Identifying Novel Diagnostic Biomarkers in Prostate Cancer

Researchers analyzed potential biomarkers for predicting the prognosis of prostate cancer and identified a valuable tool for enhancing the study and management of new therapeutic targets. Their findings were published in the journal Disease Markers.

Despite advancements, prostate cancer remains a significant global health burden. As such, it’s crucial to identify novel biomarkers for detection and prognosis to improve survival in patients with metastatic disease. As noted by the investigators, the tumor microenvironment is an important driving factor for tumor biological functions.

In this study, researchers sought to discern RNA prognostic biomarkers for prostate cancer in the tumor microenvironment by analyzing data from The Cancer Genome Atlas (TCGA) database. Bioinformatics tools Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm and weighted coexpression network analysis (WGCNA) were utilized to develop tumor microenvironment stromal-immune score-based competitive endogenous RNA (ceRNA) networks. Subsequently, Cox regression modeling was implemented to screen RNAs associated with prostate cancer survival.

According to the researchers, the differentially expressed gene profile in tumor stroma was appreciably enriched in microenvironment functions, like immune response, cancer-related pathways, and cell adhesion-related pathways. Based on these differentially expressed genes, the investigators built three ceRNA networks with over 150 RNAs associated with the prostate cancer tumor microenvironment.

Cox regression modeling screened 31 RNAs as the potential prognostic biomarkers for prostate cancer and identified eight prognostic biomarkers for prostate cancer that were deemed as “interesting”, including: lncRNA LINC01082, miRNA hsa-miR-133a-3p, and genes TTLL12, PTGDS, GAS6, CYP27A1, PKP3, and ZG16B.

“In this systematic study for ceRNA networks in the tumor environment, we screened out potential biomarkers to predict prognosis for prostate cancer,” the researchers concluded. They added that the findings “might apply a valuable tool to improve prostate cancer clinical management and the new target for mechanism study and therapy.”