Browsing by Author "Abul Basar, Md."

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Identification of Key Signaling Pathways and Novel Computational Drug Target for Depression and Coronary Artery Disease
    (2024-12-19) Hosen, Md. Faruk; Abul Basar, Md.; Shahin Uddin, Muhammad; Yasmin, Mst. Farjana; Morshed, Monir
    Psychological disorders, such as anxiety, bipolar disorder, panic disorder, stress, depression, and schizophrenia, are increasingly prevalent worldwide. Among these conditions, depression is particularly notable as one of the most common and debilitating neuropsychiatric disorders. Individuals with depression may be at a higher risk of developing oronary Artery Disease (CAD). Depression can contribute to poor lifestyle choices, such as unhealthy eating, lack of exercise, and smoking, which are risk factors for CAD. The emotional stress and anxiety associated with depression can strain the heart and exacerbate CAD symptoms. The relationship is not one-sided. CAD itself can be a significant source of emotional distress, leading to symptoms of depression and anxiety in affected individuals. Managing both conditions in tandem can be complicated. Treating CAD may involve medications, lifestyle modifications, and potentially surgical interventions. Meanwhile, depression often requires therapy, counseling, and medication. Coordinating care and addressing both conditions simultaneously is crucial. In our study, we investigated the molecular connections between CAD and Depression using GSE98793 and GSE20681 microarray datasets. After preprocessing, we identified key hub genes, including CCT2, SVIL, REPS2, ASPH, and UBC, in the shared ProteinProtein Interaction network. KEGG pathways linked these DEGs to colorectal and cancer pathways. Our next steps involve exploring microRNAs, TFs, and GO analysis. These findings offer promising leads for potential therapies, uniting CAD and Depression under a common molecular framework, advancing our understanding of these conditions.
  • Thumbnail Image
    Item
    Integrated Bioinformatics and Machine Learning Analysis Reveals Shared Key Candidate Biomarkers and Therapeutic Targets in Ulcerative Colitis and Colorectal Cancer
    (2024-10-24) Sarker, Sakib; Hosen, Md. Faruk; Abul Basar, Md.; Ahammed, Emon
    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.