Dataset Information

A Novel Gene Signature-Based Model Predicts Biochemical Recurrence-Free Survival in Prostate Cancer Patients after Radical Prostatectomy.

ABSTRACT: Abstract: Currently, decision-making regarding biochemical recurrence (BCR) following prostatectomy relies solely on clinical parameters. We therefore attempted to develop an integrated prediction model based on a molecular signature and clinicopathological features, in order to forecast the risk for BCR and guide clinical decision-making for postoperative therapy. Using high-throughput screening and least absolute shrinkage and selection operator (LASSO) in the training set, a novel gene signature for biochemical recurrence-free survival (BCRFS) was established. Validation of the prognostic value was performed in five other independent datasets, including our patient cohort. Multivariate Cox regression analysis was performed to evaluate the importance of risk for BCR. Time-dependent receiver operating characteristic (tROC) was used to evaluate the predictive power. In combination with relevant clinicopathological features, a decision tree was built to improve the risk stratification. The gene signature exhibited a strong capacity for identifying high-risk BCR patients, and multivariate Cox regression analysis demonstrated that the gene signature consistently acted as a risk factor for BCR. The decision tree was successfully able to identify the high-risk subgroup. Overall, the gene signature established in the present study is a powerful predictor and risk factor for BCR after radical prostatectomy.

SUBMITTER: Shi R

PROVIDER: S-EPMC7017310 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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A Novel Gene Signature-Based Model Predicts Biochemical Recurrence-Free Survival in Prostate Cancer Patients after Radical Prostatectomy.

Shi Run R Bao Xuanwen X Weischenfeldt Joachim J Schaefer Christian C Rogowski Paul P Schmidt-Hegemann Nina-Sophie NS Unger Kristian K Lauber Kirsten K Wang Xuanbin X Buchner Alexander A Stief Christian C Schlomm Thorsten T Belka Claus C Li Minglun M

Cancers 20191218 1

A<b>bstract</b><b>:</b> Currently, decision-making regarding biochemical recurrence (BCR) following prostatectomy relies solely on clinical parameters. We therefore attempted to develop an integrated prediction model based on a molecular signature and clinicopathological features, in order to forecast the risk for BCR and guide clinical decision-making for postoperative therapy. Using high-throughput screening and least absolute shrinkage and selection operator (LASSO) in the training set, a nov ...[more]

PMID: 31861273

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Project description:Background: Nowadays, predictions of biochemical recurrence (BCR) in localized prostate cancer (PCa) patients after radical prostatectomy (RP) are mainly based on clinical parameters with a low predictive accuracy. Given the critical role of apoptosis in PCa occurrence and progression, we aimed to establish a novel predictive model based on apoptosis-related gene signature and clinicopathological parameters that can improve risk stratification for BCR and assist in clinical decision-making. Methods: Expression data and corresponding clinical information were obtained from four public cohorts, one from The Cancer Genome Atlas (TCGA) dataset and three from the Gene Expression Omnibus (GEO) dataset. Weighted gene co-expression network analysis (WGCNA) was performed to identify candidate modules closely correlated to BCR, and univariate and multivariate Cox regression analyses were utilized to build the gene signature. Time-dependent receiver operating curve (ROC) and Kaplan-Meier (KM) survival analysis were used to assess the prognostic value. Finally, we analyzed the expression of genes in the signature and validated the results using quantitative real-time PCR (qRT-PCR). Results: The novel gene signature we established exhibited a high prognostic value and was able to act as an independent risk factor for BCR [Training set: P < 0.001, hazard ratio (HR) = 7.826; Validation set I: P = 0.006, HR = 2.655; Validation set II: P = 0.003, HR = 4.175; Validation set III: P < 0.001, HR = 3.008]. Nomogram based on the gene signature and clinical parameters was capable of distinguishing high-risk BCR patients. Additionally, functional enrichment analysis showed several enriched pathways and biological processes, which might help reveal the underlying mechanism. The expression results of qRT-PCR were consistent with TCGA results. Conclusion: The apoptosis-related gene signature could serve as a powerful predictor and risk factor for BCR in localized PCa patients after RP.

Project description:Background: D'Amico high-risk prostate cancer (Pca) patients experience poor and heterogeneous oncological outcomes. This heterogeneity highlights a need to extensively explore factors associated with poor outcomes to guide decision-making. Objective: To assess predictors of biochemical recurrence (BCR)-free survival in high-risk patients following radical prostatectomy (RP), and subsequently establish a model predicting outcomes. Methods: We retrospectively identified D'Amico high-risk non-metastatic Pca patients who underwent RP between 2013 and 2019 in our hospital. We collected data including PSA level, clinical stage, biopsy Gleason score (GS), number of D'Amico high-risk factors (RF), the inflammatory status (Neutrophil-to-lymphocyte ratio [NLR], derived NLR [dNLR], platelet-to-lymphocyte ratio [PLR] and LDH). Kaplan–Meier methods were used to analyze BCR-free survival. Univariate and multivariate analyses were performed using Cox proportional hazards model to evaluate the association between clinicopathological parameters and BCR-free survival. Results: The median follow-up time for the 101 patients' cohort was 26 months (range: 3–81 months). The number of RF (1RF vs. ?2RF), biopsy GS (<8 vs. ?8), clinical stage (?cT2c vs. >cT2c), pathological stage, and the presence of adverse pathological features were significant predictors of BCR (P < 0.05). Other parameters including inflammatory status (dNLR, NLR, PLR, and LDH) were not of predictive value. On multivariable analysis, biopsy GS (<8 vs. ?8; HR 2.439) and clinical stage (?cT2c vs. >cT2c; HR 3.271) were the independent predictors of BCR. Based on these two independent predictors, patients were stratified into three risk subgroups: favorable (0 risk factor; 47% of patients), intermediate (1 risk factor; 42 %), unfavorable (2 risk factors; 11%). The intermediate and unfavorable subgroups have a significantly shorter median BCR-free survival compared to the favorable subgroup (P < 0.001). Conclusion: Several factors are associated with BCR. Clinical stage (?cT2c vs. >cT2c) and biopsy GS (<8 vs. ?8) are the independent predictors of BCR. The stratification of high-risk patients into risk subgroups based on these two predictors shows that the intermediate and unfavorable subgroups have a significantly shorter median BCR-free survival compared to the favorable subgroup. The preoperative stratification model may help urologists and patients during decision-making. In non-metastatic high-risk patients, preoperative inflammatory markers (NLR, dNLR, PLR, and LDH) are not of prognostic value.

Project description:Background: Decision-making regarding biochemical recurrence (BCR) in localized prostate cancer (PCa) patients after radical prostatectomy (RP) mainly relies on clinicopathological parameters with a low predictive accuracy. Currently, accumulating evidence suggests that immune-associated genes (IAGs) play irreplaceable roles in tumorigenesis, progression and metastasis. Considering the critical role of immune in PCa, we therefore attempted to identify the novel IAGs signature and validate its prognostic value that can better forecast the risk for BCR and guide clinical treatment. Methods: RNA-sequencing and corresponding clinicopathological data were downloaded from the Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network analysis (WGCNA) was utilized to screen out the candidate module closely related to BCR, and univariate and LASSO Cox regression analyses were performed to build the gene signature. Kaplan-Meier (KM) survival analysis, time-dependent receiver operating curve (ROC), independent prognostic analysis and nomogram were also applied to evaluate the prognostic value of the signature. Besides, Gene ontology analysis (GO), Kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA) were used to explore potential biological pathways. Results: A total of six IAGs (SSTR1, NFATC3, NRP1, TUBB3, IL1R1, GDF15) were eventually identified and used to establish a novel IAGs signature. The Kaplan-Meier analysis revealed that patients with low-risk scores had longer recurrence-free survival (RFS) than those with high-risk scores in both GSE70769 and TCGA cohorts. Further, our signature was also proven to be a valuable independent prognostic factor for BCR. We also constructed a nomogram based on the gene signature and related clinicopathologic features, which excellently predict 1-year, 3-year and 5-year prognosis of localized PCa patients after RP. Moreover, functional enrichment analysis demonstrated the vital biological processes, and stratified GSEA revealed that a crucial immune-related pathway (T cell receptor signaling pathway) was notably enriched in the high-risk group. Conclusions: We successfully developed a novel robust IAGs signature that is powerful in BCR prediction in localized PCa patients after RP, and created a prognostic nomogram. In addition, the signature might help clinicians in selecting high-risk subpopulation, predicting survival status of patients and promoting more individualized therapies than traditional clinical factors.

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 Novel Gene Signature-Based Model Predicts Biochemical Recurrence-Free Survival in Prostate Cancer Patients after Radical Prostatectomy.

Publications

A Novel Gene Signature-Based Model Predicts Biochemical Recurrence-Free Survival in Prostate Cancer Patients after Radical Prostatectomy.

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