Analysis of the Molecular Mechanism of Comorbidity Genes Between Breast Cancer and Depression

Breast cancer and depression are both serious diseases that significantly impact women's physical health. The molecular mechanisms underlying their comorbidity remain elusive. This study aims to identify key genes and the molecular mechanisms associated with the comorbidity of breast cancer and depression using bioinformatics analysis methods.

Data files for breast cancer and depression were obtained from the TCGA database and the NCBI GEO public database, respectively. The random survival forest algorithm was utilized to identify key genes co-expressed in both breast cancer and depression. Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA) were employed to predict biological functions and signaling pathway differences influenced by these key genes in both diseases. The R package "RcisTarget" was utilized to predict molecular transcriptional regulatory relationships of the key genes. The CIBERSORT algorithm was applied for immune function correlation analysis of comorbid key genes. The differential expression of key genes was validated in breast cancer tissue and depression blood by qPCR.

The TCGA database provided original mRNA expression data for breast cancer, while the NCBI GEO public database offered the dataset GSE58430 related to depression. Through functional enrichment and random survival forest analysis, CCNB1, MLPH, PSME1, and RACGAP1 were identified as four key genes. The specific signaling pathways、strong correlation with immune cells, and the potential molecular mechanisms of these four key genes were analyzed in breast cancer and depression. Their expression levels were verified in blood and tissue samples.

This study discovered the comorbidity genes of breast cancer and depression, providing a certain direction for the prevention and treatment of these two diseases. At present, breast cancer and depression are serious diseases that affect women's physical and mental health. The connection between the two is not very clear. This study proposes that these two diseases have comorbidity genes. The risk population of the disease can be detected early through testing, so as to intervene early and improve prognosis. However, the sample size of the database analyzed in this study was relatively small, and the sample size and methods for clinical validation were insufficient. Further in-depth research will be conducted in the future.

This study identified CCNB1, MLPH, PSME1, and RACGAP1 as key genes associated with the comorbidity of breast cancer and depression.