Identification of Immune-Related Prognostic Biomarkers in Pancreatic Cancer

2020 ◽  
Vol 12 (12) ◽  
pp. 1355-1367
Author(s):  
Xiaoyan Lin ◽  
Jiakang Ma ◽  
Kaikai Ren ◽  
Mingyu Hou ◽  
Bo Zhou ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Tumor Microenvironment ◽  
Risk Score ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Individual Treatment ◽  
Survival Prediction ◽  
Lasso Regression ◽  
Immune Related Genes

Immunotherapy for pancreatic cancer (PC) faces significant challenges. It is urgent to find immunerelated genes for targeted therapy. We aimed to identify immune-related messenger ribonucleic acids (mRNAs) with multiple methods of comprehensive immunoenrichment analysis in predicting survival of PC. PC genomics and clinical data were downloaded from TCGA. We analyzed relative enrichment of 29 immune cells using ssGSEA and classified PC samples into three immuneinfiltrating subgroups. Immune cell infiltration level and pathways were evaluated by ESTIMATE data and KEGG. Independent risk factors were derived from the combined analysis of WGCNA, LASSO regression and Cox regression analyses. Immune risk score was calculated according to four mRNAs to identify its value in predicting survival. PPI analysis was used to analyze the connections and potential pathways among genes. Finally, PC samples were classified into three immuneinfiltrating subgroups. Immunity high subgroup had higher immune score, soakage of immune cells, HLA/PD-L1 expression level, immune-related pathways enrichment and better survivability. Four potential prognostic immune-related genes (ITGB7, RAC2, DNASE1L3, and TRAF1) were identified. Immune risk score could be a potential survival prediction indictor with high sensitivity and specificity (AUC values = 0.708, HR = 1.445). A PPI network with seven nodes and five potential targeted pathways were generated. In conclusion, we estimated the state of immune infiltration in the PC tumor microenvironment by calculating stromal and immune cells enrichment with ssGSEA algorithms, and identified four prognostic immune-related genes that affect the proportion and distribution of immune cells infiltration in the tumor microenvironment. They lay a theoretical foundation to be important immunity targets of individual treatment in PC.

Identification of a Novel Immune Landscape Signature for Predicting Prognosis and Response of Endometrial Carcinoma to Immunotherapy and Chemotherapy

2020 ◽  
Author(s):  
Jinhui Liu ◽  
Yichun Wang ◽  
Jie Mei ◽  
Sipei Nie ◽  
Yan Zhang
Keyword(s):  
Endometrial Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Gynecological Cancer ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Lasso Regression ◽  
Interactive Analysis ◽  
Mrna And Protein Expression ◽  
Immune Related Genes

Abstract Background: Uterine Corpus Endometrial Carcinoma (UCEC) is the most common gynecological cancer. Here, we have investigated the significance of immune-related genes in predicting the prognosis and response of UCEC patients to immunotherapy and chemotherapy.Methods: Based on TCGA database, the single-sample gene-set enrichment analysis (ssGSEA scores) was utilized to obtain enrichment of 29 immune signatures. Univariate, multivariate Cox regression and LASSO regression analyses were performed to generate an immune-related prognostic signature (IRPS). The biological functions of IRPS-associated genes were evaluated using GSEA, TIMER Database analysis, Mutation analysis, IPS analysis, Gene Expression Profiling Interactive Analysis (GEPIA), Genomics of Drug Sensitivity in Cancer (GDSC) and Immune Cell Abundance Identifier (ImmuCellAI). Potential small molecule drugs for UCEC were predicted using the connectivity map (Cmap). The mRNA and protein expression levels of IRPS-associated genes were tested via quantitative real-time PCR (qPCR) and immunohistology.Results: Two immune-related genes (CCL13 and KLRC1) were identified to construct the IRPS. Both genes were related to the prognosis of UCEC patients (P<0.05). The IRPS could distinguish patients with different prognosis and was closely associated with the infiltration of several types of immune cells. Our findings showed that patients with low IRPS benefited more from immunotherapy and developed stronger response to several chemotherapies, which was also confirmed by the results of ImmuCellAI. Finally, we identified three small molecular drugs that might improve the prognosis of patients with high IRPS. Conclusion: IRPS can be utilized to predict the prognosis of UCEC patients and provide valuable information about their therapeutic response to immunotherapy and chemotherapy.

scholarly journals Development and Validation of a Robust Apoptosis-related Prognostic Classifier in Patients With Osteosarcoma

2021 ◽  
Author(s):  
Zhifeng Zhang ◽  
Yi Wang ◽  
Fengmei Chen ◽  
Yinquan Zhang ◽  
Zhengmao Guan
Keyword(s):  
Immune Cells ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Predictive Ability ◽  
Clinical Decision ◽  
Individual Treatment ◽  
Biological Processes ◽  
Clinicopathological Factors ◽  
Lasso Regression ◽  
Development And Validation

Abstract Background: Apoptosis plays an important role in the tumorigenesis and the development of osteosarcoma, but the reliable biomarkers for individual treatment and prognosis of osteosarcoma based on apoptosis is lacking.Methods: A total of 1476 apoptosis-related genes were extracted from pathways and biological processes associated with apoptosis downloaded from MSigDB. All of those genes were used to identified the prognosis-related genes by univariate cox regression in the TARGET dataset and the ARS was constructed using the LASSO regression. The performance of the classifier was verified in the training and validation groups. The infiltration of immune cells and the expression levels of the immune checkpoint in different groups were also analyzed. Finally, a nomogram based on ARS and other Clinicopathological factors was constructed to facilitate clinical application.Results: ARS containing 22 apoptosis-related genes were identified, and its predictive ability performed well in both the training and validation groups. Macrophages M1 were highly expressed in the low-score group, and NK cells resting was highly expressed in the high-score group. The samples with low-score had higher expression of CTLA4 and PDL1. A nomogram with excellent predictive effectiveness (AUC= 0.932, 0.984, 0.939, 0.939, 0.948) was constructed to facilitate clinical decision-making.Conclusion: A prognostic classifier based on 22 apoptosis-related genes and a nomogram were constructed to predict the overall survival of patients with osteosarcoma. The classifier also provides a reference for selecting suitable patients for immunotherapy and targeted therapy.

scholarly journals A signature of seven immune‐related genes predicts overall survival in male gastric cancer patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xin Xu ◽  
Yida Lu ◽  
Youliang Wu ◽  
Mingliang Wang ◽  
Xiaodong Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Overall Survival ◽  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Survival Prediction ◽  
Immune Related Genes

Abstract Background Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC. Methods RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT. Results A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment. Conclusions Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

scholarly journals TRIM68, PIKFYVE, and DYNLL2: The Possible Novel Autophagy- and Immunity-Associated Gene Biomarkers for Osteosarcoma Prognosis

2021 ◽  
Vol 11 ◽  
Author(s):  
Jie Jiang ◽  
Dachang Liu ◽  
Guoyong Xu ◽  
Tuo Liang ◽  
Chaojie Yu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Clinical Information ◽  
Lasso Regression ◽  
Osteosarcoma Cells ◽  
Screening Process ◽  
Cox Regression Analysis ◽  
Human Osteosarcoma Cells

IntroductionOsteosarcoma is among the most common orthopedic neoplasms, and currently, there are no adequate biomarkers to predict its prognosis. Therefore, the present study was aimed to identify the prognostic biomarkers for autophagy-and immune-related osteosarcoma using bioinformatics tools for guiding the clinical diagnosis and treatment of this disease.Materials and MethodsThe gene expression and clinical information data were downloaded from the Public database. The genes associated with autophagy were extracted, followed by the development of a logistic regression model for predicting the prognosis of osteosarcoma using univariate and multivariate COX regression analysis and LASSO regression analysis. The accuracy of the constructed model was verified through the ROC curves, calibration plots, and Nomogram plots. Next, immune cell typing was performed using CIBERSORT to analyze the expression of the immune cells in each sample. For the results obtained from the analysis, we used qRT-PCR validation in two strains of human osteosarcoma cells.ResultsThe screening process identified a total of three genes that fulfilled all the screening criteria. The survival curves of the constructed prognostic model revealed that patients with the high risk presented significantly lower survival than the patients with low risk. Finally, the immune cell component analysis revealed that all three genes were significantly associated with the immune cells. The expressions of TRIM68, PIKFYVE, and DYNLL2 were higher in the osteosarcoma cells compared to the control cells. Finally, we used human pathological tissue sections to validate the expression of the genes modeled in osteosarcoma and paracancerous tissue.ConclusionThe TRIM68, PIKFYVE, and DYNLL2 genes can be used as biomarkers for predicting the prognosis of osteosarcoma.

scholarly journals Integrated analysis of single‑cell and transcriptome based RNA‑seq multilayer network and WGCNA for construction and validation of an immune cell-related prognostic model in clear cell renal cell carcinoma

2021 ◽  
Author(s):  
Guanlin Wu ◽  
Miao Liu ◽  
Weifeng Yang ◽  
Shuai Zhu ◽  
Weiming Guo ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Single Cell ◽  
Risk Score ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Marker Genes ◽  
Multilayer Network ◽  
Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cancer (RCC). The increasing incidence and poor prognosis of ccRCC after tumour metastasis makes the study of its pathogenesis extremely important. Traditional studies mostly focus on the regulation of ccRCC by single gene, while ignoring the impact of tumour heterogeneity on disease progression. The purpose of this study is to construct a prognostic risk model for ccRCC by analysing the differential marker genes related to immune cells in the single-cell database for providing help in clinical diagnosis and targeted therapy. Single-cell data and ligand-receptor relationship pair data were downloaded from related publications, and ccRCC phenotype and expression profile data were downloaded from TCGA and CPTAC. The DEGs and marker genes of the immune cell were combined and then intersected with the ligand-receptor gene data, and the 981 ligand-receptor relationship pairs obtained were intersected with the target gene of the transcription factor afterwards; 7,987 immune cell multilayer network relationship pairs were finally observed. Then, the genes in the network and the genes in TCGA were intersected to obtain 966 genes for constructing a co-expression network. Subsequently, 53 genes in black and magenta modules related to prognosis were screened by WGCNA for subsequent analysis. Whereafter, using the data of TCGA, 28 genes with significant prognostic differences were screened out through univariate Cox regression analysis. After that, LASSO regression analysis of these genes was performed to obtain a prognostic risk scoring model containing 16 genes, and CPTAC data showed that the effectiveness of this model was good. The results of correlation analysis between the risk score and other clinical factors showed that age, grade, M, T, stage and risk score were all significantly different (p < 0.05), and the results of prognostic accuracy also reached the threshold of qualification. Combined with clinical information, univariate and multivariate Cox regression analyses verified that risk score was an independent prognostic factor (p < 0.05). A nomogram constructed based on a predictive model for predicting the overall survival was established, and internal validation performed well. Our findings suggest that the predictive model built based on the immune cells scRNA-seq will enable us to judge the prognosis of patients with ccRCC and provide more accurate directions for basic relevant research and clinical practice.

scholarly journals Prognostic signature of ovarian cancer based on 14 tumor microenvironment genes

2020 ◽  
Author(s):  
Xiazi Nie ◽  
Lina Song ◽  
Xiaohua Li ◽  
Yirong Wang ◽  
Bo Qu
Keyword(s):  
Ovarian Cancer ◽  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Risk Score ◽  
Immune Cells ◽  
Prognostic Model ◽  
Cox Regression ◽  
Mrna Levels ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Ovarian cancer is one of the lethal gynecological in women. Tumor microenvironment (TME) is emerging as a pivotal biomarker for patients’ therapeutic sensitivity and prognosis. In this study, we proposed to explore the prognostic role of TME-related genes in ovarian cancer. Methods The data of whole genome expression profiles and detailed clinicopathological information of three cohorts of ovarian cancer patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Univariate Cox regression analysis was used to screen TME-related genes with significantly prognostic value based on TCGA cohort. LASSO Cox regression analysis was adapted to the construction of prognostic model. Ovarian cancer cohorts from GEO were used as validation set for verifying the reliability of the prognostic model. Relative infiltrating proportion of 22 immune cells were estimated through CIBERSORT software. Results This study identified a total of 14 TME-related genes that finally incorporated into the prognostic model. The risk score that calculated through the prognostic model was proved as an independent prognostic signature in ovarian cancer. Nomogram that contains TNM stage and risk score could reliably predict the long-term overall survival probability. Additionally, risk score was significantly associated with the relative infiltrating proportion of several immune cells in ovarian cancer and mRNA levels of some immune checkpoint genes. Conclusions This study constructed a prognostic model for ovarian cancer, which was closely associated with the prognosis and immune status. This should provide novel clue for prognosis study in ovarian cancer.

scholarly journals Development of a prognostic signature of patients with esophagus adenocarcinoma by using immune-related genes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xiangxin Zhang ◽  
Liu Yang ◽  
Ming Kong ◽  
Jian Ma ◽  
Yutao Wei
Keyword(s):  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Survival Rates ◽  
High Risk Group ◽  
Survival Prediction ◽  
Cox Regression Analysis ◽  
Related Data ◽  
Cox Analysis ◽  
Immune Related Genes

Abstract Background Esophageal adenocarcinoma (EAC) is an aggressive malignancy with a poor prognosis. The immune-related genes (IRGs) are crucial to immunocytes tumor infiltration. This study aimed to construct a IRG-related prediction signature in EAC. Methods The related data of EAC patients and IRGs were obtained from the TCGA and ImmPort database, respectively. The cox regression analysis constructed the prediction signature and explored the transcription factors regulatory network through the Cistrome database. TIMER database and CIBERSORT analytical tool were utilized to explore the immunocytes infiltration analysis. Results The prediction signature with 12 IRGs (ADRM1, CXCL1, SEMG1, CCL26, CCL24, AREG, IL23A, UCN2, FGFR4, IL17RB, TNFRSF11A, and TNFRSF21) was constructed. Overall survival (OS) curves indicate that the survival rate of the high-risk group is significantly shorter than the low-risk group (P = 7.26e−07), and the AUC of 1-, 3- and 5- year survival prediction rates is 0.871, 0.924, and 0.961, respectively. Compared with traditional features, the ROC curve of the risk score in the EAC patients (0.967) is significant than T (0.57), N (0.738), M (0.568), and Stage (0.768). Moreover, multivariate Cox analysis and Nomogram of risk score are indicated that the 1-year and 3-year survival rates of patients are accurate by the combined analysis of the risk score, Sex, M stage, and Stage (The AUC of 1- and 3-years are 0.911, and 0.853). Conclusion The 12 prognosis-related IRGs might be promising therapeutic targets for EAC.

scholarly journals Construction of a Six Immune-Related lncRNAs Signature to Predict Outcome, Immune Cell Infiltration and Immunotherapy Response in Patients with Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Pengcheng Zhou ◽  
Jiang Wei ◽  
Lu Yuhua ◽  
Wang Lei ◽  
Zhang Yewei
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Immune Cell Infiltration ◽  
Biological Processes ◽  
Qrt Pcr

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most lethal malignant tumors worldwide with poor prognosis. Growing evidence has demonstrated that immune-related long non-coding RNAs (lncRNAs) are relevant to tumor microenvironment (TME) and can help to assess the effects of immunotherapy and evaluate prognosis. This study aimed to identify an immune-related lncRNA signature for the prospective assessment of immunotherapy and prognosis in HCC. Methods: HCC RNA-seq data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) project database. Firstly, we used ESTIMATE to evaluate the tumor microenvironment (TME). Then, cox regression analysis was used to construct a prognostic signature and the risk score. Univariate Cox regression, multivariate Cox regression, principal components analysis (PCA), the receiver operating characteristic (ROC) curve and stratification analyses were applied to confirm. Gene set enrichment analysis (GSEA) analysis was employed to explore the biological processes and pathways. Besides, CIBERSORT was used to estimate the abundance of tumor-infiltrating immune cells (TIICs). Furthermore, the relationship between the immune-related lncRNA signature and immune checkpoint genes was investigated. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) assays were used to demonstrated the expression of the six lncRNAs. Results:.We identified a six immune-related lncRNAs (MSC-AS1, AC145207.5, SNHG3, AL365203.2, AL031985.3, NRAV) with the ability to stratify patients into high-risk and low-risk groups with significantly different survival. Univariate Cox regression, multivariate Cox regression, ROC and stratification analyses confirmed that the six immune-related lncRNA signature was a novel independent prognostic factor in HCC patients. The high-risk group and low-risk group illustrated different distributions in PCA. GSEA suggested that the six immune-related lncRNA signature is involved in the immune-related biological processes and pathways. Besides, the six immune-related lncRNA signature was associated with the infiltration of immune cells. Furthermore, the six immune-related lncRNA signature was associated with the expression of critical immune genes and could predict the clinical response of immunotherapy. Finally, qRT-PCR demonstrated that the six lncRNAs were significantly differentially expressed in HCC cell lines and normal hepatic cell line. Conclusions: In summary, we identified a six immune-related lncRNA signature with the ability to predict outcome, immune cell infiltration and immunotherapy response in patients with hepatocellular carcinoma.

scholarly journals Identification of a Novel Immune Landscape Signature for Predicting Prognosis and Response of Endometrial Carcinoma to Immunotherapy and Chemotherapy

2021 ◽  
Vol 9 ◽  
Author(s):  
Jinhui Liu ◽  
Yichun Wang ◽  
Jie Mei ◽  
Sipei Nie ◽  
Yan Zhang
Keyword(s):  
Endometrial Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Gynecological Cancer ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Selection Operator ◽  
Immune Related Genes

Uterine Corpus Endometrial Carcinoma (UCEC) is the most common gynecological cancer. Here, we have investigated the significance of immune-related genes in predicting the prognosis and response of UCEC patients to immunotherapy and chemotherapy. Based on the Cancer Genome Atlas (TCGA) database, the single-sample gene-set enrichment analysis (ssGSEA) scores was utilized to obtain enrichment of 29 immune signatures. Univariate, multivariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to generate an immune-related prognostic signature (IRPS). The biological functions of IRPS-associated genes were evaluated using GSEA, Tumor Immune Estimation Resource (TIMER) Database analysis, Mutation analysis, Immunophenoscore (IPS) analysis, Gene Expression Profiling Interactive Analysis (GEPIA), Genomics of Drug Sensitivity in Cancer (GDSC) and Immune Cell Abundance Identifier (ImmuCellAI). Potential small molecule drugs for UCEC were predicted using the connectivity map (Cmap). The mRNA and protein expression levels of IRPS-associated genes were tested via quantitative real-time PCR (qPCR) and immunohistology. Two immune-related genes (CCL13 and KLRC1) were identified to construct the IRPS. Both genes were related to the prognosis of UCEC patients (P &lt; 0.05). The IRPS could distinguish patients with different prognosis and was closely associated with the infiltration of several types of immune cells. Our findings showed that patients with low IRPS benefited more from immunotherapy and developed stronger response to several chemotherapies, which was also confirmed by the results of ImmuCellAI. Finally, we identified three small molecular drugs that might improve the prognosis of patients with high IRPS. IRPS can be utilized to predict the prognosis of UCEC patients and provide valuable information about their therapeutic response to immunotherapy and chemotherapy.

scholarly journals Endothelial Cells Immune-Related Genes Risk Signature Correlated With Tumor Immune Microenvironment Predicts Therapeutic Response and Prognosis in Glioblastoma

2021 ◽  
Author(s):  
Qijun Xie ◽  
Yuwei Zhang ◽  
Wu Huang ◽  
Haoran Wang ◽  
Fang Liu
Keyword(s):  
Endothelial Cells ◽  
Regression Analysis ◽  
Risk Score ◽  
Therapeutic Response ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Glioblastoma (GBM) is the most common and aggressive primary tumor of the central nervous system with high recurrence and extremely poor prognosis. Multiple recent studies have indicated a pivotal correlation between GBM prognosis and immune-related risk signature. Nevertheless, the potential value of endothelial cells (ECs) immune-related genes (EIRGs) in prognosis, immune infiltration, and their correlation with therapeutic response to immunotherapy and TMZ chemotherapy remain obscure, especially in GBM. Here, we screened out 11 EIRGs after intersecting the identified 59 GBM ECs related prognostic genes and the identified 438 immune-related prognostic genes. A prognostic-related 6-EIRGs signature was established through univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and patients in the high-risk group were significantly worse overall survival (OS) compared to those in the low-risk group. Additionally, univariate and multivariate Cox regression analysis confirmed that risk score was an independent predictor of OS in patients with GBM. The nomogram which comprised age, gender, IDH mutation status, radiation therapy, and risk score yielded a strong predictive ability of 0.5-, 1-, and 2-year OS for GBM patients. Our results demonstrate that the EIRGs signature, which is associated with immune cell infiltration, may play a regulatory role in the immunobiological process of TIME. Prognostic-related 6-EIRGs signature is a promising classification index for predicting the drug sensitivity to immunotherapy and TMZ chemotherapy, suggesting that EIRGs signature may serve as a biomarker to stratify patients who will benefit from immunotherapy and chemotherapy.

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