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

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, spe...

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Bibliographic Details
Published inMathematics (Basel) Vol. 12; no. 5; p. 725
Main Authors Abdullahi, Muhammad Sirajo, Suratanee, Apichat, Piro, Rosario Michael, Plaimas, Kitiporn
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2024
Subjects
Biomarkers
Blood
Cancer
Coronaviruses
COVID-19
Data analysis
Diagnosis
Disease
Disease control
Gene expression
Gene sequencing
Genes
Genomes
Genomics
hepatocellular carcinoma
Hepatoma
Homology
Information management
Liver cancer
Magnetic resonance imaging
Oncology, Experimental
persistent homology
Ribonucleic acid
RNA
RNA-seq
topological data analysis
Topology
Tumor proteins
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math12050725