Analysis of gene co‑expression network reveals prognostic significance of CNFN in patients with head and neck cancer

Liu, Baoling; Huang, Guanhong; Zhu, Hongming; Ma, Zhaoming; Tian, Xiaokang; Yin, Li; Gao, Xing-Ya; He, Xu

doi:10.3892/or.2019.7019

Cited by 17 publications

(17 citation statements)

References 55 publications

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“…The weighted gene co-expression network, "WGCNA", R package was used to construct co-expression network, we first calculated the standard deviation values for gene expression in GSE70770, ranked by it and chose the top 25% for further analysis 13,14 . Outlier samples were checked and removed.…”

Section: Wgcna Construction and Identification Of Prostate Cancer Diamentioning

confidence: 99%

WFDC2 suppresses prostate cancer metastasis by modulating EGFR signaling inactivation

et al. 2020

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WAP four-disulfide core domain 2 (WFDC2) is a small secretory protein that has been widely studied in ovarian cancer. It has been proven that WFDC2 promotes proliferation and metastasis in ovarian cancer, and serves as a diagnostic biomarker. However, the specific function of WFDC2 in prostate cancer has not been reported. Here, we first screened the diagnostic marker and favorable prognostic factor WFDC2 in prostate cancer by bioinformatics. WFDC2 expression was negatively correlated with Gleason score and metastasis in prostate cancer. Then, we revealed that overexpression of WFDC2, and addition of recombinant protein HE4 can significantly inhibit prostate cancer metastasis in vivo and in vitro. By co-immunoprecipitation and co-localization assays, we proved that WFDC2 binds to the extracellular domain of epidermal growth factor receptor (EGFR). Immunoblot showed that WFDC2 overexpression and recombinant protein HE4 addition inactivated the EGFR/AKT/GSK3B/Snail signaling pathway, and then restrained the progression of epithelial-mesenchymal transition. In conclusion, our study identified that the tumor suppressor WFDC2 can suppress prostate cancer metastasis by inactivating EGFR signaling.

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Section: Wgcna Construction and Identification Of Prostate Cancer Diamentioning

confidence: 99%

WFDC2 suppresses prostate cancer metastasis by modulating EGFR signaling inactivation

et al. 2020

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“…12 Using 'WGCNA' R package, we first deleted the outliers in each dataset. 12 Using 'WGCNA' R package, we first deleted the outliers in each dataset.…”

Section: Weighted Gene Co-expression Network Construction and Progrmentioning

confidence: 99%

“…For WGCNA analysis, we first calculated the standard deviation values for gene expression in each dataset, ranked by it and chose the top 25% for the following analysis. 12 Using 'WGCNA' R package, we first deleted the outliers in each dataset. 13 Then, to ensure a scalefree network, proper soft-thresholding parameter β was chosen and genes with similar expression pattern were clustered into the same module.…”

Section: Weighted Gene Co-expression Network Construction and Progrmentioning

confidence: 99%

An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis

Xiong

Yuan

Xiong

et al. 2019

J Cellular Molecular Medi

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The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan-cancer validation and time-dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed.Eight genes were eventually screened out as progression-related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E-MTAB-4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time-dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan-cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision-making. K E Y W O R D S bladder cancer, Gene Expression Omnibus (GEO), LASSO, prognosis, WGCNA S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section. How to cite this article: Xiong Y, Yuan L, Xiong J, et al. An outcome model for human bladder cancer: A comprehensive study based on weighted gene co-expression network analysis.

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“…WGCNA was widely used in various biological processes analysis to process differently expressed genes and interactions among genes [15][16][17]. These hub genes can be identified as therapeutic targets or candidate biomarkers in many diseases [5,[18][19][20]. Combined with the RNA-seq data and WGCNA analysis, Wei et al found that genes involved in cell adhesion, ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling pathway play crucial roles in human lung adenocarcinomas [15].…”

Section: Discussionmentioning

confidence: 99%

Identification of Hub Genes as Biomarkers Correlated with the Proliferation and Prognosis in Lung Cancer: A Weighted Gene Co-Expression Network Analysis

Huang

et al. 2020

BioMed Research International

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Lung cancer is one of the most malignant tumors in the world. Early diagnosis and treatment of lung cancer are vitally important to reduce the mortality of lung cancer patients. In the present study, we attempt to identify the candidate biomarkers for lung cancer by weighted gene co-expression network analysis (WGCNA). Gene expression profile of GSE30219 was downloaded from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) were analyzed by the limma package, and the co-expression modules of genes were built by WGCNA. UALCAN was used to analyze the relative expression of normal group and tumor subgroups based on tumor individual cancer stages. Survival analysis for the hub genes was performed by Kaplan–Meier plotter analysis with the TCGA database. A total of 2176 genes (745 upregulated and 1431 downregulated genes) were obtained from the GSE30219 database. Seven gene co-expression modules were conducted by WGCNA and the blue module might be inferred as the most crucial module in the pathogenesis of lung cancer. In the pathway enrichment analysis of KEGG, the candidate genes were enriched in the “DNA replication,” “Cell cycle,” and “P53 signaling pathway” pathways. Among these, the cell cycle pathway was the most significant pathway in the blue module with four hub genes CCNB1, CCNE2, MCM7, and PCNA which were selected in our study. Kaplan–Meier plotter analysis indicated that the high expressions of four hub genes were correlated with a worse overall survival (OS) and advanced tumors. qRT-PCR showed that mRNA expression levels of MCM7 (p=0.038) and CCNE2 (0.003) were significantly higher in patients with the TNM stage. In summary, the high expression of the MCM7 and CCNE2 were significantly related with advanced tumors and worse OS in lung cancer. Thus, the MCM7 and CCNE2 genes can be good indicators for cellular proliferation and prognosis in lung cancer.

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Analysis of gene co‑expression network reveals prognostic significance of CNFN in patients with head and neck cancer

Cited by 17 publications

References 55 publications

WFDC2 suppresses prostate cancer metastasis by modulating EGFR signaling inactivation

WFDC2 suppresses prostate cancer metastasis by modulating EGFR signaling inactivation

An outcome model for human bladder cancer: A comprehensive study based on weighted gene co‐expression network analysis

Identification of Hub Genes as Biomarkers Correlated with the Proliferation and Prognosis in Lung Cancer: A Weighted Gene Co-Expression Network Analysis

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