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Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis.

ABSTRACT: AIM:To identify and understand the relationship between co-expression pattern and clinic traits in uveal melanoma, weighted gene co-expression network analysis (WGCNA) is applied to investigate the gene expression levels and patient clinic features. Uveal melanoma is the most common primary eye tumor in adults. Although many studies have identified some important genes and pathways that were relevant to progress of uveal melanoma, the relationship between co-expression and clinic traits in systems level of uveal melanoma is unclear yet. We employ WGCNA to investigate the relationship underlying molecular and phenotype in this study. METHODS:Gene expression profile of uveal melanoma and patient clinic traits were collected from the Gene Expression Omnibus (GEO) database. The gene co-expression is calculated by WGCNA that is the R package software. The package is used to analyze the correlation between pairs of expression levels of genes. The function of the genes were annotated by gene ontology (GO). RESULTS:In this study, we identified four co-expression modules significantly correlated with clinic traits. Module blue positively correlated with radiotherapy treatment. Module purple positively correlates with tumor location (sclera) and negatively correlates with patient age. Module red positively correlates with sclera and negatively correlates with thickness of tumor. Module black positively correlates with the largest tumor diameter (LTD). Additionally, we identified the hug gene (top connectivity with other genes) in each module. The hub gene RPS15A, PTGDS, CD53 and MSI2 might play a vital role in progress of uveal melanoma. CONCLUSION:From WGCNA analysis and hub gene calculation, we identified RPS15A, PTGDS, CD53 and MSI2 might be target or diagnosis for uveal melanoma.

SUBMITTER: Shi K

PROVIDER: S-EPMC4413597 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis.

Shi Kai K Bing Zhi-Tong ZT Cao Gui-Qun GQ Guo Ling L Cao Ya-Na YN Jiang Hai-Ou HO Zhang Mei-Xia MX

International journal of ophthalmology 20150418 2

<h4>Aim</h4>To identify and understand the relationship between co-expression pattern and clinic traits in uveal melanoma, weighted gene co-expression network analysis (WGCNA) is applied to investigate the gene expression levels and patient clinic features. Uveal melanoma is the most common primary eye tumor in adults. Although many studies have identified some important genes and pathways that were relevant to progress of uveal melanoma, the relationship between co-expression and clinic traits ...[more]

PMID: 25938039

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Project description:BackgroundMelanoma is one of the most malignant and aggressive skin tumors, and its incidence is increasing worldwide. However, few studies have investigated the process of tumorigenesis from normal skin to melanoma.MethodsSeveral bioinformatics analyses, including GEO databases, Oncomine database, TCGA database, STRING, MCODE and cytoHubba plug-ins, GEPIA, TIMER and TRRUST and DGIdb, were performed to disclose the hub genes and immunology implicated in primary melanoma tumorigenesis. Finally, immunohistochemistry (IHC) and quantitative real-time PCR (qPCR) were used to validate the results of bioinformatics analysis in vitro.ResultsA total of 295 overlapping DEGs (ODEGs) (157 upregulated and 138 downregulated) and 9 hub genes were identified between primary melanoma and normal skin tissues. Functional analysis of these 9 hub genes indicated that the genes were primarily enriched in cell chemotaxis, the chemokine-mediated signaling pathway, the extracellular region, the extracellular space, chemokine activity and CXCR3 chemokine receptor binding. KEGG pathway enrichment showed that these genes were primarily involved in the chemokine signaling pathway, cytokine-cytokine receptor interaction, the toll-like receptor signaling pathway, the cytosolic DNA-sensing pathway and the TNF signaling pathway. Upregulated CCL5, CCL4, CXCL9 and CXCL10 demonstrated good overall survival (OS), and most of them have a higher expression in stage 0 and 1 of melanoma. Moreover, immune infiltration analysis showed that the above hub genes showed a strong positive correlation between their expression and infiltration of the six immune cell subsets. Transcription factor regulation network suggested that RELA and NFKB1 are the transcription factors of CCL4, CCL5, CXCL10 and CXCL2, while IRF7, IRF3 and IRF1 are the transcription factors of CCL5 and CXCL10. Drug-gene interaction analysis identified 46 drug-gene interactions. In vitro data demonstrated that the level of CCL4, CCL5, CXCL9 and CXCL10 is higher in melanoma than that in normal skin tissues, either at tissue or cell lines level.ConclusionIn summary, we identified 4 key chemokine members related to tumorigenesis and progression in primary melanoma, and these results may help to elucidate melanoma tumorigenesis and facilitate its treatment.

Project description:Background: Uveal melanoma (UM) is the most common primary intraocular cancer in adults. Genomic studies have provided insights into molecular subgroups and oncogenic drivers of UM that may lead to novel therapeutic strategies. Methods: Dataset TCGA-UVM, download from TCGA portal, were taken as the training cohort, and dataset GSE22138, obtained from GEO database, was set as the validation cohort. In training cohort, Kaplan-Meier analysis and univariate Cox regression model were applied to preliminary screen prognostic genes. Besides, the Cox regression model with LASSO was implemented to build a multi-gene signature, which was then validated in the validation cohorts through Kaplan-Meier, Cox, and ROC analyses. In addition, the correlation between copy number aberrations and risk score was evaluated by Spearman test. GSEA and immune infiltrating analyses were conducted for understanding function annotation and the role of the signature in the tumor microenvironment. Results: A ten-gene signature was built, and it was examined by Kaplan-Meier analysis revealing that significantly overall survival, progression-free survival, and metastasis-free survival difference was seen. The ten-gene signature was further proven to be an independent risk factor compared to other clinic-pathological parameters via the Cox regression analysis. Moreover, the receiver operating characteristic curve (ROC) analysis results demonstrated a better predictive power of the UM prognosis that our signature owned. The ten-gene signature was significantly correlated with copy numbers of chromosome 3, 8q, 6q, and 6p. Furthermore, GSEA and immune infiltrating analyses showed that the signature had close interactions with immune-related pathways and the tumor environment. Conclusions: Identifying the ten-gene signature (SIRT3, HMCES, SLC44A3, TCTN1, STPG1, POMGNT2, RNF208, ANXA2P2, ULBP1, and CA12) could accurately identify patients' prognosis and had close interactions with the immunodominant tumor environment, which may provide UM patients with personalized prognosis prediction and new treatment insights.

Dataset Information

Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis.

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Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis.

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