利用癌症基因组图谱进行综合基因组分析，以预测肝癌患者的生存情况。

Integrated genomic analysis for prediction of survival for patients with liver cancer using The Cancer Genome Atlas.

机构信息

Department of General Surgery, Lianyungang Clinical Medical College of Nanjing Medical University/The First People's Hospital of Lianyungang, Lianyungang 222002, Jiangsu Province, China.

Department of Liver Surgery/Liver Transplantation Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu Province, China.

出版信息

World J Gastroenterol. 2018 Jul 28;24(28):3145-3154. doi: 10.3748/wjg.v24.i28.3145.

DOI:10.3748/wjg.v24.i28.3145

PMID:30065560

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6064958/

Abstract

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 mRNA 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 miRNA data, the combination of molecular data with clinical data could significantly boost the prediction accuracy of the molecular data alone ( < 0.05). On the other hand, the combination of clinical data with methylation, miRNA and mRNA data could significantly boost the prediction accuracy of the clinical data itself ( < 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 mRNA 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.

摘要

目的

评估不同分子数据在肝癌中的预后能力。

方法

采用 Cox 回归筛选和最小绝对收缩和选择算子（least absolute shrinkage and selection operator，LASSO）选择显著的预后变量。然后计算一致性指数（concordance index）以评估预后能力。对于组合数据，基于临床 Cox 模型，组合更好拟合模型的分子特征以计算一致性指数。基于显著变量的算术求和构建预后模型。通过 Kaplan-Meier 生存曲线和对数秩检验比较生存差异。然后构建热图并进行基因集富集分析以进行通路分析。

结果

在肝癌的各种组学数据中，mRNA 数据是最具信息性的预后变量，其一致性指数（C-index）最高，为 0.61。对于拷贝数变异、甲基化和 miRNA 数据，分子数据与临床数据的组合可以显著提高分子数据单独预测的准确性（<0.05）。另一方面，临床数据与甲基化、miRNA 和 mRNA 数据的组合可以显著提高临床数据本身预测的准确性（<0.05）。基于显著的预后变量，构建了不同的预后模型。此外，热图分析、生存分析和基因集富集分析验证了预后模型的实用性。