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. 2022 Apr;10(8):448.
doi: 10.21037/atm-22-1163.

Identification of significant genes with a poor prognosis in skin cutaneous malignant melanoma based on a bioinformatics analysis

Jin Wang  1   2   3   4 , Jilong Yang  1   2
Affiliations

Identification of significant genes with a poor prognosis in skin cutaneous malignant melanoma based on a bioinformatics analysis

Jin Wang et al. Ann Transl Med. 2022 Apr.

Abstract

Background: Skin cutaneous malignant melanoma (SKCM) is a deadly mutated malignancy that arises from melanocytes in the basal layer of the skin. This study sought to identify effective treatment targets that could serve as prospective therapeutic targets to improve patient outcomes.

Methods: The GSE83583, GSE111766, and GSE104849 data sets from the GPL10558 platform in the Gene Expression Omnibus (GEO) were used in this study. The candidate genes were identified using the GEO2R tool and a Venn diagram. The Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Gene and Genome (KEGG) preliminary analyses of the differentially expressed genes (DEGs) were conducted using the Database for Annotation, Visualization and Integrated Discovery, and R software. The protein-protein interaction (PPI) network was examined using Cytoscape software. The survminer package was used to examine the overall survival of patients with the identified genes. The Human Protein Atlas (HPA) was used to verify the protein levels of significant genes with poor prognosis. The highly expressed genes in the melanoma tissues were visualized using the ggplot2 package.

Results: In total, 160 DEGs from 124 melanoma tissues and 9 normal melanocyte tissues were examined in this study. Cytoscape displayed 19 central nodes from the 160 DEGs. The re-analysis showed that the cytochrome P450 family 1 subfamily B member 1 (CYP1B1) and protein kinase C beta (PRKCB) were significantly enriched in the micro ribonucleic acids (RNAs) in cancer.

Conclusions: CYP1B1 and PRKCB were overexpressed in and correlated with the poor prognosis of SKCM. Our findings might help explore the prognosis and diagnostic markers of SKCM.

Keywords: Poor prognosis; bioinformatics analysis; biomarker; significant genes; skin cutaneous malignant melanoma (SKCM).

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Conflict of interest statement

Conflicts of Interest: Both authors have completed the ICMJE uniform disclosure form (available at https://atm.amegroups.com/article/view/10.21037/atm-22-1163/coif). JW reports that this work was funded by the Tianjin Science and Technology Committee (grant No. 18YFZCSY01090). The other author has no conflicts of interest to declare.

Figures

Figure 1
Figure 1
Authentication of 160 common DEGs in the 3 data sets (GSE83583, GSE111766, and GSE104849) using Venn diagram software (the different colors represent the different data sets and UpSet plots. (A,C) One hundred and fifty-four DEGs were upregulated in the 2 data sets (logFC >0). (B,D) Six DEGs were downregulated in 3 data sets (logFC <0). DEG, differentially expressed gene.
Figure 2
Figure 2
The overall survival analysis of the 19 core genes. Of the 19 genes, 8 had a significantly worse overall survival rate in SKCM patients (a P value <0.05 was considered significant). SKCM, skin cutaneous malignant melanoma.
Figure 3
Figure 3
In total, 8 genes were related to a poor prognosis. Of the 8 genes, 6 had significant expression levels in the T1, T2, T3, and T4 SKCM stages (ns, no significant; P≥0.05; *, P<0.05; **, P<0.01; ***, P<0.001). SKCM, skin cutaneous malignant melanoma.
Figure 4
Figure 4
Verification at the protein levels of the 2 genes. The immunohistochemistry staining of CYP1B1 (A-C) and PRKCB (D-F) from the HPA. HPA, Human Protein Atlas.
See this image and copyright information in PMC

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