021) Bifunctional iRGD-anti-CD3 enhances antitumor potency of T cells by facilitating tumor infiltration and T-cell activation. J Immunother Cancer 9(5):e001925. https://doiorg.publicaciones.saludcastillayleon.es/10.1136/jitc-2020-001925
Article PubMed PubMed Central Google Scholar
Zhu S, Zhu Z, Ma AH, Sonpavde GP, Cheng F, Pan CX (2021) Preclinical models for bladder cancer research. Hematol Oncol Clin North Am 35(3):613–632. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.hoc.2021.02.007
Article PubMed PubMed Central Google Scholar
Zong Y, Lin Y, Wei T, Cheng Q (2023) Lipid nanoparticle (LNP) enables mRNA delivery for cancer therapy. Adv Mater 35(51):e2303261. https://doiorg.publicaciones.saludcastillayleon.es/10.1002/adma.202303261
Article CAS PubMed Google Scholar

Download references

Acknowledgements

None.

Funding

This study was supported by Scientific Research Fund Project of Hebei Provincial Health Commission (Project No.: 20241550).

Author information

Authors and Affiliations

The Fourth Hospital of Hebei Medical University, No. 12, Health Road, Chang’an District, Shijiazhuang, 50000, Hebei, China
Ying Li, Fang Huang, Ruoying Deng & Da Jiang

Authors

Ying Li
View author publications
You can also search for this author inPubMed Google Scholar
Fang Huang
View author publications
You can also search for this author inPubMed Google Scholar
Ruoying Deng
View author publications
You can also search for this author inPubMed Google Scholar
Da Jiang
View author publications
You can also search for this author inPubMed Google Scholar

Contributions

YL, FH and RYD wrote the paper and conceived and designed the experiments; YL analyzed the data; DJ collected and provided the sample for this study. All authors have read and approved the final submitted manuscript.

Corresponding author

Correspondence to Da Jiang.

Ethics declarations

Ethics approval and consent to participate

All animal studies are conducted in accordance with our guidelines for the care and use of laboratory animals. The research involving human participants was reviewed and approved by the Clinical Ethics Committee of The Fourth Hospital of Hebei Medical University.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

12645_2024_290_MOESM1_ESM.jpg

Additional file 1: Figure S1. Quality Control, Filtering, and PCA of scRNA-seq Data. Note: (A) Violin plots showing the number of genes (nFeature_RNA), number of mRNA molecules (nCount_RNA), and the percentage of mitochondrial genes (percent.mt) for each cell in the scRNA-seq data; (B) Scatter plots showing the correlation between filtered data nCount_RNA and percent.mt, and the correlation between nCount_RNA and nFeature_RNA; (C) Variance analysis to select highly variable genes, with red representing the top 2000 highly variable genes and black representing low variable genes, labels indicate the top 10 genes in highly variable genes; (D) Determination of cell cycle status for each cell in the scRNA-seq data, where S.Score represents S phase and G2M.Score represents G2M phase; (E) Heatmap of top 20 most correlated genes in PCA for PC_1 to PC_6, with yellow indicating upregulation and purple indicating downregulation; (F) Distribution of cells in PC_1 and PC_2 before batch correction, with each dot representing a cell; (G) Batch correction process using Harmony, with the x-axis representing the number of interactions; (H) Distribution of standard deviation for PCs, with important PCs having a larger standard deviation

12645_2024_290_MOESM2_ESM.jpg

Additional file 2: Figure S2. Expression of 18 Intersection Genes in the scRNA-seq Dataset. Note: Violin plots showing the expression of 18 intersection genes in the scRNA-seq dataset, with green boxes highlighting consistent expression trends compared to normal samples vs. early-stage TNBC samples and early-stage vs. late-stage TNBC samples.

12645_2024_290_MOESM3_ESM.jpg

Additional file 3: Figure S3. Meta-analysis of 9 DEGs in Subgroups. Note: Meta-analysis results of 9 DEGs in subgroups, with non-significant results in the meta-analysis. The x-axis represents the SMD. HSPA1A is not present in the expression matrix of the GSE76124 and GSE157284 datasets.

12645_2024_290_MOESM4_ESM.jpg

Additional file 4: Figure S4. Sensitivity Analysis of 7 Genes via Meta-analysis. Note: Sensitivity analysis results of APOBEC3G, CCL5, CD8A, IL2RG, ISG20, ITGB2, and LAT, with the x-axis representing the SMD

Additional file 5: Figure S5. Distribution of IL2RG in Other Cell Types.

12645_2024_290_MOESM6_ESM.jpg

Additional file 6: Figure S6. Verification of IL2RG KO and Overexpression Efficiency. Note: (A-B) RT-qPCR (A) and Western blot (B) analysis of IL2RG expression in CRISPR/Cas9 gene-edited IL2RG-KO cells; (C-D) RT-qPCR (C) and Western blot (D) analysis of IL2RG expression in cells after lentiviral transduction of IL2RG plasmid. All cell experiments were repeated three times, with values presented as mean ± standard deviation. **, P < 0.01.

12645_2024_290_MOESM7_ESM.jpg

Additional file 7: Figure S7. Characterization of LCP loaded with mRNA. Note: (A) Schematic of the NPs; (B) TEM image of the LCP core, bar = 100 nm; (C) Final NP encapsulating IL2RG mRNA-encoded NPs, bar = 100 nm; (D) Dynamic light scattering size of LCP; (E) Zeta potential of LCP; (F) Inverted fluorescence microscopy determination of intracellular uptake of self-assembled NPs in CD8+ T cells (blue: DAPI-stained nuclei, red: LCP NPs, bar = 25 μm).

Additional file 8.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Cite this article

Li, Y., Huang, F., Deng, R. et al. Delivery of IL2RG mRNA via nanoparticles to enhance CD8⁺ T cell promotes anti-tumor effects against late-stage triple-negative breast cancer. Cancer Nano 15, 54 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12645-024-00290-2

Download citation

Received: 21 February 2024
Accepted: 28 August 2024
Published: 22 October 2024
DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12645-024-00290-2

You are viewing the site in preview mode

Delivery of IL2RG mRNA via nanoparticles to enhance CD8+ T cell promotes anti-tumor effects against late-stage triple-negative breast cancer

Abstract

Background

Methods

Results

Conclusions

Background

Results

scRNA-seq reveals seven distinct cell types in TNBC samples

Differential T-Cell interactions and potential immunotherapy targets identified across TNBC stages

Identification of IL2RG as a potential immunotherapy target in late-stage TNBC through comprehensive analysis and validation

IL2RG modulation influences the activity and cytotoxicity of CD8+ T cells in TNBC

IL2RG drives metabolic reprogramming and enhances glycolysis in activated CD8+ T cells

IL2RG Modulation in CD8+T cells impacts tumor cell proliferation, invasion, and survival in TNBC models

IL2RG expression influences CD8+ T cell infiltration and distribution in TNBC Mouse models

LCP@IL2RG enhances CD8+ T cell effects against late-stage TNBC

Discussion

Conclusion

Methods

Clinical sample collection

scRNA-seq

Meta-analysis and ROC curve analysis

RT-qPCR

Western blot

Immunohistochemistry

Cell culture

Flow cytometry

CRISPR/Cas9 gene editing technology

Lentivirus transduction

MTT cell viability assay

Enzyme-linked immunosorbent assay (ELISA)

ATP content detection

Immuno-fluorescence

Metabolic measurement

Multicellular spheroid (MCS) formation and 3D co-culture with CD8+ T cells

Animal modeling

Construction of LCP@IL2RG

Histopathological staining

Statistical analysis

Data availability

References

Acknowledgements

Funding

Author information

Authors and Affiliations

Contributions

Corresponding author

Ethics declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Additional information

Publisher’s note

Supplementary Information

Rights and permissions

About this article

Cite this article

Share this article

Keywords

Cancer Nanotechnology

Contact us

Delivery of IL2RG mRNA via nanoparticles to enhance CD8⁺ T cell promotes anti-tumor effects against late-stage triple-negative breast cancer

IL2RG Modulation in CD8⁺T cells impacts tumor cell proliferation, invasion, and survival in TNBC models