Background: Ulcerative colitis (UC) is an idiopathic, chronic disorder characterized by inflammation, injury, and disruption of the colonic mucosa. However, there are still many difficulties in the diagnosis and differential diagnosis of UC. An increasing amount of research has shown a connection between ferroptosis and the etiology of UC. Therefore, our study aimed to identify the key genes related to ferroptosis in UC to provide new ideas for diagnosis UC.Methods: Gene expression profiles of normal and UC samples were extracted from the Gene Expression Omnibus (GEO) database. By combining differentially expressed genes (DEGs), Weighted correlation network analysis (WGCNA) genes, and ferroptosis-related genes, hub genes were identified and then screened using Lasso regression. Based on the key genes, gene ontology (GO) and gene set enrichment analysis (GSEA) analyses were performed. We used NaiveBeyas, Logistic, IBk, and RandomForest algorithms to build a disease diagnosis model using the hub genes. The model was validated using GSE87473 as the validation set.Results: Gene expression matrices of GSE87466 and GSE75214 were downloaded from the GEO database, including 184 UC patients and 43 control samples. A total of 699 DEGs were obtained. From FerrDb, 565 genes related to ferroptosis were identified. The 1,513 genes with the highest absolute correlation coefficient value in the MEblue module were obtained from WGCNA analysis. Five hub genes (LCN2, MUC1, PARP8, PLIN2, and TIMP1) were identified using the Lasso regression algorithm based on the overlapped DEGs, WGCNA-identified genes, and ferroptosis-related genes. GO and GSEA analyses revealed that 5 hub genes were identified as being involved in the negative regulation of transcription by competitive promoter binding, cellular response to citrate cycle_tca_cycle, cytosolic_dna_sensing pathway, UV-A, and beta-alanine metabolism. The logistic algorithm's values of the area under the curve (AUC)were 1.000 and 0.995 for training and validation cohorts, and sensitivity is 0.962, specificity is 1.000, respectively, as determined by comparing various methods. Conclusions:The previously described hub genes were identified as being intimately related to ferroptosis in UC and capable of distinguishing UC patients from controls. By detecting the expression of several genes, this model may aid in diagnosing UC and understanding the etiology and treatment of the disease.
Background: Prostate cancer (PCa) is one of the most commonly diagnosed cancers and the fifth leading cause of cancer death in men. In this study, candidate biomarkers related to the diagnosis and prognosis of PCa were identified using bioinformatics approach. Methods: Differentially expressed genes (DEGs) between PCa tissues and matched normal tissues were screened using the R software. Enrichment analysis of the DEGs was performed to determine their functions and related pathways. PPI network was constructed, and 10 hub genes were screened using the STRING database and Cytoscape software. Weighted gene co-expression network analysis (WGCNA) was performed to extract key module genes, from which 5 key genes were identified by Venn diagram. Receiver operating characteristic (ROC) analysis was performed to identify the diagnostic value of the key genes, and their prognostic value was verified via survival analysis, which was further validated using the Human Protein Atlas. Results: We identified 661 DEGs (249 upregulated and 412 downregulated) between the PCa group and healthy controls. Overlap of PPI and WCCNA networks identified 5 key genes: BUB1B, HMMR, RRM2, CCNA2 and MELK, as candidate biomarkers for PCa. Although ROC analysis suggested that these genes had diagnostic potential in PCa, survival analysis showed that RRM2 and BUB1B were significantly associated with PCa prognosis. Conclusion: Our results showed that BUB1B, HMMR, RRM2, CCNA2 and MELK could be diagnostic biomarkers for PCa, while RRM2 and BUB1B were also associated with prognosis and could be potential therapeutic targets for PCa.
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