Evidence map›Paper›PMID 38755535›Full record

SynthesisBMC cancer2024

Prediction of esophageal cancer risk based on genetic variants and environmental risk factors in Chinese population.

Haiyan Liu, Keming Li, Junfen Xia, Jicun Zhu, Yifan Cheng, Xiaoyue Zhang, Hua Ye, Peng Wang

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BMC cancer, 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

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. Article
  2. Article
  3. Article
  4. Review
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.

Haiyan LiuDepartment of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou City, 450001, Henan Province, China.
Keming LiZhengzhou Center for Disease Control and Prevention, Zhengzhou City, 450042, Henan Province, China.
Junfen XiaOffice of Health Care, the Third Affiliated Hospital of Zhengzhou University, Zhengzhou City, 450052, Henan Province, China.
Jicun ZhuDepartment of Pharmacy, the First Affiliated Hospital of Zhengzhou University, Zhengzhou City, 450052, Henan Province, China.
Yifan ChengDepartment of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou City, 450001, Henan Province, China.
Xiaoyue ZhangDepartment of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou City, 450001, Henan Province, China.
Hua YeDepartment of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou City, 450001, Henan Province, China.
Peng WangDepartment of Epidemiology and Statistics, College of Public Health, Zhengzhou University, Zhengzhou City, 450001, Henan Province, China. wangpeng1658@hotmail.com.

Funding

Zhengzhou Major Project for Collaborative Innovation 18XTZX12007
6 · The paper itself

Abstract

backgroundResults regarding whether it is essential to incorporate genetic variants into risk prediction models for esophageal cancer (EC) are inconsistent due to the different genetic backgrounds of the populations studied. We aimed to identify single-nucleotide polymorphisms (SNPs) associated with EC among the Chinese population and to evaluate the performance of genetic and non-genetic factors in a risk model for developing EC.

methodsA meta-analysis was performed to systematically identify potential SNPs, which were further verified by a case-control study. Three risk models were developed: a genetic model with weighted genetic risk score (wGRS) based on promising SNPs, a non-genetic model with environmental risk factors, and a combined model including both genetic and non-genetic factors. The discrimination ability of the models was compared using the area under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI). The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to assess the goodness-of-fit of the models.

resultsFive promising SNPs were ultimately utilized to calculate the wGRS. Individuals in the highest quartile of the wGRS had a 4.93-fold (95% confidence interval [CI]: 2.59 to 9.38) increased risk of EC compared with those in the lowest quartile. The genetic or non-genetic model identified EC patients with AUCs ranging from 0.618 to 0.650. The combined model had an AUC of 0.707 (95% CI: 0.669 to 0.743) and was the best-fitting model (AIC = 750.55, BIC = 759.34). The NRI improved when the wGRS was added to the risk model with non-genetic factors only (NRI = 0.082, P = 0.037).

conclusionsAmong the three risk models for EC, the combined model showed optimal predictive performance and can help to identify individuals at risk of EC for tailored preventive measures.

Indexed as

Esophageal NeoplasmsGenetic Predisposition to DiseasePolymorphism, Single NucleotideCase-Control StudiesChinaEast Asian PeopleFemaleGene-Environment InteractionHumansMaleMiddle AgedRisk AssessmentRisk FactorsROC CurveEsophageal cancerMeta-analysisRisk prediction modelSingle-nucleotide polymorphismWeighted genetic risk score

Identifiers

PMID38755535
PMCPMC11100074

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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.