Evidence map›Paper›PMID 38874503›Full record

ArticleAging2024

Genetically predicted 91 circulating inflammatory proteins in relation to risk of urological malignancies: a Mendelian randomization study.

Jianxiong Xu, Ru Chen, Yucheng Yang, Sufang Xu, Lijing Yao

Abstract read
In one paragraph

Article in Aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Jianxiong XuDepartment of Radiotherapy, The First Hospital of Putian City, Putian, Fujian 351100, China.
Ru ChenDepartment of Radiotherapy, The First Hospital of Putian City, Putian, Fujian 351100, China.
Yucheng YangDepartment of Radiotherapy, The First Hospital of Putian City, Putian, Fujian 351100, China.
Sufang XuDepartment of Proctology of Traditional Chinese Medicine, The First Hospital of Putian City, Putian, Fujian 351100, China.
Lijing YaoDepartment of Radiotherapy, The First Hospital of Putian City, Putian, Fujian 351100, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUrological malignancies, including kidney, bladder, and prostate cancer, are major health concerns worldwide. Inflammation has been implicated in the pathogenesis of these cancers, and circulating inflammatory proteins may play a role in their development. However, the causal relationship between specific plasma proteins and urological malignancies remains unclear.

methodsWe performed a two-sample Mendelian randomization (MR) analysis using summary statistics from genome-wide association studies (GWAS). Instrumental variables representing genetic variants associated with circulating inflammatory proteins were used to infer causality on the risk of kidney, bladder, and prostate cancer. Four MR methods were utilized to provide robust effect estimates.

resultsOur analysis identified several plasma proteins associated with a lower risk of kidney and bladder cancer, including Eukaryotic translation initiation factor 4E-binding protein 1, Caspase 8, Natural killer cell receptor 2B4, and Tumor necrosis factor ligand superfamily member 12. However, after adjusting for multiple testing, these associations did not remain statistically significant. For prostate cancer, CUB domain-containing protein 1 and Interleukin-10 receptor subunit beta were found to be protective, while Glial cell line-derived neurotrophic factor and SIR2-like protein 2 were identified as risk factors. After FDR adjustment, none of the inflammatory proteins were found to be significantly associated with a lower risk of prostate cancer.

conclusionOur findings suggest that certain plasma proteins may be involved in the development of urological malignancies. Mendelian randomization provides a useful framework for investigating causal relationships between inflammatory proteins and urological cancers, offering potential insights into their underlying biology and therapeutic targets.

Indexed as

Genome-Wide Association StudyMendelian Randomization AnalysisBlood ProteinsGenetic Predisposition to DiseaseHumansInflammationKidney NeoplasmsMalePolymorphism, Single NucleotideProstatic NeoplasmsRisk FactorsUrinary Bladder NeoplasmsUrologic NeoplasmsBlood Proteinsbladder cancerinflammatory proteinskidney cancerMendelian randomizationprostate cancerurological malignancies

Identifiers

PMID38874503
PMCPMC11236305

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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.