Lian Jingxuan, Zhang Xinghong, Chen Wenjie, Lin Zheshen, Lu Ming, Liang Rong
Department of Hematology, Xijing Hospital, Air Force Medical University, Xi'an, Shaanxi, China.
J Cell Mol Med. 2025 May;29(9):e70535. doi: 10.1111/jcmm.70535.
The tumour immune microenvironment (TIME) is critical for lymphoma progression and therapy resistance, yet causal relationships between specific immune cell types and lymphoma subtypes remain poorly defined. In this study, using bidirectional Mendelian randomization (MR), genetic correlation (LDSC), and expression-QTL integration (SMR), we systematically evaluated causal relationships and genetic correlation between immune cells and various lymphomas. Additionally, we utilised the Mendelian randomization-based method of summary data-based MR (SMR), which incorporated genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) data from immune cells to identify genes associated with lymphoma. Furthermore, colocalization analysis and genetic correlation analysis were conducted for further validation of our findings. The two-sample mendelian randomization approach was employed to identify the immune cell types that exhibit a causal relationship with different lymphomas. Additionally, the genetic correlation between these immune cells and lymphomas was further analysed using the linked disequilibrium score regression method, thereby enhancing the reliability of our findings. The SMR and colocalisation analyses revealed several genes associated with these immune cells, thereby providing additional support for their putative role in the pathogenesis of lymphoma. Our study elucidates the intricate interplay between immune cells by employing genetic methodologies, thus suggesting novel therapeutic candidates that warrant experimental validation and risk predictors in different subtypes of lymphoma treatments.
肿瘤免疫微环境（TIME）对淋巴瘤的进展和治疗耐药性至关重要，但特定免疫细胞类型与淋巴瘤亚型之间的因果关系仍不明确。在本研究中，我们使用双向孟德尔随机化（MR）、遗传相关性（LDSC）和表达定量性状位点整合（SMR），系统评估了免疫细胞与各种淋巴瘤之间的因果关系和遗传相关性。此外，我们采用了基于孟德尔随机化的汇总数据MR（SMR）方法，该方法整合了全基因组关联研究（GWAS）和免疫细胞的表达定量性状位点（eQTL）数据，以识别与淋巴瘤相关的基因。此外，还进行了共定位分析和遗传相关性分析，以进一步验证我们的发现。采用两样本孟德尔随机化方法来识别与不同淋巴瘤存在因果关系的免疫细胞类型。此外，使用连锁不平衡评分回归方法进一步分析了这些免疫细胞与淋巴瘤之间的遗传相关性，从而提高了我们研究结果的可靠性。SMR和共定位分析揭示了几个与这些免疫细胞相关的基因，从而为它们在淋巴瘤发病机制中的假定作用提供了额外支持。我们的研究通过采用遗传方法阐明了免疫细胞之间的复杂相互作用，从而提出了新的治疗候选物，值得在不同亚型的淋巴瘤治疗中进行实验验证和风险预测。
