Unbiased data mining identifies cell cycle transcripts that predict non-indolent Gleason score 7 prostate cancer

Area of Science:

• Oncology
• Molecular Biology
• Bioinformatics

Background:

• Prostate cancer risk stratification relies on PSA, T category, and Gleason score.
• Current methods show significant outcome heterogeneity, especially in intermediate-risk groups.
• Improved biomarkers are needed for precise risk prediction in prostate cancer.

Purpose of the Study:

• To identify novel molecular markers for predicting biochemical recurrence in prostate cancer.
• To leverage large-scale transcriptome data for biomarker discovery.
• To validate potential biomarkers in a distinct patient cohort.

Main Methods:

• Data mining of public prostate cancer datasets using bioinformatics tools (camcAPP, cBioPortal, CRN, GDC Data Portal).
• Identification of candidate transcripts based on altered abundance, association with Gleason score 7 and recurrence, and correlation with time to recurrence.
• Validation of candidate transcripts using univariate and multivariable models in the TCGA-PRAD dataset.

Main Results:

• Twelve transcripts were validated as independent predictors of recurrence in Gleason score 7 prostate cancer.
• Ten of these transcripts remained prognostic in the lower-risk Gleason 3+4=7 subgroup.
• Upregulated transcripts are associated with mitosis and cell cycle regulation; SRD5A2 showed negative association with recurrence.

Conclusions:

• Twelve novel transcripts independently predict disease recurrence risk in Gleason score 7 prostate cancer.
• Mitotic and cell cycle proteins are crucial in prostate cancer progression, including to castration-resistant disease.
• These findings offer potential for improved risk stratification and targeted management in prostate cancer.