Objective:To explore potential signaling pathways and genes related to vitiligo progression by using bioinformatics methods.Methods:A vitiligo genechip dataset GSE75819 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened between lesional and non-lesional skin tissues from 15 Indian patients with vitiligo with the dataset GSE75819 by using LMFit and eBayes functions in R LIMma package. The Kyoto Encyclopedia of Genes and Genomes (KEGG) -based pathway analysis, Gene Ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA) were carried out to identify enriched pathways and functions of the DEGs. Protein-protein interaction networks were established to screen hub genes from the DEGs. In addition, lesional and non-lesional skin tissue specimens were obtained from 8 patients of Han nationality with vitiligo vulgaris in Hospital of Xinjiang Traditional Uyghur Medicine between January and June in 2019, and real-time quantitative PCR was performed to verify the expression of the top 10 up- or down-regulated DEGs.Results:Compared with the 15 non-lesional skin tissues, a total of 148 DEGs were identified in the 15 lesional skin tissues. Among these DEGs, KRT9, CXCL10, C8ORF59, TPSAB1 and RPL26 were the top 5 up-regulated genes, and SILV, RPPH1, TYRP1, MLANA and LOC401115 were the top 5 down-regulated genes, which were all verified by real-time quantitative PCR in the lesional and non-lesional skin tissues from the 8 patients of Han nationality with vitiligo. GO analysis showed that the DEGs were chiefly enriched in translational initiation, cellular response to lipopolysaccharide, ribosomes, ribosomal subunits and structural constituents of ribosomes. KEGG analysis showed that the DEGs were chiefly enriched in tyrosine metabolism, peroxisome proliferator-activated receptor signaling pathway, oxidative phosphorylation and Toll-like receptor signaling pathway. Four hub genes, including UPF3B, SNRPG, MRPL13 and RPL26L1, were screened out by protein-protein interaction analysis.Conclusion:KRT9, CXCL10, C8ORF59, TPSAB1, RPL26, SILV, RPPH1, TYRP1, MLANA and LOC401115 genes may serve as potential diagnostic molecular markers and therapeutic targets for vitiligo.

Background::Dual-energy computed tomography (DECT) is purported to accurately distinguish uric acid stones from non-uric acid stones. However, whether DECT can accurately discriminate ammonium urate stones from uric acid stones remains unknown. Therefore, we aimed to explore whether they can be accurately identified by DECT and to develop a radiomics model to assist in distinguishing them.Methods::This research included two steps. For the first purpose to evaluate the accuracy of DECT in the diagnosis of uric acid stones, 178 urolithiasis patients who underwent preoperative DECT between September 2016 and December 2019 were enrolled. For model construction, 93, 40, and 109 eligible urolithiasis patients treated between February 2013 and October 2022 were assigned to the training, internal validation, and external validation sets, respectively. Radiomics features were extracted from non-contrast CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was used to develop a radiomics signature. Then, a radiomics model incorporating the radiomics signature and clinical predictors was constructed. The performance of the model (discrimination, calibration, and clinical usefulness) was evaluated.Results::When patients with ammonium urate stones were included in the analysis, the accuracy of DECT in the diagnosis of uric acid stones was significantly decreased. Sixty-two percent of ammonium urate stones were mistakenly diagnosed as uric acid stones by DECT. A radiomics model incorporating the radiomics signature, urine pH value, and urine white blood cell count was constructed. The model achieved good calibration and discrimination {area under the receiver operating characteristic curve (AUC; 95% confidence interval [CI]), 0.944 (0.899–0.989)}, which was internally and externally validated with AUCs of 0.895 (95% CI, 0.796–0.995) and 0.870 (95% CI, 0.769–0.972), respectively. Decision curve analysis revealed the clinical usefulness of the model.Conclusions::DECT cannot accurately differentiate ammonium urate stones from uric acid stones. Our proposed radiomics model can serve as a complementary diagnostic tool for distinguishing them in vivo.