ArticleFrontiers in oncology2020
Bioinformatic Analysis Identifies Potential Key Genes in the Pathogenesis of Melanoma.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 16 citations in OpenAlex.
- Integrated Metabolomics, Network Pharmacology, and Molecular Dynamics Simulations Reveal the Potential Anti-Melanoma Mechanisms ofCurrent issues in molecular biology · 2026Article
- m6A RNA methylation regulator-associated genes drive metastasis and immune cell infiltration in skin cutaneous melanoma.Oncology letters · 2025Article
- A qualitative prognostic biomarker for melanoma based on the relative methylation orderings of CpG loci.Epigenetics · 2025Article
- Identification of exosome-related gene signature as a promising diagnostic and therapeutic tool for breast cancer.Heliyon · 2024Article
- Experimentally validated oxidative stress -associated prognostic signatures describe the immune landscape and predict the drug response and prognosis of SKCM.Frontiers in immunology · 2024Article
- RNA-seq and ChIP-seq Identification of Unique and Overlapping Targets of GLI Transcription Factors in Melanoma Cell Lines.Cancers · 2022Article
- Bioinformatic analysis identifies epidermal development genes that contribute to melanoma progression.Medical oncology (Northwood, London, England) · 2022Article
- Identification of Core Genes and Pathways in Melanoma Metastasis via Bioinformatics Analysis.International journal of molecular sciences · 2022Article
- Deregulation of Trace Amine-Associated Receptors (TAAR) Expression and Signaling Mode in Melanoma.Biomolecules · 2022Article
- Exploring Tumor Immune Microenvironment and Its Associations With Molecular Characteristics in Melanoma.Frontiers in oncology · 2022Article
- Development and Validation of a Combined Ferroptosis and Immune Prognostic Model for Melanoma.Journal of oncology · 2022Article
- A network pharmacology approach to evaluate the synergistic effect of dihydromyricetin and myricitrin in vine tea on the proliferation of B16F10 cells.Frontiers in nutrition · 2022Article
- New Prognostic Biomarkers and Drug Targets for Skin Cutaneous MelanomaFrontiers in oncology · 2021Article
- Novel uveal Melanocyte cell lines display distinct features from uveal melanoma.Advances in ophthalmology practice and researchArticle
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Melanoma is the deadliest skin tumor and is prone to distant metastases. The incidence of melanoma has increased rapidly in the past few decades, and current trends indicate that this growth is continuing. This study was aimed to explore the molecular mechanisms of melanoma pathogenesis and discover underlying pathways and genes associated with melanoma. We used high-throughput expression data to study differential expression profiles of related genes in melanoma. The differentially expressed genes (DEGs) of melanoma in GSE15605, GSE46517, GSE7553, and the Cancer Genome Atlas (TCGA) datasets were analyzed. Differentially expressed genes (DEGs) were identified by paired t-test. Then the DEGs were performed cluster and principal component analyses and protein-protein interaction (PPI) network construction. After that, we analyzed the differential genes through bioinformatics and got hub genes. Finally, the expression of hub genes was confirmed in the TCGA databases and collected patient tissue samples. Total 144 up-regulated DEGs and 16 down-regulated DEGs were identified. A total of 17 gene ontology analysis (GO) terms and 11 pathways were closely related to melanoma. Pathway of pathways in cancer was enriched in 8 DEGs, such as junction plakoglobin (JUP) and epidermal growth factor receptor (EGFR). In the PPI networks, 9 hub genes were obtained, such as loricrin (LOR), filaggrin (FLG), keratin 5 (KRT5), corneodesmosin (CDSN), desmoglein 1 (DSG1), desmoglein 3 (DSG3), keratin 1 (KRT1), involucrin (IVL), and EGFR. The pathway of pathways in cancer and its enriched DEGs may play important roles in the process of melanoma. The hub genes of DEGs may become promising melanoma candidate genes. Five key genes FLG, DSG1, DSG3, IVL, and EGFR were identified in the TCGA database and melanoma tissues. The results suggested that FLG, DSG1, DSG3, IVL, and EGFR might play important roles and potentially be valuable in the prognosis and treatment of melanoma. These hub genes might well have clinical significance as diagnostic markers.
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