Identification of Hub Genes Associated with Bile Duct Cancer Using Integrated Gene Expression Data with Protein-Protein Interaction Network

Narmadha Ramasamy 1, Pogala Hema Vardhan 1, Kannan Muthu 1 *
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1 Department of Bioinformatics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
* Corresponding Author
J CLIN MED KAZ, Volume 23, Issue 3, pp. 11-23. https://doi.org/10.23950/jcmk/18500
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Author Contributions: Conceptualization, M. K.; methodology, R. N., P. H. V. and M. K; validation, M. K. and P. H. V; formal analysis, R. N.; investigation, P. H. V.; resources, P. H. V.; data curation, R. N., P. H. V.; writing – original draft preparation, P. H. V. and R. N.; writing – review and editing, R. N. and P. H. V.; visualization, R. N. and P. H. V.; supervision, M. K.; project administration, M. K. All authors have read and agreed to the published version of the manuscript.

Data availability statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.

Artificial Intelligence (AI) Disclosure Statement: The authors declare no AI Tools used for preparation of this work.

ABSTRACT

Introduction: Bile duct cancer (cholangiocarcinoma) is an uncommon but highly aggressive malignancy characterized by late diagnosis and poor prognosis. Molecular profiling is essential to understand its pathogenesis and identify biomarkers for early detection and targeted therapy. This study aimed to identify key regulatory genes and molecular pathways associated with bile duct cancer using an integrated bioinformatics approach.
Methods: Gene expression datasets (GSE131027 and GSE107754) were retrieved from the NCBI Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified after normalization and statistical analysis. Protein–protein interaction (PPI) networks were constructed using the STRING database and visualized in Cytoscape. Hub genes were determined using the Cytohubba plugin based on topological parameters. Functional enrichment and pathway analyses were performed using ClueGO and BiNGO to explore associated biological processes and molecular functions.
Results: A total of over 1,000 DEGs were identified across datasets. Network and enrichment analyses revealed seven major hub genes: TP53, HIST1H3F, H2AFZ, FOS, POLR2B, CAV1, and SMAD3. These genes were strongly linked to tumor suppression, oxidative stress response, miRNA transcription regulation, and DNA repair processes. GO and KEGG analyses highlighted pathways associated with oncogenesis, reactive nitrogen species metabolism, and post-transcriptional gene regulation, emphasizing their relevance in bile duct cancer progression.
Conclusion: This integrative bioinformatics study identified critical hub genes and molecular pathways that may serve as potential biomarkers and therapeutic targets for bile duct cancer. Further validation and molecular docking studies could facilitate the development of targeted drugs and improve treatment outcomes.
Keywords: Cholangiocarcinoma, biomarkers, Hub genes, therapeutics, gene expression.

CITATION

Ramasamy N, Vardhan PH, Muthu K. Identification of Hub Genes Associated with Bile Duct Cancer Using Integrated Gene Expression Data with Protein-Protein Interaction Network. J CLIN MED KAZ. 2026;23(3):11-23. https://doi.org/10.23950/jcmk/18500

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