Weighted Gene Co-expression Network Analysis in Identification of Endometrial Cancer Prognosis Markers

Zhu, Xiao Lu; Ai, Zhi Hong; Wang, Juan; Xu, Yan; Teng, Yin

doi:10.7314/apjcp.2012.13.9.4607

Cited by 34 publications

(31 citation statements)

References 35 publications

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“…Hereby, my approach of identification of co-expressed gene modules was based on a modification of WGCNA method by applying the spectral clustering method, instead of the suggested hierarchical clustering, considering topological overlap matrix (TOM) as a similarity measure. Despite being applied in earlier studies Zhu et al, [5] and proved some utility, interpretation of hierarchical clustering is complex; besides expression patterns of individual gene sequences become less relevant as the clustering process progresses Li et al, [20]. Spectral clustering methods correspond to a family of unsupervised learning algorithms.…”

Section: Network Construction and Module Detection Proceduresmentioning

confidence: 99%

“…Transurethral resection (TUR) of the bladder tumor is the standard method used to determine the local stage of the neoplasm which also provides treatment for patients with non-muscle-invasive disease and staging information for those with muscle-invasive disease Biagioli et al, [4]. Prior efforts have identified urinary protein biomarkers of bladder cancer Wang et al, [5,6]. However, markers that can predict tumor stage have not been identified.…”

Section: Introductionmentioning

confidence: 99%

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Integration of Gene Coexpression Network, GO Enrichment Analysis for Identification Gene Expression Signature of Invasive Bladder Carcinoma

Gaballah¹

2016

Transcriptomics

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Bladder carcinoma is the most common malignancy of the urinary tract. Identification of genetic biomarkers for tumor invasiveness will help in earlier diagnosis and proper treatment. The present study aimed to integrate coexpression network and GO enrichment analysis for identification of prognostic markers and key genes that contribute to bladder cancer initiation and progression using a DNA microarray dataset (GSE 37317), invasive and noninvasive bladder cancer genes were compared by applying weighted gene co-expression network, gene ontology and pathway analysis. This study identified candidate genes (PURA, SRPK2, TRAK1, BRD2, and UPF3) that might have significant role in progression and invasiveness of bladder carcinoma. These markers might aid in early diagnosis of muscle invasiveness of bladder cancer. In conclusion; these finding may provide better understanding of the molecular mechanism of bladder cancer progression and invasiveness.

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Section: Network Construction and Module Detection Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integration of Gene Coexpression Network, GO Enrichment Analysis for Identification Gene Expression Signature of Invasive Bladder Carcinoma

Gaballah¹

2016

Transcriptomics

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“…2,6,45 The WGCNA approach has been widely used to identify potential oncogenic coexpression modules and predict the molecular targets and prognosis markers. 14,16,38,55,58 For instance, Horvath et al have successfully identified the gene ASPM as a molecular target for glioblastoma, another primary brain tumor. 14 Ivliev et al also identified transcriptional modules related to proastrocytic differentiation and sprout signaling in gliomas.…”

Section: 40mentioning

confidence: 99%

Genomic and transcriptome analysis revealing an oncogenic functional module in meningiomas

Chang

Shi

Russin

et al. 2013

FOC

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Object Meningiomas are among the most common primary adult brain tumors. Although typically benign, roughly 2%–5% display malignant pathological features. The key molecular pathways involved in malignant transformation remain to be determined. Methods Illumina expression microarrays were used to assess gene expression levels, and Illumina single-nucleotide polymorphism arrays were used to identify copy number variants in benign, atypical, and malignant meningiomas (19 tumors, including 4 malignant ones). The authors also reanalyzed 2 expression data sets generated on Affymetrix microarrays (n = 68, including 6 malignant ones; n = 56, including 3 malignant ones). A weighted gene coexpression network approach was used to identify coexpression modules associated with malignancy. Results At the genomic level, malignant meningiomas had more chromosomal losses than atypical and benign meningiomas, with average length of 528, 203, and 34 megabases, respectively. Monosomic loss of chromosome 22 was confirmed to be one of the primary chromosomal level abnormalities in all subtypes of meningiomas. At the transcriptome level, the authors identified 23 coexpression modules from the weighted gene coexpression network. Gene functional enrichment analysis highlighted a module with 356 genes that was highly related to tumorigenesis. Four intramodular hubs within the module (GAB2, KLF2, ID1, and CTF1) were oncogenic in other cancers such as leukemia. A putative meningioma tumor suppressor MN1 was also identified in this module with differential expression between malignant and benign meningiomas. Conclusions The authors' genomic and transcriptome analysis of meningiomas provides novel insights into the molecular pathways involved in malignant transformation of meningiomas, with implications for molecular heterogeneity of the disease.

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“…In the recent years, the integration analyses of gene expression data with PPI network have received considerable attention to mine the biological meanings (Lee et al, 2009;. Zhu et al applied a weighted gene co-expression network to identification prognosis markers in endometrial cancer for potential therapeutic targets (Zhu et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Protein-protein Interaction Network Analyses for Elucidating the Roles of LOXL2-delta72 in Esophageal Squamous Cell Carcinoma

Zou²,

Lv³

et al. 2014

Asian Pacific Journal of Cancer Prevention

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Lysyl oxidase-like 2 (LOXL2), a member of the lysyl oxidase (LOX) family, is a copper-dependent enzyme that catalyzes oxidative deamination of lysine residues on protein substrates. LOXL2 was found to be overexpressed in esophageal squamous cell carcinoma (ESCC) in our previous research. We later identified a LOXL2 splicing variant LOXL2-delta72 and we overexpressed LOXL2-delta72 and its wild type counterpart in ESCC cells following microarray analyses. First, the differentially expressed genes (DEGs) of LOXL2 and LOXL2-delta72 compared to empty plasmid were applied to generate protein-protein interaction (PPI) sub-networks. Comparison of these two sub-networks showed hundreds of different proteins. To reveal the potential specific roles of LOXL2-delta72 compared to its wild type, the DEGs of LOXL2-delta72 vs LOXL2 were also applied to construct a PPI sub-network which was annotated by Gene Ontology. The functional annotation map indicated the third PPI sub-network involved hundreds of GO terms, such as "cell cycle arrest", "G1/S transition of mitotic cell cycle", "interphase", "cell-matrix adhesion" and "cell-substrate adhesion", as well as significant "immunity" related terms, such as "innate immune response", "regulation of defense response" and "Toll signaling pathway". These results provide important clues for experimental identification of the specific biological roles and molecular mechanisms of LOXL2-delta72. This study also provided a work flow to test the different roles of a splicing variant with high-throughput data.

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Weighted Gene Co-expression Network Analysis in Identification of Endometrial Cancer Prognosis Markers

Cited by 34 publications

References 35 publications

Integration of Gene Coexpression Network, GO Enrichment Analysis for Identification Gene Expression Signature of Invasive Bladder Carcinoma

Integration of Gene Coexpression Network, GO Enrichment Analysis for Identification Gene Expression Signature of Invasive Bladder Carcinoma

Genomic and transcriptome analysis revealing an oncogenic functional module in meningiomas

Protein-protein Interaction Network Analyses for Elucidating the Roles of LOXL2-delta72 in Esophageal Squamous Cell Carcinoma

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