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Involvement of INF-γ functional single nucleotide polymorphism +874 T/A (rs2430561) in breast cancer risk

ABSTRACT: According Global Cancer Statistics 2020 GLOBOCAN estimates female breast cancer was found as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), and the fourth leading cause (6.9%) of cancer death among women worldwide. Identification of new diagnostic marker sharply characterize the tumor feature is intensive need. The present work was performed to investigate the involvement of the INF-γ + 874 T/A gene polymorphism in different breast cancer prognostic factors. Polymorphism detection analysis was performed on 163 subjects from breast cancer patients, 79 with inflamed cells of breast patients and 144 controls. The gene polymorphism was detected using the amplification refractory mutation system- polymerase chain reaction method (ARMS-PCR). The distribution of INF-γ T + 874A gene polymorphism shows strong significant association between INF-γ + 874 T/A genotypes TT in BC patients (ORTT: 6.41 [95% CI = 2.72–15.1] P < 0.0001) as well as strong significant association regarding T allele (ORT: 1.99 [95% CI = 1.43–2.76] P < 0.0001) when compared to the healthy control. In ICB group the strong association was noted with INF-γ + 874 T/A genotypes AT genotype (ORAT: 2.28 [95% CI = 1.22–4.29] P = 0.007). From the different histological BC hormonal markers the human epidermal growth factor receptor 2 (HER2) was showing significant association in INF-γ + 874 T/A genotypes TT (P = 0.03) and recessive model (TT versus AA + AT P = 0.03). Concerning different BC prognostic models, the poor prognostic one of luminal B, (ER+ve PR+ve Her2+ve) show significant association in the host INF-γ + 874 T/A genotype (TT, P = 0.03) and recessive model (TT versus AA + AT P = 0.02) when compared to the good prognostic hormonal status luminal A model, (ER+ve PR+ve Her2-ve). It seems that this is the first study that interested in correlate the INF-γ + 874 T/A gene polymorphisms in Egyptian BC patients. T allele, TT genotype and recessive model of the INF-γ + 874 T/A gene variants were documented as risk factors for BC pathogenesis. It may be used as practical biomarker to guide the BC carcinogenesis and risk process.

SUBMITTER: Al-Rashidi H

PROVIDER: S-EPMC8568710 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: This paper introduces and applies a genome wide predictive study to learn a model that predicts whether a new subject will develop breast cancer or not, based on her SNP profile. RESULTS: We first genotyped 696 female subjects (348 breast cancer cases and 348 apparently healthy controls), predominantly of Caucasian origin from Alberta, Canada using Affymetrix Human SNP 6.0 arrays. Then, we applied EIGENSTRAT population stratification correction method to remove 73 subjects not belonging to the Caucasian population. Then, we filtered any SNP that had any missing calls, whose genotype frequency was deviated from Hardy-Weinberg equilibrium, or whose minor allele frequency was less than 5%. Finally, we applied a combination of MeanDiff feature selection method and KNN learning method to this filtered dataset to produce a breast cancer prediction model. LOOCV accuracy of this classifier is 59.55%. Random permutation tests show that this result is significantly better than the baseline accuracy of 51.52%. Sensitivity analysis shows that the classifier is fairly robust to the number of MeanDiff-selected SNPs. External validation on the CGEMS breast cancer dataset, the only other publicly available breast cancer dataset, shows that this combination of MeanDiff and KNN leads to a LOOCV accuracy of 60.25%, which is significantly better than its baseline of 50.06%. We then considered a dozen different combinations of feature selection and learning method, but found that none of these combinations produces a better predictive model than our model. We also considered various biological feature selection methods like selecting SNPs reported in recent genome wide association studies to be associated with breast cancer, selecting SNPs in genes associated with KEGG cancer pathways, or selecting SNPs associated with breast cancer in the F-SNP database to produce predictive models, but again found that none of these models achieved accuracy better than baseline. CONCLUSIONS: We anticipate producing more accurate breast cancer prediction models by recruiting more study subjects, providing more accurate labelling of phenotypes (to accommodate the heterogeneity of breast cancer), measuring other genomic alterations such as point mutations and copy number variations, and incorporating non-genetic information about subjects such as environmental and lifestyle factors.

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Involvement of INF-γ functional single nucleotide polymorphism +874 T/A (rs2430561) in breast cancer risk

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