Multivariate analysis of the molecular mechanism related to breast cancer and depression

Abstract

Abstract Background Breast cancer is a malignant disease that seriously affects women's health,there is a certain connection between depression and it, however, the mechanism of their interrelationship is still unclear.This article aims to explore the common genetic characteristics and potential molecular mechanisms of breast cancer and depression through multiple data sets. Methods Download breast cancer and depression related datasets from TCGA database and NCBI GEO public database, use R package "Limma" to analyze the difference of molecular mechanism of data, identify the differentially expressed genes between normal samples and disease samples, use Metascape database for annotation and visualization, and carry out gene ontology (GO) analysis and Kyoto Gene Genome Encyclopedia (KEGG) pathway analysis for differentially expressed genes, Random Forest SRC software package was used to carry out random survival forest algorithm to screen characteristic genes, and key genes co expressed by breast cancer and depression were screened; In addition, CIBERSORT algorithm was used to analyze the data of patients, Pearson correlation analysis was conducted on the expression of key genes and the content of immune cells, and the transcription regulatory factors of breast cancer were predicted through R package "RcisTarget"; The R software package "pRRophic" was used to predict the drug sensitivity of each breast cancer sample; Download gene sets from the Molecular signatures database, use the GSVA algorithm to comprehensively score each gene set, and evaluate the potential biological functional changes of different samples; Further analyze the differences in signaling pathways between high and low expression groups through GSEA; A multivariate regression model was constructed using Nomogram to obtain miRNAs related to key genes from the miRcode database, and the miRNA network of key genes was visualized using Cytoscape software. Results Through random survival forest analysis, CCNB1, MLPH, PSME1 and RACGAP1 were screened as four key genes of breast cancer and depression, and the specific signal pathways of these four key genes were analyzed, which were regulated by multiple transcription factors and other common mechanisms, suggesting that they were significantly related to the expression level of genes involved in the progression of breast cancer and depression, These four key genes are the potential molecular mechanisms that affect the progression of breast cancer and depression, and have strong correlation with immune cells; Further analysis showed that it was significantly related to the common drug sensitivity in the treatment of breast cancer; The expression of key genes and clinical information will be used to construct a multivariate regression model and miRNA network analysis through Nomogram to analyze that key genes have a predictive effect on the prognosis of breast cancer. Conclusion Our work has found the key genes of comorbidity between breast cancer and depression. It is the first time to analyze the correlation between key genes and the occurrence, progress, treatment and prognosis of these two diseases through multiple factors, thus suggesting that these four key genes can be used as the biomarkers or potential therapeutic targets of comorbidity of these two diseases.