The Identification Of Key Genes In Basal Cell Carcinoma Based On Weighted Gene Co-expression Network Analysis

Objective: Basal Cell Carcinoma(BCC)is a type of epithelial tumor that exhibits local invasiveness and basal cell-like differentiation,originating from the epidermis or its appendages.It is common in clinical practice and is one of the common skin tumors in dermatology.BCC presents with various clinical and pathological features,with nodular BCC being the most common subtype.According to previous studies,the pathogenesis of BCC is the result of multiple comprehensive factors on a genetic background.The Hedgehog signaling pathway,Notch signaling pathway,MAPK pathway,and others have been shown to be closely related to the development of BCC.This study screened key genes related to the pathogenesis of BCC through bioinformatics analysis combined with clinical specimen validation.And furtherly this study provided a new perspective for studying the pathogenesis of BCC at the molecular level.Methods: The microarray or sequencing data related to BCC were searched from the Gene Expression Omnibus(GEO)database,and the dataset GSE125285 with a large sample size was used as the analysis data.The data were cleaned and preprocessed through online data analysis websites and R language,and the obtained data were used for subsequent analysis.Firstly,Weighted Gene Co-expression Network Analysis(WGCNA)was used to screen out the important gene modules with co-expression from the preprocessed genes.R language was used to standardize the raw data,and the "Limma" package was used to screen out the differentially expressed genes.To further investigate the roles and enrichment pathways of differentially expressed genes in BCC,we performed GO analysis and KEGG enrichment analysis of differentially expressed genes.To more comprehensively analyze the relationship between genes and BCC in all samples,we also did GSEA enrichment analysis for all sample genes.In order to obtain the key genes,we performed Venn diagram between the screened differentially expressed genes and the important module genes obtained by WGCNA to obtain the intersection genes.Then LASSO regression was performed on the intersection genes to obtain the key genes related to BCC,and the effectiveness of the obtained key genes was tested by ROC curve.Additionally,the study conducted immune cell infiltration analysis of the key genes to determine their ability to serve as immune biomarkers in BCC.To further clarify the expression of the key genes in cancer tissue and adjacent normal tissue,the study used the "ggpubr" package to visualize the expression levels of eight key genes(FRG2FP,belonging to the FSHD family member pseudo-gene)in cancer tissue and adjacent normal tissue.Finally,to clinically validate the key genes identified in the analysis,the study used RT-q PCR to validate some key genes using nine clinical BCC samples stored in the Skin Pathology Center of our hospital.Results: After data cleaning and preprocessing,a total of 16,383 genes were included in the WGCNA analysis,and 9 different modules related to clinical phenotype and staging were obtained.The red module,containing 1,061 genes,was the most relevant to clinical characteristics."Limma" package analysis identified a total of 1,321 differentially expressed genes,of which 781 genes were upregulated and 540 genes were downregulated in cancer tissues.GO analysis showed that the differentially expressed genes mainly participate in changes in organelles,the structural composition of the extracellular matrix,and collagen metabolism.KEGG analysis indicated that the differentially expressed genes mainly participate in the formation of basal cell carcinoma,PPAR signaling pathway,protein digestion and absorption,etc.GSEA gene enrichment analysis showed that the main gene sets enriched in cancer tissues were BCC-related gene sets and Hedgehog pathway-related gene sets.A Venn diagram was used to identify 656 intersection genes,and LASSO regression and ROC curve analysis were performed on these genes,resulting in the identification of 9 key genes related to BCC,namely FOXI3,KC6,PTCH2,ADAMTS3,SLCO1A2,CHGA,MMP11,FRG2 FP,and CTD-2554C21.2.Immune cell infiltration analysis of the key genes revealed a positive correlation between MMP11 and macrophage immune cells,a positive correlation between FRG2 FP and central memory CD8 T cells,and a negative correlation between PTCH2 and activated CD4 T cells.Using the "ggpubr" package,the expression levels of the 8 key genes were visualized in cancer and adjacent tissues,revealing high expression levels in cancer tissue and low expression levels in adjacent tissues.Finally,RT-q PCR was used to validate the clinical BCC samples,and the results showed that FOXI3,ADAMTS3,SLCO1A2,and MMP11 were highly expressed in BCC,consistent with the results of the bioinformatics analysis.Conclusion: By analyzing the GSE125285 dataset using bioinformatics methods and validating with clinical samples,this study identified four key genes associated with BCC:FOXI3,ADAMTS3,SLCO1A2,and MMP11.These four key genes may serve as potential biological markers for BCC.