ArticleTranslational andrology and urology2020
A new risk stratification system of prostate cancer to identify high-risk biochemical recurrence patients.
Article in Translational andrology and urology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Machine learning-based prognostic model integrating preoperative HALP score and lactate dehydrogenase for predicting postoperative recurrence of prostate cancer.World journal of surgical oncology · 2026Article
- KIF4A in disease pathogenesis and therapeutics: from molecular mechanisms to clinical translation.Biology direct · 2025Review
- Article
- Blood-based detection of MMP11 as a marker of prostate cancer progression regulated by the ALDH1A1-TGF-β1 signaling mechanism.Journal of experimental & clinical cancer research : CR · 2025Article
- A Comprehensive Analysis of Epoxide Hydrolase 2 (EPHX2) in Pan-Cancer.Cancer reports (Hoboken, N.J.) · 2025Article
- Predicting prostate cancer progression with a Multi-lncRNA expression-based risk score and nomogram integrating ISUP grading.Non-coding RNA research · 2024Article
- Biochemical recurrence in patients with prostate cancer after primary definitive therapy: treatment based on risk stratification.Prostate cancer and prostatic diseases · 2024Review
- Predicting prostate cancer recurrence: Introducing PCRPS, an advanced online web server.Heliyon · 2024Article
- A Novel Gene Signature Associated With "E2F Target" Pathway for Predicting the Prognosis of Prostate Cancer.Frontiers in molecular biosciences · 2022Article
- Identifying prognostic signatures in the microenvironment of prostate cancer.Translational andrology and urology · 2021Article
- Comprehensive data analysis of genomics, epigenomics, and transcriptomics to identify specific biomolecular markers for prostate adenocarcinoma.Translational andrology and urology · 2021Article
- Screening to Identify an Immune Landscape-Based Prognostic Predictor and Therapeutic Target for Prostate Cancer.Frontiers in oncology · 2021Article
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8 authors.
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Abstract
backgroundBiochemical recurrence (BCR) is considered a decisive risk factor for clinical recurrence and the metastasis of prostate cancer (PCa). Therefore, we developed and validated a signature which could be used to accurately predict BCR risk and aid in the selection of PCa treatments.
methodsA comprehensive genome-wide analysis of data concerning PCa from previous datasets of the Cancer Genome Atlas (TCGA) and the gene expression omnibus (GEO) was performed. Lasso and Cox regression analyses were performed to develop and validate a novel signature to help predict BCR risk. Moreover, a nomogram was constructed by combining the signature and clinical variables.
resultsA total of 977 patients were involved in the study. This consisted of patients from the TCGA (n=405), GSE21034 (n=131), GSE70770 (n=193) and GSE116918 (n=248) datasets. A 9-mRNA signature was identified in the TCGA dataset (composed of C9orf152, EPHX2, ASPM, MMP11, CENPF, KIF4A, COL1A1, ASPN, and FANCI) which was significantly associated with BCR (HR =3.72, 95% CI: 2.30-6.00, P<0.0001). This signature was validated in the GSE21034 (HR =7.54, 95% CI: 3.15-18.06, P=0.019), GSE70770 (HR =2.52, 95% CI: 1.50-4.22, P=0.0025) and GSE116918 datasets (HR =4.75, 95% CI: 2.51-9.02, P=0.0035). Multivariate Cox regression and stratified analysis showed that the 9-mRNA signature was a clinical factor independent of prostate-specific antigen (PSA), Gleason score (GS), or AJCC T staging. The mean AUC for 5-year BCR-free survival predictions of the 9-mRNA signature (0.81) was higher than the AUC for PSA, GS, or AJCC T staging (0.52-0.73). Furthermore, we combined the 9-mRNA signature with PSA, GS, or AJCC T staging and demonstrated that this could enhance prognostic accuracy.
conclusionsThe proposed 9-mRNA signature is a promising biomarker for predicting BCR-free survival in PCa. However, further controlled trials are needed to validate our results and explore a role in individualized management of PCa.
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