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An 8?gene signature predicts the prognosis of cervical cancer following radiotherapy.

ABSTRACT: Gene expression and DNA methylation levels affect the outcomes of patients with cancer. The present study aimed to establish a multigene risk model for predicting the outcomes of patients with cervical cancer (CerC) treated with or without radiotherapy. RNA sequencing training data with matched DNA methylation profiles were downloaded from The Cancer Genome Atlas database. Patients were divided into radiotherapy and non?radiotherapy groups according to the treatment strategy. Differently expressed and methylated genes between the two groups were identified, and 8 prognostic genes were identified using Cox regression analysis. The optimized risk model based on the 8?gene signature was defined using the Cox's proportional hazards model. Kaplan?Meier survival analysis indicated that patients with higher risk scores exhibited poorer survival compared with patients with lower risk scores (log?rank test, P=3.22x10?7). Validation using the GSE44001 gene set demonstrated that patients in the high?risk group exhibited a shorter survival time comprared with the low?risk group (log?rank test, P=3.01x10?3). The area under the receiver operating characteristic curve values for the training and validation sets were 0.951 and 0.929, respectively. Cox regression analyses indicated that recurrence and risk status were risk factors for poor outcomes in patients with CerC treated with or without radiotherapy. The present study defined that the 8?gene signature was an independent risk factor for the prognosis of patients with CerC. The 8?gene prognostic model had predictive power for CerC prognosis.

SUBMITTER: Xie F

PROVIDER: S-EPMC6755236 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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An 8‑gene signature predicts the prognosis of cervical cancer following radiotherapy.

Xie Fei F Dong Dan D Du Na N Guo Liang L Ni Weihua W Yuan Hongyan H Zhang Nannan N Jie Jiang J Liu Guomu G Tai Guixiang G

Molecular medicine reports 20190729 4

Gene expression and DNA methylation levels affect the outcomes of patients with cancer. The present study aimed to establish a multigene risk model for predicting the outcomes of patients with cervical cancer (CerC) treated with or without radiotherapy. RNA sequencing training data with matched DNA methylation profiles were downloaded from The Cancer Genome Atlas database. Patients were divided into radiotherapy and non‑radiotherapy groups according to the treatment strategy. Differently express ...[more]

PMID: 31432147

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Project description:Background: Necroptosis has been demonstrated to play a crucial role in the prognosis prediction and assessment of treatment outcome in cancers, including cervical cancer. The purpose of this study was to explore the potential prognostic value of necroptosis-related lncRNAs and their relationship with immune microenvironment and response to treatment in cervical cancer. Methods: Data from The Cancer Genome Atlas (TCGA) were collected to obtain synthetic data matrices. Necroptosis-related lncRNAs were identified by Pearson Correlation analysis. Univariate Cox and multivariate Cox regression analysis and Lasso regression were used to construct a necroptosis-related LncRNAs signature. Kaplan-Meier analysis, univariate and multivariate Cox regression analyses, receiver operating characteristic (ROC) curve, nomogram, and calibration curves analysis were performed to validate this signature. Gene set enrichment analyses (GSEA), immunoassays, and the half-maximal inhibitory concentration (IC50) were also analyzed. Results: Initially, 119 necroptosis-related lncRNAs were identified based on necroptosis-related genes and differentially expressed lncRNAs between normal and cervical cancer samples. Then, a prognostic risk signature consisting of five necroptosis-related lncRNAs (DDN-AS1, DLEU1, RGS5, RUSC1-AS1, TMPO-AS1) was established by Cox regression analysis, and LASSO regression techniques. Based on this signature, patients with cervical cancer were classified into a low- or high-risk group. Cox regression confirmed this signature as an independent prognostic predictor with an AUC value of 0.789 for predicting 1-year OS. A nomogram including signature, age, and TNM stage grade was then established, and showed an AUC of 0.82 for predicting 1-year OS. Moreover, GSEA analysis showed that immune-related pathways were enriched in the low-risk group; immunoassays showed that most immune cells, ESTIMAT scores and immune scores were negatively correlated with risk score and that the expression of immune checkpoint-proteins (CD27, CD48, CD200, and TNFRSF14) were higher in the low-risk group. In addition, patients in the low-risk group were more sensitive to Rucaparib, Navitoclax and Crizotinib than those in the high-risk group. Conclusion: We established a novel necroptosis-related lncRNA based signature to predict prognosis, tumor microenvironment and response to treatment in cervical cancer. Our study provides clues to tailor prognosis prediction and individualized immunization/targeted therapy strategies.

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An 8?gene signature predicts the prognosis of cervical cancer following radiotherapy.

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An 8‑gene signature predicts the prognosis of cervical cancer following radiotherapy.

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