Co-expression modules construction by WGCNA and identify potential prognostic markers of uveal melanoma

Wan, Qi; Tang, Jing; Han, Yoo Min; Wang, Dan

doi:10.1016/j.exer.2017.10.007

Cited by 207 publications

(171 citation statements)

References 16 publications

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“…Ze Zhang et al used WGCNA and found out 12 key genes in head and neck squamous cell carcinoma, which were TIMP2, MIR198, LAMA4, FAM198B, MIR4649, COL5A1, COL1A2, OLFML2B, MMP2, FBN1, ADAM12, and PDGFRB [18]. Another study showed that SLC17A7, NTRK2, ABTB1, and ADPRHL1 were key genes in uveal melanoma through using WGCNA and TCGA data [19]. In the present study, we used WGCNA to analysis 15,517 genes with differential expression in prostate cancer and paracancerous tissues from 490 samples, and then obtained a pink gene set containing 392 genes with a significant correlation with Gleason stage and pathological T classification of tumors.…”

Section: Discussionmentioning

confidence: 99%

Weighted gene co-expression network analysis identifies CCNA2 as a treatment target of prostate cancer through inhibiting cell cycle

Yang

Wang

et al. 2020

J. Cancer

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Prostate cancer is a malignant tumor disease that seriously harms the lives of middle-aged and elderly men. Weighted gene co-expression analysis can be used to construct gene co-expression networks to explore gene sets and genes that are significantly correlated with clinical features. In this study, the transcriptome data of prostate cancer on TCGA was analyzed by weighted gene co-expression network, and the gene with a significant correlation with disease Gleason stage and tumor T stage was identified: CCNA2. CCNA2 was significantly associated with biochemical recurrence, disease-free survival and overall survival rate of prostate cancer. The ability of cancer cell proliferation, invasion and metastasis was decreased after down-regulated expression of CCNA2 in prostate cancer cell lines. Flow cytometry revealed that tumor cells were arrested in the S phase after down-regulated the expression of CCNA2. Taken together, we used WGCNA and obtain a gene CCNA2 which is significantly associated with the prognosis of prostate cancer, which may be an indicator of the prognosis of prostate cancer and a new therapeutic target.

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Section: Discussionmentioning

confidence: 99%

Weighted gene co-expression network analysis identifies CCNA2 as a treatment target of prostate cancer through inhibiting cell cycle

Yang

Wang

et al. 2020

J. Cancer

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“…Our results provide valuable information for basic and clinical research of stroke. Although there are similar limitations with most other data mining approaches [69], we adoptedthe indicated methods to reduce possible bias. In order to increase the credibility of WGCNA results, we used matched stroke and normal samples for analysis, andexternal data from GEO database to validate the results.…”

Section: Discussionmentioning

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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“…Fourth, hub genes in the selected module were sorted by ranking their intra-modular connectivity and correlation with the module eigengenes. Hub genes are the backbones of scale-free networks [32] and have been found to be biologically meaningful in many diseases [33][34][35].…”

Section: Gene Coexpression Network Analysismentioning

confidence: 99%

Transcriptome differences in adipose stromal cells derived from pre- and postmenopausal women

et al. 2020

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Background: As the population ages, an increasing number of postmenopausal women are donors of adipose stromal cells (ASCs) and may benefit from autologous ASC-related treatments. However, the effect of menopausal status on ASCs has not been investigated. Methods: RNA sequencing data were downloaded, and differentially expressed genes (DEGs) were identified. Hierarchical clustering, Gene Ontology, and pathway analyses were applied to the DEGs. Two gene coexpression network analysis approaches were applied to the DEGs to provide a holistic view and preserve gene interactions. Hub genes of the gene coexpression network were identified, and their expression profiles were examined with clinical samples. ASCs from pre-and postmenopausal women were co-cultured with monocytes and T cells to determine their immunoregulatory role. Results: In total, 2299 DEGs were identified and presented distinct expression profiles between pre-and postmenopausal women. Gene Ontology and pathway analyses revealed some fertility-, sex hormone-, immune-, aging-, and angiogenesis-related terms and pathways. Gene coexpression networks were constructed, and the top hub genes, including TIE1, ANGPT2, RNASE1, PLVAP, CA2, and MPZL2, were consistent between the two approaches. Expression profiles of hub genes from the RNA sequencing data and clinical samples were consistent. ASCs from postmenopausal women elicit M1 polarization, while their counterparts facilitate CD3/4+ T cell proliferation. Conclusions: The present study reveals the transcriptome differences in ASCs derived from pre-and postmenopausal women and provides holistic views by preserving gene interactions via gene coexpression network analysis. The top hub genes identified by this study could serve as potential targets to enhance the therapeutic potential of ASCs.

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Co-expression modules construction by WGCNA and identify potential prognostic markers of uveal melanoma

Cited by 207 publications

References 16 publications

Weighted gene co-expression network analysis identifies CCNA2 as a treatment target of prostate cancer through inhibiting cell cycle

Weighted gene co-expression network analysis identifies CCNA2 as a treatment target of prostate cancer through inhibiting cell cycle

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

Transcriptome differences in adipose stromal cells derived from pre- and postmenopausal women

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