| Objective:According to the latest statistics from the National Cancer Center,breast cancer has surpassed lung cancer to become the tumor with the highest incidence rate among Chinese women and the highest incidence rate of breast cancer in the world.The 5-year survival rate of breast cancer in China still lags behind that in developed regions,and the mortality rate is increasing year by year.Breast cancer is still the most important tumor endangering women’s health,which brings huge challenges to China’s health care.Therefore,based on the data resources obtained by DNA methylation sequencing and transcriptome sequencing modern biological sequencing technology,this study explores the pathological mechanism in the process of breast cancer from a multi group level,excavates prognostic markers of breast cancer,and provides some new therapeutic targets and directions for breast cancer.Methods: 1.By collecting the clinical characteristics and transcriptome data of breast cancer tissue samples and paracancerous tissue samples in the TCGA database,the differential expression genes(DEGs)were screened after pretreatment.With DEGs as background data,pearson correlation algorithm is used to cluster differentially expressed genes,construct weighted gene co-expression network,and identify module genes.Further study the correlation analysis between module genes and clinical features Event,Age,M,N,T,select highly correlated modules for GO enrichment analysis,and use cytoscape software to build PPI(protein interaction)network to identify hub genes.Finally,the hub gene was verified by survival analysis.2.By collecting the clinical characteristic data and DNA methylation chip data of breast cancer tissue samples and cancer tissue samples in the TCGA database,we screened out the differential methylation sites and corresponding differential methylation genes after sample quality control analysis.Next,pearson correlation algorithm is used to analyze the data of differential methylation sites,build a weighted co-methylation network and identify the co-methylation module.Then use spearman correlation analysis to calculate the correlation between clinical features Event,Age,M,N,T and module genes,select the modules highly related to them for GO enrichment analysis,and identify the key methylation sites.Finally,the methylation sites were verified by survival analysis.Results:1.There are 1217 samples of transcriptome data,including1072 cancer tissue samples,99 para-cancer tissue samples,60488 genes,and 14174 DEG(set log2 Fold Change>0,padj<0.05),including 2804 upregulated genes and 2052 down-regulated genes.In the weighted gene coexpression network analysis,six modules were identified by clustering,among which the gene module highly related to clinical feature Event was blue module;The gene module highly related to clinical feature Age is turquoise module;The gene modules highly related to clinical characteristics M are yellow module and brown module;The gene module highly related to clinical feature N is yellow module;The gene module highly related to clinical feature T is blue module.The blue module highly related to the clinical characteristics of Event is mainly involved in the regulation of cell division and cell cycle;The yellow module,which is highly related to clinical characteristics M and N,is mainly involved in the regulation of extracellular matrix,the regulation of microfilament formation,and the development of skeletal system;The turquoise module,which is highly related to clinical characteristics Age and T,mainly involves biological processes such as the activity and development of muscle system,activation and regulation of ion channels.According to the survival score and ROC curve,nine key genes(CDK1、COL11A1、COL10A1、MMP13、CENPF、COMP、TPX2、NCAPG、AURKA)are closely related to the prognosis of breast cancer.2.There were 1888 samples of methylation data of breast cancer,including 1785 cancer tissue samples and 98 adjacent tissue samples.221086 differential methylation sites were screened,corresponding to 20622 differential methylation genes.In the analysis of differential methylation co-expression network,9 modules were identified,and the gene module highly related to clinical feature Event was blue module;The gene module highly related to clinical feature Age is turquoise module;The gene module highly related to clinical feature M is yellow module;The gene module highly related to clinical feature N is turquoise module;The gene module highly related to clinical feature T is blue module.The enrichment analysis results showed that the blue module was mainly involved in the development of embryonic organs,the development of urogenital system,the formation of microfilament and actin cytoskeleton in cell composition,and the regulation of DNA transcription,protein serine /threonine/tyrosine kinase signal transduction pathway in molecular function.K-M survival analysis showed that the methylation status of four methylation sites in the module,TECR(cg23690893),MAD1L1(cg05434287),ASAP1(cg07799299),SLC12A4(cg01817009),was closely related to the prognosis and survival of the tumor.Conclusion: 1.From the transcriptome level and DNA methylation level,this study found that the regulation of extracellular matrix,the regulation of microfilament and actin skeleton formation,and the development of skeletal system may jointly affect the tumor microenvironment to promote the recurrence and metastasis of breast cancer.At the same time,biological processes such as cell division,cell cycle regulation and activation of intracellular signaling pathways are also closely related to the invasion and proliferation of breast cancer.2.CDK1,COL11A1,COL10A1,MMP13,CENPF,COMP,TPX2,NCAPG,AURKA play an important role in the occurrence and metastasis of breast cancer,and are closely related to the prognosis and diagnosis of breast cancer.The methylation sites of four genes TECR(cg23690893),MAD1L1(cg05434287),ASAP1(cg07799299)and SLC12A4(cg01817009)are closely related to the prognosis of breast cancer,and these genes may become new target markers of breast cancer. |
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