Skip to content
#protein folding Open access

Integrated computational analysis prioritizes candidate targets and pathways linking ochratoxin A exposure to hepatocellular carcinoma

Aug 2026 · PLoS ONE · Vol 21 · 0 citations · 32 references
Medicine

TL;DR

An integrated computational workflow was used to prioritize candidate targets and pathways potentially linking OTA exposure with HCC and provides a reproducible hypothesis-generating framework for exploring potential metabolic, genomic-instability-related, and immune-microenvironment links between OTA exposure and HCC.

Abstract

Ochratoxin A (OTA), a food-borne mycotoxin, has been implicated in hepatotoxicity and potential carcinogenic processes, yet the molecular links between OTA exposure and hepatocellular carcinoma (HCC) remain incompletely understood. This study used an integrated computational workflow to prioritize candidate targets and pathways potentially linking OTA exposure with HCC. OTA-related and HCC-related targets were collected from public databases, intersected, and subjected to functional enrichment analysis. Transcriptomic data from the GSE36376 discovery dataset were analyzed to identify differentially expressed genes, followed by LASSO and SVM-RFE feature selection, immune-cell deconvolution, molecular docking, and molecular dynamics simulation. A total of 214 overlapping OTA-HCC-associated targets were identified and were enriched in pathways related to signal transduction, apoptosis, metabolism, and immune regulation. In GSE36376, 443 differentially expressed genes were identified using p < 0.05 and |log2 fold change| > 1, and overlap analysis yielded 13 shared target genes. Five candidate targets, CYP3A4, KIFC1, AKR1C3, CA2, and TTR, were further prioritized. KIFC1 and AKR1C3 were upregulated in HCC samples, whereas CYP3A4, CA2, and TTR were downregulated. These genes showed apparent discriminatory ability within the discovery dataset, with AUC values ranging from 0.866 to 0.958. Molecular docking predicted favorable OTA-target interactions, with docking energies ranging from −7.4 to −10.8 kcal/mol. CYP3A4 showed the lowest predicted docking energy (−10.8 kcal/mol) and was further evaluated by molecular dynamics simulation, with a protein-fitted OTA RMSD of 1.435 ± 0.097 nm and complex Rg of 2.308 ± 0.010 nm during the equilibrated 20–100 ns trajectory. Overall, this study provides a reproducible hypothesis-generating framework for exploring potential metabolic, genomic-instability-related, and immune-microenvironment links between OTA exposure and HCC. Future validation in independent datasets and experimental models will be important to further assess the biological relevance of these candidate targets and pathways.

Read PDF

Similar papers

Open access Sep 2026

Integrated multi-omics and experimental analyses reveal VIPR1 as a potential mediator linking environmental bisphenol A exposure to MASH-HCC progression.

Findings identify a VIPR1-centered molecular vulnerability linking BPA exposure with malignant-associated alterations under lipotoxic conditions and provide candidate biomarkers and therapeutic targets for further investigation.

Meng-Yue Li, Yan-Ming Yang, Li-Ye Zhong et al. · 0 citations
Sep 2026

Integrated Transcriptomic, Network Pharmacology, and Machine-Learning Framework Identifies Candidate Phytochemical-Associated Targets in Lung Adenocarcinoma

CA4 is prioritized as a reproducible LUAD-associated candidate supported by transcriptomic, machine-learning, immune, survival, and docking evidence, but the computational findings do not demonstrate direct phytochemical regulation of CA4 or therapeutic efficacy.

Awaji Y. Safhi, Fahad Y. Sabei, Ali Hanbashi et al. · 0 citations
Open access Sep 2026

Computational prioritization of candidate TCDD-associated targets in retinoblastoma using network toxicology and transcriptomic analysis

CDK1 emerged as a candidate hub associated with cell-cycle dysregulation, whereas CA14, PIP5K1B, GRIA2, and KDR were linked to broader disturbances in cellular homeostasis and microenvironmental regulation.

Ying-Luan Wang, Qiang Hu, Zhan Cao et al. · 0 citations
Open access Oct 2026

Integrative Network Toxicology Identifies NFKB1 and KIF11 as Candidate Genes Associated with 2-Hydroxyphenanthrene in the Context of Psoriasis

Background 2-Hydroxyphenanthrene (2-PHE), a polycyclic aromatic hydrocarbon metabolite, has been implicated in immune and inflammatory dysregulation, yet its potential toxicological contribution to psoriasis remains unclear. This study aimed to identify candidate genes and pathways through which 2-PHE might influence p...

Hai-Xia Shi, Yuan Guo, Hao-Chen Yuan et al. · 0 citations
Open access Aug 2026

Multi-omics screening and functional validation identify SLC5A6 as a candidate disulfidptosis-related gene and prognostic biomarker in hepatocellular carcinoma

The findings suggest that SLC5A6 may participate in glucose-deprivation-induced disulfide stress, while its direct role in regulating disulfidptotic cell death remains to be established.

Chong Fu, Ze-Min Fang, Yan-Ping Zhang et al. · 0 citations
Sep 2026

Network toxicology and machine learning analysis of MC-LR-associated hepatocellular carcinoma.

Microcystin-LR (MC-LR), a hepatotoxic aquatic contaminant classified by IARC as a Group 2B carcinogen, was investigated using an integrated network toxicology and machine learning framework to explore potential molecular mechanisms underlying MC-LR-related hepatocarcinogenesis. By integrating GEO transcriptomic dataset...

Zhi-Peng Xu, Ji Luo, Jian-Min Liang et al. · 0 citations

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.