Identification of CKS2 and RRM2 as potential markers of vitiligo using bioinformatics analysis.

Previous studies have attempted to elucidate the molecular mechanism of vitiligo; however, its pathogenesis remains unclear. This study aimed to explore biomarkers related to vitiligo through bioinformatic analysis. The microarray datasets GSE53146 and GSE65127 were downloaded from the Gene Expression Omnibus database. Firstly, differentially expressed genes (DEGs) in GSE53146 were screened, and then an enrichment analysis was performed. Secondly, the protein-protein interaction (PPI) network of DEGs was constructed using the STRING database, and the key genes were screened using the MCODE plugin in Cytoscape and verified using GSE65127. Finally, quantiseq was used to evaluate immune cell infiltration in vitiligo, then to observe the correlation between biomarkers and immune cells. In total, 544 DEGs were identified, including 342 upregulated and 202 downregulated genes. Gene Ontology (GO) enrichment showed that DEGs were related to inflammatory and immune responses, and Kyoto Encyclopedia of Genes and Genomes enrichment showed that DEGs were involved in many autoimmune diseases. In the PPI network, 7 key genes, CENPN, CKS2, PLK4, RRM2, TPX2, CCNA2, and CDC45 were identified by MCODE cluster and verified using the GSE65127 dataset. With an area under the curve (AUC) > 0.8 as the standard, 2 genes were screened, namely CKS2 and RRM2. Further immune infiltration analysis showed that M2 macrophages were involved in the pathogenesis of vitiligo, whereas CKS2 and RRM2 were both related to M2 macrophages. This study shows that CKS2 and RRM2 have potential as biomarkers of vitiligo and provides a theoretical basis for a better understanding of the pathogenesis of vitiligo.

Miao Y ，Su D ，Fu Q ，Chen T ，Ji Y ，Zhang F ... -  《-》

Identification of key genes and evaluation of immune cell infiltration in vitiligo.

Zhang X ，Hu W ，Lei Z ，Wang H ，Kang X ... -  《-》

Identification of candidate biomarkers and pathways associated with SCLC by bioinformatics analysis.

Wen P ，Chidanguro T ，Shi Z ，Gu H ，Wang N ，Wang T ，Li Y ，Gao J ... -  《-》

Screening Hub Genes as Prognostic Biomarkers of Hepatocellular Carcinoma by Bioinformatics Analysis.

Zhou Z ，Li Y ，Hao H ，Wang Y ，Zhou Z ，Wang Z ，Chu X ... -  《-》

CKS2 (CDC28 protein kinase regulatory subunit 2) is a prognostic biomarker in lower grade glioma: a study based on bioinformatic analysis and immunohistochemistry.

Hu M ，Li Z ，Qiu J ，Zhang R ，Feng J ，Hu G ，Ren J ... -  《-》