Machine learning and deep learning to identifying subarachnoid haemorrhage macrophage-associated biomarkers by bulk and single-cell sequencing

作者全名：Yang, Sha; Hu, Yunjia; Wang, Xiang; Deng, Mei; Ma, Jun; Hao, Yin; Ran, Zhongying; Luo, Tao; Han, Guoqiang; Xiang, Xin; Liu, Jian; Shi, Hui; Tan, Ying

作者地址：[Yang, Sha; Hu, Yunjia; Wang, Xiang; Xiang, Xin; Liu, Jian] Guizhou Med Univ, Dept Neurosurg, Affiliated Hosp, Guiyang 550004, Peoples R China; [Yang, Sha; Liu, Jian] Guizhou Univ, Med Coll, Guiyang, Peoples R China; [Deng, Mei; Ma, Jun; Hao, Yin; Ran, Zhongying; Luo, Tao; Han, Guoqiang; Liu, Jian; Tan, Ying] Guizhou Prov Peoples Hosp, Dept Neurosurg, Guiyang 550002, Peoples R China; [Shi, Hui] Chongqing Med Univ, Dept Neurosurg, Yongchuan Hosp, Chongqing 400016, Peoples R China

通信作者：Xiang, X; Liu, J (通讯作者)，Guizhou Med Univ, Dept Neurosurg, Affiliated Hosp, Guiyang 550004, Peoples R China.; Liu, J; Tan, Y (通讯作者)，Guizhou Prov Peoples Hosp, Dept Neurosurg, Guiyang 550002, Peoples R China.; Shi, H (通讯作者)，Chongqing Med Univ, Dept Neurosurg, Yongchuan Hosp, Chongqing 400016, Peoples R China.

来源：JOURNAL OF CELLULAR AND MOLECULAR MEDICINE

ESI学科分类：MOLECULAR BIOLOGY & GENETICS

WOS号：WOS:001258445300001

JCR分区：Q2

影响因子：4.3

年份：2024

卷号：28

期号：9

开始页：　

结束页：　

文献类型：Article

关键词：deep learning; hdWGCNA; machine learning; single-cell sequencing; subarachnoid haemorrhage rat model

摘要：We investigated subarachnoid haemorrhage (SAH) macrophage subpopulations and identified relevant key genes for improving diagnostic and therapeutic strategies. SAH rat models were established, and brain tissue samples underwent single-cell transcriptome sequencing and bulk RNA-seq. Using single-cell data, distinct macrophage subpopulations, including a unique SAH subset, were identified. The hdWGCNA method revealed 160 key macrophage-related genes. Univariate analysis and lasso regression selected 10 genes for constructing a diagnostic model. Machine learning algorithms facilitated model development. Cellular infiltration was assessed using the MCPcounter algorithm, and a heatmap integrated cell abundance and gene expression. A 3 x 3 convolutional neural network created an additional diagnostic model, while molecular docking identified potential drugs. The diagnostic model based on the 10 selected genes achieved excellent performance, with an AUC of 1 in both training and validation datasets. The heatmap, combining cell abundance and gene expression, provided insights into SAH cellular composition. The convolutional neural network model exhibited a sensitivity and specificity of 1 in both datasets. Additionally, CD14, GPNMB, SPP1 and PRDX5 were specifically expressed in SAH-associated macrophages, highlighting its potential as a therapeutic target. Network pharmacology analysis identified some targeting drugs for SAH treatment. Our study characterised SAH macrophage subpopulations and identified key associated genes. We developed a robust diagnostic model and recognised CD14, GPNMB, SPP1 and PRDX5 as potential therapeutic targets. Further experiments and clinical investigations are needed to validate these findings and explore the clinical implications of targets in SAH treatment.

基金机构：Guizhou Provincial People's Hospital Youth Fund

基金资助正文：We sincerely thank public databases such as GEO for providing this platform and the investigators for sharing their important data sets.