Functional analysis of gene expression profiling-based prediction in bladder cancer

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Bladder cancer (BC) is a significant health concern with complex genetic underpinnings.
• Understanding gene expression modifications is crucial for identifying novel therapeutic targets.

Purpose of the Study:

• To identify and functionally assess differentially expressed genes (DEGs) in bladder cancer using bioinformatics.
• To uncover key genes and pathways associated with BC development.

Main Methods:

• Downloaded and analyzed two microarray datasets (GSE24152, GSE42089) from the Gene Expression Omnibus database.
• Screened for DEGs, performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.
• Constructed protein-protein interaction (PPI) networks to identify key genes and pathways.

Main Results:

• Identified 619 common DEGs between the two datasets.
• DEGs were significantly enriched in pathways related to mitotic and chromosome assembly (e.g., nucleosome assembly, spindle checkpoint, DNA replication).
• Key genes including progesterone receptor (PGR), MAFG, CDC6, and MCMs, along with histones, were identified as crucial factors in BC.

Conclusions:

• DEGs, particularly PGR, MAFG, CDC6, MCMs, and histones, are closely associated with bladder cancer development.
• Mitotic and chromosome assembly pathways play a significant role in BC pathogenesis.
• These findings provide potential targets for future bladder cancer research and treatment.