Prolonged asbestos exposure can lead to occurrence of different asbestos-related diseases, including pleural plaques and asbestosis, as well as several cancers. Use and production of asbestos was largely banned after it was classified as a carcinogen, but it is still legally used in mostly developing countries and it can still be found in the environment.1,2 Asbestos-related diseases often occur long after initial asbestos exposure and their incidence continues to rise.1
The most problematic asbestos-related disease is malignant mesothelioma (MM), a rare but very aggressive cancer. However, only a minority of asbestos-exposed people develops MM. Other factors, such as genetic variability may contribute to carcinogenesis and development of MM.3 Among asbestos-exposed workers, several familial cases of MM were described, emphasizing that genetic factors could contribute to MM development.4 In recent years, germline BRCA1-associated protein 1 (BAP1) mutations were shown to predispose to the development of MM and other cancers. Additionally, studies suggest that numerous chromosomal deletions can accumulate in most MM cases, usually associated with the loss or inactivation of tumor suppressor genes.5,6 Despite advances in treatment, prognosis and survival of MM patients remain poor.7,8 Therefore, MM diagnosis and treatment have become increasingly focused on molecular mechanisms.9
To confirm MM diagnosis, several tumor markers are routinely analysed using immunohistochemical staining.10 One of the established immunohistochemical markers is calretinin10, a calcium binding protein and calcium sensor crucial for neuron function that is also expressed on mesothelial cells.11 It has been shown to affect mesothelial cell proliferation and migration and epithelialto-mesenchymal transition. It was also associated with focal adhesion kinase signaling pathway and signaling pathways associated with response to asbestos.12 Calretinin is encoded by the
As MM diagnosis is usually made when the disease is already advanced, blood-based biomarkers such as mesothelin and fibulin-3 that would enable an earlier diagnosis and better prognosis of MM are extensively studied.14,15 Recently, calretinin was also proposed as a soluble biomarker in MM, as increased plasma or serum levels were observed in MM patients compared to subjects with other asbestos-related diseases or healthy controls.8,16,17,18 However, interindividual variability limits the sensitivity and specificity of calretinin as a diagnostic biomarker and several clinical characteristics were previously associated with soluble calretinin levels.19 Low tumor calretinin expression was associated with lower protein concentration in the bloodstream, but there was no clear correlation with tumor size.20 Higher calretinin concentrations were observed in patients with epithelioid or biphasic MM compared to patients with sarcomatoid MM.8,20,21 Calretinin levels were also higher in women compared to men and in subjects with renal dysfunction.22
Molecular mechanisms regulating calretinin expression in various tissues or in cancer could also contribute to interindividual variability of serum calretinin concentration, but the knowledge of these processes is limited.23 Calretinin expression may be affected by several factors, including transcription factors or miRNAs. Among transcription factors, calretinin expression was found to be influenced by septin 7, E2F transcription factor 2 (E2F2) and nuclear respiratory factor 1 (NRF-1) in previous studies.23,24 Additionally, miR-335-5p was proposed as a regulator of
Our aim was to determine whether genetic polymorphisms in the
Our retrospective study included patients with MM, subjects with asbestosis, subjects with pleural plaques, and subjects that were occupationally exposed to asbestos but, did not develop any asbestos-related disease.
Patients with MM were treated at the Institute of Oncology Ljubljana between November 2001 and March 2019. The diagnosis of pleural or peritoneal MM was performed by thoracoscopy or laparoscopy, respectively, and confirmed histologically by an experienced pathologist, mostly in others tertiary institutions in Slovenia. Stage of MM was determined using the TNM staging system for pleural MM. Performance status of MM patients was determined using Eastern Cooperative Oncology Group (ECOG) scores.
Subjects with asbestosis, subjects with pleural plaques and asbestos-exposed subjects who did not develop any asbestos-related disease were selected from a cohort of occupationally exposed workers who were evaluated by the State Board for the Recognition of Occupational Asbestos Diseases at the Clinical Institute of Occupational, Traffic and Sports Medicine in Ljubljana between September 1998 and April 2007. The diagnosis of asbestos-related diseases was based on the Helsinki Criteria for Diagnosis and Attribution of Asbestos Diseases31 and the American Thoracic Society recommendations.32 Follow-up was performed for all subjects in 2018 to confirm they did not develop any other asbestos-related disease.
For all subjects, data on demographic (sex, age, smoking) and clinical characteristics were obtained from the medical records or during an interview. All participants provided written informed consent. The study has been approved by the National Medical Ethics Committee of the Republic of Slovenia (31/07/04, 39/04/06 and 41/02/09) and was carried out according to the Declaration of Helsinki.
Using bioinformatic analysis, we identified common SNPs that could affect calretinin expression: SNPs in the 5′ UTR and 3′ UTR of the calretinin gene (
Using LD Tag SNP Selection tool34 and dbSNP database35, we identified all SNPs in 5′ UTR, 3′ UTR and near gene regions (± 1000 base pairs) of
Genomic DNA was extracted from peripheral venous blood samples using Qiagen FlexiGene Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. For a subset of subjects that did not develop any asbestos-related disease, DNA was extracted from capillary blood samples collected on Whatman FTA cards using MagMaxTM DNA Multi-Sample Kit (Applied Biosystems, Foster City, California, USA). The genotyping of all selected SNPs was carried out using a fluorescence-based competitive allele-specific polymerase chain reaction (KASP) assay, according to the manufacturer's instructions (LGC Genomics, UK). For all SNPs, 15% of samples were genotyped in duplicates. Genotyping quality control criteria were 100% duplicate call rate and 95% SNP-wise call rate.
Serum samples were collected at diagnosis for MM patients and at inclusion in the study for all other subjects. Samples were prepared within 6 hours of blood collection and stored at −20°C. Serum calretinin levels were determined using a commercially available enzyme-linked immunosorbent Calretinin ELISA assay (DLD Diagnostika GmbH, Germany) according to the manufacturer's instructions as previously described.8,16,21
Continuous and categorical variables were described using median with interquartile range and frequencies, respectively. Nonparametric Mann-Whitney test or Kruskal-Wallis test with post hoc Bonferroni corrections for pairwise comparisons were used to compare the distribution of continuous variables. Chi square test was used to compare the distribution of categorical variables among different groups and to evaluate deviation from Hardy-Weinberg equilibrium. For all investigated SNPs, both additive and dominant models were used in the analysis. Univariate and multivariate logistic regression was used to compare genotype frequencies between groups and to determine odds ratios (ORs) and 95% confidence intervals (CIs). Demographic and clinical parameters, significantly associated with asbestos-related disease susceptibility in univariate analysis, were used for adjustment in multivariate models. Receiver operating characteristic (ROC) curve analysis was used to determine area under the curve (AUC), sensitivity and specificity. Cut-off values were determined as the values with the highest sum of sensitivity and specificity. All statistical tests were two-sided and the level of significance was set at 0.05. The statistical analyses were carried out by using IBM SPSS Statistics version 27.0 (IBM Corporation, Armonk, NY, USA). To assess the combined effect of all
Among 904 subjects included in our study, 288 (31.9%) had MM. Among 616 non-MM subjects that were occupationally exposed to asbestos, 153 subjects had asbestosis, 380 subjects had pleural plaques and 83 did not develop any asbestos-related disease. Characteristics of each subject group are presented in Table 1. Patients with MM were older than all other groups (P < 0.001), but there were no significant differences regarding sex (P = 0.180) and smoking (P = 0.205).
Clinical characteristics of the subjects included in the study
Sex | Male, N (%) | 61 (73.5) | 262 (68.9) | 119 (77.8) | 213 (74.0) | 0.1801 |
Female, N (%) | 22 (26.5) | 118 (31.1) | 34 (22.2) | 75 (26.0) | ||
Age | Median (25%–75%) | 53.4 (48.5–59.2) | 54.8 (48.8–62.7) | 59.4 (51.3–66.1) | 66.0 (59–73) | < 0.0012 |
Smoking | No, N (%) | 46 (55.4) | 187 (49.2) | 74 (48.4) | 158 (56.4) [8] | 0.2051 |
Yes, N (%) | 37 (44.6) | 193 (50.8) | 79 (51.6) | 122 (43.6) |
calculated using chi-square test;
calculated using Kruskal-Wallis test.
Number of missing data is presented in [] brackets.
MM = malignant mesothelioma
Among patients with MM, 217 (75.3%) patients had epithelioid histological type, 26 (9.0%) patients had biphasic type, and 26 (9.0%) patients had sarcomatoid type, while histological type could not be determined in 19 (6.6%) patients. According to cancer stage, 19 (6.6%) patients had stage 1 MM, 63 (22.0%) patients had stage 2 MM, 85 (29.6%) patients had stage 3 MM, and 87 (30.3%) patients had stage 4 MM, while no data were available for one patient. Additionally, 33 (11.5%) patients had peritoneal MM. Regarding ECOG performance status, 18 patients (6.3%) had score 0, 142 (49.5%) score 1, 110 (38.3%) score 2 and 17 (5.9%) score 3, while no data was available for one patient.
Based on available literature and publicly available databases, we identified genes and SNPs that could influence calretinin expression and serum levels: SNPs in 5′ and 3′ UTR of
In total, seven SNPs fulfilling all inclusion criteria were included in the study:
Genotype frequencies of investigated single nucleotide polymorphisms (SNPs) in the whole study group, their variant allele frequency (VAF) and agreement with Hardy-Weinberg equilibrium (HWE) in subjects without any asbestos-related disease (controls)
rs1862818 | c.-828C>T | May influence transcription factor binding, may alter chromatin states and regulatory motifs | CC | 479 (53.0) | 0.27 | 0.617 | |
CT | 346 (38.3) | ||||||
TT | 79 (8.7) | ||||||
rs889704 | c.-634C>A | May influence transcription factor binding, may alter chromatin states and regulatory motifs | CC | 708 (78.4) [1] | 0.14 | 0.814 | |
CA | 182 (20.2) | ||||||
AA | 13 (1.4) | ||||||
rs8063760 | c.*138T>C | May influence miRNA binding, may alter regulatory motifs | CC | 527 (58.4) [2] | 0.23 | 0.322 | |
CT | 319 (35.4) | ||||||
TT | 56 (6.2) | ||||||
rs2075995 | c.678C>A, p.Gln226His | Nonsynonymous, may influence splicing | CC | 187 (20.7) | 0.61 | 0.209 | |
CA | 468 (51.8) | ||||||
AA | 249 (27.5) | ||||||
rs3807348 | g.130496266G>A | Downstream transcript variant, may influence transcription factor binding | GG | 228 (25.3) [3] | 0.49 | 0.376 | |
GA | 446 (49.5) | ||||||
AA | 227 (25.2) | ||||||
rs13241028 | c.*1321T>C | May influence miRNA binding | TT | 547 (60.5) | 0.22 | 0.061 | |
TC | 313 (34.6) | ||||||
CC | 44 (4.9) | ||||||
rs3801339 | c.1168-4451T>C | Genic downstream transcript variant1 | TT | 164 (18.1) | 0.63 | 0.187 | |
TC | 401 (44.4) | ||||||
CC | 339 (37.5) |
previously classified as a nonsynonymous variant.
Number of missing data is presented in [] brackets.
A = adenine; C = cytosine; G = guanine; SNP = single nucleotide polymorphisms; T = thymine
In the whole study group, we evaluated if selected polymorphisms were associated with MM susceptibility. Genotype frequencies in MM patients and subjects without MM and are presented in Table 3. Carriers of two polymorphic
Association of investigated single nucleotide polymorphisms (SNPs) with malignant mesothelioma (MM) susceptibility
CC | 340 (55.2) | 139 (48.3) | Reference | Reference | |||
CT | 226 (36.7) | 120 (41.7) | 1.30 (0.97–1.75) | 0.084 | 1.35 (0.97–1.87) | 0.073 | |
TT | 50 (8.1) | 29 (10.1) | 1.42 (0.86–2.34) | 0.169 | 1.34 (0.77–2.32) | 0.299 | |
CT+TT | 276 (44.8) | 149 (51.7) | 1.32 (1.00–1.75) | 0.052 | 1.35 (0.99–1.83) | 0.059 | |
CC | 485 (78.9) [1] | 223 (77.4) | Reference | Reference | |||
CA | 121 (19.7) | 61 (21.2) | 1.10 (0.78–1.55) | 0.602 | 1.03 (0.70–1.51) | 0.899 | |
AA | 9 (1.5) | 4 (1.4) | 0.97 (0.29–3.17) | 0.955 | 0.55 (0.15–1.94) | 0.349 | |
CA+AA | 130 (21.1) | 65 (22.6) | 1.09 (0.78–1.52) | 0.626 | 0.98 (0.67–1.42) | 0.912 | |
CC | 352 (57.3) [2] | 175 (60.8) | Reference | Reference | |||
CT | 222 (36.2) | 97 (33.7) | 0.88 (0.65–1.19) | 0.398 | 0.91 (0.66–1.26) | 0.576 | |
TT | 40 (6.5) | 16 (5.6) | 0.80 (0.44–1.48) | 0.483 | 0.82 (0.42–1.60) | 0.554 | |
CT+TT | 262 (42.7) | 113 (39.2) | 0.87 (0.65–1.15) | 0.329 | 0.90 (0.65–1.23) | 0.493 | |
CC | 117 (19.0) | 70 (24.3) | Reference | Reference | |||
CA | 319 (51.8) | 149 (51.7) | 0.78 (0.55–1.11) | 0.171 | 0.83 (0.56–1.23) | 0.349 | |
AA | 180 (29.2) | 69 (24.0) | 0.64 (0.43–0.96) | 0.68 (0.44–1.07) | 0.093 | ||
CA+AA | 499 (81.0) | 218 (75.7) | 0.73 (0.52–1.02) | 0.067 | 0.78 (0.53–1.13) | 0.182 | |
GG | 158 (25.8) [3] | 70 (24.3) | Reference | Reference | |||
GA | 307 (50.1) | 139 (48.3) | 1.02 (0.72–1.44) | 0.902 | 1.00 (0.68–1.46) | 0.98 | |
AA | 148 (24.1) | 79 (27.4) | 1.20 (0.81–1.78) | 0.352 | 1.22 (0.79–1.87) | 0.376 | |
GA+AA | 455 (74.2) | 218 (75.7) | 1.08 (0.78–1.50) | 0.636 | 1.07 (0.75–1.52) | 0.724 | |
TT | 374 (60.7) | 173 (60.1) | Reference | Reference | |||
TC | 210 (34.1) | 103 (35.8) | 1.06 (0.79–1.43) | 0.699 | 1.08 (0.78–1.50) | 0.636 | |
CC | 32 (5.2) | 12 (4.2) | 0.81 (0.41–1.61) | 0.550 | 0.92 (0.44–1.93) | 0.823 | |
TC+CC | 242 (39.3) | 115 (39.9) | 1.03 (0.77–1.37) | 0.853 | 1.06 (0.78–1.45) | 0.711 | |
TT | 109 (17.7) | 55 (19.1) | Reference | Reference | |||
TC | 266 (43.2) | 135 (46.9) | 1.01 (0.68–1.48) | 0.976 | 1.05 (0.69–1.61) | 0.815 | |
CC | 241 (39.1) | 98 (34.0) | 0.81 (0.54–1.20) | 0.291 | 0.76 (0.49–1.18) | 0.218 | |
TC+CC | 507 (82.3) | 233 (80.9) | 0.91 (0.64–1.30) | 0.610 | 0.91 (0.61–1.35) | 0.627 |
Number of missing data is presented in [] brackets.
A = adenine; Adj = adjusted for age; C = cytosine; CI = confidence interval; G = guanine; OR = odds ratio; T= thymine
Serum calretinin concentration was determined in 545 subjects. Calretinin concentration significantly differed among subject groups (P < 0.001): MM patients (N = 163) had median calretinin concentration 0.52 (0.23–1.43) ng/ml, subjects with asbestosis (N = 117) 0.13 (0.08–0.20) ng/ml, subjects with pleural plaques (N = 195) 0.18 (0.12–0.25) ng/ml and subjects without disease (N = 70) 0.12 (0.07–0.19) ng/ml.
The association of selected SNPs with serum calretinin concentration is presented in Table 4 and Figure 1. In all subjects, carriers of at least one polymorphic
Association of selected single nucleotide polymorphisms (SNPs) with serum calretinin concentration:
Association of selected SNPs with serum calretinin concentration
CC | 0.18 (0.11–0.34) | 0.622 | 0.422 | 0.15 (0.09–0.22) | 0.751 | 0.865 | 0.64 (0.22–1.45) | 0.952 | 0.802 | |
CT | 0.19 (0.11–0.41) | 0.16 (0.09–0.24) | 0.51 (0.23–1.41) | |||||||
TT | 0.18 (0.10–0.37) | 0.13 (0.08–0.20) | 0.38 (0.21–3.57) | |||||||
CT+TT | 0.19 (0.11–0.40) | 0.15 (0.09–0.24) | 0.48 (0.23–1.43) | |||||||
CC | 0.19 (0.11–0.37) | 0.099 | 0.15 (0.10–0.23) | 0.130 | 0.069 | 0.52 (0.25–1.43) | 0.508 | 0.441 | ||
CA | 0.17 (0.08–0.27) | 0.16 (0.08–0.21) | 0.44 (0.14–1.35) | |||||||
AA | 0.21 (0.05–0.77) | 0.10 (0.02–0.21) | 1.07 (0.28–1.84) | |||||||
CA+AA | 0.17 (0.08–0.28) | 0.14 (0.07–0.21) | 0.50 (0.15–1.51) | |||||||
CC | 0.18 (0.11–0.38) | 0.955 | 0.770 | 0.14 (0.09–0.22) | 0.382 | 0.647 | 0.53 (0.24–1.44) | 0.326 | 0.768 | |
CT | 0.18 (0.12–0.32) | 0.16 (0.1–0.24) | 0.44 (0.19–1.30) | |||||||
TT | 0.21 (0.06–0.51) | 0.12 (0.05–0.22) | 0.86 (0.50–2.30) | |||||||
CT+TT | 0.19 (0.11–0.34) | 0.16 (0.09–0.24) | 0.51 (0.21–1.43) | |||||||
CC | 0.19 (0.10–0.46) | 0.512 | 0.481 | 0.14 (0.08–0.2) | 0.161 | 0.059 | 0.72 (0.33–1.45) | 0.189 | 0.117 | |
CA | 0.18 (0.12–0.34) | 0.16 (0.1–0.23) | 0.53 (0.20–1.48) | |||||||
AA | 0.18 (0.10–0.33) | 0.14 (0.09–0.24) | 0.40 (0.18–0.90) | |||||||
CA+AA | 0.18 (0.11–0.34) | 0.15 (0.1–0.23) | 0.48 (0.20–1.44) | |||||||
GG | 0.18 (0.09–0.34) | 0.057 | 0.151 | 0.14 (0.08–0.2) | 0.081 | 0.44 (0.26–1.43) | 0.400 | 0.978 | ||
GA | 0.18 (0.11–0.34) | 0.14 (0.09–0.22) | AA |
0.50 (0.18–1.16) | ||||||
AA | 0.21 (0.13–0.39) | 0.18 (0.11–0.26) | 0.65 (0.27–1.80) | |||||||
GA+AA | 0.19 (0.11–0.37) | 0.15 (0.1–0.23) | 0.52 (0.22–1.44) | |||||||
TT | 0.19 (0.12–0.36) | 0.272 | 0.144 | 0.16 (0.1–0.23) | 0.096 | 0.52 (0.21–1.15) | 0.381 | 0.672 | ||
TC | 0.18 (0.10–0.33) | 0.14 (0.08–0.21) | 0.64 (0.25–1.67) | |||||||
CC | 0.17 (0.07–0.36) | 0.15 (0.07–0.3) | 0.24 (0.07–1.18) | |||||||
TC+CC | 0.18 (0.09–0.34) | 0.14 (0.08–0.21) | 0.46 (0.24–1.53) | |||||||
TT | 0.18 (0.11–0.34) | 0.403 | 0.419 | 0.14 (0.09–0.2) | 0.424 | 0.288 | 0.35 (0.17–1.05) | 0.079 | 0.080 | |
TC | 0.18 (0.11–0.33) | 0.15 (0.09–0.22) | 0.51 (0.21–1.23) | |||||||
CC | 0.20 (0.11–0.45) | 0.16 (0.09–0.25) | 0.72 (0.38–1.48) | |||||||
TC+CC | 0.19 (0.11–0.37) | 0.15 (0.09–0.23) | 0.64 (0.26–1.45) |
A = adenine; Add = additive model, calculated using Kruskal-Wallis test; C = cytosine; Dom = dominant model, calculated using Mann-Whitney test; G = guanine; MM = malignant mesothelioma, SNP = single nucleotide polymorphism, T = thymine
Association of selected SNPs with serum calretinin concentration in subjects with asbestosis, subjects with pleural plaques and subjects without disease is shown in Supplementary Table 3. In subjects without asbestos-related disease, carriers of at least one polymorphic
Receiver operating characteristic (ROC) curve analysis according to individual genotypes for selected single nucleotide polymorphisms: comparison of malignant mesothelioma (MM) patients with all other subjects
Overall analysis in the whole group | / | 0.825 (0.781–0.868) | < 0.001 | 0.32 | 0.681 | 0.887 |
CC | 0.831 (0.782–0.880) | < 0.001 | 0.32 | 0.695 | 0.876 | |
CA | 0.779 (0.667–0.891) | < 0.001 | 0.31 | 0.607 | 0.935 | |
AA2 | 0.958 (0.837–1.000) | 0.019 | 0.21 | 1.000 | 0.833 | |
CA+AA | 0.801 (0.702–0.901) | < 0.001 | 0.31 | 0.625 | 0.940 | |
CC | 0.906 (0.845–0.968) | < 0.001 | 0.26 | 0.810 | 0.903 | |
CA | 0.803 (0.736–0.869) | < 0.001 | 0.32 | 0.671 | 0.888 | |
AA | 0.781 (0.686–0.876) | < 0.001 | 0.33 | 0.615 | 0.877 | |
CA+AA | 0.797 (0.742–0.851) | < 0.001 | 0.32 | 0.653 | 0.881 | |
GG | 0.853 (0.766–0.940) | < 0.001 | 0.29 | 0.757 | 0.872 | |
GA | 0.803 (0.739–0.867) | < 0.001 | 0.32 | 0.643 | 0.892 | |
AA | 0.845 (0.765–0.925) | < 0.001 | 0.35 | 0.738 | 0.881 | |
GA+AA | 0.815 (0.764–0.866) | < 0.001 | 0.32 | 0.675 | 0.881 | |
TT | 0.812 (0.754–0.871) | < 0.001 | 0.32 | 0.693 | 0.884 | |
TC | 0.868 (0.804–0.931) | < 0.001 | 0.23 | 0.818 | 0.798 | |
CC3 | 0.664 (0.406–0.922) | 0.203 | 0.18 | 0.714 | 0.700 | |
TC+CC | 0.842 (0.777–0.907) | < 0.001 | 0.23 | 0.790 | 0.785 |
Cut-off with the highest sum of sensitivity and specificity;
based on 10 subjects,
based on 27 subjects.
A = adenine; AUC = area under the curve; C = cytosine; G = guanine; SNP = single nucleotide polymorphism; T = thymine
Analysis of
Association of
CCC | 0.457 | 0.431 | Reference | Reference | |||
TCC | 0.245 | 0.294 | 1.26 (0.0–991.60) | 0.061 | 1.26 (0.97–1.64) | 0.084 | 0.272 |
CCT | 0.176 | 0.147 | 0.88 (0.65–1.20) | 0.415 | 0.94 (0.66–1.34) | 0.731 | 0.125 |
CAT | 0.058 | 0.066 | 1.21 (0.77–1.89) | 0.408 | 1.08 (0.64–1.81) | 0.782 | 0.731 |
CAC | 0.045 | 0.047 | 1.11 (0.64–1.91) | 0.713 | 0.99 (0.55–1.79) | 0.974 | 0.852 |
The SNPs are ordered from the 5′- to 3′-end as follows: rs1862818, rs889704, rs8063760.
A = adenine; Adj = adjusted for age, C = cytosine; CI = confidence interval; MM = malignant mesothelioma; OR = odds ratio; SNP = single nucleotide polymorphism; T = thymine
In the present study, we evaluated the role of genetic variability in
Using bioinformatic analysis, we identified seven common putatively functional SNPs that could affect calretinin expression: three SNPs in
Among
Three important transcription factors were previously associated with regulation of calretinin.23,24 E2F2 is a transcription factor that binds to
The second important calretinin-related transcription factor is NRF-1. It binds to
Septin 7 has also been identified as a factor that binds to the
MiRNAs affect gene expression on the post-transcriptional level and are often deregulated in cancer.58 Among miRNAs predicted to modify calretinin expression, common polymorphisms were only described for miR-335. In our study, carriers of two polymorphic
As several genetic factors were associated with calretinin, we also evaluated how these factors influence serum calretinin cut off values. We found that four SNPs,
This is the first study to show that genetic factors can affect serum calretinin levels and that accounting for these genetic factors may improve the predictive value of serum calretinin. We have also shown that genetic factors associated with calretinin may play a role in the development of mesothelioma. A limitation of our study is that we only had serum calretinin concentrations available for a subgroup of participants included in the study. On the other hand, we performed a comprehensive analysis of the factors that could affect calretinin expression using literature review and detailed bioinformatics analysis. Genetic variability was evaluated in a large cohort, which gives additional power to the study. However, other polymorphisms in the investigated genes could also affect calretinin concentration and other factors could affect calretinin regulation. In the future, further studies in this field and validation of these results in an independent population are needed.
The present study showed that genetic variability in