Identification of common genetic factors and immune-related pathways associating more than two autoimmune disorders: implications on risk, diagnosis, and treatment.

Autoimmune disorders (ADs) are chronic conditions resulting from failure or breakdown of immunological tolerance, resulting in the host immune system attacking its cells or tissues. Recent studies report shared effects, mechanisms, and evolutionary origins among ADs; however, the possible factors connecting them are unknown. This study attempts to identify gene signatures commonly shared between different autoimmune disorders and elucidate their molecular pathways linking the pathogenesis of these ADs using an integrated gene expression approach. We employed differential gene expression analysis across 19 datasets of whole blood/peripheral blood cell samples with five different autoimmune disorders (rheumatoid arthritis, multiple sclerosis, systemic lupus erythematosus, Crohn's disease, and type 1 diabetes) to get nine key genes-EGR1, RUNX3, SMAD7, NAMPT, S100A9, S100A8, CYBB, GATA2, and MCEMP1 that were primarily involved in cell and leukocyte activation, leukocyte mediated immunity, IL-17, AGE-RAGE signaling in diabetic complications, prion disease, and NOD-like receptor signaling confirming its role in immune-related pathways. Combined with biological interpretations such as gene ontology (GO), pathway enrichment, and protein-protein interaction (PPI) network, our current study sheds light on the in-depth research on early detection, diagnosis, and prognosis of different ADs.

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

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 identify 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 enrichment, 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 binding, Gliogenesis and negative regulation of mitochondrial depolarization. WGCNA identified a gene co-expression module that was primarily involved in mitochondrial translational elongation, mitochondrial translational termination, mitochondrial translation, mitochondrial respiratory chain complex, mRNA splicing via spliceosome pathways, etc.; Both the analysis result emphasizes that the mitochondrial 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.

Recursive Feature Elimination-based Biomarker Identification for Open Neural Tube Defects.

Background: Open spina bifida (myelomeningocele) is the result of the failure of spinal cord closing completely and is the second most common and severe birth defect. Open neural tube defects are multifactorial, and the exact molecular mechanism of the pathogenesis is not clear due to disease complexity for which prenatal treatment options remain limited worldwide. Artificial intelligence techniques like machine learning tools have been increasingly used in precision diagnosis. Objective: The primary objective of this study is to identify key genes for open neural tube defects using a machine learning approach that provides additional information about myelomeningocele in order to obtain a more accurate diagnosis. Materials and Methods: Our study reports differential gene expression analysis from multiple datasets (GSE4182 and GSE101141) of amniotic fluid samples with open neural tube defects. The sample outliers in the datasets were detected using principal component analysis (PCA). We report a combination of the differential gene expression analysis with recursive feature elimination (RFE), a machine learning approach to get 4 key genes for open neural tube defects. The features selected were validated using five binary classifiers for diseased and healthy samples: Logistic Regression (LR), Decision tree classifier (DT), Support Vector Machine (SVM), Random Forest classifier (RF), and K-nearest neighbour (KNN) with 5-fold cross-validation. Results: Growth Associated Protein 43 (GAP43), Glial fibrillary acidic protein (GFAP), Repetin (RPTN), and CD44 are the important genes identified in the study. These genes are known to be involved in axon growth, astrocyte differentiation in the central nervous system, post-traumatic brain repair, neuroinflammation, and inflammation-linked neuronal injuries. These key genes represent a promising tool for further studies in the diagnosis and early detection of open neural tube defects. Conclusion: These key biomarkers help in the diagnosis and early detection of open neural tube defects, thus evaluating the progress and seriousness in diseases condition. This study strengthens previous literature sources of confirming these biomarkers linked with open NTD's. Thus, among other prenatal treatment options present until now, these biomarkers help in the early detection of open neural tube defects, which provides success in both treatment and prevention of these defects in the advanced stage.