همایش ملی بیوانفورماتیک ایران

صفحه اصلی / 4th international edition and 13th Iranian Conference on Bioinformatics

Identification of potential RNA interference targets in bladder cancer through bioinformatics approaches

نویسندگان :

Sana Sajjadi¹ Mohammad ali Takhshid²

1- shiraz 2- shir

کلمات کلیدی :

Bladder cancer،Hub gene،Bioinformatics،Systems biology

چکیده :

Background: Bladder cancer is a urothelial carcinoma with histological heterogeneity, making it difficult to manage. High recurrence rates and the lack of effective targeted therapies highlight the need for less invasive therapeutic options. In the present study, we aim to identify potential miRNAs to suppress upregulated key genes related to bladder cancer using bioinformatics approaches to pave the way for more effective treatment strategies. Methods: Microarray data of bladder cancer and normal tissues were retrieved from the Gene Expression Omnibus (GEO) database (GSE121711). After preprocessing the data, differentially expressed genes (DEGs) were identified using the R/Bioconductor package limma, focusing on upregulated genes for further analysis. A protein-protein interaction (PPI) network was reconstructed to explore interactions among upregulated hub genes, based on centrality scores within the network. Gene expression levels of the identified hub genes were confirmed using GEPIA. Additionally, NetworkAnalyst was employed to analyze hub gene interactions with miRNAs, constructing gene-miRNA interaction networks. Results: A total of 9,781 differentially expressed genes were identified. The PPI network nodes were ranked using four topological analysis methods from the CytoHubba plugin in Cytoscape: Degree, Maximal Clique Centrality (MCC), Closeness, and Betweenness. Additionally, the MCODE (Molecular Complex Detection) plugin of Cytoscape was used to determine gene clusters in the constructed network. The hub genes identified were TOP2A, NUSAP1, TPX2, CENPF, ASPM, MKI67, CCNB2, CENPE, CDC6, ANLN, and PRC1. The results from GEPIA also confirmed the differential expression of these hub genes across multiple bladder cancer datasets. Notably, miRNAs such as hsa-miR-192-5p, hsa-miR-215-5p, hsa-miR-193b-3p, hsa-miR-92a-3p, and hsa-miR-218-5p demonstrated substantial associations and regulatory interactions with the identified hub genes. Conclusion: In this study, we identified several miRNAs that target key biomarkers related to bladder cancer through bioinformatics approaches. These miRNAs may serve as potential biomarkers and therapeutic targets in future bladder cancer treatments.