Multi-Omics Analysis of Primary Prostate Cancer Datasets Reveals Novel Biomarkers

Area of Science:

• Oncology
• Molecular Biology
• Bioinformatics

Background:

• Prostate cancer (PCa) is a leading cause of cancer-related deaths in men.
• Current diagnostic screenings lack sufficient early-stage detection capabilities.
• There is a critical need for novel diagnostic biomarkers for early PCa identification.

Purpose of the Study:

• To conduct a meta-analysis identifying potential microRNA (miRNA), messenger RNA (mRNA), and methylation biomarkers for early-stage prostate cancer.
• To validate potential biomarkers through network analysis and gene target prediction.
• To assess the expression of key candidate biomarkers in prostate cell lines.

Main Methods:

• Meta-analysis of microarray data from the Gene Expression Omnibus (GEO) dataset comparing PCa tissue with benign prostatic hyperplasia (BPH) or normal adjacent tissue.
• Identification of miRNA target genes using miRWalk and miRDB databases.
• Network analysis for visualization of biomarker interactions.
• Quantitative real-time PCR (qPCR) for validating expression levels of selected miRNA and genes in prostate cell lines (RWPE-1 and 22RV1).

Main Results:

• Meta-analysis revealed miR-7-5p overexpression in PCa.
• Upregulation of CAMKK2 and TMEM97 mRNA and downregulation of CLIP1 mRNA were observed, with these genes identified as targets of miR-7-5p.
• CAMKK2, TMEM97, and CLIP1 exhibited hypermethylation.
• Expression changes in cell lines did not fully align with meta-analysis findings, suggesting limitations of specific cell line validation.

Conclusions:

• The meta-analysis identified promising miRNA, mRNA, and methylation candidates for early prostate cancer diagnosis.
• miR-7-5p, CAMKK2, TMEM97, and CLIP1 represent potential biomarkers warranting further investigation in human prostate tissues.
• While cell line validation presented challenges, the meta-analysis provides a strong foundation for future prostate cancer biomarker research.