ArticleComputational and mathematical methods in medicine2021
Identification of Keratinocyte Differentiation-Involved Genes for Metastatic Melanoma by Gene Expression Profiles.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- In silico analysis of gene expression signatures and drug repurposing associated with metastatic progression in melanoma (skin cancer).Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Protein content of extracellular vesicles from patients with advanced melanoma changes upon progression to anti-PD1 therapy.Scientific reports · 2026Article
- Integrated Meta-Analysis Identifies Keratin Family Genes and Associated Genes as Key Biomarkers and Therapeutic Targets in Metastatic Cutaneous Melanoma.Diagnostics (Basel, Switzerland) · 2025Article
- In-silico identification and exploration of small molecule coumarin-1,2,3-triazole hybrids as potential EGFR inhibitors for targeting lung cancer.Molecular diversity · 2024Article
- Review
- A pan-cancer analysis of the oncogenic and immunological roles of transglutaminase 1 (TGM1) in human cancer.Journal of cancer research and clinical oncology · 2024Article
- Envoplakin Inhibits Macrophage Polarization by Altering the Inflammatory Tumor Microenvironment of Melanoma Through the RAS / ERK Signaling Pathway.Journal of inflammation research · 2024Article
- Article
- An effective convolutional neural network for classification of benign and malignant breast and thyroid tumors from ultrasound images.Physical and engineering sciences in medicine · 2023Article
- Construction and evaluation of a prognostic risk model of tumor metastasis-related genes in patients with non-small cell lung cancer.BMC medical genomics · 2022Article
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Abstract
backgroundMelanoma is the deadliest type of skin cancer. Until now, its pathological mechanisms, particularly the mechanism of metastasis, remain largely unknown. Our study on the identification of genes in association with metastasis for melanoma provides a novel understanding of melanoma.
methodsFrom the Gene Expression Omnibus (GEO) database, the gene expression microarray datasets GSE46517, GSE7553, and GSE8401 were downloaded. We made use of R aiming at analyzing the differentially expressed genes (DEGs) between metastatic and nonmetastatic melanoma. R was also used in differentially expressed miRNA (DEM) data mining from GSE18509, GSE19387, GSE24996, GSE34460, GSE35579, GSE36236, and GSE54492 datasets referring to Li's study. Based on the DEG and DEM data, we performed functional enrichment analysis through the application of the DAVID database. Furthermore, we constructed the protein-protein interaction (PPI) network and established functional modules by making use of the STRING database. Through making use of Cytoscape, the PPI results were visualized. We predicted the targets of the DEMs through applying TargetScan, miRanda, and PITA databases and identified the overlapping genes between DEGs and predicted targets, followed by the construction of DEM-DEG pair network. The expressions of these keratinocyte differentiation-involved genes in Module 1 were identified based on the data from TCGA.
results239 DEGs were screened out in all 3 datasets, which were inclusive of 21 positively regulated genes and 218 negatively regulated genes. Based on these 239 DEGs, we finished constructing the PPI network which was formed from 225 nodes and 846 edges. We finished establishing 3 functional modules. And we analyzed 92 overlapping genes and 26 miRNA, including 11 upregulated genes targeted by 11 negatively regulated DEMs and 81 downregulated genes targeted by 15 positively regulated DEMs. As proof of the differential expression of metastasis-associated genes, eleven keratinocyte differentiation-involved genes, including LOR, EVPL, SPRR1A, FLG, SPRR1B, SPRR2B, TGM1, DSP, CSTA, CDSN, and IVL in Module 1, were obviously downregulated in metastatic melanoma tissue in comparison with primary melanoma tissue based on the data from TCGA.
conclusion239 melanoma metastasis-associated genes and 26 differentially expressed miRNA were identified in our study. The keratinocyte differentiation-involved genes may take part in melanoma metastasis, providing a latent molecular mechanism for this disease.
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