Identification of Unique Genetic Biomarkers of Various Subtypes of Glomerulonephritis Using Machine Learning and Deep Learning.
Identification of Unique Genetic Biomarkers of Various Subtypes of Glomerulonephritis Using Machine Learning and Deep Learning.
复制标题
使用机器学习和深度学习鉴定肾小球肾炎各种亚型的独特遗传生物标志物
DOI：
10.3390/biom12091276
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发表时间：
2022-09-10
期刊：
影响因子：
5.5
通讯作者：
Li, Yafeng
中科院分区：
文献类型：
--
作者：
Qing, Jianbo;Zheng, Fang;Zhi, Huiwen;Yaigoub, Hasnaa;Tirichen, Hasna;Li, Yaheng;Zhao, Juanjuan;Qiang, Yan;Li, Yafeng
(1)目的：鉴定各种肾小球肾炎（glomerulonephritis， GN）亚型的潜在遗传生物标志物，探索GN的分子机制。(2)方法：从Gene Expression Omnibus （GEO）数据库中下载4个GN微阵列数据集，合并得到8个GN亚型的基因表达谱。然后，通过鉴定差异表达免疫相关基因（DIRGs）来探索GN的分子机制，并通过单样本基因集富集分析（ssGSEA）来发现GN中的异常炎症。此外，利用R软件包“glmnet”生成了nomogram模型，并绘制了校准曲线来评估nomogram模型的预测能力。最后，基于多层感知器（MLP）网络的深度学习（DL）来探索GN的特征基因。(3)结果：筛选出肾小球和小管间质中常见的274种上调或下调的dirg。这些DIRGs主要参与t细胞分化、RAS信号通路和MAPK信号通路。ssGSEA提示DC（树突状细胞）和巨噬细胞显著增加，肾小球中性粒细胞和NKT细胞显著减少，小管间质单核细胞和NK细胞显著增加。基于7个dirg构建预测GN的nomogram模型，并选取肾小球和小管间质GN各亚型的20个dirg作为特征基因。(4)结论：本研究提示DIRGs与GN的发病密切相关，可作为GN的遗传生物标志物。DL进一步确定了确定GN发病机制所必需的特征基因，并开发了针对8种GN亚型的靶向治疗方法。
(1) Objective: Identification of potential genetic biomarkers for various glomerulonephritis (GN) subtypes and discovering the molecular mechanisms of GN. (2) Methods: four microarray datasets of GN were downloaded from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression profiles of eight GN subtypes. Then, differentially expressed immune-related genes (DIRGs) were identified to explore the molecular mechanisms of GN, and single-sample gene set enrichment analysis (ssGSEA) was performed to discover the abnormal inflammation in GN. In addition, a nomogram model was generated using the R package “glmnet”, and the calibration curve was plotted to evaluate the predictive power of the nomogram model. Finally, deep learning (DL) based on a multilayer perceptron (MLP) network was performed to explore the characteristic genes for GN. (3) Results: we screened out 274 common up-regulated or down-regulated DIRGs in the glomeruli and tubulointerstitium. These DIRGs are mainly involved in T-cell differentiation, the RAS signaling pathway, and the MAPK signaling pathway. ssGSEA indicates that there is a significant increase in DC (dendritic cells) and macrophages, and a significant decrease in neutrophils and NKT cells in glomeruli, while monocytes and NK cells are increased in tubulointerstitium. A nomogram model was constructed to predict GN based on 7 DIRGs, and 20 DIRGs of each subtype of GN in glomeruli and tubulointerstitium were selected as characteristic genes. (4) Conclusions: this study reveals that the DIRGs are closely related to the pathogenesis of GN and could serve as genetic biomarkers in GN. DL further identified the characteristic genes that are essential to define the pathogenesis of GN and develop targeted therapies for eight GN subtypes.
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影响因子：
9.8
作者：
Bhattacharya S;Dunn P;Thomas CG;Smith B;Schaefer H;Chen J;Hu Z;Zalocusky KA;Shankar RD;Shen-Orr SS;Thomson E;Wiser J;Butte AJ
通讯作者：
Butte AJ
影响因子：
3.2
作者：
Farris AB;Colvin RB
通讯作者：
Colvin RB
影响因子：
4.6
作者：
Krebs CF;Steinmetz OM
通讯作者：
Steinmetz OM
影响因子：
5.8
作者：
Alikhan MA;Huynh M;Kitching AR;Ooi JD
通讯作者：
Ooi JD
DOI：
10.1109/tnnls.2019.2945113
发表时间：
2020-09-01
影响因子：
10.4
作者：
Dittmer, Soeren;King, Emily J.;Maass, Peter
通讯作者：
Maass, Peter