Four novel biomarkers for bladder cancer identified by weighted gene coexpression network analysis

Yan, Xin; Guo, Zixin; Li, Xiaoping; Feng, Yujia; Zhao, Yingjie; Liu, Tong‐Zu; Li, Sheng

doi:10.1002/jcp.28546

Cited by 20 publications

(15 citation statements)

References 49 publications

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“…The majority of researchers have just focused on the differential expression of genes associated with BC and have largely ignored the high degree of interconnectivity among genes, where genes with semblable expression patterns might be functionally correlated. Weighted gene co-expression network analysis (WGCNA) is a distinct approach in identifying modules with different categories of correlated genes, and candidate biomarkers or therapeutic targets for cancer management can be picked out according to the connectivity between the internal linkage of the gene sets in specific modules and by calculating the association between the gene sets and related clinical features (6)(7)(8)(9). Furthermore, WGCNA has been successfully applied to identify biomarkers or potential therapeutic targets for BC (8,10).…”

Section: Introductionmentioning

confidence: 99%

“…Weighted gene co-expression network analysis (WGCNA) is a distinct approach in identifying modules with different categories of correlated genes, and candidate biomarkers or therapeutic targets for cancer management can be picked out according to the connectivity between the internal linkage of the gene sets in specific modules and by calculating the association between the gene sets and related clinical features ( 6 – 9 ). Furthermore, WGCNA has been successfully applied to identify biomarkers or potential therapeutic targets for BC ( 8 , 10 ). Hence, we attempt to construct a network through a comprehensive bioinformatics method in the light of WGCNA and identified prognostic genes of BC.…”

Section: Introductionmentioning

confidence: 99%

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Novel Potential Biomarkers Associated With Epithelial to Mesenchymal Transition and Bladder Cancer Prognosis Identified by Integrated Bioinformatic Analysis

et al. 2020

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Bladder cancer (BC) is one of the most common malignancies in terms of incidence and recurrence worldwide. The aim of this study was to identify novel prognostic biomarkers related to BC progression utilizing weighted gene co-expression network analysis (WGCNA) and further bioinformatic analysis. First, we constructed a co-expression network by using WGCNA among 274 TCGA-BLCA patients and preliminarily screened out four genes (CORO1C, TMPRSS4, PIK3C2B, and ZNF692) associated with advanced clinical traits. In support, GSE19915 and specimens from 124 patients were used to validate the genes selected by WGCNA; then, CORO1C and TMPRSS4 were confirmed as hub genes with strong prognostic values in BC. Moreover, the result of gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) indicated that CORO1C and TMPRSS4 might be involved in the process of epithelial to mesenchymal transition (EMT) reversely. In addition, high expression of CORO1C was found to be significantly correlated with tumor-infiltrating neutrophils (TINs), a negative regulatory component that facilitates tumor distant progression and induces poor clinical outcome. In conclusion, our study first identified CORO1C and TMPRSS4 as vital regulators in the process of tumor progression through influencing EMT and could be developed to effective prognostic and therapeutic targets in future BC treatment.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Potential Biomarkers Associated With Epithelial to Mesenchymal Transition and Bladder Cancer Prognosis Identified by Integrated Bioinformatic Analysis

et al. 2020

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“…In present, WGCNA has been widely used to screen out novel and effective molecular biomarkers in bioinformatics eld [28,29]. In our previous study, we have made great efforts to use WGCNA as the main method to identify prognosis biomarkers for bladder cancer [30]. Thus, we rstly identi ed genes associated with survival of ACC in the present study.…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of a novel prognosis prediction model in adrenocortical carcinoma by integrative bioinformatics analysis and statistics and machine learning

Yan

Guo

et al. 2020

Preprint

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Background: Adrenocortical carcinoma (ACC) is a rare malignancy with poor prognosis. Thus, we aimed to establish a gene signature to predict the prognosis for ACC. Methods: Firstly, “WGCNA” package was used to construct a co-expression network and screen key module. Secondly, survival associated genes were identified by performing survival analysis. Thirdly, regression models were constructed by using the Ridge, ELASTIC-NET, and LASSO methods. Time-dependent ROC analysis, Cox regression analysis, GSEA, DCA and nomogram were performed to validate the model. Fourthly, mutations and CNVs of genes in the model were explored. Finally, LDA, KNN, SVM, PPI network and meta-analysis were used screened and validated meaningful prognostic biomarkers.Results: Two key modules were selected and 93 survival associated genes were identified. Furthermore, 11 models were constructed and two models were further selected, which were validated in each dataset (training set, internal validation set, GSE19750, and GSE76021). Model 2 was further identified as the best model (training set: survival analysis: p < 0.0001; AUC: 0.92 at 1 year, 0.91 at 3 years and 0.95 at 5 years). In genes in the best model, MKI67 was altered most (12%). Six hub genes were further analyzed by constructing a PPI network and validated by meta-analysis. Conclusion: In summary, we constructed and validated a prognostic multi-gene model and six powerful prognostic biomarkers, which might be useful instruments for predicting the prognosis of ACC patients.

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“…[ 34 ] In addition, the study of Yan X had demonstrated that CCNB1 was a key gene and associated with the prognosis of BC. [ 35 ]…”

Section: Discussionmentioning

confidence: 99%

Identification of grade-related genes and construction of a robust genomic-clinicopathologic nomogram for predicting recurrence of bladder cancer

Peng

Wang

et al. 2020

Medicine

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Background: Bladder cancer (BC) is a common tumor in the urinary system with a high recurrence rate. The individualized treatment and follow-up after surgery is the key to a successful outcome. Currently, the surveillance strategies are mainly depending on tumor stage and grade. Previous evidence has proved that tumor grade was a significant and independent risk factor of BC recurrence. Exploring the grade-related genes may provide us a new approach to predict prognosis and guide the post-operative treatment in BC patients. Methods: In this study, the weighted gene co-expression network analysis was applied to identify the hub gene module correlated with BC grade using GSE71576. After constructing a protein–protein interaction (PPI) network with the hub genes inside the hub gene module, we identified some potential core genes. TCGA and another independent dataset were used for further validation. Results: The results revealed that the expression of AURKA, CCNA2, CCNB1, KIF11, TTK, BUB1B, BUB1, and CDK1 were significantly higher in high-grade BC, showing a strong ability to distinguish BC grade. The expression levels of the 8 genes in normal, paracancerous, tumorous, and recurrent bladder tissues were progressively increased. By conducting survival analysis, we proved their prognostic value in predicting the recurrence of BC. Eventually, we constructed a prognostic nomogram by combining the 8-core-gene panel with clinicopathologic features, which had shown great performance in predicting the recurrence of BC. Conclusion: We identified 8 core genes that revealed a significant correlation with the tumor grade as well as the recurrence of BC. Finally, we proved the value of a novel prognostic nomogram for predicting the relapse-free survival of BC patients after surgery, which could guide their treatment and follow-up.

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Four novel biomarkers for bladder cancer identified by weighted gene coexpression network analysis

Cited by 20 publications

References 49 publications

Novel Potential Biomarkers Associated With Epithelial to Mesenchymal Transition and Bladder Cancer Prognosis Identified by Integrated Bioinformatic Analysis

Novel Potential Biomarkers Associated With Epithelial to Mesenchymal Transition and Bladder Cancer Prognosis Identified by Integrated Bioinformatic Analysis

Identification and validation of a novel prognosis prediction model in adrenocortical carcinoma by integrative bioinformatics analysis and statistics and machine learning

Identification of grade-related genes and construction of a robust genomic-clinicopathologic nomogram for predicting recurrence of bladder cancer

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