Construction of a set of novel and robust gene expression signatures predicting prostate cancer recurrence

Jiang, Yanzhi; Mei, Wenjuan; Gu, Yan; Lin, Xiaozeng; He, Lizhi; Zeng, Hui; Wei, Fengxiang; Wan, Xinhong; Yang, Huixiang; Major, Pierre; Tang, Damu

doi:10.1002/1878-0261.12359

Cited by 21 publications

(27 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Jiang et al . 38 developed a 15-gene signature using Elastic-net analysis, the signature showed a predict AUC value of 0.766 at 11.5 months, 0.738 at 22.3 months, and 0.764 at 48.4 months. Therefore, it is meaningful to establish the prognosis predict signature to distinguish the low risk and high-risk PCa patients, as well as provide the appropriate treatment for them.…”

Section: Discussionmentioning

confidence: 99%

The prognostic value and potential mechanism of Matrix Metalloproteinases among Prostate Cancer

Geng¹,

Chen²,

Huang³

et al. 2020

Int. J. Med. Sci.

View full text Add to dashboard Cite

Background: Matrix Metalloproteinases (MMPs) play an indispensable role in the initial alteration and development of PCa. We tried to generate an MMP-related prognostic signature (MMPS) in prostate cancer (PCa). Methods: TCGA-PRAD, MSKCC/GSE21032, GSE116918, GSE70769 cohorts were enrolled to assess the prognostic value of MMPs. The least absolute shrinkage and selection operator (LASSO) Cox regression was employed to generate the MMPS signature. The log-rank test and Kaplan-Meier (K-M) survival curve were applied to show the difference RFS, The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) was plotted to predict the accuracy of signature. CIBERSORT was conducted to analyze the different immune infiltration in MMPS-H and MMPS-L groups. Potential signaling pathways activated in the MMPS-H groups by Metascape. Results: MMP1, MMP7, MMP11, MMP24 and MMP26 were selected by LASSO regression and established the MMPS predict signature. The MMPS showed the high prognostic value in TCGA-PRAD training cohort (AUC=0.714) and validation cohorts (GSE116918: AUC=0.976, GSE70769: AUC=0.738, MSKCC: AUC=0.793). Pid integrin1 pathway, G2M checkpoint, and response to growth factor signaling pathways were activated in MMPS-H group, patients with the high MMPS risk score and low M2 macrophage showed the worst recurrence-free survival (RFS). Conclusion: MMPs involved and played an essential role in the tumorigenesis and biochemical recurrence in PCa patients. The MMPS signature could accurately predict the recurrence of PCa patients and validated in several cohorts.

show abstract

Section: Discussionmentioning

confidence: 99%

The prognostic value and potential mechanism of Matrix Metalloproteinases among Prostate Cancer

Geng¹,

Chen²,

Huang³

et al. 2020

Int. J. Med. Sci.

View full text Add to dashboard Cite

show abstract

Section: Gene Expression-based Biomarkersmentioning

confidence: 99%

“…Instead of focusing on a particular pathway, a 15-gene signature has recently been formulated from the MUC1 network (SigMuc1NW) (156); the signature was validated in the MSKCC dataset. SigMuc1NW stratifies the BCR risk in the MSKCC dataset at P-value 3.11e-15 (156). MUC1 is the most intensively investigated tumor-associated antigen (157-159) and is an attractive target for developing immunotherapies for multiple tumor types (160).…”

Section: Gene Expression-based Biomarkersmentioning

confidence: 99%

Assessment of biochemical recurrence of prostate cancer (Review)

Lin

Kapoor

et al. 2019

Int J Oncol

Self Cite

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

“…In the current study, both internal and external validation techniques were employed to evaluate the performance of models. First, the training dataset is used to develop prediction models and standard 10-fold cross-validation is used for performing internal validation, which is commonly employed in the literature (Burton et al, 2012;Bastani et al, 2013;Kourou et al, 2015;Bhalla et al, 2017;Jiang et al, 2018;Bhalla et al, 2019;Kaur et al, 2019). It is important to evaluate the realistic performance of the model on the external validation dataset, which should not be used for training and testing during model development.…”

Section: Performance Evaluation Of the Prediction Modelsmentioning

confidence: 99%

Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma Using Large-Scale Transcriptomics Data

et al. 2020

View full text Add to dashboard Cite

The high mortality rate of hepatocellular carcinoma (HCC) is primarily due to its late diagnosis. In the past, numerous attempts have been made to design genetic biomarkers for the identification of HCC; unfortunately, most of the studies are based on small datasets obtained from a specific platform or lack reasonable validation performance on the external datasets. In order to identify a universal expression-based diagnostic biomarker panel for HCC that can be applicable across multiple platforms, we have employed large-scale transcriptomic profiling datasets containing a total of 2,316 HCC and 1,665 non-tumorous tissue samples. These samples were obtained from 30 studies generated by mainly four types of profiling techniques (Affymetrix, Illumina, Agilent, and High-throughput sequencing), which are implemented in a wide range of platforms. Firstly, we scrutinized overlapping 26 genes that are differentially expressed in numerous datasets. Subsequently, we identified a panel of three genes (FCN3, CLEC1B, and PRC1) as HCC biomarker using different feature selection techniques. Three-genes-based HCC biomarker identified HCC samples in training/validation datasets with an accuracy between 93 and 98%, Area Under Receiver Operating Characteristic curve (AUROC) in a range of 0.97 to 1.0. A reasonable performance, i.e., AUROC 0.91-0.96 achieved on validation dataset containing peripheral blood mononuclear cells, concurred their non-invasive utility. Furthermore, the prognostic potential of these genes was evaluated on TCGA-LIHC and GSE14520 cohorts using univariate survival analysis. This analysis revealed that these genes are prognostic indicators for various types of the survivals of HCC patients (e.g., Overall Survival, Progression-Free Survival, Disease-Free Survival). These genes significantly stratified high-risk and low-risk HCC patients (p-value <0.05). In conclusion, we identified a universal platform-independent three-genes-based biomarker that can predict HCC patients with high precision and also possess significant prognostic potential. Eventually, we developed a web server HCCpred based on the above study to facilitate scientific community (http://webs.iiitd.edu.in/raghava/hccpred/).

show abstract

Construction of a set of novel and robust gene expression signatures predicting prostate cancer recurrence

Cited by 21 publications

References 67 publications

The prognostic value and potential mechanism of Matrix Metalloproteinases among Prostate Cancer

The prognostic value and potential mechanism of Matrix Metalloproteinases among Prostate Cancer

Assessment of biochemical recurrence of prostate cancer (Review)

Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma Using Large-Scale Transcriptomics Data

Contact Info

Product

Resources

About