Dataset Information

Construction and validation of an eight-gene signature with great prognostic value in bladder cancer.

ABSTRACT: Bladder cancer (BC) is one of the most common malignancies in urinary system with a common malignancy in urinary system with a high mortality and recurrence rate, so we attempt to construct a gene signature to predict the prognosis of BCs. We initially established a co-expression network by performing WGCNA analysis and further identified magenta module as key module (P = 8e-05, R2 = 0.4). Subsequently, we screened 12 genes associated with survival from the key module, which were selected to construct an eight-gene signature by establishing a LASSO Cox model. Moreover, we reckoned the risk score (RS) of each sample, through which we could divide samples into two groups (the high-risk and low-risk groups) and verify the signature, in the training set and 3 validation sets (internal test set, GSE13507and E-MTAB-4321). This signature could distinguish between the high- and low- risk patients well (survival analysis: P = 0.015; AUC: 0.61 at 1 year, 0.61 at 3 years and 0.61 at 5 years). In the validation sets, this signature also showed good performance, which was consistent with the training test. Furthermore, we plotted a nomogram to predict the possibility of the overall survival (OS) and three calibration curves to predict the effectiveness of the nomogram, which suggested good value and clinical utility of the nomogram. In conclusion, we established an eight-gene signature, which was probably effective in the prediction of prognosis of patients with BC.

SUBMITTER: Yan X

PROVIDER: S-EPMC7052873 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Construction and validation of an eight-gene signature with great prognostic value in bladder cancer.

Yan Xin X Fu Xun X Guo Zi-Xin ZX Liu Xiao-Ping XP Liu Tong-Zu TZ Li Sheng S

Journal of Cancer 20200117 7

Bladder cancer (BC) is one of the most common malignancies in urinary system with a common malignancy in urinary system with a high mortality and recurrence rate, so we attempt to construct a gene signature to predict the prognosis of BCs. We initially established a co-expression network by performing WGCNA analysis and further identified magenta module as key module (P = 8e-05, R2 = 0.4). Subsequently, we screened 12 genes associated with survival from the key module, which were selected to con ...[more]

PMID: 32194788

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Project description:ObjectiveBladder cancer (BC) is one of the top ten cancers endangering human health but we still lack accurate tools for BC patients' risk stratification. This study aimed to develop an autophagy-related signature that could predict the prognosis of BC. In order to provide clinical doctors with a visual tool that could precisely predict the survival probability of BC patients, we also attempted to establish a nomogram based on the risk signature.MethodsWe screened out autophagy-related genes (ARGs) combining weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) in BC. Based on the screened ARGs, we performed survival analysis and Cox regression analysis to identify potential prognostic biomarkers. A risk signature based on the prognostic ARGs by multivariate Cox regression analysis was established, which was validated by using seven datasets. To provide clinical doctors with a useful tool for survival possibility prediction, a nomogram assessed by the ARG-based signature and clinicopathological features was constructed, verified using four independent datasets.ResultsThree prognostic biomarkers including BOC (P = 0.008, HR = 1.104), FGF7(P = 0.030, HR = 1.066), and MAP1A (P = 0.001, HR = 1.173) were identified and validated. An autophagy-related risk signature was established and validated. This signature could act as an independent prognostic feature in patients with BC (P = 0.047, HR = 1.419). We then constructed two nomograms with and without ARG-based signature and subsequent analysis indicated that the nomogram with ARG signature showed high accuracy for overall survival probability prediction of patients with BC (C-index = 0.732, AUC = 0.816). These results proved that the ARG signature improved the clinical net benefit of the standard model based on clinicopathological features (age, pathologic stage).ConclusionsThree ARGs were identified as prognosis biomarkers in BC. An ARG-based signature was established for the first time, showing strong potential for prognosis prediction in BC. This signature was proven to improve the clinical net benefit of the standard model. A nomogram was established using this signature, which could lead to more effective prognosis prediction for BC patients.

Project description:Purpose: To identify whether ferroptosis-related genes play predictive roles in bladder cancer patients and to develop a ferroptosis-related gene signature to predict overall survival outcomes. Materials and Methods: We downloaded the mRNA expression files and clinical data of 256 bladder samples (188 bladder tumour and 68 nontumour samples) from the GEO database and 430 bladder samples (411 bladder tumour and 19 nontumour samples) from the TCGA database. A multigene signature based on prognostic ferroptosis-related genes was constructed by least absolute shrinkage and selection operator Cox regression analysis in the GEO cohort. The TCGA cohort was used to validate the ferroptosis-related gene signature. Next, functional enrichment analysis, including both Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses, was performed to elucidate the mechanism underlying the signature. The ssGSEA scores of 16 immune cells and 13 immune-related pathway activities between the high-risk and low-risk groups were also analysed in our study. Results: Thirty-three (67.3%) ferroptosis-related genes were differentially expressed between bladder tumour samples and nontumour samples in the GEO cohort. The intersection of prognostic ferroptosis-related genes and differentially expressed genes identified four prognostic targets, including ALOX5, FANCD2, HMGCR and FADS2. The least absolute shrinkage and selection operator Cox regression successfully built a 4-gene signature: risk score value = esum (each gene's normalized expression * each gene's coefficient). Univariate and multivariate Cox regression analyses were performed in both the GEO and TCGA cohorts to test the independent prognostic value of the 4-gene risk signature. Multivariate Cox regression analysis in the GEO cohort identified age (p < 0.001), grade (p = 0.129) and risk score (p = 0.016) as independent prognostic predictors for overall survival. Multivariate Cox regression analysis in the TCGA cohort also identified age (p = 0.002), stage (p < 0.001) and risk score (p = 0.006) as independent prognostic predictors for overall survival. The type II IFN response was determined to be significantly weakened in the high-risk group in both the GEO and TCGA cohorts. Conclusion: We successfully built a ferroptosis-related gene signature of significant predictive value for bladder cancer. These results suggest a novel research direction for targeted therapy of bladder cancer in the future.

Project description:Human bladder cancer (BCa) is the most common urogenital system malignancy. Patients with BCa have limited treatment efficacy in clinical practice. Novel biomarkers could provide more crucial information conferring to cancer diagnosis, treatment, and prognosis. Here, we aimed to explore and identify novel biomarkers associated with cancer-specific survival of patients with BCa to build a prognostic signature. Based on univariate Cox regression, Lasso regression, and multivariate Cox regression analysis, we conducted an integrated analysis in the training set (GSE32894) and established a six-gene signature to predict the cancer-specific survival for human BCa. The six genes were Cyclin Dependent Kinase 4 (CDK4), E2F Transcription Factor 7 (E2F7), Collagen Type XI Alpha 1 Chain (COL11A1), Bradykinin Receptor B2 (BDKRB2), Yip1 Interacting Factor Homolog B (YIF1B), and Zinc Finger Protein 415 (ZNF415). Then, we validated the prognostic value of the model by using two other datasets (GSE13507 and TCGA). Also, we conducted univariate and multivariate Cox regression analyses, and results indicated that the six-gene signature was an independent prognostic factor of cancer-specific survival of patients with BCa. Functional analysis was performed based on the differentially expressed genes of low- and high-risk patients, and we found that they were enriched in lipid metabolic and cell division-related biological processes. Meanwhile, the gene set enrichment analysis (GSEA) revealed that high-risk samples were enriched in cell cycle and cancer-related pathways [G2/M checkpoint, E2F targets, mitotic spindle, mTOR signaling, spermatogenesis, epithelial-mesenchymal transition (EMT), DNA repair, PI3K/AKT/mTOR signaling, unfolded protein response (UPR), and MYC targets V2]. Lastly, we detected the relative expression of each signature in BCa cell lines by quantitative real-time PCR (qRT-PCR). As far as we know, currently, the present study is the first research that developed and validated a cancer-specific survival prognostic index based on three independent cohorts. The results revealed that this six-gene signature has a predictive ability for cancer-specific prognosis. Moreover, we also verified the relative expression of these six signatures between the bladder cell line and four BCa cell lines by qRT-PCR. Nevertheless, experiments to further explore the function of six genes are lacking.

Project description:Background: Gastric cancer (GC) is the most common malignant tumor. Due to the lack of practical molecular markers, the prognosis of patients with advanced gastric cancer is still poor. A number of studies have confirmed that the coagulation system is closely related to tumor progression. Therefore, the purpose of this study was to construct a coagulation-related gene signature and prognostic model for GC by bioinformatics methods. Methods: We downloaded the gene expression and clinical data of GC patients from the TCGA and GEO databases. In total, 216 coagulation-related genes (CRGs) were obtained from AmiGO 2. Weighted gene co-expression network analysis (WGCNA) was used to identify coagulation-related genes associated with the clinical features of GC. Last absolute shrinkage and selection operator (LASSO) Cox regression was utilized to shrink the relevant predictors of the coagulation system, and a Coag-Score prognostic model was constructed based on the coefficients. According to this risk model, GC patients were divided into high-risk and low-risk groups, and overall survival (OS) curves and receiver operating characteristic (ROC) curves were drawn in the training and validation sets, respectively. We also constructed nomograms for predicting 1-, 2-, and 3-year survival in GC patients. Single-sample gene set enrichment analysis (ssGSEA) was exploited to explore immune cells' underlying mechanisms and correlations. The expression levels of coagulation-related genes were verified by real-time quantitative polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC). Results: We identified seven CRGs employed to construct a Coag-Score risk model using WGCNA combined with LASSO regression. In both training and validation sets, GC patients in the high-risk group had worse OS than those in the low-risk group, and Coag-Score was identified as an independent predictor of OS, and the nomogram provided a quantitative method to predict the 1-, 2-, and 3-year survival rates of GC patients. Functional analysis showed that Coag-Score was mainly related to the MAPK signaling pathway, complement and coagulation cascades, angiogenesis, epithelial-mesenchymal transition (EMT), and KRAS signaling pathway. In addition, the high-risk group had a significantly higher infiltration enrichment score and was positively associated with immune checkpoint gene expression. Conclusion: Coagulation-related gene models provide new insights and targets for the diagnosis, prognosis prediction, and treatment management of GC patients.

Dataset Information

Construction and validation of an eight-gene signature with great prognostic value in bladder cancer.

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Construction and validation of an eight-gene signature with great prognostic value in bladder cancer.

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