Integrated genomic analysis for prediction of survival for patients with liver cancer using The Cancer Genome Atlas
Integrated genomic analysis for prediction of survival for patients with liver cancer using The Cancer Genome Atlas作者机构：Department of General Surgery Lianyungang Clinical Medical College of Nanjing Medical University/The First People’s Hospital of Lianyungang Department of Liver Surgery/Liver Transplantation Center The First Affiliated Hospital of Nanjing Medical University Department of General Surgery The First People’s Hospital of Lianyungang
出 版 物：《World Journal of Gastroenterology》 (世界胃肠病学杂志（英文版）)
年 卷 期：2018年第24卷第28期
页 面：3145-3154页
核心收录：
基 金：National Institutes of Health, NIH National Institutes of Health, NIH
主 题：Liver cancer Prognosis Molecular marker Evaluation C-index
摘 要：AIM To evaluate the prognostic power of different molecular data in liver cancer.METHODS Cox regression screen and least absolute shrinkage and selection operator were performed to select significant prognostic variables. Then the concordance index was calculated to evaluate the prognostic power. For the combination data, based on the clinical cox model, molecular features that better fit the model were combined to calculate the concordance index. Prognostic models were built based on the arithmetic summation of the significant variables. Kaplan-Meier survival curve and log-rank test were performed to compare the survival difference. Then a heatmap was constructed and gene set enrichment analysis was performed for pathway analysis.RESULTS The m RNA data were the most informative prognostic variables in all kinds of omics data in liver cancer, with the highest concordance index(C-index) of 0.61. For the copy number variation, methylation and mi RNA data, the combination of molecular data with clinical data could significantly boost the prediction accuracy of the molecular data alone(P 0.05). On the other hand, the combination of clinical data with methylation, mi RNA and m RNA data could significantly boost the prediction accuracy of the clinical data itself(P 0.05). Based on the significant prognostic variables, different prognostic models were built. In addition, the heatmap analysis, survival analysis, and gene set enrichment analysis validated the practicability of the prognostic models.CONCLUSION In all kinds of omics data in liver cancer, the m RNA data might be the most informative prognostic variable. The combination of clinical data with molecular data might be the future direction for cancer prognosis and prediction.
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