ArticleCancer medicine2024
A polygenetic risk score combined with environmental factors better predict susceptibility to hepatocellular carcinoma in Chinese population.
Article in Cancer medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
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Who cites it
5 citing papers in PubMed.
- Association Between Allostatic Load, Genetic Susceptibility, and Liver Cancer Incidence: A Large-Scale Prospective Cohort Study.International journal of cancer · 2026Article
- AI-driven integration of genomic and exposome data for cancer risk: the combined risk score (CRS).Human genomics · 2026Review
- The immunoecology of occult hepatitis B virus infection: genetic remodeling and the immune-mediated microcosm.Frontiers in immunology · 2026Review
- Putative Imprinting Control Regions with Aberrant Blood-Based DNA Methylation are Associated with Hepatocellular Carcinoma Risk.Journal of hepatocellular carcinoma · 2026Article
- A polygenetic risk score combined with environmental factors better predict susceptibility to hepatocellular carcinoma in Chinese population.Cancer medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
aimsThis study aimed to investigate environmental factors and genetic variant loci associated with hepatocellular carcinoma (HCC) in Chinese population and construct a weighted genetic risk score (wGRS) and polygenic risk score (PRS).
methodsA case-control study was applied to confirm the single nucleotide polymorphisms (SNPs) and environmental variables linked to HCC in the Chinese population, which had been screened by meta-analyses. wGRS and PRS were built in training sets and validation sets. Area under the curve (AUC), net reclassification improvement (NRI), integrated discrimination improvement (IDI), Akaike information criterion (AIC), and Bayesian information criterion (BIC) were applied to evaluate the performance of the models.
resultsA total of 13 SNPs were included in both risk prediction models. Compared with wGRS, PRS had better accuracy and discrimination ability in predicting HCC risk. The AUC for PRS in combination with drinking history, cirrhosis, HBV infection, and family history of HCC in training sets and validation sets (AUC: 0.86, 95% CI: 0.84-0.89; AUC: 0.85, 95% CI: 0.81-0.89) increased at least 20% than the AUC for PRS alone (AUC: 0.63, 95% CI: 0.60-0.67; AUC: 0.65, 95% CI: 0.60-0.71).
conclusionsA novel model combining PRS with alcohol history, HBV infection, cirrhosis, and family history of HCC could be applied as an effective tool for risk prediction of HCC, which could discriminate at-risk individuals for precise prevention.
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Registered trials
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.