scholarly journals Identification and validation of a three-gene signature as a candidate prognostic biomarker for lower grade glioma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8312 ◽  
Author(s):  
Kai Xiao ◽  
Qing Liu ◽  
Gang Peng ◽  
Jun Su ◽  
Chao-Ying Qin ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Prognostic Biomarker ◽  
Risk Group ◽  
Gene Signature ◽  
Low Risk ◽  
Low Grade ◽  
Lower Grade ◽  
Lower Grade Glioma ◽  
Robust Likelihood

Background Lower grade glioma (LGG) are a heterogeneous tumor that may develop into high-grade malignant glioma seriously shortens patient survival time. The clinical prognostic biomarker of lower-grade glioma is still lacking. The aim of our study is to explore novel biomarkers for LGG that contribute to distinguish potential malignancy in low-grade glioma, to guide clinical adoption of more rational and effective treatments. Methods The RNA-seq data for LGG was downloaded from UCSC Xena and the Chinese Glioma Genome Atlas (CGGA). By a robust likelihood-based survival model, least absolute shrinkage and selection operator regression and multivariate Cox regression analysis, we developed a three-gene signature and established a risk score to predict the prognosis of patient with LGG. The three-gene signature was an independent survival predictor compared to other clinical parameters. Based on the signature related risk score system, stratified survival analysis was performed in patients with different age group, gender and pathologic grade. The prognostic signature was validated in the CGGA dataset. Finally, weighted gene co-expression network analysis (WGCNA) was carried out to find the co-expression genes related to the member of the signature and enrichment analysis of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were conducted for those co-expression network. To prove the efficiency of the model, time-dependent receiver operating characteristic curves of our model and other models are constructed. Results In this study, a three-gene signature (WEE1, CRTAC1, SEMA4G) was constructed. Based on the model, the risk score of each patient was calculated with LGG (low-risk vs. high-risk, hazard ratio (HR) = 0.198 (95% CI [0.120–0.325])) and patients in the high-risk group had significantly poorer survival results than those in the low-risk group. Furthermore, the model was validated in the CGGA dataset. Lastly, by WGCNA, we constructed the co-expression network of the three genes and conducted the enrichment of GO and KEGG. Our study identified a three-gene model that showed satisfactory performance in predicting the 1-, 3- and 5-year survival of LGG patients compared to other models and may be a promising independent biomarker of LGG.

scholarly journals A Novel Four-Gene Signature Associated With Immune Checkpoint for Predicting Prognosis in Lower-Grade Glioma

2020 ◽  
Vol 10 ◽  
Author(s):  
Youchao Xiao ◽  
Gang Cui ◽  
Xingguang Ren ◽  
Jiaqi Hao ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Immune Checkpoint ◽  
Risk Groups ◽  
Gene Signature ◽  
Low Risk ◽  
Lower Grade ◽  
Lower Grade Glioma ◽  
Risk Patients

The overall survival of patients with lower grade glioma (LGG) varies greatly, but the current histopathological classification has limitations in predicting patients’ prognosis. Therefore, this study aims to find potential therapeutic target genes and establish a gene signature for predicting the prognosis of LGG. CD44 is a marker of tumor stem cells and has prognostic value in various tumors, but its role in LGG is unclear. By analyzing three glioma datasets from Gene Expression Omnibus (GEO) database, CD44 was upregulated in LGG. We screened 10 CD44-related genes via protein–protein interaction (PPI) network; function enrichment analysis demonstrated that these genes were associated with biological processes and signaling pathways of the tumor; survival analysis showed that four genes (CD44, HYAL2, SPP1, MMP2) were associated with the overall survival (OS) and disease-free survival (DFS)of LGG; a novel four-gene signature was constructed. The prediction model showed good predictive value over 2-, 5-, 8-, and 10-year survival probability in both the development and validation sets. The risk score effectively divided patients into high- and low- risk groups with a distinct outcome. Multivariate analysis confirmed that the risk score and status of IDH were independent prognostic predictors of LGG. Among three LGG subgroups based on the presence of molecular parameters, IDH-mutant gliomas have a favorable OS, especially if combined with 1p/19q codeletion, which further confirmed the distinct biological pattern between three LGG subgroups, and the gene signature is able to divide LGG patients with the same IDH status into high- and low- risk groups. The high-risk group possessed a higher expression of immune checkpoints and was related to the activation of immunosuppressive pathways. Finally, this study provided a convenient tool for predicting patient survival. In summary, the four prognostic genes may be therapeutic targets and prognostic predictors for LGG; this four-gene signature has good prognostic prediction ability and can effectively distinguish high- and low-risk patients. High-risk patients are associated with higher immune checkpoint expression and activation of the immunosuppressive pathway, providing help for screening immunotherapy-sensitive patients.

scholarly journals Gene signature for prognosis in comparison of pancreatic cancer patients with diabetes and non-diabetes

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10297
Author(s):  
Mingjun Yang ◽  
Boni Song ◽  
Juxiang Liu ◽  
Zhitong Bing ◽  
Yonggang Wang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
High Risk ◽  
Prognostic Value ◽  
Prognostic Biomarker ◽  
Risk Group ◽  
Gene Signature ◽  
Low Risk ◽  
Risk Genes ◽  
Genetic Biomarkers ◽  
Patients With Diabetes

Background Pancreatic cancer (PC) has much weaker prognosis, which can be divided into diabetes and non-diabetes. PC patients with diabetes mellitus will have more opportunities for physical examination due to diabetes, while pancreatic cancer patients without diabetes tend to have higher risk. Identification of prognostic markers for diabetic and non-diabetic pancreatic cancer can improve the prognosis of patients with both types of pancreatic cancer. Methods Both types of PC patients perform differently at the clinical and molecular levels. The Cancer Genome Atlas (TCGA) is employed in this study. The gene expression of the PC with diabetes and non-diabetes is used for predicting their prognosis by LASSO (Least Absolute Shrinkage and Selection Operator) Cox regression. Furthermore, the results are validated by exchanging gene biomarker with each other and verified by the independent Gene Expression Omnibus (GEO) and the International Cancer Genome Consortium (ICGC). The prognostic index (PI) is generated by a combination of genetic biomarkers that are used to rank the patient’s risk ratio. Survival analysis is applied to test significant difference between high-risk group and low-risk group. Results An integrated gene prognostic biomarker consisted by 14 low-risk genes and six high-risk genes in PC with non-diabetes. Meanwhile, and another integrated gene prognostic biomarker consisted by five low-risk genes and three high-risk genes in PC with diabetes. Therefore, the prognostic value of gene biomarker in PC with non-diabetes and diabetes are all greater than clinical traits (HR = 1.102, P-value < 0.0001; HR = 1.212, P-value < 0.0001). Gene signature in PC with non-diabetes was validated in two independent datasets. Conclusions The conclusion of this study indicated that the prognostic value of genetic biomarkers in PCs with non-diabetes and diabetes. The gene signature was validated in two independent databases. Therefore, this study is expected to provide a novel gene biomarker for predicting prognosis of PC with non-diabetes and diabetes and improving clinical decision.

scholarly journals Establishment of a novel CNV-related prognostic signature predicting prognosis in patients with breast cancer

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Wei Hu ◽  
Mingyue Li ◽  
Qi Zhang ◽  
Chuan Liu ◽  
Xinmei Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
High Risk ◽  
Risk Score ◽  
Copy Number ◽  
Risk Group ◽  
Gene Signature ◽  
Low Risk ◽  
Breast Cancer Dataset ◽  
Cox Analysis

Abstract Background Copy number variation (CNVs) is a key factor in breast cancer development. This study determined prognostic molecular characteristics to predict breast cancer through performing a comprehensive analysis of copy number and gene expression data. Methods Breast cancer expression profiles, CNV and complete information from The Cancer Genome Atlas (TCGA) dataset were collected. Gene Expression Omnibus (GEO) chip data sets (GSE20685 and GSE31448) containing breast cancer samples were used as external validation sets. Univariate survival COX analysis, multivariate survival COX analysis, least absolute shrinkage and selection operator (LASSO), Chi square, Kaplan-Meier (KM) survival curve and receiver operating characteristic (ROC) analysis were applied to build a gene signature model and assess its performance. Results A total of 649 CNV related-differentially expressed gene obtained from TCGA-breast cancer dataset were related to several cancer pathways and functions. A prognostic gene sets with 9 genes were developed to stratify patients into high-risk and low-risk groups, and its prognostic performance was verified in two independent patient cohorts (n = 327, 246). The result uncovered that 9-gene signature could independently predict breast cancer prognosis. Lower mutation of PIK3CA and higher mutation of TP53 and CDH1 were found in samples with high-risk score compared with samples with low-risk score. Patients in the high-risk group showed higher immune score, malignant clinical features than those in the low-risk group. The 9-gene signature developed in this study achieved a higher AUC. Conclusion The current research established a 5-CNV gene signature to evaluate prognosis of breast cancer patients, which may innovate clinical application of prognostic assessment.

scholarly journals Identification of the 11-lncRNA signatures associated with the prognosis of endometrial carcinoma

2021 ◽  
Vol 104 (1) ◽  
pp. 003685042110065
Author(s):  
Jing Wan ◽  
Peigen Chen ◽  
Yu Zhang ◽  
Jie Ding ◽  
Yuebo Yang ◽  
...  
Keyword(s):  
High Risk ◽  
Endometrial Carcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Training Set ◽  
Lncrna Expression ◽  
Robust Likelihood

Endometrial carcinoma (EC) is the fourth most common cancer in women. Some long non-coding RNAs (lncRNAs) are regarded as potential prognostic biomarkers or targets for treatment of many types of cancers. We aim to screen prognostic-related lncRNAs and build a possible lncRNA signature which can effectively predict the survival of patients with EC. We obtained lncRNA expression profiling from the TCGA database. The patients were classified into training set and verification set. By performing Univariate Cox regression model, Robust likelihood-based survival analysis, and Cox proportional hazards model, we developed a risk score with the Cox co-efficient of individual lncRNAs in the training set. The optimum cut-off point was selected by ROC analysis. Patients were effectively divided into high-risk group and low-risk group according to the risk score. The OS of the low-risk patients was significantly prolonged compared with that of the high-risk group. At last, we validated this 11-lncRNA signature in the verification set and the complete set. We identified an 11-lncRNA expression signature with high stability and feasibility, which can predict the survival of patients with EC. These findings provide new potential biomarkers to improve the accuracy of prognosis prediction of EC.

Nine glycolysis-related gene signature predicting the survival of patients with endometrial adenocarcinoma

2020 ◽  
Author(s):  
liu jinhui ◽  
Li siyue ◽  
Gao feng ◽  
meng huangyang ◽  
Nie sipei ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
High Risk ◽  
Risk Score ◽  
Prognostic Model ◽  
Risk Group ◽  
Gene Signature ◽  
Low Risk ◽  
Cellular Respiration ◽  
Immunohistochemistry Staining ◽  
Glycolysis Pathway

Abstract Background: Endometrial cancer is the fourth most common cancer in women. The death rate for endometrial cancer has increased. Glycolysis of cellular respiration is a complex reaction and is the first step in most carbohydrate catabolism, which was proved to participate in tumors. Methods: We analyzed the sample data of over 500 patients from TCGA database. The bioinformatic analysis included GSEA, cox and lasso regression analysis to select prognostic genes, as well as construction of a prognostic model and a nomogram for OS evaluation. The immunohistochemistry staining, survival analysis and expression level validation were also performed. Maftools package was for mutation analysis. GSEA identified Glycolysis was the most related pathway to EC. Results: According to the prognostic model using the train set, 9 glycolysis-related genes including B3GALT6, PAM, LCT, GMPPB, GLCE, DCN, CAPN5, GYS2 and FBP2 were identified as prognosis-related genes. Based on nine gene signature, the EC patients could be classified into high and low risk subgroups, and patients with high risk score showed shorter survival time. Time-dependent ROC analysis and Cox regression suggested that the risk score predicted EC prognosis accurately and independently. Analysis of test and train sets yielded consistent results A nomogram which incorporated the 9‐mRNA signature and clinical features was also built for prognostic prediction. Immunohistochemistry staining and TCGA validation showed that expression levels of these genes do differ between EC and normal tissue samples. GSEA revealed that the samples of the low-risk group were mainly concentrated on Bile Acid Metabolism. Patients in the low-risk group displayed obvious mutation signatures compared with those in the high-risk group. Conclusion: This study found that the Glycolysis pathway is associated with EC and screened for hub genes on the Glycolysis pathway, which may serve as new target for the treatment of EC.

scholarly journals A Glycolysis-Related Five-Gene Signature Predicts Biochemical Recurrence-Free Survival in Patients With Prostate Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zijun Xu ◽  
Lijuan Xu ◽  
Liping Liu ◽  
Hai Li ◽  
Jiewen Jin ◽  
...  
Keyword(s):  
High Risk ◽  
Biochemical Recurrence ◽  
Risk Group ◽  
Predictive Biomarkers ◽  
Gene Signature ◽  
Low Risk ◽  
Free Survival ◽  
Training Cohort ◽  
Increased Risk ◽  
Robust Likelihood

Prostate cancer (PCa) is one of the most frequently diagnosed cancers in males worldwide. Approximately 25% of all patients experience biochemical recurrence (BCR) after radical prostatectomy (RP) and BCR indicates increased risk for metastasis and castration resistance. PCa patients with highly glycolytic tumors have a worse prognosis. Thus, this study aimed to explore glycolysis-based predictive biomarkers for BCR. Expression data and clinical information of PCa samples were retrieved from three publicly available datasets. One from The Cancer Genome Atlas (TCGA) dataset was used as the training cohort, and two from the Gene Expression Omnibus (GEO) dataset (GSE54460 and GSE70769) were used as validation cohorts. Using the training cohort, univariate Cox regression survival analysis, robust likelihood-based survival model, and stepwise multiply Cox analysis were sequentially applied to explore predictive glycolysis-related candidates. A five-gene risk score was then constructed based on the Cox coefficient as the following: (−0.8367*GYS2) + (0.3448*STMN1) + (0.3595*PPFIA4) + (−0.1940*KDELR3) + (0.4779*ABCB6). Receiver operating characteristic curve (ROC) analysis was used to identify the optimal cut-off point, and patients were divided into low risk and high risk groups. Kaplan–Meier analysis revealed that high risk group had significantly shorter BCR free survival time as compared with that in low risk group in training and validation cohorts. In conclusion, our data support the glycolysis-based five-gene signature as a novel and robust signature for predicting BCR of PCa patients.

scholarly journals Identification of Iron Metabolism-Related Genes as Prognostic Indicators for Lower-Grade Glioma

2021 ◽  
Vol 11 ◽  
Author(s):  
Shenbin Xu ◽  
Zefeng Wang ◽  
Juan Ye ◽  
Shuhao Mei ◽  
Jianmin Zhang
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Iron Metabolism ◽  
Risk Group ◽  
High Risk Group ◽  
Lower Grade ◽  
Who Grade ◽  
Lower Grade Glioma ◽  
Score Model

Lower-grade glioma (LGG) is characterized by genetic and transcriptional heterogeneity, and a dismal prognosis. Iron metabolism is considered central for glioma tumorigenesis, tumor progression and tumor microenvironment, although key iron metabolism-related genes are unclear. Here we developed and validated an iron metabolism-related gene signature LGG prognosis. RNA-sequence and clinicopathological data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were downloaded. Prognostic iron metabolism-related genes were screened and used to construct a risk-score model via differential gene expression analysis, univariate Cox analysis, and the Least Absolute Shrinkage and Selection Operator (LASSO)-regression algorithm. All LGG patients were stratified into high- and low-risk groups, based on the risk score. The prognostic significance of the risk-score model in the TCGA and CGGA cohorts was evaluated with Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analysis. Risk- score distributions in subgroups were stratified by age, gender, the World Health Organization (WHO) grade, isocitrate dehydrogenase 1 (IDH1) mutation status, the O6‐methylguanine‐DNA methyl‐transferase (MGMT) promoter-methylation status, and the 1p/19q co-deletion status. Furthermore, a nomogram model with a risk score was developed, and its predictive performance was validated with the TCGA and CGGA cohorts. Additionally, the gene set enrichment analysis (GSEA) identified signaling pathways and pathological processes enriched in the high-risk group. Finally, immune infiltration and immune checkpoint analysis were utilized to investigate the tumor microenvironment characteristics related to the risk score. We identified a prognostic 15-gene iron metabolism-related signature and constructed a risk-score model. High risk scores were associated with an age of &gt; 40, wild-type IDH1, a WHO grade of III, an unmethylated MGMT promoter, and 1p/19q non-codeletion. ROC analysis indicated that the risk-score model accurately predicted 1-, 3-, and 5-year overall survival rates of LGG patients in the both TCGA and CGGA cohorts. KM analysis showed that the high-risk group had a much lower overall survival than the low-risk group (P &lt; 0.0001). The nomogram model showed a strong ability to predict the overall survival of LGG patients in the TCGA and CGGA cohorts. GSEA analysis indicated that inflammatory responses, tumor-associated pathways, and pathological processes were enriched in high-risk group. Moreover, a high risk score correlated with the infiltration immune cells (dendritic cells, macrophages, CD4+ T cells, and B cells) and expression of immune checkpoint (PD1, PDL1, TIM3, and CD48). Our prognostic model was based on iron metabolism-related genes in LGG, can potentially aid in LGG prognosis, and provides potential targets against gliomas.

scholarly journals A ferroptosis-associated gene signature for the prediction of prognosis and therapeutic response in luminal-type breast carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yang Peng ◽  
Haochen Yu ◽  
Yingzi Zhang ◽  
Fanli Qu ◽  
Zhenrong Tang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Breast Carcinoma ◽  
Risk Score ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Luminal Type

AbstractFerroptosis is a new form of regulated cell death (RCD), and its emergence has provided a new approach to the progression and drug resistance of breast cancer (BRCA). However, there is still a great gap in the study of ferroptosis-related genes in BRCA, especially luminal-type BRCA patients. We downloaded the mRNA expression profiles and corresponding clinical data of BRCA patients from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and The Cancer Genome Atlas (TCGA) databases. Then, we built a prognostic multigene signature with ferroptosis-related differentially expressed genes (DEGs) in the METABRIC cohort and validated it in the TCGA cohort. The predictive value of this signature was investigated in terms of the immune microenvironment and the probability of a response to immunotherapy and chemotherapy. The patients were divided into a high-risk group and a low-risk group according to the ferroptosis-associated gene signature, and the high-risk group had a worse overall survival (OS). The risk score based on the 10 ferroptosis-related gene-based signature was determined to be an independent prognostic predictor in both the METABRIC and TCGA cohorts (HR, 1.41, 95% CI, 1.14–1.76, P = 0.002; HR, 2.19, 95% CI, 1.13–4.26, P = 0.02). Gene set enrichment analysis indicated that the term “cytokine-cytokine receptor interaction” was enriched in the high-risk score subgroup. Moreover, the immune infiltration scores of most immune cells were significantly different between the two groups, the low-risk group was much more sensitive to immunotherapy, and six drugs might have potential therapeutic implications in the high-risk group. Finally, a nomogram incorporating a classifier based on the 10 ferroptosis-related genes, tumor stage, age and histologic grade was established. This nomogram showed favorable discriminative ability and could help guide clinical decision-making for luminal-type breast carcinoma.

scholarly journals Identification of Metastasis-Related Signature For Predicting Survival In Osteosarcoma

2021 ◽  
Author(s):  
Song Shi ◽  
Shuaijie Yang ◽  
Zhenyu Zhou ◽  
Kai Sun ◽  
Ran Tao ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Signature ◽  
Low Risk ◽  
Risk Scores ◽  
Activation Markers

Abstract BackgroundRNA sequencing has become a powerful tool for exploring tumor recurrence or metastasis mechanisms. In this study, we aimed to develop a signature to improve the prognostic predictions of osteosarcoma.Materials and methodsBy comparing the expression profiles between metastatic and non-metastatic samples, we obtained 57 metastatic-related gene signatures. Then we constructed a 3‐gene signature to predict the prognostic risk of osteosarcoma patients by the Cox proportional hazards regression model. The risk score derived from this signature could successfully stratify osteosarcoma patients into subgroups with different survival outcomes.ResultsPatients in the low-risk group showed more prolonged overall survival than those in the high-risk group. And the performance was validated with another independent dataset. Multivariate cox regression revealed that the risk score served as an independent risk factor. Besides, we found that patients with low-risk scores had higher expression levels of immune-related signatures, suggesting an active immune status in those patients. Using the CIBERSORT database, we further systematically analyzed the relationships between the risk score and immune cell infiltration levels, as well as the immune activation markers. Higher infiltration of immune cells (CD8 T cells, monocytes, M2 macrophages, and memory B cells) and higher levels of immune cytotoxic markers (GZMA, GMZB, IFNG, and TNF) were observed in patients in the low-risk group.ConclusionsIn summary, this 3-gene signature could be a reliable marker for prognostic evaluation and help clinicians identify high‐risk patients.

scholarly journals DNA Repair and Replication-Related Gene Signature Based on Tumor Mutation Burden Reveals Prognostic and Immunotherapy Response in Gastric Cancer

2022 ◽  
Vol 2022 ◽  
pp. 1-17
Author(s):  
Lei Zhang ◽  
Dahai Hu ◽  
Shuchen Huangfu ◽  
Jiaxin Zhou ◽  
Wei Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Risk Scores ◽  
Tumor Mutation Burden ◽  
Mutation Burden

The genomic variant features (mutations, deletions, structural variants, etc.) within gastric cancer impact its evolution and immunogenicity. The tumor has developed several coping strategies to respond to these changes by DNA repair and replication (DRR). However, the intrinsic relationship between the associated DRR-related genes and gastric cancer progression remained unknown. This study selected DRR-related genes with tumor mutation burden based on the TCGA (The Cancer Genome Atlas) database of gastric cancer transcriptome and mutation data. The prognosis model of seven genes (LAMA2, CREB3L3, SELP, ABCC9, CYP1B1, CDH2, and GAMT) was constructed by a univariate and LASSO regression analysis and divided into high-risk and low-risk groups with the median risk score. Survival analysis showed that overall survival (OS) was lower in the high-risk group than that in the low-risk group. Moreover, patients with gastric cancer in the high-risk group have worse survival in different subgroups, including age, gender, histological grade, and TNM stage. The nomogram that included risk scores for DRR-related genes could accurately foresee OS of patients with gastric cancer. Interestingly, the tumor mutation burden score was higher in the low-risk group than that in the high-risk group, and the risk score for DRR-related genes was negatively correlated with tumor mutation burden in gastric cancer. Next, we further combined the risk score and tumor mutation burden to evaluate the prognosis of gastric cancer patients. The low-risk cohort had a better prognosis than the high-risk cohort in the high tumor mutation burden subgroup. The number of mutation types in the high-risk group was lower than that in the low-risk group. In the immune microenvironment of gastric cancer, more naïve B cells, memory resting CD4+ T cells, Treg cells, monocytes cells, and resting mast cells were infiltrated in the high-risk group. At last, PD-L1 and IAP expressions were negatively correlated with the risk scores; patients with gastric cancer in the low-risk group showed better immunotherapy outcomes than those in the high-risk group. Overall, the DRR-related gene signature based on tumor mutation burden is a novel biomarker for prognostic and immunotherapy response in patients with gastric cancer.

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