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Cholangiocarcinoma (CCA) is a heterogenous malignancy that can occur anywhere along the biliary tract. Its heterogeneous and aggressive behaviour necessitates the identification of biomarkers influencing the overall survival of patients. The current work utilizes clinical and RNA-Seq data of 36 CCA patients from the TCGA database. Cox Proportional Hazard (Cox PH) model was employed to evaluate the transcriptomic information and clinical factors in determining their relative effects on patient survival. The univariate analysis revealed that FRMD5, CCNA1, ALG1L2, and POPDC3 genes are significantly related to the patient’s survival, while the multivariate analysis done for all the genes together confirmed a significant relation of some genes on the overall survival. And, the combined analysis of clinical and transcriptomic data suggests that mutation counts, neoplasmic grade, and patient’s weight are significantly correlated with the patient’s survival. Additionally, the expression pattern of selected genes was validated using the firebrowse database. This integrated analytical study reveals POPDC3, FRMD5, CCNA1, and ALG1L2 as novel and crucial prognostic biomarkers of CCA and their high expression correlates to poor survival of CCA patients.

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Machine Learning and Bioinformatics Analysis Reveal POPDC3, FRMD5, CCNA1, and ALG1L2 as Novel Prognostic Biomarkers in Cholangiocarcinoma

  • 会议论文

  • 发表日期：

    2025年

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  • Aakansha Singh,
  • Anjana Dwivedi

摘要

Cholangiocarcinoma (CCA) is a heterogenous malignancy that can occur anywhere along the biliary tract. Its heterogeneous and aggressive behaviour necessitates the identification of biomarkers influencing the overall survival of patients. The current work utilizes clinical and RNA-Seq data of 36 CCA patients from the TCGA database. Cox Proportional Hazard (Cox PH) model was employed to evaluate the transcriptomic information and clinical factors in determining their relative effects on patient survival. The univariate analysis revealed that FRMD5, CCNA1, ALG1L2, and POPDC3 genes are significantly related to the patient’s survival, while the multivariate analysis done for all the genes together confirmed a significant relation of some genes on the overall survival. And, the combined analysis of clinical and transcriptomic data suggests that mutation counts, neoplasmic grade, and patient’s weight are significantly correlated with the patient’s survival. Additionally, the expression pattern of selected genes was validated using the firebrowse database. This integrated analytical study reveals POPDC3, FRMD5, CCNA1, and ALG1L2 as novel and crucial prognostic biomarkers of CCA and their high expression correlates to poor survival of CCA patients.