Evidence map›Paper›PMID 41118010›Full record

ArticleDiscover oncology2025

Identification of promising lung cancer targets from human plasma proteins via Mendelian randomization.

Xiao-Dong Shao, Zhou-Lin Miao, Wei-Jie Yu

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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Xiao-Dong ShaoRadiology Department, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, Shandong, China.
Zhou-Lin MiaoRadiology Department, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, Shandong, China.
Wei-Jie YuDepartment of thoracic surgery, The third affiliated hospital of Xinjiang medical university, Urumuqi, China. 342278431@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung cancer (LC) is the most prevalent form of malignant neoplasm globally, as well as the major cause of cancer-related death. Identifying effective pharmaceutical targets is paramount in advancing the development of treatment modalities for LC.

methodProtein-wide Mendelian randomization (MR) was used in this study. The present study collated data on plasma proteins from a protein quantitative trait loci (pQTL) study with a total of 4907 individuals. Genetic associations with LC were obtained from GWAS, including 3791 cases and 489012 controls. Integration of pQTL and LC genome-wide association study (GWAS) data was employed to identify candidate proteins. MR used single nucleotide polymorphisms (SNPs) as a genetic tool to estimate the causal effect of exposure on the outcome, while reverse Mendelian randomization was performed to assess the presence of false positives. The present study utilized these approaches to evaluate the causal relationship between plasma proteins and LC. Finally, protein-protein interaction (PPI) and functional enrichment analyses were performed to illustrate potential links between proteins and current LC drugs. Finally, drug prediction and molecular docking were performed to predict drugs and explored the expression distribution of key genes by single-cell sequencing.

resultWe identified 46 plasma proteins that are strongly associated with LC Fifteen of these proteins have protective effects. Among them, MMP8(OR = 0.87, 95%CI:0.78-0.97, p = 0.013) had the most significant protective effect. In contrast, 31 proteins increased the risk of LC. IL36A༈OR = 1.20, 95%CI:1.041-1.38, p = 0.012) exhibited the most significant MR result. Notably, COL2A1, MMP19 showed reverse causality. This was further verified by enrichment analysis, which confirmed the causal effect of these proteins. Additionally, the researchers utilized the DSigDB database to predict potentially effective intervening drugs, identifying nine possible candidates. Molecular docking showed that the drugs bind very much to the proteins. KDR and ANGPTL4 are abundantly expressed in lung tissue and are differentially expressed between cells.

conclusionThe present study has revealed six potential drug targets for the treatment of LC. Drugs designed to target these proteins will be more likely to attain success in clinical trials and are expected to assist in the development of LC drugs and reduce drug development costs.

Indexed as

Drug targetLCMendelian randomizationPlasma protein

Identifiers

PMID41118010
PMCPMC12540209

What Socratic holds

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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 Socratic graph.