Identification Of Key Genes In Cutaneous Squamous Cell Carcinoma Using Bioinformatic Sanalysis

Objective: Cutaneous squamous cell carcinoma is an epidermal keratinocyte-derived skin tumor that comprise about 20% of all nonmelanoma skin cancers.Long-term UV exposure is a primary risk factor for AK,and it is recognized that the incidence of AK will rise given an aging population.AKs are a common keratinocyte-derived precancerous lesion in humans,AK progress to CSCC at a rate estimated between 0.025% and 16% for an individual lesion per year.The two diseases have a lot in common in terms of etiology,pathogenesis and genetic characteristics,However,it is not yet possible to predict which AK may progress to CSCC.Bioinformatics is a crossing discipline which utilizes modern computational technology to handle and research the data of biology.In this study,we use bioinformatics tools to search,collate,and analyze the high-throughput microarray data of CSCC and AK,we screen DEGs and common expressed genes in the pathogenesis of both diseases.Explore which biological processes or molecular functions or signaling pathways these genes are involved in,and also further explore the common pathogenesis of the two diseases.We sought to exploit potential key genes in the pathogenesis of squamous cell carcinoma of the skin.It provides new ideas for the research of pathogenesis of CSCC and the development of targeted drugs.We sought to identify the potentially key genes linked to the development of cutaneous squamous cell carcinoma and provide new ideas for the study of the pathogenesis of CSCC and the development of targeted drugs.Method:Two datasets with the accession number GSE45216 and GSE98774 were respectively downloaded from NCBI GEO DataSets.The screening of differentially expressed genes(DEGs)was conducted by the use of the R Project.we draw heat map,volcano map and Venn diagram through online software to visualize differential gene expression more intuitively.Gene Ontology and KEGG pathways analyses of DEGs were performed using Metascape.Construction of protein-protein interaction(PPI)network and identification of hub genes were conducted using STRING,Cytoscape and CytoNCA.MCODE plugin was used to extract the most important sub-network modules.Then the GO and KEGG Pathway enrichment analysis were carried of the genes in the module.Result: 1.A total of 319 DEGs were screened from the GSE45164 dataset,of which 161 were up-regulated and 158 were down-regulated.The GSE98774 dataset screened 294 DEGs,of which 171 were up-regulated and 123 were down-regulated.39 public geneswere found in the intersection of the DEGs.2.The results of GO enrichment and KEGG pathway enrichment analysis showed that:(1)The up-regulated DEGs of GSE45164 dataset are significantly enriched in biological processes such as immune response,mitot-ic cell cycle phase transition,keratinization,etc.,molecular functions such as protease binding,etc.,cell components such as the basolateral membrane,etc.,KEGG pathways such as cell cycle,pyrimidine metabolism,etc..Down-regulated DEGs are significantly enriched in biochemical processes such as chemotaxis,glial cell development,and connective tissue development,etc.,molecular functions such as extracellular matrix structural constituent,etc.,cellular components such as neuromuscular junctions,KEGG pathway such as Cytokine-cytokine receptor interaction.(2)The up-regulated DEGs of GSE98774 dataset are significantly enriched in biological processes such as keratinization,defense response,cell killing,etc.,molecular functions such as serine-type endopeptidase inhibitor activity,etc.,cellular components such as intermediate filament cytoskeleton,etc.,KEGG pathways such as IL-17 signaling pathways and pyrimidine metabolism.Down-regulated DEGs are mainly enriched in: biological processes such as muscle system processes,mesenchymal migration,and regulation of inflammatory responses,etc.,molecular functions such as receptor ligand activity;cellular components such as extracellular matrix;KEGG pathway such as PPAR signaling pathway,metabolism of xenobiotics by cytochrome P450,etc..(3)The common DEGs of the two datasets are mainly enriched in: biological processes such as keratinization and cell killing,molecular functions such as RAGE receptor binding,KEGG pathway such as pyrimidine metabolism.3.PPI network and module functional analysis: The PPI network of the GSE45164 dataset DEGs contained 150 nodes and 347 edges.The top 10 nodes with higher topological scores are: CDK1,CCNB1,CCNB2,CDC20,SMC4,KIF23,UBE2 C,NDC80,RRM2,MELK;The PPI network of the GSE45164 dataset DEGs contained 114 nodes and 275 edges.The top 10 nodes with higher topological scores are: IVL,C3,SPRR1 B,CXCL10,SPRR3,SPRR1 A,RSAD2.IFIT1,OASL,PI3;RRM2,CCNB1 and PI3 are exist in the 39 common genes.(2)we further conducted functional enrichment analysis on the highest topological gene modules of the two datasets,GSE45164 gene modules are mainly enriched in cell division and p53 signaling pathway.GSE45164 gene modules are mainly enriched in keratinization.Conclusion:(1)Genes involved in immune response,keratinization and pyrimidine metabolism may contribute to the progression of actinic keratosis to cutaneous squamous cell carcinoma.(2)P53 signaling pathway is the key pathway of skin squamous cell carcinoma,(3)RRM2,CCNB1 and PI3 may be associated with AK progress to CSCC,which can be the potential key genes in the pathogenesis of cutaneous squamous cell carcinoma.