AUTHOR=Zhou Yufei , Shi Wenxiang , Zhao Di , Xiao Shengjue , Wang Kai , Wang Jing 

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

JOURNAL=Frontiers in Immunology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.937886

DOI=10.3389/fimmu.2022.937886

ISSN=1664-3224

ABSTRACT=Background: Immune system dysregulation plays the 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.
Methods: Three AVC and one MS datasets were downloaded from the GEO database. Differentially expressed genes (DEGs) and module gene identification via Limma and weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (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 curve (ROC) were developed. Finally, immune cell infiltration was created to investigate immune cell dysregulation in AVC.
Results: The merged AVC dataset included 587 DEGs, and 1438 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 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 curve from 0.732 to 0.982). Various dysregulated immune cells were observed as well.
Conclusion: Five immune-associated candidate hub genes (BEX2, SPRY2, CXCL16, ITGAL, 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 with MS patients.