Development of a Novel four-gene Model for Monitoring the Progression from Metabolic Dysfunction-associated Steatotic Liver Disease to Hepatocellular Carcinoma in Males

Yuchuan Jiang; Jiejian Chen; Lin Xu; Lin Lv; Xiaoning Gan

doi:10.7150/jca.100724

Development of a Novel four-gene Model for Monitoring the Progression from Metabolic Dysfunction-associated Steatotic Liver Disease to Hepatocellular Carcinoma in Males

J Cancer. 2025 Jan 1;16(3):917-931. doi: 10.7150/jca.100724. eCollection 2025.

Authors

Yuchuan Jiang^{1

2}, Jiejian Chen¹, Lin Xu¹, Lin Lv¹, Xiaoning Gan^{1

3}

Affiliations

¹ Department of Medical Oncology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong 510180, China.
² Department of Gastroenterology, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330000, China.
³ Department, University, City, Postcode, Country Department of Physiology, Michigan State University, East Lansing, MI 48824, USA.

Abstract

The pathogenesis of metabolic dysfunction-associated steatotic liver disease-associated hepatocellular carcinoma (MASLD-HCC) is complex and exhibits sex-specific differences. Effective methods for monitoring MASLD progression to HCC are lacking. Transcriptomic data from liver tissue samples sourced from multiple public databases were integrated. Utilizing both differential expression analysis and robust rank aggregation analysis, differentially expressed genes (DEGs) in patients with MASLD-HCC were identified. Based on these DEGs, diagnostic prediction models for MASLD (DP.MASLD) and HCC (DP.HCC) were constructed using elastic net analysis for various comparisons, including steatosis versus normal, steatohepatitis versus steatosis, and cancer versus non-cancer. Weighted gene correlation network analysis and gene set enrichment analysis were conducted to unveil the underlying pathogenesis of MASLD-HCC in males. Five overlapping DEGs with diagnostic significance in the progression from MASLD to HCC were identified, namely, AKR1B10, CYR61, FABP4, GNMT, and THBS1. DP.HCC demonstrated excellent predictive accuracy, with an area under the curve of 0.910 in the training group and 0.981 in the validation group. Similarly, DP.MASLD showed robust predictive accuracy. The pathogenesis of MASLD-HCC in males primarily involves extracellular matrix-receptor interaction, DNA replication, cell cycle, and T-cell receptor signaling. Overall, our study provides a quantitative assessment tool for the early detection and monitoring of MASLD-HCC, highlighting the male-specific molecular characteristics involved in its progression.

Keywords: diagnostic prediction model; differentially expressed genes; hepatocellular carcinoma (HCC); metabolic dysfunction-associated steatotic liver disease (MASLD); metabolic-associated steatohepatitis (MASH).