文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期,新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

整合性表达数量性状基因座(eQTL)与孟德尔随机化分析揭示了间皮瘤中的关键遗传标记。

Integrative eQTL and Mendelian randomization analysis reveals key genetic markers in mesothelioma.

作者信息

Li Jinsong, Wang Xingmeng, Lin Yaru, Li Zhengliang, Xiong Wei

机构信息

Department of Biochemistry and Molecular Biology, College of Basic Medical Sciences, Dali University, Dali, Yunnan, China.

Key Laboratory of Clinical Biochemistry Testing in Universities of Yunnan Province, College of Basic Medical Sciences, Dali University, Dali, Yunnan, China.

出版信息

Respir Res. 2025 Apr 13;26(1):140. doi: 10.1186/s12931-025-03219-4.


DOI:10.1186/s12931-025-03219-4
PMID:40223054
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11995628/
Abstract

BACKGROUND: Mesothelioma is a rare cancer that originates from the pleura and peritoneum, with its incidence increasing due to asbestos exposure. Patients are frequently diagnosed at advanced stages, resulting in poor survival rates. Therefore, the identification of molecular markers for early detection and diagnosis is essential. METHODS: Three mesothelioma datasets were downloaded from the GEO database for differential gene expression analysis. Instrumental variables (IVs) were identified based on expression quantitative trait locus (eQTL) data for Mendelian randomization (MR) analysis using mesothelioma Genome-Wide Association Study (GWAS) data from the FINNGEN database. The intersecting genes from MR-identified risk genes and differentially expressed genes were identified as key co-expressed genes for mesothelioma. Functional enrichment analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA), as well as immune cell correlation analysis, were performed to elucidate the roles of key genes in mesothelioma. Additionally, the differential expression of key genes in mesothelioma was validated in independent GEO datasets and TCGA datasets. This integrative research combining multiple databases and analytical methods established a robust model for identifying mesothelioma risk genes. RESULTS: The research conducted in our study identified 1608 genes that were expressed differentially in mesothelioma GEO datasets. By combining these genes with 192 genes from MR analysis, we identified 14 key genes. Notably, MPZL1, SOAT1, TACC3, and CYBRD1 are linked to a high risk of mesothelioma, while TGFBR3, NDRG2, EPAS1, CPA3, MNDA, PRKCD, MTUS1, ALOX15, LRRN3, and ITGAM are associated with a lower risk. These genes were found to be enriched in pathways associated with superoxide metabolism, cell cycle regulation, and proteasome function, all of which are linked to the development of mesothelioma. Noteworthy observations included a significant infiltration of M1 macrophages and CD4 + T cells in mesothelioma, with genes SOAT1, MNDA, and ITGAM showing a positive correlation with the level of M1 macrophage infiltration. Furthermore, the differential expression analyses conducted on the GEO validation set and TCGA data confirmed the significance of the identified key genes. CONCLUSION: This integrative eQTL and Mendelian randomization analysis provides evidence of a positive causal association between 14 key co-expressed genes and mesothelioma genetically. These disease critical genes are implicated in correlations with biological processes and infiltrated immune cells related to mesothelioma. Moreover, our study lays a theoretical foundation for further research into the mechanisms of mesothelioma and potential clinical applications.

摘要

背景:间皮瘤是一种起源于胸膜和腹膜的罕见癌症,由于接触石棉,其发病率正在上升。患者常被诊断为晚期,导致生存率较低。因此,识别用于早期检测和诊断的分子标志物至关重要。 方法:从GEO数据库下载了三个间皮瘤数据集用于差异基因表达分析。基于表达数量性状位点(eQTL)数据鉴定工具变量(IVs),以便使用来自FINNGEN数据库的间皮瘤全基因组关联研究(GWAS)数据进行孟德尔随机化(MR)分析。将MR鉴定的风险基因与差异表达基因的交集基因确定为间皮瘤的关键共表达基因。进行了功能富集分析,包括基因本体论(GO)、京都基因与基因组百科全书(KEGG)和基因集富集分析(GSEA),以及免疫细胞相关性分析,以阐明关键基因在间皮瘤中的作用。此外,在独立的GEO数据集和TCGA数据集中验证了间皮瘤中关键基因的差异表达。这种结合多个数据库和分析方法的综合研究建立了一个强大的模型来识别间皮瘤风险基因。 结果:我们研究中的研究确定了1608个在间皮瘤GEO数据集中差异表达的基因。通过将这些基因与MR分析中的192个基因相结合,我们确定了14个关键基因。值得注意的是,MPZL1、SOAT1、TACC3和CYBRD1与间皮瘤的高风险相关,而TGFBR3、NDRG2、EPAS1、CPA3、MNDA、PRKCD、MTUS1、ALOX15、LRRN3和ITGAM与较低风险相关。发现这些基因在与超氧化物代谢、细胞周期调控和蛋白酶体功能相关的途径中富集,所有这些都与间皮瘤的发展有关。值得注意的观察结果包括间皮瘤中M1巨噬细胞和CD4 + T细胞的显著浸润,基因SOAT1、MNDA和ITGAM与M1巨噬细胞浸润水平呈正相关。此外,在GEO验证集和TCGA数据上进行的差异表达分析证实了所鉴定关键基因的重要性。 结论:这种综合的eQTL和孟德尔随机化分析提供了14个关键共表达基因与间皮瘤之间存在正向因果关联的遗传证据。这些疾病关键基因与间皮瘤相关的生物学过程和浸润免疫细胞存在相关性。此外,我们的研究为进一步研究间皮瘤的机制和潜在临床应用奠定了理论基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/e7449211e48a/12931_2025_3219_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/4a3ebf64a32e/12931_2025_3219_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/851372d1e20b/12931_2025_3219_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/e415d37f8a5e/12931_2025_3219_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/aba069868195/12931_2025_3219_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/3dbca056698c/12931_2025_3219_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/44a52837b512/12931_2025_3219_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/3a7246b05d66/12931_2025_3219_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/26ce19303a05/12931_2025_3219_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/78de41087bf7/12931_2025_3219_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/e7449211e48a/12931_2025_3219_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/4a3ebf64a32e/12931_2025_3219_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/851372d1e20b/12931_2025_3219_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/e415d37f8a5e/12931_2025_3219_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/aba069868195/12931_2025_3219_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/3dbca056698c/12931_2025_3219_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/44a52837b512/12931_2025_3219_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/3a7246b05d66/12931_2025_3219_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/26ce19303a05/12931_2025_3219_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/78de41087bf7/12931_2025_3219_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9be2/11995628/e7449211e48a/12931_2025_3219_Fig10_HTML.jpg

相似文献

[1]
Integrative eQTL and Mendelian randomization analysis reveals key genetic markers in mesothelioma.

Respir Res. 2025-4-13

[2]
Exploring Pathogenic Genes in Frozen Shoulder through weighted gene co-expression network analysis and Mendelian Randomization.

Int J Med Sci. 2024

[3]
Identification of atrial fibrillation-related genes through transcriptome data analysis and Mendelian randomization.

Front Cardiovasc Med. 2024-7-11

[4]
Amino acid metabolism-related genes as potential biomarkers and the role of MATN3 in stomach adenocarcinoma: A bioinformatics, mendelian randomization and experimental validation study.

Int Immunopharmacol. 2024-12-25

[5]
Exploring potential therapeutic targets for small cell lung cancer based on transcriptomics combined with Mendelian randomization analysis.

Front Immunol. 2025-1-13

[6]
Mendelian randomization analysis of blood uric acid and risk of preeclampsia: based on GWAS and eQTL data.

J Matern Fetal Neonatal Med. 2025-12

[7]
Multi-omic biomarkers associated with multiple sclerosis: from Mendelian randomization to drug prediction.

Sci Rep. 2025-3-19

[8]
Integrated Analysis of Bulk RNA Sequencing, eQTL, GWAS, and Single-Cell RNA Sequencing Reveals Key Genes in Hepatocellular Carcinoma.

J Cell Mol Med. 2025-1

[9]
Transcriptomics in idiopathic pulmonary fibrosis unveiled: a new perspective from differentially expressed genes to therapeutic targets.

Front Immunol. 2024

[10]
Investigating potential biomarkers of acute pancreatitis in patients with a BMI>30 using Mendelian randomization and transcriptomic analysis.

Lipids Health Dis. 2024-4-22

本文引用的文献

[1]
MPZL1 as an HGF/MET signaling amplifier promotes cell migration and invasion in glioblastoma.

Genes Dis. 2023-9-9

[2]
Salvianolic acid B inhibits the growth and metastasis of A549 lung cancer cells through the NDRG2/PTEN pathway by inducing oxidative stress.

Med Oncol. 2024-6-7

[3]
SOAT1 regulates cholesterol metabolism to induce EMT in hepatocellular carcinoma.

Cell Death Dis. 2024-5-9

[4]
An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs.

Sci China Life Sci. 2024-6

[5]
NDRG2 acts as a negative regulator of the progression of small-cell lung cancer through the modulation of the PTEN-AKT-mTOR signalling cascade.

Toxicol Appl Pharmacol. 2024-4

[6]
TACC3: a multi-functional protein promoting cancer cell survival and aggressiveness.

Cell Cycle. 2023

[7]
MPZL1 suppresses the cancer stem-like properties of lung cancer through β-catenin/TCF4 signaling.

Funct Integr Genomics. 2023-9-19

[8]
A pan-cancer analysis identifies SOAT1 as an immunological and prognostic biomarker.

Oncol Res. 2023

[9]
New Markers for Management of Mesothelioma.

Semin Respir Crit Care Med. 2023-8

[10]
MPZL1 Promotes Lung Adenocarcinoma Progression by Enhancing Tumor Proliferation, Invasion, Migration, and Suppressing Immune Function via Transforming Growth Factor-β1.

Hum Gene Ther. 2023-6

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索