Identification of Immune-Associated Genes in Diagnosing Aortic Valve Calcification With Metabolic Syndrome by Integrated Bioinformatics Analysis and Machine Learning

Abstract

BackgroundImmune system dysregulation plays a critical role in aortic valve calcification (AVC) and metabolic syndrome (MS) pathogenesis. The study aimed to identify pivotal diagnostic candidate genes for AVC patients with MS.MethodsWe obtained three AVC and one MS dataset from the gene expression omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and module gene via Limma and weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, protein–protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and random forest) were used to identify candidate immune-associated hub genes for diagnosing AVC with MS. To assess the diagnostic value, the nomogram and receiver operating characteristic (ROC) curve were developed. Finally, immune cell infiltration was created to investigate immune cell dysregulation in AVC.ResultsThe merged AVC dataset included 587 DEGs, and 1,438 module genes were screened out in MS. MS DEGs were primarily enriched in immune regulation. The intersection of DEGs for AVC and module genes for MS was 50, which were mainly enriched in the immune system as well. Following the development of the PPI network, 26 node genes were filtered, and five candidate hub genes were chosen for nomogram building and diagnostic value evaluation after machine learning. The nomogram and all five candidate hub genes had high diagnostic values (area under the curve from 0.732 to 0.982). Various dysregulated immune cells were observed as well.ConclusionFive immune-associated candidate hub genes (BEX2, SPRY2, CXCL16, ITGAL, and MORF4L2) were identified, and the nomogram was constructed for AVC with MS diagnosis. Our study could provide potential peripheral blood diagnostic candidate genes for AVC in MS patients.

Development and Validation of a Predictive Model for Chronic Postsurgical Pain After Arthroscopic Rotator Cuff Repair: A Prospective Cohort Study

“…In our study, we employed the LASSO regression method as a strategy to mitigate the impact of overfitting and collinearity, thereby enhancing the accuracy of variable prediction. 28 One significant factor we investigated was age, which emerged as a crucial determinant in the development of chronic pain. This finding is consistent with several previous studies, [29][30][31] which have also noted that younger patients tend to experience higher levels of postoperative pain intensity when compared to older patients.…”

Investigation of the shared molecular mechanisms and hub genes between myocardial infarction and depression

“…Based on the methods of previous studies, hub genes for depression diagnosis were selected (14,15). Four machine learning methods, including least absolute shrinkage and selection operator (LASSO) regression, random forest model (RF), support vector machine model (SVM), generalized linear model (GLM) were performed to evaluate the value of S-DEGs in diagnosing depression.…”

Identification and validation of diagnostic signature genes in non-obstructive azoospermia by machine learning

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