[1]魏 星a,陈芊颖,龚厚文b,等.基于GEO 数据库生物信息学筛选动脉粥样硬化铁死亡关键基因和实验验证[J].现代检验医学杂志,2024,39(05):112-119.[doi:10.3969/j.issn.1671-7414.2024.05.021]
 WEI Xinga,CHEN Qianying,GONG Houwenb,et al.Screening Key Genes of Ferroptosis in Atherosclerosis Based on GEO Database Bioinformatics and Experimental Validation[J].Journal of Modern Laboratory Medicine,2024,39(05):112-119.[doi:10.3969/j.issn.1671-7414.2024.05.021]
点击复制

基于GEO 数据库生物信息学筛选动脉粥样硬化铁死亡关键基因和实验验证()
分享到:

《现代检验医学杂志》[ISSN:/CN:]

卷:
第39卷
期数:
2024年05期
页码:
112-119
栏目:
论著
出版日期:
2024-09-15

文章信息/Info

Title:
Screening Key Genes of Ferroptosis in Atherosclerosis Based on GEO Database Bioinformatics and Experimental Validation
文章编号:
1671-7414(2024)05-112-08
作者:
魏 星1a陈芊颖2龚厚文1b许锋成1b韩立明1c葛 斌1a龚大彩1a
(1. 成都医学院第三附属医院/ 成都市郫都区人民医院a. 检验科;b. 心内科;c. 超声科,成都 611730;2. 成都医学院检验学院,成都 610500)
Author(s):
WEI Xing1a CHEN Qianying2 GONG Houwen1b XU Fengcheng1b HAN Liming1c GE Bin1a GONG Dacai1a
(1a. Department of Clinical Laboratory;1b. Department of Cardiology;1c. Department of Ultrasound, the Third Affiliated Hospital of Chengdu Medical College / Chengdu Pidu District People’s Hospital, Chengdu 611730, China; 2.College of Laboratory Medicine,
关键词:
生物信息学铁死亡动脉粥样硬化差异表达基因采用实时荧光定量PCR
分类号:
R543.5;R446
DOI:
10.3969/j.issn.1671-7414.2024.05.021
文献标志码:
A
摘要:
目的 利用生物信息学方法,筛选动脉粥样硬化(atherosclerosis,AS)铁死亡关键基因,并分析关键基因的生物学功能,以便深入了解AS 发病机制。方法 从GEO(gene expression omnibus)数据库下载AS 基因表达谱芯片数据集GSE100927,以P<0.05,|logFC>1| 为条件筛选AS 的差异基因,并与铁死亡数据集Ferroptosis 基因取交集,筛选出AS 铁死亡相关基因,并进行基因本体(GO)功能注释和京都基因与基因组百科全书(KEGG)富集分析,随后通过 STRING 在线分析工具联合 Cytoscape 可视化软件挖掘在AS 生物学过程中发挥关键作用的基因,并采用AS 数据集GSE9874 作为验证集,验证关键基因的表达,最后,收集2023 年1 ~ 3 月所在医院心血管内科30 例AS 确诊患者血液样本作为实验组,同时收集20 例健康志愿者血液样本作为对照组,提取样本RNA,采用实时荧光定量PCR(quantitativereal time polymerase chain reaction,qRT-PCR)方法对筛选基因验证。结果 采用生物信息学方法共筛选出10 个AS 铁死亡相关差异基因。GO 功能富集分析结果表明,差异基因主要涉及炎症、细胞凋亡、氧化应激等生物学过程,KEGG通路富集分析表明差异基因主要涉及铁死亡、HIF-1 信号通路、白细胞经内皮细胞迁移通路等。蛋白互作网络筛选出7个基因构建的关键模块,分别是FTL,SLC40A1,CYBB,NCF2,HMOX1,DPP4 和ALOX5,采用GSE9874 进行验证,最终筛选出ALOX5,DPP4,FTL,SLC40A1,NCF2 等5 个关键基因;qRT-PCR 对临床样本中关键基因表达检测显示,相对于对照组,AS 组血液中表达上调的基因有DPP4(t=1.795,P=0.046),FTL(t=2.218,P=0.029),SLC40A1(t=2.859,P=0.009);表达下调的基因有ALOX5(t=8.039,P<0.001),NCF2(t=11.867,P<0.001),差异具有统计学意义;实验结果与生物信息学分析结果一致。亚组分析发现斑块组DPP4 表达高于内膜增厚组,差异具有统计学意义(t=2.843,P=0.036)。结论 通过生物信息学筛选出AS 铁死亡关键基因ALOX5,DPP4,FTL,SLC40A1 和NCF2,可能成为AS 诊断治疗的潜在靶点,为AS 的临床诊疗提供新的思路。
Abstract:
Objective To screen key genes for ferroptosis in atherosclerosis (AS) using bioinformatics methods and analyze the biological functions of key genes to gain an in-depth understanding of the pathogenesis of AS. Methods AS gene expression profile chip dataset GSE100927 was downloaded from the Gene Expression Omnibus (GEO) database. P<0.05 and |logFC> 1| were used as the conditions to screen the differential genes of AS. These genes were intersected with ferroptosis gene dataset to screen out the genes related to AS ferroptosis. Gene ontology (GO) functional annotation and enrichment analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out. The STRING online analysis tool combined with Cytoscape visualization software was subsequently used to mine genes that play a key role in the biological process of AS, and the AS dataset GSE9874 was used as the verification set to verify the expression of key genes. Blood samples from 30 confirmed AS patients in the cardiovascular department of our hospital from January to March 2023 were collected as the experimental group, while blood samples from 20 healthy volunteers were collected as the control group. Sample RNA was extracted, and quantitative real time polymerase chain reaction (qRT-PCR) was used to verify the selected genes. Results A total of 10 differential genes related to ferroptosis were screened by bioinformatics method. GO functional enrichment analysis showed that differential genes were mainly involved in inflammation, apoptosis, oxidative stress and other biological processes, while KEGG pathway enrichment analysis showed that differential genes were mainly involved in ferroptosis, HIF-1 signaling pathway, and leukocyte migration pathway through endothelial cells. Seven key modules of gene construction were screened out through the protein interaction network, which were FTL, SLC40A1, CYBB, NCF2, HMOX1, DPP4 and ALOX5. GSE9874 was used for verification, and 5 key genes including ALOX5, DPP4, FTL, SLC40A1 and NCF2 were finally screened out. The expression detection of key genes in clinical samples by qRT-PCR showed that compared with the control group, the up-regulated genes in blood of AS group were DPP4 (t=1.795, P=0.046), FTL (t=2.218, P=0.029) and SLC40A1 (t=2.859, P=0.009), and the downregulated genes were ALOX5 (t=8.039, P<0.001) and NCF2 (t=11.867, P<0.001), and the differences were significant. The experimental results were consistent with the results of bioinformatics analysis. Subgroup analysis showed that DPP4 expression in plaque group was higher than that in intima thickening group, and the difference was significant (t=2.843, P=0.036). Conclusion The key genes of ferroptosis screened by bioinformatics are AS, ALOX5, DPP4, FTL, SLC40A1 and NCF2, which may be potential targets for the diagnosis and treatment of AS, providing new ideas for the clinical diagnosis and treatment of AS.

参考文献/References:

[1] RANGARAJAN S, ORUJYAN D, RANGCHAIKUL P, et al. Critical role of inflammation and specialized pro-resolving mediators in the pathogenesis of atherosclerosis[J]. Biomedicines, 2022, 10(11): 2829.
[2] FREDMAN G, SERHAN C N. Specialized pro-resolving mediators in vascular inflammation and atherosclerotic cardiovascular disease[J]. Nature Reviews Cardiology, 2024:DOI: 10.1038/s41569-023-00984-x.
[3] 王雪琦, 赵斓婷, 马春燕. 颈动脉及下肢动脉粥样硬化与冠心病相关性的研究进展[J]. 实用临床医药杂志, 2022, 26(3): 125-129. WANG Xueqi, ZHAO Lanting, MA Chunyan. Research progress of correlation of carotid artery and lower limb atherosclerosis with coronary heart disease[J]. Journal of Clinical Medicine in Practice, 2022, 26(3): 125-129.
[4] 王冯宇, 李亘, 林晓伊, 等. 铁死亡在肿瘤治疗作用中的研究进展[J]. 基础医学与临床, 2022, 42(11):1781-1784. WANG Fengyu, LI Gen, LIN Xiaoyi, et al. Research progress on the role of ferroptosis in the treatment of tumors[J]. Basic & Clinical Medicine, 2022, 42(11): 1781-1784.
[5] YU Yi, YAN Yuan, NIU Fanglin, et al. Ferroptosis: a cell death connecting oxidative stress, inflammation and cardiovascular diseases[J]. Cell Death Discovery, 2021, 7(1): 193.
[6] LIN Lin, ZHANG Muxin, ZHANG Lei, et al. Autophagy, pyroptosis, and ferroptosis: new regulatory mechanisms for atherosclerosis[J]. Frontiers in Cell and Developmental Biology, 2021, 9: 809955.
[7] FANG Xuexian, ARDEHALI H, MIN Junxia, et al. The molecular and metabolic landscape of iron and ferroptosis in cardiovascular disease[J]. Nature Reviews Cardiology, 2023, 20(1): 7-23.
[8] 吴良银, 李文丽, 刘俊. 基于GEO 数据的病毒相关性肝癌潜在生物基因标志物的筛选及生物信息学分析[J]. 现代检验医学杂志, 2021, 36(6): 106-110. WU Liangyin, LI Wenli, LIU Jun. Screening and bioinformatics analysis of potential biomarkers for virus-associated hepatocellular carcinoma based on GEO data[J]. Journal of Modern Laboratory Medicine, 2021, 36(6): 106-110.
[9] 侯丽, 张丽, 唐婧, 等. 基于GEO 对多发性骨髓瘤关键基因生物信息学分析及免疫浸润模式与验证[J]. 现代检验医学杂志, 2023, 38(5): 23-28. HOU Li, ZHANG Li, TANG Jing, et al. Bioinformatics analysis and verify core genes and immune infiltration patterns in multiple myeloma based on GEO[J]. Journal of Modern Laboratory Medicine, 2023, 38(5): 23-28.
[10] 侯芳霞, 刘琳, 张维, 等. 基于GEO 数据库筛选稳定性心绞痛患者外周血关键差异基因及诊断模型构建[J]. 现代检验医学杂志, 2022, 37(6): 19-23, 69. HOU Fangxia, LIU Lin, ZHANG Wei, et al. Identification of hub genes and differential expression genes for peripheral blood samples of stable angina pectoris based on GEO databases[J]. Journal of Modern Laboratory Medicine, 2022, 37(6): 19-23, 69.
[11] 晏子钦, 魏明波, 程波. 基于铁死亡相关基因的口腔鳞状细胞癌的生物信息学分析[J]. 临床口腔医学杂志, 2022, 38(1): 19-22. YAN Ziqin, WEI Mingbo, CHENG Bo. Bioinformatics analysis of oral squamous cell carcinoma based on the expression of ferroptosis-related genes[J]. Journal of Clinical Stomatology, 2022, 38(1): 19-22.
[12] B?CK M, YURDAGUL A J, TABAS I, et al. Inflammation and its resolution in atherosclerosis: mediators and therapeutic opportunities[J]. Nature Reviews Cardiology, 2019, 16(7): 389-406.
[13] DIXON S J, LEMBERG K M, LAMPRECHT M R, et al. Ferroptosis: an iron-dependent form of nonapoptotic cell death[J]. Cell, 2012, 149(5): 1060-1072.
[14] OUYANG Siyu, YOU Jia, ZHI Chenxi, et al. Ferroptosis: the potential value target in atherosclerosis[J]. Cell Death & Disease, 2021, 12(8): 782.
[15] XIAO Lin, LUO Gang, GUO Xiaoping, et al. Macrophage iron retention aggravates atherosclerosis: evidence for the role of autocrine formation of hepcidin in plaque macrophages[J]. Biochimica Et Biophysica Acta Molecular and Cell Biology of Lipids, 2020, 1865(2): 158531.
[16] BAI Tao, LI Mingxing, LIU Yuanfeng, et al. Inhibition of ferroptosis alleviates atherosclerosis through attenuating lipid peroxidation and endothelial dysfunction in mouse aortic endothelial cell[J]. Free Radical Biology and Medicine, 2020, 160: 92-102.
[17] YANG Yunfan, LIN Ting, KUANG Pu, et al. Ferritin L-subunit gene mutation and hereditary hyperferritinaemia cataract syndrome (HHCS): a case report and literature review[J]. Hematology, 2021, 26(1): 896-903.
[18] UYY E, SUICA V I, BOTEANU R M, et al. Regulated cell death joins in atherosclerotic plaque silent progression[J]. Scientific Reports, 2022, 12(1): 2814.
[19] WANG Yuqin, ZHAO Yajie, YE Ting, et al. Ferroptosis signaling and regulators in atherosclerosis[J]. Frontiers in Cell and Developmental Biology, 2021, 9: 809457.
[20] RAJENDRAN P, AMMAR R B, AL-SAEEDI F J, et al. Kaempferol inhibits zearalenone-induced oxidative stress and apoptosis via the PI3K/Akt-mediated Nrf2 signaling pathway: in vitro and in vivo studies[J]. International Journal of Molecular Sciences, 2020, 22(1): 217.
[21] CHEN S R, YANG L Q, CHONG Y T, et al. Novel gain of function mutation in the SLC40A1 gene associated with hereditary haemochromatosis type 4[J]. Internal Medicine Journal, 2015, 45(6): 672-676.
[22] HERMAN A B, TSITSIPATIS D, ANERILLAS C, et al. DPP4 inhibition impairs senohemostasis to improve plaque stability in atherosclerotic mice[J]. Journal of Clinical Investigation, 2023, 133(12): e165933.
[23] 钟玉梅, 钟雪玉, 周卫平, 等. 2 型糖尿病颈动脉粥样硬化患者血清二肽基肽酶4 和氧化低密度脂蛋白水平改变[J]. 中国糖尿病杂志, 2022, 30(2): 111-115. ZHONG Yumei, ZHONG Xueyu, ZHOU Weiping, et al. Changes of serum dipeptidyl peptidase-4 and oxidized low-density lipoprotein levels in type 2 diabetic patients with carotid atherosclerosis[J]. Chinese Journal of Diabetes, 2022, 30(2): 111-115.
[24] 谢金波, 彭波. NCF2/p67phox 在泛肿瘤中的表达调控及临床意义研究进展[J]. 同济大学学报( 医学版),2023, 44(6): 889-896. XIE Jinbo, PENG Bo. Expression and clinical significance of NCF2/p67phox in pan-tumor[J]. Journal of Tongji University (Medical Science), 2023, 44(6): 889-896.
[25] TARAZONA-SANTOS E, MACHADO M, MAGALH?ES W C S, et al. Evolutionary dynamics of the human NADPH oxidase genes CYBB, CYBA, NCF2, and NCF4: functional implications[J]. Molecular Biology and Evolution, 2013, 30(9): 2157-2167.
[26] 赵倩倩, 孙之, 潘月眉, 等. ALOX5 可作为与免疫细胞浸润相关的非小细胞肺癌预后生物标志物[J].中南大学学报( 医学版), 2023, 48(3):311-322. ZHAO Qianqian, SUN Zhi, PAN Yuemei, et al. Role of ALOX5 in non-small cell lung cancer: a potential therapeutic target associated with immune cell infiltration[J]. Journal of Central South University(Medical Science), 2023, 48(3): 311-322.
[27] CAMACHO-MEJORADO R, G?MEZ R, TORRESS?NCHEZ L E, et al. ALOX5, LPA, MMP9 and TPO gene polymorphisms increase atherothrombosis susceptibility in middle-aged Mexicans[J]. Royal Society Open Science, 2020, 7(1): 190775.

相似文献/References:

[1]陈龙梅,杨振华.基于GEO数据库对类风湿性关节炎相关基因筛选及生物信息学分析[J].现代检验医学杂志,2021,36(02):49.[doi:doi:10.3969/j.issn.1671-7414.2021.02.012]
 CHEN Long-mei,YANG Zhen-hua.Gene Screening and Bioinformatics Analysis of Rheumatoid Arthritis Based on GEO Database[J].Journal of Modern Laboratory Medicine,2021,36(05):49.[doi:doi:10.3969/j.issn.1671-7414.2021.02.012]
[2]吴良银a,李文丽b,刘 俊b.基于GEO数据的病毒相关性肝癌潜在生物基因标志物的筛选及生物信息学分析[J].现代检验医学杂志,2021,36(06):106.[doi:10.3969/j.issn.1671-7414.2021.06.022]
 WU Liang-yin,LI Wen-li,LIU Jun.Screening and Bioinformatics Analysis of Potential Biomarkers for Virus-associated Hepatocellular Carcinoma Based on GEO Data[J].Journal of Modern Laboratory Medicine,2021,36(05):106.[doi:10.3969/j.issn.1671-7414.2021.06.022]
[3]张涛元,丁雪梅,李 俏,等.人非小细胞肺癌组织中转录因子E2F 家族表达与临床病理特征及预后的相关性分析[J].现代检验医学杂志,2022,37(04):87.[doi:10.3969/j.issn.1671-7414.2022.04.017]
 ZHANG Tao-yuan,DING Xue-mei,LI Qiao,et al.Correlation Analysis of Transcription Factor E2F Family Expression with Clinicopathological Features and Prognosis in Human Non-small Cell Lung Cancer[J].Journal of Modern Laboratory Medicine,2022,37(05):87.[doi:10.3969/j.issn.1671-7414.2022.04.017]
[4]侯芳霞,刘 琳,张 维,等.基于GEO 数据库筛选稳定性心绞痛患者外周血关键差异基因及诊断模型构建[J].现代检验医学杂志,2022,37(06):19.[doi:10.3969/j.issn.1671-7414.2022.06.004]
 HOU Fang-xia,LIU Lin,ZHANG Wei,et al.Identification of Hub Genes and Differential Expression Genes for Peripheral Blood Samples of Stable Angina Pectoris Based on GEO Databases[J].Journal of Modern Laboratory Medicine,2022,37(05):19.[doi:10.3969/j.issn.1671-7414.2022.06.004]
[5]毛 俊,沈秀芬,马 润,等.基于TCGA 和GEO 数据库建立了肝内胆管癌的预后风险模型及验证分析[J].现代检验医学杂志,2023,38(03):40.[doi:10.3969/j.issn.1671-7414.2023.03.008]
 MAO Jun,SHEN Xiu-fen,MA Run,et al.Establishment and Verification of Prognostic Risk Model of Intrahepatic Cholangiocarcinoma Based on TCGA and GEO Database[J].Journal of Modern Laboratory Medicine,2023,38(05):40.[doi:10.3969/j.issn.1671-7414.2023.03.008]
[6]侯 丽,张 丽,唐 婧,等.基于GEO 对多发性骨髓瘤关键基因生物信息学分析及免疫浸润模式与验证[J].现代检验医学杂志,2023,38(05):23.[doi:10.3969/j.issn.1671-7414.2023.05.005]
 HOU Li,ZHANG Li,TANG Jing,et al.Bioinformatics Analysis and Verify Core Genes and Immune Infiltration Patterns in Multiple Myeloma Based on GEO[J].Journal of Modern Laboratory Medicine,2023,38(05):23.[doi:10.3969/j.issn.1671-7414.2023.05.005]
[7]曹 君,金婕妤,张 胜,等.生物信息学方法筛选IL-3和IL-3+SCF诱导的小鼠骨髓来源肥大细胞的差异表达基因及相关信号通路分析[J].现代检验医学杂志,2024,39(01):16.[doi:10.3969/j.issn.1671-7414.2024.01.004]
 CAO Jun,JIN Jieyu,ZHANG Sheng,et al.Screening of IL-3 and IL-3+SCF Induce Differentially Expressed Genes and Signaling Pathways in Bone Marrow-derived Mast Cells Based on Bioinformatics[J].Journal of Modern Laboratory Medicine,2024,39(05):16.[doi:10.3969/j.issn.1671-7414.2024.01.004]
[8]刁 迅,范绮雨,耿良栋,等.基于生物信息学分析双硫死亡相关基因PDLIM1 mRNA在多种肿瘤中的表达及临床应用价值[J].现代检验医学杂志,2024,39(01):36.[doi:10.3969/j.issn.1671-7414.2024.01.007]
 DIAO Xun,FAN Qiyu,GENG Liangdong,et al.Analysis of Expression in Disulfidptosis-Related Gene PDLIM1 mRNA in Various Tumors and Its Clinical Application Value Based on Bioinformatics[J].Journal of Modern Laboratory Medicine,2024,39(05):36.[doi:10.3969/j.issn.1671-7414.2024.01.007]
[9]钟双泽,陈尚金,林汉胜,等.基于TCGA 数据库生物信息学分析构建肾癌N6- 甲基腺苷相关LncRNA 配对模型及其预后预测价值研究[J].现代检验医学杂志,2024,39(02):68.[doi:10.3969/j.issn.1671-7414.2024.02.013]
 ZHONG Shuangze,CHEN Shangjin,LIN Hansheng,et al.Construction of N6-methyladenosine Related LncRNA Pairing Model for Renal Cell Carcinoma Based on Bioinformatics Analysis of TCGA Database and Its Prognostic Value Research[J].Journal of Modern Laboratory Medicine,2024,39(05):68.[doi:10.3969/j.issn.1671-7414.2024.02.013]
[10]陆 兵,李明虎,文 宁,等.基于TCGA 和HPA 数据库生物学信息分析肝癌组织中YEATS2 表达水平与临床预后及治疗价值[J].现代检验医学杂志,2024,39(03):8.[doi:10.3969/j.issn.1671-7414.2024.03.002]
 LU Bing,LI Minghu,WEN Ning,et al.Analysis of YEATS2 Expression Level in Hepatocellular Carcinoma Tissues with Clinical Prognosis and Therapeutic Value Based on Biological Information from TCGA and HPA Databases[J].Journal of Modern Laboratory Medicine,2024,39(05):8.[doi:10.3969/j.issn.1671-7414.2024.03.002]

备注/Memo

备注/Memo:
基金项目: 成都市卫健委2022 年课题(立项编号:2022504);2021 年四川省医学会青年创新科研课题(立项编号)Q21094;成都医学院校级课题(CYZYB20-23)。
作者简介:魏星(1984-),男,硕士,副主任技师,主要研究方向:生物信息学,E-mail:weixing3009@163.com。
通讯作者:龚大彩(1979-),男,大学本科,主任技师,主要研究方向:临床检验诊断学,E-mail:603632601@qq.com。
更新日期/Last Update: 2024-09-15