Multiscale Embedded Gene Co-Expression Network Combined with Mendelian Randomisation Analysis for the Molecular Pathogenesis of Breast Cancer.

[OBJECTIVE] To integrate multiscale embedded gene co-expression network analysis (MEGENA) and Mendelian randomisation (MR) to identify new pathogenic factors associated with breast cancer (BC).

[STUDY DESIGN] A descriptive study. Place and Duration of the Study: Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, China, from January to December 2024.

[METHODOLOGY] The raw mRNA expression data were downloaded from the Cancer Genome Atlas (TCGA) database, and differentially expressed genes were used for MEGENA analysis. MR analysis was applied to explore causal relationships. A sensitivity analysis using leave-one-out tests ensured robust results. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were used to examine the functions and mechanisms of the newly identified targets.

[RESULTS] MEGENA analysis identified 257 targets. The MR analysis demonstrated that ALOX15B (0.874; 0.809-0.943; p = 0.001) and TLE3 (0.807; 0.667-0.976; p = 0.027) were associated with a low risk of the disease; while the genes FAAH (1.064; 1.003-1.129; p = 0.038), HDGF (1.158; 1.012- 1.326; p = 0.032), KLF5 (1.110; 1.020-1.208; p = 0.015), LSM4 (1.071; 1.001-1.145; p = 0.046), and TNS1 (1.073; 1.015-1.135; p = 0.013) were associated with a high risk of BC. Six (ALOX15B, FAAH, HDGF, KLF5, TLE3, and TNS1) of these seven genes have been further validated for their reliability through sensitivity analysis. These six genes are closely correlated with immune cell infiltration and multiple tumour-related pathways.

[CONCLUSION] This study demonstrated the causal effect of six key genes on BC and provided a potential molecular link between these genes and BC.

[KEY WORDS] Breast cancer, Mendelian randomisation, MEGENA, Biomarker, Immune infiltration.