摘要：Abstract Ulcerative colitis is a chronic, idiopathic, and inflammatory disease of the rectal and colonic mucosa, the behavior of which is of heterogeneity in individuals. Here, we explored the multifactor-mediated functional modules associated with ulcerative colitis classification in the whole genome. Datasets downloaded from the GEO database were used to identify differentially expressed genes between ulcerative colitis patients and healthy individuals initially, followed by acquisition of the remaining ulcerative colitis -related genes from the OMIM and STRING databases. The results identified 914 ulcerative colitis-related genes, of which 60 were differentially expressed genes obtained from GEO datasets. Through weighted co-expression network analysis of ulcerative colitis-related genes, four modules were obtained, three of which were related to ulcerative colitis. Following interactions between microRNA, long noncoding RNA, transcription factors, and module hub genes were predicted and used to construct ulcerative colitis multifactor networks. Additionally, we performed consensus clustering of the ulcerative colitis samples. The results revealed that ulcerative colitis could be divided into four subtypes, with six hub genes identified as potential biomarkers for classification. These findings offer novel insights into ulcerative colitis and a basis for disease classification of ulcerative colitis.
其他摘要：Abstract Ulcerative colitis is a chronic, idiopathic, and inflammatory disease of the rectal and colonic mucosa, the behavior of which is of heterogeneity in individuals. Here, we explored the multifactor-mediated functional modules associated with ulcerative colitis classification in the whole genome. Datasets downloaded from the GEO database were used to identify differentially expressed genes between ulcerative colitis patients and healthy individuals initially, followed by acquisition of the remaining ulcerative colitis -related genes from the OMIM and STRING databases. The results identified 914 ulcerative colitis-related genes, of which 60 were differentially expressed genes obtained from GEO datasets. Through weighted co-expression network analysis of ulcerative colitis-related genes, four modules were obtained, three of which were related to ulcerative colitis. Following interactions between microRNA, long noncoding RNA, transcription factors, and module hub genes were predicted and used to construct ulcerative colitis multifactor networks. Additionally, we performed consensus clustering of the ulcerative colitis samples. The results revealed that ulcerative colitis could be divided into four subtypes, with six hub genes identified as potential biomarkers for classification. These findings offer novel insights into ulcerative colitis and a basis for disease classification of ulcerative colitis.
