Department of Geriatric Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China.
Fujian Provincial Center for Geriatrics, Fujian Provincial Hospital, Fuzhou, Fujian, China.
Front Immunol. 2022 Dec 8;13:1046410. doi: 10.3389/fimmu.2022.1046410. eCollection 2022.
BACKGROUND: Using interpretable machine learning, we sought to define the immune microenvironment subtypes and distinctive genes in AD. METHODS: ssGSEA, LASSO regression, and WGCNA algorithms were used to evaluate immune state in AD patients. To predict the fate of AD and identify distinctive genes, six machine learning algorithms were developed. The output of machine learning models was interpreted using the SHAP and LIME algorithms. For external validation, four separate GEO databases were used. We estimated the subgroups of the immunological microenvironment using unsupervised clustering. Further research was done on the variations in immunological microenvironment, enhanced functions and pathways, and therapeutic medicines between these subtypes. Finally, the expression of characteristic genes was verified using the AlzData and pan-cancer databases and RT-PCR analysis. RESULTS: It was determined that AD is connected to changes in the immunological microenvironment. WGCNA revealed 31 potential immune genes, of which the greenyellow and blue modules were shown to be most associated with infiltrated immune cells. In the testing set, the XGBoost algorithm had the best performance with an AUC of 0.86 and a P-R value of 0.83. Following the screening of the testing set by machine learning algorithms and the verification of independent datasets, five genes (CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12) that were closely associated with AD pathological biomarkers and allowed for the accurate prediction of AD progression were found to be immune microenvironment-related genes. The feature gene-based nomogram may provide clinical advantages to patients. Two immune microenvironment subgroups for AD patients were identified, subtype2 was linked to a metabolic phenotype, subtype1 belonged to the immune-active kind. MK-866 and arachidonyltrifluoromethane were identified as the top treatment agents for subtypes 1 and 2, respectively. These five distinguishing genes were found to be intimately linked to the development of the disease, according to the Alzdata database, pan-cancer research, and RT-PCR analysis. CONCLUSION: The hub genes associated with the immune microenvironment that are most strongly associated with the progression of pathology in AD are CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12. The hypothesized molecular subgroups might offer novel perceptions for individualized AD treatment.
背景：我们使用可解释的机器学习方法，旨在确定 AD 中的免疫微环境亚型和特征基因。
方法：使用 ssGSEA、LASSO 回归和 WGCNA 算法评估 AD 患者的免疫状态。为了预测 AD 的命运并确定特征基因，我们开发了六种机器学习算法。使用 SHAP 和 LIME 算法解释机器学习模型的输出。为了外部验证，我们使用了四个独立的 GEO 数据库。我们使用无监督聚类估计免疫微环境亚群。进一步研究了这些亚群之间免疫微环境、增强功能和途径以及治疗药物的变化。最后，使用 AlzData 和泛癌数据库以及 RT-PCR 分析验证特征基因的表达。
结果：确定 AD 与免疫微环境变化有关。WGCNA 显示 31 个潜在的免疫基因，其中绿色黄色和蓝色模块与浸润免疫细胞最相关。在测试集中，XGBoost 算法的表现最佳，AUC 为 0.86，P-R 值为 0.83。通过机器学习算法对测试集进行筛选，以及对独立数据集的验证，发现 5 个与 AD 病理生物标志物密切相关且能够准确预测 AD 进展的基因（CXCR4、PPP3R1、HSP90AB1、CXCL10 和 S100A12）是与免疫微环境相关的基因。基于特征基因的诺模图可能为患者提供临床优势。确定了两种 AD 患者的免疫微环境亚群，亚型 2 与代谢表型相关，亚型 1 属于免疫活跃型。MK-866 和花生四烯酸三氟甲醚分别被鉴定为亚型 1 和 2 的首选治疗药物。根据 AlzData 数据库、泛癌研究和 RT-PCR 分析，这五个区别基因与疾病的发展密切相关。
结论：与 AD 病理进展最密切相关的与免疫微环境相关的关键基因是 CXCR4、PPP3R1、HSP90AB1、CXCL10 和 S100A12。假设的分子亚群可能为个体化 AD 治疗提供新的见解。
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