ArticleClinical and experimental gastroenterology2012
Role of XPC, XPD, XRCC1, GSTP genetic polymorphisms and Barrett's esophagus in a cohort of Italian subjects. A neural network analysis.
Article in Clinical and experimental gastroenterology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Gene-Environment Interactions and the Risk of Barrett's Esophagus in Three US Cohorts.The American journal of gastroenterology · 2019Article
- The Genetics of Barrett's Esophagus: A Familial and Population-Based Perspective.Digestive diseases and sciences · 2016Review
- Application of artificial neural networks to investigate one-carbon metabolism in Alzheimer's disease and healthy matched individuals.PloS one · 2013Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBarrett's esophagus (BE), a metaplastic premalignant disorder, represents the primary risk factor for the development of esophageal adenocarcinoma. Chronic gastroesophageal reflux disease and central obesity have been associated with BE and esophageal adenocarcinoma, but relatively little is known about the specific genes that confer susceptibility to BE carcinogenesis.
methodsA total of 74 patients with BE and 67 controls coming from six gastrointestinal Italian units were evaluated for six polymorphisms in four genes: XPC, XPD nucleotide excision repair (NER) genes, XRCC1 (BER gene), and glutathione S-transferase P1. Smoking status was analyzed together with the genetic data. Statistical analysis was performed through Artificial Neural Networks.
resultsDistributions of sex, smoking history, and polymorphisms among BE cases and controls did not show statistically significant differences. The r-value from linear correlation allowed us to identify possible protective factors as well as possible risk factors. The application of advanced intelligent systems allowed for the selection of a subgroup of nine variables. Artificial Neural Networks applied on the final data set reached mean global accuracy of 60%, reaching as high as 65.88%.
conclusionWe report here results from an exploratory study. Results from this study failed to find an association among the tested single nucleotide polymorphisms and BE phenotype through classical statistical methods. On the contrary, advanced intelligent systems are really able to handle the disease complexity, not treating the data with reductionist approaches unable to detect multiple genes of smaller effect in predisposing to the disease. IMPACT: To detect multiple genes of smaller effects in predisposing individuals to Barrett's esophagus.
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