Clinical feature and gene expression analysis in low prostate-specific antigen, high-grade prostate cancer

Abstract

Background: Prostate cancer (PCa) patients with low prostate-specific antigen (PSA) levels can occasionally present high-grade disease. These patients often exhibit resistance to androgen deprivation therapy and have poor outcomes. The mechanisms underlying these observations remain poorly understood. This study aimed to investigate the clinical characteristics and potential gene expression mechanisms in this subgroup.

Patients and methods: Clinical data from 365,558 PCa patients were categorized into four groups based on PSA levels and Gleason score (GS): Group 1 (PSA ≤ 2.5 ng/mL, GS < 8), Group 2 (PSA ≤ 2.5 ng/mL, GS ≥ 8), Group 3 (PSA > 2.5 ng/mL, GS < 8), and Group 4 (PSA > 2.5 ng/mL, GS ≥ 8). Clinical characteristics were compared using Kruskal-Wallis H and Pearson's chi-squared tests. Competing-risks regression assessed prostate cancer-specific mortality (PCSM). Gene set enrichment analysis (GSEA) was performed on 219 PCa patients to compare Group A (PSA ≤ 2.5 ng/mL, GS ≥ 8) with Group B (PSA > 2.5 ng/mL, GS ≥ 8).

Results: Group 2 had a significantly higher tumor stage (p < 0.001) and increased hazard ratio for PCSM (p < 0.001). GSEA in Group A identified 156 upregulated gene sets and highlighted several enriched pathways, including the polycomb repressive complex 2, the epidermal growth factor receptor family, retrograde axonal transport, the tumor necrosis factor/nuclear factor-κB pathway, the Rho guanine nucleotide exchange factor/RhoA pathway, and the phosphoinositide 3-kinase signaling pathways (p < 0.05, false discovery rate-adjusted p < 0.25).

Conclusion: PCa patients with low PSA levels and high GS demonstrated an increased risk of PCSM. They were characterized by the aberrant activation of multiple signaling pathways. Targeted therapeutic strategies aimed at these pathways warrant further investigation for their potential to improve outcomes in this aggressive PCa subtype.

Fig 1. Flow chart of the patient cohort selection.

Fig 2. Cumulative incidence function of PCSM across groups.

Abbreviation: PCSM = prostate cancer-specific mortality.

Fig 3. Top three most significant gene sets identified in GSEA of the GEO Cohort.

Abbreviation: GSEA = Gene set enrichment analysis, GEO = Gene Expression Omnibus, IGF = insulin-like growth factor, IGFR = insulin-like growth factor receptor, PI3K = phosphoinositide 3-kinase, NF-κB = nuclear factor-κB, EGF = epidermal growth factor, EGFR = epidermal growth factor receptor.

Fig 4. Network visualization of enriched pathways based on GSEA results.

Abbreviation: GSEA = Gene set enrichment analysis, PRC = polycomb repressive complex, EGF = epidermal growth factor, ERBB2 = erythroblastic oncogene B 2, RAS = rat sarcoma virus, ERK = extracellular signal-regulated kinase, EGFR = epidermal growth factor receptor, PI3K = phosphoinositide 3-kinase, HIV = human immunodeficiency virus, TNF = tumor necrosis factor, NF-κB = nuclear factor-κB, GEF = guanine nucleotide exchange factor, GF = growth factor, RTK = receptor tyrosine kinase, NES = normalized enrichment scores.