---
title: "Computational Analysis Identifies Key Targets Linking Ochratoxin A Exposure to Hepatocellular Carc"
id: "plos-one-0-integrated-computational-analysis-prioritizes-candidate-targets-and-pathways"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-0-integrated-computational-analysis-prioritizes-candidate-targets-and-pathways"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594"
published_at: "2026-08-31T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Computational Analysis Identifies Key Targets Linking Ochratoxin A Exposure to Hepatocellular Carc
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-0-integrated-computational-analysis-prioritizes-candidate-targets-and-pathways
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594)
- **Published At:** 2026-08-31T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- Ochratoxin A (OTA) is a mycotoxin contaminating various foods and is associated with hepatotoxicity and carcinogenic risks, particularly hepatocellular carcinoma (HCC). - The study applied an integrated computational approach combining public database mining, transcriptomic analysis, machine learning, immune cell profiling, molecular docking, and molecular dynamics simulations to explore OTA-HCC molecular links. - Researchers identified 214 overlapping genes linked to both OTA toxicity and HCC from multiple databases. These genes were enriched in pathways involving signal transduction, apoptosis, metabolism, and immune regulation. - Transcriptomic data (GSE36376) revealed 443 differentially expressed genes in HCC, with 13 genes overlapping OTA-HCC targets. Five key candidate genes (CYP3A4, KIFC1, AKR1C3, CA2, TTR) were prioritized using feature selection methods. - Among these, KIFC1 and AKR1C3 were upregulated in HCC, whereas CYP3A4, CA2, and TTR were downregulated; these genes demonstrated strong predictive power (AUC 0.866–0.958) in distinguishing HCC samples. - Molecular docking showed favorable OTA binding to these targets, with CYP3A4 having the strongest predicted interaction (docking energy −10.8 kcal/mol). Molecular dynamics simulations of the CYP3A4-OTA complex indicated stable binding over time. - The study suggests potential metabolic, genomic instability, and immune microenvironment pathways linking OTA exposure to HCC development. - This computational framework provides hypotheses for future experimental and clinical validation of OTA-related hepatocarcinogenesis mechanisms.
## Clinical Analysis & Structured Key Points
[ Skip to main content ](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594#main-content) Advertisement * [plos.org](https://plos.org/) * [Create account](https://community.plos.org/registration/new) * [Sign in](https://journals.plos.org/user/secure/login?page=%2Fplosone%2Farticle%3Fid%3D10.1371%2Fjournal.pone.0357594) * * About * Browse * Publish * [](https://journals.plos.org/plosone/ "PLOS One") * Search [advanced search](https://journals.plos.org/plosone/search) * [Browse Topics](https://journals.plos.org/plosone/subjectAreaBrowse) Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click [here](https://github.com/PLOS/plos-thesaurus/blob/master/README.md "Link opens in new window"). [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594) [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594) * 0 [Save](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357594#savedHeader) [Total Mendeley and Citeulike bookmarks.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357594#savedHeader) * 0 [Citation](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357594#citedHeader) [Paper's citation count computed by Dimensions.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357594#citedHeader) * 31 [View](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357594#viewedHeader) [PLOS views and downloads.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357594#viewedHeader) * 0 [Share](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357594#discussedHeader) [Sum of Facebook, Twitter, Reddit and Wikipedia activity.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357594#discussedHeader) Open Access Peer-reviewed Research Article # Integrated computational analysis prioritizes candidate targets and pathways linking ochratoxin A exposure to hepatocellular carcinoma * Shili Yang, Roles Conceptualization, Data curation, Formal analysis, Software, Visualization, Writing – original draft Affiliation Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0009-0009-4125-3323 ](https://orcid.org/0009-0009-4125-3323 "ORCID Registry") ⨯ * Huaiquan Liu, Roles Writing – review & editing Affiliation Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China ⨯ * Haiyang Kou, Roles Methodology Affiliation Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China ⨯ * Lingyan Lai, Roles Investigation Affiliation Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China ⨯ * Xinyan Zhang, Roles Methodology, Resources Affiliation Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China ⨯ * Yunling Xu, Roles Resources Affiliation Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China ⨯ * Yu Sun, Roles Investigation, Resources Affiliation Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China ⨯ * Bo Chen Roles Conceptualization, Funding acquisition, Project administration, Supervision, Writing – review & editing * E-mail: 19078821420@163.com Affiliation Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou Province, China [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0009-0003-1150-727X ](https://orcid.org/0009-0003-1150-727X "ORCID Registry") ⨯ # Integrated computational analysis prioritizes candidate targets and pathways linking ochratoxin A exposure to hepatocellular carcinoma * Shili Yang, * Huaiquan Liu, * Haiyang Kou, * Lingyan Lai, * Xinyan Zhang, * Yunling Xu, * Yu Sun, * Bo Chen ![PLOS](https://journals.plos.org/resource/img/logo-plos-full-color.svg) x * Published: August 31, 2026 * * [Article](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594) * [Authors](https://journals.plos.org/plosone/article/authors?id=10.1371/journal.pone.0357594) * [Metrics](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357594) * [Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0357594) * [Media Coverage](http://plos.altmetric.com/details/doi/10.1371/journal.pone.0357594) * [Abstract](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594#abstract0) * [Introduction](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594#sec001) * [Materials and methods](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594#sec002) * [Results](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594#sec014) * [Discussion](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594#sec024) * [References](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594#references) * [Reader Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0357594) * [Figures](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594) ## 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 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. ## Figures ![Fig 9](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357594.g009) ![Fig 10](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357594.g010) ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357594.g001) ![Fig 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357594.g002) ![Fig 3](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357594.g003) ![Fig 4](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357594.g004) ![Fig 5](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357594.g005) ![Fig 6](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357594.g006) ![Fig 7](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357594.g007) ![Fig 8](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357594.g008) ![Table 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357594.t001) ![Fig 9](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357594.g009) ![Fig 10](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357594.g010) ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357594.g001) ![Fig 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357594.g002) ![Fig 3](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357594.g003) **Citation:** Yang S, Liu H, Kou H, Lai L, Zhang X, Xu Y, et al. (2026) Integrated computational analysis prioritizes candidate targets and pathways linking ochratoxin A exposure to hepatocellular carcinoma. PLoS One 21(8): e0357594. https://doi.org/10.1371/journal.pone.0357594 **Editor:** Serkan Yılmaz, Ankara University: Ankara Universitesi, TÜRKIYE **Received:** January 2, 2026; **Accepted:** August 18, 2026; **Published:** August 31, 2026 **Copyright:** © 2026 Yang et al. This is an open access article distributed under the terms of the [Creative Commons Attribution License](http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. **Data Availability:** All relevant data and code supporting the findings of this study are publicly available. The transcriptomic dataset used for differential expression analysis is available from the NCBI Gene Expression Omnibus under accession number GSE36376 ([https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36376)](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36376). The 2D molecular structure and SMILES string of Ochratoxin A were obtained from PubChem, CID 442530 ([https://pubchem.ncbi.nlm.nih.gov/compound/442530)](https://pubchem.ncbi.nlm.nih.gov/compound/442530). OTA-related target data were obtained from ChEMBL ([https://www.ebi.ac.uk/chembl)](https://www.ebi.ac.uk/chembl), the Similarity Ensemble Approach database ([https://sea.bkslab.org/)](https://sea.bkslab.org/), and SwissTargetPrediction ([https://www.swisstargetprediction.ch)](https://www.swisstargetprediction.ch), using “Ochratoxin A” as the query and restricting the species to Homo sapiens where applicable. HCC-related disease target data were obtained from GeneCards ([https://www.genecards.org)](https://www.genecards.org) and OMIM ([https://omim.org)](https://omim.org) using “hepatocellular carcinoma” as the search term. Protein structural templates used for molecular docking and molecular dynamics simulations were obtained from the Protein Data Bank ([https://www.rcsb.org)](https://www.rcsb.org) using the following PDB IDs: AKR1C3 (1RY0), CA2 (1CA3), CYP3A4 (1TQN), KIFC1 (5WDH), and TTR (1FH2). All author-generated code used in this study has been deposited in Zenodo and is publicly available under the MIT License at . **Funding:** This work was supported by the National Natural Science Foundation of China (grant number 82360976 to B.C.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. **Competing interests:** The authors have declared that no competing interests exist. ## Introduction Hepatocellular carcinoma (HCC), the most common primary liver malignancy, is a leading cause of cancer-related mortality worldwide and accounts for a substantial global disease burden [[1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594#pone.0357594.ref001)]. More than half of the world’s new annual liver cancer cases occur in China, where patient prognosis is generally poor, with an overall 5-year survival rate of only about 10% [[2](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594#pone.0357594.ref002)]. Therefore, research on the prevention and treatment of HCC holds significant practical and clinical value. The etiology of HCC is complex. The main pathogenic factors in the Chinese population include chronic Hepatitis B virus (HBV) infection and aflatoxin exposure. In Japan, Hepatitis C virus (HCV) infection is predominant, while alcohol and metabolic disorders are more common in Western populations [[3](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594#pone.0357594.ref003)]. Current research suggests that HCC development results from the interaction of multiple factors, involving complex interplay among genetic susceptibility, environmental toxins, and metabolic dysregulation [[4](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594#pone.0357594.ref004)]. Among these, Ochratoxin A (OTA), a mycotoxin widely present worldwide, is a secondary metabolite produced by Penicillium and Aspergillus fungi [[5](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594#pone.0357594.ref005)] and can be detected in various countries. Due to climatic conditions or improper food storage, OTA may contaminate grains, meat, fruits, wine, beer, coffee, and other foods, and has been associated with several toxic effects, particularly nephrotoxicity and hepatotoxicity [[6](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594#pone.0357594.ref006)]. Currently, epidemiological and preclinical studies increasingly indicate a close association between OTA and HCC [[7](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594#pone.0357594.ref007)]. Although previous studies have provided background information on the potential association between OTA exposure and HCC, the underlying molecular mechanisms remain incompletely understood. Traditional toxicology studies are often limited to the analysis of single targets or pathways, showing certain limitations when explaining diversified toxicity regulation networks. With the development of computational toxicology, network-based system toxicology has emerged as a useful approach for integrating compound-target-pathway-disease relationships and exploring potential toxicological cascades [[7](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594#pone.0357594.ref007)]. Machine learning methods can further support candidate feature prioritization and pattern recognition in biomedical datasets [[8](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594#pone.0357594.ref008)]. Previous HCC studies have reported alterations in hepatic xenobiotic metabolism, mitotic regulation, reductase-related pathways, carbonic anhydrase activity, and liver-derived transport processes, including studies involving CYP3A4, KIFC1, AKR1C3, CA2, and TTR, thereby providing biological context for interpreting computationally prioritized genes in these functional categories [[9](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594#pone.0357594.ref009)–[13](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357594#pone.0357594.ref013)]. In the present study, we integrated network toxicology, machine learning, immune infiltration estimation, molecular docking, and molecular dynamics simulation to explore potential molecular associations between OTA exposure and HCC. This workflow was designed to prioritize OTA-HCC-associated candidate targets and pathways, evaluate their expression patterns and immune-cell associations, and further characterize potential ligand-protein interactions through structural modeling. ## Materials and methods ### Preparation of OTA compound information The Simplified Molecular Input Line Entry System (SMILES) of OTA was searched and saved via the PubChem database ( ), and its 2D structure was downloaded for later use. ### Analysis of OTA-related toxicity targets OTA-related toxicity targets were retrieved from the ChEMBL database ( ), the SEA database ( ), and the SwissTargetPrediction database ( ), with the species limited to “Homo sapiens.” After removing duplicate targets from the search results, the Uniprot database ( ) was used for gene name standardization. The OTA-related toxicity target genes were then organized. Finally, the intersection of target genes from the three databases was taken using a Venn diagram, and visual analysis was completed. ### Analysis of HCC-related disease targets Using “hepatocellular carcinoma” as the search keyword in the GeneCards database ( ) and the OMIM database ( ), target genes with a Relevance score > 5 in the GeneCards database were screened, and related target genes from the OMIM database were extracted. Then, the intersection of the two types of target genes was taken using a Venn diagram to determine HCC-related disease targets. ### Intersection of OTA target genes and HCC-related genes The previously obtained OTA-related toxicity target genes and HCC-related target genes were intersected using a Venn diagram to obtain the intersecting genes between them. ### GO and KEGG enrichment analyses The screened intersecting genes were converted from gene symbols to Entrez IDs based on the human gene annotation database org.Hs.e.g.,db. Invalid genes without matching IDs were filtered out to obtain the analysis gene set. Using R packages such as clusterProfiler and enrichplot, GO and KEGG enrichment analyses were sequentially performed on the analysis gene set. GO enrichment analysis was conducted independently for three ontologies: Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). The thresholds for both p-value and q-value were set at 0.05 to screen significantly enriched terms, and bubble charts were used to visualize the analysis results. KEGG pathway enrichment analysis was performed with the human (hsa) species background. The screening thresholds were set at p ) to search for related diseases, with conditions limited to “Series,” “expression profiling by array,” and “Homo sapiens.” The probe matrix file and platform file (GSE36376) related to HCC in this database were downloaded. Genes with p 1 were defined as significantly differentially expressed genes and visualized. This combined threshold was used to identify genes with both statistical significance and biologically meaningful expression changes, thereby reducing the inclusion of genes with very small but statistically significant differences. The resulting DEGs were then cross-referenced with th
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