Integrative In Silico and Experimental Analysis Reveals MT1A and HSP70 as Components of a Dual Biomarker Axis for...

Heavy metal exposure in industrial environments constitutes a significant occupational health risk and can induce systemic oxidative and proteotoxic stress. In this study, in silico toxicogenomics was integrated with population-based validation to identify and confirm candidate biomarkers of heavy metal exposure. Publicly available PBMC microarray data (GSE37567; lead-related exposure model) were analyzed to identify significantly dysregulated genes and enriched biological pathways. Subsequent pathway and protein-protein interaction (PPI) network prioritization, along with toxicogenomics cross-evidence from the Comparative Toxicogenomics Database (CTD), further refined candidate selection. For human validation, PBMC gene expression was assessed by endpoint RT-PCR in a subgroup of four occupationally exposed tannery workers (S1-S4), an additional exposed group from the affected area (n = 10), and (5 + 2) seven controls. Transcriptomic analysis identified 319 significantly dysregulated genes (FDR < 0.05), with notable enrichment of metal homeostasis and cellular stress-response pathways. Metallothionein-related and heat-shock response genes emerged as key network modules, leading to the prioritization of MT1A and HSP70 as a complementary biomarker pair for metal sequestration and proteostasis protection. In the human cohort, MT1A demonstrated consistent induction in exposed individuals (approximately 2.1-2.6-fold, with a maximum of 2.63-fold), while HSP70 expression varied among exposed samples, reflecting heterogeneous exposure and stress-response dynamics. Water analysis from the industrial zone revealed polymetallic contamination, including Pb, Cd, and Cr, supporting the biological plausibility of combined metal-stress responses. Collectively, these findings indicate that MT1A functions as a reliable indicator of metal exposure, whereas HSP70 represents variable stress responses. Together, they offer a complementary framework for assessing stress associated with heavy metal exposure.