| Objective:Melanoma is a tumor commonly found in the skin,which is highly malignant,aggressive,has a poor prognosis,and develops rapidly.In this study,molecular typing of melanoma was performed using pyroptosis-related genes and cell differentiation-related genes found by single-cell RNA sequencing,respectively,and the differences in survival time and clinical phenotype between different molecular typing were evaluated,so as to explore the pathogenesis and personalized treatment of cutaneous melanoma.Cutaneous squamous cell carcinoma(CSCC),usually secondary to actinic keratosis(AK),is the second most common skin cancer worldwide.To reduce the risk of metastasis,early diagnosis and treatment of CSCC are important.The aim of this study was to identify hub genes associated with CSCC and AK,in order to explore the pathogenesis of skin squamous cell carcinoma and provide clues for personalized treatment.Methods:First,RNA expression data of skin tissues from TCGA melanoma patients and GTEx normal subjects were downloaded from UCSC Xena,and 20 genes related to pyroptosis were retrieved by searching the literature,and 19 differentially expressed pyropsis-related genes were used to perform unsupervised clustering typing of melanoma patients,and the differences in survival time and clinical characteristics between molecular subtypes were compared;univariate Cox analysis was used to select genes with higher correlation with prognosis and construct a Lasso regression model,and the patients were divided into high and low risk groups according to the risk score of the model,and the differences in survival time between the two groups were compared and the association between risk score and other clinical characteristics was determined.The independent factors affecting the prognosis of melanoma were screened by univariate independent prognostic analysis and multivariate independent prognostic analysis.Protein expression levels of prognostic genes were verified using the HPA database.Secondly,4645 melanoma cells were clustered using GSE72056,a single-cell RNA sequencing(sc RNA-seq)dataset from GEO database,and the cells were divided into 16clusters by t-SNE analysis,and each cluster was annotated to obtain cell characteristics of different clusters.Cell trajectories were used to analyze and identify genes involved in melanoma cell differentiation.Differentially expressed genes(DEGs)in cell trajectories were screened by R language,and 79 melanoma patients in the melanoma GEO dataset GSE54467 were molecularly typed using genes found to be associated with cell differentiation.The relationship with typing was determined by survival time,tumor microenvironment,immune cells,and immune checkpoint analysis.Finally,two skin squamous cell carcinoma datasets GSE45216-98774 and GSE108008were used.We applied weighted gene co-expression network analysis(WGCNA)to investigate key modules and hub genes associated with CSCC and AK.Differentially expressed genes between CSCC and AK,AK and normal samples were also analyzed.We took the hub genes common to the 2 datasets as validated hub genes.Next,we analyzed whether hub gene expression was also different in other cancers using 33 TCGA pan-cancer data and whether survival outcomes could be predicted in patients with other cancers.Results:In melanoma,patients can be divided into two molecular subtypes by unsupervised clustering using pyroptosis-related genes,and the survival time is significantly different between the two groups,with 5-year survival rates of 51.8%and 71.6%,respectively.It was also found that there were significant differences among the three clinical characteristics of tumor stage,T stage,and age among different molecular subtypes.A Lasso regression model was constructed using six genes significantly associated with prognosis(GSDMD,STAT3,AIM2,CD274,GZMA,NLRC4),and patients were divided into high-risk and low-risk groups according to the risk score of the Lasso regression model.It was found that there was a significant difference in the survival time between the high and low risk groups,and the overall survival time of patients in the high risk group was significantly lower than that of patients in the low risk group.Associations between risk scores and clinical traits were also identified,with significant differences in tumor stage,T stage,and survival status between high and low risk groups.Univariate independent prognostic analysis and multivariate independent prognostic analysis showed that risk value,age,T and N stages were all independent prognostic factors that could be used to predict the survival time of patients.The protein expression levels of GSDMD,STAT3,AIM2 and GZMA were also different between normal and melanoma tissues as verified by HPA database.By sc RNA-seq,16 cell subsets were identified from melanoma samples,containing seven cell types,melanoma cells,T cells,monocytes,tissue stem cells,B cells,fibroblasts,and endothelial cells.Pseudo-temporal analysis was performed on melanoma cells,and different clusters of skin melanoma cells were divided into three branches,with clusters clustered on branch 1 mainly 11,13,14,and 15,clusters clustered on branch 2 mainly 0,1,8,9,and 10,and clusters clustered on branch 3 mainly 6,11,and 13.Tissue stem cells,and melanoma cells were mainly accumulated on branch 1,B cells and T cells were mainly accumulated on branch 2,and monocytes,endothelial cells,and fibroblasts were mainly accumulated on branch 3.Seventy-nine melanoma samples from the GSE54467 expression dataset of the GEO database were divided into three types using the resulting 619 cell trajectory differential genes,and it was found that patients with different types had significant differences in survival time,immune microenvironment,immune cell types,and immune checkpoint gene expression.By studying the correlation between immune cell types and tumor prognosis,M1 macrophages and resting CD4~+T cells were found to be closely related to prognosis which may be good therapeutic targets.Immune checkpoint results showed that immune checkpoint genes CD8A,CD40,CD40LG,CD80,CD86,CD274,HAVCR2,ICOS,JAK2,LGALS9,PDCD1,PDCD1LG2,PTPRC,TNFSF4,TNFRSF18,and TNFRSF9 were associated with prognosis.In both datasets,by WGCNA analysis we identified the modules most associated with CSCC and AK,respectively.Combined with differential gene expression analysis,we identify hub genes for each module.Finally,we identify and validate seven hub genes of CSCC:GATM,ARHGEF26,PTHLH,MMP1,POU2F3,MMP10,and GATA3.AK associated hub genes were not validated.The hub genes PTHLH,MMP1,and MMP10 were up-regulated in most pan-cancer tumor samples,and GATM and ARHGEF26 were mainly down-regulated.Increased expression of MMP1,MMP10,and PTHLH was mainly associated with increased survival disadvantage.In contrast,increased expression of GATM,ARHGEF26,and POU2F3 was predominantly associated with survival advantage,and only GATA3 was significantly associated with melanoma survival in congeneric skin cancers.Conclusions:Pyroptosis-related genes and differentiation-related genes found by single-cell RNA sequencing are closely related to the prognosis and clinical characteristics of melanoma.Pyroptosis-related genes and differentiation-related genes can be used for molecular typing of melanoma and contribute to the exploration of personalized treatment for melanoma patients.In addition,we found seven hub genes that play an important role in cutaneous squamous cell carcinoma and help to understand the pathogenesis of cutaneous squamous cell carcinoma,which may become biomarkers or therapeutic targets for the diagnosis and treatment of cutaneous squamous cell carcinoma in the future. |
