Dataset Information

Network-based co-expression analysis for exploring the potential diagnostic biomarkers of metastatic melanoma.

ABSTRACT: Metastatic melanoma is an aggressive skin cancer and is one of the global malignancies with high mortality and morbidity. It is essential to identify and verify diagnostic biomarkers of early metastatic melanoma. Previous studies have systematically assessed protein biomarkers and mRNA-based expression characteristics. However, molecular markers for the early diagnosis of metastatic melanoma have not been identified. To explore potential regulatory targets, we have analyzed the gene microarray expression profiles of malignant melanoma samples by co-expression analysis based on the network approach. The differentially expressed genes (DEGs) were screened by the EdgeR package of R software. A weighted gene co-expression network analysis (WGCNA) was used for the identification of DEGs in the special gene modules and hub genes. Subsequently, a protein-protein interaction network was constructed to extract hub genes associated with gene modules. Finally, twenty-four important hub genes (RASGRP2, IKZF1, CXCR5, LTB, BLK, LINGO3, CCR6, P2RY10, RHOH, JUP, KRT14, PLA2G3, SPRR1A, KRT78, SFN, CLDN4, IL1RN, PKP3, CBLC, KRT16, TMEM79, KLK8, LYPD3 and LYPD5) were treated as valuable factors involved in the immune response and tumor cell development in tumorigenesis. In addition, a transcriptional regulatory network was constructed for these specific modules or hub genes, and a few core transcriptional regulators were found to be mostly associated with our hub genes, including GATA1, STAT1, SP1, and PSG1. In summary, our findings enhance our understanding of the biological process of malignant melanoma metastasis, enabling us to identify specific genes to use for diagnostic and prognostic markers and possibly for targeted therapy.

SUBMITTER: Wang LX

PROVIDER: S-EPMC5788335 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Network-based co-expression analysis for exploring the potential diagnostic biomarkers of metastatic melanoma.

Wang Li-Xin LX Li Yang Y Chen Guan-Zhi GZ

PloS one 20180129 1

Metastatic melanoma is an aggressive skin cancer and is one of the global malignancies with high mortality and morbidity. It is essential to identify and verify diagnostic biomarkers of early metastatic melanoma. Previous studies have systematically assessed protein biomarkers and mRNA-based expression characteristics. However, molecular markers for the early diagnosis of metastatic melanoma have not been identified. To explore potential regulatory targets, we have analyzed the gene microarray e ...[more]

PMID: 29377892

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Project description:Background Glioblastoma (GBM) is the most common malignant tumor in the central system with a poor prognosis. Due to the complexity of its molecular mechanism, the recurrence rate and mortality rate of GBM patients are still high. Therefore, there is an urgent need to screen GBM biomarkers to prove the therapeutic effect and improve the prognosis. Results We extracted data from GBM patients from the Gene Expression Integration Database (GEO), analyzed differentially expressed genes in GEO and identified key modules by weighted gene co-expression network analysis (WGCNA). GSE145128 data was obtained from the GEO database, and the darkturquoise module was determined to be the most relevant to the GBM prognosis by WGCNA (r = − 0.62, p = 0.01). We performed enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to reveal the interaction activity in the selected modules. Then Kaplan-Meier survival curve analysis was used to extract genes closely related to GBM prognosis. We used Kaplan-Meier survival curves to analyze the 139 genes in the darkturquoise module, identified four genes (DARS/GDI2/P4HA2/TRUB1) associated with prognostic GBM. Low expression of DARS/GDI2/TRUB1 and high expression of P4HA2 had a poor prognosis. Finally, we used tumor genome map (TCGA) data, verified the characteristics of hub genes through Co-expression analysis, Drug sensitivity analysis, TIMER database analysis and GSVA analysis. We downloaded the data of GBM from the TCGA database, the results of co-expression analysis showed that DARS/GDI2/P4HA2/TRUB1 could regulate the development of GBM by affecting genes such as CDC73/CDC123/B4GALT1/CUL2. Drug sensitivity analysis showed that genes are involved in many classic Cancer-related pathways including TSC/mTOR, RAS/MAPK.TIMER database analysis showed DARS expression is positively correlated with tumor purity (cor = 0.125, p = 1.07e−02)), P4HA2 expression is negatively correlated with tumor purity (cor =−0.279, p = 6.06e−09). Finally, GSVA analysis found that DARS/GDI2/P4HA2/TRUB1 gene sets are closely related to the occurrence of cancer. Conclusion We used two public databases to identify four valuable biomarkers for GBM prognosis, namely DARS/GDI2/P4HA2/TRUB1, which have potential clinical application value and can be used as prognostic markers for GBM.

Project description:Acute myocardial infarction (AMI) is a common cause of death in numerous countries. Understanding the molecular mechanisms of the disease and analyzing potential biomarkers of AMI is crucial. However, specific diagnostic biomarkers have thus far not been fully established and candidate regulatory targets for AMI remain to be determined. In the present study, the AMI gene chip dataset GSE48060 comprising blood samples from control subjects with normal cardiac function (n=21) and patients with AMI (n=26) was downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs) between the AMI and control groups were identified with the online tool GEO2R. The co-expression network of DEGs was analyzed by calculating the Pearson correlation coefficient of all gene pairs, mutual rank screening and cutoff threshold screening. Subsequently, the Gene Ontology (GO) database was used to analyze the genes' functions and pathway enrichment of genes in the most important modules was performed. Kyoto Encyclopedia of Genes and Genomes (KEGG) Disease and BioCyc were used to analyze the hub genes in the module to determine important sub-pathways. In addition, the expression of hub genes was confirmed by reverse transcription-quantitative PCR in AMI and control specimens. In the present study, 52 DEGs, including 26 upregulated and 26 downregulated genes, were identified. As key hub genes, three upregulated genes (AKR1C3, RPS24 and P2RY12) and three downregulated genes (ACSL1, B3GNT5 and MGAM) were identified from the co-expression network. Furthermore, GO enrichment analysis of all AMI co-expression network genes revealed functional enrichment mainly in 'RAGE receptor binding' and 'negative regulation of T cell cytokine production'. In addition, KEGG Disease and BioCyc analysis indicated functional enrichment of the genes RPS24 and P2RY12 in 'cardiovascular diseases', of AKR1C3 in 'cardenolide biosynthesis', of MGAM in 'glycogenolysis', of B3GNT5 in 'glycosphingolipid biosynthesis' and of ACSL1 in 'icosapentaenoate biosynthesis II'. In conclusion, the hub genes AKR1C3, RPS24, P2RY12, ACSL1, B3GNT5 and MGAM are potential markers of AMI, and have potential application value in the diagnosis of AMI.

Project description:This study aims to explore the predictive noninvasive biomarker for obstructive coronary artery disease (CAD). By using the data set GSE90074, weighted gene co-expression network analysis (WGCNA), and protein-protein interactive network, construction of differentially expressed genes in peripheral blood mononuclear cells was conducted to identify the most significant gene clusters associated with obstructive CAD. Univariate and multivariate stepwise logistic regression analyses and receiver operating characteristic analysis were used to predicate the diagnostic accuracy of biomarker candidates in the detection of obstructive CAD. Furthermore, functional prediction of candidate gene biomarkers was further confirmed in ST-segment elevation myocardial infarction (STEMI) patients or stable CAD patients by using the datasets of GSE62646 and GSE59867. We found that the blue module discriminated by WGCNA contained 13 hub-genes that could be independent risk factors for obstructive CAD (P?<?.05). Among these 13 hub-genes, a four-gene signature including neutrophil cytosol factor 2 (NCF2, P?=?.025), myosin-If (MYO1F, P?=?.001), sphingosine-1-phosphate receptor 4 (S1PR4, P?=?.015), and ficolin-1 (FCN1, P?=?.012) alone or combined with two risk factors (male sex and hyperlipidemia) may represent potential diagnostic biomarkers in obstructive CAD. Furthermore, the messenger RNA levels of NCF2, MYO1F, S1PR4, and FCN1 were higher in STEMI patients than that in stable CAD patients, although S1PR4 showed no statistical difference (P?>?.05). This four-gene signature could also act as a prognostic biomarker to discriminate STEMI patients from stable CAD patients. These findings suggest a four-gene signature (NCF2, MYO1F, S1PR4, and FCN1) alone or combined with two risk factors (male sex and hyperlipidemia) as a promising prognostic biomarker in the diagnosis of STEMI. Well-designed cohort studies should be implemented to warrant the diagnostic value of these genes in clinical purpose.

Dataset Information

Network-based co-expression analysis for exploring the potential diagnostic biomarkers of metastatic melanoma.

Publications

Network-based co-expression analysis for exploring the potential diagnostic biomarkers of metastatic melanoma.

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