Dataset Information

Identification of prognostic biomarkers for malignant melanoma using microarray datasets.

ABSTRACT: Malignant melanoma is one of the most common types of cancer worldwide. Efforts have been made to elucidate the pathology of malignant melanoma. However, its molecular mechanisms remain unclear. Therefore, the microarray datasets GSE3189, GSE4570 and GSE4587 from the Gene Expression Omnibus database were used for the elucidation of candidate genes involved in the initiation and progression of melanoma. Assessment of the microarray datasets led to the identification of differentially expressed genes (DEGs), which were subsequently used for function enrichment analysis. These data were utilized in the construction of the protein-protein interaction network and module analysis was conducted using STRING and Cytoscape software. The results of these analyses led to the identification of a total of 182 DEGs, including 52 downregulated and 130 upregulated genes. The functions and pathways found to be enriched in the DEGs were GTPase activity, transcription from RNA polymerase II promoter, apoptotic processes, cell adhesion, membrane related pathways, calcium signaling cascade and the PI3K-Akt signaling pathway. The identified genes were demonstrated to belong to a set of 10 hub genes biologically involved in proliferation, apoptosis, cytokinesis, adhesion and migration. Survival analysis and Oncomine database analysis revealed that the calmodulin gene family, BAX and VEGFA genes, may be associated with the initiation, invasion or recurrence of melanoma. In conclusion, the DEGs and hub genes identified in the present study may be used to understand the molecular pathways involved in the initiation and progression of malignant melanoma. Furthermore, the present study may aid in the identification of possible targets for the diagnosis and treatment of melanoma.

SUBMITTER: Lin G

PROVIDER: S-EPMC6788168 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Identification of prognostic biomarkers for malignant melanoma using microarray datasets.

Lin Guanyu G Yin Guoqian G Yan Yuyong Y Lin Bojie B

Oncology letters 20190924 5

Malignant melanoma is one of the most common types of cancer worldwide. Efforts have been made to elucidate the pathology of malignant melanoma. However, its molecular mechanisms remain unclear. Therefore, the microarray datasets GSE3189, GSE4570 and GSE4587 from the Gene Expression Omnibus database were used for the elucidation of candidate genes involved in the initiation and progression of melanoma. Assessment of the microarray datasets led to the identification of differentially expressed ge ...[more]

PMID: 31620197

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Project description:Microarray experiments are capable of determining the relative expression of tens of thousands of genes simultaneously, thus resulting in very large databases. The analysis of these databases and the extraction of biologically relevant knowledge from them are challenging tasks. The identification of potential cancer biomarker genes is one of the most important aims for microarray analysis and, as such, has been widely targeted in the literature. However, identifying a set of these genes consistently across different experiments, researches, microarray platforms, or cancer types is still an elusive endeavor. Besides the inherent difficulty of the large and nonconstant variability in these experiments and the incommensurability between different microarray technologies, there is the issue of the users having to adjust a series of parameters that significantly affect the outcome of the analyses and that do not have a biological or medical meaning. In this study, the identification of potential cancer biomarkers from microarray data is casted as a multiple criteria optimization (MCO) problem. The efficient solutions to this problem, found here through data envelopment analysis (DEA), are associated to genes that are proposed as potential cancer biomarkers. The method does not require any parameter adjustment by the user, and thus fosters repeatability. The approach also allows the analysis of different microarray experiments, microarray platforms, and cancer types simultaneously. The results include the analysis of three publicly available microarray databases related to cervix cancer. This study points to the feasibility of modeling the selection of potential cancer biomarkers from microarray data as an MCO problem and solve it using DEA. Using MCO entails a new optic to the identification of potential cancer biomarkers as it does not require the definition of a threshold value to establish significance for a particular gene and the selection of a normalization procedure to compare different experiments is no longer necessary.

Dataset Information

Identification of prognostic biomarkers for malignant melanoma using microarray datasets.

Publications

Identification of prognostic biomarkers for malignant melanoma using microarray datasets.

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