Dataset Information

Single Nucleotide Variants Associated with Colorectal Cancer Among Iranian Patients: A Narrative Review.

ABSTRACT: Colorectal cancer has been considered as one of the complicated multi-stage processes after adenoma-carcinoma sequence. Therefore, studies of the molecular dysregulation basis could present information on the recognition of the potent biomarkers and treatment targets for this disease. Even though outcomes of the patients with colorectal cancer have been improved largely with current annual screening plans, it is necessary to have reliable prognostic biomarkers because of the disease heterogeneity. There is a significant relationship between SNP in IL1RN* 2 (IL1ra), -509 C/T (TGFB1), rs11556218 T>G and rs4778889 T/C (IL16), miRNA-binding site polymorphisms in IL16, rs4464148 (SMAD7), rs6983267 (EGF), GSTT1, TACG haplotype (CTLA4), 1793G> A (MTHFR), Leu/Leu genotype of (EXO1), -137 G/C (IL18), C/T genotype (XRCC3), I3434T (XRCC7), MGMT, C3435T (MDR1), ff genotype of FokI, 677CT+TT (MTHFR), G2677T/A (MDR1) and CRC. Increased risk has been observed in VDR ApaI genotype "aa". Finally, the protective effect has been explored in the TACA haplotype (CTLA4). According to the findings, the genetic polymorphisms in the immunity-associated genes are related to the CRC amongst the Iranian patients. Therefore, more large-scale functional investigations are necessary for confirming the results.

SUBMITTER: Jamshidi M

PROVIDER: S-EPMC7280057 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Single Nucleotide Variants Associated with Colorectal Cancer Among Iranian Patients: A Narrative Review.

Jamshidi Mohammad M Mohammadi Pour Somayeh S Mahmoudian-Sani Mohammad-Reza MR

Pharmacogenomics and personalized medicine 20200603

Colorectal cancer has been considered as one of the complicated multi-stage processes after adenoma-carcinoma sequence. Therefore, studies of the molecular dysregulation basis could present information on the recognition of the potent biomarkers and treatment targets for this disease. Even though outcomes of the patients with colorectal cancer have been improved largely with current annual screening plans, it is necessary to have reliable prognostic biomarkers because of the disease heterogeneit ...[more]

PMID: 32581566

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Project description:BackgroundIn recent years the number of human genetic variants deposited into the publicly available databases has been increasing exponentially. The latest version of dbSNP, for example, contains ~50 million validated Single Nucleotide Variants (SNVs). SNVs make up most of human variation and are often the primary causes of disease. The non-synonymous SNVs (nsSNVs) result in single amino acid substitutions and may affect protein function, often causing disease. Although several methods for the detection of nsSNV effects have already been developed, the consistent increase in annotated data is offering the opportunity to improve prediction accuracy.ResultsHere we present a new approach for the detection of disease-associated nsSNVs (Meta-SNP) that integrates four existing methods: PANTHER, PhD-SNP, SIFT and SNAP. We first tested the accuracy of each method using a dataset of 35,766 disease-annotated mutations from 8,667 proteins extracted from the SwissVar database. The four methods reached overall accuracies of 64%-76% with a Matthew's correlation coefficient (MCC) of 0.38-0.53. We then used the outputs of these methods to develop a machine learning based approach that discriminates between disease-associated and polymorphic variants (Meta-SNP). In testing, the combined method reached 79% overall accuracy and 0.59 MCC, ~3% higher accuracy and ~0.05 higher correlation with respect to the best-performing method. Moreover, for the hardest-to-define subset of nsSNVs, i.e. variants for which half of the predictors disagreed with the other half, Meta-SNP attained 8% higher accuracy than the best predictor.ConclusionsHere we find that the Meta-SNP algorithm achieves better performance than the best single predictor. This result suggests that the methods used for the prediction of variant-disease associations are orthogonal, encoding different biologically relevant relationships. Careful combination of predictions from various resources is therefore a good strategy for the selection of high reliability predictions. Indeed, for the subset of nsSNVs where all predictors were in agreement (46% of all nsSNVs in the set), our method reached 87% overall accuracy and 0.73 MCC. Meta-SNP server is freely accessible at http://snps.biofold.org/meta-snp.

Dataset Information

Single Nucleotide Variants Associated with Colorectal Cancer Among Iranian Patients: A Narrative Review.

Publications

Single Nucleotide Variants Associated with Colorectal Cancer Among Iranian Patients: A Narrative Review.

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