Identification of Hub Genes in Thyroid Cancer: Robust Rank Aggregation and Weighted Gene Co-Expression Network Analysis

2019 ◽  
Author(s):  
Yang Liu ◽  
Ting-Yu Chen ◽  
Zhi-Yan Yang ◽  
Wei Fang ◽  
Chao Zhang
Keyword(s):  
Thyroid Cancer ◽  
Network Analysis ◽  
Rank Aggregation ◽  
Hub Genes

scholarly journals Identification of hub genes in prostate cancer using robust rank aggregation and weighted gene co-expression network analysis

Aging ◽  
2019 ◽  
Vol 11 (13) ◽  
pp. 4736-4756 ◽  
Author(s):  
Zhen-yu Song ◽  
Fan Chao ◽  
Zhiyuan Zhuo ◽  
Zhe Ma ◽  
Wenzhi Li ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Network Analysis ◽  
Rank Aggregation ◽  
Hub Genes

scholarly journals Potential Novel Modules and Hub Genes as Prognostic Candidates of Thyroid Cancer by Weighted Gene Co-Expression Network Analysis

2021 ◽  
Vol Volume 14 ◽  
pp. 9433-9444
Author(s):  
Zhiqiang Shi ◽  
Xinghui Li ◽  
Long Zhang ◽  
Yilang Luo ◽  
Bikal Shrestha ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Network Analysis ◽  
Hub Genes

scholarly journals CENPL, ISG20L2, LSM4 and MRPL3 Are Four Novel Hub Genes Involved in Breast Cancer Development and Progression

2020 ◽  
Author(s):  
Jinbao Yin ◽  
Chen Lin ◽  
Meng jiang ◽  
Xinbing Tang ◽  
Danlin Xie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Candidate Genes ◽  
Molecular Mechanism ◽  
Molecular Mechanisms ◽  
Rank Aggregation ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Ppi Networks ◽  
Kaplan Meier

Abstract BackgroundAs a highly prevalent tumor disease worldwide, Further elucidation of the molecular mechanisms of the occurrence, development and prognosis of breast cancer remain an urgent need. Identifying hub genes involved in these pathogenesis and progression can potentially help to unveil its mechanism and provide novel diagnostic and prognostic markers for breast cancer.MethodsIn this study, we systematically integrated multiple bioinformatic methods, including robust rank aggregation (RRA), functional enrichment analysis, protein-protein interaction (PPI) networks construction and analysis, weighted gene co-expression network analysis (WGCNA), ROC and Kaplan-Meier analyses, DNA methylation analyses and genomic mutation analyses, GSEA and GSVA, based on ten mRNA datasets to identify and investigate novel hub genes involved in breast cancer. In parallel, RNA in situ detection technology was applied to validate those novel hub gene.ResultsEZH2 was recognized as a key gene by PPI network analysis. CENPL, ISG20L2, LSM4 and MRPL3 were identified as four novel hub genes through the WGCNA analysis and literate search. Among those five hub genes, many studies on EZH2 gene in breast cancer have been reported, but no studies are related to the roles of CENPL, ISG20L2, MRPL3 and LSM4 in breast cancer. These novel four hub genes were up-regulated in breast cancer tissues and associated with tumor progression. ROC and Kaplan-Meier indicated these four hub genes all showed good diagnostic performance and prognostic values for breast cancer. The preliminary analysis revealed those novel hub genes are four potentially candidate genes for further exploring the molecular mechanism of breast cancer.ConclusionWe identify four novel hub genes (CENPL, ISG20L2, MRPL3, and LSM4) that are likely playing key roles in the molecular mechanism of occurrence and development of breast cancer. Those hub genes are four potentially candidate genes served as promising candidate diagnostic biomarkers and prognosis predictors for breast cancer, and their exact functional mechanisms in breast cancer deserve further in-depth study.

scholarly journals Comprehensive Analysis of mRNA Expression Profiles in Head and Neck Cancer by Using Robust Rank Aggregation and Weighted Gene Coexpression Network Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Zaizai Cao ◽  
Yinjie Ao ◽  
Yu Guo ◽  
Shuihong Zhou
Keyword(s):  
Network Analysis ◽  
Head And Neck ◽  
Cell Cancer ◽  
Rank Aggregation ◽  
Coexpression Network ◽  
Gene Coexpression Network ◽  
Methylation Analysis ◽  
Hub Genes ◽  
Gene Coexpression ◽  
Coexpression Network Analysis

Background. Head and neck squamous cell cancer (HNSCC) is the sixth most common cancer in the world; its pathogenic mechanism remains to be further clarified. Methods. Robust rank aggregation (RRA) analysis was utilized to identify the metasignature dysregulated genes, which were then used for potential drug prediction. Weighted gene coexpression network analysis (WGCNA) was performed on all metasignature genes to find hub genes. DNA methylation analysis, GSEA, functional annotation, and immunocyte infiltration analysis were then performed on hub genes to investigate their potential role in HNSCC. Result. A total of 862 metasignature genes were identified, and 6 potential drugs were selected based on these genes. Based on the result of WGCNA, six hub genes (ITM2A, GALNTL1, FAM107A, MFAP4, PGM5, and OGN) were selected ( GS > 0.1 , MM > 0.75 , GS p value < 0.05, and MM p value < 0.05). All six genes were downregulated in tumor tissue ( FDR < 0.01 ) and were related to the clinical stage and prognosis of HNSCC in different degrees. Methylation analysis showed that the dysregulation of ITM2A, GALNTL1, FAM107A, and MFAP4 may be caused by hypermethylation. Moreover, the expression level of all 6 hub genes was positively associated with immune cell infiltration, and the result of GSEA showed that all hub genes may be involved in the process of immunoregulation. Conclusion. All identified hub genes could be potential biomarkers for HNSCC and provide a new insight into the diagnosis and treatment of head and neck tumors.

scholarly journals Weighted gene co-expression network analysis identified underlying hub genes and mechanisms in the occurrence and development of viral myocarditis

2020 ◽  
Vol 8 (21) ◽  
pp. 1348-1348
Author(s):  
Zetao Ma ◽  
Zhida Shen ◽  
Yingchao Gong ◽  
Jiaqi Zhou ◽  
Xiaoou Chen ◽  
...  
Keyword(s):  
Network Analysis ◽  
Viral Myocarditis ◽  
Hub Genes

scholarly journals Identification of key modules and hub genes in glioblastoma multiforme based on co‐expression network analysis

FEBS Open Bio ◽  
2021 ◽  
Author(s):  
Chun Li ◽  
Bangming Pu ◽  
Long Gu ◽  
Mingwei Zhang ◽  
Hongping Shen ◽  
...  
Keyword(s):  
Glioblastoma Multiforme ◽  
Network Analysis ◽  
Hub Genes

scholarly journals The potential role of chemotaxis and the complement system in the formation and progression of thoracic aortic aneurysms inferred from the weighted gene coexpression network analysis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chuxiang Lei ◽  
Dan Yang ◽  
Wenlin Chen ◽  
Haoxuan Kan ◽  
Fang Xu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Clinical Significance ◽  
Immune Cells ◽  
Complement System ◽  
Aortic Wall ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
The Complement System ◽  
Coexpression Network Analysis

Abstract Background Thoracic aortic aneurysm (TAA) can be life-threatening due to the progressive weakening and dilatation of the aortic wall. Once the aortic wall has ruptured, no effective pharmaceutical therapies are available. However, studies on TAA at the gene expression level are limited. Our study aimed to identify the driver genes and critical pathways of TAA through gene coexpression networks. Methods We analyzed the genetic data of TAA patients from a public database by weighted gene coexpression network analysis (WGCNA). Modules with clinical significance were identified, and the differentially expressed genes (DEGs) were intersected with the genes in these modules. Gene Ontology and pathway enrichment analyses were performed. Finally, hub genes that might be driving factors of TAA were identified. Furthermore, we evaluated the diagnostic accuracy of these genes and analyzed the composition of immune cells using the CIBERSORT algorithm. Results We identified 256 DEGs and two modules with clinical significance. The immune response, including leukocyte adhesion, mononuclear cell proliferation and T cell activation, was identified by functional enrichment analysis. CX3CR1, C3, and C3AR1 were the top 3 hub genes in the module correlated with TAA, and the areas under the curve (AUCs) by receiver operating characteristic (ROC) analysis of all the hub genes exceeded 0.7. Finally, we found that the proportions of infiltrating immune cells in TAA and normal tissues were different, especially in terms of macrophages and natural killer (NK) cells. Conclusion Chemotaxis and the complement system were identified as crucial pathways in TAA, and macrophages with interactive immune cells may regulate this pathological process.

scholarly journals Weighted gene co-expression network analysis of the salt-responsive transcriptomes reveals novel hub genes in green halophytic microalgae Dunaliella salina

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bahman Panahi ◽  
Mohammad Amin Hejazi
Keyword(s):  
Network Analysis ◽  
Salinity Stress ◽  
Empirical Bayes ◽  
Dunaliella Salina ◽  
Sulfur Metabolism ◽  
Extensive Study ◽  
Mitogen Activated Protein Kinase ◽  
Hub Genes ◽  
Chlorophyll Metabolism ◽  
Scale Free

AbstractDespite responses to salinity stress in Dunaliella salina, a unicellular halotolerant green alga, being subject to extensive study, but the underlying molecular mechanism remains unknown. Here, Empirical Bayes method was applied to identify the common differentially expressed genes (DEGs) between hypersaline and normal conditions. Then, using weighted gene co-expression network analysis (WGCNA), which takes advantage of a graph theoretical approach, highly correlated genes were clustered as a module. Subsequently, connectivity patterns of the identified modules in two conditions were surveyed to define preserved and non-preserved modules by combining the Zsummary and medianRank measures. Finally, common and specific hub genes in non-preserved modules were determined using Eigengene-based module connectivity or module membership (kME) measures and validation was performed by using leave-one-out cross-validation (LOOCV). In this study, the power of beta = 12 (scale-free R2 = 0.8) was selected as the soft-thresholding to ensure a scale-free network, which led to the identification of 15 co-expression modules. Results also indicate that green, blue, brown, and yellow modules are non-preserved in salinity stress conditions. Examples of enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in non-preserved modules are Sulfur metabolism, Oxidative phosphorylation, Porphyrin and chlorophyll metabolism, Vitamin B6 metabolism. Moreover, the systems biology approach was applied here, proposed some salinity specific hub genes, such as radical-induced cell death1 protein (RCD1), mitogen-activated protein kinase kinase kinase 13 (MAP3K13), long-chain acyl-CoA synthetase (ACSL), acetyl-CoA carboxylase, biotin carboxylase subunit (AccC), and fructose-bisphosphate aldolase (ALDO), for the development of metabolites accumulating strains in D. salina.

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