Meta-analysis of the association of MTHFR polymorphisms with multiple myeloma risk

Computational Prediction of Probable Single Nucleotide Polymorphism-Cancer Relationships

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.

A Mendelian Randomization Study of Plasma Homocysteine and Multiple Myeloma

Observational studies have demonstrated an association between elevated homocysteine (Hcy) level and risk of multiple myeloma (MM). However, it remains unclear whether this relationship is causal. We conducted a Mendelian randomization (MR) study to evaluate whether genetically increased Hcy level influences the risk of MM. We used the methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism as an instrumental variable, which affects the plasma Hcy levels. Estimate of its effect on plasma Hcy level was based on a recent genome-wide meta-analysis of 44,147 individuals, while estimate of its effect on MM risk was obtained through meta-analysis of case-control studies with 2,092 cases and 4,954 controls. By combining these two estimates, we found that per one standard-deviation (SD) increase in natural log-transformed plasma Hcy levels conferred a 2.67-fold increase in risk for MM (95% confidence interval (CI): 1.12–6.38; P = 2.7 × 10⁻²). Our study suggests that elevated Hcy levels are causally associated with an increased risk of developing MM. Whether Hcy-lowering therapy can prevent MM merits further investigation in long-term randomized controlled trials (RCTs).