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Polycyclic aromatic hydrocarbons (PAHs), particularly benzo[a]pyrene (B[a]P), found in cigarette smoke and air pollution, is an important carcinogen. Nevertheless, early molecular events and related regulatory effects of B[a]P-mediated cell transformation and tumor initiation remain unclear. This study found that EGR1 was significantly downregulated during human bronchial epithelial cell transformation and mice lung carcinogenesis upon exposure to B[a]P and its active form BPDE, respectively. In contrast, overexpression of EGR1 inhibited the BPDE-induced cell malignant transformation. Moreover, miR-377-3p was strongly enhanced by BPDE/B[a]P exposure and crucial for the inhibition of EGR1 expression by targeting the 3'UTR of EGR1. MiR-377-3p antagomir reversed the effect of EGR1 downregulation in cell malignant transformation and tumor initiation models. Furthermore, the B[a]P-induced molecular changes were evaluated by IHC in clinical lung cancer tissues and examined with a clinic database. Mechanistically, EGR1 inhibition was also involved in the regulation of Wnt/β-catenin transduction, promoting lung tumorigenesis following B[a]P/BPDE exposure. Taken together, the results demonstrated that bBenzo[a]pyrene exposure might induce lung tumorigenesis through miR-377-3p-mediated reduction of EGR1 expression, suggesting an important role of EGR1 in PAHs-induced lung carcinogenesis. [ABSTRACT FROM AUTHOR]

Mining cancer-omics databases deepens our understanding of cancer biology and can lead to potential breakthroughs in cancer treatment. Here, we propose an integrative analytical approach to reveal across-cancer expression patterns and identify potential clinical impacts for genes of interest from five representative public databases. Using ribonucleotide reductase (RR), a key enzyme in DNA synthesis and cancer-therapeutic targeting, as an example, we characterized the mRNA expression profiles and inter-component associations of three RR subunit genes and assess their differing pathological and prognostic significance across over 30-types of cancers and their related subtypes. Findings were validated by immunohistochemistry with clinical tissue samples (n = 211) collected from multiple cancer centers in China and with clinical follow-up. Underlying mechanisms were further explored and discussed using co-expression gene network analyses. This framework represents a simple, efficient, accurate, and comprehensive approach for cancer-omics resource analysis and underlines the necessity to separate the tumors by their histological or pathological subtypes during the clinical evaluation of molecular biomarkers. [ABSTRACT FROM AUTHOR]