The correlation of combined OGG1, CYP1A1 and GSTP1 gene variants and risk of lung cancer of male Iraqi waterpipe tobacco smokers

Genic-intergenic polymorphisms of CYP1A genes and their clinical impact

The humancytochrome P450 1A (CYP1A) subfamily genes, CYP1A1 and CYP1A2, encoding monooxygenases are critically involved in biotransformation of key endogenous substrates (estradiol, arachidonic acid, cholesterol) and exogenous compounds (smoke constituents, carcinogens, caffeine, therapeutic drugs). This suggests their significant involvement in multiple biological pathways with a primary role of maintaining endogenous homeostasis and xenobiotic detoxification. Large interindividual variability exist in CYP1A gene expression and/or catalytic activity of the enzyme, which is primarily due to the existence of polymorphic alleles which encode them. These polymorphisms (mainly single nucleotide polymorphisms, SNPs) have been extensively studied as susceptibility factors in a spectrum of clinical phenotypes. An in-depth understanding of the effects of polymorphic CYP1A genes on the differential metabolic activity and the resulting biological pathways is needed to explain the clinical implications of CYP1A polymorphisms. The present review is intended to provide an integrated understanding of CYP1A metabolic activity with unique substrate specificity and their involvement in physiological and pathophysiological roles. The article further emphasizes on the impact of widely studied CYP1A1 and CYP1A2 SNPs and their complex interaction with non-genetic factors like smoking and caffeine intake on multiple clinical phenotypes. Finally, we attempted to discuss the alterations in metabolism/physiology concerning the polymorphic CYP1A genes, which may underlie the reported clinical associations. This knowledge may provide insights into the disease pathogenesis, risk stratification, response to therapy and potential drug targets for individuals with certain CYP1A genotypes.

A metabolism-related gene signature for predicting the prognosis in thyroid carcinoma

Metabolic reprogramming is one of the cancer hallmarks, important for the survival of malignant cells. We investigated the prognostic value of genes associated with metabolism in thyroid carcinoma (THCA). A prognostic risk model of metabolism-related genes (MRGs) was built and tested based on datasets in The Cancer Genome Atlas (TCGA), with univariate Cox regression analysis, LASSO, and multivariate Cox regression analysis. We used Kaplan-Meier (KM) curves, time-dependent receiver operating characteristic curves (ROC), a nomogram, concordance index (C-index) and restricted mean survival (RMS) to assess the performance of the risk model, indicating the splendid predictive performance. We established a three-gene risk model related to metabolism, consisting of PAPSS2, ITPKA, and CYP1A1. The correlation analysis in patients with different risk statuses involved immune infiltration, mutation and therapeutic reaction. We also performed pan-cancer analyses of model genes to predict the mutational value in various cancers. Our metabolism-related risk model had a powerful predictive capability in the prognosis of THCA. This research will provide the fundamental data for further development of prognostic markers and individualized therapy in THCA.

Alterations of DNA methylation and mRNA levels of CYP1A1, GSTP1, and GSTM1 in human bronchial epithelial cells induced by benzo[a]pyrene

Benzo[a]pyrene (B[a]P) is a known human carcinogen and plays a major function in the initiation of lung cancer at its first proximity. However, the underlying molecular mechanisms are less well understood. In this study, we investigated the impact of B[a]P treatment on the DNA methylation and mRNA levels of CYP1A1, GSTP1, and GSTM1 in human bronchial epithelial cells (16HBEs), and provide scientific evidence for the mechanism study on the carcinogenesis of B[a]P. We treated 16HBEs with DMSO or concentrations of B[a]P at 1, 2, and 5 mmol/L for 24 h, observed the morphological changes, determined the cell viability, DNA methylation, and mRNA levels of CYP1A1, GSTP1, and GSTM1. Compared to the DMSO controls, B[a]P treatment had significantly increased the neoplastic cell number and cell viability in 16HBEs at all three doses (1, 2, and 5 mmol/L), and had significantly reduced the CYP1A1 and GSTP1 DNA promoter methylation levels. Following B[a]P treatment, the GSTM1 promoter methylation level in 16HBEs was profoundly reduced at low dose group compared to the DMSO controls, yet it was significantly increased at both middle and high dose groups. The mRNA levels of CYP1A1, GSTP1, and GSTM1 were significantly decreased in 16HBEs following B[a]P treatment at all three doses. The findings demonstrate that B[a]P promoted cell proliferation in 16HBEs, which was possibly related to the altered DNA methylations and the inhibited mRNA levels in CYP1A1, GSTP1, and GSTM1.

Association of GSTP1 p.Ile105Val (rs1695, c.313A > G) Variant with the Risk of Breast Carcinoma among Egyptian Women

Several reports examined the association of the GSTP1 p.Ile105Val (rs1695, c.313A > G) variant with the elevated risk of multiple cancerous diseases involving breast carcinoma, but with inconclusive findings. The primary purpose of this study is to test the association of this essential variant with the risk of breast carcinoma among Egyptian females. This case-control study was conducted based on 200 participants involving 100 women diagnosed with breast carcinoma and 100 unrelated cancer-free controls from the same district. The genomic DNA for all participants was genotyped utilizing T-ARMS-PCR procedure. The frequencies of the GSTP1 p.Ile105Val (rs1695, c.313A > G) variant indicated a statistically significant with the elevated risk of breast carcinoma under various genetic models, including allelic (OR = 2.48, P-value < 0.001) and dominant (OR = 2.36, P-value = 0.003) models. In conclusion, the GSTP1 p.Ile105Val (rs1695, c.313A > G) variant was considered as an independent risk factor for breast carcinoma among Egyptian women.

Genetic interactions effects for cancer disease identification using computational models: a review

Genome-wide association studies (GWAS) provide clear insight into understanding genetic variations and environmental influences responsible for various human diseases. Cancer identification through genetic interactions (epistasis) is one of the significant ongoing researches in GWAS. The growth of the cancer cell emerges from multi-locus as well as complex genetic interaction. It is impractical for the physician to detect cancer via manual examination of SNPs interaction. Due to its importance, several computational approaches have been modeled to infer epistasis effects. This article includes a comprehensive and multifaceted review of all relevant genetic studies published between 2001 and 2020. In this contemporary review, various computational methods are as follows: multifactor dimensionality reduction-based approaches, statistical strategies, machine learning, and optimization-based techniques are carefully reviewed and presented with their evaluation results. Moreover, these computational approaches' strengths and limitations are described. The issues behind the computational methods for identifying the cancer disease through genetic interactions and the various evaluation parameters used by researchers have been analyzed. This review is highly beneficial for researchers and medical professionals to learn techniques adapted to discover the epistasis and aids to design novel automatic epistasis detection systems with strong robustness and maximum efficiency to address the different research problems in finding practical solutions effectively.

The CHRNA5 Polymorphism (rs16969968) and its Association with Waterpipe Smoking Addiction among Jordanians

Waterpipe smoking is a form of tobacco use that causes nicotine/tobacco dependence and has become a global health problem. In the current study, the association of rs16969968 SNP in the CHRNA5 gene with waterpipe dependence was investigated. A total of 386 men and women who used a waterpipe to smoke tobacco were recruited and divided into less dependent and more dependent smokers based on their score on the Lebanon Waterpipe Dependence Scale (LWDS). Results showed a significant difference in the distribution of GG, GA, and AA genotypes by waterpipe dependence status (P<0.001). The more dependent group showed a higher frequency of the AA genotype than the less dependent smokers' group (38% versus 23% respectively). In addition, the more dependent smokers exhibited more A allele than less dependent smokers (53% versus 37% respectively, P<0.001). In conclusion, there is an association between the rs16969968 SNP and waterpipe dependence as assessed by the LWDS.