Xiang-Yong Hao1,2, Tao Wang1,2, Hui Cai1,2, An-Qiang Li1,2, Hao Shi1,2, Tian-Kang Guo1,2, Yan-Fei Shen3, Yuan Deng1,2, Li-Tian Wang1,2. 1. Department of General Surgery, Gansu Provincial Hospital, Lanzhou, China. 2. Key Laboratory of Molecular Diagnosis and Precision Treatment of Surgical Tumors in Gansu Province, Lanzhou, China. 3. Department of Medical Management, Gansu Provincial Hospital, Lanzhou, China.
Abstract
PURPOSE: To build a novel predictive model for hepatocellular carcinoma (HCC) patients based on DNA methylation data. METHODS: Four independent DNA methylation datasets for HCC were used to screen for common differentially methylated genes (CDMGs). Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to explore the biological roles of CDMGs in HCC. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were performed to identify survival-related CDMGs (SR-CDMGs) and to build a predictive model. The importance of this model was assessed using Cox regression analysis, propensity score-matched (PSM) analysis and stratification analysis. A validation group from the Cancer Genome Atlas (TCGA) was constructed to further validate the model. RESULTS: Four SR-CDMGs were identified and used to build the predictive model. The risk score of this model was calculated as follows: risk score = (0.01489826 × methylation level of WDR69) + (0.15868618 × methylation level of HOXB4) + (0.16674959 × methylation level of CDKL2) + (0.16689301 × methylation level of HOXA10). Kaplan-Meier analysis demonstrated that patients in the low-risk group had a significantly longer overall survival (OS; log-rank P-value =0.00071). The Cox model multivariate analysis and PSM analysis identified the risk score as an independent prognostic factor (P<0.05). Stratified analysis results further confirmed this model performed well. By analyzing the validation group, the results of receiver operating characteristic (ROC) curve analysis and survival analysis further validated this model. CONCLUSION: Our DNA methylation-based prognosis predictive model is effective and reliable in predicting prognosis for patients with HCC.
PURPOSE: To build a novel predictive model for hepatocellular carcinoma (HCC) patients based on DNA methylation data. METHODS: Four independent DNA methylation datasets for HCC were used to screen for common differentially methylated genes (CDMGs). Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were used to explore the biological roles of CDMGs in HCC. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were performed to identify survival-related CDMGs (SR-CDMGs) and to build a predictive model. The importance of this model was assessed using Cox regression analysis, propensity score-matched (PSM) analysis and stratification analysis. A validation group from the Cancer Genome Atlas (TCGA) was constructed to further validate the model. RESULTS: Four SR-CDMGs were identified and used to build the predictive model. The risk score of this model was calculated as follows: risk score = (0.01489826 × methylation level of WDR69) + (0.15868618 × methylation level of HOXB4) + (0.16674959 × methylation level of CDKL2) + (0.16689301 × methylation level of HOXA10). Kaplan-Meier analysis demonstrated that patients in the low-risk group had a significantly longer overall survival (OS; log-rank P-value =0.00071). The Cox model multivariate analysis and PSM analysis identified the risk score as an independent prognostic factor (P<0.05). Stratified analysis results further confirmed this model performed well. By analyzing the validation group, the results of receiver operating characteristic (ROC) curve analysis and survival analysis further validated this model. CONCLUSION: Our DNA methylation-based prognosis predictive model is effective and reliable in predicting prognosis for patients with HCC.
Authors: Christian von Mering; Martijn Huynen; Daniel Jaeggi; Steffen Schmidt; Peer Bork; Berend Snel Journal: Nucleic Acids Res Date: 2003-01-01 Impact factor: 16.971