Evidence map›Paper›PMID 38648753›Full record

ArticleJournal of the National Cancer Institute2024

Systematic investigation of genetically determined plasma and urinary metabolites to discover potential interventional targets for colorectal cancer.

Jing Sun, Jianhui Zhao, Siyun Zhou, Xinxuan Li, Tengfei Li, Lijuan Wang, Shuai Yuan, Dong Chen, Philip J Law, Susanna C Larsson and 5 more

Open access · hybridAbstract read
In one paragraph

Article in Journal of the National Cancer Institute, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
4.0field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 17 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 7 institutions in 3 countries.

Jing SunDepartment of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.ORCID 0000-0003-0046-6663
Jianhui ZhaoDepartment of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Siyun ZhouDepartment of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Xinxuan LiDepartment of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Tengfei LiDepartment of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Lijuan WangCentre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK.
Shuai YuanUnit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.ORCID 0000-0001-5055-5627
Dong ChenDepartment of Colorectal Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Philip J LawDivision of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.
Susanna C LarssonUnit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
Susan M FarringtonCancer Research UK Edinburgh Centre, Medical Research Council Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
Richard S HoulstonDivision of Genetics and Epidemiology, The Institute of Cancer Research, London, UK.
Malcolm G DunlopCancer Research UK Edinburgh Centre, Medical Research Council Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
Evropi TheodoratouCentre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK.
Xue LiDepartment of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.ORCID 0000-0001-6880-2577
Second Affiliated Hospital of Zhejiang University · CNEdinburgh Cancer Research · GBInstitute of Cancer Research · GBFirst Affiliated Hospital Zhejiang University · CNKarolinska Institutet · SEUniversity of Edinburgh · GBUppsala University · SE

Funding

Cancer Research UK DRCPGM\100012Cancer Research UK Scotland CentreCRUK Career Development C31250/A22804Hjärt-Lungfonden 20190247National Nature Science Foundation of China 82204019Natural Science Fund for Distinguished Young Scholars of Zhejiang Province LR22H260001Swedish Cancer SocietySwedish Heart-Lung FoundationSwedish Research CouncilVetenskapsrådet 2019-00977
6 · The paper itself

Abstract

backgroundWe aimed to identify plasma and urinary metabolites related to colorectal cancer (CRC) risk and elucidate their mediator role in the associations between modifiable risk factors and CRC.

methodsMetabolite quantitative trait loci were derived from 2 published metabolomics genome-wide association studies, and summary-level data were extracted for 651 plasma metabolites and 208 urinary metabolites. Genetic associations with CRC were obtained from a large-scale genome-wide association study meta-analysis (100 204 cases, 154 587 controls) and the FinnGen cohort (4957 cases, 304 197 controls). Mendelian randomization and colocalization analyses were performed to evaluate the causal roles of metabolites in CRC. Druggability evaluation was employed to prioritize potential therapeutic targets. Multivariable Mendelian randomization and mediation estimation were conducted to elucidate the mediating effects of metabolites on the associations between modifiable risk factors and CRC.

resultsThe study identified 30 plasma metabolites and 4 urinary metabolites for CRC. Plasma sphingomyelin and urinary lactose, which were positively associated with CRC risk, could be modulated by drug interventions (ie, olipudase alfa, tilactase). Thirteen modifiable risk factors were associated with 9 metabolites, and 8 of these modifiable risk factors were associated with CRC risk. These 9 metabolites mediated the effect of modifiable risk factors (Actinobacteria, body mass index, waist to hip ratio, fasting insulin, smoking initiation) on CRC.

conclusionThis study identified key metabolite biomarkers associated with CRC and elucidated their mediator roles in the associations between modifiable risk factors and CRC. These findings provide new insights into the etiology and potential therapeutic targets for CRC and the etiological pathways of modifiable environmental factors with CRC.

Indexed as

Colorectal NeoplasmsGenome-Wide Association StudyMendelian Randomization AnalysisQuantitative Trait LociBiomarkers, TumorCase-Control StudiesFemaleHumansMaleMetabolomeMetabolomicsPolymorphism, Single NucleotideRisk FactorsBiomarkers, Tumor

Identifiers

PMID38648753
PMCPMC11308169
OpenAlexW4394992327

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.