Occupational pesticide exposure is linked to the prevalence of Luminal B breast cancer and poor prognosis features in Brazilian rural women

Occupational pesticide exposure is linked to the prevalence of Luminal B breast cancer and poor prognosis features in Brazilian rural women | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Occupational pesticide exposure is linked to the prevalence of Luminal B breast cancer and poor prognosis features in Brazilian rural women Isabella Cazagranda, Rafaela de Almeida, Lucca Smaniotto, Maria Paula Berny, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4182249/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Brazil is among the top three global consumers of pesticides, despite evidence concerning the negative impact on public health. The exposure on female rural workers has been neglected, and the incidence of pesticide-induced diseases such as breast cancer is growing. In this context, this study analyzed the impact of occupational/household pesticide exposure on the clinicopathological profile of breast cancer in rural women. Clinicopathological data were collected from medical records and analyzed from a total of 386 patients (208 exposed and 141 unexposed to pesticides). Was used descriptive and inferential statistics methods to characterize the patient data, including the chi-square test and Fisher's exact test, to evaluate associations between variables. This data was grouped as patient, tumor, and disease characteristics. Exposed patients had a prevalence of Luminal B subtype (32.83%), while unexposed patients had a prevalence of Luminal A molecular subtype (37.78%). Exposed patients also had higher disease recurrence (10.19%), chemoresistance (21.26%), and death occurrence (7.21%) than unexposed patients. Breast cancer patients exposed to pesticides were also more likely to have distant metastases and lymph node invasion compared to breast cancer unexposed patients. These findings indicate that pesticide exposure favors the occurrence of more aggressive breast cancer in rural women through occupational exposure. This results indicate that this occupational information could be added to the screening process and risk determination for breast cancer severity. Biological sciences/Ecology/Agri ecology Health sciences/Oncology pesticides breast cancer luminal B subtype chemoresistance recurrence Figures Figure 1 Figure 2 1. Introduction The use of pesticides in agriculture began with the “Green Revolution” movement in the 50s, using the argument of increasing food production. 1 Unfortunately, at that time, there was no understanding of the potential risks of its use for the environment and human health. 2 Several studies have demonstrated pesticide exposure risks to human health, such as their carcinogenic potential, 3,4 endocrine disruption, 5 genotoxicity, 6–8 and compromised immune system. 9–12 With increased negative evidence on pesticide use, one of the main discussions in this field is the fact that several pesticides have carcinogenic potential. They have been classified by the International Agency for Cancer Research (IARC) as potentially, probably, or proven carcinogens. A variety of cancers have been linked to pesticide exposure, including thyroid, 13 colorectal, 14 bladder, 15 blood, 16 brain, 17 and breast cancer. 18 In the context of pesticide health risks, it is important to note the growth of the feminization movement in agriculture; it has been estimated that women represent 43% of the world's agricultural workforce. 19 This trend has been observed in several regions of the world, such as the European Union, where women represent 29% of rural workers, 20 Brazil, where they represent 45% of women, and certain regions of Africa and Asia, where women’s representation can reach up to 60%. 21 The feminization of agriculture may lead to an increase in the incidence of breast cancer in women. Due to the extensive exposure of rural women in agriculture and the endocrine disruption properties of pesticides, attention has been drawn to the incidence of female tumors, such as breast cancer. Several studies have addressed the relationship between pesticide exposure and the increased risk of developing this pathology. 22–24 Endocrine deregulation can occur by mainly two exposure models: programming, which modifies tissues during embryonic development until puberty, making them susceptible to cancer, and worsening, where subsequent exposure leads to malignant evolution of precancerous cells or benign lesions. 25 Considering little is known about the impact of chronic and continued occupational pesticide exposure on the clinicopathological profiling of breast cancer, the present study focused on understanding the issue. To reach this goal, we performed extensive data collection from patients diagnosed with breast cancer who had visited a public hospital located in Paraná state, a Brazilian setting known for its high use of pesticides and female rural work. Information concerning patient profiles and tumor characteristics was obtained and analyzed using descriptive and inferential statistical methods, aiming to determine a clinicopathological signature associated with pesticide exposure in women with breast cancer. 2. Methods 2.1. Study design and data collection This is a descriptive, cross-sectional, and quantitative exploratory study with the objective of determining a clinicopathological signature associated with exposure to pesticides in rural women with breast cancer. Figure 1 illustrates the study design. This study complied with the national and international regulatory standards for research involving humans and was approved by the Research Ethics Committee (CEP) of the State University of Western Paraná (UNIOESTE), under the number CAAE 35524814.4.0000.0107. All volunteers signed a free and informed consent form. Data, including unique materials, documentation, and codes used in the analysis, are available at https://github.com/Laboratorio-de-Analise-de-Dados/Article_pesticide_exposure. A total of 923 women who attended the Francisco Beltrão Cancer Hospital (Ceonc) from May 2015 to April 2023 with images suggestive of breast lesions identified by mammograms and ultrasound were included. The study comprised the Eighth Paraná Health Region, which comprises 27 municipalities characterized predominantly by rural family work. To obtain the diagnosis of breast cancer, a biopsy of the suspicious lesion was performed by a pathologist, followed by anatomopathological analysis and immunohistochemistry. After excluding patients with benign lesions, 386 patients were included in the study as having a breast cancer diagnosis. Patients were categorized as occupationally exposed (n = 208) or unexposed to pesticides (n = 141). The Eighth Regional Health Region of Paraná comprises about 500,000 inhabitants living in the following 27 municipalities: Ampére, Barracão, Bela Vista da Caroba, Boa Esperança do Iguaçu, Bom Jesus do Sul, Capanema, Cruzeiro do Iguaçu, Dois Vizinhos, Éneas Marques, Flor da Serra Do Sul, Francisco Beltrão, Manfrinópolis, Marmeleiro, Nova Esperança do Sudoeste, Nova Prata do Iguaçu, Pérola D'oeste, Pinhal de São Bento, Planalto; Pranchita, Realeza, Renascença, Salgado Filho, Salto do Lontra, Santa Izabel do Oeste, Santo Antônio do Sudoeste, São Jorge D'Oeste, and Verê. We chose this region to develop the study because Paraná is the state that sells the fourth highest amount of pesticides in Brazil. 26 This reflects an extensive use of pesticides in the state’s agricultural activities, which play a significant role in the Gross Domestic Product (GDP) of the 27 municipalities that make up this health area. 26 More than 50% of the region’s inhabitants engage in agricultural activities, with a particular focus on family farming. This population is subject to considerable pesticide exposure, especially glyphosate, atrazine, and 2,4-dichlorophenoxyacetic (2,4-D), which are widely used in soybean, corn, and wheat monocultures in the region. 26 Figure 1A depicts the correlation between breast cancer cases and the amount of pesticides used by municipalities in the Eighth Health Region of Paraná. Data were grouped into three categories: patient characteristics, tumor characteristics, and disease characteristics (Figure 1B). 2.1.1. Patient characteristics The following data were used as defining parameters of patient characteristics: age in years at diagnosis and menopausal status at diagnosis, dichotomized as presence and absence. The patient's weight at diagnosis was also obtained in kilograms (kg), height in meters (m), and body mass index (BMI) in kg/m2. The profile of occupational pesticide exposure was obtained using a standardized data collection instrument validated for this purpose. 27 The exposure criteria are based on continuous, unprotected, and direct handling of pesticides. Thus, included in the group of pesticide-exposed patients were rural women with a history of direct handling of pesticides without use of protective gloves during preparation and/or dilution of the poisonous solution, application of pesticides, and/or decontamination of personal protective equipment and/or washing of clothes used during spraying, who reported living at least 50% of their lives under direct handling of pesticides at least twice a week for every week of the year. The group of women unexposed to pesticides comprises urban workers with no previous or current history of occupational pesticide exposure. 2.1.2. Tumor characteristics The following parameters were considered to contain the tumor characteristics: estrogen (ER) and progesterone (PR) receptors' expression profile in percentage (%), considering values greater than zero as positive, and zero as negative; amplification of the epidermal human growth factor receptor 2 (HER2) considering values of "3+" and "2+" with the positive FISH amplification test as positive and values zero, "1+" and "2+" without the FISH amplification test as negative; proliferation index Ki67 in %, considering values below/equal to 14% or above as cut-off (≤14% as low and <14% as high proliferation); molecular subtyping of breast tumors considering the classes Luminal A = any positivity for ER and/or PR and ki67 below/equal to 14%, Luminal B = any positivity for ER and/or PR and ki67 above 14%, HER2-amplified = any ER/PR/ki67 value and presence of amplification for HER2, and Triple Negative = ER/PR/HER2 negative and any ki67 value (as described by the St. Gallen Consensus) 28 ; tumor size represented in mm, histological grade categorized as low (grades 1 and 2) and high (grade 3). Lymph node invasion, presence of angiolymphatic emboli, and occurrence of distant metastases were dichotomized as presence or absence. 2.1.3. Disease characteristics The parameters considered disease characteristics were stratification of death risk and recurrence (stratified into low risk, intermediate risk, and high risk, as described in Joint Ordinance No. 5 of April 18, 2019), 29 chemoresistance development, disease recurrence, and death. All were dichotomized as presence or absence. 2.2. Data analysis All data wore processed in Python version 3.10.12. A descriptive statistical data analysis was performed, including disease, tumor, and patient characteristics. A ratio was calculated (dividing the frequencies of each molecular subtype) and the differences analyzed by Chi-square and Fisher's exact test for samples with n less than 5. A p>0.05 was considered significant. The parameters that showed significance were subjected to the contingency chi-square test to confirm their independence. 3. Results 3.1. Breast cancer patients occupationally exposed to pesticides have a prevalence of the Luminal B molecular subtype Table 1 shows clinicopathological data of the study population according to their pesticide exposure profile. Breast cancer patients (n=386) had a mean age of 56 years, ranging from 22 to 96 years. Average BMI was 27.95 kg/m2 (16.4 – 51.26 kg/m2). About 60% of the patients were occupationally exposed to pesticides. Of this population, 8.25% were stratified into low death risk and recurrence, 55.87% into intermediate risk, and 35.87% were classified as high risk. Regarding the molecular subtype, 33.24% of the patients were classified as Luminal A, 33.8% as Luminal B, 16.62% as HER2-amplified, and 16.34% as Triple-Negative. Also, 28.21% of the tumors were grade 1, 51.68% were grade 2, and 20.11% were grade 3. About 7% of the patients died, 9.36% of the patients had disease recurrence, and 18.97% of the patients developed chemoresistance. This population was segregated according to the profile of occupational pesticide exposure and presented summary measures similar to those obtained by the total study population (n=386). Breast cancer patients occupationally exposed to pesticides (n=208) had a prevalence of the Luminal B molecular subtype (32.83%), and unexposed patients (n=141) had a prevalence of the Luminal A molecular subtype (37.78%). From the division of the percentages of patients not exposed to pesticides and exposed to pesticides of the Luminal A molecular subtype, it was identified that unexposed patients were 1.2 times more likely to present a molecular subtype associated with a better prognosis of the disease compared to occupationally exposed women. And when dividing the percentages of patients exposed to pesticides and those not exposed to pesticides, the Triple-negative molecular subtype was identified as being 1.5 times more likely for exposed patients to present a molecular subtype associated with a worse prognosis of the disease compared to unexposed women. Exposed patients also have a higher incidence of recurrence (10.19%), chemoresistance (21.26%), and death (7.21%) compared to patients unexposed to pesticides and the total population analyzed, suggesting a worse prognosis. 3.2. Patients exposed to pesticides are more likely to have distant metastasis and affected lymph nodes We sought to determine which characteristics were significantly distinct in breast cancer patients according to their occupational pesticide exposure profile. Comparisons were exhaustively performed, and only the statistically significant and clinically relevant results were reported here, as summarized in Tables 2 and 3. All variables were evaluated using Chi-square analysis for risk stratification of death and/or recurrence, and molecular subtypes. Among patients with positive menopause at diagnosis, the number of patients in each molecular subtype was comparatively significant, both for exposed and unexposed patients. Unexposed patients at menopause showed an increase in the frequency of Luminal B patients compared to Triple-negative (p= 0.03 and ratio=2.08) and HER2-amplified (p=<0.01 and ratio=2.78) (Table 3). Exposed patients at menopause showed an increase in the frequency of Luminal B patients compared to Triple-negative (p=0.01 and ratio=1.84) and HER2-amplified (p=<0.01 and ratio=3.29) (Table 2). Among the tumor characteristics that were significant in the comparison of pesticide exposure are the parameters of distant metastasis and positive lymph node invasion. Breast cancer patients exposed to pesticides were 1.4 times more likely to develop metastasis compared to breast cancer patients unexposed to pesticides. (Figure 2). For the analyses performed on patients exposed to pesticides, an increase in the frequency of Luminal B patients compared to HER2-amplified (p=<0.01 and ratio= 2.31) and Triple-negative (p=<0.01 and ratio=2.73) was observed (Table 2) in the positive subgroup for the distant metastasis parameter. For unexposed patients positive for distant metastasis, there was an increase in the frequency of Luminal B patients compared to Triple-negative (p=0.02 and ratio=3.20) (Table 3), but to a lesser extent. Regarding the lymph node invasion, breast cancer patients exposed to pesticides were 1.3 times more likely of disease spreading compared to breast cancer patients unexposed to pesticides (Figure 2). For the analysis performed on exposed patients belonging to the positive lymph node invasion subgroup, an increase in the frequency of Luminal B patients compared to HER2-amplified molecular subtypes (p=<0.01 and ratio=3.33) (Table 2) was identified. For unexposed patients from the lymph node invasion subgroup, an increase in the frequency of Luminal B patients compared to Triple-negative (p=0.03 and ratio=3.0) was identified (Table 3). Among the disease characteristics found to be significant, the presence of angiolymphatic emboli were observed. The subgroup of unexposed patients and those positive for the presence of angiolymphatic emboli did not show significant differences in relation to the molecular subtype (Table 3). In the subgroup of exposed patients positive for the presence of angiolymphatic emboli, there was an increase in the frequency of Luminal B patients compared to HER2-amplified (p=0.01 and ratio=3.0) (Table 2). The parameters menopausal status, distant metastases, lymph node invasion and angiolymphatic emboli were subjected to the contingency chi-square test and confirmed their independence. 4. Discussion Our study indicates that occupational pesticide exposure is linked to the occurrence of breast tumors with more aggressive clinicopathological characteristics. In the exposed population, we observed an increased frequency of disease recurrence, chemoresistance to treatment, death, and predominance of the molecular subtype Luminal B. To the best of our knowledge, this is the first study that uses the Chi-square test for independence and Fisher's exact test for samples with n less than 5 to predict the relationship of variables related to breast cancer severity in a population categorized according to their pesticide exposure profile. We identified that unexposed women were 1.2 times more likely to have a molecular subtype associated with a better prognosis of the disease compared to women occupationally exposed to pesticides. The most prevalent molecular subtype in these patients was Luminal A, which has slow-growing characteristics characterized by low rates of ki67 proliferation. 30 On the other hand, in pesticide-exposed women, we observed they were 1.5 times more likely to have the triple-negative molecular subtype, characterized by more aggressive clinical behavior. 30 This trend towards a worse prognosis of women exposed to pesticides was also observed in a toxicoproteomics study conducted by Pizzatti et al. (2020) 31 and another by Scandolara et al. (2022), 3 corroborating that pesticide-exposed women may have a poor prognosis for breast cancer. In the exploratory analysis, a higher prevalence of patients with the Luminal B molecular subtype (33.8%) was identified, followed by a minimal difference between Luminal A patients (33.24%). This small difference in frequency between the Luminal subtypes found in the study population differs from the percentage found in the populations of China (65.3% Luminal A and 19% Luminal B) 32 and the United States (55% Luminal A and 17% Luminal B), 33 countries that are among world leaders in the use of pesticides along with Brazil. 34 Although the population of these countries faces similar pesticide exposure as the Brazilian population, one probable reason why a prevalence of more aggressive molecular subtypes is not observed is the lack of studies that segregate the exposed population for a more detailed evaluation. This inclination towards the emergence of tumors with molecular subtypes with an unfavorable prognosis may be related to the promotion of cell proliferation induced by pesticide exposure. 35,36 Pesticide exposure is known to be associated with dysregulation of immune and inflammatory responses. 37 The study conducted by Silva et al. (2022) 9 identified a predominance of intermediate death risk and recurrence for women who were exposed to pesticides. Due to its uncertain characteristics, intermediate risk may imply the recurrence of disease or even systemic damage and has been linked to poor immunological profiles. Other studies have reported immune deregulation in women exposed to pesticides, 38,39 which may favor the development of more aggressive tumors. For example, failure to produce Th1 immune responses can influence the development of large tumor masses in the long term, potentially resulting in aggressive tumor behaviors, such as the occurrence of metastases observed in the exposed patients described in this study. 40 Luminal B breast cancer patients exposed to pesticides were more likely to develop lymph node invasion and distant metastasis than other subtypes. Comparatively, we observed an increased ratio when compared to samples from other molecular subtypes. Li et al. (2019) 41 suggested that lymph node involvement and tumor metastasis have a strong association with pathogenic alterations in TP53 expression. These modifications were observed with a higher mutagenic frequency in patients exposed to pesticides in the study conducted by Scandolara et al. (2022), 3 demonstrating the cascade of progressive damage generated by pesticide exposure, including oncogenesis. Further aggravating harm to the health of the population exposed to pesticides, a study conducted on a population chronically exposed to pesticides—even at low exposure doses—found an accumulation of DNA lesions due to failures in the genetic material repair system. 42 Thus, the accumulation of mutations observed in the immune and inflammatory responses triggered by pesticide exposure, 3,31,38,39 which generally result in the inactivation of tumor suppressor genes, are indications of genomic instability. 43 This genomic instability increases susceptibility to metastasis development. 44 Song et al. (2011) 45 found a greater predisposition to lymph node involvement in patients with angiolymphatic emboli, indicating a greater tendency to a worse prognosis. However, studies on the influence of the presence of angiolymphatic emboli on breast cancer are still scarce. In addition, due to the hypoxic environment that the presence of angiolymphatic emboli provides, some authors consider it as a precursor in the development of metastasis in cancer patients, 37,46,47 reinforcing the idea of a greater tendency to an unfavorable prognosis for the patient, especially in the context of pesticide exposure. Pesticides are known endocrine disruptors and can influence the development of tumors in the female reproductive system, 25 increase aromatase activity and estrogen production, 48,49 reduce fertility, 48 augment estrogen production, 50 increase androgen availability, 51,52 competitively bind to estrogen cell receptors, 53 enhance proliferation of estrogen-sensitive cells, and inhibit corticosterone synthesis in the adrenal cortex. 48,51,54 In this context, we identify 1.8 times more likely of exposed patients having menopause at diagnosis compared to unexposed patients. Pizzatti et al. (2020) 31 reported that pesticide-exposed patients in menopause had significantly reduced levels of tumor necrosis factor-alpha (TNF-α) when compared to unexposed patients, suggesting that exposure may affect the production of TNF-α in the absence of estrogen, resulting in worsening of the disease due to the failure of antitumor mechanisms. This could help to explain the higher incidence of the triple-negative molecular subtype in the population exposed to pesticides. This study has limitations, including the modest sample size and the lack of other risk factors, such as dietary habits and lifestyle. Its strength is that the correlational analysis of pesticide exposure with clinicopathological parameters of breast cancer may be influencing the worse prognosis found in patients living in southwestern Paraná. Therefore, we reiterate the urgency of discussing and changing policies regulating the use of pesticides and the need for screening exposed populations at risk of developing more aggressive disease. Declarations Data availability statement The original contributions presented in the study are included in the article Materials and Methods. Further inquiries can be directed to the corresponding author. Data, including unique materials, documentation, and codes used in the analysis, are available at https://github.com/Laboratorio-de-Analise-de-Dados/Article_pesticide_exposure. Ethics statement The studies involving humans were approved by the Research Ethics Committee (CEP) of the State University of Western Paraná (UNIOESTE) under the number CAAE 35524814.4.0000.0107. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Author Contributions: Conceptualization: ICC, CP, and GFS Methodology: ICC, RFA, LLS, MPAB, SCG, LZPC, DR, CP, and GFS Investigation: ICC, RFA, LLS, MPAB, SCG, LZPC, DR, CP, and GFS Visualization: ICC, CP, and GFS Supervision: CP and GFS Writing—original draft: ICC Writing—review & editing: ICC, DR, CP, and GFS Funding The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. Part of this research was supported by Carlos Chagas Institute, Fiocruz/PR, and by Conselho Nacional de Desenvolvimento Científico e Tecnológico – CNPq. Conflict of interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher. References Gomes AM da S, da Silva JM, dos Santos CB. O uso indiscriminado de agrotóxicos e suas consequências na saúde humana e no ambiente: revisão bibliográfica | Diversitas Journal. Published online August 19, 2021. https://www.diversitasjournal.com.br/diversitas_journal/article/view/1041 Pelaez V, da Silva LR, Araújo EB. Regulation of pesticides: A comparative analysis*. Sci Public Policy . 2013;40(5):644-656. doi:10.1093/scipol/sct020 Scandolara TB, Valle SF, Esteves C, et al. 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The pesticides endosulfan, toxaphene, and dieldrin have estrogenic effects on human estrogen-sensitive cells. Environ Health Perspect . 1994;102(4):380-383. doi:10.1289/ehp.94102380 Tables Table 1 - Clinicopathological characteristics of the patients included in the study. Total population All patients (n) Exposed, frequency Exposed, number (n) Unexposed, frequency Unexposed, number (n) Patient Characteristics Mean age at diagnosis (years) 56 353 56 197 56 134 BMI (kg/m²) 27.95 259 27.82 150 27.92 97 Occupational pesticide exposure 59.6% 208 100.0% 208 0.0% 0 Tumor Characteristics Average tumor size (mm) 29,21 357 30,82 201 28,11 134 Molecular subtyping Luminal A 33.24% 118 30.81% 61 37.78% 51 Luminal B 34.37% 122 33.33% 66 34.81% 47 HER2-amplified 16.62% 59 16.67% 33 15.56% 21 Triple-negative 15.77% 56 19.19% 38 11.85% 16 Grade 1 28.21% 101 29.15% 58 29.2% 40 Grade 2 51.68% 185 52.26% 104 48.91% 67 Grade 3 20.11% 72 18.59% 37 21.9% 30 Disease characteristics Stratification of death risk and recurrence Low 8.25% 26 8.24% 15 8.26% 10 Intermediate 55.87% 176 52.2% 95 60.33% 73 High 35.87% 113 39.56% 72 31.4% 38 Recurrence 9.36% 35 10.19% 21 9.93% 14 Chemoresistance 18.97% 70 21.26% 44 17.27% 24 Death 7.01% 27 7.21% 15 7.09% 10 The parameters age at diagnosis (years), BMI (kg/m²), and tumor size are presented by the population mean analyzed, and the parameters occupational pesticide exposure, molecular subtyping, histological grade, stratification of death risk and recurrence, recurrence, and chemoresistance are presented as a percentage of the analyzed group in relation to the total number of individuals in the population. The number of patients in each variable is different due to missing values (all variables have less than 15% of missing values, except for the variable BMI with 28%). n represents the number of patients evaluated for each parameter. Table 2 - Frequency, ratio and p-value of the patients exposed to pesticides for molecular subtypes. Molecular subtype Total Lum A Lum B HER2-amplified Triple-negative Ratio Lum B/ Lum A p-value Ratio Lum B / HER2-amplified p-value Ratio Lum B/Triple-negative p-value Estrogen receptor expression 133 58 65 10 0 1.12 0.53 6.50 <0.01 0 <0.01 Progesterone receptor expression 89 43 38 8 0 0.88 0.58 4.75 <0.01 0 <0.01 HER2-amplified 24 0 0 24 0 0.00 NA 0.00 <0.01 0 NA KI67 (%) 120 8 66 27 19 8.25 <0.01 2.44 <0.01 3.47 <0.01 Presence of angiolymphatic emboli 52 15 18 6 13 1.20 0.6 3.00 0.01 1.38 0.37 Lymph node invasion 68 19 30 9 10 1.58 0.12 3.33 <0.01 3.00 <0.01 Distant metastasis 76 22 30 13 11 1.36 0.27 2.31 <0.01 2.73 <0.01 Menopausal status 127 42 46 14 25 1.10 0.67 3.29 <0.01 1.84 0.01 Pesticide exposure 198 61 66 33 38 1.08 0.66 2.00 <0.01 1.74 <0.01 Chemoresistance 44 8 15 10 11 1.88 0.14 1.50 0.32 1.36 0.43 Recurrence 21 4 7 5 5 1.75 0.54 α 1.40 0.56 1.40 0.56 Death 15 2 3 2 8 1.50 1.00 α 1.50 1.00 α 0.38 0.02 α A ratio was calculated (dividing the frequencies of each molecular subtype) and the differences analyzed by Chi-square and Fisher's exact test for samples with n less than 5. A p<0.05 was considered significant. The acronym Lum corresponds to the molecular subtype Luminal. NA represents when the calculation was not applicable. α represents that p-values were calculated using Fisher's exact test, the other values were calculated using the chi-square test for independence. Table 3 - Frequency, ratio and p-value of the patients unexposed to pesticides for molecular subtypes. Molecular subtype Total (n) Lum A Lum B HER2-amplified Triple-negative Ratio Lum B/ Lum A P-value Ratio Lum B / HER2-amplified P-value Ratio Lum B /Triple-negative P-value Estrogen receptor expression 106 51 47 8 0 0.92 0.69 5.88 <0.01 0 <0.01 Progesterone receptor expression 78 35 36 7 0 1.03 0.91 5.14 <0.01 0 <0.01 HER2-amplified 18 0 0 18 0 0.00 NA 0.00 <0.01 0 NA KI67 (%) 84 6 46 18 14 7.67 <0.01 2.56 <0.01 3.29 <0.01 Presence of angiolymphatic emboli 31 7 16 4 4 2.29 0.06 4.00 0.26 α 4.00 0.75 α Lymph node invasion 32 9 15 3 5 1.67 0.22 5.00 0.73 α 3.00 0.03 Distant metastasis 35 10 16 4 5 1.60 0.24 4.00 0.53 α 3.20 0.02 Menopausal status 85 39 25 9 12 0.64 0.08 2.78 <0.01 2.08 0.03 Pesticide exposure 0 0 0 0 0 0.00 NA 0.00 NA 0.00 NA Chemoresistance 23 6 11 3 3 1.83 0.23 3.67 0.52 α 3.67 1.00 α Recurrence 14 4 5 2 3 1.25 0.73 α 2.50 1.00 α 1.67 0.41 α Death 8 0 2 0 6 0.00 0.23 α 0.00 1.00 α 0.33 0.00 α A ratio was calculated (dividing the frequencies of each molecular subtype) and the differences analyzed by Chi-square and Fisher's exact test for samples with n less than 5. A p<0.05 was considered significant. The acronym Lum corresponds to the molecular subtype Luminal. NA represents when the calculation was not applicable. α represents that p-values were calculated using Fisher's exact test, the other values were calculated using the chi-square test for independence. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4182249","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":288976643,"identity":"29d4e77f-d946-4541-aabd-d6f0817bed4e","order_by":0,"name":"Isabella Cazagranda","email":"","orcid":"","institution":"Carlos Chagas Institute","correspondingAuthor":false,"prefix":"","firstName":"Isabella","middleName":"","lastName":"Cazagranda","suffix":""},{"id":288976645,"identity":"0c55c88b-5b9c-4c0c-b037-545e63e43744","order_by":1,"name":"Rafaela de Almeida","email":"","orcid":"","institution":"Universidade Estadual do Oeste do Paraná (UNIOESTE)","correspondingAuthor":false,"prefix":"","firstName":"Rafaela","middleName":"","lastName":"de Almeida","suffix":""},{"id":288976646,"identity":"5c3c9b91-1877-41ca-883a-199af6608116","order_by":2,"name":"Lucca Smaniotto","email":"","orcid":"","institution":"Universidade Estadual do Oeste do Paraná (UNIOESTE)","correspondingAuthor":false,"prefix":"","firstName":"Lucca","middleName":"","lastName":"Smaniotto","suffix":""},{"id":288976649,"identity":"61edc8a1-f5a1-4c29-9d85-6b12848eb329","order_by":3,"name":"Maria Paula Berny","email":"","orcid":"","institution":"Universidade Estadual do Oeste do Paraná (UNIOESTE)","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Paula","lastName":"Berny","suffix":""},{"id":288976650,"identity":"fa65725a-7037-4325-8a1a-98a5ef090f5f","order_by":4,"name":"Shaiane Gaboardi","email":"","orcid":"","institution":"Instituto Federal Catarinense","correspondingAuthor":false,"prefix":"","firstName":"Shaiane","middleName":"","lastName":"Gaboardi","suffix":""},{"id":288976651,"identity":"dcd7259c-9d89-4b02-8bac-84d0213b78aa","order_by":5,"name":"Luciano Candiotto","email":"","orcid":"","institution":"Universidade Estadual do Oeste do Paraná (UNIOESTE)","correspondingAuthor":false,"prefix":"","firstName":"Luciano","middleName":"","lastName":"Candiotto","suffix":""},{"id":288976652,"identity":"2dc2bd87-2e4e-4cc5-b55c-141aaa13e801","order_by":6,"name":"Daniel Rech","email":"","orcid":"","institution":"Universidade Estadual do Oeste do Paraná (UNIOESTE)","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Rech","suffix":""},{"id":288976653,"identity":"d1dcf512-8d28-4ca2-8826-c935184dc3b2","order_by":7,"name":"Carolina Panis","email":"","orcid":"","institution":"Universidade Estadual do Oeste do Paraná (UNIOESTE)","correspondingAuthor":false,"prefix":"","firstName":"Carolina","middleName":"","lastName":"Panis","suffix":""},{"id":288976654,"identity":"bf728f2e-ebc0-443b-a589-d50f349f5153","order_by":8,"name":"Guilherme Silveira","email":"data:image/png;base64,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","orcid":"","institution":"Carlos Chagas Institute","correspondingAuthor":true,"prefix":"","firstName":"Guilherme","middleName":"","lastName":"Silveira","suffix":""}],"badges":[],"createdAt":"2024-03-28 12:27:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4182249/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4182249/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54514460,"identity":"93f62380-db61-41c2-be8d-1f13031fccf8","added_by":"auto","created_at":"2024-04-11 16:17:41","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":237396,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design. Of the 923 patients with images suggestive of breast lesions identified by mammograms and ultrasound, 386 patients were included in the study analyses because they had a diagnosis of breast cancer determined by a pathologist. To achieve the study's objective, descriptive and inferential statistical analyses were performed on the segregated population of patients exposed and unexposed to pesticides. Letter A presents a graph with the average volume of pesticides sold per hectare cultivated and the number of cases of breast cancer diagnosed between 2015 and 2023 in the 27 municipalities that make up the Eighth Health Region of the state of Paraná, the region of the population used. The numbers present in the municipalities present in the graph refer to 1 - Ampére, 2 - Barracão, 3 - Bela Vista da Caroba, 4 - Boa Esperança do Iguaçu, 5 - Bom Jesus do Sul, 6 - Capanema, 7 - Cruzeiro do Iguaçu, 8 - Dois Vizinhos, 9 - Éneas Marques, 10 - Flor da Serra Do Sul, 11 - Francisco Beltrão, 12 - Manfrinópolis, 13 - Marmeleiro, 14 - Nova Esperança do Sudoeste, 15 - Nova Prata do Iguaçu, 16 - Pérola D' west, 17 - Pinhal de São Bento, 18 - Planalto; 19 - Pranchita, 20 - Royalty, 21 - Renaissance, 22 - Salgado Filho, 23 - Salto do Lontra, 24 - Santa Izabel do Oeste, 25 - Santo Antônio do Sudoeste, 26 - São Jorge D'Oeste, and 27 – Verê. Letter B presents a table with the profile and grouping of the parameters analyzed in the study. The characteristics of the patients include the following parameters: age at diagnosis in years, menopausal status at diagnosis; weight at diagnosis, height in cm; body mass index (BMI); exposure to pesticides. Among the tumor characteristics are the following parameters: estrogen receptor expression in %; progesterone receptor expression in %; amplification of human epidermal growth factor 2 (HER2); Ki67 profiler index in %; molecular subtype; tumor size in mm; histological grade; presence of angiolymphatic emboli; lymph node invasion; presence of distant metastasis. Among the characteristics of the disease are the following parameters: stratification of the risk of death and recurrence, recurrence profile, chemoresistance to treatment and occurrence of death.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4182249/v1/8bdb912c011aea48ce035db4.jpg"},{"id":54514459,"identity":"b7e52f4a-e0c7-4a4a-8e3d-a9a35780d289","added_by":"auto","created_at":"2024-04-11 16:17:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":29655,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the relative frequency of parameters that showed significance for patients exposed and unexposed to pesticides. * represents that there is no statistical evidence of dependence between the variables using the chi-square test of independence.\u003c/p\u003e","description":"","filename":"Picture2.png","url":"https://assets-eu.researchsquare.com/files/rs-4182249/v1/5b5c8bf3ba65eb6dd01c2c40.png"},{"id":58107952,"identity":"f4f8be5e-58f6-4c16-962a-fd1518d4cfc9","added_by":"auto","created_at":"2024-06-11 08:24:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":972842,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4182249/v1/7937db7b-d3c3-448b-b19a-1d80d47e69b4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Occupational pesticide exposure is linked to the prevalence of Luminal B breast cancer and poor prognosis features in Brazilian rural women","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe use of pesticides in agriculture began with the \u0026ldquo;Green Revolution\u0026rdquo; movement in the 50s, using the argument of increasing food production.\u003csup\u003e1\u003c/sup\u003e Unfortunately, at that time, there was no understanding of the potential risks of its use for the environment and human health.\u003csup\u003e2\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral studies have demonstrated pesticide exposure risks to human health, such as their carcinogenic potential,\u003csup\u003e3,4\u003c/sup\u003e endocrine disruption,\u003csup\u003e5\u003c/sup\u003e genotoxicity,\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e and compromised immune system.\u003csup\u003e9\u0026ndash;12\u003c/sup\u003e With increased negative evidence on pesticide use, one of the main discussions in this field is the fact that several pesticides have carcinogenic potential. They have been classified by the International Agency for Cancer Research (IARC) as potentially, probably, or proven carcinogens. A variety of cancers have been linked to pesticide exposure, including thyroid,\u0026nbsp;\u003csup\u003e13\u003c/sup\u003e colorectal,\u003csup\u003e14\u003c/sup\u003e bladder,\u003csup\u003e15\u003c/sup\u003e blood,\u003csup\u003e16\u003c/sup\u003e brain,\u003csup\u003e17\u003c/sup\u003e and breast cancer.\u003csup\u003e18\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the context of pesticide health risks, it is important to note the growth of the feminization movement in agriculture; it has been estimated that women represent 43% of the world\u0026apos;s agricultural workforce.\u003csup\u003e19\u003c/sup\u003e This trend has been observed in several regions of the world, such as the European Union, where women represent 29% of rural workers,\u003csup\u003e20\u003c/sup\u003e Brazil, where they represent 45% of women, and certain regions of Africa and Asia, where women\u0026rsquo;s representation can reach up to 60%.\u003csup\u003e21\u003c/sup\u003e The feminization of agriculture may lead to an increase in the incidence of breast cancer in women.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDue to the extensive exposure of rural women in agriculture and the endocrine disruption properties of pesticides, attention has been drawn to the incidence of female tumors, such as breast cancer. Several studies have addressed the relationship between pesticide exposure and the increased risk of developing this pathology.\u003csup\u003e22\u0026ndash;24\u003c/sup\u003e Endocrine deregulation can occur by mainly two exposure models: programming, which modifies tissues during embryonic development until puberty, making them susceptible to cancer, and worsening, where subsequent exposure leads to malignant evolution of precancerous cells or benign lesions.\u003csup\u003e25\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eConsidering little is known about the impact of chronic and continued occupational pesticide exposure on the clinicopathological profiling of breast cancer, the present study focused on understanding the issue. To reach this goal, we performed extensive data collection from patients diagnosed with breast cancer who had visited a public hospital located in Paran\u0026aacute; state, a Brazilian setting known for its high use of pesticides and female rural work. Information concerning patient profiles and tumor characteristics was obtained and analyzed using descriptive and inferential statistical methods, aiming to determine a clinicopathological signature associated with pesticide exposure in women with breast cancer.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e2.1.\u0026nbsp;Study design and data collection\u003c/p\u003e\n\u003cp\u003eThis is a descriptive, cross-sectional, and quantitative exploratory study with the objective of determining a clinicopathological signature associated with exposure to pesticides in rural women with breast cancer. Figure 1 illustrates the study design.\u003c/p\u003e\n\u003cp\u003eThis study complied with the national and international regulatory standards for research involving humans and was approved by the Research Ethics Committee (CEP) of the State University of Western Paran\u0026aacute; (UNIOESTE), under the number CAAE 35524814.4.0000.0107. All volunteers signed a free and informed consent form. Data, including unique materials, documentation, and codes used in the analysis, are available at https://github.com/Laboratorio-de-Analise-de-Dados/Article_pesticide_exposure.\u003c/p\u003e\n\u003cp\u003eA total of 923 women who attended the Francisco Beltr\u0026atilde;o Cancer Hospital (Ceonc) from May 2015 to April 2023 with images suggestive of breast lesions identified by mammograms and ultrasound were included. The study comprised the Eighth Paran\u0026aacute; Health Region, which comprises 27 municipalities characterized predominantly by rural family work. To obtain the diagnosis of breast cancer, a biopsy of the suspicious lesion was performed by a pathologist, followed by anatomopathological analysis and immunohistochemistry. After excluding patients with benign lesions, 386 patients were included in the study as having a breast cancer diagnosis. Patients were categorized as occupationally exposed (n = 208) or unexposed to pesticides (n = 141).\u003c/p\u003e\n\u003cp\u003eThe Eighth Regional Health Region of Paran\u0026aacute; comprises about 500,000 inhabitants living in the following 27 municipalities: Amp\u0026eacute;re, Barrac\u0026atilde;o, Bela Vista da Caroba, Boa Esperan\u0026ccedil;a do Igua\u0026ccedil;u, Bom Jesus do Sul, Capanema, Cruzeiro do Igua\u0026ccedil;u, Dois Vizinhos, \u0026Eacute;neas Marques, Flor da Serra Do Sul, Francisco Beltr\u0026atilde;o, Manfrin\u0026oacute;polis, Marmeleiro, Nova Esperan\u0026ccedil;a do Sudoeste, Nova Prata do Igua\u0026ccedil;u, P\u0026eacute;rola D\u0026apos;oeste, Pinhal de S\u0026atilde;o Bento, Planalto; Pranchita, Realeza, Renascen\u0026ccedil;a, Salgado Filho, Salto do Lontra, Santa Izabel do Oeste, Santo Ant\u0026ocirc;nio do Sudoeste, S\u0026atilde;o Jorge D\u0026apos;Oeste, and Ver\u0026ecirc;.\u0026nbsp;We chose this region to develop the study because Paran\u0026aacute; is the state that sells the fourth highest amount of pesticides in Brazil.\u003csup\u003e26\u003c/sup\u003e This reflects an extensive use of pesticides in the state\u0026rsquo;s agricultural activities, which play a significant role in the Gross Domestic Product (GDP) of the 27 municipalities that make up this health area.\u003csup\u003e26\u003c/sup\u003e More than 50% of the region\u0026rsquo;s inhabitants engage in agricultural activities, with a particular focus on family farming. This population is subject to considerable pesticide exposure, especially glyphosate, atrazine, and 2,4-dichlorophenoxyacetic (2,4-D), which are widely used in soybean, corn, and wheat monocultures in the region.\u003csup\u003e26\u003c/sup\u003e Figure 1A depicts the correlation between breast cancer cases and the amount of pesticides used by municipalities in the Eighth Health Region of Paran\u0026aacute;.\u003c/p\u003e\n\u003cp\u003eData were grouped into three categories: patient characteristics, tumor characteristics, and disease characteristics (Figure 1B).\u003c/p\u003e\n\u003cp\u003e2.1.1.\u0026nbsp;\u0026nbsp;Patient characteristics\u003c/p\u003e\n\u003cp\u003eThe following data were used as defining parameters of patient characteristics: age in years at diagnosis and menopausal status at diagnosis, dichotomized as presence and absence. The patient\u0026apos;s weight at diagnosis was also obtained in kilograms (kg), height in meters (m), and body mass index (BMI) in kg/m2. The profile of occupational pesticide exposure was obtained using a standardized data collection instrument validated for this purpose.\u003csup\u003e27\u003c/sup\u003e The exposure criteria are based on continuous, unprotected, and direct handling of pesticides. Thus, included in the group of pesticide-exposed patients were rural women with a history of direct handling of pesticides without use of protective gloves during preparation and/or dilution of the poisonous solution, application of pesticides, and/or decontamination of personal protective equipment and/or washing of clothes used during spraying, who reported living at least 50% of their lives under direct handling of pesticides at least twice a week for every week of the year. The group of women unexposed to pesticides comprises urban workers with no previous or current history of occupational pesticide exposure.\u003c/p\u003e\n\u003cp\u003e2.1.2.\u0026nbsp;\u0026nbsp;Tumor characteristics\u003c/p\u003e\n\u003cp\u003eThe following parameters were considered to contain the tumor characteristics: estrogen (ER) and progesterone (PR) receptors\u0026apos; expression profile in percentage (%), considering values greater than zero as positive, and zero as negative; amplification of the epidermal human growth factor receptor 2 (HER2) considering values of \u0026quot;3+\u0026quot; and \u0026quot;2+\u0026quot; with the positive FISH amplification test as positive and values zero, \u0026quot;1+\u0026quot; and \u0026quot;2+\u0026quot; without the FISH amplification test as negative; proliferation index Ki67 in %, considering values below/equal to 14% or above as cut-off (\u0026le;14% as low and \u0026lt;14% as high proliferation); molecular subtyping of breast tumors considering the classes Luminal A = any positivity for ER and/or PR and ki67 below/equal to 14%, Luminal B = any positivity for ER and/or PR and ki67 above 14%, HER2-amplified = any ER/PR/ki67 value and presence of amplification for HER2, and Triple Negative = ER/PR/HER2 negative and any ki67 value (as described by the St. Gallen Consensus)\u003csup\u003e28\u003c/sup\u003e; tumor size represented in mm, histological grade categorized as low (grades 1 and 2) and high (grade 3). Lymph node invasion, presence of angiolymphatic emboli, and occurrence of distant metastases were dichotomized as presence or absence.\u003c/p\u003e\n\u003cp\u003e2.1.3.\u0026nbsp;\u0026nbsp;Disease characteristics\u003c/p\u003e\n\u003cp\u003eThe parameters considered disease characteristics were stratification of death risk and recurrence (stratified into low risk, intermediate risk, and high risk, as described in Joint Ordinance No. 5 of April 18, 2019),\u003csup\u003e29\u003c/sup\u003e chemoresistance development, disease recurrence, and death. All were dichotomized as presence or absence.\u003c/p\u003e\n\u003cp\u003e2.2.\u0026nbsp;Data analysis\u003c/p\u003e\n\u003cp\u003eAll data wore processed in Python version 3.10.12. A descriptive statistical data analysis was performed, including disease, tumor, and patient characteristics. A ratio was calculated (dividing the frequencies of each molecular subtype) and the differences analyzed by Chi-square and Fisher\u0026apos;s exact test for samples with n less than 5. A p\u0026gt;0.05 was considered significant. The parameters that showed significance were subjected to the contingency chi-square test to confirm their independence.\u003c/p\u003e"},{"header":"3.\tResults","content":"\u003cp\u003e3.1.\u0026nbsp;Breast cancer patients occupationally exposed to pesticides have a prevalence of the Luminal B molecular subtype\u003c/p\u003e\n\u003cp\u003eTable 1 shows clinicopathological data of the study population according to their pesticide exposure profile. Breast cancer patients (n=386) had a mean age of 56 years, ranging from 22 to 96 years. Average BMI was 27.95 kg/m2 (16.4 \u0026ndash; 51.26 kg/m2). About 60% of the patients were occupationally exposed to pesticides. Of this population, 8.25% were stratified into low death risk and recurrence, 55.87% into intermediate risk, and 35.87% were classified as high risk. Regarding the molecular subtype, 33.24% of the patients were classified as Luminal A, 33.8% as Luminal B, 16.62% as HER2-amplified, and 16.34% as Triple-Negative. Also, 28.21% of the tumors were grade 1, 51.68% were grade 2, and 20.11% were grade 3. About 7% of the patients died, 9.36% of the patients had disease recurrence, and 18.97% of the patients developed chemoresistance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis population was segregated according to the profile of occupational pesticide exposure and presented summary measures similar to those obtained by the total study population (n=386). Breast cancer patients occupationally exposed to pesticides (n=208) had a prevalence of the Luminal B molecular subtype (32.83%), and unexposed patients (n=141) had a prevalence of the Luminal A molecular subtype (37.78%). From the division of the percentages of patients not exposed to pesticides and exposed to pesticides of the Luminal A molecular subtype, it was identified that unexposed patients were 1.2 times more likely to present a molecular subtype associated with a better prognosis of the disease compared to occupationally exposed women. And when dividing the percentages of patients exposed to pesticides and those not exposed to pesticides, the Triple-negative molecular subtype was identified as being 1.5 times more likely for exposed patients to present a molecular subtype associated with a worse prognosis of the disease compared to unexposed women. \u0026nbsp;Exposed patients also have a higher incidence of recurrence (10.19%), chemoresistance (21.26%), and death (7.21%) compared to patients unexposed to pesticides and the total population analyzed, suggesting a worse prognosis.\u003c/p\u003e\n\u003cp\u003e3.2.\u0026nbsp;Patients exposed to pesticides are more likely to have distant metastasis and affected lymph nodes\u003c/p\u003e\n\u003cp\u003eWe sought to determine which characteristics were significantly distinct in breast cancer patients according to their occupational pesticide exposure profile. Comparisons were exhaustively performed, and only the statistically significant and clinically relevant results were reported here, as summarized in Tables 2 and 3. All variables were evaluated using Chi-square analysis for risk stratification of death and/or recurrence, and molecular subtypes.\u003c/p\u003e\n\u003cp\u003eAmong patients with positive menopause at diagnosis, the number of patients in each molecular subtype was comparatively significant, both for exposed and unexposed patients. Unexposed patients at menopause showed an increase in the frequency of Luminal B patients compared to Triple-negative (p= 0.03 and ratio=2.08) and HER2-amplified (p=\u0026lt;0.01 and ratio=2.78) (Table 3). Exposed patients at menopause showed an increase in the frequency of Luminal B patients compared to Triple-negative (p=0.01 and ratio=1.84) and HER2-amplified (p=\u0026lt;0.01 and ratio=3.29) (Table 2).\u003c/p\u003e\n\u003cp\u003eAmong the tumor characteristics that were significant in the comparison of pesticide exposure are the parameters of distant metastasis and positive lymph node invasion. Breast cancer patients exposed to pesticides were 1.4 times more likely to develop metastasis compared to breast cancer patients unexposed to pesticides. (Figure 2). \u0026nbsp;For the analyses performed on patients exposed to pesticides, an increase in the frequency of Luminal B patients compared to HER2-amplified (p=\u0026lt;0.01 and ratio= 2.31) and Triple-negative (p=\u0026lt;0.01 and ratio=2.73) was observed (Table 2) in the positive subgroup for the distant metastasis parameter. For unexposed patients positive for distant metastasis, there was an increase in the frequency of Luminal B patients compared to Triple-negative (p=0.02 and ratio=3.20) (Table 3), but to a lesser extent. Regarding the lymph node invasion, breast cancer patients exposed to pesticides were 1.3 times more likely of disease spreading compared to breast cancer patients unexposed to pesticides (Figure 2). For the analysis performed on exposed patients belonging to the positive lymph node invasion subgroup, an increase in the frequency of Luminal B patients compared to HER2-amplified molecular subtypes (p=\u0026lt;0.01 and ratio=3.33) (Table 2) was identified. For unexposed patients from the lymph node invasion subgroup, an increase in the frequency of Luminal B patients compared to Triple-negative (p=0.03 and ratio=3.0) was identified (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the disease characteristics found to be significant, the presence of angiolymphatic emboli were observed. The subgroup of unexposed patients and those positive for the presence of angiolymphatic emboli did not show significant differences in relation to the molecular subtype (Table 3). In the subgroup of exposed patients positive for the presence of angiolymphatic emboli, there was an increase in the frequency of Luminal B patients compared to HER2-amplified (p=0.01 and ratio=3.0) (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe parameters menopausal status, distant metastases, lymph node invasion and angiolymphatic emboli were subjected to the contingency chi-square test and confirmed their independence.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study indicates that occupational pesticide exposure is linked to the occurrence of breast tumors with more aggressive clinicopathological characteristics. In the exposed population, we observed an increased frequency of disease recurrence, chemoresistance to treatment, death, and predominance of the molecular subtype Luminal B. To the best of our knowledge, this is the first study that uses the Chi-square test for independence and Fisher\u0026apos;s exact test for samples with n less than 5 to predict the relationship of variables related to breast cancer severity in a population categorized according to their pesticide exposure profile.\u003c/p\u003e\n\u003cp\u003eWe identified that unexposed women were 1.2 times more likely to have a molecular subtype associated with a better prognosis of the disease compared to women occupationally exposed to pesticides. The most prevalent molecular subtype in these patients was Luminal A, which has slow-growing characteristics characterized by low rates of ki67 proliferation.\u003csup\u003e30\u003c/sup\u003e On the other hand, in pesticide-exposed women, we observed they were 1.5 times more likely to have the triple-negative molecular subtype, characterized by more aggressive clinical behavior.\u003csup\u003e30\u003c/sup\u003e This trend towards a worse prognosis of women exposed to pesticides was also observed in a toxicoproteomics study conducted by Pizzatti et al. (2020)\u003csup\u003e31\u003c/sup\u003e and another by Scandolara et al. (2022),\u003csup\u003e3\u003c/sup\u003e corroborating that pesticide-exposed women may have a poor prognosis for breast cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the exploratory analysis, a higher prevalence of patients with the Luminal B molecular subtype (33.8%) was identified, followed by a minimal difference between Luminal A patients (33.24%). This small difference in frequency between the Luminal subtypes found in the study population differs from the percentage found in the populations of China (65.3% Luminal A and 19% Luminal B)\u003csup\u003e32\u003c/sup\u003e and the United States (55% Luminal A and 17% Luminal B),\u003csup\u003e33\u003c/sup\u003e countries that are among world leaders in the use of pesticides along with Brazil.\u003csup\u003e34\u003c/sup\u003e Although the population of these countries faces similar pesticide exposure as the Brazilian population, one probable reason why a prevalence of more aggressive molecular subtypes is not observed is the lack of studies that segregate the exposed population for a more detailed evaluation. This inclination towards the emergence of tumors with molecular subtypes with an unfavorable prognosis may be related to the promotion of cell proliferation induced by pesticide exposure.\u003csup\u003e35,36\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003ePesticide exposure is known to be associated with dysregulation of immune and inflammatory responses.\u003csup\u003e37\u003c/sup\u003e The study conducted by Silva et al. (2022)\u003csup\u003e9\u003c/sup\u003eidentified a predominance of intermediate death risk and recurrence for women who were exposed to pesticides. Due to its uncertain characteristics, intermediate risk may imply the recurrence of disease or even systemic damage and has been linked to poor immunological profiles. Other studies have reported immune deregulation in women exposed to pesticides,\u003csup\u003e38,39\u003c/sup\u003e which may favor the development of more aggressive tumors. For example, failure to produce Th1 immune responses can influence the development of large tumor masses in the long term, potentially resulting in aggressive tumor behaviors, such as the occurrence of metastases observed in the exposed patients described in this study.\u003csup\u003e40\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eLuminal B breast cancer patients exposed to pesticides were more likely to develop lymph node invasion and distant metastasis than other subtypes. Comparatively, we observed an increased ratio when compared to samples from other molecular subtypes. Li et al. (2019)\u003csup\u003e41\u003c/sup\u003e suggested that lymph node involvement and tumor metastasis have a strong association with pathogenic alterations in TP53 expression. These modifications were observed with a higher mutagenic frequency in patients exposed to pesticides in the study conducted by Scandolara et al. (2022),\u003csup\u003e3\u003c/sup\u003e demonstrating the cascade of progressive damage generated by pesticide exposure, including oncogenesis.\u003c/p\u003e\n\u003cp\u003eFurther aggravating harm to the health of the population exposed to pesticides, a study conducted on a population chronically exposed to pesticides\u0026mdash;even at low exposure doses\u0026mdash;found an accumulation of DNA lesions due to failures in the genetic material repair system.\u003csup\u003e42\u003c/sup\u003e Thus, the accumulation of mutations observed in the immune and inflammatory responses triggered by pesticide exposure,\u003csup\u003e3,31,38,39\u003c/sup\u003ewhich generally result in the inactivation of tumor suppressor genes, are indications of genomic instability.\u003csup\u003e43\u003c/sup\u003e This genomic instability increases susceptibility to metastasis development.\u003csup\u003e44\u003c/sup\u003e Song et al. (2011)\u003csup\u003e45\u003c/sup\u003e found a greater predisposition to lymph node involvement in patients with angiolymphatic emboli, indicating a greater tendency to a worse prognosis. However, studies on the influence of the presence of angiolymphatic emboli on breast cancer are still scarce. In addition, due to the hypoxic environment that the presence of angiolymphatic emboli provides, some authors consider it as a precursor in the development of metastasis in cancer patients,\u003csup\u003e37,46,47\u003c/sup\u003ereinforcing the idea of a greater tendency to an unfavorable prognosis for the patient, especially in the context of pesticide exposure.\u003c/p\u003e\n\u003cp\u003ePesticides are known endocrine disruptors and can influence the development of tumors in the female reproductive system,\u003csup\u003e25\u003c/sup\u003e increase aromatase activity and estrogen production,\u003csup\u003e48,49\u003c/sup\u003e reduce fertility,\u003csup\u003e48\u003c/sup\u003e augment estrogen production,\u003csup\u003e50\u003c/sup\u003e increase androgen availability,\u003csup\u003e51,52\u003c/sup\u003e competitively bind to estrogen cell receptors,\u003csup\u003e53\u003c/sup\u003e enhance proliferation of estrogen-sensitive cells, and inhibit corticosterone synthesis in the adrenal cortex.\u0026nbsp;\u003csup\u003e48,51,54\u003c/sup\u003e In this context, we identify 1.8 times more likely of exposed patients having menopause at diagnosis compared to unexposed patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePizzatti et al. (2020)\u003csup\u003e31\u003c/sup\u003e reported that pesticide-exposed patients in menopause had significantly reduced levels of tumor necrosis factor-alpha (TNF-\u0026alpha;) when compared to unexposed patients, suggesting that exposure may affect the production of TNF-\u0026alpha; in the absence of estrogen, resulting in worsening of the disease due to the failure of antitumor mechanisms. This could help to explain the higher incidence of the triple-negative molecular subtype in the population exposed to pesticides.\u003c/p\u003e\n\u003cp\u003eThis study has limitations, including the modest sample size and the lack of other risk factors, such as dietary habits and lifestyle. Its strength is that the correlational analysis of pesticide exposure with clinicopathological parameters of breast cancer may be influencing the worse prognosis found in patients living in southwestern Paran\u0026aacute;. Therefore, we reiterate the urgency of discussing and changing policies regulating the use of pesticides and the need for screening exposed populations at risk of developing more aggressive disease.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability statement\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article Materials and Methods. Further inquiries can be directed to the corresponding author. Data, including unique materials, documentation, and codes used in the analysis, are available at https://github.com/Laboratorio-de-Analise-de-Dados/Article_pesticide_exposure.\u003c/p\u003e\n\u003cp\u003eEthics statement\u003c/p\u003e\n\u003cp\u003eThe studies involving humans were approved by the Research Ethics Committee (CEP) of the State University of Western Paran\u0026aacute; (UNIOESTE) under the number\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCAAE 35524814.4.0000.0107. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConceptualization: ICC, CP, and GFS\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Methodology: ICC, RFA, LLS, MPAB, SCG, LZPC, DR, CP, and GFS\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Investigation: ICC, RFA, LLS, MPAB, SCG, LZPC, DR, CP, and GFS\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Visualization: ICC, CP, and GFS\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Supervision: CP and GFS\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Writing\u0026mdash;original draft: ICC\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Writing\u0026mdash;review \u0026amp; editing: ICC, DR, CP, and GFS\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that financial support was received for the research, authorship, and/or publication of this article.\u0026nbsp;Part of this research was supported by Carlos Chagas Institute, Fiocruz/PR, and by Conselho Nacional de Desenvolvimento Cient\u0026iacute;fico e Tecnol\u0026oacute;gico \u0026ndash; CNPq.\u003c/p\u003e\n\u003cp\u003eConflict of interest:\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003ePublisher\u0026rsquo;s note\u003c/p\u003e\n\u003cp\u003eAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGomes AM da S, da Silva JM, dos Santos CB. O uso indiscriminado de agrot\u0026oacute;xicos e suas consequ\u0026ecirc;ncias na sa\u0026uacute;de humana e no ambiente: revis\u0026atilde;o bibliogr\u0026aacute;fica | Diversitas Journal. Published online August 19, 2021. https://www.diversitasjournal.com.br/diversitas_journal/article/view/1041\u003c/li\u003e\n\u003cli\u003ePelaez V, da Silva LR, Ara\u0026uacute;jo EB. Regulation of pesticides: A comparative analysis*. \u003cem\u003eSci Public Policy\u003c/em\u003e. 2013;40(5):644-656. doi:10.1093/scipol/sct020\u003c/li\u003e\n\u003cli\u003eScandolara TB, Valle SF, Esteves C, et al. 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Toxicoproteomics Disclose Pesticides as Downregulators of TNF-\u0026alpha;, IL-1\u0026beta; and Estrogen Receptor Pathways in Breast Cancer Women Chronically Exposed. \u003cem\u003eFront Oncol\u003c/em\u003e. 2020;10. doi:10.3389/fonc.2020.01698\u003c/li\u003e\n\u003cli\u003eZhu X, Ying J, Wang F, Wang J, Yang H. Estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 status in invasive breast cancer: a 3,198 cases study at National Cancer Center, China. \u003cem\u003eBreast Cancer Res Treat\u003c/em\u003e. 2014;147(3):551-555. doi:10.1007/s10549-014-3136-y\u003c/li\u003e\n\u003cli\u003eBhargava R, Striebel J, Beriwal S, et al. Prevalence, Morphologic Features and Proliferation Indices of Breast Carcinoma Molecular Classes Using Immunohistochemical Surrogate Markers. \u003cem\u003eInt J Clin Exp Pathol\u003c/em\u003e. 2009;2(5):444-455.\u003c/li\u003e\n\u003cli\u003eFAO. 2023. \u003cem\u003ePesticides use and trade, 1990\u0026ndash;2021\u003c/em\u003e. FAOSTAT Analytical Briefs Series No. 70. Rome.\u0026nbsp;\u003ca href=\"https://doi.org/10.4060/cc6958en\"\u003ehttps://doi.org/10.4060/cc6958en\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eCalaf GM. Chapter Four - Breast carcinogenesis induced by organophosphorous pesticides. In: Costa M, ed. \u003cem\u003eAdvances in Pharmacology\u003c/em\u003e. Vol 96. Environmental Carcinogenesis. Academic Press; 2023:71-117. doi:10.1016/bs.apha.2022.10.003\u003c/li\u003e\n\u003cli\u003eCabello G, Valenzuela M, Vilaxa A, et al. A rat mammary tumor model induced by the organophosphorous pesticides parathion and malathion, possibly through acetylcholinesterase inhibition. \u003cem\u003eEnviron Health Perspect\u003c/em\u003e. 2001;109(5):471-479. doi:10.1289/ehp.01109471\u003c/li\u003e\n\u003cli\u003eLiu Z ji, Semenza GL, Zhang H feng. Hypoxia-inducible factor 1 and breast cancer metastasis. \u003cem\u003eJ Zhejiang Univ-Sci B\u003c/em\u003e. 2015;16(1):32-43. doi:10.1631/jzus.B1400221\u003c/li\u003e\n\u003cli\u003eda Silva RGS, Ferreira MO, Komori IMS, et al. Brief research report pesticide occupational exposure leads to significant inflammatory changes in normal mammary breast tissue. \u003cem\u003eFront Public Health\u003c/em\u003e. 2023;11. doi:10.3389/fpubh.2023.1229422\u003c/li\u003e\n\u003cli\u003eSantos SBG dos, da Silva JC, Jaques H dos S, et al. Occupational exposure to pesticides dysregulates systemic Th1/Th2/Th17 cytokines and correlates with poor clinical outcomes in breast cancer patients. \u003cem\u003eFront Immunol\u003c/em\u003e. 2023;14. doi:10.3389/fimmu.2023.1281056\u003c/li\u003e\n\u003cli\u003eMirlekar B, Pylayeva-Gupta Y. IL-12 Family Cytokines in Cancer and Immunotherapy. \u003cem\u003eCancers\u003c/em\u003e. 2021;13(2):167. doi:10.3390/cancers13020167\u003c/li\u003e\n\u003cli\u003eLi Y, Zhang X, Qiu J, Pang T, Huang L, Zeng Q. Comparisons of p53, KI67 and BRCA1 expressions in patients.\u003c/li\u003e\n\u003cli\u003eNascimento F de A, Silva D de M e., Pedroso TMA, Ramos JSA, Parise MR. Farmers exposed to pesticides have almost five times more DNA damage: a meta-analysis study. \u003cem\u003eEnviron Sci Pollut Res\u003c/em\u003e. 2022;29(1):805-816. doi:10.1007/s11356-021-15573-z\u003c/li\u003e\n\u003cli\u003eCalaf GM, Bleak TC, Roy D. Signs of carcinogenicity induced by parathion, malathion, and estrogen in human breast epithelial cells (Review). \u003cem\u003eOncol Rep\u003c/em\u003e. 2021;45(4):1-1. doi:10.3892/or.2021.7975\u003c/li\u003e\n\u003cli\u003eFares J, Fares MY, Khachfe HH, Salhab HA, Fares Y. Molecular principles of metastasis: a hallmark of cancer revisited. \u003cem\u003eSignal Transduct Target Ther\u003c/em\u003e. 2020;5(1):1-17. doi:10.1038/s41392-020-0134-x\u003c/li\u003e\n\u003cli\u003eSong YJ, Shin SH, Cho JS, Park MH, Yoon JH, Jegal YJ. The Role of Lymphovascular Invasion as a Prognostic Factor in Patients with Lymph Node-Positive Operable Invasive Breast Cancer. \u003cem\u003eJ Breast Cancer\u003c/em\u003e. 2011;14(3):198-203. doi:10.4048/jbc.2011.14.3.198\u003c/li\u003e\n\u003cli\u003eFidler IJ. Metastasis: Quantitative Analysis of Distribution and Fate of Tumor Emboli Labeled With 125I-5-Iodo-2\u0026prime; -deoxyuridine23. \u003cem\u003eJNCI J Natl Cancer Inst\u003c/em\u003e. 1970;45(4):773-782. doi:10.1093/jnci/45.4.773\u003c/li\u003e\n\u003cli\u003eChaffer CL, Weinberg RA. A Perspective on Cancer Cell Metastasis. \u003cem\u003eScience\u003c/em\u003e. 2011;331(6024):1559-1564. doi:10.1126/science.1203543\u003c/li\u003e\n\u003cli\u003eCocco P. On the rumors about the silent spring: review of the scientific evidence linking occupational and environmental pesticide exposure to endocrine disruption health effects. \u003cem\u003eCad Sa\u0026uacute;de P\u0026uacute;blica\u003c/em\u003e. 2002;18(2):379-402. doi:10.1590/S0102-311X2002000200003\u003c/li\u003e\n\u003cli\u003eRaun Andersen H, Vinggaard AM, H\u0026oslash;j Rasmussen T, Gjermandsen IM, Cecilie Bonefeld-J\u0026oslash;rgensen E. Effects of Currently Used Pesticides in Assays for Estrogenicity, Androgenicity, and Aromatase Activity \u003cem\u003ein Vitro\u003c/em\u003e. \u003cem\u003eToxicol Appl Pharmacol\u003c/em\u003e. 2002;179(1):1-12. doi:10.1006/taap.2001.9347\u003c/li\u003e\n\u003cli\u003eSanderson JT, Seinen W, Giesy JP, van den Berg M. 2-Chloro-s-Triazine Herbicides Induce Aromatase (CYP19) Activity in H295R Human Adrenocortical Carcinoma Cells: A Novel Mechanism for Estrogenicity? \u003cem\u003eToxicol Sci\u003c/em\u003e. 2000;54(1):121-127. doi:10.1093/toxsci/54.1.121\u003c/li\u003e\n\u003cli\u003eTr\u0026ouml;sken ER, Scholz K, Lutz RW, V\u0026ouml;lkel W, Zarn JA, Lutz WK. Comparative Assessment of the Inhibition of Recombinant Human CYP19 (Aromatase) by Azoles Used in Agriculture and as Drugs for Humans. \u003cem\u003eEndocr Res\u003c/em\u003e. 2004;30(3):387-394. doi:10.1081/ERC-200035093\u003c/li\u003e\n\u003cli\u003eHurst MR, Sheahan DA. The potential for oestrogenic effects of pesticides in headwater streams in the UK. \u003cem\u003eSci Total Environ\u003c/em\u003e. 2003;301(1):87-96. doi:10.1016/S0048-9697(02)00288-7\u003c/li\u003e\n\u003cli\u003eGr\u0026uuml;nfeld HT, Bonefeld-Jorgensen EC. Effect of in vitro estrogenic pesticides on human oestrogen receptor \u0026alpha; and \u0026beta; mRNA levels. \u003cem\u003eToxicol Lett\u003c/em\u003e. 2004;151(3):467-480. doi:10.1016/j.toxlet.2004.03.021\u003c/li\u003e\n\u003cli\u003eSoto AM, Chung KL, Sonnenschein C. The pesticides endosulfan, toxaphene, and dieldrin have estrogenic effects on human estrogen-sensitive cells. \u003cem\u003eEnviron Health Perspect\u003c/em\u003e. 1994;102(4):380-383. doi:10.1289/ehp.94102380\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 - Clinicopathological characteristics of the patients included in the study.\u0026nbsp;\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"510\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal population\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll patients (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposed, frequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\n \u003cp\u003e\u003cstrong\u003eExposed, number (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnexposed, frequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnexposed, number (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient Characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean age at diagnosis (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\n \u003cp\u003e353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u0026sup2;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\n \u003cp\u003e27.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e27.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e27.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOccupational pesticide exposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\n \u003cp\u003e59.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e100.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor Characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage tumor size (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\n \u003cp\u003e29,21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\n \u003cp\u003e357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e30,82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e28,11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular subtyping\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuminal A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\n \u003cp\u003e33.24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e30.81%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e37.78%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuminal B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\n \u003cp\u003e34.37%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e33.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e34.81%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHER2-amplified\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\n \u003cp\u003e16.62%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e16.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e15.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriple-negative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\n \u003cp\u003e15.77%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e19.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e11.85%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\n \u003cp\u003e28.21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e29.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e29.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\n \u003cp\u003e51.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e52.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e48.91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\n \u003cp\u003e20.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e18.59%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e21.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStratification of death risk and recurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\n \u003cp\u003e8.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e8.24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e8.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntermediate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\n \u003cp\u003e55.87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e52.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e60.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\n \u003cp\u003e35.87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e39.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e31.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRecurrence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\n \u003cp\u003e9.36%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e10.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e9.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemoresistance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\n \u003cp\u003e18.97%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e21.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e17.27%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.784313725490197%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeath\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.92156862745098%\"\u003e\n \u003cp\u003e7.01%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.784313725490197%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\"\u003e\n \u003cp\u003e7.21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.96078431372549%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.901960784313726%\"\u003e\n \u003cp\u003e7.09%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.705882352941176%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe parameters age at diagnosis (years), BMI (kg/m\u0026sup2;), and tumor size are presented by the population mean analyzed, and the parameters occupational pesticide exposure, molecular subtyping, histological grade, stratification of death risk and recurrence, recurrence, and chemoresistance are presented as a percentage of the analyzed group in relation to the total number of individuals in the population. The number of patients in each variable is different due to missing values (all variables have less than 15% of missing values, except for the variable BMI with 28%). n represents the number of patients evaluated for each parameter.\u003c/p\u003e\n\u003cp\u003eTable 2 - Frequency, ratio and p-value of the patients exposed to pesticides for molecular subtypes.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"822\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.233009708737864%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.854368932038835%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"29.00485436893204%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eMolecular subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.616504854368932%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.91747572815534%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.315533980582524%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.91747572815534%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.91747572815534%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.27493917274939%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003eLum A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003eLum B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.907542579075426%\"\u003e\n \u003cp\u003eHER2-amplified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\"\u003e\n \u003cp\u003eTriple-negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.637469586374696%\"\u003e\n \u003cp\u003eRatio Lum B/ Lum A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\"\u003e\n \u003cp\u003eRatio Lum B / HER2-amplified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\"\u003e\n \u003cp\u003eRatio Lum B/Triple-negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.27493917274939%\" valign=\"bottom\"\u003e\n \u003cp\u003eEstrogen receptor expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.907542579075426%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.637469586374696%\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\"\u003e\n \u003cp\u003e6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.27493917274939%\" valign=\"bottom\"\u003e\n \u003cp\u003eProgesterone receptor expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.907542579075426%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.637469586374696%\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\"\u003e\n \u003cp\u003e4.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.27493917274939%\" valign=\"bottom\"\u003e\n \u003cp\u003eHER2-amplified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.907542579075426%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.637469586374696%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.27493917274939%\" valign=\"bottom\"\u003e\n \u003cp\u003eKI67 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.907542579075426%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.637469586374696%\"\u003e\n \u003cp\u003e8.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\"\u003e\n \u003cp\u003e2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.27493917274939%\" valign=\"bottom\"\u003e\n \u003cp\u003ePresence of angiolymphatic emboli\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.907542579075426%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.637469586374696%\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.27493917274939%\" valign=\"bottom\"\u003e\n \u003cp\u003eLymph node invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.907542579075426%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.637469586374696%\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.27493917274939%\" valign=\"bottom\"\u003e\n \u003cp\u003eDistant metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.907542579075426%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.637469586374696%\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.27493917274939%\" valign=\"bottom\"\u003e\n \u003cp\u003eMenopausal status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.907542579075426%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.637469586374696%\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\"\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.27493917274939%\" valign=\"bottom\"\u003e\n \u003cp\u003ePesticide exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.907542579075426%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.637469586374696%\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.27493917274939%\" valign=\"bottom\"\u003e\n \u003cp\u003eChemoresistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.907542579075426%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.637469586374696%\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.27493917274939%\" valign=\"bottom\"\u003e\n \u003cp\u003eRecurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.907542579075426%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.637469586374696%\"\u003e\n \u003cp\u003e1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e0.54\u003csup\u003e\u0026nbsp;\u0026alpha;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.27493917274939%\" valign=\"bottom\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.907542579075426%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.637469586374696%\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u0026nbsp;\u0026alpha;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u0026nbsp;\u0026alpha;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\"\u003e\n \u003cp\u003e0.02\u003csup\u003e\u0026nbsp;\u0026alpha;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA ratio was calculated (dividing the frequencies of each molecular subtype) and the differences analyzed by Chi-square and Fisher\u0026apos;s exact test for samples with n less than 5. A p\u0026lt;0.05 was considered significant. The acronym Lum corresponds to the molecular subtype Luminal. NA represents when the calculation was not applicable. \u003csup\u003e\u0026alpha;\u003c/sup\u003e represents that p-values were calculated using Fisher\u0026apos;s exact test, the other values were calculated using the chi-square test for independence.\u003c/p\u003e\n\u003cp\u003eTable 3 - Frequency, ratio and p-value of the patients unexposed to pesticides for molecular subtypes.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"823\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.592233009708737%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.854368932038835%\" colspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"29.12621359223301%\" colspan=\"8\" valign=\"bottom\"\u003e\n \u003cp\u003eMolecular subtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.223300970873787%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.766990291262136%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.072815533980583%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.796116504854369%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.315533980582524%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.91747572815534%\" colspan=\"2\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.3349514563106797%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.62530413625304%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\" colspan=\"2\"\u003e\n \u003cp\u003eTotal (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003eLum A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003eLum B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.02919708029197%\" colspan=\"2\"\u003e\n \u003cp\u003eHER2-amplified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\" colspan=\"2\"\u003e\n \u003cp\u003eTriple-negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\" colspan=\"2\"\u003e\n \u003cp\u003eRatio Lum B/ Lum A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.785888077858881%\" colspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.097323600973237%\" colspan=\"2\"\u003e\n \u003cp\u003eRatio Lum B / HER2-amplified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\" colspan=\"2\"\u003e\n \u003cp\u003eRatio Lum B /Triple-negative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\" colspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.338199513381995%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.963503649635037%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eEstrogen receptor expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\" colspan=\"2\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.02919708029197%\" colspan=\"2\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\" colspan=\"2\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.785888077858881%\" colspan=\"2\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.097323600973237%\" colspan=\"2\"\u003e\n \u003cp\u003e5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.963503649635037%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eProgesterone receptor expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\" colspan=\"2\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.02919708029197%\" colspan=\"2\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\" colspan=\"2\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.785888077858881%\" colspan=\"2\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.097323600973237%\" colspan=\"2\"\u003e\n \u003cp\u003e5.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.963503649635037%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eHER2-amplified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\" colspan=\"2\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.02919708029197%\" colspan=\"2\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\" colspan=\"2\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.785888077858881%\" colspan=\"2\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.097323600973237%\" colspan=\"2\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\" colspan=\"2\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.963503649635037%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eKI67 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\" colspan=\"2\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.02919708029197%\" colspan=\"2\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\" colspan=\"2\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\" colspan=\"2\"\u003e\n \u003cp\u003e7.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.785888077858881%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.097323600973237%\" colspan=\"2\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\" colspan=\"2\"\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.963503649635037%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003ePresence of angiolymphatic emboli\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\" colspan=\"2\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.02919708029197%\" colspan=\"2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\" colspan=\"2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\" colspan=\"2\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.785888077858881%\" colspan=\"2\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.097323600973237%\" colspan=\"2\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e0.26 \u003csup\u003e\u0026alpha;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\" colspan=\"2\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\" colspan=\"2\"\u003e\n \u003cp\u003e0.75\u003csup\u003e\u0026nbsp;\u0026alpha;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.963503649635037%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eLymph node invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\" colspan=\"2\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.02919708029197%\" colspan=\"2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\" colspan=\"2\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\" colspan=\"2\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.785888077858881%\" colspan=\"2\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.097323600973237%\" colspan=\"2\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e0.73\u003csup\u003e\u0026nbsp;\u0026alpha;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\" colspan=\"2\"\u003e\n \u003cp\u003e3.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\" colspan=\"2\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.963503649635037%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eDistant metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\" colspan=\"2\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.02919708029197%\" colspan=\"2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\" colspan=\"2\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\" colspan=\"2\"\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.785888077858881%\" colspan=\"2\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.097323600973237%\" colspan=\"2\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e0.53\u003csup\u003e\u0026nbsp;\u0026alpha;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\" colspan=\"2\"\u003e\n \u003cp\u003e3.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\" colspan=\"2\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.963503649635037%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eMenopausal status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\" colspan=\"2\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.02919708029197%\" colspan=\"2\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\" colspan=\"2\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\" colspan=\"2\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.785888077858881%\" colspan=\"2\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.097323600973237%\" colspan=\"2\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\" colspan=\"2\"\u003e\n \u003cp\u003e2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\" colspan=\"2\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.963503649635037%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003ePesticide exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.02919708029197%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\" colspan=\"2\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.785888077858881%\" colspan=\"2\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.097323600973237%\" colspan=\"2\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\" colspan=\"2\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\" colspan=\"2\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.963503649635037%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eChemoresistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\" colspan=\"2\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.02919708029197%\" colspan=\"2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\" colspan=\"2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\" colspan=\"2\"\u003e\n \u003cp\u003e1.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.785888077858881%\" colspan=\"2\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.097323600973237%\" colspan=\"2\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e0.52\u003csup\u003e\u0026nbsp;\u0026alpha;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\" colspan=\"2\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\" colspan=\"2\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u0026nbsp;\u0026alpha;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.963503649635037%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eRecurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\" colspan=\"2\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.02919708029197%\" colspan=\"2\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\" colspan=\"2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\" colspan=\"2\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.785888077858881%\" colspan=\"2\"\u003e\n \u003cp\u003e0.73\u003csup\u003e\u0026nbsp;\u0026alpha;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.097323600973237%\" colspan=\"2\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u0026nbsp;\u0026alpha;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\" colspan=\"2\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\" colspan=\"2\"\u003e\n \u003cp\u003e0.41\u003csup\u003e\u0026nbsp;\u0026alpha;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.963503649635037%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.866180048661801%\" colspan=\"2\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.02919708029197%\" colspan=\"2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.299270072992701%\" colspan=\"2\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.245742092457421%\" colspan=\"2\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.785888077858881%\" colspan=\"2\"\u003e\n \u003cp\u003e0.23\u003csup\u003e\u0026nbsp;\u0026alpha;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.097323600973237%\" colspan=\"2\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.81265206812652%\" colspan=\"2\"\u003e\n \u003cp\u003e1.00\u003csup\u003e\u0026nbsp;\u0026alpha;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.340632603406325%\" colspan=\"2\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.934306569343065%\" colspan=\"2\"\u003e\n \u003cp\u003e0.00\u003csup\u003e\u0026nbsp;\u0026alpha;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA ratio was calculated (dividing the frequencies of each molecular subtype) and the differences analyzed by Chi-square and Fisher\u0026apos;s exact test for samples with n less than 5. A p\u0026lt;0.05 was considered significant. The acronym Lum corresponds to the molecular subtype Luminal. NA represents when the calculation was not applicable. \u003csup\u003e\u0026alpha;\u003c/sup\u003e represents that p-values were calculated using Fisher\u0026apos;s exact test, the other values were calculated using the chi-square test for independence.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"pesticides, breast cancer, luminal B subtype, chemoresistance, recurrence","lastPublishedDoi":"10.21203/rs.3.rs-4182249/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4182249/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Brazil is among the top three global consumers of pesticides, despite evidence concerning the negative impact on public health. The exposure on female rural workers has been neglected, and the incidence of pesticide-induced diseases such as breast cancer is growing. In this context, this study analyzed the impact of occupational/household pesticide exposure on the clinicopathological profile of breast cancer in rural women. Clinicopathological data were collected from medical records and analyzed from a total of 386 patients (208 exposed and 141 unexposed to pesticides). Was used descriptive and inferential statistics methods to characterize the patient data, including the chi-square test and Fisher's exact test, to evaluate associations between variables. This data was grouped as patient, tumor, and disease characteristics. Exposed patients had a prevalence of Luminal B subtype (32.83%), while unexposed patients had a prevalence of Luminal A molecular subtype (37.78%). Exposed patients also had higher disease recurrence (10.19%), chemoresistance (21.26%), and death occurrence (7.21%) than unexposed patients. Breast cancer patients exposed to pesticides were also more likely to have distant metastases and lymph node invasion compared to breast cancer unexposed patients. These findings indicate that pesticide exposure favors the occurrence of more aggressive breast cancer in rural women through occupational exposure. This results indicate that this occupational information could be added to the screening process and risk determination for breast cancer severity.","manuscriptTitle":"Occupational pesticide exposure is linked to the prevalence of Luminal B breast cancer and poor prognosis features in Brazilian rural women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-11 16:17:33","doi":"10.21203/rs.3.rs-4182249/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"254e4e7c-788a-4d28-a8b2-c88aaccf9091","owner":[],"postedDate":"April 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30427240,"name":"Biological sciences/Ecology/Agri ecology"},{"id":30427241,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2024-06-11T08:15:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-11 16:17:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4182249","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4182249","identity":"rs-4182249","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}