Construction of an immune-related gene prognostic model with experimental validation and analysis of immune cell infiltration in lung adenocarcinoma
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- Published online on: May 1, 2024 https://doi.org/10.3892/ol.2024.14430
- Article Number: 297
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Copyright: © Yang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Lung cancer is the second most common malignant tumor worldwide (1). Lung cancer is primarily divided into non-small cell lung cancer (NSCLC) and small cell lung cancer based on histopathological features (2). Lung adenocarcinoma (LUAD) accounts for >40% of NSCLC (3) and is a common type of lung cancer tissue (4). LUAD patients are typically older when diagnosed, at a later disease stage, possess a worse prognosis, and have a 5-year survival rate <20% (5). Surgery, radiotherapy and chemotherapy are the primary treatment modalities for lung cancer. However, their efficacy is often limited in patients with metastatic lung cancer. Therefore, an in-depth exploration of the molecular mechanisms related to the pathogenesis of LUAD could support the search for optimized approaches to early diagnosis and treatment targeting specific genes.
The role of the immune system in the occurrence and development of tumors is complex. It can eliminate tumor cells in specific tissues, establish an inflammatory environment that prevents tumorigenesis by removing pathogens and inflammation, and promote tumor growth through immunoediting amongst other methods (6). Moreover, tumor cells can adopt various methods to evade immune system attack, including changing surface antigens and inhibiting immune cell activity. The primary components of tumor immunity are an immunosuppressive tumor microenvironment (TME) and dysfunctional anti-tumor T cells (7). The complex surroundings around the tumor, including components such as molecules, blood vessels, and other non-tumor cells, are known as the TME and influence both the anti-tumor immune response and immunotherapy effectiveness (8). Development in immunotherapy have been remarkably successful in treating certain cancers. In LUAD, patients were administered immune checkpoint inhibitors (ICIs) to target programmed cell death 1, programmed cell death ligand 1 and cytotoxic T lymphocyte antigen 4 which improved survival (9). However, there are some issues with immunotherapy, such as acquired resistance and severe side effects (10). A previous study reported that tumor cell and TME interaction in spatiotemporal dynamics was essential for tumor progression (11). Thus, tumor growth promotes the development of immunological tolerance in the body, weakening the therapeutic effect of ICIs (12). Not every lung adenocarcinoma patient responds to ICI treatment due to the effects of immune tolerance. Moreover, the abnormal expression of tumor immune-related genes in tumor escape has become a novel direction of focus in tumor research (13). However, to the best of our knowledge, only a few reports exist on how abnormal immune-related genes affect lung adenocarcinoma prognosis and immune cell infiltration. Therefore, elucidating the immune genes associated with lung adenocarcinoma prognosis and constructing a LUAD prognosis model based on immune cell infiltration in such patients, could have clinical value.
In the present study, LUAD-related data were retrieved from The Cancer Genome Atlas (TCGA) database and bioinformatics were used to analyze the immune-related genes significantly linked with the prognosis of LUAD patients. An immune-related genes prognostic model was constructed and its independent prediction power was assessed. The study also used reverse transcription-quantitative (RT-qPCR) to analyze immune-related prognostic gene expression while evaluating immune cell infiltration and function levels in LUAD. Thus, an immune-related prognostic risk model was developed for LUAD based on disease transcriptomics, offering novel targets for therapeutic immunotherapy.
Materials and methods
Data sources
The RNA sequencing and clinical data of 535 LUAD samples and 59 normal tissues were retrieved from the TCGA database (http://www.cancer.gov/). The gene expression data were corrected in batches and those with missing information regarding clinical features were eliminated. Finally, there were 522 LUAD samples and normal tissues. A total of 2,483 immune-related genes were downloaded from the Immunology Database and Analysis Portal (ImmPort) database (http://www.immport.org/). LM22 marker genes were downloaded from the CIBERSORT website (https://cibersortx.stanford.edu). The Human Protein Atlas (https://www.proteinatlas.org) database was used to retrieve prognostic immune-related gene protein expression data. The TIDE algorithm was used to predict responses to ICIs therapy (14).
Screening of immune-related differential genes and enrichment analysis
Differential analysis was performed across all LUAD patient genes using the limma package (https://bioinf.wehi.edu.au/limma/), with fdrFilter=0.05 and logFCfilter=2, followed by construction of the differential gene volcano diagram with graphics (https://www.R-project.org/). The differentially expressed immune-related genes were obtained by assessing the intersection of the differentially expressed genes (DEGs) with 2483 immune-related genes with the Venn diagram depicting the intersection results by venn package (https://CRAN.R-project.org/package=venn). Gene Set Enrichment Analysis (GSEA) (15), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (https://ggplot2.tidyverse.org) enrichment analysis were performed on the differentially expressed immune-related genes previously identified. Univariate Cox regression analysis was used to identify the immune-related genes associated with prognosis and DEGs with P<0.05 were included in the multifactor Cox proportional hazards model analysis to obtain the immune-related genes which were subsequently used to construct the prognostic model.
Construction and analysis of prognostic risk models
The expression levels of the prognostic immune-related genes, identified by the aforementioned multivariate Cox regression analyses, were multiplied using the coefficients obtained from their respective multivariate Cox regression analyses to determine the risk score of each patient. A prognostic risk model was then constructed using multifactor Cox proportional hazards model analysis. Patients were divided into high and low-risk groups with the median risk score as the cutoff value, followed by the plotting of the Kaplan-Meier survival curve (https://CRAN.R-project.org/package=survival), receiver operating characteristic (ROC) curve (16) and nomogram (https://CRAN.R-project.org/package=rms). Univariate and multivariate Cox regression analyses were also performed for the hazard ratio (HR) of the risk score and clinical characteristics including age, gender, stage and, tumor (T), node (N) and metastasis (M) scores.
Immune infiltration analysis in LUAD
The LUAD immune cells of LM22 marker genes were analyzed using the CIBERSORT algorithm, with P<0.05 being considered to indicate a statistically significantly difference. The Wilcox.test was used to assess the differences in immune cell expression in LUAD, and a differential heatmap of lung adenocarcinoma immune cells was drawn. Student's t-test was used to evaluate differences in immune cells and the relationship between prognostic genes and immune cells in high and low-risk groups. Single sample GSEA was used to compare Tumor Immune Dysfunction and Exclusion (TIDE) scores between high and low-risk groups and to determine immune-related functional activity.
Cell culture
Human normal lung epithelial MRC5 cells and human NSCLC A549 and H1975 cells were purchased from The Cell Bank of Type Culture Collection of the Chinese Academy of Sciences. The cells were cultured in DMEM high glucose (H) medium containing 10% fetal bovine serum and incubated at 37°C with 5% CO2, with the medium replaced every 2–3 days.
RNA extraction and RT-qPCR
Total RNA was extracted from MRC5, A549 and H1975 cells using TRIzol® (Invitrogen; Thermo Fisher Scientific, Inc.). RT-PCR was performed after removing genomic DNA, according to the instructions of the PrimeScript™ RT reagent kit with gDNA Eraser (Takara Biotechnology Co., Ltd.). RT-qPCR primers were designed and synthesized by Sangon Biotech Co., Ltd. (Table I). The RT-qPCR solution comprised 12.5 µl TB Green Premix Ex TaqII (Takara Biotechnology Co., Ltd.), 1.0 µl PCR forward primer (10 µΜ), 1.0 µl PCR reverse primer (10 µΜ), 2 µl RT reaction solution (complimentary DNA solution), and 8.5 µl sterilized water, for a total volume of 25 µl. The RT-qPCR reaction conditions were as follows: 95°C for 30 sec, then 40 cycles of 95°C for 5 sec and 60°C for 30 sec. The relative gene expression was determined using the 2−∆∆Cq method (17), with GAPDH used as an internal control. Each experiment was performed in triplicate.
Statistical analysis
All statistical analysis and presentations were performed using the R 4.3.0 software package (https://mirrors.tuna.tsinghua.edu.cn/CRAN/). The Wilcox.test was used to compare the differential expression of immune-related genes in LUAD and normal tissues. Immune-related genes linked with poor prognosis in LUAD were identified using multivariate Cox regression analysis. The correlation of prognostic genes and transcription factors was assessed using t-test. The comparison of the expression levels of prognostic genes in MRC5, H1975 and A549 cells was performed using one-way ANOVA with Dunnett's multiple comparisons test as the post-hoc test. Kaplan-Meier curves were used for survival analyses. P<0.05 was considered to indicate a statistically significant difference.
Results
Differential immune-related gene expression and enrichment analysis in LUAD
The clinicopathological characteristics of the 522 LUAD samples from the TCGA database were assessed (Table II). The intersection of DEGs and immune related genes in LUAD identified 505 differentially immune-related genes using logFCfilter=1 and fdrFilter=0.05 for these genes in LUAD (Fig. 1B). Among these, 338 genes were up-regulated and 167 were down-regulated (Fig. 1A and B). The volcano diagram was drawn using the DEGs. GO enrichment analysis of these differentially immune-related genes indicated that they were primarily enriched in biological processes such as ‘immunoglobulin production’ and ‘production of molecular mediator of immune response’, cellular components such as ‘immunoglobulin complex’ and ‘external side of plasma membrane’, and molecular functions such as ‘antigen binding’ and ‘receptor ligand activity’ (Fig. 1C). KEGG analysis indicated the differentially immune-related genes were mainly enriched in ‘cytokine-cytokine receptor interaction’, ‘neuroactive receptor-ligand interaction’, ‘viral protein ineraction with cytokine and cytokine receptor’ and ‘chemokine signaling pathway’ pathways (Fig. 1D). GSEA analysis demonstrated primary enrichment in ‘antimicrobial peptides’, ‘regulation of insulin-like growth factor (IGF) transport and uptake by IGF’, ‘post-translational protein modification’ and ‘metabolism of proteins’ (Fig. 1E).
Construction of a prognostic model using immune-related genes
Seven differential immune-related genes associated with prognosis were obtained using univariate Cox regression analysis (Fig. 2A). Multifactor Cox proportional hazards model analysis was performed on these seven genes, from which six prognostic genes (S100A16, FURIN, FGF2, LGR4, TNFRSF11A and VIPR1) were significantly associated with the occurrence and development of LUAD (Table III). The multivariate regression coefficients of the six prognostic immune-related genes were multiplied with their respective expression levels in each sample. Thus, the risk score of each sample was calculated as follows: risk score=S100A16 expression level × 0.002303 + FURIN expression level × 0.00073 + FGF2 expression level × 0.24979 + LGR4 expression level × 0.015309 + TNFRSF11A expression level × 0.176773 - VIPR1 expression level × 0.14724. Depending on the median risk score, the samples were differentiated into high and low-risk groups. The risk curve indicated the mortality of most patients in the high-risk group; with an increase in the patient's risk value, more patients died (Fig. 2B and C). The survival heat map indicated that the expression levels of S100A16, FURIN, FGF2, LGR4 and TNFRSF11A gradually increased with the risk score. In contrast, VIPR1 gene expression gradually decreased (Fig. 2D).
Independent prognostic analysis
The survival analysis of the prognostic risk model indicated that the survival time of patients with LUAD in the high-risk score group was significantly lower than those in the low-risk score group. The 5-year overall survival rate of patients in the high-risk score group was ~23%, while that of those in the low-risk score group was ~51% (Fig. 3A). The ROC curve indicated an area under the curve of 0.707 (Fig. 3B), which suggested that the prognostic model possessed strong predictive ability. A nomogram was built as a quantitative method for the six immune-related genes to predict LUAD prognosis. The nomogram point scale made it possible to assign a value to a single variable by constructing a straight line between the predicted curve and the total point axis. Moreover, the nomogram could be used to estimate the 1, 3 and 5-year overall survival rates of the patients with LUAD (Fig. 3C) through the predictive power of the prognostic risk model. The univariate Cox regression analysis of the risk score of the prognostic model and the clinical characteristics of LUAD patients (age, sex, stage and TNM scores) revealed that the stage, T, N and risk score all independently predicted the prognosis of patients LUAD (Fig. 3D). Multivariate Cox regression analysis indicated that the risk score independently predicted the prognosis of patients with LUAD (Fig. 3E).
Prognostic immune-related gene expression and survival analysis
The expression levels of S100A16, FURIN, LGR4 and TNFRSF11A were significantly higher in tumor tissues compared with normal tissues and the expression levels of VIPR1 and FGF2 were significantly lower in tumor tissues compared with normal tissues (Fig. 4A-F). Patients with high S100A16, FURIN and LGR4 gene expression had a significantly worse prognosis compared with the low expression group, while patients with low VIPR1 gene expression had a significantly worse prognosis compared with the high expression group. However, FGF2 and TNFRSF11A did not demonstrate a significant impact on the survival time (Fig. 4G-L).
Prognostic gene validation
S100A16, FURIN, LGR4, TNFRSF11A and VIPR1 mRNA expression levels were significantly higher in human lung cancer H1975 and A549 cells compared with normal human embryonic lung MRC5 cells. FURIN mRNA expression levels were significantly higher in human lung cancer H1975 compared with normal human embryonic lung MRC5 cells. However, FGF2 expression was significantly lower in the human lung cancer H1975 and A549 cells compared with normal human embryonic lung MRC5 cells (Fig. 5A). Due to the low copy numbers of S100A16, FURIN and FGF2 in all three cell lines, the significance of gene expression differences between the cell lines should be treated with caution. S100A16, FURIN, TNFRSF11A and VIPR1 were not stained for in normal alveolar tissue cells, with weak/moderate expression in human lung tumor tissue cells. Additionally, FGF2 staining was weak in normal alveolar and lung tumor tissue cells (Fig. 5B-D). These results indicated the inconsistency of the VIPR1 expression between LUAD and lung cancer and differences in FGF2 expression in cell lines and tissues.
Analysis of immune cell infiltration in LUAD
The heat map of immune cell infiltration indicated higher levels of macrophages M0, T cells CD4 memory resting and macrophages M2 cells within LUAD tissue (Fig. 6A). Immune cell component analysis indicated that most immune cells were differentially distributed between the lung adenocarcinoma samples and adjacent tissues (Fig. 6B). The degree of infiltration of B cells naive, plasma cells, T cell CD4 memory activated, T cell follicular helper, T cells regulatory (Tregs), macrophages M2 and dendritic cells resting were significantly higher in lung adenocarcinoma tissue than normal tissues. However degree of infiltration of T cells CD4 memory resting, NK cells resting, macrophages M0, macrophages M1, mast cells resting, eosinophils and neutrophils were significantly lower in lung adenocarcinoma tissue than normal tissues. The correlation between risk genes and immune cells indicated that FURIN was significantly positively associated with T cells CD4 memory resting, and significantly negatively associated with T cells regulatory (Tregs), T cells CD8, B cells memory, macrophages M1, dendritic cells resting and mast cells resting. FGF2 was significantly positively associated with mast cells resting, monocytes, NK cells resting, Tregs and significantly negatively associated with plasma cells and mast cells activated. TNFRSF11A was significantly positively associated with macrophages M0 and mast cells resting, and was significantly negatively associated with plasma cells. VIPR1 was positively associated with dendritic cells activated, macrophages M2, mast cells resting, monocytes, and significantly negatively associated with T cells CD4 memory activated, dendritic cells resting, neutrophils, plasma cells and macrophages M0 (Fig. 6C).
Immune-infiltrating cell survival analysis
The prognosis of patients with LUAD with high expression of macrophages M1, T cells CD4 memory activated, NK cells resting and T cells CD8 activated was significantly worse compared with the low expression group (Fig. 7A-D). However, the low expression of plasma cells, mast cells resting and monocytes indicated a significantly worse prognosis compared with the high expression group (Fig. 7E-G).
Immune-related functional analysis
Antigen presenting cell (APC) co-stimulation, APC co-inhibition, chemokine receptor (CCR), check-point, cytolytic-activity, inflammation-promoting, major histocompatibility complex (MHC)-class-I, parainflammation, T-cell-co-inhibition and Type-I–IFN-Response immune function were expressed at a significantly higher level within the high-risk group (Fig. 8A). Most tumor proliferation-associated function was improved in the high-risk group, which suggested that tumor proliferation was more active (Fig. 8A). The risk model tumor immune escape analysis demonstrated significantly lower TIDE and dysfunction scores within the high-risk group compared with the low-risk group, however the exclusion score was significantly higher in the high-risk group compared with the low-risk group (Fig. 8B-E).
Discussion
Non-small cell lung cancer is the leading cause of lung cancer related deaths worldwide (18). LUAD is the primary lung cancer subtype with specific epidemiological, molecular and clinical features (19). Currently, lung cancer treatment comprises surgery, radiotherapy, chemotherapy (20), targeted drug therapy and immunotherapy (2). For patients with metastatic lung cancer, conventional radiotherapy and chemotherapy may have limited efficacy (21). Tumor immunotherapy is a promising treatment for use following surgery, radiotherapy, chemotherapy or targeted therapy. Previous clinical studies have reported that many tumors are insensitive to immunotherapy due to the immune tumor microenvironment, with immune genes serving a vital role in this insensitivity. Previous studies have reported that CD3, IL-12 and IL-17 expression in the tumor stroma is significantly linked with postoperative recurrence of early LUAD (22–24). Furthermore, many immune factors and cells (such as, neutrophils, macrophages and lymphocytes) are associated with angiogenesis, cell proliferation and invasion in LUAD (25). Therefore, the elucidation of immune-related genes with biological significance and assessment of their prognostic value could aid the diagnosis and treatment of patients with LUAD.
Previous studies have demonstrated that abnormally expressed genes can be utilized as prognostic LUAD markers (26–28). However, only a few previous studies have reported the abnormally expressed immune genes and their immune infiltration role (29–31). The present study first identified six immune-related genes closely related to the prognosis of LUAD patients, including S100A16, FURIN, FGF2, LGR4, TNFRSF11A and VIPR1. These genes serve an important biological role in the occurrence and development of tumors. Among these genes, S100A16 is highly conserved in mammals (32), and its high expression is linked with tumor cell growth and EMT (33). S100A16 is a prognostic marker for various tumors, including LUAD and colorectal cancer (34,35). The present study showed that S100A16 was highly expressed in NSCLC cells and that its high expression was associated with a worse prognosis in LUAD. FURIN is a protein conversion enzyme expressed in breast cancer, squamous cell carcinoma and striated muscle tumors, which indicates that it may hold an important role. FURIN expression is elevated in NSCLC, and upregulated RURIN activity correlates with accelerated tumor progression (36,37). The present study found high FURIN expression was associated with a worse prognosis in patients with LUAD and with an increased number of infiltrated immune cells. FGF2 is a fibroblast growth factor family member and exerts mitogenic activity by binding to fibroblast factor receptors (38). It is a tumor angiogenesis factor, and neutrophils are reported to enhance the growth of liver metastases through FGF2-dependent angiogenesis, converting tumor-associated macrophages into pro-tumorigenic M2 macrophages (39). The present study demonstrated that the low FGF2 expression in LUAD cells was closely linked with increased numbers of infiltrated immune cells. LGR4 is a receptor of the G protein-coupled receptors superfamily, which regulate signaling pathways in normal and pathological processes. LGR4 is commonly activated by R-spondins, Norrin and receptor activators of NF-κβ ligand. LGR4 activation leads to signaling in the Wnt/β-catenin and G protein-associated pathways (40). LGR4 is highly expressed in several cancers, enhances tumorigenesis and metastasis, regulates cancer stem cells and is linked with poor patient prognosis (41–43). The present study showed a high level of LGR4 expression in NSCLC cells, with a worse prognosis for patients with LUAD. TNFRSF11A is a crucial regulator of cell differentiation, proliferation and survival, an inducer for the activation of dendritic cells, and a critical factor in maintaining immune tolerance (44). TNFRSF11A promotes cervical cancer cell proliferation and inhibits cell apoptosis (45). The present study showed high TNFRSF11A expression in LUAD cells which was related to increased infiltrated immune cells. VIP is a vital neuropeptide controlling lung physiology and mainly functions through two receptor subtypes, VAPC1 and VAPC2 (46). VIP or VPAC1 receptor antagonist strengthens the ability of chemotherapy to kill breast cancer cells (47) while improving anti-viral immunity (48). VIPR1 inhibits the growth of certain cancers including prostate cancer and lymphoblastoma, and VIPR1 overexpression inhibits the proliferation of LUAD H1299 cells (49). The present study found a low VIPR1 expression in LUAD cells, which was associated with infiltrated immune cells, and patients with low expression of VIPR1 had a worse prognosis.
The prognostic risk model generated in the present study depicted significant prognostic differences between the high and low-risk groups. Immune cell infiltration, immune cell-related functions and TIDE were compared in high and low-risk patient groups to elucidate the potential reasons behind the prognostic differences. We jypothesize that the differences in the degree of tumor immune cell infiltration, immune-related functions and low TIDE score may be potential mechanisms affecting the prognosis of patients with LUAD. The present study indicated that immune cells, including T cells CD4 memory resting, T cells CD8, macrophages M0, macrophages M1, plasma cells and B cells naive were dominant in both LUAD and normal tissue. Thus, monocyte macrophages, NK cells resting and T cells follicular helper were also expressed in LUAD. Macrophages M1 and M2 are crucial immune cells polarized by circulating monocytes (50). Macrophages M1 generate reactive oxygen/nitrogen species and pro-inflammatory factors critical for host defense and tumor cell death. The increase in the levels of macrophages M1 has been reported to promote the tumor-antagonistic effect of M1-polarized macrophages and enhances the survival trend of colorectal cancer patients (51). However, macrophages M2 promote angiogenesis and matrix remodeling, which enhance tumor progression and metastasis, and suppress immune surveillance of tumor cells (52). Although NK cells are essential for tumor immune surveillance, a previous study reported that their ability to kill target cells and produce IFN-γ may be decreased in NSCLC tumors (53). The current study demonstrated that levels of NK cells activated were lower in LUAD; thus, infiltrating NK cells is inhibited. NK cells resting in normal tissues express more without releasing their toxic particles before non-specific target cell recognition. In recent years, increasing evidence has demonstrated effective interaction between NK cells and B cells (54). Although B cells can release cytokines with cytotoxic T-cell activity and function as effective antigen-presenting cells, their anti-tumor role in the TME remains debatable. Nevertheless, previous studies have described that B cells can recruit myeloid-derived suppressor cells, improve vascular survival by producing cytokines (55) and potentially induce tumors by blocking cytotoxic T lymphocyte function (56).
Immune function scores, including APC-co-inhibition, APC co-stimulation, CCR, MHC-class-I, parainflammation and T-cell-co-inhibition were significantly elevated in the high-risk group. Thus, the density of immune dysfunction was higher, with a worse prognosis in the high-risk group. The present study also assessed the difference in TIDE scores between high and low-risk groups, and the result showed the high-risk group with a lower TIDE score. TIDE analysis focuses on the function and status of T cells without reflecting the complexity of the immune cell infiltration in the TME which is associated with immunotherapy responses (57). However, TIDE can predict patient response to immunotherapy (58). The TIDE score calculates the efficacy of ICIs for treating tumors. A high TIDE score indicates poor ICI efficacy with a short survival period post-ICI treatment. The current study demonstrated a low TIDE score within the prognostic model for the high-risk group. Thus, LUAD tumors of the high-risk group are less likely to escape immune killing, enhancing their treatment using better immunotherapy. Higher TIDE scores are linked with a greater likelihood of immune evasion and reduced survival in patients receiving ICIs, rendering immunotherapy less effective (14). The low-risk group had higher immune evasion potential. This indicated that the high-risk group could derive greater benefit from immunotherapy with an improved prognosis.
The current study has certain limitations. First, some of the roles and mechanisms of genes in the LUAD model remain unclear. Therefore, further experiments are required to assess their functions and mechanisms. Second, the study data came from public databases and most patients did not receive immunotherapy. Deficiencies also exist in predicting the prognosis of lung cancer patients who underwent immunotherapy, highlighting the need for additional clinical samples. Third, these results were primarily obtained using bioinformatic analysis and lack clinical, in vitro and in vivo studies to support the results.
In conclusion, the present study identified six immune-related genes linked with patient prognosis, and a prognostic risk model was constructed for LUAD based on bioinformatic analysis. The survival times of patients with LUAD could be independently predicted using the prognostic risk model. It is anticipated that future treatments for LUAD molecular targets will require targeting of these genes to guide the diagnosis and treatment of LUAD patients.
Acknowledgments
The authors would like to thank Ms. Mao Yang, Miss Wenjun Wang and Miss Xianyu Zhu (Institute for Cancer Medicine, School of Basic Medicine Sciences, Southwest Medical University, Luzhou, Sichuan, China) for their support in performing experiments.
Funding
The present study was supported by the Sichuan Science and Technology Program (grant no. 2022YFS0623), the Sichuan Science and Technology Program Joint Innovation Grant (grant no. 2022YFS0623-B3), Southwest Medical University Grant (grant no. 2021ZKZD005) and Sichuan Science and Technology Grant (grant no. 2020YFH0121).
Availability of data and materials
The data generated in the present study may be requested from the corresponding author.
Authors' contributions
JY and WY designed the study, drafted the manuscript and confirm the authenticity of all the raw data. JY performed TCGA data analysis and drew the figures. CT performed the cellular experiments and qPCR, and analyzed the data. CL helped perform the cellular experiments and analyzed the data. XL helped design the study, provided the cells and critically revised the drafted work for content. All the authors read and approved the final version of the manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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