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Colon cancer (CC) and obesity are significant public health challenges with a strong bidirectional association. Obesity contributes to systemic inflammation and alters key biological pathways, substantially increasing CC probability. This study identifies potential therapeutic candidates and molecular pathways using bioinformatic approaches and machine learning (ML) methodologies. The GSE44076 and GSE24185 microarray datasets were used for CC and obesity samples. We identified concordant differentially expressed genes (DEGs) with regulatory frameworks shown using Venn diagrams. Gene selection employed the SHapley Additive exPlanations (SHAP) approach to identify the most relevant DEGs, followed by ML-based DEG discovery. We constructed a protein-protein interaction (PPI) network using overlapped genes, identifying the most active genes through topological analysis. HMOX2, DICER, MAML1, FBXW2, and NSMCE4A emerged as main hub genes from the PPI network. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicates shared DEGs relate to significant metabolic pathways associated with CC and obesity. Future possibilities include gene ontology (GO), transcription factor, and miRNA analysis, plus module analysis networks. Therapeutic molecules are proposed based on concordant DEGs.

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Comprehensive Bioinformatics and Machine Learning Analyses to Identify Molecular Signatures and Pathways in Colon Cancer and Obesity

  • 会议论文

  • 发表日期：

    2026年

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  • Francis Rudra D. Cruze,
  • Jeba Wasima,
  • Md. Faruk Hosen,
  • Hafizul Islam,
  • Md. Nasimul Kader,
  • Mohammad Sarwar Hossain Mollah

摘要

Colon cancer (CC) and obesity are significant public health challenges with a strong bidirectional association. Obesity contributes to systemic inflammation and alters key biological pathways, substantially increasing CC probability. This study identifies potential therapeutic candidates and molecular pathways using bioinformatic approaches and machine learning (ML) methodologies. The GSE44076 and GSE24185 microarray datasets were used for CC and obesity samples. We identified concordant differentially expressed genes (DEGs) with regulatory frameworks shown using Venn diagrams. Gene selection employed the SHapley Additive exPlanations (SHAP) approach to identify the most relevant DEGs, followed by ML-based DEG discovery. We constructed a protein-protein interaction (PPI) network using overlapped genes, identifying the most active genes through topological analysis. HMOX2, DICER, MAML1, FBXW2, and NSMCE4A emerged as main hub genes from the PPI network. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicates shared DEGs relate to significant metabolic pathways associated with CC and obesity. Future possibilities include gene ontology (GO), transcription factor, and miRNA analysis, plus module analysis networks. Therapeutic molecules are proposed based on concordant DEGs.