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A Three-Gene Classifier Associated With MicroRNA-Mediated Regulation Predicts Prostate Cancer Recurrence After Radical Prostatectomy.

ABSTRACT: Background and Objective:After radical prostatectomy (RP), prostate cancer (PCa) patients may experience biochemical recurrence (BCR) and clinical recurrence, which remains a dominant issue in PCa treatment. The purpose of this study was to identify a protein-coding gene classifier associated with microRNA (miRNA)-mediated regulation to provide a comprehensive prognostic index to predict PCa recurrence after RP. Methods:Candidate classifiers were constructed using two machine-learning algorithms (a least absolute shrinkage and selector operation [LASSO]-based classifier and a decision tree-based classifier) based on a discovery cohort (n = 156) from The Cancer Genome Atlas (TCGA) database. After selecting the LASSO-based classifier based on the prediction accuracy, both an internal validation cohort (n = 333) and an external validation cohort (n = 100) were used to examined the classifier using survival analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and univariate and multivariate Cox proportional hazards regression analyses. Functional enrichment analysis of co-expressed genes was carried out to explore the underlying moleculer mechanisms of the genes included in the classifier. Results:We constructed a three-gene classifier that included FAM72B, GNE, and TRIM46, and we identified four upstream prognostic miRNAs (hsa-miR-133a-3p, hsa-miR-222-3p, hsa-miR-1301-3p, and hsa-miR-30c-2-3p). The classifier exhibited a remarkable ability (area under the curve [AUC] = 0.927) to distinguish PCa patients with high and low Gleason scores in the discovery cohort. Furthermore, it was significantly associated with clinical recurrence (p < 0.0001, log rank statistic = 20.7, AUC = 0.733) and could serve as an independent prognostic factor of recurrence-free survival (hazard ratio: 1.708, 95% CI: 1.180-2.472, p < 0.001). Additionally, it was a predictor of BCR according to BCR-free survival analysis (p = 0.0338, log rank statistic = 4.51). Conclusions:The three-gene classifier associated with miRNA-mediated regulation may serve as a novel prognostic biomarker for PCa patients after RP.

SUBMITTER: Cheng B

PROVIDER: S-EPMC7011265 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A Three-Gene Classifier Associated With MicroRNA-Mediated Regulation Predicts Prostate Cancer Recurrence After Radical Prostatectomy.

Cheng Bo B He Qidan Q Cheng Yong Y Yang Haifan H Pei Lijun L Deng Qingfu Q Long Hao H Zhu Likun L Jiang Rui R

Frontiers in genetics 20200204

<h4>Background and objective</h4>After radical prostatectomy (RP), prostate cancer (PCa) patients may experience biochemical recurrence (BCR) and clinical recurrence, which remains a dominant issue in PCa treatment. The purpose of this study was to identify a protein-coding gene classifier associated with microRNA (miRNA)-mediated regulation to provide a comprehensive prognostic index to predict PCa recurrence after RP.<h4>Methods</h4>Candidate classifiers were constructed using two machine-lear ...[more]

PMID: 32117427

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Project description:Accurate estimation of recurrence risk is needed for optimal treatment of clinically localized prostate cancer (PCa) patients. We combined classical clinicopathological predictors of biochemical recurrence (BCR) and molecular markers into a new risk score for predicting BCR after radical prostatectomy (RP). A retrospective study which included 122 PCa patients who attended our department between 2000 and 2007 was conducted. Total RNA was isolated from formalin-fixed paraffin embedded PCa tissue. Gene expression patterns were analyzed in 21 selected samples from 7 localized, 6 locally advanced, and 8 metastatic PCa patients using Illumina microarrays. Expression levels of 41 genes were validated by quantitative PCR in an independent retrospective series of 101 patients with ≥ 10 years follow-up who underwent RP for clinically localized PCa. Univariate and multivariate logistic regression analysis were used to identify individual predictors of BCR. A risk score (RS) for predicting BCR including clinicopathological and gene expression data was developed and Kaplan-Meier curves were generated. Interaction networks between the genes of the model were built by GeneMANIA Cytoscape plugin. In a median follow-up of 120 months (range 3-190), 37 patients developed BCR (36.6%). Expression levels of 7,930 transcripts differed between clinically localized and locally advanced-metastatic PCa groups (FDR<0.1). A set of 41 genes were validated by quantitative PCR. Regression analysis showed that expression of ASF1B and MCL1 as well as Gleason score, extracapsular extension, seminal vesicle invasion and positive margins were independent prognostic factors of BCR. A RS generated using these gene expression and clinicopathological variables was able to discriminate between two groups of patients with a significantly different probability of BCR (HR 6.24; CI 3.23-12.4, p<0.01), improving the individual discriminative performance of each of these variables on their own. Direct interactions between the two genes of the model were not found. In conclusion, identification of new gene expression patterns associated with a high probability of BCR in clinically localized PCa patients treated with RP, and their combination with clinicopathological variables in a robust, easy-to-use and reliable classifier may contribute to improve PCa risk stratification and, consequently, to tailor treatment and surveillance strategies in these patients.

Project description:Purpose: Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis. Methods: A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry that underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 that experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases - men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set. Results: Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67 - 0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression. Conclusion: A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer.

Project description:PurposeClinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis.MethodsA nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry who underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 who experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases--men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set.ResultsExpression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67-0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression.ConclusionA genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer.

Project description:Purpose: Available tools for prostate cancer (PC) diagnosis and prognosis are suboptimal and novel biomarkers are urgently needed. Here, we investigated the regulation and biomarker potential of the GABRE~miR-452~miR-224 genomic locus. Experimental design: GABRE/miR-452/miR-224 transcriptional expression was quantified in 80 non-malignant and 281 PC tissue samples. GABRE promoter methylation was determined by methylation-specific qPCR (MethyLight) in 35 non-malignant, 293 PC (radical prostatectomy (RP) cohort 1) and 198 PC tissue samples (RP cohort 2). Diagnostic/prognostic biomarker potential of GABRE methylation was evaluated by ROC, Kaplan-Meier, uni- and multivariate Cox regression analyses. Functional roles of miR-224 and miR-452 were investigated in PC3 and DU145 cells by viability, migration, and invasion assays and gene-set enrichment analysis (GSEA) of post-transfection transcriptional profiling data. Results: GABRE~miR-452~miR-224 was significantly downregulated in PC compared to non-malignant prostate tissue and had highly cancer-specific aberrant promoter hypermethylation (AUC=0.98). Functional studies and GSEA suggested that miR-224 and miR-452 inhibit proliferation, migration, and invasion of PC3 and DU145 cells by direct/indirect regulation of pathways related to the cell cycle and cellular motility. Finally, in uni- and multivariate analyses, high GABRE promoter methylation was significantly associated with biochemical recurrence in RP cohort 1, which was successfully validated in RP cohort 2. Conclusion: The GABRE~miR-452~miR-224 locus is downregulated and hypermethylated in PC and is a new promising epigenetic candidate biomarker for PC diagnosis and prognosis. Tumor suppressive functions of the intronic miR-224 and miR-452 were demonstrated in two PC cell lines, suggesting that epigenetic silencing of GABRE~miR-452~miR-224 may be selected for in PC. Affymetrix GeneChip Human Gene 1.0 ST Arrays were used for whole-genome transcriptional profiling of DU145 and PC3 cells at 48 hours post-transfection with either miR-224, miR-452, or scrambled miRNA mimics, or untransfected. All experiments were performed in duplicate. Transcript expression levels were determined after RMA16 normalization in GeneSpringGX 11.0 software (Agilent). PC3 and DU145 arrays were normalized separately. DU145 48 t Scr2a were excluded from the study because of bad performance of this microarray.

Dataset Information

A Three-Gene Classifier Associated With MicroRNA-Mediated Regulation Predicts Prostate Cancer Recurrence After Radical Prostatectomy.

Publications

A Three-Gene Classifier Associated With MicroRNA-Mediated Regulation Predicts Prostate Cancer Recurrence After Radical Prostatectomy.

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