Integrating machine learning and molecular docking to decipher the molecular network of aflatoxin B1-induced...

Area of Science:

• Hepatocellular carcinoma (HCC) research
• Toxicology
• Bioinformatics

Background:

• Hepatocellular carcinoma (HCC) is a major global health concern.
• Aflatoxin B1 (AFB1) is a potent hepatotoxin and a known carcinogen linked to HCC development.
• Understanding the molecular mechanisms of AFB1-induced HCC is crucial for prevention and treatment strategies.

Purpose of the Study:

• To elucidate the molecular mechanisms of hepatocellular carcinoma (HCC) induced by Aflatoxin B1 (AFB1).
• To identify key genes and pathways involved in AFB1-mediated hepatocarcinogenesis.
• To explore the binding interactions between AFB1 and its molecular targets.

Main Methods:

• Differential gene expression analysis across multiple datasets.
• Application of machine learning algorithms for target gene identification.
• Network toxicology and molecular docking for assessing AFB1-protein interactions.

Main Results:

• Identification of 48 potential target genes for AFB1-induced HCC.
• Prioritization of six core regulatory genes (RND3, PCK1, AURKA, BCAT2, UCK2, CCNB1) using machine learning.
• Confirmation of significant differential expression for these six genes and strong binding affinity of AFB1 to key targets via molecular docking.

Conclusions:

• AFB1 promotes HCC pathogenesis through the modulation of specific genes and signaling pathways.
• Six core genes identified by machine learning are critical regulators in AFB1-induced hepatocarcinogenesis.
• Molecular docking validates the high binding affinity of AFB1 to its targets, providing mechanistic insights.