Identification of an autophagy-related gene signature for survival prediction in patients with cervical cancer

Abstract Autophagy controls the survival and death of cancerous cells by regulating the degradation process of cytoplasm and cellular organelle. In the present study, the differentially expressed autophagy-related genes (ARGs) between healthy and cancerous cervical tissues (squamous cell neoplasms) were obtained using data from GTEx and The Cancer Genome Atlas (TCGA) database. The functionalities of the differentially expressed ARGs were analyzed using GO and KEGG database. After univariate and multivariate cox regression assay, we got a six ARG-related prognostic signature for the survival prediction of patients with squamous cell cervical cancer (Risk score= -0.63*ATG3-0.42*BCL2+0.85*CD46-0.38*IFNG+0.23*NAMPT+0.82*TM9SF1). Kaplan-Meier curve demonstrated that patients with a high-risk score tend to have a poor prognosis (P<0.001). The value for area under the curves corresponding to the receiver operating characteristic (ROC) was 0.740. As observed, the expression of IFNG was negatively associated with lymph node metastasis (P=0.026), while a high-risk score was significantly associated with increased age (P=0.008). To further validate the prognostic signature, we carried out a permutation test and confirmed the performance of the risk score.

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Identification of an autophagy-related gene signature for survival prediction in patients with cervical cancer

Journal of Ovarian Research ◽

10.1186/s13048-020-00730-8 ◽

2020 ◽

Vol 13 (1) ◽

Author(s):

Hengyu Chen ◽

Qingchun Deng ◽

Wenwen Wang ◽

Huishan Tao ◽

Ying Gao

Keyword(s):

Cervical Cancer ◽

High Risk ◽

Risk Score ◽

Squamous Cell ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Survival Prediction ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Cervical cancer is one of the most common female malignancy that occurs worldwide and is reported to cause over 300,000 deaths in 2018. Autophagy controls the survival and death of cancerous cells by regulating the degradation process of cytoplasm and cellular organelle. In the present study, the differentially expressed autophagy-related genes (ARGs) between healthy and cancerous cervical tissues (squamous cell neoplasms) were obtained using data from GTEx and The Cancer Genome Atlas (TCGA) database. The functionalities of the differentially expressed ARGs were analyzed using Gene Ontology (GO) as well as the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Next, we conducted univariate Cox regression assay and obtained 12 ARGs that were associated with the prognosis of cervical cancer patients. We carried out a multivariate Cox regression analysis and developed six ARG-related prognostic signature for the survival prediction of patients with squamous cell cervical cancer (Risk score = − 0.63*ATG3–0.42*BCL2 + 0.85*CD46–0.38*IFNG+ 0.23*NAMPT+ 0.82*TM9SF1). Following the calculation of risk score using the signature, the patients were divided into high and low-risk groups according to the median value. Kaplan-Meier curve demonstrated that patients with a high-risk score tend to have a poor prognosis (P < 0.001). The value for area under the curves corresponding to the receiver operating characteristic (ROC) was 0.740. As observed, the expression of IFNG was negatively associated with lymph node metastasis (P = 0.026), while a high-risk score was significantly associated with increased age (P = 0.008). To further validate the prognostic signature, we carried out a permutation test and confirmed the performance of the risk score. In conclusion, our study developed six ARG-related prognostic signature for patients with squamous cell cervical cancer, which might help in improving the prognostic predictions of such patients.

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A 13-Gene Metabolic Prognostic Signature Is Associated With Clinical and Immune Features in Stomach Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.612952 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zaisheng Ye ◽

Miao Zheng ◽

Yi Zeng ◽

Shenghong Wei ◽

He Huang ◽

...

Keyword(s):

Risk Score ◽

Molecular Mechanisms ◽

Cancer Metabolism ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Stomach Adenocarcinoma ◽

Prognostic Prediction

Patients with advanced stomach adenocarcinoma (STAD) commonly show high mortality and poor prognosis. Increasing evidence has suggested that basic metabolic changes may promote the growth and aggressiveness of STAD; therefore, identification of metabolic prognostic signatures in STAD would be meaningful. An integrative analysis was performed with 407 samples from The Cancer Genome Atlas (TCGA) and 433 samples from Gene Expression Omnibus (GEO) to develop a metabolic prognostic signature associated with clinical and immune features in STAD using Cox regression analysis and least absolute shrinkage and selection operator (LASSO). The different proportions of immune cells and differentially expressed immune-related genes (DEIRGs) between high- and low-risk score groups based on the metabolic prognostic signature were evaluated to describe the association of cancer metabolism and immune response in STAD. A total of 883 metabolism-related genes in both TCGA and GEO databases were analyzed to obtain 184 differentially expressed metabolism-related genes (DEMRGs) between tumor and normal tissues. A 13-gene metabolic signature (GSTA2, POLD3, GLA, GGT5, DCK, CKMT2, ASAH1, OPLAH, ME1, ACYP1, NNMT, POLR1A, and RDH12) was constructed for prognostic prediction of STAD. Sixteen survival-related DEMRGs were significantly related to the overall survival of STAD and the immune landscape in the tumor microenvironment. Univariate and multiple Cox regression analyses and the nomogram proved that a metabolism-based prognostic risk score (MPRS) could be an independent risk factor. More importantly, the results were mutually verified using TCGA and GEO data. This study provided a metabolism-related gene signature for prognostic prediction of STAD and explored the association between metabolism and the immune microenvironment for future research, thereby furthering the understanding of the crosstalk between different molecular mechanisms in human STAD. Some prognosis-related metabolic pathways have been revealed, and the survival of STAD patients could be predicted by a risk model based on these pathways, which could serve as prognostic markers in clinical practice.

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

Cancer Cell International ◽

10.1186/s12935-021-01823-0 ◽

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.

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A signature of seven immune-related genes predicts overall survival in male gastric cancer patients

10.21203/rs.3.rs-52209/v2 ◽

2021 ◽

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.Method: 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.

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A Hypoxia-related LncRNA Signature Predicts Survival of Primary Lower-grade glioma

10.21203/rs.3.rs-335140/v1 ◽

2021 ◽

Author(s):

Yanjia Hu ◽

Jing Zhang ◽

Jing Chen

Keyword(s):

High Risk ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

Lower Grade ◽

Prognostic Signature ◽

Gene Set Enrichment

Abstract Background Hypoxia-related long non-coding RNAs (lncRNAs) have been proven to play a role in multiple cancers and can serve as prognostic markers. Lower-grade gliomas (LGGs) are characterized by large heterogeneity. Methods This study aimed to construct a hypoxia-related lncRNA signature for predicting the prognosis of LGG patients. Transcriptome and clinical data of LGG patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LGG cohort in TCGA was chosen as training set and LGG cohorts in CGGA served as validation sets. A prognostic signature consisting of fourteen hypoxia-related lncRNAs was constructed using univariate and LASSO Cox regression. A risk score formula involving the fourteen lncRNAs was developed to calculate the risk score and patients were classified into high- and low-risk groups based on cutoff. Kaplan-Meier survival analysis was used to compare the survival between two groups. Cox regression analysis was used to determine whether risk score was an independent prognostic factor. A nomogram was then constructed based on independent prognostic factors and assessed by C-index and calibration plot. Gene set enrichment analysis and immune cell infiltration analysis were performed to uncover further mechanisms of this lncRNA signature. Results LGG patients with high risk had poorer prognosis than those with low risk in both training and validation sets. Recipient operating characteristic curves showed good performance of the prognostic signature. Univariate and multivariate Cox regression confirmed that the established lncRNA signature was an independent prognostic factor. C-index and calibration plots showed good predictive performance of nomogram. Gene set enrichment analysis showed that genes in the high-risk group were enriched in apoptosis, cell adhesion, pathways in cancer, hypoxia etc. Immune cells were higher in high-risk group. Conclusion The present study showed the value of the 14-lncRNA signature in predicting survival of LGGs and these 14 lncRNAs could be further investigated to reveal more mechanisms involved in gliomas.

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Development and Verification of the Hypoxia- and Immune-Associated Prognostic Signature for Pancreatic Ductal Adenocarcinoma

Frontiers in Immunology ◽

10.3389/fimmu.2021.728062 ◽

2021 ◽

Vol 12 ◽

Author(s):

Dongjie Chen ◽

Hui Huang ◽

Longjun Zang ◽

Wenzhe Gao ◽

Hongwei Zhu ◽

...

Keyword(s):

High Risk ◽

Pancreatic Ductal Adenocarcinoma ◽

Risk Score ◽

Cox Regression ◽

Pearson Correlation ◽

Risk Groups ◽

Low Risk ◽

Ductal Adenocarcinoma ◽

Prognostic Signature ◽

Score Model

We aim to construct a hypoxia- and immune-associated risk score model to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC). By unsupervised consensus clustering algorithms, we generate two different hypoxia clusters. Then, we screened out 682 hypoxia-associated and 528 immune-associated PDAC differentially expressed genes (DEGs) of PDAC using Pearson correlation analysis based on the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression project (GTEx) dataset. Seven hypoxia and immune-associated signature genes (S100A16, PPP3CA, SEMA3C, PLAU, IL18, GDF11, and NR0B1) were identified to construct a risk score model using the Univariate Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, which stratified patients into high- and low-risk groups and were further validated in the GEO and ICGC cohort. Patients in the low-risk group showed superior overall survival (OS) to their high-risk counterparts (p < 0.05). Moreover, it was suggested by multivariate Cox regression that our constructed hypoxia-associated and immune-associated prognosis signature might be used as the independent factor for prognosis prediction (p < 0.001). By CIBERSORT and ESTIMATE algorithms, we discovered that patients in high-risk groups had lower immune score, stromal score, and immune checkpoint expression such as PD-L1, and different immunocyte infiltration states compared with those low-risk patients. The mutation spectrum also differs between high- and low-risk groups. To sum up, our hypoxia- and immune-associated prognostic signature can be used as an approach to stratify the risk of PDAC.

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A Risk Signature of Nine Autophagy-Related Genes Associated With Immune Microenvironment and Clinical Prognosis in Wilms Tumor

10.21203/rs.3.rs-892534/v1 ◽

2021 ◽

Author(s):

Shaopei Ye ◽

Wenbin Tang ◽

Ke Huang

Keyword(s):

Regression Analysis ◽

High Risk ◽

Risk Score ◽

Cox Regression ◽

Wilms Tumor ◽

Risk Group ◽

High Risk Group ◽

Differentially Expressed ◽

Clinical Prognosis ◽

Cox Regression Analysis

Abstract Background: Autophagy is a biological process to eliminate dysfunctional organelles, aggregates or even long-lived proteins. . Nevertheless, the potential function and prognostic values of autophagy in Wilms Tumor (WT) are complex and remain to be clarifed. Therefore, we proposed to systematically examine the roles of autophagy-associated genes (ARGs) in WT.Methods: Here, we obtained differentially expressed autophagy-related genes (ARGs) between healthy and Wilms tumor from Therapeutically Applicable Research To Generate Effective Treatments(TARGET) and The Cancer Genome Atlas (TCGA) database. The functionalities of the differentially expressed ARGs were analyzed using Gene Ontology. Then univariate COX regression analysis and multivariate COX regression analysis were performed to acquire nine autophagy genes related to WT patients’ survival. According to the risk score, the patients were divided into high-risk and low-risk groups. The Kaplan-Meier curve demonstrated that patients with a high-risk score tend to have a poor prognosis.Results: Eighteen DEARGs were identifed, and nine ARGs were fnally utilized to establish the FAGs based signature in the TCGA cohort. we found that patients in the high-risk group were associated with mutations in TP53. We further conducted CIBERSORT analysis, and found that the infiltration of Macrophage M1 was increased in the high-risk group. Finally, the expression levels of crucial ARGs were verifed by the experiment, which were consistent with our bioinformatics analysis.Conclusions: we emphasized the clinical significance of autophagy in WT, established a prediction system based on autophagy, and identified a promising therapeutic target of autophagy for WT.

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Prognostic Gene Signature for Squamous Cell Carcinoma with a Higher Risk for Treatment Failure and Accelerated MEK-ERK Pathway Activity

Cancers ◽

10.3390/cancers13205182 ◽

2021 ◽

Vol 13 (20) ◽

pp. 5182

Author(s):

Bohai Feng ◽

Kai Wang ◽

Esther Herpel ◽

Michaela Plath ◽

Wilko Weichert ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

High Risk ◽

Cell Carcinoma ◽

Squamous Cell ◽

Cox Regression ◽

Human Cancer ◽

Cancer Cell Line ◽

The Cancer Genome Atlas ◽

Mek Inhibitors ◽

Prognostic Gene

Squamous cell carcinoma (SCC) is the most prevalent histological type of human cancer, including head and neck squamous cell carcinoma (HNSCC). However, reliable prognostic gene signatures for SCC and underlying genetic and/or epigenetic principles are still unclear. We identified 37 prognostic candidate genes by best cutoff computation based on survival in a pan-SCC cohort (n = 1334) of The Cancer Genome Atlas (TCGA), whose expression stratified not only the pan-SCC cohort but also independent HNSCC validation cohorts into three distinct prognostic subgroups. The most relevant prognostic genes were prioritized by a Least Absolute Shrinkage and Selection Operator Cox regression model and were used to identify subgroups with high or low risks for unfavorable survival. An integrative analysis of multi-omics data identified FN1, SEMA3A, CDH2, FBN1, COL5A1, and ADAM12 as key nodes in a regulatory network related to the prognostic phenotype. An in-silico drug screen predicted two MEK inhibitors (Trametinib and Selumetinib) as effective compounds for high-risk SCC based on the Cancer Cell Line Encyclopedia, which is supported by a higher p-MEK1/2 immunohistochemical staining of high-risk HNSCC. In conclusion, our data identified a molecular classifier for high-risk HNSCC as well as other SCC patients, who might benefit from treatment with MEK inhibitors.

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Expression profiling analysis of autophagy-related genes in cervical cancer

10.21203/rs.3.rs-595676/v1 ◽

2021 ◽

Author(s):

Zhiyuan Huang ◽

He Wang ◽

Min Liu ◽

Xinrui Li ◽

Lei Zhu ◽

...

Keyword(s):

Risk Factors ◽

Cervical Cancer ◽

Overall Survival ◽

Risk Score ◽

Roc Curve ◽

Normal Tissue ◽

Cox Regression ◽

Risk Group ◽

Differentially Expressed ◽

Tissue Samples

Abstract Background: It has been demonstrated by studies globally that autophagy took part in the development of cervical cancer (CC). Few studies concentrated on the correlation between overall survival and CC patients. We retrieved significant autophagy-related genes (ARGs) correlated to the process of cervical cancer. They may be used as prognosis marker or treatment target for clinical application.Methods: Expressions level of genes in cervical cancer and normal tissue samples were obtained from GTEx and TCGA database. Autophagy-related genes (ARGs) were retrieved accroding to the gene list from HaDB. Differentially expressed autophagy related genes (DE-ARGs) related to cervical cancer were identified by Wilcoxon signed-rank test. ClusterProfiler package worked in R software was used to perform GO and KEGG enrichment analyses. Univariate propotional hazard cox regression and multivariate propotional hazard cox regressions were applied to identify DE-ARGs equipped with prognostic value and other clinical independent risk factors. ROC curve was drawn for comparing the survival predict feasibility of risk score with other risk factors in CC patients. Nomogram was drawn to exhibit the prediction model constructed accroding to multivariate cox regression. Correlations between Differentially expressed autophagy related genes (DE-ARGs) and other clinical features were investigated by t test or Cruskal wallis analysis. Correlation between Immune and autophagy in cervical cancer was investigated by ssGSEA and TIMER database. Results: Fifty-six differentially expressed ARGs (DE-ARGs) were retrieved from cervical cancer tissue and normal tissue samples. GO enrichment analysis showed that these ARGs involved in autophagy, ubiquitination of protein and apoptosis. Cox regression medel showed that there were six ARGs significantly associated with overall survival of cervical caner patients. VAMP7 (HR = 0.599, P= 0.033) and TP73 (HR = 0.671, P= 0.014) played protective roles in survival among these six genes. Stage (Stage IV vs Stage I HR = 3.985, P<0.001) and risk score (HR = 1.353, P< 0.001) were sorted as independent prognostic risk factors based on multivariate cox regression. ROC curve validated that risk score was preferable to predict survival of CC patients than other risk factors. Additionally, we found some of these six predictor ARGs were correlated significantly in statistic with tumor grade or stage, clinical T stage, clinical N stage, pathology or risk score (all P< 0.05). The immune cells and immune functions showed a lower activity in high risk group than low risk group which is distincted by median risk score. Conclusion: Our discovery showed that autophagy genes involved in the progress of cervical cancer. Many autophagy-related genes could probably serve as prognostic biomarkers and accelerate the discovery of treatment targets for CC patients.

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Analysis of DNA damage response gene signature for prognosis prediction of esophageal squamous cell carcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e16073 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e16073-e16073

Author(s):

Weitao Zhuang ◽

Xiao-song Ben ◽

Dan Tian ◽

Zihao Zhou ◽

Gang Chen ◽

...

Keyword(s):

High Risk ◽

Dna Damage Response ◽

Squamous Cell ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Prognostic Signature ◽

Training Set ◽

Prognosis Prediction ◽

Damage Response

e16073 Background: Esophageal squamous cell cancer (ESCC) is a malignant tumor with a poor 5-year relative survival. A prognosis prediction signature associated with DNA Damage Response (DDR) genes in ESCC was explored in this study. Methods: The clinical and gene expression profiles of ESCC patients were downloaded from the GEO and TCGA database. Univariate Cox regression and 1000 iterations of 10-fold cross-validation of LASSO Cox regression with binomial deviance minimization criteria were used to identify DDR genes as potential object and a prognostic signature for ESCC survival prediction, followed by validation of the signature via TCGA cohort and identification of independent prognostic predictors. A nomogram for prognosis prediction was built and Gene Set Enrichment Analysis (GSEA) was performed to further understand the underlying molecular mechanisms. Results: A signature of 8 DDR genes were constructed as being significantly associated with overall survival (OS) among patients with esophageal squamous cell carcinoma. The pronostic signature stratified ESCC patients into low- vs high-risk groups in terms of OS in the training set, testing set and the validation cohorts, and remained as an independent prognostic factor in multivariate analyses (hazard ratio (HR) in training set, 0.17 [95% CI, 0.09-0.35; P < 0 .001], HR in testing set, 0.38 [95% CI, 0.16-0.93; P = 0.029], HR in discovery cohort, 0.171 [95% CI, 0.03-0.48; P < 0 .001]) after adjusting for clinicopathological factors. The 8-DDR gene signature achieved a higher accuracy (C-index, 0.69; AUCs for 1-, 3- and 5-year OS, 0.74, 0.77 and 0.76, respectively) than 7 previously reported multigene signatures (C-index range, 0.53 to 0.60; AUCs range, 0.55to 0.66, 0.54 to 0.64 and 0.62 to 0.66, respectively) for estimation of survival in comparable cohorts. A nomogram incorporating tumor location, grade, adjuvant therapy and signature-based risk group showed better predictive performance for 1- and 3- year survival than for 5 year survival. Moreover, GSEA revealed that the DNA repair was more prominently enriched in the high-risk group while the low-risk group had not enrichment of any process (P > 0.05 for all). Conclusions: Taken together, our study identified 8 DDR genes related to the prognosis of ESCC patients, and constructed a robust prognostic signature to effectively stratify ESCC patients with different survival rates, which may help recognize high-risk patients potentially benefiting from more aggressive treatment.

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