Evidence map›Paper›PMID 39692937›Full record

ArticleDiscover oncology2024

Comprehensive Mendelian randomization analysis of low-density lipoprotein cholesterol and multiple cancers.

Hengchang Liang, Chunling Tang, Yue Sun, Mingwei Wang, Tong Tong, Qinquan Gao, Hui Xie, Tao Tan

Abstract read
In one paragraph

Article in Discover oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Cholesterol as a metabolic integrator of oncogenic signaling, immune evasion, and therapy resistance.Apoptosis : an international journal on programmed cell death · 2026
    Review
  3. Article
  4. Article
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

8 authors.

Hengchang LiangFaulty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China.
Chunling TangCentre for Craniofacial and Regenerative Biology, King's College London, London, SE1 9RT, UK.
Yue SunFaulty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China.
Mingwei WangDepartment of Dardiovascular Medicine, Affiliated Hospital of Hangzhou Normal University, Clinical School of Medicine, Hangzhou Normal University, Hangzhou Institute of Cardiovascular Diseases, Hangzhou, 310015, China.
Tong TongThe College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China.
Qinquan GaoThe College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China.
Hui XieFaulty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China. h.xie@xnu.edu.cn.
Tao TanFaulty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China. taotan@mpu.edu.mo.

Funding

Macao Polytechnic University RP/FCA-15/2022
6 · The paper itself

Abstract

purposeThe aim of this study was to investigate the causal relationship between low-density lipoprotein cholesterol (LDL-C) and five cancers (breast, cervical, thyroid, prostate and colorectal) using the Mendelian Randomization (MR) method, with a view to revealing the potential role of LDL-C in the development of these cancers.

methodsWe used gene variant data and disease data from the Genome-Wide Association Study (GWAS) database to assess the causal relationship between LDL-C and each cancer by Mendelian randomisation analysis methods such as inverse variance weighting and MR-Egger. Specifically, we selected Proprotein convertase subtilisin/kexin type 9 (PCSK9) and 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), genes associated with LDL-C levels, as instrumental variables, extracted the corresponding single nucleotide polymorphism (SNP) data and analysed the associations of these SNPs with five cancers.In addition, sensitivity analyses and heterogeneity tests were performed to ensure the reliability of the results.

resultsThe analyses showed that when using HMGCR gene, LDL-C were significantly and positively associated with breast (OR:1.200, 95% CI:1.082-1.329, p = 0.001), prostate (OR:1.198, 95% CI:1.050-1.366, p = 0.007), and thyroid cancers (OR:8.291, 95% CI:3.189- 21.555, p = 0.00001) were significantly positively correlated, whereas they were significantly negatively correlated with colorectal cancer (OR:0.641, 95% CI:0.442-0.928, p = 0.019); the results for cervical cancer were not significant (p = 0.050). When using the PCSK9 gene, LDL-C levels were significantly and positively associated with breast (OR:1.107, 95%:CI 1.031-1.187, p = 0.005) and prostate (OR:1.219, 95%:CI 1.101-1.349, p = 0.0001) cancers, but not with cervical (p = 0.294), thyroid cancer (p = 0.759) and colorectal cancer ( p = 0.572).

conclusionAnalyses using both the HMGCR and PCSK9 genes have shown that LDL-C may be a potential risk factor for breast and prostate cancer, while analyses of the HMGCR gene have also suggested that LDL-C may increase the risk of thyroid cancer and decrease the risk of colorectal cancer.

Indexed as

CancerCausalityHMGCRLDL cholesterolMendelian randomizationPSCK9

Identifiers

PMID39692937
PMCPMC11655734

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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.