Meta-analysis of GEO datasets identifies specific gene expression alterations in aflatoxin-associated intrahepatic cholangiocarcinomaCould aflatoxin exposure change specific genes in liver cancer, and what does that mean for prevention?

This meta-analysis utilized five Gene Expression Omnibus (GEO) expression datasets to investigate genomic and transcriptomic insights into aflatoxin-induced intrahepatic cholangiocarcinoma (ICC). The study aimed to identify genes associated with aflatoxin exposure and ICC pathogenesis through literature data mining and meta-analytic techniques. The analysis identified 1,754 ICC-associated genes, 427 linked to aflatoxin, and 154 potential intermediate regulators. No specific patient population demographics, such as age, sex, or geographic origin, were reported in the input data.

The primary outcomes focused on expression alterations in genes associated with aflatoxin exposure and ICC. Meta-analysis results revealed significant upregulation of CRP, CDK2, AXL, and MIR221, with overexpression exceeding 50% and a p-value less than 0.05. Conversely, F2 and BUB1B demonstrated downregulation with reduced expression greater than 60% and a p-value less than 0.014. Co-expression analysis among regulators indicated strong interactions, defined by a Fisher's Z statistic greater than 0.53 and a p-value less than 0.05.

Safety and tolerability data were not reported, as the study analyzed existing expression datasets rather than conducting an interventional trial. Consequently, no adverse events, serious adverse events, or discontinuations were documented. The study did not report specific limitations, funding sources, or potential conflicts of interest. The input data did not provide a causality note or a specific certainty rating regarding the strength of the evidence.

The authors suggest these findings may facilitate exposure-informed biomarker discovery and potential preventive or therapeutic strategies, particularly in regions where aflatoxin exposure remains prevalent. However, because the population details are not reported and the evidence is derived from retrospective data mining, clinical application remains theoretical. Further validation in prospective clinical cohorts is necessary to confirm these genomic associations and establish their utility in patient management.