Biomarker-driven drug repurposing for NAFLD-associated hepatocellular carcinoma using machine learning integrated ensemble feature selection

Subhajit Ghosh ¹ Sukhen Das Mandal ^2* Subarna Thakur ^1*

• ¹ Department of Bioinformatics, University of North Bengal, Darjeeling, West Bengal, India
• ² Ghani Khan Choudhury Institute of Engineering & Technology (GKCIT), West Bengal, India

The incidence of non-alcoholic fatty liver disease (NAFLD), encompassing the more severe non-alcoholic steatohepatitis (NASH), is rising alongside the surges in diabetes and obesity.Increasing evidence indicates that NASH is responsible for a significant share of idiopathic hepatocellular carcinoma (HCC) cases, a fatal cancer with a five-year survival rate below 22%. Biomarkers can facilitate early screening and monitoring of at-risk NAFLD/NASH patients and assist in identifying potential drug candidates for treatment. This study utilized an ensemble feature selection framework to analyze transcriptomic data, identifying biomarker genes associated with the stage-wise progression of NAFLD-related HCC. Seven machine learning algorithms were assessed for disease stage classification. Twelve feature selection methods including correlation-based techniques, mutual information-based methods, and embedded techniques were utilized to rank the top genes as features, through this approach, multiple feature selection methods were combined to yield more robust features important in this disease progression. Cox regression-based survival analysis was carried out to evaluate the biomarker potentiality of these genes. Furthermore, multiphase drug repurposing strategy and molecular docking were employed to identify potential drug candidates against these biomarkers. Among the seven machine learning models initially evaluated, DISCR resulted as the most accurate disease stage classifier. Ensemble feature selection identified ten top genes, among which eight were recognized as potential biomarkers based on survival analysis. These include genes ABAT, ABCB11, MBTPS1, and ZFP1 mostly involved in alanine and glutamate metabolism, butanoate metabolism, and ER protein processing. Through drug repurposing, 81 candidate drugs were found to be effective against these markers genes, with Diosmin, Esculin, Lapatinib, and Phenelzine as the best candidates screened through molecular docking and MMGBSA. The consensus derived from multiple methods enhances the accuracy of identifying relevant robust biomarkers for NAFLD-associated HCC. The use of these biomarkers in a multiphase drug repurposing strategy highlights potential therapeutic options for early intervention, which is essential to stop disease progression and improve outcomes.