Excavation And Analysis Of Potential Biomarkers Of Breast Cancer

Breast cancer is a very common disease that poses a huge threat to human health.With the development of breast cancer treatment methods,the demand for the molecular mechanism of breast cancer has increased,and the research of biomarkers has become a hot spot.Biomarkers are molecules that can monitor pathological processes and serve as therapeutic targets.Genes and proteins associated with breast cancer may be potential biomarkers.In recent years,the development of data analysis methods such as high-throughput sequencing technology and weighted gene co-expression network has enabled us to analyze and understand the complex inter-genetic roles in breast cancer from a global perspective,and to explore potential biomarkers of breast cancer.We obtained breast cancer gene expression data and clinical data from TCGA,and classified breast cancer gene expression data into paracancerous samples and four stages of cancer(?-?)samples based on clinical stage information.We use WGCNA to construct the weighted gene co-expression network and the co-expression module for the expression data of each stage,use the gene expression difference analysis to screen the differential genes at each stage,and use the differential gene to screen the co-expression module.KEGG enrichment analysis of the obtained co-expression modules revealed that these modules may be associated with breast cancer.In the co-expression module,we use the correlation between genes and traits and the status of genes in the co-expression module to mine core genes,and use the core gene construction support vector machine to classify and predict breast cancer samples,and verify the importance of core genes.In turn,the core gene is identified as a potential biomarker for breast cancer.We performed gene distribution information analysis,GO enrichment analysis and survival analysis on core genes,and verified them in combination with existing studies.After the above analysis,we extracted 13,9,13,16 co-expression modules that may be associated with breast cancer and have specific functions in each stage of breast cancer,and tapped 251 potential biomarkers(from various stages)..Analysis of these genes found that these genes play a major role in the extracellular matrix;containing 135non-differentiated genes;different expression levels of 42 genes have a significant impact on the survival of breast cancer patients;and the genes also show the stage in function Sex.It is proved by combining existing research that the extracted genes have a reference effect.We used gene expression data without differential gene screening for co-expression network construction,and mapped the co-expression modules by mapping differential genes to co-expression modules to enable non-differentiated genes to be retained.The gene expression data was analyzed in a phased manner,and the obtained core gene(potential biomarker of breast cancer)has a stage characteristic.Combine the phase characteristics to analyze the core genes to understand the expression characteristics of the genes at different stages.Finally,251 potential biomarkers of breast cancer were obtained,which can help the mechanism research of breast cancer disease development and provide new ideas and directions for further diagnosis and treatment research.