| 杨丽婉，高倩，王彬，高利娟，胡江伟.GEO数据库联合孟德尔随机化分析：基因决定的
高血脂与2型糖尿病周围性神经病变的效应关系[J].神经损伤功能重建,2024,(12):746-751 |
| GEO数据库联合孟德尔随机化分析：基因决定的
高血脂与2型糖尿病周围性神经病变的效应关系 |
| GEO Database Combined with Mendelian Randomization Analysis: The Effect RelationshipBetween Gene-Determined Hyperlipidemia and Diabetic Peripheral Neuropathy in Type 2Diabetes |
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| DOI： |
| 中文关键词: 2型糖尿病 周围神经病变 潜在影响机制 生物信息学 基因因果效应 |
| 英文关键词: type 2 diabetes peripheral neuropathy mechanisms of potential impact bioinformatics genetic causal effects |
| 基金项目:邢台市重点研发计
划（动脉微灌注治
疗2型糖尿病周围
神经血管病变的临
床应用研究，No. 2
022ZC184） |
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| 摘要点击次数: 167 |
| 全文下载次数: 158 |
| 中文摘要: |
| 目的：通过生物信息学技术确定影响2型糖尿病（diabetes mellitus type 2，T2DM）周围神经病变（diabetic peripheral neuropathy，DPN）发生的潜在机制。方法：采用Gene Expression Omnibus（GEO）数据库下载
相关数据集（数据集编号：GSE24290），R 包‘limma’对数据集进行差异表达分析，利用‘org.Hs.eg.db’和
‘clusterProfiler’包对靶标基因进行GO和KEGG富集分析，利用STRING网站（https://cn.string-db.org/）获得
相关基因的蛋白-蛋白交互作用网络。基于 Python 中的‘Networkx’和‘Netwulf’库对分子网络进行可视
化。引用临床样本确定筛选到的影响疾病发生的因素是否符合中国人样本。最后采用孟德尔随机化分析
方法确定筛选到的影响因素与T2DM、T2DM DPN风险存在的基因层面因果效应关系。结果：GEO数据库
数据相关数据经差异分析后，GO 与 KEGG 结果显示，差异基因生物学通路主要集中在脂肪代谢过程。
STRING结果显示，与脂质代谢相关途径的基因共同点是促进高血脂发生和发展。在中国人临床样本中
T2DM 群体、T2DM DPN群体的总胆固醇、甘油三酯、高密度脂蛋白、低密度脂蛋白、载脂蛋白B表达水平高
于健康对照组（P＜0.05），与T2DM 组相比血脂相关指标表达水平较高，但差异无统计学意义（P＞0.05）。
MR 分析结果显示，血脂-T2DM、高血脂-T2DM DPN 不存在效果效应关系。MR-Egger、WME、Weighted
mode、Simple mode确定MR结果的稳定性，散点图、留一法、漏斗图证实结果可靠。结论：高血脂是T2DM
DPN发生潜在影响因子，但基因层面并未发现存在因果效应关系。 |
| 英文摘要: |
| To identify potential mechanisms influencing the occurrence of diabetic peripheral
neuropathy (DPN) in type 2 diabetes mellitus (T2DM) through bioinformatics techniques. Methods: The
relevant dataset (GSE24290) was downloaded from the Gene Expression Omnibus (GEO) database. Differential
expression analysis was conducted on the dataset using the R package 'limma'. GO and KEGG enrichment
analyses of the target genes were performed using the 'org.Hs.eg.db' and 'clusterProfiler' packages. The
protein-protein interaction network of the relevant genes was obtained from the STRING website (https://cn.
string-db.org/). The molecular network was visualized using the 'Networkx' and 'Netwulf' libraries in Python.
Clinical samples were referenced to determine whether the identified factors influencing disease occurrence
were consistent with Chinese samples. Finally, Mendelian randomization (MR) analysis was employed to
determine the gene-level causal effect relationship between the identified influencing factors and the risk of
T2DM and T2DM DPN. Results: After differential analysis of the GEO database data, the GO and KEGG
results showed that the biological pathways of differential genes were mainly concentrated in fat metabolism
processes. The STRING results indicated that the commonality of genes related to lipid metabolism pathways
was to promote the occurrence and development of hyperlipidemia. In Chinese clinical samples, the expression
levels of total cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein, and apolipoprotein B
were higher in the T2DM group and T2DM DPN group compared to the healthy control group (P<0.05).
Compared to the T2DM group, the expression levels of blood lipid-related indicators were higher, but the
difference was not statistically significant (P>0.05). The MR analysis results showed no causal effect
relationship between blood lipids-T2DM and hyperlipidemia-T2DM DPN. MR-Egger, WME, Weighted mode,
and Simple mode were used to determine the stability of the MR results, and scatter plots, leave-one-out
analysis, and funnel plots confirmed the reliability of the results. Conclusion: Hyperlipidemia is a potential
influencing factor for the occurrence of T2DM DPN, but no causal effect relationship was found at the gene
level. |
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