# Identification of potential biomarkers and therapeutic targets for underactive bladder based on bioinformatics analysis and experimental validation

## TL;DR

This study identifies key genes and regulatory networks in underactive bladder to find potential biomarkers and therapeutic targets.

## Contribution

The study combines bioinformatics and experimental validation to discover novel biomarkers and regulatory mechanisms for underactive bladder.

## Key findings

- Eighty-five differentially expressed genes were identified in underactive bladder.
- Six hub genes (CLEC4E, CSF3R, CXCR2, FPR2, and IDO1) showed high diagnostic potential with AUC values over 0.76.
- Five transcription factors and five miRNAs were found to regulate multiple hub genes, suggesting new therapeutic targets.

## Abstract

Underactive bladder (UAB) is a common disorder that significantly affects patients’ quality of life, necessitating the exploration of underlying molecular mechanisms for more effective management. This study aims to elucidate the gene expression profiles associated with UAB by employing a combination of bioinformatics analyses and experimental validation to identify pivotal hub genes and potential therapeutic targets. We accessed the GSE122060 and GSE100219 datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs), followed by functional enrichment analysis, construction of a protein-protein interaction (PPI) network, screening for hub genes and assess the accuracy and diagnostic value of the hub genes with the validation dataset GSE28242. Eighty-five DEGs were identified from the GEO dataset, with functional enrichment analysis focusing primarily on biological processes like neutrophil migration, cell chemotaxis, and bacterial defense responses. Twelve key genes were identified in the PPI network using CytoHubba and MCODE plugins. Of these, C3, CLEC4E, CSF3R, CXCR2, FPR2, and IDO1 showed significant upregulation in the validation set compared to the control group. Receiver operating characteristic (ROC) curve analysis demonstrated that these six hub genes possess high diagnostic potential, with area under the curve (AUC) values greater than 0.76. Additionally, a hub gene-transcription factor (TF) interaction network, a hub gene-TF-miRNA co-regulatory network and a hub gene-drug interaction network were constructed, revealing that five TFs and five miRNAs regulate three or more hub genes. Quantitative real-time polymerase chain reaction (qRT-PCR) validation confirmed the differential expression patterns of the 12 key genes in the PPI network in TGF-β1 treated SV-HUC-1 cells. In conclusion, our findings suggest that CLEC4E, CSF3R, CXCR2, FPR2, and IDO1 can serve as promising diagnostic biomarkers for UAB, while the identified TFs and miRNAs could unveil new avenues for drug discovery and therapeutic interventions targeting UAB progression.