Toxicogenomic Characterization of Common Genes and Signaling Pathways Associated with Multiple Toxic Metal(loid)s in Lung Cancer: A Comparative Analysis of Small-Cell and Non-Small-Cell Lung Cancer

Abstract

Background/Objectives: This study aimed to identify common target genes, molecular interaction networks, and signaling pathways shared across the investigated toxic metal(loid)s—lead (Pb), cadmium (Cd), methylmercury compounds (MMC), arsenic (As), nickel (Ni), and hexavalent chromium (Cr(VI))—and associated with lung cancer overall, non-small-cell lung cancer (NSCLC), and small-cell lung cancer (SCLC). Methods: The Comparative Toxicogenomics Database (CTD) was used to identify associated genes, GeneMANIA to characterize molecular interaction networks, and ToppFun for pathway enrichment analysis. Results: Two common genes (MTOR and TP53) were identified for SCLC, eight for NSCLC (CAT, CCNB1, MTOR, NQO1, SOD2, STAT3, TP53, and VEGFA), and eighteen for lung cancer overall. Gene network analysis revealed predominantly physical interactions in SCLC, pathway interactions in NSCLC, and co-expression in lung cancer overall. Several toxic metal(loid)s exhibited concordant effects on TP53, VEGFA, HMOX1, and IL6 expression. TP53 was the only gene common to all investigated toxic metal(loid)s and lung cancer categories. Pathway enrichment identified PI3K–AKT–mTOR signaling in both SCLC and NSCLC, VEGF signaling in NSCLC, and MAPK, interleukin, and macrophage-stimulating protein (MSP) signaling in lung cancer overall. Conclusions: The identified genes and pathways suggest molecular mechanisms potentially relevant to the association between exposure to the investigated toxic metal(loid)s and lung carcinogenesis, particularly those related to cell survival, inflammation, oxidative stress, and angiogenesis. TP53 may represent a common molecular link warranting further investigation. The identified pathways represent candidate biomarkers and mechanistic targets, highlighting the biological heterogeneity of lung cancer and the importance of subtype-specific analyses.