The complexity of Interstitial Cystitis/bladder Pain Syndrome (IC/BPS) has led to a great deal of uncertainty in terms of diagnosis and prevalence of the condition. Under these situations the chronic inflammation has reported to be arise after surgery in IC/BPS patients which is frequently misdiagnosed. Here, we have identified the possible candidate genes through integrated analysis of Gene Expression Omnibus (GEO) datasets and confirmed experimentally in order to predict the correct pathologic diagnosis for IC/BPS.

Bioinformatic analyses were conducted using two GEO datasets (GSE11783 and GSE57560) to determine whether individual biomarkers associated with diagnosis of IC/BPS and to predict the possible signaling pathway. Based on the results, we assayed hub genes expression of tissue samples from IC/BPS patients using RT-PCR and immunohistochemistry analysis. We prospectively enrolled IC/BPS patients, on the basis of cystoscopic findings, as having Hunner lesions. Specimens obtained from the posterior wall in Hunner-type IC cases during transurethral resection were evaluated. Radical cystectomy specimen without malignancy was selected as controls.

Our Data mining analysis of GEO datasets revealed a total of 39 (25 upregulated and 14 downregulated) common differentially expressed genes (DEGs) in IC/BPS. A PPI network was then constructed with those 39 common DEGs using cytoscape v7.1 and subsequently, six hub genes (AQP9, S100A8, FPR1, CSF3R, S100A12, and NCF2, respectively) were identified using cytoscape through cytoHubba v0.1 tool. Moreover, enrichment analysis of common DEGs was revealed that cell cycle and TP53 signaling pathway were prominently involved with common 25 upregulated DEGs whereas common 14 downregulated DEGs might regulate G alpha (z) and Transmembrane transport of small molecules signaling pathway in IC/BPS. Among the all six hub genes, AQP9 and NCF2 expression were significantly augmented in our IC/BPS patient’s sample compared to their normal counterparts.

In this study, we have performed an bioinformatics analysis, using patient-derived tissue genomics data to reveal the molecular signatures of IC/BPS. The identified common DEGs were predominantly enriched in immunity and metabolism signaling pathways. Six key hub genes were identified using a PPI network analysis of DEGs from three GEO datasets which were significantly upregulated. These hub genes might be considered as candidate biomarkers of IC/BPS.

In addition, these hub genes may also be regulated by various key transcription factors which may have a potential role in the development of IC/BPS. Our RT-qPCR data also confirmed the upregulation of hub genes in our IC/BPS patient tissues compared to normal tissue. Thus, these six hub genes might play a potential role in IC/BPS development and serve as diagnostic biomarkers of IC/BPS, which deserve further investigation.