Persistent Homology Identifies Pathways Associated with Hepatocellular Carcinoma from Peripheral Blood Samples

Muhammad Sirajo Abdullahi, Apichat Suratanee, Rosario Michael Piro and Kitiporn Plaimas ()
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Muhammad Sirajo Abdullahi: Advanced Virtual and Intelligence Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
Apichat Suratanee: Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
Rosario Michael Piro: Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, 20133 Milan, Italy
Kitiporn Plaimas: Advanced Virtual and Intelligence Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand

Mathematics, 2024, vol. 12, issue 5, 1-27

Abstract: Topological data analysis (TDA) methods have recently emerged as powerful tools for uncovering intricate patterns and relationships in complex biological data, demonstrating their effectiveness in identifying key genes in breast, lung, and blood cancer. In this study, we applied a TDA technique, specifically persistent homology (PH), to identify key pathways for early detection of hepatocellular carcinoma (HCC). Recognizing the limitations of current strategies for this purpose, we meticulously used PH to analyze RNA sequencing (RNA-seq) data from peripheral blood of both HCC patients and normal controls. This approach enabled us to gain nuanced insights by detecting significant differences between control and disease sample classes. By leveraging topological descriptors crucial for capturing subtle changes between these classes, our study identified 23 noteworthy pathways, including the apelin signaling pathway, the IL-17 signaling pathway, and the p53 signaling pathway. Subsequently, we performed a comparative analysis with a classical enrichment-based pathway analysis method which revealed both shared and unique findings. Notably, while the IL-17 signaling pathway was identified by both methods, the HCC-related apelin signaling and p53 signaling pathways emerged exclusively through our topological approach. In summary, our study underscores the potential of PH to complement traditional pathway analysis approaches, potentially providing additional knowledge for the development of innovative early detection strategies of HCC from blood samples.

Keywords: topological data analysis; persistent homology; RNA-seq; gene expression; cancer; hepatocellular carcinoma; pathway analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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