Evaluation of Oxidative Stress Response Related Genetic Variants, Pro-oxidants, Antioxidants and Prostate Cancer

Exposure to environmental pollutants and genetic variants related to oxidative stress and xenobiotic metabolism-Association with prostate cancer

This study assessed whether genetic variants coding for certain enzymes involved in xenobiotic detoxification, antioxidant defences and DNA repair, along with exposure to environmental chemicals, were associated with an increased prostate cancer (PCa) risk. The study population consisted of 300 men (150 PCa cases and 150 controls) which underwent prostate biopsy as their serum prostate specific antigen (PSA) levels were greater than 4 ng/ml. Genetic variants in GSTM1, GSTP1, SOD2, CAT, GPX1, XRCC1 were determined and data for chemical exposures was obtained through a structured questionnaire and by biomonitoring in a subsample of cases and controls. High serum PSA levels were associated with a greater risk of PCa, while physical exercise appears to exert a protective effect against its development. In addition, elevated urinary levels of certain organic pollutants, such as benzo(a)pyrene (BaP), bisphenol A (BPA), and ethyl-paraben (EPB), were associated with an increased risk of PCa.

The rs1801280 SNP is associated with non-small cell lung carcinoma by exhibiting a highly deleterious effect on N-acetyltransferase 2

PURPOSE

N-acetyltransferase 2 is an enzyme that is involved in the detoxification of carcinogens in the human body, so any damage to this protein may lead to the emergence of several metabolic dysfunctions. This work was conducted to determine the association between NAT2 polymorphism and non-small cell lung carcinoma (NSCLC) that is increasingly reported in the Iraqi population.

METHODS

PCR sequencing was conducted to assess the possible association between genetic variants and NSCLC. Several in silico tools were implemented to investigate the effect of the observed SNPs on the structure, function, and stability of the altered NAT2.

RESULTS

Five SNPS of NAT2 (rs1208, rs1041983, rs1799929, rs1799930, and rs1801280) were identified in high frequencies in the amplified fragment. These SNPs showed variable distributions of haplotypes between cases and controls. No significant association of rs1208, rs1041983, rs1799929, and rs1799930 with NSCLC was shown in the investigated population. In contrast, rs1801280: CC genotype showed a highly significant (P = 0.009) association with the NSCLC, and individuals with this genotype had 2.19 more chances for developing NSCLC (OR 2.19; Cl_95% 1.21-3.94). Association analysis of rs1801280 SNP distribution among the investigated patients showed that patients with CC genotype showed a significant (P = 0.02, OR 2.65) association with family history, which entailed a high hereditary possibility of this genotype among Iraqi patients. It was predicted that this SNP showed high damaging effects on the activity of NAT2 enzyme, with various deleterious outcomes on enzyme structure, function, and stability.

CONCLUSION

Data indicated that rs1801280 SNP exerted a tight association with NSCLC since individuals with CC genotype exhibited the most damaging effects on the NAT2 that may be behind the low acetylation rates of this enzyme in patients with NSCLC.

Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art

Artificial intelligence (AI) and machine learning models are today frequently used for classification and prediction of various biochemical processes and phenomena. In recent years, numerous research efforts have been focused on developing such models for assessment, categorization, and prediction of oxidative stress. Supervised machine learning can successfully automate the process of evaluation and quantification of oxidative damage in biological samples, as well as extract useful data from the abundance of experimental results. In this concise review, we cover the possible applications of neural networks, decision trees and regression analysis as three common strategies in machine learning. We also review recent works on the various weaknesses and limitations of artificial intelligence in biochemistry and related scientific areas. Finally, we discuss future innovative approaches on the ways how AI can contribute to the automation of oxidative stress measurement and diagnosis of diseases associated with oxidative damage.

Lipid peroxidation processes in men with type 1 diabetes mellitus following α-lipoic acid treatment

<abstract> <p>In various pathological conditions such as diabetes mellitus, the cellular redox balance can be disturbed and these alterations may persist even after blood glucose levels have returned to normal. Antioxidant therapies, including α-lipoic acid, are widely used to treat various systemic diseases including diabetes mellitus. The aim of this study was to measure the lipid metabolism parameters, as well as the activity of lipid peroxidation and antioxidant defense reactions, in men with type 1 diabetes mellitus (T1DM) during treatment with α-lipoic acid. Twenty-four reproductive-age T1DM males with an unsatisfactory glycemic profile were examined. Twenty-eight practically healthy men of similar age were used as the control group. Spectrophotometric, fluorometric, and enzyme-linked immunoassay methods were used. T1DM was characterized by increased values of lipid metabolism components, including total cholesterol, triacylglycerides (TG), and very-low-density lipoproteins (VLDL). In the lipid peroxidation system, increased levels of the primary products (conjugated dienes), secondary products (thiobarbituric acid reactants), and final products (Schiff bases) were observed in T1DM patients compared to the control group. Retinol values were also increased. After treatment, there was a decrease in TG, VLDL, and Schiff bases levels and an increase in the retinol level compared to before treatment. These results expand our understanding of the pathogenetic mechanisms of T1DM and suggest that α-LA treatment may be beneficial for type 1 diabetics.</p> </abstract>

Genetic variants in xenobiotic detoxification enzymes, antioxidant defenses and hormonal pathways as biomarkers of susceptibility to prostate cancer

Cancer is considered a complex disease that in many cases results from the interaction between chemical exposures, either from environmental or dietary sources, and genetic polymorphisms of xenobiotic-metabolizing enzymes (XME) or antioxidant enzymatic defenses. This study explored associations and interactions between genetic and environmental risk factors on the risk of prostate cancer (PCa) in 323 subjects that underwent prostate biopsy due to prostate specific antigen (PSA) levels above 4 ng/ml (161 PCa and 162 non-PCa). Eleven genes involved directly or indirectly in xenobiotic detoxification, oxidative stress and estrogen signaling were studied (GSTM1, GPX1 (rs1050450 and rs17650792), NAT2 (rs1801280), TXNRD1 (rs7310505), PRDX3 (rs3740562), CYP17A1 (rs743572), PON1 (rs662), SOD1 (rs10432782), SOD2 (rs4880), CAT (rs1001179), and ESR1 (rs746432)). A structured questionnaire was administered to all individuals to assess environmental and dietary chemical exposures. Medical data was collected by urologists. GPX1 rs17650792 polymorphism was the only one showing a significant inverse association with PCa risk. PRDX3 and GPX1 (rs17650792) genetic polymorphisms were significantly associated with Gleason score and PSA levels, respectively. The intake of nuts and soya products was associated with a reduced risk of PCa, as well as the performance of physical activity. Moreover, a number of gene-environmental interactions were found to increase the risk of PCa, particularly exposure to pesticides and rs1801280 (NAT2) and tobacco smoking and rs1050450 (GPX1). These findings suggest that the association of genetic and environmental risk factors with PCa risk should be assessed jointly for a better understanding of this complex disease.

Impregnation of Curcumin into a Biodegradable (Poly-lactic-co-glycolic acid, PLGA) Support, to Transfer Its Well Known In Vitro Effect to an In Vivo Prostate Cancer Model

Prostate cancer (PCa) is one of the most common cancers in older men and is associated with high mortality. Despite advances in screening for early detection of PCa, a large proportion of patients continue to be diagnosed with metastatic disease, with ~20% of men showing a high tumor grade and stage. Medicinal plant extracts have a great potential to prevent/treat PCa, as well as to reduce its incidence/prevalence and improve survival rates. One of the most promising extracts is curcumin, which is a major, nontoxic, bioactive compound of Curcuma longa. Curcumin has strong antitumor activity in vitro. However, its potential beneficial in vivo affects are limited by its low intestinal absorption and rapid metabolism. In this study, curcumin was impregnated into a biodegradable poly(lactic-co-glycolic) acid (PLGA) support and characterized by FTIR and DSC, and its release by UV spectrophotometry. PLGA-curcumin was tested in different subcutaneous PCa xenograft models (PC3, 22rv1, and DU145 PCa cell-lines), and its effects evaluated by tumor progression an immuno-histochemical analysis (Trichromic, Ki67 and TUNEL stainings), were compared with those of a commercial curcumin preparation. Our results indicate that curcumin-impregnated PLGA is significantly more active (~2-fold increase) with respect to oral curcumin, which supports its use for subcutaneous administration.

Serum selenium levels and prostate cancer risk: A MOOSE-compliant meta-analysis

Some observational studies have shown that elevated serum selenium levels are associated with reduced prostate cancer risk; however, not all published studies support these results. A literature search of PubMed, Embase, Medline, and the Cochrane Library up until September 2016 identified 17 studies suitable for further investigation. A meta-analysis was conducted on these studies to investigate the association between serum selenium levels and subsequent prostate cancer risk. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) were used to evaluate the overall OR of prostate cancer for the highest versus the lowest levels of serum selenium. We found a pooled OR (95% CI) of 0.76 (0.64, 0.91; P < 0.05). In subgroup analysis, an inverse association between serum selenium levels and prostate cancer risk was found in each of case-control studies, current and former smokers, high-grade cancer cases, advanced cancer cases, and different populations. Such correlations were not found for subgroups containing each of cohort studies, nonsmokers, low-grade cancer cases, and early stage cancer cases. In conclusion, our study suggests an inverse relationship between serum selenium levels and prostate cancer risk. However, further cohort studies and randomized control trials based on non-Western populations are required.