Computational Prediction of Probable Single Nucleotide Polymorphism-Cancer Relationships

Abstract

Genetic variations such as single nucleotide polymorphisms (SNPs) can cause susceptibility to cancer. Although thousands of genetic variants have been identified to be associated with different cancers, the molecular mechanisms of cancer remain unknown. There is not a particular dataset of relationships between cancer and SNPs, as a bipartite network, for computational analysis and prediction. Link prediction as a computational graph analysis method can help us to gain new insight into the network. In this article, after creating a network between cancer and SNPs using SNPedia and Cancer Research UK databases, we evaluated the computational link prediction methods to foresee new SNP-Cancer relationships. Results show that among the popular scoring methods based on network topology, for relation prediction, the preferential attachment (PA) algorithm is the most robust method according to computational and experimental evidence, and some of its computational predictions are corroborated in recent publications. According to the PA predictions, rs1801394-Non-small cell lung cancer, rs4880-Non-small cell lung cancer, and rs1805794-Colorectal cancer are some of the best probable SNP-Cancer associations that have not yet been mentioned in any published article, and they are the most probable candidates for additional laboratory and validation studies. Also, it is feasible to improve the predicting algorithms to produce new predictions in the future.

Graphylo: A deep learning approach for predicting regulatory DNA and RNA sites from whole-genome multiple alignments

“…Computational predictors are also key to interpreting the potential consequences of sequence variants. 10 …”

Association of KDR (rs2071559, rs1870377), CFH (rs1061170, rs1410996) genes variants and serum levels with pituitary adenoma

“…Abnormal gene expression is linked to genetic changes such as gene amplification or allele loss and epigenetic changes such as promoter methylation (Vandeva et al, 2010). While thousands of genetic variants, including single nucleotide polymorphisms (SNPs), are related to different types of cancer, the molecular mechanisms of the diseases are still not fully understood (Bakhtiari et al, 2020). SNP analysis is a valuable predictor of cancer risk in particular populations where this polymorphism is frequently detected (Deng et al, 2017).…”