A five-pseudouridylation-associated-LncRNA classifier for primary prostate cancer prognosis prediction

Area of Science:

• Oncology
• Molecular Biology
• Bioinformatics

Background:

• Prostate cancer (PCa) is a prevalent malignancy in males globally.
• Biochemical recurrence (BCR) affects approximately one-third of localized PCa patients post-treatment, often leading to poor prognosis.
• The role of pseudouridylation in long non-coding RNAs (lncRNAs) within PCa pathogenesis remains largely unexplored.

Purpose of the Study:

• To investigate the role of pseudouridylation in lncRNAs in prostate cancer development and recurrence.
• To develop and validate a novel prognostic classifier for predicting biochemical recurrence in PCa patients.
• To explore the potential molecular mechanisms underlying the function of key pseudouridylation-related lncRNAs in PCa.

Main Methods:

• Utilized Spearman correlation and univariate Cox regression to identify prognostic pseudouridylation-related lncRNAs.
• Developed a predictive model using the LASSO regression algorithm and validated it with Kaplan-Meier survival analysis and ROC curves.
• Conducted cell assays, constructed a ceRNA network, and performed GO/KEGG pathway analysis for functional exploration.

Main Results:

• A five-pseudouridine-related lncRNA classifier, the 'ψ-lnc score,' was developed to stratify PCa patients based on BCR risk.
• High ψ-lnc scores correlated with increased BCR incidence, and the score demonstrated superior predictive performance compared to other clinical indicators.
• External dataset validation confirmed the model's efficacy, and RP11-468E2.5 was identified as a potential tumor promoter.

Conclusions:

• Pseudouridylation in lncRNAs plays a significant role in prostate cancer progression.
• The developed ψ-lnc score serves as a novel and effective prognostic classifier for clinical application in PCa.
• Further research into RP11-468E2.5 and its role in PCa is warranted.