AI Insight
This study identifies three genes (ABCB1, PIM2, and TSHR) as core targets through which the industrial chemical bisphenol S (BPS) may promote the spread of cutaneous melanoma, the deadliest form of skin cancer. Using computational analysis of melanoma patient data and molecular modeling techniques, researchers developed a prognostic model demonstrating that higher expression of these genes correlates with better patient survival, and confirmed that BPS binds strongly to all three proteins. The findings suggest BPS influences melanoma metastasis through mechanisms involving drug resistance pathways, kinase signaling, and receptor-mediated processes.
Why it matters
This research raises concerns about environmental BPS exposure as a potential factor in melanoma progression and provides molecular targets for understanding this relationship. The prognostic model could potentially aid in predicting melanoma patient outcomes and identifying those at higher risk of metastasis.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Background: Cutaneous melanoma is the most aggressive malignant skin tumor, and metastasis represents the primary cause of patient mortality. Bisphenol S (BPS) has an unclear influence on melanoma metastasis and its underlying molecular mechanisms. Methods: Potential BPS targets were predicted using the SEA, SwissTargetPrediction, and SuperPred databases. Based on TCGA-SKCM transcriptomic data, differential expression analysis was performed, and Weighted Gene Co-expression Network Analysis (WGCNA) was employed to construct a gene co-expression network. Candidate genes were obtained by integrating BPS-related targets, differentially expressed genes (DEGs), module genes, and univariate Cox regression genes, followed by Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein-protein interaction (PPI) network construction. Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was applied to screen core prognostic genes and construct a risk prediction model. Further analyses included network construction, molecular docking, and 100 ns molecular dynamics (MD) simulation. Results: Integration of BPS-related targets, DEGs, WGCNA module genes, and Cox regression results yielded 13 candidate genes enriched in kinase activity regulation and melanoma-related pathways. LASSO-Cox regression ultimately identified three core prognostic genes–ABCB1, PIM2, and TSHR–all significantly upregulated in metastatic tissues, with area under the curve (AUC) values of approximately 0.7. High-expression patients exhibited significantly better overall survival than low-expression patients (P < 0.05). A nomogram incorporating the three genes and clinical parameters demonstrated good calibration performance. Within the ceRNA network, MALAT1 and hsa-miR-155-5p were identified as key regulatory molecules, and 37 potential transcription factors were predicted, including CEBPA, JUN, and STAT3. Molecular docking revealed strong binding affinities of BPS toward ABCB1 , PIM2, and TSHR, and MD simulations confirmed the structural stability of all three complexes. Conclusion: ABCB1, PIM2, and TSHR are the core target genes through which BPS influences melanoma metastasis via multidrug resistance, kinase signaling, and receptor-mediated signal transduction. The prognostic model based on these three genes demonstrates good clinical applicability, and the ceRNA and transcription factor regulatory networks provide a systematic molecular basis for understanding the association between BPS exposure and melanoma metastasis.