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

Shi, Run; Bao, Xuanwen; Schaefer, Christian; Rogowski, Paul; Schmidt-Hegemann, Nina-Sophie; Unger, Kristian; Lauber, Kirsten; Wang, Xuanbin; Büchner, Alexander; Stief, Christian G.; Schlomm, Thorsten; Belka, Claus; Li, Minglun

doi:10.3390/cancers12010001

Cited by 25 publications

(25 citation statements)

References 39 publications

(44 reference statements)

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“…Using the training and validation datasets, we first identified 6 preserved PCa-driven modules and then screened 9 prognosis-related genes (including 3 lncRNAs: DLG5-AS1, MAGI2-AS3, and RHPN1-AS1; and 6 mRNAs: GINS2, NLGN2, EBNA1BP2, MELK, EIF5AL1, and G6PC3) from these modules to construct the risk score. The ROC curve analysis demonstrated the prediction accuracy of this molecular risk score was higher than that of clinical indicators (the Gleason score [AUC = 0.945 vs.0.57], PSA [AUC = 0.945 vs.0.578], and combined [AUC = 0.945 vs.0.673]), which was in line with the studies of Li et al [ 9 ], Shi et al [ 10 ], Huang et al [ 11 ], and Xu et al [ 12 ]. More importantly, our integrated model seemed to be more effective than the single mRNA model (Xu et al: 4-mRNA, AUC = 0.945 vs.0.904 [ 26 ]) for OS prediction, which was also observed in our study (AUC = 0.945 vs.0.81) ( Figure 7 ).…”

Section: Discussionsupporting

confidence: 89%

“…Shi et al established a prognostic risk score based on 9 protein-coding genes. Receiver operating characteristic (ROC) analysis indicated that the prediction accuracy of this risk score for BCR-free survival was higher than that of Gleason score (area under curve (AUC) = 0.836 vs.0.742) and pathological T stage (AUC = 0.836 vs.0.780) [ 10 ]. Similar superiority of the molecular risk score was also observed in the study of Huang et al who found the four-long noncoding RNA- (lncRNA-) based risk score was independent of the American Joint Committee on Cancer T stage and Gleason score for the prediction of BCR-free survival and disease-free survival.…”

Section: Introductionmentioning

confidence: 99%

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Coexpression Network Analysis Identifies a Novel Nine-RNA Signature to Improve Prognostic Prediction for Prostate Cancer Patients

Cai

Chen

et al. 2020

BioMed Research International

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Background. Prostate cancer (PCa) is the most common malignancy and the leading cause of cancer death in men. Recent studies suggest the molecular signature was more effective than the clinical indicators for the prognostic prediction, but all of the known studies focused on a single RNA type. The present study was to develop a new prognostic signature by integrating long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) and evaluate its prognostic performance. Methods. The RNA expression data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA) or Gene Expression Omnibus database (GSE17951, GSE7076, and GSE16560). The PCa-driven modules were identified by constructing a weighted gene coexpression network, the corresponding genes of which were overlapped with differentially expressed RNAs (DERs) screened by the MetaDE package. The optimal prognostic signature was screened using the least absolute shrinkage and selection operator analysis. The prognostic performance and functions of the combined prognostic signature was then assessed. Results. Twelve PCa-driven modules were identified using TCGA dataset and validated in the GSE17951 and GSE7076 datasets, and six of them were considered to be preserved. A total of 217 genes in these 6 modules were overlapped with 699 DERs, from which a nine-gene prognostic signature was identified (including 3 lncRNAs and 6 mRNAs), and the risk score of each patient was calculated. The overall survival was significantly shortened in patients having the risk score higher than the cut-off, which was demonstrated in TCGA (p=5.063E−03) dataset and validated in the GSE16560 (p=3.268E−02) dataset. The prediction accuracy of this risk score was higher than that of clinical indicators (the Gleason score and prostate-specific antigen) or the single RNA type, with the area under the receiver operator characteristic curve of 0.945. Besides, some new therapeutic targets and mechanisms (MAGI2-AS3-SPARC/GJA1/CYSLTR1, DLG5-AS1-DEFB1, and RHPN1-AS1-CDC45/ORC) were also revealed. Conclusion. The risk score system established in this study may provide a novel reliable method to identify PCa patients at a high risk of death.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Coexpression Network Analysis Identifies a Novel Nine-RNA Signature to Improve Prognostic Prediction for Prostate Cancer Patients

Cai

Chen

et al. 2020

BioMed Research International

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“…The LASSO regression analysis is an accepted method for the reduction in high dimensional data, as described previously 13 . In this study, LASSO regression analysis was performed by R package “glmnet” to filtrate most powerful prognostic markers of m1A-related regulatory genes, and disease risk prediction score was established in HCC patients.…”

Section: Methodsmentioning

confidence: 99%

Gene signatures and prognostic values of m1A-related regulatory genes in hepatocellular carcinoma

Shi

Xue

Yuan

et al. 2020

Sci Rep

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Hepatocellular carcinoma (HCC) ranks fourth in cancer-related mortality worldwide. N1-methyladenosine (m1A), a methylation modification on RNA, is gaining attention for its role across diverse biological processes. However, m1A-related regulatory genes expression, its relationship with clinical prognosis, and its role in HCC remain unclear. In this study, we utilized The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) database to investigate alterations within 10 m1A-related regulatory genes and observed a high mutation frequency (23/363). Cox regression analysis and least absolute shrinkage and selection operator were used to explore the association between m1A-related regulatory genes expression and HCC patient survival and identified four regulators that were remarkably associated with HCC patient prognosis. Additionally, an independent cohort from International Cancer Genome Consortium was studied to validate our discoveries and found to be consistent with those in the TCGA dataset. In terms of mechanism, gene set enrichment analysis linked these four genes with various physiological roles in cell division, the MYC pathway, protein metabolism, and mitosis. Kyoto Encyclopedia of Genes and Genomes analysis revealed that PI3K/Akt signaling pathway had potential relevance to m1A-related regulatory genes in HCC. These findings indicate that m1A-related regulatory genes may play crucial roles in regulating HCC progression and be exploited for diagnostic and prognostic purposes.

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“…Therefore, more ideal biomarker and predictive model are warranted to be developed. Recent studies in many malignancies, including PCa, suggested that multigene classifier or gene signature can make a good prediction of tumor prognosis [ 9 – 11 ]. However, limited robust signature has been constructed to predict early BCR [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Construction and Validation of a Robust Cancer Stem Cell-Associated Gene Set-Based Signature to Predict Early Biochemical Recurrence in Prostate Cancer

Liu

et al. 2020

Disease Markers

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Background. Postoperative early biochemical recurrence (BCR) was an essential indicator for recurrence and distant metastasis of prostate cancer (PCa). The aim of this study was to construct a cancer stem cell- (CSC-) associated gene set-based signature to identify a subgroup of PCa patients who are at high risk of early BCR. Methods. The PCa dataset from The Cancer Genome Atlas (TCGA) was randomly separated into discovery and validation set. Patients in discovery set were divided into early BCR group and long-term survival group. Propensity score matching analysis and differentially expressed gene selection were used to identify candidate CSC-associated genes. The LASSO Cox regression model was finally performed to filter the most useful prognostic CSC-associated genes for predicting early BCR. Results. By applying the LASSO Cox regression model, we built a thirteen-CSC-associated gene-based early BCR-predicting signature. In the discovery set, patients in high-risk group showed significantly poorer BCR free survival than that patients in low-risk group (HR: 4.91, 95% CI: 2.75–8.76, P < 0.001 ). The results were further validated in the internal validation set (HR: 2.99, 95% CI: 1.34–6.70, P = 0.005 ). Time-dependent ROC at 1 year suggested that the CSC gene signature ( AUC = 0.800 ) possessed better predictive value than any other clinicopathological features in the entire TCGA cohort. Additionally, survival decision curve analysis revealed a considerable clinical usefulness of the CSC gene signature. Conclusions. We successfully developed a CSC-associated gene set-based signature that can accurately predict early BCR in PCa cancer.

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

Cited by 25 publications

References 39 publications

Coexpression Network Analysis Identifies a Novel Nine-RNA Signature to Improve Prognostic Prediction for Prostate Cancer Patients

Coexpression Network Analysis Identifies a Novel Nine-RNA Signature to Improve Prognostic Prediction for Prostate Cancer Patients

Gene signatures and prognostic values of m1A-related regulatory genes in hepatocellular carcinoma

Construction and Validation of a Robust Cancer Stem Cell-Associated Gene Set-Based Signature to Predict Early Biochemical Recurrence in Prostate Cancer

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