Association between polymorphic sites in thymidylate synthase gene and risk of non-Hodgkin lymphoma: a systematic review and pooled analysis

Artificial Intelligence Analysis of Gene Expression Predicted the Overall Survival of Mantle Cell Lymphoma and a Large Pan-Cancer Series

Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients' overall survival and highlight new markers. As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9). Further reduction identified 10 genes: KIF18A, YBX3, PEMT, GCNA, and POGLUT3 that associated with a poor survival; and SELENOP, AMOTL2, IGFBP7, KCTD12, and ADGRG2 with a favorable survival. Correlation with the proliferation index (Ki67) was also made. Interestingly, these genes, which were related to cell cycle, apoptosis, and metabolism, also predicted the survival of diffuse large B-cell lymphoma (GSE10846, n = 414), and a pan-cancer series of The Cancer Genome Atlas (TCGA, n = 7289), which included the most relevant cancers (lung, breast, colorectal, prostate, stomach, liver, etcetera). Secondly, survival was predicted using 10 oncology panels (transcriptome, cancer progression and pathways, metabolic pathways, immuno-oncology, and host response), and TYMS was highlighted. Finally, using machine learning, C5 tree and Bayesian network had the highest accuracy for prediction and correlation with the LLMPP MCL35 proliferation assay and RGS1 was made. In conclusion, artificial intelligence analysis predicted the overall survival of MCL with high accuracy, and highlighted genes that predicted the survival of a large pan-cancer series.

TSER polymorphism is not associated with risk of pediatric acute lymphoblastic leukemia: A meta-analysis

BACKGROUND

Accumulating studies have explored the effect of thymidylate synthase enhancer region (TSER) variation on risk of pediatric acute lymphoblastic leukemia (ALL) with controversial results. Therefore, this quantitative meta-analysis was performed to assess synthetically the association of TSER variation with susceptibility to develop pediatric ALL.

METHODS

The PubMed, ScienceDirect, Google Scholar, Wanfang Database, and China National Knowledge Infrastructure were systematically retrieved to obtain the published case-control studies about the relationship between TSER variation and pediatric ALL risk. The quality assessment of the included studies was preformed and relevant information was collected. Odds ratios (ORs) and 95% confidence intervals (CIs) were applied to evaluate the strength of association.

RESULTS

This meta-analysis finally included 2681 children with ALL and 3854 matched controls from 11 investigations. The quantitative synthesis results found no significant association between TSER variation and susceptibility to pediatric ALL in overall comparisons under 5 genetic models (2R/3R vs 3R/3R: OR = 0.95, 95% CI = 0.84-1.07, P = 0.41; 2R/2R vs 3R/3R: OR = 0.99, 95% CI = 0.84-1.16, P = 0.90; 2R2R vs 3R/3R+2R/3R: OR = 1.05, 95% CI = 0.92-1.21, P = 0.45; 2R/3R+2R/2R vs 3R/3R: OR = 0.97, 95% CI = 0.87-1.09, P = 0.63; 2R vs 3R: OR = 1.03, 95% CI = 0.92-1.15, P = 0.61). Similarly, there was no significant association existed in the stratification analyses according to ethnicity, control source, and quality score.

CONCLUSION

This meta-analysis shows that TSER variation is not related to the development risk of pediatric ALL.

Genetic polymorphisms in the one-carbon metabolism pathway genes and susceptibility to non-Hodgkin lymphoma

Corroborating evidence related to the role of aberrations on one-carbon metabolism (OCM) genes has been inconsistent. We evaluated the association between polymorphisms in 12 single nucleotide polymorphisms (SNPs) in 8 OCM genes (CBS, FPGS, FTHFD, MTRR, SHMT1, SLC19A1, TCN1, and TYMS), and non-Hodgkin lymphoma (NHL) risk in a multi-ethnic population which includes Malay, Chinese and Indian ethnic subgroups. Cases (N = 372) and controls (N = 722) were genotyped using the Sequenom MassARRAY platform. Our results of the pooled subjects showed a significantly enhanced NHL risk for CBS Ex9 + 33C > T (T versus C: OR 1.55, 95% CI 1.22-1.96, P = 0.0003), CBS Ex18-319G > A (A versus G: OR 1.15, 95% CI 1.14-1.83; P = 0.002), SHMT1 Ex12 + 236 T > C (T versus C: OR 1.44, 95% CI 1.15-1.81, P = 0.002), and TYMS Ex8 + 157C > T (T versus C: OR 1.29, 95% CI 1.06-1.57, P = 0.01). Haplotype analysis for CBS SNPs showed a significantly decreased risk of NHL in subjects with haplotype CG (OR 0.69, 95% CI 0.56-0.86, P = <0.001). The GG haplotype for the FTHFD SNPs showed a significant increased risk of NHL (OR 1.40, 95% CI 1.12-1.76, P = 0.002). For the TYMS gene, haplotype CAT at TYMS (OR 0.67, 95% CI 0.49-0.90, P = 0.007) was associated with decreased risk of NHL, while haplotype TAC (OR 1.29, 95% CI 1.05-1.58, P = 0.01) was found to confer increased risk of NHL. Our study suggests that variation in several OCM genes (CBS, FTHFD, SHMT1, TCN1, and TYMS) may influence susceptibility to NHL.