作者：方柔柔1 ，杨启帆1 ，韩若冰1 ，邬东东1 ，孙娜1 ，李娟1 ，徐守竹1 ，赵晶2

单位：

1.712046陕西省咸阳市，陕西中医药大学公共卫生学院　2.712046陕西省咸阳市，陕西中医药大学陕西省针药结合重点实验室

单位（英文）：

1.School of Public Health, Shaanxi University of Chinese Medicine, Xianyang 712046, China 2.Shaanxi Key Laboratory of Acupuncture & Medicine, Shaanxi University of Chinese Medicine, Xianyang 712046, China

关键词：

动脉粥样硬化；加权基因共表达网络分析；铁死亡；核心基因；免疫浸润细胞

关键词（英文）：

Atherosclerosis; Weighted gene co-expression network analysis; Ferroptosis; Core gene; Immune infiltrating cells

中图分类号：

R 543.5

DOI：

10.12114/j.issn.1008-5971.2024.00.058

基金项目：

国家自然科学基金资助项目（82100488）；陕西省科技计划项目（2022SF-318）；陕西省教育厅科学研究项目 （21JS012，21JK0597）；国家级大学生创新创业训练计划项目（202210716019）；陕西中医药大学研究生质量提升工程专项项目 （CXSJ202322）；陕西中医药大学优势特色与交叉学科支持计划（2022XKZC04）

摘要：

目的　基于加权基因共表达网络分析（WGCNA）筛选动脉粥样硬化（AS）中铁死亡核心基因，并分 析其与免疫浸润细胞的关系。方法　从基因表达综合（GEO）数据库下载AS转录组数据集GSE100927〔筛选出颈动脉 样本41个，包含12个健康对照者的颈动脉样本（对照组）和29个AS患者的颈动脉样本（AS组）〕。从FerrDb数据库下 载铁死亡相关基因（FRG），去除重复基因后，最终纳入了564个FRG。利用R软件（版本4.2.3）进行数据预处理、差 异表达基因（DEG）筛选、WGCNA，通过在线String数据库进行蛋白质-蛋白质相互作用网络（PPI）分析及确定核心 基因，比较GSE100927中对照组和AS组核心基因表达水平，并分析GSE20129和GSE226790中核心基因表达水平，同时 进行单样本基因集富集分析（ssGSEA）及免疫浸润分析。结果　共筛选出了508个DEG。网络拓扑分析结果显示，软 阈值为2。共识别到5个基因模块，其中绿松石色基因模块与AS相关性最强（r=－0.96，P＜0.001），该基因模块共包 含15 087个基因。将绿松石色基因模块中的基因、FRG、DEG取交集，共得到17个交集基因。PPI分析结果显示，构建 出1个含有17个节点、60条边的PPI网络图；核心基因分别为IL1B、CTSB、HMOX1、CDKN2A、ALOX5。在GSE100927 中，AS组IL1B、CTSB、HMOX1、CDKN2A、ALOX5表达水平高于对照组（P＜0.05）；在GSE20129中，AS组IL1B、 HMOX1表达水平高于对照组（P＜0.05）；在GSE20129中，对照组与AS组CDKN2A、ALOX5表达水平比较，差异无统 计学意义（P＞0.05）；在GSE226790中，对照组与AS组CTSB表达水平比较，差异无统计学意义（P＞0.05）。ssGSEA 结果显示，IL1B、CTSB、HMOX1、CDKN2A、ALOX5主要涉及铁死亡、脂肪消化吸收等机制。AS组静息CD4记忆T淋 巴细胞、浆细胞表达水平低于对照组，活化肥大细胞、单核细胞、滤泡辅助性T淋巴细胞、记忆B细胞表达水平高于 对照组（P＜0.05）。相关性分析结果显示，IL1B表达水平与活化肥大细胞、中性粒细胞表达水平呈正相关，与滤泡 辅助性T淋巴细胞、记忆B细胞、M0型巨噬细胞表达水平呈负相关（P＜0.05）；CTSB、HMOX1表达水平与22种免疫 浸润细胞表达水平均无直线相关关系（P＞0.05）；CDKN2A表达水平与中性粒细胞表达水平呈负相关（P＜0.05）； ALOX5表达水平与活化CD4记忆T淋巴细胞、活化树突状细胞、M2型巨噬细胞表达水平呈负相关，与活化肥大细胞、 调节性T淋巴细胞、M1型巨噬细胞表达水平呈正相关（P＜0.05）。结论　本研究基于WGCNA共筛选出5个AS中铁死亡 核心基因，分别为IL1B、CTSB、HMOX1、CDKN2A、ALOX5，其中IL1B、CDKN2A、ALOX5表达水平与部分免疫浸润 细胞表达水平有直线相关关系，而CTSB、HMOX1表达水平与22种免疫浸润细胞表达水平均无直线相关关系。

英文摘要：

Objective To screen the core genes of ferroptosis in atherosclerosis (AS) based on weighted gene co-expression network analysis (WGCNA) , and analyze their relationship with immune infiltrating cells. Methods AS transcriptome dataset GSE100927 was downloaded from the gene expression omnibus (GEO) database [41 carotid vascular samples were screened, including 12 healthy carotid vascular samples (control group) and 29 AS carotid vascular samples (AS group) ] . After downloading ferroptosis related genes (FRGs) from the FerrDb database and removing duplicate genes, 564 FRGs were eventually included. R software (version 4.2.3) was used for data preprocessing, differentially expressed genes (DEG) screening, and WGCNA. Protein-protein interaction networks (PPI) analysis and identification of core genes were performed using an online String database. The expression levels of core genes were compared between control group and AS group in GSE100927, and the expression levels of core genes in GSE20129 and GSE226790 were analyzed. At the same time, single sample gene set enrichment analysis (ssGSEA) and immune infiltration analysis were performed. Results A total of 508 DEGs were screened. The result of network topology analysis showed that the soft threshold was 2. A total of 5 gene modules were identified, among which the turquoise gene module had the strongest correlation with AS (r=－0.96, P < 0.001) . This gene module contained a total of 15 087 genes. By intersecting the genes in the turquoise gene module, FRG, and DEG, a total of 17 intersecting genes were obtained. The PPI analysis results showed that a PPI network diagram with 17 nodes and 60 edges had been constructed; the core genes were IL1B, CTSB, HMOX1, CDKN2A, and ALOX5, respectively. In GSE100927, the expression levels of IL1B, CTSB, HMOX1, CDKN2A and ALOX5 in AS group were higher than those in control group (P < 0.05) . In GSE20129, the expression levels of IL1B and HMOX1 in AS group were higher than those in control group (P < 0.05) . In GSE20129, there was no significant difference in the expression levels of CDKN2A and ALOX5 between control group and AS group (P > 0.05) . In GSE226790, there was no significant difference in CTSB expression levels between control group and AS group (P > 0.05) . The results of ssGSEA showed that IL1B, CTSB, HMOX1, CDKN2A and ALOX5 were mainly involved in the mechanism of iron death, fat digestion and absorption. The expression levels of resting CD4 memory T lymphocytes and plasma cells in AS group were lower than those in control group, and the expression levels of activated mast cells, monocytes, follicular helper T lymphocytes and memory B cells in AS group were higher than those in control group (P < 0.05) . Correlation analysis showed that the expression level of IL1B was positively correlated with the expression levels of activated mast cells and neutrophils, and negatively correlated with the expression levels of follicular T-helper lymphocytes, memory B cells and M0 macrophages (P < 0.05) ; there was no linear correlation between the expression levels of CTSB and HMOX1 and the expression levels of 22 kinds of immune infiltrating cells (P > 0.05) ; the expression level of CDKN2A was negatively correlated with the expression level of neutrophils (P < 0.05) ; the expression level of ALOX5 was negatively correlated with the expression levels of activated CD4 memory T lymphocytes, activated dendritic cells and M2 macrophages, and positively correlated with the expression levels of activated mast cells, regulatory T lymphocytes and M1 macrophages (P < 0.05) . Conclusion In this study, five core genes of iron death in AS are screened out based on WGCNA, namely IL1B, CTSB, HMOX1, CDKN2A and ALOX5 respectively, among which the expression levels of IL1B, CDKN2A and ALOX5 are linearly correlated with the expression levels of some immune infiltrating cells, while there was no linear correlation between the expression levels of CTSB and HMOX1 and the expression levels of 22 kinds of immune infiltrating cells.

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