基于多芯片联合分析的肝癌患者预后标志物筛选。

Screening of Prognostic Markers for Hepatocellular Carcinoma Patients Based on Multichip Combined Analysis.

机构信息

Department of Medical Oncology, Sir Run Run Shaw Hospital, College of Medicine, Zhejiang University, No. 3 Qingchun East Road, Jianggan District, Hangzhou, Zhejiang 310016, China.

Department of Medical Oncology, Quzhou People's Hospital, No. 2, Zhongloudi, Kecheng District, Quzhou, Zhejiang 324000, China.

出版信息

Comput Math Methods Med. 2022 Jul 14;2022:6881600. doi: 10.1155/2022/6881600. eCollection 2022.

METHODS

GSE (14520, 36376, 57957, 76427) datasets were accessed from GEO database. 55 differential mRNAs (DEGs) were obtained by differential analysis based on the datasets. GO and KEGG analysis results indicated that the DEGs were enriched in xenobiotic metabolic process and other pathways. Expression profiles and clinical data of TCGA-LIHC mRNAs were from TCGA database. We established a prognostic model of HCC through univariate and multivariate Cox risk regression analyses. ROC curve analysis was used to examine the prognostic model performance. GSEA analysis was performed between the high- and low-risk score sample groups.

RESULTS

A 4-gene HCC prognostic model was constructed, in which the gene expressions correlated to HCC patients' survival. The AUC value presented 0.734 in the ROC analysis for the prognostic model.

CONCLUSION

The four-gene model could be introduced as an independent prognostic factors to assess HCC patients' survival status.

方法

从 GEO 数据库中获取 GSE（14520、36376、57957、76427）数据集。基于数据集进行差异分析，获得 55 个差异表达 mRNA（DEGs）。GO 和 KEGG 分析结果表明，DEGs 富集在外源物代谢过程和其他途径中。TCGA-LIHC mRNAs 的表达谱和临床数据来自 TCGA 数据库。我们通过单因素和多因素 Cox 风险回归分析建立了 HCC 的预后模型。ROC 曲线分析用于检验预后模型的性能。对高风险评分和低风险评分样本组进行 GSEA 分析。