免疫细胞与乳腺癌的关联：来自孟德尔随机化与荟萃分析的启示

Breast cancer (BC) is the most common cancer among women worldwide, with 2.3 million new cases and 685 000 deaths annually. It has the highest incidence in North America, Europe, and Australia and lower rates in parts of Asia and Africa. Risk factors include age, family history, hormone replacement therapy, obesity, alcohol consumption, and lack of physical activity. BRCA1 and BRCA2 gene mutations significantly increase the risk. The 5-year survival rate is over 90% in developed countries but lower in developing ones. Early screening and diagnosis, using mammography and MRI, are crucial for reducing mortality. In recent years, significant progress has been made in studying BC immunophenotyping, particularly in multicolor flow cytometry, molecular imaging techniques, and tumor microenvironment analysis. These technologies improve diagnosis, classification, and detection of minimal residual disease. Novel immunotherapies targeting the tumor microenvironment, like CAR-T cell therapy, show high efficiency and fewer side effects. High levels of tumor-infiltrating lymphocytes correlate with better prognosis, while immune checkpoint molecules (PD-1, PD-L1) help cancer cells evade the immune system. Tumor-associated macrophages promote invasion and metastasis. Blocking molecules like CTLA-4, LAG-3, and TIM-3 enhance antitumor responses, and cytokines like IL-10 and TGF- aid tumor growth and immune evasion. Mendelian randomization (MR) studies use genetic variants to reduce confounding bias and avoid reverse causation, providing robust causal inferences about immune cell phenotypes and BC. This approach supports the development of precision medicine and personalized treatment strategies for BC.

This study aims to conduct MR analysis on 731 immune cell phenotypes with BC in the BCAC and Finngen R10 datasets, followed by a meta-analysis of the primary results using the inverse-variance weighted (IVW) method and multiple corrections for the significance P -values from the meta-analysis. Specifically, the study is divided into three parts: First, data on 731 immune cell phenotypes and BC are obtained and preprocessed from the GWAS Catalog and Open GWAS (BCAC) and the Finngen R10 databases. Second, MR analysis is performed on the 731 immune cell phenotypes with BC data from the BCAC and Finngen R10 databases, followed by a meta-analysis of the primary results using the IVW method, with multiple corrections for the significance P -values from the meta-analysis. Finally, the positively identified immune cell phenotypes are used as outcome variables, and BC as the exposure variable for reverse MR validation.

The study found that two immune phenotypes exhibited strong significant associations in MR analysis combined with meta-analysis and multiple corrections. For the immune phenotype CD3 on CD28+ CD4-CD8- T cells, the results were as follows: in the BCAC dataset, the IVW result was odds ratio (OR) = 0.942 (95% CI: 0.915-0.970, P =6.76 10 -5 ), =-0.059; MR Egger result was =-0.095; and the weighted median result was =-0.060. In the Finngen R10 dataset, the IVW result was OR=0.956 (95% CI: 0.907-1.01, P =0.092), =-0.045; MR Egger result was =-0.070; and weighted median result was =-0.035. The values were consistent in direction across all three MR methods in both datasets. The meta-analysis of the IVW results from both datasets showed OR=0.945 (95% CI: 0.922-0.970, P =1.70 10 -5 ). After Bonferroni correction, the significant P- value was P =0.01, confirming the immune phenotype as a protective factor against BC. For the immune phenotype HLA DR on CD33- HLA DR+, the results were as follows: in the BCAC dataset, the IVW result was OR=0.977 (95% CI: 0.964-0.990, P =7.64 10 -4 ), =-0.023; MR Egger result was =-0.016; and the weighted median result was =-0.019. In the Finngen R10 dataset, the IVW result was OR=0.960 (95% CI: 0.938-0.983, P =6.51 10 -4 ), =-0.041; MR Egger result was =-0.064; and weighted median result w