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Updated: May 9, 2026
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Identification of potential crucial genes shared in psoriasis and ulcerative colitis by machine learning and
Jing Zhang1, Shuo Feng1, Minfei Chen1
1Department of Dermatology, the First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi Province, China.
Background:
Mounting evidence suggest that there are an association between psoriasis and ulcerative colitis (UC), although the common pathogeneses are not fully understood. Our study aimed to find potential crucial genes in psoriasis and UC through machine learning and integrated bioinformatics.
Methods:
The overlapping differentially expressed genes (DEGs) of the datasets GSE13355 and GSE87466 were identified. Then the functional enrichment analysis was performed. The overlapping genes in LASSO, SVM-RFE and key module in WGCNA were considered as potential crucial genes. The receiver operator characteristic (ROC) curve was used to estimate their diagnostic confidence. The CIBERSORT was conducted to evaluate immune cell infiltration. Finally, the datasets GSE30999 and GSE107499 were retrieved to validate.
Results:
112 overlapping DEGs were identified in psoriasis and UC and the functional enrichment analysis revealed they were closely related to the inflammatory and immune response. Eight genes, including S100A9, PI3, KYNU, WNT5A, SERPINB3, CHI3L2, ARNTL2, and SLAMF7, were ultimately identified as potential crucial genes. ROC curves showed they all had high confidence in the test and validation datasets. CIBERSORT analysis indicated there was a correlation between infiltrating immune cells and potential crucial genes.
Conclusion:
In our study, we focused on the comprehensive understanding of pathogeneses in psoriasis and UC. The identification of eight potential crucial genes may contribute to not only understanding the common mechanism, but also identifying occult UC in psoriasis patients, even serving as therapeutic targets in the future.
