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Scopus Indexed Publications

Title: Integrated Bioinformatics and Machine Learning Analysis Reveals Shared Key Candidate Biomarkers and Therapeutic Targets in Ulcerative Colitis and Colorectal Cancer

Abstract: The interplay between ulcerative colitis (UC) and
colorectal cancer (CRC) has garnered significant research interest
due to their potential shared molecular mechanisms. This study
aims to identify common significant biomarkers and potential
therapeutic targets for UC and CRC. We utilized two microarray
datasets to perform differential expression analysis, identifying
DEGs for both conditions. Subsequent ML-based gene selection
was conducted using SHapley Additive exPlanations (SHAP)
algorithm models on the respective datasets. Common ML-based
DEGs were then identified and a protein-protein interaction (PPI)
network was constructed using the STRING database. The PPI
network was visualized and analyzed in Cytoscape, with the
top ten hub genes identified using the Degree method in the
cytoHubba plugin. The hub genes identified were CDC20, ANLN,
HMMR, CCNB1, CDK1, KIF20A, ECT2, KIF11, NUF2, and
CCNA2. These genes were further validated through survival
analysis, establishing their significance in patient outcomes.
Finally, we explored the drug-gene interaction network to identify
potential therapeutic drugs targeting these hub genes. This
comprehensive bioinformatics approach provides insights into the
shared molecular pathways in UC and CRC and highlights poten-
tial therapeutic targets for future research and drug development.

Journal or Conference Name: 2nd International Conference on Information and Communication Technology, ICICT 2024