Comparative analysis of telomere length in peripheral blood of breast cancer BRCA1 mutation carriers and non-carriers

Abstract

Introduction

Breast cancer is the most prevalent malignancy among women worldwide. A significant portion of patients possess homologous recombination deficiency (HRD), often caused by BRCA1/2 mutations, which may sensitize tumors to PARP inhibitors and platinum-based chemotherapy through synthetic lethality. Since mutations in BRCA genes have been previously suggested in association with impaired biology of telomeres, in the present study we investigated leukocyte telomere length (LTL) to evaluate its potential utility as a biomarker for BRCA1 mutations and HRD.

Methodology

LTL was measured using multiplex monochrome real-time qPCR in four groups: breast cancer patients with pathogenic hereditary BRCA1 mutations (n = 99), age-matched non-cancerous controls carrying the same BRCA1 mutations (n = 99), breast cancer patients with wild-type BRCA1 (n = 105), and age-matched non-cancerous controls with wild-type BRCA1 (n = 107). BRCA1 mutations were tested by the DNA sequencing approach.

Results

A significant negative correlation between age and LTL was observed across all studied groups, except in breast cancer patients carrying pathogenic hereditary BRCA1 mutations. Interestingly, after adjusting for age, BRCA1 mutation carriers had shorter LTL compared to non-carriers, regardless of the presence of cancer (P = 0.024).

Conclusion

LTL shortening is associated with BRCA1 mutations, regardless of cancer status. Further validation studies are needed.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-04007-8.

Introduction

Breast cancer is one of the most commonly diagnosed cancers worldwide, with approximately 1 in 8 women expected to be diagnosed during their lifetime. It remains the leading cause of cancer-related deaths among females [1]. Individuals carrying germline pathogenic BRCA1/2 variants have a significantly higher risk of developing breast and other cancers throughout their lifetime. While the prevalence of BRCA1/2 mutations in the general population is about 0.3%, BRCA carriers face a 60% lifetime risk of developing breast cancer and are five times more likely to develop contralateral breast cancer compared to those with BRCA wild-type status, highlighting the critical role of these mutations in the disease’s pathogenesis [2].

BRCA carriers exhibit homologous recombination deficiency (HRD), leading to reliance on error-prone non-homologous end joining to repair double-stranded DNA breaks [3]. HRD testing is crucial for identifying patients who are more likely to benefit from PARP inhibitors (such as olaparib, niraparib, rucaparib, and talazoparib) and platinum-based chemotherapy [4]. It also aids in refining patient prognosis, as HRD-positive individuals generally show significantly worse overall survival [2]. Current biomarkers for assessing HRD status include the analysis of BRCA mutations and/or genomic instability markers, such as telomeric allelic imbalances and other large-scale structural chromosomal rearrangements [5]. Although studies suggest a link between HRD and altered telomere length, available results remain inconclusive [5].

Telomeres, tandem repetitive DNA sequences (5′ TTAGGG 3′ in vertebrates) along with associated protective proteins, form dynamic complexes capping the ends of chromosomes, protecting them from deterioration or fusion [6]. Leukocyte telomere length (LTL) averages around 10 kb in newborns, reflecting a heritable trait passed down from parents, and progressively shortens with each cell division [7]. Deviations in LTL, whether longer or shorter than observed in the general population, have been linked to an increased risk of various diseases, including different cancer types [8]. Telomerase, an enzyme primarily responsible for maintaining telomere length, adds telomeric repeats using its catalytic subunit hTERT and RNA template hTR. While inactive in most somatic cells, telomerase is active in stem cells and about 75% of human cancer cell lines, contributing to cellular immortalization [9].

Several studies have hypothesized a link between BRCA mutations and impaired telomere biology. For illustration, there is evidence that under physiological circumstances BRCA1 localized at telomeres through interactions with telomere-associated protein subunits and DNA repair proteins like Rad50, regulates the telomeric 3′ overhang length and directly stimulates telomerase activity [10]. Another study has shown that BRCA1 overexpression inhibits TERT expression and reduces telomerase activity, leading to telomere shortening in breast cancer cells [11]. Studies on populations documented that BRCA carriers are characterized by shorter LTL compared to non-carriers, along with differential expression of telomere-associated genes, including the Shelterin complex components. However, this concrete study included only 40 participants [12]. The next population study, including over 4,800 individuals, compared the LTL between BRCA carriers and unaffected, BRCA wild-type family members, revealed significant differences in LTL between the groups, particularly in families with BRCA2 mutations. The authors concluded that increased telomere length may result from the BRCA mutations, although it is not directly linked to a higher cancer risk [13]. These previous findings raise the question whether the telomere length is affected in BRCA carriers involving cancer patients and/ or non-cancer individuals.

Herein we hypothesize that carriers of pathogenic BRCA1 variants exhibit variations in LTL. Furthermore, we propose that noninvasive and rapid analysis of LTL in breast cancer patients could serve as an initial screening tool to identify candidates for further HRD testing, as relying solely on currently used BRCA mutation testing is insufficient to identify all patients with the HRD phenotype [3]. While HRD testing offers a more comprehensive evaluation, its implementation is limited by long turnaround times, centralized testing, and high costs, highlighting the need for new biomarkers to improve HRD testing and expand treatment accessibility. Moreover, detecting variations in LTL in BRCA carriers may help identify BRCA1 wild-type individuals with HRD, as 20–30% of breast cancer patients are believed to have HRD regardless of BRCA status [14]. This approach could aid in selecting patients who may benefit from HRD testing and therapies targeting synthetic lethality.

Methods

Studied group of patients

The study cohort comprised 410 participants and LTL was successfully analysed in all of them. The analysed groups included breast cancer patients with a germline pathogenic BRCA1 mutation (n = 99), age-matched non-cancerous individuals carrying the same BRCA1 mutation (n = 99), breast cancer patients with BRCA1 wild-type status (n = 105), and age-matched healthy female controls with BRCA1 wild-type status (n = 107). All participants were recruited in the hospital in Police, Pomerania, Poland. The more detailed clinicopathological data such as age at the time of blood sample collection, age at the breast cancer diagnosis, particular type of BRCA1 mutation, smoking status, and the status of female carriers and healthy controls whether they had undergone any hormone replacement therapy (HRT), adnexectomy, consumption of any oral contraceptive: (OC) alone or in combination with HRT (OC + HRT) and history of taking OC in the past 25 years (OC < 25 yrs) were recorded for all the patients.

All participants provided informed written consent for participation and use of their biological samples for genetic analyses. This study was conducted in accordance with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Institute of Experimental Medicine of the CAS (No. 2020/13), and by the Ethics Committee of the Institute for Clinical and Experimental Medicine and Thomayer Hospital (No. 13252/20).

The distribution of the three analysed germline BRCA1 mutations among the patients was as follows (Table 1; the number of non-cancerous controls with the same mutation is equal for comparison):

Table 1.

Provides a description of the BRCA1 mutations analysed in this study, along with the number of patients

Number of Patients (n)	Mutation	Nucleotide Change	Protein Change	Clinical Significance	Condition Associated	Description
17	c .181T > G	NM_007294.3(BRCA1): c.181T > G	p.Cys61Gly (C61G)	Pathogenic	Hereditary breast and ovarian cancer	Missense mutation leading to a cysteine-to-glycine substitution at codon 61. It disrupts the BRCA1 RING domain, crucial for its tumor suppressor function.
5	c .4035delA	NM_007294.3(BRCA1): c.4035delA	p.Glu1346Lysfs*20 (frameshift)	Pathogenic	Hereditary breast and ovarian cancer	A frameshift mutation caused by the deletion of one adenine nucleotide, resulting in a truncated, nonfunctional BRCA1 protein.
77	c.5263insC>	NM_007294.3(BRCA1): c.5263insC	p.Gln1755Profs*74 (frameshift)	Pathogenic	Hereditary breast and ovarian cancer	An insertion of a cytosine nucleotide that causes a frameshift, leading to a premature stop codon and truncated protein, impairing BRCA1 function.

BRCA1 genetic analysis

DNA was isolated and its quality assessed using the Qubit dsDNA HS Assay kit and Qubit 2.0 Fluorometer (Life Technologies). Library preparation targeted all BRCA1 coding exons and their flanking regions using the BRCA Hereditary Cancer MASTR Plus kit (Multiplicom, Agilent). Amplicon purification was performed with Agencourt AMPure XP reagent, and quality control was carried out using the High-Sensitivity DNA kit on an Agilent Bioanalyzer. Sequencing was conducted on the Illumina Miseq platform, with raw data analysed using SeqGenomize and Sophia Genetics software. Variants were annotated according to HGVS nomenclature, and pathogenicity was evaluated using SIFT, PROVEAN, and CADD scores. Mutation allele frequencies were obtained from the ExAc database, and all variants were validated by Sanger sequencing.

Measurement of telomere length

LTL was determined using the multiplex monochrome real-time qPCR method, as previously described [15–17]. In brief, the HOT FIREPol^® Probe qPCR Mix Plus ROX (Solis BioDyne, Tartu, Estonia) was used in combination with primers specific for telomeric sequences and albumin (see Supplementary Table 1), along with SYTO 9 Green Fluorescent Nucleic Acid Stain (Thermo Fisher Scientific Inc., Massachusetts, U.S.), in two sequential qPCR reactions performed on the QuantStudio™ 6 Flex Real-Time PCR System (Applied Biosystems, USA).

For the first reaction, which amplified telomeres, the PCR protocol was: (1) 95 °C for 15 min (1 cycle), (2) 95 °C for 20 s, 49 °C for 1 min (2 cycles), (3) 85 °C for 20 s, 59 °C for 30 s (25 cycles, with signal detection). For the second reaction, which amplified albumin, the PCR conditions were: (1) 95 °C for 15 s, 85 °C for 30 s, 84 °C for 30 s (35 cycles), and (2) a melt curve stage: 95 °C for 15 s, 60 °C for 1 min, 95 °C for 15 s (with gradual signal detection between 60 °C and 95 °C).

Only the PCR plates with amplification efficiencies ranging from 90 to 110% were involved for further analysis. All samples were measured in triplicate, with a Ct standard deviation (SD) ≤ 0.3, and the Ct values were averaged. Quantification of products was based on a calibration curve derived from serial dilutions of pooled genomic DNA from 30 healthy individuals (15 males and 15 females, aged 40–55). Telomere length was expressed as the telomere/albumin ratio.

Statistical analysis

LTL values were adjusted for age using linear regression analyses based on correlation with age. The Shapiro-Wilk test was utilized to evaluate normality. Differences between groups were assessed using appropriate statistical tests based on the distribution of normality and standard deviation differences; either the unpaired t-test, Welch’s t-test, or Mann-Whitney U test was employed. Statistical analyses were conducted using R Studio for linear regression, while other statistical evaluations were performed using Prism 8 GraphPad software (San Diego, CA, USA). All p-values were two-tailed, with statistical significance defined as p ≤ 0.05. The final results were visually presented using both Prism 8 GraphPad software (San Diego, CA, USA) and Statistica software (StatSoft, Inc., Tulsa, OK, USA).

Results

All the results including P values and clinicopathological details of patients are summarized in Table 2.

Table 2.

The table shows comparisons between the patient and control groups, LTL values, and clinicopathological characteristics of the studied population

	BRCA1 carriers		BRCA1 non-carriers (wild-type)
	Breast cancer patients	Non-cancerous controls	Breast cancer patients	Non-cancerous controls
Total no. of participants (n)	99 females	99 females	105 females	107 females
Mean Age (Min; Max)	42,26 (26;76)	42,15 (23;78)	60,73 (37;91)	59,64 (38;88)
Median LTL ratio [IQR]	1.09 [0.98–1.30]	1.12 [0.94–1.26]	1.13 [0.94–1.27]	1.07 [0.93–1.23]

Relative Leukocyte Telomere Length values (LTL) and Age-adjusted p values (P)
	n	Mean ± SD	Median [IQR]	P	n	Mean ± SD	Median [IQR]	P	n	Mean ± SD	Median [IQR]	P	n	Mean ± SD	Median [IQR]	P
A) BRCA1 mutation
c.181T/G	17	1,15 ± 0,21	1,15 [1,03 − 1,33]	0.869	17	1,12 ± 0,22	1,06 [0,96 − 1,25]	0.920	Not Applicable
c.4035delA	5	1,16 ± 0,29	1,03 [0,93 − 1,47]		5	1,2 ± 0,28	1,03 [0,98 − 1,51]
c.5263insC	77	1,13 ± 0,24	1,09 [0,97 − 1,26]		77	1,1 ± 0,22	1,13 [0,92 − 1,25]
B) Smoking status
Current smoker	15	1,16 ± 0,21	1,09 [0,93 − 1,34]	0.323	15	1,23 ± 0,21	1,28 [1,04 − 1,37]	0.038	24	1,1 ± 0,2	1,12 [0,95 − 1,17]	0.203	26	1,06 ± 0,19	1,01 [0,92 − 1,23]	0.379
Former smoker	19	1,21 ± 0,27	1,15 [1,02 − 1,31]		19	1,05 ± 0,14	1,07 [0,96 − 1,17]		27	1,1 ± 0,22	1,08 [0,87 − 1,36]		27	1,11 ± 0,21	1,11 [0,89 − 1,2]
Non-smoker	65	1,11 ± 0,23	1,08 [0,96 − 1,26]		65	1,1 ± 0,24	1,12 [0,91 − 1,22]		54	1,15 ± 0,23	1,17 [0,98 − 1,29]		54	1,09 ± 0,22	1,05 [0,97 − 1,24]
C) Adnexectomy
Yes	1	1,06 ± 0	1,06 [1,06 − 1,06]	-	6	0,76 ± 0,15	0,77 [0,66 − 0,89]	0.000	5	1,07 ± 0,17	1,04 [0,92 − 1,23]	0.721	5	1,02 ± 0,19	1,04 [0,86 − 1,17]	0.638
No	98	1,14 ± 0,23	1,1 [0,98 − 1,3]		93	1,13 ± 0,21	1,13 [0,97 − 1,27]		100	1,13 ± 0,22	1,14 [0,94 − 1,27]		102	1,09 ± 0,21	1,09 [0,93 − 1,23]
D) Intake of Hormones
Yes	35	1,06 ± 0,23	1,02 [0,87 − 1,25]	0.003	35	1,11 ± 0,25	1,1 [0,89 − 1,35]	0.396	28	1,16 ± 0,25	1,12 [0,93 − 1,36]	0.667	31	1,13 ± 0,22	1,12 [0,94 − 1,25]	0.329
No	64	1,18 ± 0,23	1,11 [1,04 − 1,34]		64	1,11 ± 0,21	1,13 [0,97 − 1,22]		77	1,12 ± 0,21	1,13 [0,96 − 1,26]		76	1,07 ± 0,2	1,05 [0,91 − 1,2]
E) OC, HRT, OC/HRT status*
OC	34	1,07 ± 0,22	1,03 [0,87 − 1,26]	-	32	1,13 ± 0,24	1,11 [0,91 − 1,36]	0.043	13	1,24 ± 0,27	1,28 [0,93 − 1,44]	0.375	16	1,09 ± 0,19	1,07 [0,94 − 1,19]	0.225
HRT	1	0,66 ± 0	0,66 [0,66 − 0,66]		2	0,74 ± 0,03	0,74 [0,71 − 0,76]		14	1,1 ± 0,22	1,06 [0,93 − 1,21]		14	1,15 ± 0,26	1,18 [0,93 − 1,39]
OC/HRT	0	-			1	0,97 ± 0	0,97 [0,97 − 0,97]		1	0,82 ± 0	0,82 [0,82 − 0,82]		1	1,36 ± 0	1,36 [1,36 − 1,36]
F) Oral Contraceptive intake OC < 25 yrs
Yes	22	1,13 ± 0,21	1,15 [0,92 − 1,3]	0.584	23	1,19 ± 0,24	1,19 [0,97 − 1,37]	0.226	4	1,2 ± 0,23	1,22 [0,97 − 1,41]	0.692	4	1,1 ± 0,22	1,07 [0,91 − 1,33]	0.770
No	77	1,14 ± 0,24	1,08 [0,99 − 1,3]		76	1,09 ± 0,21	1,11 [0,93 − 1,19]		101	1,12 ± 0,22	1,13 [0,94 − 1,26]		103	1,09 ± 0,21	1,06 [0,93 − 1,22]

Number of patients (n), Age-adjusted p values (P), Oral contraceptives intake (OC), Hormone Receptor Therapy (HRT)

*Numbers of patients may not add up to 100% of available subjects due to the lack of clinicopathological data

First, a correlation analysis between age and LTL was performed within each individual group. The results revealed no significant correlation in BRCA1 carriers with breast cancer (P = 0.193, r = -0.132, Spearman correlation). Similarly, no significant correlation was found in early-onset BRCA1 carriers with breast cancer (including 49% of all studied BRCA1 carriers with cancer disease; P = 0.744, r = 0.048). However, all the remaining groups showed a statistically significant inverse relationship between age and LTL (see Fig. 1), including BRCA1 non-carriers with cancer (P = 0.007, r = -0.2667), healthy BRCA1 carriers (P = 0.001, r = -0.3177), and healthy BRCA1 non-carriers (P = 0.003, r = -0.2924).

Fig. 1.

Correlation of age and LTL in BRCA1 carriers and non-carriers

After adjusting for age, no statistically significant differences in LTL were observed between the patient groups and non-cancerous controls (P = 0.395, ANOVA). Specifically, there was no difference in LTL between BRCA1 carriers with breast cancer and age-matched non-cancerous BRCA1 carriers (P = 0.56, Wilcoxon matched-pairs signed-rank test). Similarly, no difference in LTL was found between BRCA1 non-carriers with breast cancer and age-matched non-cancerous BRCA1 non-carriers (P = 0.173, paired t-test). Interestingly, no difference in LTL was observed between BRCA1 carriers and non-carriers among all studied breast cancer patients (P = 0.90, Mann-Whitney test). However, when comparing LTL between all BRCA1 carriers and non-carriers, regardless of cancer status, a significant difference was found (P = 0.024, Mann-Whitney test, Fig. 2).

Fig. 2.

Effect of BRCA1 mutation on relative LTL

Discussion

This study represents, to our best knowledge, the largest dataset investigating the effect of BRCA1 mutations on LTL in well-characterized groups of breast cancer patients carrying either pathogenic BRCA1 mutations or their non-relatives without BRCA1 mutations, who were also diagnosed with breast cancer. Additionally, a set of age-matched non-cancerous controls, similarly classified as BRCA1 mutated or BRCA1 wild-type, was included for comparison. This case-control study aims to explore LTL’s potential as a biomarker for inherited BRCA1 mutations and possibly HRD as well, ultimately indicating susceptibility to PARP inhibitors.

As we have previously summarized, BRCA1 function is crucial for proper telomere maintenance [18]. The present study is based on the premise that carriers of BRCA1 mutations have impaired DNA repair, which may lead to replicative stress and the accumulation of DNA damage in proliferating cells, potentially affecting telomere biology and resulting in significantly changed telomeres over time. This effect may also be observed in surrogate cells of BRCA1 carriers, such as lymphocytes, due to BRCA1 haploinsufficiency [19].

In this study, age was a significant modifier of LTL in all groups except for BRCA1 carriers with breast cancer. We initially suggested that these differences in telomere biology could be attributed to the high proportion of early-onset breast cancer patients in our study, given the age-dependent nature of LTL. In correlation with this, the mean age of BRCA1 carriers with cancer was 42 (including 49% of early-onset cancer patients), compared to 60 in the BRCA1 wild-type group. However, no significant correlation between LTL and age was observed within the early-onset group of patients with BRCA1 mutation. There are almost no studies investigating biology of telomeres in early-onset breast cancer patients. In one longitudinal study including 70 women aged between 23 and 71 years of age, the authors found that both race and age of the patients significantly affect the LTL (shorter telomeres) in women who are treated for early-onset breast cancer, however, the authors did not distinguish between BRCA1 carriers and non-carriers [20]. The correlation of LTL with age observed in the remaining patient and control groups in our study aligns with findings from our previous research, including study of 611 breast cancer patients, where a weak inverse linear correlation between age and LTL was identified. Furthermore, this correlation was stronger in healthy subjects [17].

Following the adjustment for age, we did not observe any differences in LTL between all the patients and non-cancerous controls irrespective of the BRCA status. This finding contrasts with our previous study including nearly 800 participants (611 breast cancer patients), where we observed longer LTL in patients compared to healthy controls [17]. In our additional study, 151 breast cancer patients exhibited significantly longer LTL compared to control individuals [21]. The differences in results between the studies may be attributed to the smaller sample sizes and variations in patient composition, as half of the patients in the current study are BRCA1 carriers, with a significant proportion being early-onset breast cancer patients. Another reason for this discrepancy remains unclear, but, as concluded by us in the former study, various confounders, including single-nucleotide polymorphisms in telomere-related genes, may influence LTL as well [17]. Overall, there are inconsistencies across studies examining LTL differences in breast cancer patients and healthy controls. However, a most recent systematic review of 44 articles found no significant difference in LTL between women with and without breast cancer [22].

Additionally, we found no statistically significant differences in LTL between cancer patients who were BRCA1 carriers and those who were non-carriers. However, shorter LTL was found in all BRCA1 carriers compared to BRCA1 non-carriers regardless of cancer status. Only a few studies so far have analysed the potential impact of BRCA mutations on LTL with inconsistent outcomes. One of these studies focused on LTL analysis in a group of 131 BRCA1 carriers, 109 BRCA2 carriers, and 43 controls, half of whom had been diagnosed with breast cancer. The authors found no significant association between BRCA mutation status and LTL, either considering BRCA1 and BRCA2 mutation carriers as separate groups, or as a combined set [23]. In another study involving 198 hereditary breast cancer patients (48 BRCA1 carriers, 45 BRCA2 carriers, and 105 non-carriers), 267 controls, and 71 sporadic breast cancer patients, the authors observed that women with BRCA mutations exhibited shorter telomeres [24]. Another study analysing LTL in 4,822 subjects including BRCA1 carriers and their relatives, representing the largest analysis of LTL in BRCA1 mutation breast cancer families so far, had not found evidence that cancer cases from BRCA1 families differ in mean LTL compared to their unaffected, BRCA1 relatives. However, the authors found that BRCA1 carriers (regardless of whether cancer-affected or unaffected) had longer mean LTL than their non-carrier relatives, particularly in families with BRCA2 mutations [13]. In contrast to our study, Pooley et al. [13] included male breast cancer patients and various types of tumors in their study, which could have caused the opposite trend in LTL. Another study involving 169 BRCA2 carriers, 561 sporadic breast cancer patients, and 537 healthy controls revealed that breast cancer cases had significantly shorter LTL compared to unaffected women, including BRCA2 carriers and non-carriers [25]. These inconsistencies in previous findings may be attributed to other modulating factors that are often not considered, including single nucleotide polymorphisms in telomerase-associated genes [17] and additional factors such as body mass index [26], psychological stress [27], physical activity [28], and sleep disorders [29], all of which have been linked to LTL across populations.

Overall, while previous studies remain inconclusive, our findings support the hypothesis that LTL may serve as a marker for inherited BRCA1 mutations, regardless of cancer status. However, further research is needed to confirm this observation.

We acknowledge several limitations of our study, the first being its retrospective design. Although the initially studied groups of patients and controls were well-balanced by BRCA1 mutation status, breast cancer presence, and age matching, further subdivision—especially based on specific BRCA1 mutations—led to small sample sizes in some subgroups, thus reducing the ability to detect differences in LTL. Another limitation is the inability to confirm HRD status in our cohort of BRCA1 mutation carriers diagnosed with cancer, as HRD testing through established assays or genomic scarring analyses was not performed. Consequently, while our study indicates a potential association between BRCA1 mutation and LTL, and suggests that LTL could act as a proxy for HRD, this relationship remains indirect due to the lack of access to HRD status.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

Funding acquisition: M.K., L.V., Measurement of the data: M.K., K.T., P.H., Writing and original draft: M.K., K.B., A.U., K.S., Review and editing: K.H., L.V., P.V., J.L.

Funding

This study was supported by the Ministry of Health of the Czech Republic in cooperation with the Czech Health Research Council under project No. NU22J-03-00028 and No. NU21-03-00145. All rights reserved. The project National Institute for Cancer Research (Programme EXCELES, ID Project No. LX22NPO5102) - Funded by the European Union – Next Generation EU and by the Czech Science Foundation grant (Project No. GX21-04607X). This work was supported by the Cooperatio Program, research area „Oncology and Haematology“.

Data availability

As the GEO database team confirmed that datasets containing only two genes do not qualify as high-throughput data and therefore cannot be added to the GEO database, we followed their suggestion and uploaded the raw data along with the manuscript as a supplementary file.

Declarations

Ethics approval and consent to participate

This study was conducted in accordance with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of the Institute of Experimental Medicine of the CAS (No. 2020/13), and by the Ethics Committee of the Institute for Clinical and Experimental Medicine and Thomayer Hospital (No. 13252/20). All participants provided informed consent to take part in this study.

Competing interests

The authors declare no competing interests.