A novel tumor 4-driver gene signature for the prognosis of hepatocellular carcinoma

Houtian Guo; Fei Lu; Rongqi Lu; Meiqi Huang; Xuejing Li; Jianhui Yuan; Feng Wang

doi:10.1016/j.heliyon.2023.e17054

A novel tumor 4-driver gene signature for the prognosis of hepatocellular carcinoma

Heliyon. 2023 Jun 7;9(6):e17054. doi: 10.1016/j.heliyon.2023.e17054. eCollection 2023 Jun.

Authors

Houtian Guo¹, Fei Lu¹, Rongqi Lu¹, Meiqi Huang¹, Xuejing Li², Jianhui Yuan³, Feng Wang^{4

5}

Affiliations

¹ First Clinical College of Guangxi Medical University, Nanning, China.
² Department of Physiology, School of Basic Medical Sciences, Guangxi Medical University, Nanning, China.
³ Department of Physics, School of Basic Medical Sciences, Guangxi Medical University, Nanning, China.
⁴ Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Guangxi Medical University, Nanning, China.
⁵ Key Laboratory of Biological Molecular Medicine Research, Guangxi Medical University, Education Department of Guangxi Zhuang Autonomous Region, Nanning, China.

Abstract

Background: Hepatocellular carcinoma (HCC), the main type of liver cancer, is the second most lethal tumor worldwide, with a 5-year survival rate of only 18%. Driver genes facilitate cancer cell growth and spread in the tumor microenvironment. Here, a comprehensive driver gene signature for the prognosis of HCC was developed.

Methods: HCC driver genes were analyzed comprehensively to develop a better prognostic signature. The dataset of HCC patients included mRNA sequencing data and clinical information from the TCGA, the ICGC, and the Guangxi Medical University Cancer Hospital cohorts. First, LASSO was performed to develop a prognostic signature for differentially expressed driver genes in the TCGA cohort. Then, the robustness of the signature was assessed using survival and time-dependent ROC curves. Furthermore, independent predictors were determined using univariate and multivariate Cox regression analyses. Stepwise multi-Cox regression analysis was employed to identify significant variables for the construction of a nomogram that predicts survival rates. Functional analysis by Spearman correlation analysis, enrichment analysis (GO, KEGG, and GSEA), and immunoassay (ssGSEA and xCell) were performed.

Result: A 4-driver gene signature (CLTC, DNMT3A, GMPS, and NRAS) was successfully constructed and showed excellent predictive efficiency in three cohorts. The nomogram indicated high predictive accuracy for the 1-, 3-, and 5-year prognoses of HCC patients, which included clinical information and risk score. Enrichment analysis revealed that driver genes were involved in regulating oncogenic processes, including the cell cycle and metabolic pathways, which were associated with the progression of HCC. ssGSEA and xCell showed differences in immune infiltration and the immune microenvironment between the two risk groups.

Conclusion: The 4-driver gene signature is closely associated with the survival prediction of HCC and is expected to provide new insights into targeted therapy for HCC patients.

Keywords: Driver genes; Hepatocellular carcinoma; Immune; Nomogram; Prognosis.