Pub Date : 2026-01-28DOI: 10.1186/s40360-026-01092-5
Junyi Zhuo, Hua Wu, Xiaoling Zhou, Xi Wang, Tianqi Qiu, Min Lin, Yu Tang
Background: Ochratoxin A (OTA), a common food-borne mycotoxin, is a potential human carcinogen, yet the specific molecular mechanisms linking it to hepatocellular carcinoma (HCC) remain unclear.
Methods: We integrated network toxicology to predict OTA targets and intersected them with HCC transcriptomic data to identify key candidate genes. Functional enrichment analysis was then conducted. Multiple machine learning algorithms were applied to screen and validate core genes. Furthermore, molecular docking and molecular dynamics (MD) simulations were employed to evaluate the binding stability between OTA and key target proteins.
Results: A total of 50 key genes were identified as potential targets for potential OTA-associated hepatocarcinogenesis. Enrichment analysis revealed their significant involvement in critical processes such as xenobiotic metabolism and oxidative stress response. Machine learning analysis prioritized eight core genes (AURKA, GABARAPL1, CA2, PARP1, LMNA, SLC27A5, EPHX2, and GSTP1), and a combined diagnostic model demonstrated outstanding performance (AUC = 0.986). Structural analyses via molecular docking and MD simulations confirmed stable binding interactions between OTA and these core targets.
Conclusions: This integrated computational study identifies a set of candidate genes through which OTA may potentially interact with HCC-associated molecular networks. The robust binding predicted between OTA and the core targets provides a structural basis for these interactions. These findings offer a prioritized list of targets and a theoretical framework for subsequent experimental validation and investigation into OTA's toxicological role in HCC.
{"title":"Decrypting potential mechanisms linking ochratoxin A to hepatocellular carcinoma: an integrated approach combining toxicology, machine learning, molecular docking, and molecular dynamics simulation.","authors":"Junyi Zhuo, Hua Wu, Xiaoling Zhou, Xi Wang, Tianqi Qiu, Min Lin, Yu Tang","doi":"10.1186/s40360-026-01092-5","DOIUrl":"10.1186/s40360-026-01092-5","url":null,"abstract":"<p><strong>Background: </strong>Ochratoxin A (OTA), a common food-borne mycotoxin, is a potential human carcinogen, yet the specific molecular mechanisms linking it to hepatocellular carcinoma (HCC) remain unclear.</p><p><strong>Methods: </strong>We integrated network toxicology to predict OTA targets and intersected them with HCC transcriptomic data to identify key candidate genes. Functional enrichment analysis was then conducted. Multiple machine learning algorithms were applied to screen and validate core genes. Furthermore, molecular docking and molecular dynamics (MD) simulations were employed to evaluate the binding stability between OTA and key target proteins.</p><p><strong>Results: </strong>A total of 50 key genes were identified as potential targets for potential OTA-associated hepatocarcinogenesis. Enrichment analysis revealed their significant involvement in critical processes such as xenobiotic metabolism and oxidative stress response. Machine learning analysis prioritized eight core genes (AURKA, GABARAPL1, CA2, PARP1, LMNA, SLC27A5, EPHX2, and GSTP1), and a combined diagnostic model demonstrated outstanding performance (AUC = 0.986). Structural analyses via molecular docking and MD simulations confirmed stable binding interactions between OTA and these core targets.</p><p><strong>Conclusions: </strong>This integrated computational study identifies a set of candidate genes through which OTA may potentially interact with HCC-associated molecular networks. The robust binding predicted between OTA and the core targets provides a structural basis for these interactions. These findings offer a prioritized list of targets and a theoretical framework for subsequent experimental validation and investigation into OTA's toxicological role in HCC.</p>","PeriodicalId":9023,"journal":{"name":"BMC Pharmacology & Toxicology","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12924422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146060027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-24DOI: 10.1186/s40360-025-01077-w
Duncan C Gilbert, Ruth E Langley, Dami Ayadi, Mannab Berhanu, Lakshmi Kowdley Hemanth, Seunghee Kwon, Hossameldin Abdallah, Angela Meade, Noel Clarke, Silke Gillessen, Nicholas James, Gauthier Bouche, Mahesh Parmar, Matthew Nankivell, Laura Murphy
{"title":"Drug re-purposing to improve outcomes in the management of prostate cancer - aims, outcome measures and design of current phase III trials.","authors":"Duncan C Gilbert, Ruth E Langley, Dami Ayadi, Mannab Berhanu, Lakshmi Kowdley Hemanth, Seunghee Kwon, Hossameldin Abdallah, Angela Meade, Noel Clarke, Silke Gillessen, Nicholas James, Gauthier Bouche, Mahesh Parmar, Matthew Nankivell, Laura Murphy","doi":"10.1186/s40360-025-01077-w","DOIUrl":"10.1186/s40360-025-01077-w","url":null,"abstract":"","PeriodicalId":9023,"journal":{"name":"BMC Pharmacology & Toxicology","volume":" ","pages":"35"},"PeriodicalIF":2.7,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12910801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146043837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Mechanistic study of deoxycholic acid in colorectal cancer based on network toxicology and machine learning approaches.","authors":"Yulai Yin, Xueqing Li, Yixuan Xie, Shuang Liu, Shufa Tan, Chen Xu","doi":"10.1186/s40360-026-01091-6","DOIUrl":"10.1186/s40360-026-01091-6","url":null,"abstract":"","PeriodicalId":9023,"journal":{"name":"BMC Pharmacology & Toxicology","volume":" ","pages":"32"},"PeriodicalIF":2.7,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12903602/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146008923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Compound 7 h exerts its anti-oncogenic effects on colorectal cancer cells by inducing death-receptor-mediated apoptosis, promoting DNA damage, and obstructing autophagic flux.","authors":"Donglin Yang, Yanlai Fu, Jiuhong Huang, Tianzhi Zhang, Hongyi Nie, Yajun Zhang","doi":"10.1186/s40360-026-01087-2","DOIUrl":"10.1186/s40360-026-01087-2","url":null,"abstract":"","PeriodicalId":9023,"journal":{"name":"BMC Pharmacology & Toxicology","volume":" ","pages":"33"},"PeriodicalIF":2.7,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12903496/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146008999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-16DOI: 10.1186/s40360-025-01081-0
Shu Yang, Lijun Hu, Guohua Liu, Xiaohong Yuan, Xiaoling Chen
{"title":"Development and validation of a nomogram for predicting thrombocytopenia in sepsis patients treated with linezolid.","authors":"Shu Yang, Lijun Hu, Guohua Liu, Xiaohong Yuan, Xiaoling Chen","doi":"10.1186/s40360-025-01081-0","DOIUrl":"10.1186/s40360-025-01081-0","url":null,"abstract":"","PeriodicalId":9023,"journal":{"name":"BMC Pharmacology & Toxicology","volume":" ","pages":"29"},"PeriodicalIF":2.7,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12892453/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145988056","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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