Multi-Dimensional Scaling Analysis of Key Regulatory Genes in Prostate Cancer Using the TCGA Database.

Area of Science:

• Oncology
• Genomics
• Molecular Biology

Background:

• Prostate cancer (PC) is a complex polygenic disease.
• Identifying predictors of aggressive PC is crucial for effective treatment strategies.
• Understanding gene interactions within the tumor microenvironment is key to evaluating risk factors.

Purpose of the Study:

• To analyze transcriptome data from PC patients to identify key regulatory genes.
• To develop a combined score using gene expression to predict patient survival and disease-free intervals.
• To investigate the relationship between gene expression patterns and PC aggressiveness.

Main Methods:

• Utilized transcriptome data from 243 PC patients from The Cancer Genome Atlas (TCGA) database.
• Selected key regulatory genes involved in proliferation, stress, and inflammation.
• Applied multi-dimensional scaling (MDS) to model gene interactions and created a combined score.
• Performed Kaplan-Meier analyses to assess the relationship between gene clusters and patient outcomes.

Main Results:

• Survival correlated positively with cortisol expression and negatively with Mini-Chromosome Maintenance 7 (MCM7) and Breast-Related Cancer Antigen 2 (BRCA2) expression.
• Disease-free interval was negatively associated with enhancer of zeste homolog 2 (EZH2), MCM7, BRCA2, and programmed cell death 1 ligand 1 (PD-L1) expression.
• Multi-dimensional scaling identified three distinct gene clusters; only Cluster 3 (EZH2, MCM7, BRCA2, c-Myc) significantly predicted a shorter disease-free interval.

Conclusions:

• High activity of proliferation and DNA repair genes (Cluster 3) is associated with a lower probability of a longer disease-free time in PC.
• This suggests that patients with high proliferation and DNA repair activity may have aggressive PC with metastatic potential.
• Early identification of such patients could improve prognostic accuracy and guide treatment decisions.