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Discovery of lncRNA-Based ProsRISK Score in Serum as Potential Biomarkers for Improved Accuracy of Prostate Cancer Detection

  • 0Department of Clinical Laboratory, Qilu Hospital, Shandong University, Jinan, Shandong Province, People's Republic of China.
Journal of Cellular and Molecular Medicine +

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April 21, 2025

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April 21, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study developed a novel risk score using four specific long non-coding RNAs (lncRNAs) and prostate-specific antigen (PSA) to accurately detect prostate cancer (PCA). The ProsRISK model shows significant potential for improved early detection and surveillance of PCA.

Area Of Science

  • Oncology
  • Biomarker Discovery
  • Molecular Diagnostics

Background

  • Circulating long non-coding RNAs (lncRNAs) are emerging as valuable biomarkers for cancer diagnosis.
  • Accurate detection of prostate cancer (PCA) remains a clinical challenge, necessitating improved diagnostic tools.

Purpose Of The Study

  • To develop and validate a risk prediction model for prostate cancer (PCA) detection using serum lncRNAs.
  • To assess the diagnostic performance of the novel model compared to existing biomarkers.

Main Methods

  • Quantitative reverse transcription PCR (RT-qPCR) was employed to analyze serum lncRNA levels.
  • A risk prediction score (ProsRISK) was constructed using four differentially expressed lncRNAs (NEAT1, ARLNC1, FOXP4-AS1, DSCAM-AS1) and prostate-specific antigen (PSA).
  • Receiver operating characteristic (ROC) curve analysis was performed for validation.

Main Results

  • Four serum lncRNAs were identified with differential expression in PCA patients versus controls.
  • The ProsRISK score demonstrated superior accuracy in discriminating PCA from healthy and benign controls (AUCs 0.926 and 0.837, respectively).
  • The model also showed strong performance in detecting early-stage PCA (I-II) with AUCs of 0.905 and 0.819 against healthy and benign controls.

Conclusions

  • The developed ProsRISK score, integrating lncRNAs and PSA, offers a reliable tool for prostate cancer risk stratification.
  • This model holds significant potential for enhancing the precision and clinical utility of PCA surveillance.

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