Bioinformatics analysis of the Hub genes and key pathways of interstitial cystitis pathogenesis. Issue 1 (29th October 2019)

• Record Type: Journal Article
• Authors: Liu, Junjiang; Zhang, Yunxia; Li, Shoubin; Sun, Fuzhen; Wang, Gang; Wei, Dong; Yang, Tao; Gu, Shouyi
• Is Part Of: Neurourology and urodynamics. Volume 39:Issue 1(2020:Jan.)
• Page Start: 133
• Page End: 143
• Publication Date: 2019-10-29
• Subjects: bioinformatic -- differentially expressed genes -- interstitial cystitis; Urinary organs -- Periodicals; Urodynamics -- Periodicals; Urology -- Periodicals; 616.6
• DOI: 10.1002/nau.24196
• Languages: English
• ISSNs: 0733-2467

Abstract

Abstract: Aims: This study aimed to identify suitable datasets for reanalysis and then explore potential key genes and related pathways of interstitial cystitis (IC). Methods: We searched the Gene Expression Omnibus database and three expression profile datasets and included 23 lesions of IC and 9 normal tissues in the analysis. Eight urine specimens of patients with IC and five urine specimens of healthy controls were also included. Then, these datasets were reanalyzed to determine the differentially expressed genes (DEGs), which were used to perform Gene Ontology and pathway enrichment analyses. These identified candidate genes were also applied to generate a protein‐protein interaction (PPI) network. Results: Forty‐two common DEGs were sorted and identified from two datasets, both of which included the samples of bladder lesions. Based on their functions and signaling pathways, these 42 DEGs are mainly classified as cell‐surface proteins and are involved in the immune and inflammatory responses. The PPI network included 41 nodes. In this network, we identified 11 genes as central nodes that are involved in the immune system and the inflammatory response. Furthermore, IC with Hunner's lesions shared the same DEGs with IC without Hunner's lesions. In both subgroups (IC with and without Hunner's lesions), we identified some common DEGs shared between bladder lesions and urine samples. Conclusion: Using bioinformatics, we integrated different IC‐related datasets and identified potential critical genes involved in IC that may contribute to future research on IC.