Integrating multi-cohort machine learning and clinical sample validation to explore peripheral blood mRNA diagnostic biomarkers for prostate cancer

Background The global incidence of prostate cancer (PCa) has been rising annually, and early diagnosis and treatment remain pivotal for improving therapeutic outcomes and patient prognosis. Concurrently, advancements in liquid biopsy technology have facilitated disease diagnosis and monitoring, with...

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Veröffentlicht in:Cancer cell international Jg. 25; H. 1; S. 158 - 11
Hauptverfasser: Zhong, Xingyu, Yang, Yuxuan, He, Haodong, Xiong, Yifan, Zhong, Mingliang, Wang, Shaogang, Xia, Qidong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 22.04.2025
Springer Nature B.V
BMC
Schlagworte:
Algorithms
Antigens
Biomarker
Biomarkers
Biomedical and Life Sciences
Biomedicine
Biopsy
Cancer Research
Cell Biology
Cell-free RNA
Circulating Biomarkers for Early Cancer Detection and Treatment
Datasets
Diagnosis
Disease
DNA methylation
Epithelial cells
Feature selection
Gene expression
Hyperplasia
KCNQ5 protein
Learning algorithms
Liquid biopsy
Machine learning
Medical prognosis
Medical screening
MicroRNAs
mRNA
Patients
Peripheral blood
Plasma
Potassium channels (voltage-gated)
Precision diagnostic
Prediction models
Prostate cancer
Prostate-specific antigen
Urine
United States > US
China
Liquid biopsy
Biomarker
Cell-free RNA
Prostate cancer
Precision diagnostic
ISSN:1475-2867, 1475-2867
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Zusammenfassung:Background The global incidence of prostate cancer (PCa) has been rising annually, and early diagnosis and treatment remain pivotal for improving therapeutic outcomes and patient prognosis. Concurrently, advancements in liquid biopsy technology have facilitated disease diagnosis and monitoring, with its minimally invasive nature and low heterogeneity positioning it as a promising approach for predicting disease progression. However, current liquid biopsy strategies for PCa predominantly rely on prostate-specific antigen (PSA), which lacks specificity and compromises diagnostic accuracy. Thus, there is an urgent need to identify novel liquid biopsy biomarkers to enable early and precise PCa diagnosis. Methods We integrated 12 machine learning algorithms to construct 113 combinatorial models, screening and validating an optimal PCa diagnostic panel across five datasets from TCGA and GEO databases. Subsequently, the biological feasibility of the selected predictive model was verified in one prostate epithelial cell line and five PCa cell lines. Robust RNA diagnostic targets were further validated for their expression in plasma samples to establish an RNA-based liquid biopsy strategy for PCa. Finally, plasma samples from PCa and benign prostatic hyperplasia (BPH) patients at Wuhan Tongji Hospital were collected to evaluate the strategy’s clinical significance. Results Differential analysis identified 1,071 candidate mRNAs, which were input into the integrated machine learning framework. Among the 113 combinatorial models, the 9-gene diagnostic panel selected by the Stepglm[both] and Enet[alpha = 0.4] algorithms demonstrated the highest diagnostic efficacy (mean AUC = 0.91), including JPH4 , RASL12 , AOX1 , SLC18A2 , PDZRN4 , P2RY2 , B3GNT8 , KCNQ5 , and APOBEC3C . Cell line experiments further validated AOX1 and B3GNT8 as robust RNA biomarkers, both exhibiting consistent PCa-specific expression in human plasma samples. In liquid biopsy analyses, AOX1 and B3GNT8 outperformed PSA in diagnostic accuracy, achieving a combined AUC of 0.91. Notably, these biomarkers also demonstrated diagnostic utility in patients with ISUP ≤ 2. Conclusions Through an integrated machine learning approach and clinical validation, we developed an RNA-based diagnostic panel for PCa. Specifically, we identified AOX1 and B3GNT8 as novel liquid biopsy biomarkers with promising clinical diagnostic value. These findings provide new targets and insights for early and precise PCa diagnosis.
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ISSN:1475-2867
1475-2867
DOI:10.1186/s12935-025-03788-w