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Weighted Gene Co-Expression Network Analysis Identifies Hub Genes Associated with Occurrence and Prognosis of Oral Squamous Cell Carcinoma.

ABSTRACT: BACKGROUND The aim of this study was to identify biomarkers closely related to the pathogenesis and prognosis of oral squamous cell carcinoma (OSCC) by using weighted gene co-expression network analysis (WGCNA) based on integrative transcriptome datasets. MATERIAL AND METHODS Gene expression profiles of OSCC were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained and we then performed with Gene ontology (GO) and pathway enrichment analysis as well as protein-protein interactions (PPI) network analysis. WGCNA was used to construct the co-expression network. Multipart results were intersected to acquire the candidate genes, and survival analysis was used to identify the hub genes. RESULTS A total of 568 DEGs, including 272 upregulated genes and 296 downregulated genes, were identified. GO and pathway analyses revealed that these DEGs were mainly enriched in extracellular matrix (ECM), ECM organization, structural constituent of muscle, and ECM-receptor interaction. The PPI network of DEGs was established, comprising 428 nodes and 1944 edges. In the co-expression network, pink module was the key module, in which 34 genes with high connectivity were identified. After the intersection of multipart results, 24 common genes were chosen as the candidate genes, among which 7 hub genes (PLAU, SERPINE1, LAMC2, ITGA5, TGFBI, FSCN1, and HLF) were identified using survival analysis. CONCLUSIONS Seven potential biomarkers were identified as being closely related with the initiation and prognosis of OSCC and might serve as potential targets for early diagnosis and personalized therapy of OSCC.

SUBMITTER: Ge Y

PROVIDER: S-EPMC6778410 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Weighted Gene Co-Expression Network Analysis Identifies Hub Genes Associated with Occurrence and Prognosis of Oral Squamous Cell Carcinoma.

Ge You Y Li Wei W Ni Qian Q He Yan Y Chu Jinjin J Wei Pingmin P

Medical science monitor : international medical journal of experimental and clinical research 20190928

BACKGROUND The aim of this study was to identify biomarkers closely related to the pathogenesis and prognosis of oral squamous cell carcinoma (OSCC) by using weighted gene co-expression network analysis (WGCNA) based on integrative transcriptome datasets. MATERIAL AND METHODS Gene expression profiles of OSCC were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained and we then performed with Gene ontology (GO) and pathway enrichment anal ...[more]

PMID: 31562292

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Project description:Introduction: Acute myeloid leukemia (AML) is the most common type of leukemia in adults. However, there is a gap in understanding the molecular basis of the disease, partly because key genes associated with AML have not been extensively explored. In the current study, we aimed to identify genes that have strong association with AML based on a cross-species integrative approach. Methods: We used Weighted Gene Co-Expression Network Analysis (WGCNA) to identify co-expressed gene modules significantly correlated with human AML, and further selected the genes exhibiting a significant difference in expression between AML and healthy mouse. Protein-protein interactions, transcription factors, gene function, genetic regulation, and coding sequence variants were integrated to identify key hub genes in AML. Results: The cross-species approach identified a total of 412 genes associated with both human and mouse AML. Enrichment analysis confirmed an association of these genes with hematopoietic and immune-related functions, phenotypes, processes, and pathways. Further, the integrated analysis approach identified a set of important module genes including Nfe2, Trim27, Mef2c, Ets1, Tal1, Foxo1, and Gata1 in AML. Six of these genes (except ETS1) showed significant differential expression between human AML and healthy samples in an independent microarray dataset. All of these genes are known to be involved in immune/hematopoietic functions, and in transcriptional regulation. In addition, Nfe2, Trim27, Mef2c, and Ets1 harbor coding sequence variants, whereas Nfe2 and Trim27 are cis-regulated, making them attractive candidates for validation. Furthermore, subtype-specific analysis of the hub genes in human AML indicated high expression of NFE2 across all the subtypes (M0 through M7) and enriched expression of ETS1, LEF1, GATA1, and TAL1 in M6 and M7 subtypes. A significant correlation between methylation status and expression level was observed for most of these genes in AML patients. Conclusion: Findings from the current study highlight the importance of our cross-species approach in the identification of multiple key candidate genes in AML, which can be further studied to explore their detailed role in leukemia/AML.

Dataset Information

Weighted Gene Co-Expression Network Analysis Identifies Hub Genes Associated with Occurrence and Prognosis of Oral Squamous Cell Carcinoma.

Publications

Weighted Gene Co-Expression Network Analysis Identifies Hub Genes Associated with Occurrence and Prognosis of Oral Squamous Cell Carcinoma.

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