Dataset Information

Identification of DNA-Repair-Related Five-Gene Signature to Predict Prognosis in Patients with Esophageal Cancer.

ABSTRACT: Esophageal cancer (ESCA) is a leading cause of cancer-related mortality, with poor prognosis worldwide. DNA damage repair is one of the hallmarks of cancer. Loss of genomic integrity owing to inactivation of DNA repair genes can increase the risk of cancer progression and lead to poor prognosis. We aimed to identify a novel gene signature related to DNA repair to predict the prognosis of ESCA patients. Based on gene expression profiles of ESCA patients from The Cancer Genome Atlas and gene set enrichment analysis, 102 genes related to DNA repair were identified as candidates. After stepwise Cox regression analysis, we established a five-gene prognostic model comprising DGCR8, POM121, TAF9, UPF3B, and BCAP31. Kaplan-Meier survival analysis confirmed a strong correlation between the prognostic model and survival. Moreover, we verified the clinical value of the prognostic signature under the influence of different clinical parameters. We found that small-molecule drugs (trametinib, selumetinib, and refametinib) could help to improve patient survival. In summary, our study provides a novel and promising prognostic signature based on DNA-repair-related genes to predict survival of patients with ESCA. Systematic data mining provides a theoretical basis for further exploring the molecular pathogenesis of ESCA and identifying therapeutic targets.

SUBMITTER: Wang L

PROVIDER: S-EPMC8262199 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Identification of DNA-Repair-Related Five-Gene Signature to Predict Prognosis in Patients with Esophageal Cancer.

Wang Lin L Li Xueping X Zhao Lan L Jiang Longyang L Song Xinyue X Qi Aoshuang A Chen Ting T Ju Mingyi M Hu Baohui B Wei Minjie M He Miao M Zhao Lin L

Pathology oncology research : POR 20210330

Esophageal cancer (ESCA) is a leading cause of cancer-related mortality, with poor prognosis worldwide. DNA damage repair is one of the hallmarks of cancer. Loss of genomic integrity owing to inactivation of DNA repair genes can increase the risk of cancer progression and lead to poor prognosis. We aimed to identify a novel gene signature related to DNA repair to predict the prognosis of ESCA patients. Based on gene expression profiles of ESCA patients from The Cancer Genome Atlas and gene set e ...[more]

PMID: 34257547

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Project description:BackgroundThe tumor immune microenvironment is closely related to the malignant progression and treatment resistance of glioma. Long non-coding RNA (lncRNA) plays a regulatory role in this process. We investigated the pathological mechanisms within the glioma microenvironment and potential immunotherapy resistance related to lncRNAs.MethodWe downloaded datasets derived from glioma patients and analyzed them by hierarchical clustering. Next, we analyzed the immune microenvironment of glioma, related gene expression, and patient survival. Coexpressed lncRNAs were analyzed to generate a model of lncRNAs and immune-related genes. We analyzed the model using survival and Cox regression. Then, univariate, multivariate, receiver operating characteristic (ROC), and principle component analysis (PCA) methods were used to verify the accuracy of the model. Finally, GSEA was used to evaluate which functions and pathways were associated with the differential genes.ResultsNormal brain tissue maintains a low-medium immune state, and gliomas are clearly divided into three groups (low to high immunity). The stromal, immune, and estimate scores increased along with immunity, while tumor purity decreased. Further, human leukocyte antigen (HLA), programmed cell death-1 (PDL1), T cell immunoglobulin and mucin domain 3 (TIM-3), B7-H3, and cytotoxic T lymphocyte-associated antigen-4 (CTLA4) expression increases concomitantly with immune state, and the patient prognosis worsens. Five immune gene-related lncRNAs (AP001007.1, LBX-AS1, MIR155HG, MAPT-AS1, and LINC00515) were screened to construct risk models. We found that risk scores are related to patient prognosis and clinical characteristics, and are positively correlated with PDL1, TIM-3, and B7-H3 expression. These lncRNAs may regulate the tumor immune microenvironment through cytokine-cytokine receptor interactions, complement, and coagulation cascades, and may promote CD8 + T cell, regulatory T cell, M1 macrophage, and infiltrating neutrophils activity in the high-immunity group. In vitro, the abnormal expression of immune-related lncRNAs and the relationship between risk scores and immune-related indicators (PDL1, CTLA4, CD3, CD8, iNOS) were verified by q-PCR and immunohistochemistry (IHC).ConclusionFor the first time, we constructed immune gene-related lncRNA risk models. The risk score may be a new biomarker for tumor immune subtypes and provide molecular targets for glioma immunotherapy.

Project description:Background: Increasing evidence have depicted that DNA repair-related genes (DRGs) are associated with the prognosis of colorectal cancer (CRC) patients. Thus, the aim of this study was to evaluate the impact of DNA repair-related gene signature (DRGS) in predicting the prognosis of CRC patients. Method: In this study, we retrospectively analyzed the gene expression profiles from six CRC cohorts. A total of 1,768 CRC patients with complete prognostic information were divided into the training cohort (n = 566) and two validation cohorts (n = 624 and 578, respectively). The LASSO Cox model was applied to construct a prediction model. To further validate the clinical significance of the model, we also validated the model with Genomics of Drug Sensitivity in Cancer (GDSC) and an advanced clear cell renal cell carcinoma (ccRCC) immunotherapy data set. Results: We constructed a prognostic DRGS consisting of 11 different genes to stratify patients into high- and low-risk groups. Patients in the high-risk groups had significantly worse disease-free survival (DFS) than those in the low-risk groups in all cohorts [training cohort: hazard ratio (HR) = 2.40, p < 0.001, 95% confidence interval (CI) = 1.67-3.44; validation-1: HR = 2.20, p < 0.001, 95% CI = 1.38-3.49 and validation-2 cohort: HR = 2.12, p < 0.001, 95% CI = 1.40-3.21). By validating the model with GDSC, we could see that among the chemotherapeutic drugs such as oxaliplatin, 5-fluorouracil, and irinotecan, the IC50 of the cell line in the low-risk group was lower. By validating the model with the ccRCC immunotherapy data set, we can clearly see that the overall survival (OS) of the objective response rate (ORR) with complete response (CR) and partial response (PR) in the low-risk group was the best. Conclusions: DRGS is a favorable prediction model for patients with CRC, and our model can predict the response of cell lines to chemotherapeutic agents and potentially predict the response of patients to immunotherapy.

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Dataset Information

Identification of DNA-Repair-Related Five-Gene Signature to Predict Prognosis in Patients with Esophageal Cancer.

Publications

Identification of DNA-Repair-Related Five-Gene Signature to Predict Prognosis in Patients with Esophageal Cancer.

Similar Datasets

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