Evidence map›Paper›PMID 35003328›Full record

ArticleComputational and mathematical methods in medicine2021

Identification of Keratinocyte Differentiation-Involved Genes for Metastatic Melanoma by Gene Expression Profiles.

Kezhu Li, Shu Guo, Shuang Tong, Qiang Sun, Shifeng Jin, Bingran Qi, Yining Shao, Nan Xu

Abstract readValidation Study
In one paragraph

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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0cells of the map it votes in
10citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Kezhu LiDepartment of Plastic Surgery, The First Hospital of China Medical University, Shenyang, 11000 Liaoning Province, China.
Shu GuoDepartment of Plastic Surgery, The First Hospital of China Medical University, Shenyang, 11000 Liaoning Province, China.
Shuang TongDepartment of Plastic Surgery, The First Hospital of China Medical University, Shenyang, 11000 Liaoning Province, China.
Qiang SunDepartment of Plastic Surgery, The First Hospital of China Medical University, Shenyang, 11000 Liaoning Province, China.
Shifeng JinDepartment of Plastic Surgery, The First Hospital of China Medical University, Shenyang, 11000 Liaoning Province, China.
Bingran QiDepartment of Plastic Surgery, The First Hospital of China Medical University, Shenyang, 11000 Liaoning Province, China.
Yining ShaoDepartment of Plastic Surgery, The First Hospital of China Medical University, Shenyang, 11000 Liaoning Province, China.
Nan XuDepartment of Plastic Surgery, The First Hospital of China Medical University, Shenyang, 11000 Liaoning Province, China.ORCID https://orcid.org/0000-0002-8811-6459

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Gene Regulatory NetworksBiomarkers, TumorCell DifferentiationComputational BiologyDatabases, GeneticGene Expression ProfilingGene Expression Regulation, NeoplasticHumansKeratinocytesMelanomaMicroRNAsProtein Interaction MapsSkin NeoplasmsBiomarkers, TumorMicroRNAs

Identifiers

PMID35003328
PMCPMC8728391

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