Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods.
Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods.
复制标题
使用共表达基因模块和机器学习方法的哮喘风险预测模型的开发和验证。
DOI：
10.1038/s41598-023-35866-2
复制
发表时间：
2023-07-12
影响因子：
4.6
通讯作者：
Mersha, Tesfaye B.
中科院分区：
文献类型：
--
作者：
Dessie, Eskezeia Y.;Gautam, Yadu;Ding, Lili;Altaye, Mekibib;Beyene, Joseph;Mersha, Tesfaye B.
哮喘是一种以气道炎症和阻塞为特征的异质性呼吸系统疾病。尽管最近的进展，哮喘发病机制的遗传调控仍然是未知的。基因表达谱技术非常适合研究包括哮喘在内的复杂疾病。在这项研究中，差异表达基因（DEG），然后加权基因共表达网络分析（WGCNA）和机器学习技术，使用气道上皮细胞（AEC）和鼻上皮细胞（NEC）生成的数据集，用于识别候选基因和途径，并开发哮喘分类和预测模型。使用支气管上皮细胞（BEC）、气道平滑肌（ASM）和全血（WB）数据集验证了这些模型。DEG和WGCNA，然后是最小绝对收缩和选择算子（LASSO）方法，鉴定了30个和34个基因签名，并且这些基因签名与支持向量机（SVM）一起区分哮喘受试者和对照者的AEC（曲线下面积：AUC = 1）和NEC（AUC = 1）。我们进一步在BEC（AUC = 0.72）、ASM（AUC = 0.74）和WB（AUC = 0.66）中验证了AEC来源的基因签名。类似地，在BEC（AUC = 0.75）、ASM（AUC = 0.82）和WB（AUC = 0.69）中验证了NEC来源的基因签名。基于AEC和NEC的基因签名都显示出具有高灵敏度和特异性的强诊断性能。来自AEC和NEC的基因签名的功能注释在与IL-13、PI 3 K/AKT和凋亡信号传导相关的通路中富集。包括SERPINB 2和CTSC基因在内的几个哮喘相关基因被优先考虑，这些基因在多种组织/细胞类型中显示出功能相关性，并与哮喘发病机制相关。总之，基于上皮基因特征的模型可以作为包括BEC在内的难以获得的组织的可靠替代模型，以改善哮喘的分子病因学。
Asthma is a heterogeneous respiratory disease characterized by airway inflammation and obstruction. Despite recent advances, the genetic regulation of asthma pathogenesis is still largely unknown. Gene expression profiling techniques are well suited to study complex diseases including asthma. In this study, differentially expressed genes (DEGs) followed by weighted gene co-expression network analysis (WGCNA) and machine learning techniques using dataset generated from airway epithelial cells (AECs) and nasal epithelial cells (NECs) were used to identify candidate genes and pathways and to develop asthma classification and predictive models. The models were validated using bronchial epithelial cells (BECs), airway smooth muscle (ASM) and whole blood (WB) datasets. DEG and WGCNA followed by least absolute shrinkage and selection operator (LASSO) method identified 30 and 34 gene signatures and these gene signatures with support vector machine (SVM) discriminated asthmatic subjects from controls in AECs (Area under the curve: AUC = 1) and NECs (AUC = 1), respectively. We further validated AECs derived gene-signature in BECs (AUC = 0.72), ASM (AUC = 0.74) and WB (AUC = 0.66). Similarly, NECs derived gene-signature were validated in BECs (AUC = 0.75), ASM (AUC = 0.82) and WB (AUC = 0.69). Both AECs and NECs based gene-signatures showed a strong diagnostic performance with high sensitivity and specificity. Functional annotation of gene-signatures from AECs and NECs were enriched in pathways associated with IL-13, PI3K/AKT and apoptosis signaling. Several asthma related genes were prioritized including SERPINB2 and CTSC genes, which showed functional relevance in multiple tissue/cell types and related to asthma pathogenesis. Taken together, epithelium gene signature-based model could serve as robust surrogate model for hard-to-get tissues including BECs to improve the molecular etiology of asthma.
登录
查看更多内容
影响因子：
--
作者：
Ai, Xiaoshun;Shen, Hong;Wang, Yangyanqiu;Zhuang, Jing;Zhou, Yani;Niu, Furong;Zhou, Qing
通讯作者：
Zhou, Qing
影响因子：
3.5
作者：
Chen, Linbo;Lu, Dewen;Xu, Feng
通讯作者：
Xu, Feng
WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
影响因子：
3
作者：
Kursa MB
通讯作者：
Kursa MB
影响因子：
5.8
作者：
Kursa, Miron B.;Rudnicki, Witold R.
通讯作者：
Rudnicki, Witold R.