Shengyun Cai1, Xiaomin Yu1, Zhongyi Gu1, Qingqing Yang1, Biwei Wen1, Jizi Sheng1, Rui Guan2. 1. Department of Obstetrics and Gynecology, Changhai Hospital, Second Military Medical University, NO.168, Changhai Road, Shanghai, 200433, People's Republic of China. 2. Department of Obstetrics and Gynecology, Changhai Hospital, Second Military Medical University, NO.168, Changhai Road, Shanghai, 200433, People's Republic of China. ruiguan_fck@126.com.
Abstract
PURPOSE: Cervical cancer (CC) patients usually have poor prognosis. The present study aims to find a DNA methylation signature for predicting survival of CC patients. METHODS: We selected CC patients at pathological stage I-III with corresponding information on radiotherapy and overall survival (OS) from TCGA. Differential expression and methylation analysis was done between patients with and without radiotherapy. We selected feature genes using recursive feature elimination algorithm to build a support vector machine classifier. DNA methylation biomarkers predictive of prognosis were identified using a LASSO Cox-Proportional Hazards model to construct a prognostic scoring model. The classifier and the prognostic model were tested on the training set and the validation set. Nomogram combining risk score and prognostic clinical factors were used. RESULTS: We obtained 497 differentially expressed genes (DEGs) and 865 differentially methylated genes (DMGs). Fifteen feature genes were selected from the 292 common genes between the DEGs and the DMGs to construct a classification model for radiotherapy. A DNA methylation signature including 10 genes was identified and used to establish a prognostic scoring model. The 10-gene methylation signature could effectively separate patients into two risk groups with markedly different OS time. Predictive capability of the methylation signature was successfully confirmed on the validation set. A nomogram comprised of risk score, radiotherapy, and recurrence was applied, with calibration plots displaying good concordance between predicted and actual OS. The DEGs were involved in 12 KEGG pathways most of which were correlated with metastasis and proliferation of various cancers, such as pathways in cancer, basal cell carcinoma, transcriptional misregulation in cancer and ECM-receptor interaction. CONCLUSION: We Identified a 10-gene methylation signature for risk stratification of CC patients at pathological stages I-III, and ten methylation biomarkers might be novel therapeutic targets for CC.
PURPOSE: Cervical cancer (CC) patients usually have poor prognosis. The present study aims to find a DNA methylation signature for predicting survival of CC patients. METHODS: We selected CC patients at pathological stage I-III with corresponding information on radiotherapy and overall survival (OS) from TCGA. Differential expression and methylation analysis was done between patients with and without radiotherapy. We selected feature genes using recursive feature elimination algorithm to build a support vector machine classifier. DNA methylation biomarkers predictive of prognosis were identified using a LASSO Cox-Proportional Hazards model to construct a prognostic scoring model. The classifier and the prognostic model were tested on the training set and the validation set. Nomogram combining risk score and prognostic clinical factors were used. RESULTS: We obtained 497 differentially expressed genes (DEGs) and 865 differentially methylated genes (DMGs). Fifteen feature genes were selected from the 292 common genes between the DEGs and the DMGs to construct a classification model for radiotherapy. A DNA methylation signature including 10 genes was identified and used to establish a prognostic scoring model. The 10-gene methylation signature could effectively separate patients into two risk groups with markedly different OS time. Predictive capability of the methylation signature was successfully confirmed on the validation set. A nomogram comprised of risk score, radiotherapy, and recurrence was applied, with calibration plots displaying good concordance between predicted and actual OS. The DEGs were involved in 12 KEGG pathways most of which were correlated with metastasis and proliferation of various cancers, such as pathways in cancer, basal cell carcinoma, transcriptional misregulation in cancer and ECM-receptor interaction. CONCLUSION: We Identified a 10-gene methylation signature for risk stratification of CC patients at pathological stages I-III, and ten methylation biomarkers might be novel therapeutic targets for CC.
Entities:
Keywords:
DNA methylation; Gene signature; Nomogram; Overall survival; Risk score
Authors: Mari K Halle; Marte Sødal; David Forsse; Hilde Engerud; Kathrine Woie; Njål G Lura; Kari S Wagner-Larsen; Jone Trovik; Bjørn I Bertelsen; Ingfrid S Haldorsen; Akinyemi I Ojesina; Camilla Krakstad Journal: Br J Cancer Date: 2021-03-15 Impact factor: 7.640