Predicting prostate cancer progression with a Multi-lncRNA expression-based risk score and nomogram integrating ISUP...

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Prostate cancer prognosis is challenging due to disease heterogeneity and lack of specific biomarkers.
• Integrating genomic, transcriptomic, and clinico-pathological data is crucial for improved clinical practice.

Purpose of the Study:

• To develop a predictive model incorporating long non-coding RNA (lncRNA) expression and clinico-pathological data for prostate cancer progression.
• To identify specific lncRNAs associated with progression-free survival in prostate cancer patients.

Main Methods:

• Bioinformatics analysis of RNA-seq data from 5 public datasets (n=178) across various prostate cancer stages.
• Identification of differentially expressed lncRNAs and association with time-to-progression using multivariate survival analysis.
• Development of a multi-lncRNA score and a combined risk score/nomogram integrating lncRNA data and ISUP group.

Main Results:

• 30 differentially expressed lncRNAs were identified, with 7 significantly associated with time-to-progression.
• A multi-lncRNA score indicated a 4-fold increased risk of progression in high-score patients.
• A combined risk score, including ISUP group, showed an 8-fold increased risk of progression in high-risk patients.

Conclusions:

• The developed multi-lncRNA score and nomogram effectively predict prostate cancer progression risk.
• Integration of lncRNA expression and clinico-pathological features aids tailored risk assessment and treatment strategies.
• This approach can improve clinical decision-making for prostate cancer management.