Dataset Information

Gastric Cancer Risk Prediction Using an Epidemiological Risk Assessment Model and Polygenic Risk Score.

ABSTRACT: We investigated the performance of a gastric cancer (GC) risk assessment model in combination with single-nucleotide polymorphisms (SNPs) as a polygenic risk score (PRS) in consideration of Helicobacter pylori (H. pylori) infection status. Six SNPs identified from genome-wide association studies and a marginal association with GC in the study population were included in the PRS. Discrimination of the GC risk assessment model, PRS, and the combination of the two (PRS-GCS) were examined regarding incremental risk and the area under the receiver operating characteristic curve (AUC), with grouping according to H. pylori infection status. The GC risk assessment model score showed an association with GC, irrespective of H. pylori infection. Conversely, the PRS exhibited an association only for those with H. pylori infection. The PRS did not discriminate GC in those without H. pylori infection, whereas the GC risk assessment model showed a modest discrimination. Among individuals with H. pylori infection, discrimination by the GC risk assessment model and the PRS were comparable, with the PRS-GCS combination resulting in an increase in the AUC of 3%. In addition, the PRS-GCS classified more patients and fewer controls at the highest score quintile in those with H. pylori infection. Overall, the PRS-GCS improved the identification of a GC-susceptible population of people with H. pylori infection. In those without H. pylori infection, the GC risk assessment model was better at identifying the high-risk group.

SUBMITTER: Park B

PROVIDER: S-EPMC7923020 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Gastric Cancer Risk Prediction Using an Epidemiological Risk Assessment Model and Polygenic Risk Score.

Park Boyoung B Yang Sarah S Lee Jeonghee J Choi Il Ju IJ Kim Young-Il YI Kim Jeongseon J

Cancers 20210219 4

We investigated the performance of a gastric cancer (GC) risk assessment model in combination with single-nucleotide polymorphisms (SNPs) as a polygenic risk score (PRS) in consideration of <i>Helicobacter pylori</i> (<i>H. pylori)</i> infection status. Six SNPs identified from genome-wide association studies and a marginal association with GC in the study population were included in the PRS. Discrimination of the GC risk assessment model, PRS, and the combination of the two (PRS-GCS) were exami ...[more]

PMID: 33669642

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Project description:BackgroundGenetic variants of Helicobacter pylori (H. pylori) are involved in gastric cancer occurrence. Single nucleotide polymorphisms (SNPs) of H. pylori that are associated with gastric cancer have been reported. The combined effect of H. pylori SNPs on the risk of gastric cancer remains unclear.AimTo assess the performance of a polygenic risk score (PRS) based on H. pylori SNPs in predicting the risk of gastric cancer.MethodsA total of 15 gastric cancer-associated H. pylori SNPs were selected. The associations between these SNPs and gastric cancer were further validated in 1022 global strains with publicly available genome sequences. The PRS model was established based on the validated SNPs. The performance of the PRS for predicting the risk of gastric cancer was assessed in global strains using quintiles and random forest (RF) methods. The variation in the performance of the PRS among different populations of H. pylori was further examined.ResultsAnalyses of the association between selected SNPs and gastric cancer in the global dataset revealed that the risk allele frequencies of six SNPs were significantly higher in gastric cancer cases than non-gastric cancer cases. The PRS model constructed subsequently with these validated SNPs produced significantly higher scores in gastric cancer. The odds ratio (OR) value for gastric cancer gradually increased from the first to the fifth quintile of PRS, with the fifth quintile having an OR value as high as 9.76 (95% confidence interval: 5.84-16.29). The results of RF analyses indicated that the area under the curve (AUC) value for classifying gastric cancer and non-gastric cancer was 0.75, suggesting that the PRS based on H. pylori SNPs was capable of predicting the risk of gastric cancer. Assessing the performance of the PRS among different H. pylori populations demonstrated that it had good predictive power for cancer risk for hpEurope strains, with an AUC value of 0.78.ConclusionThe PRS model based on H. pylori SNPs had a good performance for assessment of gastric cancer risk. It would be useful in the prediction of final consequences of the H. pylori infection and beneficial for the management of the infection in clinical settings.

Project description:Endometriosis is a major health care challenge because many young women with endometriosis go undetected for an extended period, which may lead to pain sensitization. Clinical tools to better identify candidates for laparoscopy-guided diagnosis are urgently needed. Since endometriosis has a strong genetic component, there is a growing interest in using genetics as part of the clinical risk assessment. The aim of this work was to investigate the discriminative ability of a polygenic risk score (PRS) for endometriosis using three different cohorts: surgically confirmed cases from the Western Danish endometriosis referral Center (249 cases, 348 controls), cases identified from the Danish Twin Registry (DTR) based on ICD-10 codes from the National Patient Registry (140 cases, 316 controls), and replication analysis in the UK Biobank (2,967 cases, 256,222 controls). Patients with adenomyosis from the DTR (25 cases) and from the UK Biobank (1,883 cases) were included for comparison. The PRS was derived from 14 genetic variants identified in a published genome-wide association study with more than 17,000 cases. The PRS was associated with endometriosis in surgically confirmed cases [odds ratio (OR) = 1.59, p = 2.57× 10-7] and in cases from the DTR biobank (OR = 1.50, p = 0.0001). Combining the two Danish cohorts, each standard deviation increase in PRS was associated with endometriosis (OR = 1.57, p = 2.5× 10-11), as well as the major subtypes of endometriosis; ovarian (OR = 1.72, p = 6.7× 10-5), infiltrating (OR = 1.66, p = 2.7× 10-9), and peritoneal (OR = 1.51, p = 2.6 × 10-3). These findings were replicated in the UK Biobank with a much larger sample size (OR = 1.28, p < 2.2× 10-16). The PRS was not associated with adenomyosis, suggesting that adenomyosis is not driven by the same genetic risk variants as endometriosis. Our results suggest that a PRS captures an increased risk of all types of endometriosis rather than an increased risk for endometriosis in specific locations. Although the discriminative accuracy is not yet sufficient as a stand-alone clinical utility, our data demonstrate that genetics risk variants in form of a simple PRS may add significant new discriminatory value. We suggest that an endometriosis PRS in combination with classical clinical risk factors and symptoms could be an important step in developing an urgently needed endometriosis risk stratification tool.

Dataset Information

Gastric Cancer Risk Prediction Using an Epidemiological Risk Assessment Model and Polygenic Risk Score.

Publications

Gastric Cancer Risk Prediction Using an Epidemiological Risk Assessment Model and Polygenic Risk Score.

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