Huang Peiying, Yan Li, Li Zhishang, Zhao Shuai, Feng Yuchao, Zeng Jing, Chen Li, Huang Afang, Chen Yan, Lei Sisi, Huang Xiaoyan, Deng Yi, Xie Dan, Guan Hansu, Peng Weihang, Yu Liyuan, Chen Bojun
The Second Clinical Medical School of Guangzhou University of Chinese Medicine, Guangzhou, China.
Department of Neurosurgery of Shenyang Second Hospital of Traditional Chinese Medicine, Shenyang, China.
Comput Biol Med. 2023 Jan;152:106450. doi: 10.1016/j.compbiomed.2022.106450. Epub 2022 Dec 21.
Atherosclerosis and depression contribute to each other; however, mechanisms linking them at the genetic level remain unexplored. This study aimed to identify shared gene signatures and related pathways between these comorbidities.
Atherosclerosis-related datasets were downloaded from the Gene Expression Omnibus database. Differential and weighted gene co-expression network analyses were employed to identify atherosclerosis-related genes. Depression-related genes were downloaded from the DisGeNET database, and the overlaps between atherosclerosis-related genes and depression-related genes were characterized as crosstalk genes. The functional enrichment analysis and protein-protein interaction network were performed in these gene sets. Subsequently, the Boruta algorithm and Recursive Feature Elimination algorithm were performed to identify feature-selection genes. A support vector machine was constructed to measure the accuracy of calculations, and two external validation sets were included to verify the results.
Based on two atherosclerosis-related datasets (GSE28829 and GSE43292), 165 genes were determined as atherosclerosis-related genes. Meanwhile, 1478 depression-related genes were obtained. After intersecting, 24 crosstalk genes were identified, and two pathways, "lipid and atherosclerosis" and "tryptophan metabolism," were revealed as mutual pathways according to the enrichment analysis results. Through the protein-protein interaction network, Molecular Complex Detection plugin, and cytoHubba plugin, PTPRC and MMP9 were identified as the hub gene. Moreover, SLC22A3, CASP1, AMPD3, and PIK3CG were recognized as feature-selection genes. Based on two external validation sets, CASP1 and MMP9 were finally determined as the critical crosstalk genes.
"Lipid and atherosclerosis" and "tryptophan metabolism" were possibly the pathways of atherosclerosis secondary to depression and depression due to atherosclerosis, respectively. CASP1 and MMP9 were revealed as the most pivotal candidates linking atherosclerosis and depression by mediating these two pathways. Further experimentation is needed to confirm these conclusions.
动脉粥样硬化与抑郁症相互影响;然而,在基因水平上连接它们的机制仍未得到探索。本研究旨在识别这些共病之间的共享基因特征和相关通路。
从基因表达综合数据库下载与动脉粥样硬化相关的数据集。采用差异基因共表达网络分析和加权基因共表达网络分析来识别与动脉粥样硬化相关的基因。从疾病基因网络数据库下载与抑郁症相关的基因,并将与动脉粥样硬化相关的基因和与抑郁症相关的基因之间的重叠部分表征为串扰基因。对这些基因集进行功能富集分析和蛋白质-蛋白质相互作用网络分析。随后,进行博鲁塔算法和递归特征消除算法以识别特征选择基因。构建支持向量机来衡量计算的准确性,并纳入两个外部验证集以验证结果。
基于两个与动脉粥样硬化相关的数据集(GSE28829和GSE43292),确定了165个与动脉粥样硬化相关的基因。同时,获得了1478个与抑郁症相关的基因。交叉分析后,识别出24个串扰基因,根据富集分析结果,“脂质与动脉粥样硬化”和“色氨酸代谢”这两条通路被揭示为共同通路。通过蛋白质-蛋白质相互作用网络、分子复合物检测插件和细胞枢纽插件,识别出PTPRC和MMP9为枢纽基因。此外,SLC22A3、CASP1、AMPD3和PIK3CG被识别为特征选择基因。基于两个外部验证集,最终确定CASP1和MMP9为关键串扰基因。
“脂质与动脉粥样硬化”和“色氨酸代谢”可能分别是抑郁症继发动脉粥样硬化以及动脉粥样硬化导致抑郁症的通路。CASP1和MMP9被揭示为通过介导这两条通路连接动脉粥样硬化和抑郁症的最关键候选基因。需要进一步的实验来证实这些结论。