Construction a six-gene prognostic model for hepatocellular carcinoma based on WGCNA co-expression network

Objective: Currently, the incidence of hepatocellular carcinoma remains high, and the prognosis of patients is poor. Prognostic biomarkers are still worth exploring. Methods: Based on The Cancer Genome Atlas (TCGA) database, the differentially expressed genes (DEGs) were screened. Subsequently, a mo...

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Bibliographic Details
Published in:Journal of Holistic Integrative Pharmacy 2024-06, Vol.5 (2), p.90-102
Main Authors: Wang, Tian, Fan, Yu-Chun, Zhang, Lin-Li, Nong, Min-Yu, Zheng, Guang-Fei, Wei, Wan-Shuo, Jiang, Li-He
Format: Article
Language:English
Subjects:
Bioinformatics
Drug sensitivity analysis
Hepatocellular carcinoma
Methylation analysis
Prognostic model
WGCNA
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Summary:Objective: Currently, the incidence of hepatocellular carcinoma remains high, and the prognosis of patients is poor. Prognostic biomarkers are still worth exploring. Methods: Based on The Cancer Genome Atlas (TCGA) database, the differentially expressed genes (DEGs) were screened. Subsequently, a modular analysis of these DEGs was performed using the weighted gene co-expression network analysis (WGCNA). A prognostic model for liver cancer patients was constructed employing the Cox proportional hazards model. Through univariate and multivariate Cox regression analyses, we developed a Cox proportional-hazards model specifically for hepatocellular carcinoma. Subsequently, International Cancer Genome Consortium (ICGC) cohort data were used to validate the accuracy of the Cox proportional-hazards model. Following this, we conducted further analyses of prognostic genes, encompassing functional enrichment analysis and survival analysis. Additionally, we utilized the BBcancer database to investigate whether these prognostic genes have the potential to serve as blood markers. Notably, in this six-gene prognostic model, we also analyzed the genes' drug susceptibility. Results: Leveraging the candidate genes identified from the WGCNA analysis, we constructed a Cox proportional-hazards model with an AUC value greater than 0.7. This model incorporates HMMR, E2F2, WDR62, KIF11, MSH4, and KCNF1, revealing that patients with low expression levels of these genes had significantly better survival prognosis compared to those with high expression levels (P ​
ISSN:2707-3688
DOI:10.1016/j.jhip.2024.06.005