Exploring lipotoxicity-related prognostic genes and experimental validation in breast cancer using electrochemical sensors and single-cell transcriptomics analysis

Breast cancer (BRCA) is the leading cause of cancer-related deaths among women. Despite the continuous advancement of treatment methods, some patients still face the challenges of recurrence and metastasis. Research on electrochemical sensors in detecting tumor markers and monitoring disease progression is gradually increasing, providing new ideas and methods for precision medicine of diseases such as breast cancer. This study aims to explore lipotoxic-related prognostic genes in breast cancer through a combination of electrochemical sensors and single-cell transcriptomics analysis, and to reveal their mechanism of action in the occurrence and development of the disease. By integrating with single-cell transcriptomics analysis, genes related to the prognosis of breast cancer will be screened out. MR Analysis was used to evaluate the causal relationship between these candidate genes and breast cancer. Further, a prognostic model was constructed through a machine learning model and verified in an independent dataset. The expression levels of prognostic genes in different tissues and breast cancer cell lines were also analyzed, and their localization on chromosomes was explored. Based on single-cell transcriptomics data, identify the differential cell types in breast cancer, and analyze the communication networks between key cell types as well as the dynamic expression changes of prognostic genes during cell differentiation. The expression of prognostic genes was verified by RT-qPCR experiments. MR Analysis identified 19 candidate genes with stable causal relationships with breast cancer. Eventually, four prognostic genes (MYD88, JAK2, ZMYND10 and GPT) were determined through univariate Cox regression analysis and LASSO regression analysis. The constructed prognostic model demonstrated excellent discriminatory ability and diagnostic performance in both the training set and the validation set. Single-cell analysis revealed the distribution differences of nine cell types in breast cancer, among which macrophages were identified as the key cell type, and TWIK acid-sensitive K + channels (TASK) associated with these cells were discovered. The RT-qPCR results indicated that, compared with the adjacent tissue samples, the expression of ZMYND10 in breast cancer tissue samples was significantly increased ( p < 0.05), while the expressions of JAK2 and GPT were significantly decreased (p < 0.05).