Identification a novel set of 6 differential expressed genes in prostate cancer that can potentially predict biochemical recurrence after curative surgery

Li, F.; Ji, Junpeng; Xu, Yong; Liu, Ran-Lu

doi:10.1007/s12094-018-02029-z

Cited by 19 publications

(13 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…Univariate and multivariate Cox regression analyses showed that this risk score was independent of TNM stage for biochemical recurrence- (BCR-) free survival prediction (hazard ratio (HR) = 3.045, 95% confidence interval (CI) = 1.655 − 5.602; p < 0.001). A subgroup analysis revealed that there were also significant survival differences when the patients with the same Gleason score (≤7 or >7) were classified into the high-risk score group and the low-risk score group [ 9 ]. Shi et al established a prognostic risk score based on 9 protein-coding genes.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Even with the construction of Oncotype DX GPS and Prolaris CCP multigene panels, there is clearly a need to improve the assessment of BCR. To fulfill this need, there are numerous additional multigene sets reported (Table IV), including a 6-differentially expressed gene (DEG) panel (150), an 8-gene panel with its risk scores predicting BCR at P=5e-7 (151), and a 10-gene panel HDDA10 (152) (Table IV).…”

Section: Gene Expression-based Biomarkersmentioning

confidence: 99%

Assessment of biochemical recurrence of prostate cancer (Review)

Lin

Kapoor

et al. 2019

Int J Oncol

View full text Add to dashboard Cite

The assessment of the risk of biochemical recurrence (BCR) is critical in the management of males with prostate cancer (PC). Over the past decades, a comprehensive effort has been focusing on improving risk stratification; a variety of models have been constructed using PC-associated pathological features and molecular alterations occurring at the genome, protein and RNA level. Alterations in RNA expression (lncRNA, miRNA and mRNA) constitute the largest proportion of the biomarkers of BCR. In this article, we systemically review RNA-based BCR biomarkers reported in PubMed according to the PRISMA guidelines. Individual miRNAs, mRNAs, lncRNAs and multigene panels, including the commercially available signatures, Oncotype DX and Prolaris, will be discussed; details related to cohort size, hazard ratio and 95% confidence intervals will be provided. Mechanistically, these individual biomarkers affect multiple pathways critical to tumorigenesis and progression, including epithelial-mesenchymal transition (EMT), phosphatase and tensin homolog (PTEN), Wnt, growth factor receptor, cell proliferation, immune checkpoints and others. This variety in the mechanisms involved not only validates their associations with BCR, but also highlights the need for the coverage of multiple pathways in order to effectively stratify the risk of BCR. Updates of novel biomarkers and their mechanistic insights are considered, which suggests new avenues to pursue in the prediction of BCR. Additionally, the management of patients with BCR and the potential utility of the stratification of the risk of BCR in salvage treatment decision making for these patients are briefly covered. Limitations will also be discussed. Contents 1. Introduction 2. Stratification of BCR risk: An update 3. Searching methods for RNA-based BCR biomarkers 4. Gene expression-based biomarkers 5. Management of patients with biochemical recurrence 6. Perspectives

show abstract

“…For instance, TPC2 was observed to be overexpressed in oral squamous cell carcinoma cell lines, raising intriguing questions regarding the role of TPC2 as a driver of oncogenesis [ 102 ]. In addition, TPCN2 was found to be significantly associated with survival in bladder cancer [ 103 ], and has also been reported to be one of the six gene signatures correlated with prostate cancer to predict postoperative biochemical recurrence [ 104 ]. Many studies on the association between TPCs and cancers have revealed the role of NAADP/TPC/Ca 2+ signaling ( Figure 3 ).…”

Section: Two-pore Channels (Tpcs)mentioning

confidence: 99%

Roles of NAD+ and Its Metabolites Regulated Calcium Channels in Cancer

Cai

Liang

et al. 2020

Molecules

View full text Add to dashboard Cite

Nicotinamide adenine dinucleotide (NAD+) is an essential cofactor for redox enzymes, but also moonlights as a regulator for ion channels, the same as its metabolites. Ca2+ homeostasis is dysregulated in cancer cells and affects processes such as tumorigenesis, angiogenesis, autophagy, progression, and metastasis. Herein, we summarize the regulation of the most common calcium channels (TRPM2, TPCs, RyRs, and TRPML1) by NAD+ and its metabolites, with a particular focus on their roles in cancers. Although the mechanisms of NAD+ metabolites in these pathological processes are yet to be clearly elucidated, these ion channels are emerging as potential candidates of alternative targets for anticancer therapy.

show abstract

Identification a novel set of 6 differential expressed genes in prostate cancer that can potentially predict biochemical recurrence after curative surgery

Cited by 19 publications

References 29 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

Assessment of biochemical recurrence of prostate cancer (Review)

Roles of NAD+ and Its Metabolites Regulated Calcium Channels in Cancer

Contact Info

Product

Resources

About