Emerging Biomarkers in Bladder Cancer Identified by Network Analysis of Transcriptomic Data

Giulietti, Matteo; Occhipinti, Giulia; Righetti, Alessandra; Bracci, Massimo; Conti, Alessandro; Ruzzo, Annamaria; Cerigioni, Elisabetta; Cacciamani, Tiziana; Principato, Giovanni; Piva, Francesco

doi:10.3389/fonc.2018.00450

Cited by 56 publications

(43 citation statements)

References 87 publications

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“…Subsequently, the WGCNA analysis was performed on RNAseq data obtained from TCGA on those 185 DEGs to identify two co-expressed modules (blue and turquoise). WGCNA is a recently developed method to construct a weighted gene coexpression network and a new analytic approach to move beyond single-gene comparisons (Giulietti et al, 2018). The WGCNA algorithm has been used to identify disease-related genes, biological pathways and therapeutic targets for diseases such as familial combined hyperlipidemia, Osteoporosis, Autistic, and Alzheimer disease (Goh et al, 2007;He et al, 2011;Tang et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Integrated Analysis to Evaluate the Prognostic Value of Signature mRNAs in Glioblastoma Multiforme

Yang

Wang

et al. 2020

Front. Genet.

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Background: Gliomas are the most common intracranial tumors and are classified as I-IV. Among them, glioblastoma multiforme (GBM) is the most common invasive glioma with a poor prognosis. New molecular biomarkers that can predict clinical outcomes in GBM patients must be identified, which will help comprehend their pathogenesis and supply personalized treatment. Our research revealed four powerful survival indicators in GBM by reanalyzing microarray data and genetic sequencing data in public databases. Moreover, it unraveled new potential therapeutic targets which could help improve the survival time and quality of life of GBM patients. Materials and Methods: To identify prognostic signatures in GBMs, we analyzed the gene profiling data of GBM and standard brain samples from the Gene Expression Omnibus, including four datasets and RNA sequencing data from The Cancer Genome Atlas (TCGA) containing 152 glioblastoma tissues. We performed the differential analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, weighted gene co-expression network analysis (WGCNA) and Cox regression analysis. Results: After differential analysis in GSE12657, GSE15824, GSE42656 and GSE50161, overlapping differentially expressed genes were identified. We identified 110 up-regulated DEGs and 75 down-regulated DEGs in the GBM samples. Significantly enriched subclasses of the GO classification of these genes included mitotic sister chromatid separation, mitotic nuclear division and so on. In KEGG pathway analysis, the most abundant terms were ECM-receptor interaction and protein digestion and absorption. WGCNA analysis was performed on these 185 DEGs in 152 glioblastoma samples obtained from TCGA, and gene co-expression networks were constructed. We then performed a multivariate Cox analysis and established a Cox proportional hazards regression model using the top 20 genes significantly correlated with survival time. We

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Section: Discussionmentioning

confidence: 99%

Integrated Analysis to Evaluate the Prognostic Value of Signature mRNAs in Glioblastoma Multiforme

Yang

Wang

et al. 2020

Front. Genet.

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“…Instead of linking thousands of genes to the disease, this technology focuses on relationship between gene modules and disease traits. Through WGCNA, hidden biological models of the disease can be discovered (Giulietti et al, 2018;Vella et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Key genes and co-expression modules involved in asthma pathogenesis

Huang

Liu

Zuo

et al. 2020

PeerJ

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Machine learning and weighted gene co-expression network analysis (WGCNA) have been widely used due to its well-known accuracy in the biological field. However, due to the nature of a gene’s multiple functions, it is challenging to locate the exact genes involved in complex diseases such as asthma. In this study, we combined machine learning and WGCNA in order to analyze the gene expression data of asthma for better understanding of associated pathogenesis. Specifically, the role of machine learning is assigned to screen out the key genes in the asthma development, while the role of WGCNA is to set up gene co-expression network. Our results indicated that hormone secretion regulation, airway remodeling, and negative immune regulation, were all regulated by critical gene modules associated with pathogenesis of asthma progression. Overall, the method employed in this study helped identify key genes in asthma and their roles in the asthma pathogenesis.

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“…WGCNA is useful in understanding various biological processes. It helpsunravel the interactions between genes in different modulesand hence can be used foridenti cation of candidate biomarkers or therapeutic targets [18,19]. In addition,WGCNA links microarray data directly to clinical traits thus revealing mechanisms of drug resistance [20].Further, WGCNAis used for identi cation of factors for predicting pathological stage and prognosis of disease [21].…”

Section: Introductionmentioning

confidence: 99%

Hub Genes of Stroke Identified by Weighted Gene Co-expression Network Analysis

Zhai

et al. 2020

Preprint

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BACKGROUD: Microarray-based gene expression profiling is widely used in biomedical research. Weighted gene co-expression network analysis (WGCNA) links microarray data directly to clinical traits and identifies rules for predicting pathological stage and prognosis of disease.WGCNA is useful in understandingmany biological processes. Stroke is a common disease worldwide, however, molecular mechanisms of its pathogenesis are largely unknown. The aim of this study was to construct gene co-expression networks for identification of key modules and hub genes associated with stroke pathogenesis.METHODS: Gene microarray expression profiles of stroke samples were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened by the limma package in R software. WGCNA was used to construct free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify key modules and hub genes. Subsequently, functional enrichment analyses were performed. Further, receiver operating characteristic (ROC) curve analysis was carried out to validate expression of hub genes and literature validation was performed as well.RESULTS: A total of 11,747 most variant genes were used for co-expression network construction. Pink and yellow modules were significantly correlated to stroke pathogenesis. Functional enrichment analysis showed that the pink module was mainly involved in regulation of neuron regeneration, and repair of DNA damage.On the other hand, yellow module was mainly enriched in ion transport system dysfunction which was correlated with neuron death. A total of eight hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) were identified and validated at transcriptional levels and through existing literature.CONCLUSION: The eight hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) identified in the study are potentialbiomarkers and therapeutic targets for effective diagnosis and treatment of stroke.

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Emerging Biomarkers in Bladder Cancer Identified by Network Analysis of Transcriptomic Data

Cited by 56 publications

References 87 publications

Integrated Analysis to Evaluate the Prognostic Value of Signature mRNAs in Glioblastoma Multiforme

Integrated Analysis to Evaluate the Prognostic Value of Signature mRNAs in Glioblastoma Multiforme

Key genes and co-expression modules involved in asthma pathogenesis

Hub Genes of Stroke Identified by Weighted Gene Co-expression Network Analysis

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