Dataset Information

Identification of a Robust Five-Gene Risk Model in Prostate Cancer: A Robust Likelihood-Based Survival Analysis.

ABSTRACT: Aim:In this paper, we aimed to develop and validate a risk prediction method using independent prognosis genes selected robustly in prostate cancer. Method:We considered 723 samples obtained from TCGA (the Cancer Genome Atlas), GSE46602, and GSE21032. Prostate cancer prognosis-related genes with P < 0.05 were selected using Univariable Cox regression analysis. We then built the lowest AIC (Akaike information criterion score) optimal gene model using the "Rbsurv" package in TCGA train set. The coefficients were obtained by Multivariable Cox regression analysis. We named the new prognosis method CMU5. The CMU5 risk score was verified in TCGA test set, GSE46602, and GSE21032. Results:FAM72D, ARHGAP33, TACR2, PLEK2, and FA2H were identified as independent prognosis factors in prostate cancer patients. We built the computing model as follows: CMU5 risk score?=?1.158?FAM72D?+?1.737?ARHGAP33?-?0.737?TACR2?-?0.651?PLEK2?-?0.793?FA2H. The AUC of DFS was 0.809 in the train set (274 samples), 0.710 in the test set (273 samples), and 0.768 in the complete set (547 samples). The benign prediction capacity of CMU5 was verified by GSE46602 (36 samples; AUC = 0.6039) and GSE21032 GPL5188 (140 samples; AUC = 0.7083). Using the cut-off point of 2.056, a significant difference was shown between high- and low-risk groups. Conclusion:A prognosis-related risk score formula named CMU5 was built and verified, providing reliable prediction of prostate cancer outcome. This signature might provide a basis for individualized treatment of prostate cancer.

SUBMITTER: Wang Y

PROVIDER: S-EPMC7285394 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification of a Robust Five-Gene Risk Model in Prostate Cancer: A Robust Likelihood-Based Survival Analysis.

Wang Yutao Y Lin Jiaxing J Yan Kexin K Wang Jianfeng J

International journal of genomics 20200527

<h4>Aim</h4>In this paper, we aimed to develop and validate a risk prediction method using independent prognosis genes selected robustly in prostate cancer.<h4>Method</h4>We considered 723 samples obtained from TCGA (the Cancer Genome Atlas), GSE46602, and GSE21032. Prostate cancer prognosis-related genes with <i>P</i> < 0.05 were selected using Univariable Cox regression analysis. We then built the lowest AIC (Akaike information criterion score) optimal gene model using the "Rbsurv" package in ...[more]

PMID: 32566639

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Project description:Background: Papillary renal cell carcinoma (PRCC) accounts for 15% of all renal cell carcinomas. The molecular mechanisms of renal papillary cell carcinoma remain unclear, and treatments for advanced disease are limited.Result: We built the computing model as follows: Risk score = 1.806 * TPX2 - 0.355 * TXNRD2 - 0.805 * SLC6A20. The 3-year AUC of overall survival was 0.917 in the training set (147 PRCC samples) and 0.760 in the test set (142 PRCC samples). Based on the robust model, M2 macrophages showed positive correlation with risk score, while M1 macrophages were the opposite. PRCC patients with low risk score showed higher tumor mutation burden. TPX2 is a risk factor, and co-expression factors were enriched in cell proliferation and cancer-related pathways. Finally, the proliferation and invasion of PRCC cell line were decreased in the TPX2 reduced group, and the differential expression was identified. TPX2 is a potential risk biomarker which involved in cell proliferation in PRCC.Conclusion: We conducted a study to develop a three gene model for predicting prognosis in patients with papillary renal cell carcinoma. Our findings may provide candidate biomarkers for prognosis that have important implications for understanding the therapeutic targets of papillary renal cell carcinoma.Method: Gene expression matrix and clinical data were obtained from TCGA (The Cancer Genome Atlas), GSE26574, GSE2048, and GSE7023. Prognostic factors were identified using "survival" and "rbsurv" packages, and a risk score was constructed using Multivariate Cox regression analysis. The co-expression networks of the factors in model were constructed using the "WGCNA" package. The co-expression genes of factors were enriched and displayed the biological process. Based on this robust risk model, immune cells infiltration proportions and tumor mutation burdens were compared between risk groups. Subsequently, using the PRCC cell line, the role of TPX2 was determined by Cell proliferation assay, 5-Ethynyl-20-deoxyuridine assay and Transwell assay.

Project description:BackgroundBiochemical recurrence (BCR) after initial treatment, such as radical prostatectomy, is the most frequently adopted prognostic factor for patients who suffer from prostate cancer (PCa). In this study, we aimed to construct a prognostic model consisting of gene expression profiles to predict BCR-free survival.MethodsWe analyzed 70 metabolic pathways in 152 normal prostate samples and 494 PCa samples from the UCSC Xena dataset (training set) via gene set enrichment analysis (GSEA) to select BCR-related genes and constructed a BCR-related gene risk score (RS) model. We tested the power of our model using Kaplan-Meier (K-M) plots and receiver operator characteristic (ROC) curves. We performed univariate and multivariate analyses of RS using other clinicopathological features and established a nomogram model, which has stronger prediction ability. We used GSE70770 and DFKZ 2018 datasets to validate the results. Finally, we performed differential expression and quantitative real-time polymerase chain reaction analyses of the UCSC data for further verification of the findings.ResultsA total of 194 core enriched genes were obtained through GSEA, among which 16 BCR-related genes were selected and a three-gene RS model based on the expression levels of CA14, LRAT, and MGAT5B was constructed. The outcomes of the K-M plots and ROC curves verified the accuracy of the RS model. We identified the Gleason score, pathologic T stage, and RS model as independent predictors through univariate and multivariate Cox analyses and constructed a nomogram model that presented better predictability than the RS model. The outcomes of the validation set were consistent with those of the training set. Finally, the results of differential expression analyses support the effectiveness of our model.ConclusionWe constructed an RS model based on metabolic genes that could predict the prognosis of PCa patients. The model can be easily used in clinical applications and provide important insights into future research on the underlying mechanism of PCa.

Dataset Information

Identification of a Robust Five-Gene Risk Model in Prostate Cancer: A Robust Likelihood-Based Survival Analysis.

Publications

Identification of a Robust Five-Gene Risk Model in Prostate Cancer: A Robust Likelihood-Based Survival Analysis.

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