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Identification of Shared Genes and Functional Pathways Between Skin Cancer and Skin Aging Based on Integrated Bioinformatic Analyses
Xingyu Chen1, Xiaoyi Li2*
1International Curriculum Center, The High School Affiliated to Remin University of China, Beijing, P.R. China
2Department of Epidemiology and Health Statistics, School of Public Health, Peking University, Beijing, China
Corresponding Author: Xiaoyi Li
Address: Department of Epidemiology and Health Statistics, School of public health, Peking University, No.38 Road, Haidian District, Beijing, 100191, China.
Received date: 11 March 2025; Accepted date: 02 October 2025; Published date: 09 October 2025
Citation: Chen X, Li X. Identification of Shared Genes and Functional Pathways Between Skin Cancer and Skin Aging Based on Integrated Bioinformatic Analyses. J Health Care and Research. 2025 Oct 09;6(3):55-68.
Copyright © 2025 Chen X, Li X. This is an open-access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.
Keywords: Skin Aging, Melanoma, Non-Melanoma Skin Cancer, Biomarker
Abbreviations: NMSC: Nonmelanoma Skin Cancer; Including Squamous Cell Carcinoma; MSC: Melanoma Skin Cancer; GPL17077: Agilent-039494 Sureprint G3 Human GE V2 8x60k Microarray 039381; GPL96: Affymetrix Human Genome U133A Ar-Ray
Abstract
Backgrounds: Skin cancer (SC) and skin aging (SA) are polygenic phenotypes posing significant health risks. While molecular biomarkers and related pathways for each have been studied separately, shared mechanisms remain unclear. This study aimed to identify shared biomarkers and mechanisms between SC and SA using gene expression profiling, offering new insights for future research. This study identified shared differentially expressed genes (DEGs) of the two phenotypes, their related important pathways, and interactions between significant functional proteins.
Method: DEGs of SA and SC were identified via LIMMA analysis based on mRNA datasets from the Gene Expression Omnibus and ArrayExpress databases. Pathway enrichment analysis was conducted using the overrepresentation method to identify shared DEGs-associated KEGG pathways and GO terms. Using Cytoscape, protein-protein interaction (PPI) networks were constructed based on the STRING database. Core networks and top functional genes were identified using the MCODE plugin and CytoHubba plugin.
Results: Results showed SPRR1A and S100A2 as significant shared DEGs (GSE85358: PSPRR1A = 0.0028, logFCSPRR1A = -0.93; PS100A2 = 0.0086, logFCS100A2 = -0.60; GSE2503: PSPRR1A = 0.0089, logFCSPRR1A = 2.5; P S100A2 = 1.5, logFCS100A2 = 0.0095; GSE3189: PSPRR1A = 1.7E-07, logFCSPRR1A = -3.4; PS100A2 = 1.0E-14, logFCS100A2 = -4.5). Those shared genes enriched in immune response, endothelial cell migration, cellular process, and peptide cross-linking. In PPI analysis, top hub genes of networks were PIK3R1, NANOG, VAV3, SMTN, SPRR1B, MET, MYLK, EPCAM, SPRR1A, and GATA3.
Conclusions: Our findings elucidate shared genetic architectures between SC and SA. The identification of shared genes and protein-protein interaction networks associated with both SA and SC suggests an underlying molecular genetic mechanism, offering opportunities to develop therapeutic strategies against SA and SC comorbidity.