Introduction

Biomedical Reports

2049-9434 2049-9442

D.A. Spandidos

BR-21-4-01832

10.3892/br.2024.1832

Articles

Comprehensive breast cancer risk analysis with whole exome sequencing and the prevalence of BRCA1 and ABCG2 mutations and oncogenic HPV

Bumrungthai

Sureewan

1 2 3 Duangjit

Sureewan

4 Passorn

Supaporn

5 Pongpakdeesakul

Sutida

5 Butsri

Siriwoot

1 3 Janyakhantikul

Somwang

1 3

1Division of Biopharmaceutical Sciences, Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand 2Division of Microbiology and Parasitology, School of Medical Sciences, University of Phayao, Phayao 56000, Thailand 3Center for Pharmacogenomics and Clinical Translational Research, Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand 4Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, Ubon Ratchathani 34190, Thailand 5Division of Biotechnology, School of Agriculture and Natural resources, University of Phayao, Phayao 56000, Thailand

Correspondence to: Dr Somwang Janyakhantikul, Division of Biopharmaceutical Sciences, Faculty of Pharmaceutical Sciences, Ubon Ratchathani University, 85 Sathonlamark Road, Warin Chamrap, Ubon Ratchathani 34190, Thailand somwang.j@ubu.ac.th

10 2024

07 08 2024

21 4

144

11 04 2024 02 07 2024

2024

This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Breast cancer is the most prevalent cancer and also the leading cause of cancer death in women worldwide. A comprehensive understanding of breast cancer risk factors and their incidences is useful information for breast cancer prevention and control planning. The present study aimed to provide information on single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) in breast cancer, the allele frequency of two SNPs in breast cancer-related genes BRCA1 DNA repair associated (BRCA1; rs799917) and ATP binding cassette subfamily G member 2 (ABCG2; rs2231142), and the prevalence of human papillomavirus (HPV) infections in a normal population living in Phayao Province, Northern Thailand. One breast cancer and 10 healthy samples were investigated by whole exome sequencing (WES) and compared for genetic variation. The WES data contained SNPs in genes previously implicated in breast cancer and provided data on CNVs. The allele frequencies for SNPs rs799917 and rs2231142 were also examined. The SNP genotype frequencies were 35.88% CC, 46.54% CT, and 17.58% TT for rs799917 and 33.20% CC, 46.88% CA, and 19.92% AA for rs2231142. A total of 825 human whole blood samples were examined for HPV infection by PCR, and the pooled DNA was tested for HPV infection using metagenomic sequencing. No HPV infections were detected among all 825 samples or the pooled blood samples. The incidence of breast cancer among the tested samples was estimated based on acceptable breast cancer risk factors and demographic data and was 1.47%. The present study provided data on SNPs and CNVs in breast cancer-related genes. The associations between SNPs rs2231142 and rs799917 and breast cancer should be further investigated in a case-control study since heterozygous and homozygous variants are more common. Based on the detection of HPV infection in the blood samples, HPV may not be associated with breast cancer, at least in the Northern Thai population.

breast cancer whole exome sequencing copy number variation BRCA1 ABCG2 human papillomavirus Thailand

Funding: The present study was funded by the University of Phayao (grant no. FF64-RIB005) and the Faculty of Pharmaceutical Sciences, Ubon Ratchathani University (grant nos. 0604.11-3/2566 and grant no. 0604.11-6/2567).

Introduction

Breast cancer is the most common cancer in females. The global cancer statistics indicate that in 2020, breast cancer was the leading newly diagnosed cancer worldwide, accounting for 2,261,419 new cases (11.7%), and the leading cause of cancer-related mortality worldwide, accounting for 684,996 deaths (6.9%) (1). Xu et al (2) showed that the global incidence of breast cancer increased by 123% between 1990 and 2017 and it is expected to continue increasing yearly. Furthermore, the authors estimated that there will be 4,781,849 cases of breast cancer and 1,503,694 breast cancer-related mortality worldwide in 2050. In Thailand, breast cancer is a common malignancy, ranking third after liver and lung cancer in new cases and mortality annually (3). Therefore, a comprehensive understanding of breast cancer risk factors and their incidences is valuable for breast cancer prevention, including screening, diagnosis, prognosis, personal lifestyle modification, and effective therapy strategies, such as precision medicine, although the latter will require collecting data on breast cancer genetics, environmental factors, and lifestyle behaviors.

Numerous non-modifiable (e.g., female sex, older age, family history and genetic mutations) and modifiable (e.g., obesity, alcohol consumption and smoking) risk factors have been reported for breast cancer (4-6). Single nucleotide polymorphisms (SNPs) in the breast cancer-related genes BRCA1 DNA repair associated gene (BRCA1; rs799917) and ATP-binding cassette subfamily G member 2 (ABCG2; rs2231142) are some examples of genetic mutations that have been reported. BRCA1 is a tumor suppressor. The mutations in BRCA1 may cause BRCA1 dysfunction and affect cancer risk, especially breast and ovarian cancer (7). However, recent meta-analysis studies have revealed no significant correlation between SNP rs799917 and breast cancer (8,9) or overall cancer (10,11) risk. Moreover, SNP rs799917 in BRCA1 could be a protective factor for non-breast cancer in Asian populations (10,11). ABCG2, or breast cancer resistance protein (BCRP), is an efflux transporter that functions as a xenobiotic transporter. Homozygosity for the A allele of SNP rs2231142 has recently been reported to be associated with an increased risk of breast cancer (12,13) and axillary lymph node status (14).

One recent additional and intriguing potential risk factor for breast cancer is human papillomavirus (HPV) infection, which has been reported to increase the risk of breast carcinoma in a large-scale systemic review and meta-analysis of case-control studies (15). The mechanism by which HPV causes breast carcinogenesis is also not well studied, but some reports reveal that HPV DNA in serum-derived extracellular vesicles was found in breast cancer patients, where E6 and E7 oncoproteins act as stimulators of host cell proliferation and inhibit apoptosis (16,17). However, the mode of HPV transmission underlying breast cancer remains unclear (18). There is a report that HPV was identified in 1.7% of the blood samples or peripheral blood mononuclear cells (PBMCs) of healthy individuals (19) but the association between HPV transmission and breast cancer in blood samples in Thailand has not yet been verified.

The present study aimed to explore genetic mutations [i.e., SNPs and copy number variations (CNVs)] in breast cancer and, in particular, the allele frequencies for two SNPs (BRCA1; rs799917) and (ABCG2; rs2231142), as well as the prevalence of HPV infections in individuals living in Phayao Province, Thailand.

Materials and methods <sec> <title>Participants

The present study collected 825 human whole blood samples from normal individuals aged 3-90 years (mean age 51.25±19.20 years old, with 236 males and 589 females, sex ratio: 0.40) living in Phayao Province, Thailand, along with demographic data on their sex, body mass index (BMI), family history of cancer, exercise, alcohol consumption, smoking, secondhand smoke and cleanliness of drinking water. The participants were randomly selected between March 2020 and July 2022 by researchers at the Division of Microbiology and Parasitology, School of Medical Sciences, The University of Phayao, Thailand. The present study was approved by the Committee on Human Research Ethics in Health Sciences and Science and Technology at the University of Phayao (1.3/023/63 and 1.3/013/65) and the Ubonratchathani University (UBU-REC-68/2567).

Blood collection and DNA extraction

Human whole blood samples were randomly collected from 825 individuals aged 3-90 years living in Phayao Province, Thailand. DNA was extracted from these samples using the Genomic DNA Isolation Kit (cat. no. PDC11-0100; Bio-Helix Co., Ltd.) as in previous studies (20,21).

Whole exome sequencing (WES)

WES was used to examine point mutations in 11 human samples, consisting of one female patient with breast cancer and 10 healthy individuals of both sexes who were born and live in Phayao Province, Thailand. The 10 healthy samples were aged 11-20, 21-30, 31-40, 41-50, and 51-60 years. The details of all samples, including sex, age, and family history of cancer, are provided in Table I. The DNA was extracted as previously described (20,21) and eluted in TE buffer. DNA quality control, library preparation, library quality control, cluster generation, and sequencing were performed as previous studies (20,21). Briefly, a qualified DNA sample was fragmented using an ultrasonicator for 20-40 kHz, 360 sec, at 4˚C. Then, the interrupted DNA fragment was constructed into a high-throughput sequencing library through the steps of terminal repair, adding a base A tail, adapter ligation, purification and pre-amplification, quantitative, exon capture and PCR enrichment. After the completion of library preparation, the size and concentration of each sample were determined and the Qubit fluorometer (Thermo Fisher Scientific, Inc.) was used for accurate measurement of DNA concentration. The accuracy and concentration of the sequencing library were assessed. Finally, to ensure the accuracy of the library concentration and data output, the effective concentration of the library mixture was measured. The optical signal under the four fluorescent channels scanned by the built-in software Illumina Real Time Analysis (RTA) software (version 1.17.28; Illumina, Inc.) and was converted to base calling files in real time. After the base calling, Illumina's official software, bcl2fastq (v2.17; Illumina, Inc.), was used to demultiplex the data according to the sample index sequence and convert it into FASTQ format. The primary analysis was conducted using the built-in High-Content Screening (HCS) sequencer software to determine whether the reads would pass filter (PF; the first 25 cycles have ≤2 bases, whose chastity value is <0.6) based on the purity of the first 25 cycles of the read signal or not. The PF clusters stored in the FASTQ format after a conversation are called PF data, or raw data. For paired-end data, sequence data consists of two FASTQ files that hold each end of the sequence read.

SNPs genotyping. BRCA1 (rs799917) detection by high-resolution melting (HRM) analysis

BRCA1 SNP rs799917 was genotyped in 825 samples by HRM as previously described (22) using the forward and reverse primers listed in Table SI, which produced a PCR product of 47 bp. The quantitative (q)PCR reaction used the 5X FiREPOL Eva Green HRM Mix Plus (Solis BioDyne OÜ). The qPCR conditions were as follows: Initial denaturation at 95˚C for 12 min, then 40 cycles of denaturation at 95˚C for 15 sec, annealing/elongation at 60˚C for 20 sec and melting at 65-95˚C for 5 sec/step. A DNA sample known to have the BRCA1 SNP rs799917 (C>T) was used as a positive control. DNase- and RNase-free water was used as a negative control.

Following HRM analysis, 60 samples were randomly selected to repeat for verification of SNP genotyping results by Sanger sequencing. Sanger sequencing was conducted to confirm the BRCA1 SNP rs799917 (C>T) with primers for BRCA1 amplifying a 719 bp fragment (Table SI). The sequences were computationally compared to the reference sequence in the GenBank database using the BioEdit (version 7.2) biological sequence alignment editor, which was developed by Tom Hall and downloaded from https://bioedit.software.informer.com/7.2/.

ABCG2 (rs2231142) detection by PCR-restriction fragment length polymorphism (PCR-RFLP) analysis

ABCG2 SNP rs2231142 was genotyped in 256 samples, randomly selected from 825 samples, using the PCR-RFLP method described by Wu et al (12), with some modifications. Briefly, the 25 µl PCR mixture comprised 12.5 µl of One PCR (GeneDireX, Inc.), 1 µl (10 µM) of both the forward and reverse primers (Table SI), and 10-100 ng of genomic DNA. The PCR amplification was conducted with the following parameters: Initial denaturation step of 5 min at 94˚C; followed by 35 cycles of 30 sec at 94˚C, 30 sec at 58˚C, and 1 min at 72˚C and then a final elongation of 5 min at 72˚C. The PCR products (302 bp) were digested with TaaI (Thermo Fisher Scientific, Inc.) at 65˚C for 2 h. The A allele was uncut, and the C allele was cut into 252 and 50 bp fragments.

DNA sequencing of the ABCG2 promoter

Males and females in 18 samples were randomly selected for DNA sequencing in the ABCG2 promoter region (chr4:89,079,995-89,080,518; hg19), according to Eclov et al (23) with some modifications. The PCR was conducted using the forward primer and reverse primers listed in Table SI and the following conditions: 95^˚C for 2 min; followed by 35 cycles of 30 sec at 95˚C, 30 sec at 60˚C and 1 min at 68˚C and then a final extension of 10 min at 72˚C. The 524 bp PCR product was confirmed by gel electrophoresis and sequenced.

HPV DNA detection by PCR

HPV DNA was detected in 825 samples by PCR using the primers listed in Table SI, which produced a PCR product of 154 bp (24). Caski cell line (kindly provided by HPV & EBV and Carcinogenesis Research Group, Khon Kaen University, Thailand) was used as a positive control. The PCR reaction used 5X FiREPOL Eva Green HRM Mix Plus (Solis BioDyne OÜ). The hemoglobin subunit β gene was used as the housekeeping control gene, producing a PCR product of 268 bp, as in a previous study (20).

Metagenomics

The pooled DNA extracted from the 825 samples was analyzed by shotgun metagenomic sequencing (next-generation sequencing) as in a previous study (20) to detect oncogenic HPV DNA. According to WES in the material and methods.

CNV analysis

CNVs are a type of structural variation that represents repeated sections of the genome, and the number of repeats varies among individuals. They include gains and losses. Studies on CNVs have been seminal in rare genetic diseases. The present study used Control-FREEC to detect CNVs based on the change in read depth across the genome, annotated them using Annovar and counted their number (some examples are shown in Fig. 1).

Statistical analysis

The data were analyzed using the SPSS software (version 16; IBM Corp.). Pearson's χ² test was used to compare categorical variables between groups. An independent Student's t-test was used to compare means ± standard deviation (SD) between pairs of groups. P<0.05 was considered to indicate a statistically significant difference.

Results <sec> <title>Study population characteristics

The demographic characteristics of participants (i.e., sex, age, family history of cancer, BMI, exercise, cleanliness of water, alcohol consumption, smoking and secondhand smoke) are shown in Table II. Their age ranged from 3-90 years, with a mean of 51.25 years (SD 19.20 years). Notably, nearly 25% of the participants had a family history of cancer; 15.03 and 20.48% were at risk of cancer from smoking and secondhand smoke, respectively.

Genotype frequencies of SNPs rs799917 and rs2231142 and the DNA sequencing of the ABCG2 promoter

The genotyped SNPs (rs799917 and rs2231142) were both in the Hardy-Weinberg equilibrium (P=1.119 and P=0.534, respectively) and had a minor allele frequency (MAF) of 0.408 and 0.434, respectively. The allele frequencies of these SNPs in the present study and other populations are compared in Table SII. The genotype frequencies were 35.88% CC, 46.54% CT, and 17.58% TT for rs799917, and 33.20% CC, 46.88% CA, and 19.92% AA for rs2231142. The genotype frequencies for SNP rs799917 were not associated with sex or family history of cancer (Table II). However, notably, the genotype frequencies for SNP rs2231142 differed between males and females, with the most common genotype being homozygous wide-type (CC) in males and heterozygous (CA) in females (P<0.001; Table II).

The present study investigated rare SNPs in the ABCG2 promoter using DNA sequencing (~524 bp) in 18 samples, finding that one (5.56%) had a heterozygous (CT) at the SNP rs76656413 (C>T).

HPV detection by PCR and metagenomic sequencing

Blood was collected from 825 Thai donors, and HPV DNA was detected by PCR. However, HPV DNA was not detected in any examined sample (0/825; 0%). The metagenomic sequencing of pool blood DNA samples also detected no HPV DNA in the samples (Fig. 2).

The predicted number of breast cancer cases in the tested samples

The number of breast cancer cases among 68 tested samples (of 256 samples) from females aged ≥50 years who were tested for SNP rs2231142 was estimated based on all collected data on breast cancer risk factors, including demographic factors (i.e., female and aged ≥50 years), genetic factors (i.e., SNP rs2231142 and family history of cancer), environmental factors (i.e., HPV infection and secondhand smoke) and lifestyle behaviors (i.e., alcohol consumption, smoking, obesity and overweight, and physical activity). One of the 68 samples (1.47%) was predicted to have a high risk of breast cancer.

WES analysis

WES identified SNPs in the following genes (Fig. 3 and Table III): ADAM, ADCY, ANKRD, APOBEC, ARHGEF, ASTN2, NUAK, ATAD, ATAD, BCL, BCL2L, BABAM, CNTROB, CASP, CCDC, CDCA, CDKAL, CMSS, MYC, CNTNAP, COX, CREB, CUX, DCLRE, DNAH, DNAJC, SREBF, PERM, ESRP, MRPS, FGFR, FLJ, GRHL, KANSL, L3MBTL, LSP1, MYEOV, OTUD, PDE, MAP3K, MCM, PEX, PIK3C, PLA2G, JMJD, PLA2G4F, PPRC, RAD51, RAD54, RGPD, RBL, RNF, SETBP, SLC, TNRC, TP53BP1, WDR, ZBTB, ZC3H, and ZNF. Notably, SNPs were identified in the NBPF9, NBPF1, SLC9B1, PABPC3, SLC9B1, PABPC3, PPIAL4G, PPIAL4H, H2BFS and ZC3H11B genes in all 11 samples, including a patient with breast cancer.

Discussion

Breast cancer risk factors and their incidences are helpful for breast cancer prevention. In the present study, WES was used to detect SNPs and CNVs in one patient with breast cancer and 10 healthy individuals, and then the SNPs were compared. It also investigated the genotype frequencies of two SNPs (rs799917 and rs2231142) in breast cancer-related genes and HPV infection. Finally, all data on breast cancer risk factors, including genetic factors, environmental factors, and lifestyle behaviors, were gathered to predict the number of breast cancer cases among the tested samples.

CNVs are associated with breast cancer risk and diagnosed for breast cancer subtypes (36,37). Dennis et al (38) found that 0.5% of 86,788 cases had a deletion in one of the known breast cancer susceptibility genes. In the present study, CNVs were detected in a Thai patient with breast cancer (Fig. 1). While there are numerous duplication and deletion sites on chromosome 17 where several breast cancer-related genes are located (e.g., HER2, TOP2A, TAU, p53, BRCA1, and HIC-1) (39), the present study did not detect CNVs in these genes in this patient. While various germline and somatic CNVs associated with breast cancer risk have been reported, such as in BRCA1, CHEK2, ATM, BRCA2, ERBB2, MYC, NBN, CCND1, and MCL1 (38,40), The present study also did not detect them in its Thai patient with breast cancer. It was hypothesized that the main reason may be the difference in ethnic ancestry. However, the present study detected a CNV in RAD51D associated with breast cancer risk that had been published in the Thai population (41,42). RAD51D is involved in homologous recombinant DNA repair, and it carries mutations that are known to be pathogenic. While it is not the most commonly mutated gene, it is mutated more often in Thai patients with breast cancer (41,42), supporting the inclusion of RAD51D in breast cancer genetic testing (43).

In the present study, SNPs were also characterized in one patient with breast cancer and 10 healthy individuals by WES. Notably, no SNPs were detected in the patient with breast cancer in the top genes previously reported in the Thai population (41,42). However, the results revealed that some SNPs were in previously reported breast cancer genes (Table III). A comparison of SNPs between the breast cancer patient and 10 healthy individuals (Fig. 3) identified several that were common in the tested samples.

BRCA1 is a tumor suppressor located on chromosome 17q21. Its protein has multiple functions in cell homeostasis during the cell cycle, including DNA replication and apoptosis. Therefore, mutations in BRCA1 may cause BRCA1 dysfunction and affect cancer risk through its intracellular functions, as has been reported for various types of cancer, especially breast and ovarian (7). However, the association between SNP rs799917, a missense mutation (p.P871L), and breast cancer susceptibility remains unclear. Nicoloso et al (44) found inconsistent results in which the carrier (T) allele of this SNP was associated with a weaker microRNA 638-dependent BRCA1 reduction. However, while it was shown to be associated with breast cancer risk in their case-control study and other studies (45), meta-analysis studies have revealed no significant correlation between SNP rs799917 and breast cancer (8,9) or overall cancer (10,11) risk. Moreover, SNP rs799917 in BRCA1 could be a protective factor for non-breast cancer in Asian populations (10,11). One study reported that this SNP was a neutral variant found in 17.6% of 190 healthy Thai individuals (46). Unexpectedly, in the present study, the frequency of this SNP was 17.6%, the same as in Ahmad et al (46). Due to its high prevalence in the Thai population, this protective factor for breast cancer should be investigated further in a large case-control study to confirm this hypothesis.

ABCG2, formerly called breast cancer resistance protein (BCRP), is an efflux transporter that functions as a xenobiotic transporter. While ABCG2 contains numerous SNPs, one of the most important is rs2231142 (C421A), which affects ABCG2 protein expression and function with clinical significance in diseases (e.g., gout) (47,48) and drug pharmacokinetics (e.g., statins) (49). Homozygosity for the A allele of SNP rs2231142 has recently been reported to be associated with an increased risk of breast cancer (12,13) and axillary lymph node status (14). The frequency of this SNP differs among populations; it is more common in Asians than Caucasians and is rarely found in Africans (50,51). In the present Thai study, its MAF was 0.434 and its most common genotype was the heterozygote (CA). Therefore, it could be pathogenic and a risk factor for breast cancer in the samples of the present study. However, the relationship between SNP rs2231142 and breast cancer risk requires further study to be confirmed. Moreover, it was also found that one of 18 samples (5.56%) had a polymorphism in the ABCG2 promoter (SNP rs76656413), which has been reported to markedly reduce promoter activity by 70% (23). Notably, this volunteer also had a homozygous variant (AA) genotype for SNP rs2231142. Therefore, the allele frequency for SNP rs76656413 should be investigated further for its prevalence and clinical relevance in the Thai population, especially in those who carry both SNPs.

The association between HPV infection and risk factors for breast cancer is controversial since the reported frequency of HPV infection in patients with breast cancer varies widely from 0-86.2% and there is a lack of evidence to support HPV transmission to the breast (18). However, a recent meta-analysis of 3,607 breast cancer cases and 1,728 controls confirmed that being HPV-positive increases breast cancer risk (15). In Thailand, one study reported a low HPV-positive frequency, detecting HPV DNA in 25/700 (3.57%) of Thai women with breast cancer and benign breast tumors (52). Our previous study also detected HPV DNA at a low HPV-positive frequency, detecting HPV DNA in 3.7% of oral rinses from 594 healthy participants in Northern Thailand (53). The present study is the first to detect HPV in human blood samples from Thai individuals, including the pool meta-genomic results, detecting no HPV DNA. Therefore, it appeared that HPV infection in a blood sample HPV might not be associated with breast cancer, at least in the Northern Thai population.

Notably, an analysis considering all data on breast cancer risk factors, including genetic factors, environmental factors and lifestyle behaviors, predicted a relatively high number of breast cancer cases among the 68 tested samples of 1.47%. Therefore, all causes should be further investigated and modifiable risk factors for breast cancer should be used to prevent and control breast cancer risk to reduce the number of cases in the future.

However, the present study also had limitations, such as the following: First, the present study is not a case-control study; all participants in the present study were healthy individuals, excluding one breast cancer patient. Therefore, a large case-control study still needs to be further investigated. Second, all the volunteers in the present study were individuals who live in Phayao Province, which is located in the northern part of Thailand. Thus, in some respects, the results of the present study might not be properly representative of the Thai population. Third, the present study revealed only information about WES in one breast cancer patient and some participants among 10 healthy individuals had a family history of cancer. Consequently, the interpretation of results must be based on awareness, and more information about breast cancer patients is still needed to confirm results in the future.

In conclusion, the present study provided information on SNPs and CNVs in breast cancer-related genes. The associations between SNPs rs2231142 of ABCG2, rs76656413 in the promoter of ABCG2 and rs799917 in BRCA1 and breast cancer should be further investigated in case-controlled studies since their variant genotypes are more common in the Thai population. Moreover, based on the predicted number of breast cancer cases, strategies for modifiable breast cancer risk factors should be applied to prevent and control breast cancer risk. The oncogenic HPV in the whole blood might not be a breast cancer-associated risk factor in the Northern Thai population. However, a larger sample size might be needed for further investigation to confirm this.

Supplementary Material

Forward and reverse primers.

Minor allele frequencies of single nucleotide polymorphisms rs799917 and rs2231142 in different populations.

Acknowledgements

Not applicable.

Availability of data and materials

The data generated in the present study may be requested from the corresponding author. The WES of the breast cancer patient was submitted to the SRA database (BioProject ID PRJNA1126023; https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA1126023).

Authors' contributions

SuB and SJ were responsible for conceptualization, methodology, validation, data curation, and writing, reviewing, and editing the manuscript. SuB and SupP was responsible for sample collection. SuB, SD, SiB, SutP and SJ were responsible for the investigation. SuB, SD, SiB, SupP and SJ were responsible for funding acquisition. SuB and SJ confirm the authenticity of all the raw data. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The present study was approved by the Committee on Human Research Ethics in Health Sciences and Science and Technology, University of Phayao (approval nos. UP-HEC 1.3/023/63 and 1.3/013/65) and the Ubonratchathani University (approval no. UBU-REC-68/2567), Thailand. Informed consent was obtained from all subjects involved in the present study. All procedures involving human participants performed in the study were in accordance with the ethical standards of the Declaration of Helsinki, the Belmont Report, the Council for International Organizations of Medical Sciences guidelines, and the International Conference on Harmonization in Good Clinical Practice.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Figure 1

Copy number variants identified by whole exome sequencing in one breast cancer sample. The χ-coordinate shows the chromosome's position (in X.X x 10^x). The y-coordinate shows the copy number, with the normal copy number shown in green, duplications shown in red, and deletions shown in blue.

Figure 2

Metagenomic sequencing data. The white circle is an unclassified microbe. The orange circlet is Ortervirales and Herpesvirales, separated by a white line. The green circlet is Ortervirales. The blue circlet is Retroviridae. The yellow circlet is a group of viruses that are separated by a white line.

Figure 3

Comparison of single nucleotide polymorphisms between one patient with breast cancer and 10 healthy individuals of different ages. M, man; W, woman; BC, breast cancer.

Table I

Characteristics of a female patient with breast cancer and 10 healthy individuals.

ID	Sex	Age, years	Family history of cancer
1	Female	16	No disease
2	Male	13	No disease
3	Female	29	Mother with breast cancer
4	Male	22	No disease
5	Female	32	Mother with breast cancer
6	Male	38	No disease
7	Female	43	No disease
8	Male	49	Father with liver cancer
9	Female	59	Sister with colon cancer
10	Male	56	Mother with liver cancer and sister with breast cancer
11	Female	63	Patient with breast cancer

Table II

Demographic characteristics of participants and genotype frequencies for SNPs rs799917 and rs2231142.

		Genotype frequencies of rs799917				Genotype frequencies of rs2231142
Demographical factors	Total (n=825) (%)	CC (296)	CT (384)	TT (145)	P-value	CC (85)	CA (120) (%)	AA (51)	P-value
Sex					0.596				<0.001
Male	589 (71.39%)	205 (34.80%)	279 (47.37%)	105 (17.83%)		51 (43.59%)	30 (25.64%)	36 (30.77%)
Female	236 (28.61%)	91 (38.56%)	105 (44.49%)	40 (16.95%)		34 (24.46%)	90 (64.75%)	15 (10.79%)
Age (years)
Mean	51.25
SD	19.20
Family history of cancer					0.488				0.748
Yes	186 (22.55%)	66 (35.48%)	82 (44.09%)	38 (20.43%)		20 (33.33%)	30 (50.00%)	10 (16.67%)
No	639 (77.45%)	230 (35.99%)	302 (47.26%)	107 (16.75%)		65 (33.16%)	90 (45.92%)	41 (20.92%)
BMI (kg/m²)
Low (<18.50)	102 (12.37%)
Normal (18.50-22.90)	489 (59.27%)
High (≥23.00)	234 (28.36%)
Exercise
Yes	418 (50.67%)
No	407 (49.33%)
Alcohol consumption
Yes	326 (39.52%)
No	499 (60.48%)
Smoking
Yes	124 (15.03%)
No	701 (84.97%)
Secondhand smoke
Yes	169 (20.48%)
No	656 (79.52%)

SD, standard deviation; BMI, body mass index.

Table III

Breast cancer-related genes containing SNPs in 10 healthy individuals and one patient with breast cancer that have been reported in published articles (25-35).

Gene from whole exome sequencing	Genes reported in published articles	SNP rsID	Gene from whole exome sequencing	Genes reported in published articles	SNP rsID
ADAMTSL4	ADAM29	rs587742902	L3MBTL1	L3MBTL3	rs772502710
ADAM15		rs113878254	L3MBTL1		rs372050022
ADAM15			LSP1	LSP1	rs552802699
ADAMTS12		rs759606612	LSP1		rs148262402
ADAMTS6		rs368191265	MAP3K11	MAP3K1	rs11227236
ADAMDEC1		rs200134300	MAP3K9		rs34322726
ADAM32			MAP3K10
ADAMTS13			MCM10	MCM8	rs187685058
ADAMTS14		rs147256643	MCM8		rs760412395
ADAMTS14		rs376614311	MCM5	MDM4	rs751091748
ADAM20		rs567945895	MDM2
ADAMTSL3			MYEOV	MYEOV, CCNDL	rs148448631
ADAMTS17		rs371570653	OTUD4	OTUD7B	rs369626183
ADAMTS1		rs751984218	PDE4DIP	PDE4D	rs151058495
ADCY10	ADCY3	rs200816878	PDE4DIP		rs782064349
ADCY4		rs532277226	PDE4DIP		rs140993521
AKAP9			PDE4DIP		rs201403178
AKAP9		rs746860114	PDE4DIP		rs142679243
AKAP9		rs77447750	PDE4DIP
AKAP13			PDE4DIP		rs573724
AKAP4			PDE2A		rs561445982
ANKRD34A	ANKRD16	rs781900571	PDE8A		rs189073229
ANKRD65		rs574552814	PDE4C		rs149614671
ANKRD36C		rs773442285	PDE4C		rs202177222
ANKRD36C		rs768682466	PDE9A
ANKRD39		rs528356666	PEX6	PEX14	rs200115671
ANKRD36		rs751998840	PIK3CG	PIK3R3
ANKRD36		rs745923584	PLA2G2F	PLA2G6	rs368567704
ANKRD36			PLA2G4A		rs188911054
ANKRD36		rs375602706	JMJD7-PLA2G4B, PLA2G4B		rs200327143
ANKRD36		rs768585370	PLA2G4F		rs530370813
ANKRD30BL			PPRC1	PRC1	rs565951388
ANKRD17		rs534030909	PPRC1		rs145446235
ANKRD31			RAD51AP2	RAD51C	rs575308098
ANKRD31		rs776341649	RAD54L	RAD51D
ANKRD31		rs550567797	RGPD2	RANBP9
ANKRD18B			RGPD3
ANKRD26		rs200775533	RANBP2
ANKRD52		rs758974930	RANBP2
APOBEC1	APOBEC3B	rs61753204	RANBP2		rs774306184
APOBEC3G		rs183180481	RBL2	RBL2, TOX3, TNRC9	rs555878756
ARHGEF10L	ARHGEF6	rs544006964	RNF180	RNF115	rs774684839
ARHGEF4		rs373534611	RNF8		rs542382214
ARHGEF12			RNF26		rs145592178
FARP1		rs192616500	RNF10
ASTN2	ASTN2		RNF31
NUAK2	AT	rs539524885	RNF31		rs201497992
ATAD3B	ATAD5		RNF111		rs747518713
ATAD5		rs769083129	RNF40		rs769686628
BCL11A	BCL2L11		RNFT1		rs138794420
BCL2L11			RNF213		rs371441113
BABAM2	BRCA1	rs368517485	RNF225		rs187505845
CNTROB	BRCA2	rs772562149	SETBP1	SETBP1	rs748289164
CASP7	CASP8	rs376949404	SETBP1		rs529611461
CCDC24	CCDC88C	rs187616405	SLC45A1	SLC4A7	rs373884958
CCDC121		rs767258483	SLC4A4		rs377031010
CCDC141		rs77071759	SLC44A4		rs375793445
CCDC150		rs201013091	SLC45A4
CCDC136			SLC4A1		rs757478694
CCDC171			SLC44A2		rs145954566
CCDC65		rs142550817	STXBP5L	STXBP4	rs186768873
CCDC63		rs116032516	STXBP2		rs142105943
CCDC60		rs141367042	TGIF2LX	TGFBR2
CCDC169,			TNRC18	TNRC9
CCDC169-SOHLH2 CCDC168			TNRC18		rs748968031
CCDC168		rs200872789	TP53BP1	TP53	rs548813580
CCDC168		rs540907577	WDR49	WDR43
CCDC168		rs201134938	WDR17		rs142589281
CCDC168		rs116890855	WDR41		rs77995935
CCDC88C	CCDC88C		WDR60		rs748375887
CCDC33	CCDC88C	rs373900085	WDR97		rs543467041
CCDC33		rs538344642	WDR38
CCDC189		rs182267845	WDR5		rs778304067
CCDC40		rs775239960	WDR37		rs144312898
CCDC40		rs760407697	WDR89
CCDC105		rs553990912	WDR89
CCDC114		rs377507314	WDR89		rs201238690
CCDC116			WDR89
CDCA7	CDCA7	rs138353896	WDR89
CDKAL1	CDKAL1	rs553804984	WDR89
CMSS1	CMSS1	rs145645351	WDR89
MYCBP2	cMyC		WDR89
MYCBP2			WDR89		rs74383752
CNTNAP5	CNTNAP1	rs541671672	WDR89		rs200681506
CNTNAP2			WDR24		rs775417149
CNTNAP3B			WDR24
CNTNAP3B		rs1755755	WDR81		rs780532350
COX7A2	COX11	rs138092231	WDR62		rs564143230
COX15		rs201703572	WDR13
COX7A1		rs755756129	WDR44		rs200615882
CREB3L1	CREB5	rs376081099	ZBTB48	ZBTB38	rs554036434
CUX1	CUX1	rs139293638	ZBTB40		rs148301324
DCLRE1C	DCLRE1B	rs376186052	ZBTB39		rs182966445
DNAH14	DNAH11		ZC3H11B	ZC3H11A	rs2653989
DNAH14			ZC3H14		rs80289104
DNAH6		rs375106276	ZC3H11B		rs571704621
DNAH6			ZC3H12B
DNAH7		rs764776065	ZNF687	ZNF365	rs151299620
DNAH12			ZNF638
DNAH8		rs575069902	ZNF638
DNAH8			ZNF142		rs756225038
DNAH11		rs781560218	ZNF860		rs572072037
DNAH11		rs369849556	ZNF501
DNAH11		rs183489539	ZNF80		rs572397764
DNAH11			ZNF595		rs146070291
DNAH10		rs556641156	ZNF732		rs150738695
DNAH3		rs200676672	ZNF141		rs201791423
DNAH3			ZNF141		rs79227679
DNAH3		rs777262918	ZNF141		rs79869819
DNAH3		rs539288270	ZNF141
DNAJC10	DNAJC1	rs372447298	ZNF518B		rs185283370
DNAJC15		rs115128267	ZNF330		rs548266148
SREBF1	EBF1	rs775175384	ZNF622		rs200470817
PERM1	ESR	rs528106044	ZNF354A		rs575302139
ESRP1		rs528521502	ZNF165
ESRRA		rs373399001	ZNF316
ESRP2		rs777034822	ZNF479		rs201001924
MRPS25	FGF10/MRPS30	rs377446402	ZNF679		rs375602152
MRPS18A		rs750387982	ZNF680		rs188006471
MRPS23		rs369458033	ZNF107		rs184622647
MRPS12		rs147007310	ZNF107
FGFR4	FGFR2		ZNF107
FLJ44635	FLJ43663	rs199561699	ZNF107
GRHL3	GRHL1	rs754006408	ZNF138		rs551808152
GRHL2		rs746616786	ZNF804B
HSPA6	HSPA	rs200790521	ZNF425		rs781577739
HSPA9			ZNF862		rs371880003
HSPA8			ZNF705G		rs376157799
KANSL1L	KANSL1	rs374376792	ZNF7		rs75052405
KANSL1			ZNF16		rs139521477

SNP, single nucleotide polymorphism.