Identification of Potential Genes and Critical Pathways in Postoperative Recurrence of Crohn�s Disease by Machine Learning And WGCNA Network Analysis

Rajalingam, A and Sekar, K and Ganjiwale, A (2023) Identification of Potential Genes and Critical Pathways in Postoperative Recurrence of Crohn�s Disease by Machine Learning And WGCNA Network Analysis. In: Current Genomics, 24 (2). pp. 84-99.

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Abstract

Background: Crohn's disease (CD) is a chronic idiopathic inflammatory bowel disease affecting the entire gastrointestinal tract from the mouth to the anus. These patients often experience a period of symptomatic relapse and remission. A 20-30 symptomatic recurrence rate is reported in the first year after surgery, with a 10 increase each subsequent year. Thus, surgery is done only to relieve symptoms and not for the complete cure of the disease. The determinants and the genetic factors of this disease recurrence are also not well-defined. Therefore, enhanced diagnostic efficiency and prognostic outcome are critical for confronting CD recurrence. Methods: We analysed ileal mucosa samples collected from neo-terminal ileum six months after surgery (M6=121 samples) from Crohn's disease dataset (GSE186582). The primary aim of this study is to identi-fy the potential genes and critical pathways in post-operative recurrence of Crohn�s disease. We combined the differential gene expression analysis with Recursive feature elimination (RFE), a machine learning approach to get five critical genes for the postoperative recurrence of Crohn's disease. The features (genes) selected by different methods were validated using five binary classifiers for recurrence and remission samples: Logistic Regression (LR), Decision tree classifier (DT), Support Vector Machine (SVM), Random Forest classifier (RF), and K-nearest neighbor (KNN) with 10-fold cross-validation. We also performed weighted gene co-expression network analysis (WGCNA) to select specific modules and feature genes associated with Crohn's disease postoperative recurrence, smoking, and biological sex. Combined with other biological interpretations, including Gene Ontology (GO) analysis, pathway en-richment, and protein-protein interaction (PPI) network analysis, our current study sheds light on the in-depth research of CD diagnosis and prognosis in postoperative recurrence. Results: PLOD2, ZNF165, BOK, CX3CR1, and ARMCX4, are the important genes identified from the machine learning approach. These genes are reported to be involved in the viral protein interaction with cytokine and cytokine receptors, lysine degradation, and apoptosis. They are also linked with various cellular and molecular functions such as Peptidyl-lysine hydroxylation, Central nervous system maturation, G protein-coupled chemoattractant receptor activity, BCL-2 homology (BH) domain bind-ing, Gliogenesis and negative regulation of mitochondrial depolarization. WGCNA identified a gene co-expression module that was primarily involved in mitochondrial translational elongation, mito-chondrial translational termination, mitochondrial translation, mitochondrial respiratory chain com-plex, mRNA splicing via spliceosome pathways, etc.; Both the analysis result emphasizes that the mi-tochondrial depolarization pathway is linked with CD recurrence leading to oxidative stress in promoting inflammation in CD patients. Conclusion: These key genes serve as the novel diagnostic biomarker for the postoperative recurrence of Crohn�s disease. Thus, among other treatment options present until now, these biomarkers would provide success in both diagnosis and prognosis, aiming for a long-lasting remission to prevent further complications in CD. © 2023 Bentham Science Publishers.

Item Type:	Journal Article
Publication:	Current Genomics
Publisher:	Bentham Science Publishers
Additional Information:	The copyright for this article belongs to Bentham Science Publishers.
Keywords:	chemokine receptor CX3CR1; cytokine receptor; protein bcl 2, apoptosis; Article; bioinformatics; controlled study; Crohn disease; cross validation; depolarization; diagnostic test accuracy study; differential expression analysis; differential gene expression; endoscopy; gene expression; gene ontology; hierarchical clustering; human; human tissue; immune response; inflammation; k nearest neighbor; machine learning; oxidative stress; pathway enrichment analysis; postoperative complication; protein expression; protein interaction; protein protein interaction; receiver operating characteristic; recurrent disease; RNA splicing; spliceosome; support vector machine; weighted gene co expression network analysis
Department/Centre:	Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited:	30 May 2024 06:02
Last Modified:	30 May 2024 06:02
URI:	https://eprints.iisc.ac.in/id/eprint/84354

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