ArticleOncology letters2019
Identification of prognostic biomarkers for malignant melanoma using microarray datasets.
Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Article
- Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View.International journal of molecular sciences · 2022Review
- MACF1 mutations predict poor prognosis: a novel potential therapeutic target for breast cancer.American journal of translational research · 2022Article
- Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools.Oncology reports · 2020Observational
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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,
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