Role of GSTM1 and GSTT1 genotypes in differentiated thyroid cancer and interaction with lifestyle factors: Results from case-control studies in France and New Caledonia

Effects of Glutathione S-Transferases (GSTM1, GSTT1 and GSTP1) gene variants in combination with smoking or drinking on cancers: A meta-analysis

BACKGROUND

This meta-analysis aimed to systematically summarize the association between cancer risks and glutathione s-transferases (GSTs) among smokers and drinkers.

METHODS

Literature was searched through PubMed, Web of Science, CNKI, and WANFANG published from 2001 to 2022. Stata was used with fixed-effect model or random-effect model to calculate pooled odds ratios (ORs) and the 95% confidence interval (95% CI). Sensitivity and heterogeneity calculations were performed, and publication bias was analyzed by Begg and Egger's test. Regression analysis was performed on the correlated variables about heterogeneity, and the false-positive report probabilities (FPRP) and the Bayesian False Discovery Probability (BFDP) were calculated to assess the confidence of a statistically significant association.

RESULTS

A total of 85 studies were eligible for GSTs and cancer with smoking status (19,604 cases and 23,710 controls), including 14 articles referring to drinking status (4409 cases and 5645 controls). GSTM1-null had significant associations with cancer risks (for smokers: OR = 1.347, 95% CI: 1.196-1.516, P < .001; for nonsmokers: OR = 1.423, 95% CI: 1.270-1.594, P < .001; for drinkers: OR = 1.748, 95% CI: 1.093-2.797, P = .02). GSTT1-null had significant associations with cancer risks (for smokers: OR = 1.356, 95% CI: 1.114-1.651, P = .002; for nonsmokers: OR = 1.103, 95% CI: 1.011-1.204, P = .028; for drinkers: OR = 1.423, 95% CI: 1.042-1.942, P = .026; for nondrinkers: OR = 1.458, 95% CI: 1.014-2.098, P = .042). Negative associations were found between GSTP1rs1695(AG + GG/AA) and cancer risks among nondrinkers (OR = 0.840, 95% CI: 0.711-0.985, P = .032).

CONCLUSIONS

GSTM1-null and GSTT1-null might be related cancers in combination with smoking or drinking, and GSTP1rs1695 might be associated with cancers among drinkers.

Auto repair workers exposed to PM2.5 particulate matter in Barranquilla, Colombia: telomere length and hematological parameters

Exposure to 2.5 µm particulate matter (PM_2.5) in automotive repair shops is associated with risks to health. We evaluated the effects of occupational exposure to PM_2.5 among auto repair-shop workers. Blood and urine samples were collected from 110 volunteers from Barranquilla, Colombia: 55 active workers and 55 controls. PM_2.5 concentrations were assessed at each of the sampling sites and chemical content was analyzed by SEM-EDS electron microscopy. The biological samples obtained were peripheral blood (hematological profiling, DNA extraction) and urine (malondialdehyde concentration). Telomere length was assessed by qPCR and polymorphisms in the glutathione transferase genes GSTT1 and GSTM1 by PCR-RFLP, with confirmation by allelic exclusion. White blood cell (WBC), lymphocyte (LYM%) and platelet (PLT) counts and the malondialdehyde concentration were higher (4.10 ± 0.93) in the exposed group compared to the control group (1.56 ± 0.96). TL was shorter (5071 ± 891) in the exposed individuals compared to the control group (6271 ± 805). White blood cell (WBC) and platelet counts were positively associated with exposure. Age and TBARS were correlated with TL in exposed individuals. The GSTT1 gene alleles were not in Hardy-Weinberg (H-W) equilibrium. The GSTM1 gene alleles were in H-W equilibrium and allelic exclusion analysis confirmed the presence of heterozygous GSTM1 genotypes. SEM-EDS analysis showed the presence of potentially toxic elements, including Mg, Al, Fe, Mn, Rh, Zn, and Cu. Auto repair shop workers showed effects that may be associated with exposure to mixtures of pollutants present in PM_2.5. The GSTM1 and GSTT1 genes had independent modulatory effects.

Powerful quantifiers for cancer transcriptomics

Every day, investigators find a new link between a form of cancer and a particular alteration in the sequence or/and expression level of a key gene, awarding this gene the title of “biomarker”. The clinician may choose from numerous available panels to assess the type of cancer based on the mutation or expression regulation (“transcriptomic signature”) of “driver” genes. However, cancer is not a “one-gene show” and, together with the alleged biomarker, hundreds other genes are found as mutated or/and regulated in cancer samples. Regardless of the platform, a well-designed transcriptomic study produces three independent features for each gene: Average expression level, expression variability and coordination with expression of each other gene. While the average expression level is used in all studies to identify what genes were up-/down-regulated or turn on/off, the other two features are unfairly ignored. We use all three features to quantify the transcriptomic change during the progression of the disease and recovery in response to a treatment. Data from our published microarray experiments on cancer nodules and surrounding normal tissue from surgically removed tumors prove that the transcriptomic topologies are not only different in histopathologically distinct regions of a tumor but also dynamic and unique for each human being. We show also that the most influential genes in cancer nodules [the Gene Master Regulators (GMRs)] are significantly less influential in the normal tissue. As such, “smart” manipulation of the cancer GMRs expression may selectively kill cancer cells with little consequences on the normal ones. Therefore, we strongly recommend a really personalized approach of cancer medicine and present the experimental procedure and the mathematical algorithm to identify the most legitimate targets (GMRs) for gene therapy.