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LITHUANIAN UNIVERSITY OF HEALTH SCIENCES
MEDICAL ACADEMY, FACULTY OF MEDICINE, INSTITUTE OF ONCOLOGY
Zahi Revivo
Heat shock protein coding gene HSPA1A rs1043618 and
rs562047 polymorphism analysis and the assessment of their
prognostic value in breast cancer patients
MEDICAL INTEGRATED MASTER'S STUDY PROGRAMME
Thesis Supervisor
Prof. Rasa Ugenskiene
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TABBLE OF CONTENTS
1. Summary 4-5
2. Acknowledgement 6
3. Conflict of interest 7
4. Ethics committee study permission 8
5. Abbreviation list 9-10
6. Introduction 11
7. Aim and objectives 12
8. Literature review 13-26 8.1. Cancer 8.2. Carcinogenesis 8.3. Breast cancer 8.3.1. Histological classification 8.3.2. Molecular classification of BC
8.4. Breast cancer risk factor
8.4.1. Non-modifiable risk factors: 8.4.1.1. Age
8.4.1.2. Menarche 8.4.1.3. Menopause 8.4.1.4. Genetics 8.4.2. Modifiable risk factors
8.4.2.1. Obesity 8.4.2.2. Lactation 8.4.2.3. Vitamin D
8.4.2.4. Physical activity 8.5. Genetic & Cancer
8.6. Heat shock proteins (HSPs) 8.7. HSP and cancer
8.8. HSP polymorphisms 8.9. HSP and breast cancer
9. Research and methodology 27
9.1. Research organization, study object and patient selection 9.2. Research method
3 9.2.2. DNA isolation protocol
9.2.3. HSPA1A rs1043618 analysis 9.2.4. HSPA1A rs562047 analysis 9.3. Statistical analysis of the data
10. Results and data analysis 34
10.1. Tumor characteristics and SNP’s frequencies 10.2. Allele Polymorphism and genotype distribution 10.3. Association analysis 10.4. Survival analysis 10.4.1. HSPA1A rs1043618 10.4.2. HSPA1A rs562047 11. Discussion 41 12. Conclusions 42 13. References 43-45
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1. THE SUMMARY OF RESEARCH
Author name and surname: Zahi Revivo
Title: Heat shock protein coding gene HSPA1A rs1043618 and rs562047 polymorphism analysis and the assessment of their prognostic value in breast cancer patients.
The aim of the research: To identify DNA sequence variations rs1043618 and rs562047 in a gene HSPA1A coding for heat shock protein and to assess their prognostic value in breast cancer.
The Objectives:
1. To determine the distribution of HSPA1A rs1043618 and rs562047 alleles and genotypes. 2. To assess the relationship between DNA sequence variations in HSPA1A and tumor
pathomorphological parameters.
3. To analyses the associations between HSPA1A genetic variants and breast cancer patient survival.
The methodology: A retrospective study involving 100 breast cancer patients was conducted.
The study research protocol was approved by Kaunas Regional Biomedical Research Ethical Committee (protocol number BE-2-10 and BE-2-10/2014) and Lithuanian University of Health Sciences Bioethics Center (protocol number BEC-MF-03). Patient blood tests, acquired by clinicians in a time frame from 2014-2016, were utilized for the genomic DNA extraction. rs1043618 and rs562047 polymorphisms were analyzed with polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay. Patient clinical information was gathered before from clinical records. The statistical analysis was performed using IBM “SPSS.”
The results and conclusions:
1. The distribution of alleles and genotypes in rs1043618 and rs562047 polymorphisms was as follows:
• rs1043618 allele frequency: C - 66%, G - 34%. rs1043618 genotype frequency: GG - 7%, GC - 54%, CC - 39%
• rs562047 allele frequency: C - 83.5%, T – 16.5%. HSPA1A rs562047 genotype frequency: GG - 4%, GC - 25%, CC - 71%.
2. No significant association between rs1043618 and rs562047 in HSPA1A and tumor pathomorphological parameters was determined in both genotype and allelic model.
5 3. There was no significant link between the analyzed SNPs and patient OS, PFS, and MFS in
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2. ACKNOWLEDGMENT
I want to express my appreciation and thanks to Rasa Ugenskiene from the Institute of Oncology, Lithuanian University of Health Sciences, for all her help and guidance that she has given me. You have been an extraordinary mentor for me.
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3. CONFLICT OF INTEREST
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4. ETHICS COMMITTEE STUDY PERMISSION
Kaunas Regional Biomedical Research Ethical Committee approved the study research protocol (BE-2-10 and BE-2-10/2014). This study was also approved by LUHS Bioethics center (BEC-MF-03).
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5. ABBREVIATION LIST
BC –breast cancer BP - base pairs
HSF1 – heat shock transcription factor 1 HSP- heat shock protein
BRCA1 - breast cancer type 1 susceptibility gene BRCA2 - breast cancer type 2 susceptibility gene
PTEN - phosphatase and tensin homolog STK1 - serine/threonine kinase 11 CDH1 - cadherin-1
DCIS - ductal carcinoma in situ ER - estrogen receptor
HER2 - human epidermal growth factor 2 receptor IDC - invasive ductal carcinoma
ILC - invasive lobular carcinoma DCIS - ductal carcinoma in situ
IDC-NST - invasive ductal carcinoma no specific type ICC - invasive cribriform carcinoma
LCIS - lobular carcinoma in situ MMP - matrix metalloproteinases
MT-MMP - membrane type metalloprotein matrix proteinase PFS/MFS-progression/metastasis-free survival
10 PR - progesterone receptor
SNP - single nucleotide polymorphism TNM - tumor, nodes, metastasis TP53 - tumor protein 53 gene TSG-tumor suppressor genes WHO - world health organization
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6. INTRODUCTION
Breast cancer became the most common cancer globally in women worldwide. About 1.7 million cases of breast cancer are diagnosed every year. Breast cancer is also the leading cause of cancer death in women worldwide [1]. HSPs constitute a large family of proteins that are often classified based on their molecular weight. They include HSP27, HSP40, HSP60, HSP70, and HSP90. HSPs function in several protective and physiological processes to support maintaining cellular homeostasis. Notably,
HSPs participate in protein folding under stressors such as hypoxia, heat shock, and degradation process. HSPs also play a role across various types of cancers as they are implicated in cancer-related activities
such as cell proliferation and metastasis.
Therefore, HSPs have multiple roles as biomarkers for cancer diagnosis and management [2].
HSPs overexpression has been observed in various cancers such as ovarian, gastric, breast, colon, lung,
and prostate cancers. Studies reported a correlation of high expression of HSPs with cancer prognosis. Differentiation, proliferation, invasion, and metastasis are implicated by overexpression of HSPs [3]. Their expanded expression in breast cancer is due to overexpression of oncoproteins and proliferation of mutant proteins that trigger HSP gene transcription. The elevation HSPs concentration is influenced by heat shock transcription factor 1 (HSF1), a protein that responds to unfolded proteins and leads to
HSP transcription. Therefore, HSPs are focused on breast cancer therapy, and drugs for HSP90 have
been synthesized [4]. A study on a highly homogenous breast cancer group was performed. One hundred women with breast cancer were analyzed for HSPA1A polymorphism with polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) assay.
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7. AIM AND OBJECTIVES
The aim of the research: To identify DNA sequence variations rs1043618 and rs562047 in a gene HSPA1A coding for heat shock protein and to assess their prognostic value in breast cancer.
Objectives:
1. To determine the distribution of HSPA1A rs1043618 and rs562047 alleles and genotypes. 2. To assess the relationship between DNA sequence variations in HSPA1A and tumor
pathomorphological parameters.
3. To analyses the associations between HSPA1A genetic variants and breast cancer patient survival.
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8. LITERATURE REVIEW
8.1 Cancer
Cancer is known for its critical health concern globally, and it constitutes second place in the cause of death after cardiovascular diseases. It is estimated that prostate, lung, and colorectal cancers are the most commonly diagnosed cancers among the male gender and makeup to 43% of total cases among men. Prostate cancer alone makes up more than 20%. As for women, breast, lung, and colorectal cancers are the three most common will constitute approximately half of all cancer cases, while breast cancer alone is expected to reach 30% of all women cancers.
Overall, the number of diagnosed cancer cases is expected to be 1,806,590, and mortality estimated to be 606,520 in the American population, making it more than 1600 death per day.
The incidence of different types of cancer changed dramatically through the years due to the development of different diagnostic method such as new screening methods which could detect cancer early in the asymptomatic phase. The best example is prostate cancer. During the year 1990, there was a sudden peak in prostate cancer incidence thanks to the detection of prostate-specific antigen (PSA). The trend incidence of different cancers according to gender is presented in Figure 1 [5].
Fig. 1. The trends in Incidence Rates by genders from 1975 to 2016 in the US.
14 Cancer is a final result of abnormally accumulated cells caused by an interruption in the cell cycle and cellular, molecular transduction.
Two main genes are thought to be the primary mechanism behind cancer pathophysiology: inactivation of tumor suppressor genes and oncogenes activation. When cells become cancerous, they lose their sensitivity to anti-growth signals, losing the ability to go through programmed cell death. They can invade other tissue and structures by metastasis [6,7].
In the clinical aspect, cancer can be classified by TMN classification, and this provides physicians critical information:
▪ Gives a clinical prognostic picture. ▪ Help adjust treatment plans.
▪ Improve doctors and medical centers communication. ▪ Help to evaluate patient condition.
This classification is based on characteristics like histopathology, morphology, and extent of invasion. It can be achieved by imaging tools, biopsy, surgeries, etc.
T, N, M letters stand for:
▪ T- the size of the primary tumor and its extent to invade adjacent structures.
▪ N- the degree of regional lymph nodes metastasis or absence of regional lymph nodes metastasis.
15 8.2 Carcinogenesis
Cancer is a disease caused by the formation and growth of an abnormal cell population. Analyses of cancer statistics carried out in 1950 showed that mortality increases exponentially with age. It was revealed that cancer was the result of successive cellular changes. The only cellular component that can accumulate and transmit changes through life is the DNA. According to the carcinogenesis theory, cancer is caused by DNA mutation in oncogenes and tumor – suppressor genes. Mutation in oncogenes would lead to cell proliferation, whereas tumor- suppressor genes mutation would inhibit cell death. These changes in DNA also include mutation on non-protein-coding DNA, epigenetic changes (a modification that does not involve a change in the sequence of nucleotide), and aneuploidy [9]. According to the Knudson hypothesis, the inactivation of tumor suppressor genes requires two hits. The first hit occurring in germline cells (hereditary cancer) or somatic cells (sporadic cancer), and the second hit is always happening in somatic cells. Two inactivating hits occur somatically in the sporadic form before initiation of the tumor [10].
16 8.3 Breast cancer
BC might be a fatal and aggressive disease and constitutes the second leading cause of cancer death, making it 25% of the total women cancers globally. Early diagnosis is a very crucial step in preventing the disease. In developed countries with suitable prevention methods, the five years survival rate is reaching 80%. Many risk factors can dramatically raise the risk for developing BC, mainly genetics, age, and environmental factors. The most predominant genes associated with BC is BRCA1/BRCA2 gene. Alteration in this gene is the highest contributor to BC development among all other genes related to BC [11,12]. The different types are classified according to invasion pattern, histomorphology, and expression of proteins and genes.
8.3.1 Histological classification
Can be non-invasive (in situ) or invasive: • Non-invasive breast cancer:
This breast cancer is confined to local tissue and did not invade other structures of the breast tissue.
o Ductal carcinoma in situ (DCIS):
this subtype can be further divided by grades: low, intermediate, and high. The differentiation between them is according to histological and morphological manifestations. This cancer origin is in the ducts or lobules of the breast tissue, with the opportunity to become invasive later on, depending on the tumor's grading. The prognosis of this cancer is quite good since it did not spread further yet, and the development of a new imaging technique like mammography [11,12].
o Lobular carcinoma in situ (LCIS):
Some sources consider this type a risk factor and not necessarily obligate precancerous lesions. LCIS is challenging to diagnose on gross examinations. It is usually found accidentally when a biopsy is done for other reasons, and management sometimes includes only follow-up without any operational procedures.
17 • Invasive breast cancer:
This type of cancer already invaded the surrounding tissue of the breast.
o Invasive ductal carcinoma
▪ Invasive ductal carcinoma no specific type (IDC-NST):
This type constitutes the most invasive BC (40%-70%). It does not have predictable behavior and no distinctive morphological features compared to healthy tissue. Histologically, The tumor shows a heterogenous type of growth, including diffuse cords, nests, sheets, or singly distributed cells with variable amount of ductal differentiation
▪ Tubular carcinoma:
Tubular carcinoma is relatively rare (2% of total IDC cases). It is predominant, especially in older women, and has a good prognosis because of low lymph node metastatic rate with relatively good morphological manifestations. Histologically, it is characterized by the tubules proliferation with a single layer of epithelium without supporting the outer layer of myoepithelial cell and basement membrane, with multifocal invasion of the stroma and fat at the periphery of the tumor.
▪ Invasive cribriform carcinoma (ICC):
ICC is also a rare type of BC (0.8%-3.5% of total IDC) and most common in elderly patients. Histologically the cells appear as a cribriform shape, and the invasion of stroma appears very clear.
▪ Medullary carcinoma:
Medullary carcinoma (MC) is rare (< 5% of total IDC), a special subtype of breast cancer presented by a well-defined tumor mass and anaplastic morphology.Histologically, medullary cancer is a well-circumscribed carcinoma composed of poorly differentiated cells with scanty stroma and prominent lymphoid infiltration.
The other sub-types of IDC are papillary, micropapillary, apocrine, neuroendocrine, metaplastic,lipid-rich, secretory, oncocytic, adenoid cystic, acinic cell carcinoma.
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o Invasive lobular carcinoma (ILC):
It is the second class of invasive BC besides IDC. ILC comprises 5%–15% of total invasive types. The increasing incidence among postmenopausal women may be related to hormonal replacement therapy. Histologically, ILC tumor cells are typically round, small, relatively uniform, and have characteristic growth pattern.
The other sub-types of ILC are classic type, pleomorphic lobular carcinoma, histiocytoid carcinoma, signet ring carcinoma. Signet, tubule-lobular carcinoma [12].
19 Breast cancer Histological Pre-cancerous (in situ) (25%) Ductal (80%) Lobular (20%) Invasive carcinoma (75%) Lobular (5%-15%) Pleomorphic lobular Histiocytoid Signet ring carcinoma Tubulolobular Ductal (75%) Others (10%) Tubular (2%) Cribiform (0.8%-3%) Mucinous (2%) Medullary (<%5) IDC-NST (40%-70%) Molecular Luminal A (50%) Luminal B (20%) Basal like (15%) HER2 overexpression (15%) 8.3.2 Molecular classification of BC
This classification aims to give a better clinical picture to adjust appropriate therapeutic methods. The different subtypes differ according to their genetics and molecular expression (Figure 2). Each type shows to express different amounts of estrogen and progesterone hormone receptors and. HER2 receptors.
• Luminal A (ER+| PR +/-| HER2 -): this type is the most common. It tends to have relatively slow progression and shows a good prognosis from all the other types.
• Luminal B (ER+| PR +/-| HER2 +/-): this type usually grows even faster with a lowers prognostic value comparing the luminal A-type.
• Basal-like (ER-| PR -| HER2 -): also known as "triple-negative," the most aggressive type and most common among African American women.
• HER2-enriched (ER-| PR -| HER2 +): this type is worse than Luminal A and B type. However, responsive for therapy that blocks HER2 activity, such as trastuzumab [12,13].
20 8.4 BC risk factors
Both environmental and hereditary factors influence the development of BC. The risk factors can be divided into modifiable and non-modifiable.
8.4.1 Non-modifiable risk factors 8.4.1.1 Age
Increasing age has the highest contribution for developing BC. When women reach the menopause phase, their risk reaches a maximum and then decreases slowly.
8.4.1.2 Menarche
Studies show that women who started their menses at younger age doubled their chances to develop BC by increase ovulatory cycles.
8.4.1.3 Menopause
The average age for menopause is approximately 50, and studies show that the onset of menopause has a significant influence as a risk factor. Women who became menopause earlier had a relatively lower risk than women who finished their menses after age 55 and compared those before 45 years.
8.4.1.4 Genetics
There is a wide range of genes associated with breast cancer. The most common are BRCA1 and BRCA2 autosomal dominant genes, alternation in those genes responsible for approximately 40% of the total hereditary BC. According to studies, 55%-65% of patients with BRCA1 mutations and 45% with BRCA2 eventually were sick with breast cancer when they reached age 70.
21 8.4.2 Modifiable risk factors
8.4.2.1 Obesity
Several studies proved the correlation of obesity with breast cancer. It happens due to high fatty tissue levels, which are converted from androgen to estrogen by aromatization. Insulin resistance as resulted of diabetes can also stimulate the proliferation of malignant cells. BC patients with BMI>30kg/m2 showed a lower survival rate than non-obese.
8.4.2.2 Lactation
Lactation considers lowering a risk factor due to the reduction of ovulatory cycles and thus decreasing hormonal activity. The combination of lactation and having two or more childbirth could reduce BC's risk by 50%.
8.4.2.3 Vitamin D
Stable levels of vitamin D showed decreased risk for BC and improved the outcome of the disease. Studies show that deficiency of vitamin D increases the risk by 27% comparing patients with stable levels.
8.4.2.4 Physical activity
Physical activity is well known as a positive habitus that contributes to our health and well-being. The role of it in BC, according to studies, shows to decrease the risk for developing it in postmenopausal women. Furthermore, it may reduce the mortality rate after the diagnosis is already made. The best physical activity found to be was walking 3-5 hours per week [14,15].
22 8.5 Genetic & Cancer
An alternation of 2 genes can explain BC's primary mechanism: proto-oncogenes and tumor suppressor genes (TSG). These genes, on their regular basis responsible for cellular growth, controlling the cell cycle, and repair by checkpoints through the process to prevent further mutations in the genome. Genetics is responsible for 3%-10% of all BC cases. The most predominant genes associated with BC are BRCA1 and BRCA2 and others less common: TP53, PTEN, STK11, CDH1.
BRCA1 and BRCA2 genes encode proteins responsible for damaged DNA repair and checkpoint activation through the cell cycle. Any alteration or damage to those genes can lead to the carcinogenesis process, BRCA gene mutations responsible for 40% of the total hereditary BC causes. Hence, those genes serve as a significant threat for developing BC and are highly recommended for testing [13,14].
TP53 is also a significant TSG that has a role in BC pathogenesis. It is known that approximately 50% of the total BC cases have somatic alternations in that gene. TP53 has many roles in the cell cycle, but its function is to arrest the cell cycle and induce apoptosis in mistaken DNA lesions. A classic autosomal dominant hereditary tumor predisposing disorder called Li–Fraumeni syndrome is associated with germline mutations in TP53 genes. Patients with this disease are known to have BC at a relatively early age (29 on average) [15,16].
23 8.6 Heat shock proteins (HSPs)
HSPs were discovered for the first time in flies that have undergone heat shock. Later on, these proteins were found in all organisms and showed vital functions in the cells' molecular biology, primarily acting as a "chaperone." Chaperones are proteins that take part in folding, unfolding, repairing, and transporting proteins at the molecular level. HSPs are regulated by external stressors like temperature changes, infection, and they demonstrated to be involved in inflammatory processes, neurodegenerative diseases, and different types of cancers. There are five HSPs groups based to their molecular features: HSP-100, HSP-90, HSP-70, HSP-60, and s-HSP [17,18].
HSP70 is one of the most investigated proteins, and it has a variety of essential functions like protein folding, cellular transduction, signaling, and protein denaturation. HSP70 has an important role in many types of cancers by being involved in a crucial step of cellular proliferation and survival, thus suppressing HSP70 might prevent cancer. Furthermore, HSP70 showed a link to tumor suppressor genes like TP53 and 17-AAG, which were shown to have a protective role during ischemic events. While evaluating cerebral and myocardial ischemia in mice, the levels of HSP70 had dramatically increased and showed to inhibit the ischemic process. HSP70 in rats was demonstrated to have a protective function during ischemic reperfusion injury [17].
HSP27 is a part of a small HSP group and is regulated mainly by the phosphorylation process and plays a significant role in cancer pathophysiology by affecting the tumor's progression and metastatic ability. HSP27 is shown to have a dysplastic effect on tissues and to have a considerable influence on MMP enzymes which enhance cancer metastatic ability. MMP2 activity was shown to be elevated, thus increasing prostate cancer's ability to invade the surrounding. HSP27 is also linked to breast cancer by activating VEGF on breast cancer cell receptors. HSP27 might serve as a critical chemotherapy therapy regulator in cancer [19].
24 8.7 HSP and cancer
The relation between HSP and cancer is not entirely understood. However, many hypotheses support that relation between the two. Under normal physiological conditions, HSP is expressed in low amounts. However, during stress state, when cells face external stressors that alternate the PH, oxygen, temperature, and glucose consumption, HSP expression is elevated to refold the denaturized damaged proteins and prevent further damage progression. On the other hand, HSP shows to prevent oncoprotein aggregation and thus preventing apoptosis. Cancerous cells are under constant stress conditions like hypoxia, acidosis, and proteotoxic processes, thus increasing HSP expression in those cells [20,21].
According to the study of 322 advanced gastric cancer patients, which analyzed the expression of HSP90, 69.6% of the patients had increased HSP40 expression. Moreover, the expression of HSP90 had a statistically significant different correlation with prognostic value parameters such as large tumor size, depth invasion, lymph node metastasis. The 5-year survival rate in patients with negative HSP90 expression was 75.7%, while in patients with positive HSP90 expression, the survival was much lower (30.1%) [22].
A study of 60 NSCLC patients where the expression of HSP70 and HSP27 was analyzed revealed that overexpression of HSP70 was found in 47 samples, and HSP27 overexpression was found in 43 of samples. Moreover, during the statistical analysis, samples with high HSP70 expression had statistically significant associations with crucial prognostic parameters such as lymph node metastasis, cancer stage, histopathological, and smoking history [23].
25 8.8 HSP polymorphisms
According to a study of 242 patients, which included 146 breast cancer and 96 controls, 4 SNP's of HSF1 genes were investigated (rs78202224, rs35253356, rs4977219 rs34404564). The rs78202224 (G>T) SNP was found to increase the risk of breast cancer. On the other hand, a protective role against breast cancer was found for rs34404564 (A>G) SNP. The frequency of AG genotype of rs34404564 SNP was determined at a much lower frequency among breast cancer patients than the control group. It was also found that HSF1 expression was significantly higher in breast cancer types than in the control group. The other 2 SNPs (rs4977219 and rs35253356) did not significantly contribute to breast cancer development [24].
According to a study of 346 lung cancer patients that analyzed 19 different SNP's and their effect on the lung cancer patient prognosis, rs2070804 (G>T) SNP of HSPB1 and rs3088225 (A>G) SNP of HSPA4 showed to be a positive prognostic factor for lung cancer, mainly among the male patients with NSCLC and who are more than 55-year-old [25].
26 8.9 HSP and breast cancer
Many HSP's and HSF-1 were investigated and were known to have some role in BC's etiology and development and were detected in high amounts during the cancerous transformation of mammary cells. HSP27 and HSP70 were shown to inhibit apoptosis in tumor cells and enhance cancerous cell proliferation. HSP90 has stabilizing function on the mutated oncogenes and induction growth factors on breast cancer cells. Moreover, HSP90 interacts with important BC parameters such as estrogen and tyrosine receptors. DCIS cancer showed to have high levels of HSP90. On the other hand, low amounts were measured in LBIS. The role of HSP90 is to make the cancer cells resistant to various external stressors [26].
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9. RESEARCH AND METHODOLOGY
9.1 Research organization. The object of the study and patient selection
Patient selection was organized at the Oncology Research laboratory, Oncology Institute, Lithuanian University of Health Sciences (LUHS). This work is a piece of an ongoing study at the Oncology Institute. Kaunas Regional Biomedical Research Ethical Committee approved the study research protocol (BE-2-10 and BE-2-10/2014). This study was also approved by LUHS Bioethics center (BEC-MF-03).
In this study, 100 breast cancer patients were included. Patient peripheral blood samples, acquired by clinicians in a time-frame from 2014-2016, were utilized for the genomic DNA extraction. Patient clinical information was gathered earlier by medical doctors from patient clinical records. Patient inclusion criteria were as follows:
• Age at the time of diagnosis (≤50 years). • Early BC stage (I-II).
• Premenopausal status.
Patient exclusion criteria were as follows: • Other malignancies.
• Poor execution status. • Other critical comorbidities.
• Incomplete clinical documentation.
NM_005527.3:- 855C>G (rs1043618) and NM_005345.5: -330G>C (rs562047) based on literature reports were chosen for analysis. The previous study proposed that the DNA variations in this gene may be significant for oncological disease pathogenesis and alter its cause. Even though the effect of DNA variations on cancer development is the area of active research, the contribution of heat shock protein polymorphisms to mammary cancer pathogenesis is poorly investigated. The previously mentioned perceptions laid the initiation for this study.
28 9.2 Research methods
9.2.1 DNA isolation kit and additional materials
GeneJet Genomic DNA purification Kit (Thermo Fisher Scientific) was used to isolate DNA. The components which were in the kit are as follows: Proteinase K solution, RNase A solution, Digestion solution, Lysis solution, Wash buffer I, Wash buffer II, Elution buffer, Purification columns with collection tubes and collection tubes. Additional components and instruments that are used are as follows: pipets and pipet tips, vortex, ethanol, 1.5 mL microcentrifuge tubes, microcentrifuge, thermomixer, and disposable gloves
9.2.2 DNA isolation protocol
Initially, we added 400 µL of Lysis buffer and 20 µL of Proteinase K solution to 200 µL of blood and mixed everything by vortexing or pipetting to get a uniform suspension. At that point, the examples were incubated at 56⁰C for 10 min while vortexing. 200 µL of ethanol (96%-100%) was added. The lysates were moved to GeneJET DNA columns with collection tubes and centrifuged for 1 min at 6000 x g. Subsequently, 500 µL of Wash buffer I was added to the columns, and the examples were centrifuged for 1 min at 8000 x g. In the subsequent stage, 500 µL of Wash buffer II was added to the GeneJET genomic columns, and they were centrifuged for 3 minutes at a greatest speed of <12000 X g. After that, the columns were moved to the clean 1.5 mL microcentrifuge tubes, and 200 µL of Elution buffer was added. Following 10 minutes’ incubation at room temperature, the examples were centrifuged for 1 min at 8000 X g. At last, the cleaned DNA was stored at - 20⁰C for further applications.
29 9.2.3 HSPA1A rs1043618 analysis
Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was used for rs1043618 polymorphism examination. PCR was carried out in an absolute volume of 25 µl, containing 23 µl of PCR solution and 2 µl of DNA. PCR mixture composition is presented in Table 1. PCR temperature regimes are shown in Table 2.
Table 1. PCR mixture composition for rs1043618 polymorphism analysis
Table 2. PCR program for rs1043618 polymorphism analysis
During PCR, 301 bp fragment was generated. The presence of a PCR amplicon was analyzed using 2% agarose gel electrophoresis.
Reagent 1 sample volume (µL)
10X Taq buffer 2.50
dNTP Mix (10 mM) 0.5
Forward primer (20 pmol/uL)
5’-CGATGAGCCGCTCGGTGT -3’ 0.38 Reverse primer (20 pmol/uL)
5’-AGGCTTCCCAGAGCGAAC -3’ 0.38 Taq Polimerase (5 U/ul) 0.25
MgCl2 (25 mM) 2.5
DMSO 1.25
H2O 15.25
Temperature (°C) Time Cycles
94 5 min - 94 30 s 40 60.9 30 s 72 30 s 72 10 min - 4 ∞ -
30 For polymorphism analysis, restriction fragment length polymorphism (RFLP) assay was done in a complete volume of 15 µL, containing 5 µL of RFLP mixture and 10 µL of PCR. The RFLP mixture combination is presented in Table 3. Restriction endonuclease MbiI was utilized for rs1043618 polymorphism examination. During RFLP, the examples were incubated at 37°C for 1-16 hours. The results were analyzed on 2% agarose gel electrophoresis (90 V, 40 min). The representative image of rs1043618 polymorphism examination is in Figure 3. In the case of C allele, MbiI endonuclease yielded 172, 119, and 10 bp fragments. In the samples with G allele, 291 and 10 bp fragments were obtained.
Table 3. RFLP mixture for rs1043618 polymorphism analysis
Fig. 3. Agarose gel electrophoresis for rs1043618 polymorphism analysis.
Lane M - DNA molecular marker GeneRuler Ultra Low Range DNA Ladder (Thermo Fisher Scientific Baltics, Lithuania); Lanes 6, 9, and 10 -GG genotype; Lanes 1-4, 8 -GC genotype; Lane 5,7 and 11 CC genotype.
Reagent Volume (µL) for 1 sample
H2O 3.5
10X Buffer Tango 1
31 9.2.4 HSPA1A rs562047 analysis
PCR-RFLP was implemented for rs562047 polymorphism analysis. The reaction was carried out in an absolute volume of 25 µl, containing 23 µl of PCR mixture and 2 µl of DNA. PCR mixture composition is displayed in Table 4. PCR temperature regimes are in Table 5.
Table 4. PCR mixture composition for rs562047 polymorphism analysis
Table 5. PCR program for rs562047 polymorphism analysis
During PCR, 308 bp fragments were produced. The presence of PCR fragments was checked using 2% agarose gel electrophoresis.
Reagent 1 sample volume (µL)
10X Taq buffer 2.50
dNTP Mix (10 mM) 0.5
Forward primer (20 pmol/uL)
5’- GTTGGTCACCGGGTAGCC -3’ 0.38 Reverse primer (20 pmol/uL)
5’- CCAGCTACGTGGCCTTCAC -3’ 0.38 Taq Polimerase (5 U/ul) 0.25
MgCl2 (25 mM) 2.5
DMSO 1.25
H2O 15.25
Temperature (°C) Time Cycles
95 3 min - 95 30 s 30 60.3 30 s 72 45 s 72 5 min - 4 ∞ -
32 Polymorphism analysis was based on RFLP assay, which was done in a volume of 15 µL, containing 5 µL of RFLP mixture and 10 µL PCR product. The RFLP mixture composition is presented in Table 6. Restriction endonuclease Eco31I was used for rs562047 polymorphism examination. During RFLP the examples were incubated at 37°C for 1-16 hours. The results were analyzed on 2% agarose gel (90 V, 40 min). The representative image of rs562047 polymorphism analysis is presented in Figure 4. Eco31I enzyme cuts C allele into 211 and 97 bp fragments. In the case of G allele, the amplicon remains uncut (308 bp).
Table 6. RFLP mixture for rs562047 polymorphism analysis
Fig. 4. Agarose gel electrophoresis for rs562047 polymorphism analysis.
Lane M - DNA molecular marker GeneRuler Ultra Low Range DNA Ladder (Thermo Fisher Scientific Baltics, Lithuania); Lanes 1-2, 4-5, and 8 CC genotype; Lanes 3,7, 9-10 -CG genotype; Lane 6 -GG genotype.
Reagent Volume (µL) for 1 sample
H2O 3.5
10X Buffer Tango 1
33 9.3 Statistical analysis of the data
The statistical analysis was performed using IBM “SPSS” (Statistical Package for the Social Science), version 26.0, and Microsoft Excel. Pearson’s Chi-square or Fisher’s tests were used to analyze the correlation between studied genotype and alleles with tumor characteristics. In a subsequent analysis, univariate logistic regression analysis was performed. The effect of polymorphism on PFS, MFS, and OS was analyzed with Kaplan–Meier, Log-rank test. Survival curves were generated with Kaplan– Meier. The statistically significant level was set at p<0.05. Evaluation of the Hardy-Weinberg equilibrium was performed by comparing observed and expected genotype frequencies.
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10. RESULTS AND DATA ANALYSIS
10.1 Tumor characteristics and SNP's frequencies
In our study, the distribution of tumor pathomorphological parameters was as follows: estrogen-positive (57%), progesterone estrogen-positive (48%), HER2 overexpression 22% of tumors. Triple-negative, luminal B, and HER2 molecular tumor subtypes were observed in 27%, 11 (ER+, PR-, HER2-), and % 5% (ER+, PR-, HER2+) of patients, respectively. HER2 enriched subtype was observed in 9% of the cases. Approximately half of the studied BC patients (46%) had positive lymph nodes. The majority (71%) of the tumors were well to moderate differentiated (G1), and most of them were classified as T1 (66%). The detailed overview of tumor clinicopathological characteristics is presented in Table 7.
Table 7. The clinicopathological characteristics of the study group
During a follow-up period, 26% of patients experienced distinct organ metastasis, 31% – local progress, 22% - deaths. The median follow-up of patients was 115 months.
10.2 Allele Polymorphism and genotype distribution
In our study, two polymorphisms in HSPA1A rs1043618, rs562047 genes were analyzed. In the rs1043618 polymorphism analysis, the C allele (66%) was more frequent than the G allele (34%). The distribution of genotypes was as follows: GG- 7%, CG- 54%, CC- 39% (Figure 4). In rs562047 analysis C allele (83.5%) was more common than the G allele (16.5%). The distribution of genotypes was as follows: GG - 4%, CG - 25%, CC - 71% (Figure 5). The frequencies of genotypes in HSPA1A rs1043618, rs562047 were according to the Hardy-Weinberg equilibrium.
Characteristics Subgroup and frequencies (%)
Age group 30-40 years – 35%, 41-50 years – 65%
Estrogen receptors (ERs) ER negative - 43%, ER positive - 57% Progesterone receptors (PRs) PR negative - 52%, PR positive - 48% Human epidermal growth factor receptor 2 HER2 negative - 78%, HER2 positive - 22%
Pathological lymph node involvement (N) N0 - 54%, N1 - 46%
Tumor grade (G) G1- 71%, G2 - 29%
35
Figure 4-5. Genotype distribution in the analyzed polymorphisms
10.3 Association analysis
The association between the selected SNP׳s (genotype and allele model) and tumor pathomorphological characteristics (ER, PR, HER2 status, G, T, N, L, V) was analyzed in our study. Genotype and allele distribution of HSPA1A rs1043618 and rs562047 polymorphisms by patient and tumor characteristics are presented in Table 8-9. The association analysis was performed using Chi-square or Fisher’s tests; however, the results were non-significant in both genotype and allelic model (data not provided). In further association analysis, the univariate logistic regression was implemented. There was no significant association between the analyzed polymorphisms (genotype and allelic model) and tumor pathomorphological characteristics (Table 10-12). There was no need for multivariate logistic regression analysis.
71% 25%
4%
Genotype distribution of HSPA1A rs562047
CC CT TT
39%
54% 7%
Genotype distribution of HSPA1A rs1043618
36
Table 8. Genotype and allele distributions by patient and tumor characteristics of HSPA1A rs1043618 polymorphism
Characteristic rs1043618
CC CG GG C allele carriers G allele carriers
Total 100 39(%) 54(%) 7(%) n(%) n(%)
Age (years)
30-40 14(%) 18(%) 3(%) 31 21
41-50 25(%) 36(%) 4(%) 61 41
Lymph nodes involved 46 18(46.2) 25(46.3) 3(42.9) 43(42.6%) 28(45.9%)
Progression (local) 1 11(28.2) 16(29.6) 4(57.1) 27(29) 20(32.8) Metastasis (peripheral) 1 10(25.6) 13(24.1) 3(42.9) 23(24.7) 16(26.2) Tumor size T1 26(66.7) 37(68.5) 3(42.9) 63(67.7) 40(65.6) T2 13(33.3) 17(31.5) 4(57.1) 30(32.3) 21(34.4) Grade G1-Good/moderate 26(66.7) 41(75.9) 4(57.1) 67(72) 45(73.8) G2- Poor 13(33.3) 13(24.1) 3(42.9) 26(28) 16(26.2) L 1 18(47.4) 22(41.5) 4(57.1) 40(44) 26(43.3) V 1 10(26.3) 10(19.2) 2(28.6) 20(22.2) 12(20.3) ER status Positive 24(61.5) 28(51.9) 5(71.4) 52(55.9)) 33(54.1) Negative 15(38.5) 26(48.1) 2(28.6) 41(44.1) 28(45.9) PR status Positive 19(48.7) 25(46.3) 4(57.1) 44(47.3) 29(47.5) Negative 20(51.3) 29(53.7) 3(42.9) 49(52.7) 32(52.5) HER2 status Positive 9(23.1) 11(20.4) 2(28.6) 20(21.5) 13(21.3) Negative 30(76.9) 43(79.6) 5(71.4) 73(78.5) 48(78.7)
Table 9. Genotype and allele distribution by patient and tumor characteristics of HSPA1A rs562047polymorphism
Characteristic rs562047
CC CG GG C allele carriers G allele carriers
Total 100 n(%) n(%) n(%) n(%) n(%)
Age (years)
30-40 22(31) 11(44) 2(50) 33(34.4) 13(44.8)
41-50 49(69) 14(56) 2(50) 63(65.6) 16(55.2)
Lymph nodes involved 71 32(45.1) 11(44) 3(75) 43(44.8) 14(48.3)
Progression (local) 1 21(29.6) 8(32) 2(50) 29(30.2) 10(34.5) Metastasis (peripheral) 1 17(23.9) 7(28) 2(50) 24(25) 9(31) Tumor size T1 46(64.8) 16(64) 4(100) 62(64.6) 20(69) T2 25(35.2) 9(36) - 34(35.4) 9(31) Grade G1-Good/moderate 52(73.2) 18(72) 1(25) 70(72.9) 19(65.5) G2- Poor 19(26.8) 7(28) 3(75) 26(27.1) 10(34.5) L 1 31(44.3) 11(44) 2(66.7) 42(44.2) 13(46.4) V 1 18(26.1) 4(16) 3(100) 22(23.4) 4(14.3) ER status Positive 43(60.6) 12(48) 2(50) 55(57.3) 14(48.3) Negative 28(39.4) 13(52) 2(50) 41(42.7) 15(51.7) PR status Positive 38(53.5) 9(36) 1(25) 47(49) 10(34.5) Negative 33(46.5) 16(64) 3(75) 49(51) 19(65.5) HER2 status Positive 18(25.2) 3(12) 1(25) 21(21.9) 4(13.8) Negative 53(74.6) 22(88) 3(75) 75(78.1) 25(86.2)
37
Table. 10. Univariant logistic regression analysis. The odds ratio for the association between SNP’s and tumor receptor status
SNP Genotype or alleles ER PR HER2
Odds 95% CI p Odds 95% CI P Odds 95% CI p
HSPA1A rs1043618
CG versus CC 0.631 0.276-1.440 0.274 0.958 0.411-2.235 0.921 0.806 0.304-2.134 0.664
GG versus CC 1.475 0.255-8.521 0.664 1.578 0.291-8.549 0.597 1.243 0.208-7.448 0.811
The carriers of C allele versus the non-carriers 2.416 0.298-19.586 0.409 0.943 0.135-6.583 0.953 1.578 0.264-9.419 0.617
The carriers of G allele versus the non-carriers 0.360 0.070-1.838 0.219 0.340 0.072-1.594 0.171 0.611 0.128-2.915 0.537
HSPA1A rs562047
CG versus CC 0.585 0.235-1.455 0.248 0.459 0.180-1.169 0.102 0.386 0.104-1.432 0.155
GG versus CC 0.658 0.088-4.893 0.682 0.277 0.028-2.764 0.274 0.864 0.085-8.777 0.902
The carriers of C allele versus the non-carriers 0.643 0.097-4.275 0.648 2.219 0.208-23.670 0.509 0.688 0.090-5.270 0.719
The carriers of G allele versus the non-carriers 0.595 0.251-1.406 0.236 0.430 0.177-1.044 0.062 0.448 0.139-1.447 0.179
Table. 11. Univariant logistic regression analysis. The odds ratio for the association between SNP’s and tumor grade, size and lymph node involvement
SNP Genotype or alleles Tumor grade Tumor size Lymph node involvement
Odds 95% CI p Odds 95% CI p Odds 95% CI p
HSPA1A rs1043618
CG versus CC 0.660 0.268-1.624 0.366 0.903 0.382-2.139 0.817 1.078 0.479-2.424 0.857
GG versus CC 1.552 0.303-7.936 0.598 2.610 0.511-13.319 0.249 0.935 0.186-4.699 0.935
The carriers of C allele versus the non-carriers 0.301 0.036-2.548 0.961 0.271 0.057-1.894 0.214 0.322 0.038-2.733 0.299
The carriers of G allele versus the non-carriers 0.696 0.157-3.085 0.633 0.411 0.098-1.729 0.225 1.614 0.373-6.986 0.522
HSPA1A rs562047
CG versus CC 1.054 0.383-2.900 0.919 1.029 0.401-2.645 0.952 0.985 0.396-2.449 0.975
GG versus CC 7.566 0.744-76.898 0.087 0.000 0.00- 0.999 3.694 0.370-36.907 0.266
The carriers of C allele versus the non-carriers 0.262 0.039-1.775 0.170 4.932 0.449-54.170 0.192 0.676 0.109-4.197 0.675
38
Table. 12. Univariant logistic regression analysis. The odds ratio for the association between SNP’s and tumor lymphatic and vascular infiltration
SNP Genotype or alleles L V Odds 95% CI p Odds 95% CI p HSPA1A rs1043618 CG versus CC 0.859 0.375-1.967 0.719 0.680 0.252-1.830 0.445 CC versus GG 0.610 0.319-8.119 0.564 1.121 0.188-6.691 0.900
The carriers of C allele versus the non-carriers 0.551 0.101-2.996 0.490 0.752 0.113-4.990 0.377
The carriers of G allele versus the non-carriers 0.950 0.228-3.961 0.944 0.825 0.160-4.265 0.819
HSPA1A rs562047
CG versus CC 1,029 0.413-2.564 0.952 0.537 0.163-1.768 0.306
CC versus GG 2,607 0.227-30.006 0,442 0.000 0.000- 0.999
The carriers of C allele versus the non-carriers 1.146 0.165-7.978 0.891 3.966 0.227-69.225 0.345
39 10.4 Survival analysis
10.4.1 HSPA1A rs1043618
The possible associations between HSPA1A rs1043618 polymorphism and BC patient survival were assessed using Kaplan-Meier analysis (Log Rank test). No significant link between this SNP and PFS (p=0.146), MFS (p=0.424), and OS (p=0.375) was determined in the genotype model. Furthermore, there was no significant association between rs1043618 and PFS (p=0.051, p=0.651), MFS (p=0.192, p=0.989) and OS (p=0.162, p=0.807) and C or G alleles, respectfully. Due to non-significant results, Cox survival analysis was not further performed. The representative PFS, MFS, and OS curves, generated with Kaplan–Meier, are presented in Figure 6.
40 10.4.2 HSPA1A rs562047
The possible associations between HSPA1A rs562047 polymorphism and BC patient survival were assessed (Kaplan-Meier analysis, Log Rank test). In our study, no significant link between this SNP in the genotype model and PFS (p=0.587), MFS (p=0.587), OS (p=0.226) was determined. In allelic model there was also no-significant correlation found with PFS (p=0.308, p=0.651), MFS (p=0.198, p=0.560) and OS (p=0.095, p=0.379) and C or G alleles, respectfully. Due to non-significant results, Cox survival analysis was not further performed. The representative PFS, MFS, and OS curves, generated with Kaplan–Meier, are presented in Figure 7.
41
11. DISCUSSION
SNPs are a sequence of DNA polymorphisms caused by single nucleotide variations at the genomic level and are the most prevalent genetic markers in the human genome. Single nucleotide variations can lead to modification in gene expression, determine phenotypic diversity in protein, and contribute to an individual’s susceptibility to cancer. Past studies indicated that HSP70 is a reserved protein group functioning as a molecular chaperone in the process of protein maturation. HSP70 is involved in DNA disturbance renewal, apoptosis, and cell cycle regulation. However, several DNA sequence changes, such as single nucleotide variations, appeared in the course of long-term evolution. As a crucial chaperone molecule, HSPA1A sequence variations can alter protein expression or functions, leading to variation intolerance to stress and susceptibility to certain diseases. Therefore, the investigation of HSPA1A gene polymorphism may be useful in detecting patients at high risk for lung cancer. In the initial part of this work, we analyzed the distribution of alleles and genotypes in rs1043618 and rs562047 polymorphisms. In the case of rs1043618 polymorphism, C allele and consequently GC genotype were predominant. As far as rs562047 polymorphism is concerned, C allele and homozygous CC genotype were determined in most cases. To our best knowledge, there is no study analyzing the contribution of rs1043618 (https://www.ncbi.nlm.nih.gov/snp/rs1043618#publications) and rs562047 (https://www.ncbi.nlm.nih.gov/snp/rs562047#publications) to breast tumor pathomorphological parameters and the course of the disease reported so far. Furthermore, only a limited number of studies aimed to analyze the relevance of these polymorphisms to the other conditions. It was an earlier report by Wang and colleagues that rs1043618 may be a functional polymorphism and it may affect patient susceptibility to lung cancer, and homozygous CC genotype may enhance the risk of lung cancer development. In addition, smoking along with rs1043618 may increase the risk of lung cancer [27]. Moreover, recent studies have evaluated the associations between polymorphisms of the heat-shock protein 70 (HSP70) encoding genes and noise-induced hearing loss (NIHL). In work presented by Song Lei, rs1043618 polymorphism was found to have no significant association for any genetic model in the case of noise-induced hearing loss [28]. However, a study presented by Ning-Chia Chang showed the opposite results [29]. A study presented by Malgorzata Kowalczyk aimed to determine whether genetic polymorphism in HSPA1A rs562047 is associated with risk of paranoid schizophrenia found no significant association in genotype or allele distributions tested between the schizophrenia and control groups [30]. Moreover, a study presented by Jung Jin Kim supports this hypothesis [31]. In our study, there were no significant associations between analyzed polymorphisms and tumor pathomorphological parameters and patient survival determined.
42
12. CONCLUSION
1. The distribution of alleles and genotypes in rs1043618 and rs562047 polymorphisms was as follows:
• rs1043618 allele frequency: C - 66%, G - 34%. HSPA1A rs1043618 genotype frequency: GG - 7%, GC - 54%, CC - 39%
• rs562047 allele frequency: C - 83.5%, T – 16.5%. HSPA1A rs562047 genotype frequency: GG - 4%, GC - 25%, CC - 71%.
2. No significant association between rs1043618 and rs562047 in HSPA1A and tumor pathomorphological parameters was determined in both genotype and allelic model.
3. There was no significant link between the analyzed SNPs and patient OS, PFS, and MFS in genotype and allelic models.
43
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