Evidence map›Paper›PMID 41021077›Full record

ArticleDiscover oncology2025

Multiomic Mendelian randomization analysis of metabolic gene methylation expression and protein levels in lung adenocarcinoma.

Qing Wang, Gang Liu, Jun Zhang, Qinguang Zou, Junfeng Geng

Abstract read
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Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Qing Wang *Department of Thoracic Surgery, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China.
Gang Liu *Department of Thoracic Surgery, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China.
Jun Zhang *Department of Thoracic Surgery, The Second People's Hospital of Weifang, Weifang, 261041, Shandong, China.
Qinguang ZouDepartment of Thoracic Surgery, Jilin Provincial Cancer Hospital, Changchun, 130012, Jilin, China. 21215402@qq.com.
Junfeng GengDepartment of Thoracic Surgery, Shanghai Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China. drgengjunfeng@163.com.

Funding

This study was supported by The Medical Engineering Cross research fund of Shanghai Jiaotong University "Star of Jiaotong University" program 24X010301595
6 · The paper itself

Abstract

backgroundMetabolic reprogramming is a hallmark of cancer development, including in lung adenocarcinoma (LUAD). This study aimed to explore the relationships between metabolic gene methylation, expression, and protein levels with LUAD risk using Mendelian randomization, leveraging multi-omic data to identify potential molecular targets for early detection and treatment.

methodsThe study utilized summary-level data from methylation, expression, and protein quantitative trait loci (QTL) studies. Genetic associations with LUAD risk were sourced from the TRICL Consortium for discovery analysis and validated using data from the FinnGen cohort. Mendelian randomization was conducted to evaluate associations between metabolic gene-related molecular features and LUAD risk, while colocalization analyses were performed to assess whether the identified signals shared causal genetic variants.

resultsThe analysis highlighted significant associations between LUAD risk and specific molecular features of metabolic genes. Among these, CHRNA3 emerged as a key gene of interest, with methylation at two sites significantly associated with increased LUAD risk, supported by strong colocalization evidence. Validation in the FinnGen cohort confirmed the association of one methylation site, strengthening its role in LUAD development. Additionally, expression analyses identified FLOT1 and HYKK as genes with moderate but meaningful associations with LUAD risk, with robust colocalization evidence linking their expression to disease susceptibility. Protective associations were observed for specific protein levels, notably for POGLUT3, which displayed a significant inverse relationship with LUAD risk. These findings collectively identify a set of tier 1 metabolic genes, including CHRNA3, FLOT1, HYKK, and POGLUT3, as central players in the metabolic dysregulation underlying LUAD.

conclusionThis multi-omic Mendelian randomization study provides compelling evidence of the role of metabolic genes in LUAD risk. Methylation changes in CHRNA3, altered expression of FLOT1 and HYKK, and protective protein levels of POGLUT3 represent key molecular features associated with disease susceptibility. These findings offer valuable insights into potential molecular targets for early LUAD detection and therapeutic strategies.

Indexed as

CHRNA3FLOT1Gene expressionHYKKLung adenocarcinomaMendelian randomizationMetabolic gene methylationMulti-omic insights.POGLUT3Protein quantification

Identifiers

PMID41021077
PMCPMC12480298

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.