{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T16:40:01Z","timestamp":1792600801026,"version":"4.5.0"},"reference-count":50,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,16]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Benzo[a]pyrene diol epoxide (BPDE), a key carcinogenic metabolite of the ubiquitous environmental pollutant benzo[a]pyrene, is a well-established etiological agent in lung cancer. However, a systems-level understanding of its molecular targets and pathogenic mechanisms remains incomplete. This study aimed to identify and characterize the core toxicity targets linking BPDE exposure to lung carcinogenesis using an integrative network toxicology approach.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Putative BPDE targets were retrieved from SwissTargetPrediction and TargetNet. Lung cancer-associated genes were compiled from GeneCards and TCGA datasets. The intersection yielded candidate genes, which were analyzed via protein\u2013protein interaction (PPI) network construction, functional enrichment, and machine learning (LASSO, XGBoost, Random Forest, Boruta) to identify core biomarkers. A nomogram was developed for risk prediction. The key target CA2 was further validated for prognostic value, differential expression across multiple cohorts (TCGA, GEO), and associated biological pathways via gene set enrichment analysis (GSEA) and pathway activity scoring. Molecular docking was performed to simulate the BPDE-CA2 interaction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Thirty-one overlapping targets were identified. Enrichment analysis implicated processes like inflammation, oxidative stress response, and cancer-related pathways (e.g. p53, cell cycle). Machine learning convergence highlighted four core biomarkers: PARP1, ALOX5, CA9, and CA2. Among these, CA2 demonstrated significant prognostic value, consistent downregulation in lung tumor tissues across independent datasets, and distinct molecular phenotypes: its high expression correlated with immune activation and suppressed VEGF signaling, while low expression was linked to enhanced cell cycle activity. Molecular docking revealed a stable binding interaction between BPDE and CA2.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This integrative study identifies a set of core targets, prominently featuring CA2, that potentially mediate BPDE-induced lung carcinogenesis. CA2 downregulation is associated with poor prognosis, a pro-proliferative phenotype, and may be directly modulated by BPDE, positioning it as a critical molecular link between environmental exposure and lung cancer development.<\/jats:p>\n                    <jats:p>Insight Paragraph This study provides a systems-level molecular bridge connecting environmental carcinogen exposure to lung cancer pathogenesis. By integrating network toxicology, machine learning, and multi-omics validation, we move beyond single-gene associations to identify a core set of toxicity targets, most notably CA2. We demonstrate that CA2 downregulation, potentially via direct interaction with BPDE, is linked to poor prognosis and a pro-proliferative phenotype. This integrative approach offers a novel mechanistic insight, positioning CA2 as a critical node at the intersection of environmental exposure, disrupted metabolism, and tumor progression, with implications for risk prediction and targeted prevention strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/intbio\/zyag024","type":"journal-article","created":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T11:18:14Z","timestamp":1792408694000},"source":"Crossref","is-referenced-by-count":0,"title":["A multi-omics convergence on CA2 as a prognostic hub and potential molecular target of benzo[a]pyrene diol epoxide in lung cancer"],"prefix":"10.1093","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0597-7486","authenticated-orcid":false,"given":"Jiayi","family":"He","sequence":"first","affiliation":[{"name":"Graduate School, Heilongjiang University of Chinese Medicine, No. 24 Heping Road, Xiangfang District, Harbin, Heilongjiang, 150040 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