BioMed Research International

BioMed Research International / 2020 / Article

Research Article | Open Access

Volume 2020 |Article ID 8465971 | 9 pages |

NOTCH3 T6746C and TP53 P72R Polymorphisms Are Associated with the Susceptibility to Diffuse Cutaneous Systemic Sclerosis

Academic Editor: Francesco Del Galdo
Received18 Sep 2019
Revised15 Dec 2019
Accepted21 Jan 2020
Published25 Feb 2020


Introduction. NOTCH pathway and TP53 protein are involved in the development of fibrosis and autoimmune disorders, respectively. The aim of this study was to evaluate the role of single nucleotide polymorphisms (SNPs) of NOTCH3 and TP53 genes and serum anti-TP53 antibodies with the susceptibility, clinical subset of systemic sclerosis (SSc), and clinical profile of SSc patient, particularly with lung involvement and disease activity. Objects and Methods. 124 white Polish SSc patients (101 with limited cutaneous SSc–lcSSc, and 23 with diffuse cutaneous SSc–dcSSc) and 100 healthy individuals were included in the study. Patients were assessed for the presence of autoantibodies and interstitial lung disease. Two SNPs at position 6746 of NOTCH3 gene (C/T alleles) and 215 of the TP53 gene (P/R alleles) were genotyped by PCR-restriction fragment length polymorphism. Serum levels of anti-TP53 antibodies were analyzed by means of ELISA. Results. The genotypic frequencies of the NOTCH3 gene for SSc patients diverged significantly from Hardy–Weinberg equilibrium (; χ2 = 4.63). There was no significant difference between SSc patients and the control population in allele frequencies of both SNPs. The CT + CC genotypes of NOTCH3 influenced the susceptibility to SSc (OR = 1.85, ), including dcSSc (OR = 3.43, ), and active form of SSc (OR = 5.46, ). The PR + RR genotypes of the TP53 gene were associated only with dcSSc susceptibility (OR = 3.30, ). The levels of anti-TP53 antibodies were not related to studied SNPs and clinical parameters of SSc including the presence of specific antibodies and interstitial lung disease. Conclusion. The CT + CC genotypes of NOTCH3 gene and PR + RR genotypes of the TP53 gene increased the risk of dcSSc development. Moreover, genotypes of CT + CC were associated with the active form of SSc suggesting the role of the NOTCH pathway in the pathogenesis of this disease.

1. Introduction

Systemic sclerosis (SSc) is a connective tissue disease characterized by vascular dysfunction, the presence of autoantibodies, and inflammatory-driven fibrosis of the skin and internal organs [1, 2]. The disease manifests clinically as limited cutaneous SSc (lcSSc) or diffuse cutaneous SSc (dcSSc) distinguished mainly on the pattern of skin involvement: lcSSc form is characterized by skin involvement restricted to hands, face, forearms, and feet, whereas in dcSSc skin sclerosis extends proximal to the elbow and may involve truncal areas [35]. Interstitial lung disease is observed in up to 50% of SSc patients and is featured by activation of the NOTCH pathway involved in the differentiation of myofibroblasts [6, 7]. These cells are characterized by high proliferative capacity, which produce more extracellular matrix and in many cases do not respond to apoptotic signals [7].

NOTCH pathway is a conserved signaling system mediating cell differentiation, proliferation, survival, and apoptosis [8]. The NOTCH3 receptor regulates T-cell differentiation, which may be associated with autoimmunity [9]. NOTCH3 gene (locus 19p13.12) encodes type I transmembrane receptor protein [10]. The role of single nucleotide polymorphisms (SNPs) in the coding sequence of NOTCH3 gene remains unknown. The most common SNP, present in exon 33 (T6746C), causes a substitution of T (T allele) by C (C allele) (GTG to GCG) and results in the exchange of valine to alanine in protein chain (Val2223Ala). The residue 2223 is located in the intracellular domain, which is thought to play a role in signal transduction associated with lung fibrosis or active SSc [7]. The role of this SNP in SSc was previously not analyzed.

Auto-TP53 antibodies are detected in certain autoimmune disorders including SSc [11]. TP53 protein acts as a transcription factor, which regulates the expression of genes involved in cell cycle progression, cell growth, and apoptosis. It is encoded by the TP53 gene (locus 17p13.1). The most common studied SNP (rs1042522) is located in codon 72 (exon 4) of the TP53 gene and is associated with the presence of nucleotide with G or C (CGC to CCC). This leads to a replacement of amino acid Arg (R) with Pro (P) in protein structure [12]. The allele encoding Arg (R allele–wild type allele) was shown to induce apoptosis more effectively than the P allele [13]. Increased expression of TP53 is consistent with a higher level of apoptosis [14].

NOTCH3 and TP53 signaling pathways are important in cell fate [15, 16]. The combination of common SNPs might influence both the susceptibility to the disease and specific features of the SSc phenotype [17].

The aim of our study was to evaluate possible associations of NOTCH3 and TP53 SNPs with levels of anti-TP53 antibody, clinical subsets of SSc, clinical profile of SSc patients, particular lung involvement, and disease activity.

2. Material and Methods

2.1. Patients and Samples

The study comprised 124 consecutive adult SSc patients and 100 healthy blood donors. The inclusion and exclusion criteria for SSc patients and healthy blood donors are shown in Table 1. The patients were hospitalized in the Department of Dermatology, Venerology and Pediatric Dermatology of the Medical University of Lublin between June 2017 and March 2019. All patients fulfilled the American Rheumatism Association diagnostic criteria [18, 19]. Ethical approval was obtained from the Bioethics Committee of Medical University of Lublin [KE-0254/145/2017] and each patient signed an informed consent form according to the Helsinki Declaration.

Inclusion criteriaExclusion criteria

SSc patients(i) Adult patients
(ii) Signed informed consent
(iii) Diagnosed with SSc and treated in the Chair and Department of Dermatology, Venerology and Pediatric Dermatology, Medical University of Lublin, Poland
(iv) Unrelated individuals
(v) Caucasian race from South-Western Poland
(i) Other connective tissue diseases, SSc-like illnesses related to exposures or ingestions
(ii) Non-Caucasian in race

Control group(i) Adult, healthy blood donors
(ii) Signed informed consent
(iii) Agreed to have blood donated and stored for research in Regional Blood Donation and Blood Treatment Center in Kielce, Poland
(iv) Caucasian race from South-Western Poland
(i) Known to be infected with HIV, syphilis, tuberculosis, hepatitis B or hepatitis C
(ii) A condition in which repeated blood draws or injections pose more than minimal risk for the subject such as hemophilia, other severe coagulation disorders, or significantly impaired venous access
(iii) A condition that requires active medical intervention or monitoring to avert serious danger to the participant's health or well-being
(iv) Non-Caucasian in race

The study population was divided into two groups—lcSSc (n = 101) and dcSSc (n = 23)—according to the classification criteria for systemic sclerosis subsets [5, 20]. Furthermore, based on the disease duration from the first non-Raynaud symptom, SSc patients were divided into an early (<5 years for lcSSc and <3 years for dcSSc) and late (>5 years for lcSSc and >3 years for dcSSc) stage of SSc (n = 41 and n = 83, respectively) [21]. Routine laboratory and imaging diagnostic tests were performed to determine the disease activity and lung involvement. Levels of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), and complement components 3 and 4 (C3 and C4) were analyzed. Lung involvement was evaluated by high-resolution computed tomography (HRCT), spirometry, and diffusing lung capacity for carbon monoxide (DLCO). To determine disease activity, we used a 10-point activity index for SSc developed by the European Scleroderma Study Group (EScSG). Results of ≥3 points were indicative for active disease [22]. All patients were tested for circulating autoantibodies, such as antinuclear antibodies (ANA), anticentromere antibodies (ACA), anti-topoisomerase I (anti-Scl-70), and anti-polymerase RNAIII antibodies, following standard methods. SSc patients’ characteristics are shown in Table 2.

SSc featuresAll SSc patients n = 124SSc subtypesNOTCH3TP53
lcSSc n = 101dcSSc n = 23 valueTT n = 97CT + CC n = 27 valueRR n = 72PR + PP n = 52 value

Age in years, M56.758.9648.26<0.00156.6258.220.5256.8056.70.96
Active disease, n (%)91 (73.4)73 (72.2)18 (78.3)0.5674 (76.3)10 (37)<0.00118 (25)15 (28.8)0.63
Inactive disease, n (%)33 (26.6)28 (27.8)5 (21.7)23 (23.7)17 (63)54 (75)37 (71.2)
Disease duration in years, M8.7511.1211.980.5911.0612.050.5111.4210.810.63
Early lcSSc, M4.123.703.570.773.254.110.022
Late lcSSc, M1313.7814.100.8414.9212.180.056
Early dcSSc, M2.502.252.5
Late dcSSc, M14.5514.8713.00.5114.6414.250.88

Antinuclear antibodies, n (%)
 Anti-Scl-70 (anti-topoisomerase I) positivity66 (53.2)43 (42.5)23 (100)<0.00148 (49.5)18 (66.7)0.1142 (58.4)24 (46.1)0.17
 ACA positivity49 (39.6)49 (48.5)0<0.00143 (44.3)6 (22.3)0.00325 (34.7)24 (46.1)0.19
 Anti-RNA polymerase III positivity2 (1.6)2 (2)02 (2)002 (3.8)
 Other (anti-fibrillarin, ANA-speckled pattern)7 (5.6)7 (7)04 (4.1)3 (11)5 (6.9)2 (3.8)
 Complement protein C3 level (g/L), M1.
 Complement protein C4 level (g/L), M0.
 C-reactive protein level (mg/L), M7.757.677.930.936.9310.540.216.458.580.34
 Erythrocyte sedimentation rate (mm/h), M24.022.5528.300.1522.4927.660.1722.2125.360.33
 Interstitial lung disease, n (%)98 (79)79 (78.2)19 (82.6)76 (78.4)22 (81.5)0.7355 (76.4)43 (82.7)0.39
 Total lung capacity (TLC)—% of norm97 (15.5)89.284.350.3187.4790.260.4088.9486.620.55
 TLC ≥ 80%8976130.0769200.7651380.77
 TLC < 80%3525102872114

M: mean. Too small group for analysis.

Control peripheral blood samples were collected from 100 adult, healthy blood donors (50 males and 50 females) attending the Regional Blood Donation and Blood Treatment Center in Kielce, Poland.

2.2. DNA Isolation

DNA isolation from peripheral blood was performed using a commercial kit (Qiagen, Germany) according to the manufacturer’s procedure. The concentration and quality of DNA were checked using NanoDrop device (Thermo Fisher Scientific, USA). A total of 124 SSc patients and 100 healthy blood donors were genotyped.

2.3. NOTCH3 and TP53 Genotyping

Two polymorphisms were assessed by PCR-restriction fragment length polymorphism (RFLP). Each PCR mix (25 μl) contained 150 ng genomic DNA, PCR buffer (Clontech Laboratories, USA), dNTPs mix (0.25 mM), HD polymerase (Clontech Laboratories, USA), and primers (10 μM of each). The mix was heated to 94°C for 5 min and underwent 35 cycles of amplification for NOTCH3 and TP53: denaturation 98°C for 10 s and 94°C for 15 s, annealing 64°C for 10 s and 55°C for 10 s, and elongation 72°C for 20 s and 72°C for 30 s, respectively. The final elongation took 5 min at 72°C. The PCR reaction was performed using an Applied Biosystems 9700 Thermal Cycler. The following primers were used in PCR reaction:(i)NOTCH3forward 5′-CTT ACC TGG CAG TCC CAG G-3′(ii)NOTCH3reverse 5′-AGT GGC AGT GGC TGG GCT AG-3′

or(i)TP53 forward 5′-TTG CCG TCC CAA GCA ATG GAT GA-3′(ii)TP53 reverse 5′-TCT GGG AAG GGA CAG AAG ATG AC-3′

The PCR products of NOTCH3 or TP53 were digested for 16 hours at 37°C with MwoI (HpyF10VI) or BstUI restriction enzymes (Thermo Fisher Scientific, USA), respectively. RFLP products were analyzed on 3% agarose gel, stained with SimplySafe (Eurx, Poland), and visualized in G: Box (Syngene, Great Britain). The C or T alleles of the NOTCH3 gene were identified by the presence of 158 bp (CC genotype) or 203 bp (TT genotypes) fragments, respectively. Heterozygous CT genotype showed the presence of two bands—158 bp and 203 bp (Figure 1(a)). The TP53 alleles were identified by the presence of two 113 bp and 86 bp fragments (for the presence of R allele) or one fragment of 199 bp (for the presence of P allele) (Figure 1(b)). An independent PCR analysis was carried out for each sample.

The results of NOTCH3 and TP53 polymorphisms analysis obtained by PCR-RFLP were confirmed by the use of automated Sanger DNA sequencing of PCR-amplicons. The same primers for PCR-RFLP were used. Each PCR mixture (25 μl) contained 50 ng genomic DNA, PCR buffer (Clontech), dNTPs mixture (0.25 mM), HD polymerase (0,31U) (Clontech), and primers (10 μM of each). The PCR conditions were the same as described above. Sequencing PCR was performed with the use of BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems) in a thermal cycler (as previously mentioned). The sequencing PCR product was purified by the use of an exterminator kit (A&A Biotechnology). The sequencing run module was StdSeq50_POP7 in genetic analyzer 3130 (Applied Biosystems). The results were analyzed by the use of Applied Biosystems software (Figure 2).

2.4. Anti-TP53 Antibodies Analysis

Serum-circulating TP53 antibodies (p53 antibodies) were analyzed in all SSc groups by ELISA using a commercially available kit (Wuhan Fine Biological Technology, China) according to the manufacturer’s instructions. Patients treated with steroids and/or immunosuppressive medications discontinued therapy for at least one month before sample collection. No other drugs were discontinued, on the basis of their limited impact on immune system activity.

2.5. Statistical Analysis

An independent t-test was used for the analysis of continuous variables and the Chi-square test—for categorical variables. The association of studied polymorphisms with clinical or laboratory values/factors was evaluated using the Chi-square test or Fischer’s exact test (when the expected value was <5). The quantitative data was shown as frequency or percentage. Deviation of genotype frequencies in controls and cases from Hardy-Weinberg equilibrium (HWE) was assessed by the Chi-square test with one degree of freedom (df) using Michael H. Court’s (2005–2008) calculator [23]. For a 95% confidence interval (CI), we assumed and χ2 = 3.84; therefore, if the χ2 ≤ 3.84 and the corresponding , then the population is in HWE. The statistical power of the study was calculated according to Bacchetti and Leung 2008 [24]. We assumed a 5% error of inference and the related level of significance pointing to the existence of statistically significant differences. Statistical analyses were performed using the Statistica ver. 12.5 (StatSoft) software.

3. Results

124 SSc patients with a median age of 56.7 years (range 32–79) and 100 healthy blood donors with a median age of 34.4 years (range 18–61 years) were included in the study. Genotyping was successful in all individuals. The HWE test showed that genotypic frequencies of NOTCH3 gene for SSc patients diverged significantly from the equilibrium, which indicates a possible association of these genotypes with the disease (Table 3). The frequencies of TP53 genotypes were in HWE.

TTCTCCTotalHWE value and χ2RRPRPPTotalHWE value and χ2

E67303100, χ2 = 0.3859365100, χ2 = 0.22

E94282124, χ2 = 4.6372447124, χ2 = 0.43

If χ2 ≤ 3.84 and the corresponding p ≥ 0.05, then the population is in HWE.

The differences between allele frequencies of NOTCH3 and TP53 genes in control and study populations were statistically insignificant (Table 4). The CT and CC genotypes of NOTCH3 gene were analyzed as one group, because the frequency of CC homozygotes was very low in SSc patient and control groups—4% (5 cases) and 2% (2 cases), respectively. An association between CT + CC genotypes and SSc susceptibility was observed—OR (odds ratio) = 1.85, (Table 4). The statistical power of this study was 0.37. Additionally, CT + CC genotypes were associated with a 3-fold higher risk of dcSSc. The statistical power of this association was 0.66. The presence of P allele of TP53 gene was low too, and PR and PP genotypes were analyzed in the clustered group. The association between TP53 (PR + RR) genotypes and susceptibility of dcSSc was statistically significant () with statistical power being equal to 0.69. Furthermore, the combination effect of both studied SNPs did not show the influence on SSc susceptibility (Table 5).

AllelesSSc cases, n = 248 (%)Controls, n = 200 (%)p values

T216 (87%)164 (82%)0.13
C32 (13%)36 (18%)
Total:248 (100%)200 (100%)
R191 (77%)153 (76.5%)0.88
P57 (23%)47 (23.5%)
Total:248 (100%)200 (100%)

GenotypesSSc patients n = 124 (%)Controls n = 100 (%)OR95% CI value

TT97 (78%)66 (66%)Reference
CT + CC27 (22%)34 (34%)1.851.02–3.350.04
lcSSc n = 101 (%)Controls n = 100 (%)
TT77 (76%)66 (66%)Reference
CT + CC24 (24%)34 (34%)1.650.89–3.060.10
dcSSC n = 23 (%)Controls n = 100 (%)
TT20 (87%)66 (66%)Reference
CT + CC3 (13%)34 (34%)3.430.95–12.370.04
RR72 (58%)59 (59%)Reference
PR + PP52 (42%)41 (41%)0.960.56–1.640.88
lcSSc n = 101 (%)Controls n = 100 (%)
RR53 (52%)59 (59%)Reference
PR + PP48 (48%)41 (41%)0.760.43–1.340.35
dcSSC n = 23 (%)Controls n = 100 (%)
RR1959 (59%)Reference
PR + PP441 (41%)3.301.04–10.410.034

NOTCH3TP53SSc patients nControls nOR95% CI value

CT + CCPR + RR1541.00.29–3.430.75

Next, we analyzed the potential relationship between clinical values and laboratory results with SSc susceptibility. We observed the association of CT + CC genotypes and active form of SSc (OR = 5.46 (95% CI 2.20–13.59), ) (Table 2) and a higher risk of dcSSc (OR = 3.43 (95% CI 0.95–12.37), ) (Table 4). While analyzing TP53 genotypes, we found a relationship between PR + PP genotypes and a higher risk of dcSSc (OR = 3.30 (1.04–10.41), ) (Table 4).

In the case of serum level of anti-TP53 antibodies, we did not observe statistically significant differences between TT and CT + CC genotypes of NOTCH3 gene (6.41 ng/mL versus 5.90 ng/mL ()) as well as TP53 genotypes (RR versus PR + PP) (5.54 ng/mL versus 6.67 ng/mL (p = 0.14)).

4. Discussion

In this study, we have explored the association of NOTCH3 and TP53 polymorphisms, as well as the level of anti-TP53 antibodies with SSc susceptibility and pattern in the Caucasian population. To our knowledge, this is the first study to elicit the significance of SNPs in SSc. Our findings suggest that the genotypes of NOTCH3 and TP53 genes are associated with higher susceptibility of dcSSc.

DcSSc affected various skin areas and visceral organs including lungs [25]. The development of dcSSc may be induced by cell cycle arrest, activation of DNA repair mechanisms, and inactivation of the apoptosis pathway [26]. It is known that the inhibition of apoptosis is a key mechanism of fibrosis [27]. In the present study, the relationship between PR and PP genotypes of the TP53 gene and dcSSc susceptibility was observed. TP53 P72R polymorphism affects the function and expression of TP53 protein. The P72R polymorphism is present in the segment encoding the transactivation domain and may increase the expression of this gene and the levels of anti-TP53 antibodies [28].

Several studies were focused on the role of serum anti-TP53 antibodies in the development of malignant disorders [29, 30]. Patients with various types of cancers and TP53 mutations show a specific autoimmune response to TP53 protein [31]. However, some studies suggested that serum anti-TP53 antibodies might be detected in certain autoimmune disorders [32]. For example, patients with systemic lupus erythematosus secrete an antibody to the C-terminal domain of TP53, which can inhibit the function of this protein [33]. Mimura and coworkers in the study of 25 patients with SSc and 20 healthy controls found no statistical difference in the level of these antibodies between both study groups [34]. Moreover, Mahmoudi showed no significant alteration in mRNA expression levels of the TP53 gene in fibroblasts from SSc patients compared with healthy controls [35]. This is why we did not analyze serum anti-TP53 antibody in the control population, since we focused on the association of these antibodies with SSc susceptibility and pattern: this included the levels of serum antinuclear antibodies. In our study, we did not observe a relationship between anti-TP53 antibodies level and SSc susceptibility, activity, nor interstitial lung disease.

The NOTCH pathway is involved in the pathogenesis of diseases associated with abnormal fibrosis, including the development of idiopathic pulmonary fibrosis and SSc [3638]. Activation of the NOTCH pathway in the endothelium leads to morphological, phenotypic, and functional changes in epithelial cells [39]. Several papers suggested that an overexpression of NOTCH signaling may have fibrogenic effects in a wide spectrum of diseases, including SSc [40]. Dees et al. found the activation of the NOTCH pathway in SSc with a prominent expression of ligand Jag-1 in infiltrating T-cells [37]. In human adults, NOTCH3 is expressed only in arterial smooth muscle cells (SMCs), and its product participates in artery maturation and specification and responses to vascular injury, regulating vascular SMCs growth and apoptosis [41]. Vascular damage is thought to be involved in SSc development [42]. Early stages of SSc are associated with reduced capillary density [43]. A possible role of NOTCH3 in myofibroblast differentiation was postulated in an animal model with NOTCH3 synthesis inhibition [44]. In our study, we found a relationship between CT + CC genotypes with the susceptibility to various forms of SSc. These genotypes were not in HWE, which suggests their possible association with disease development.

It is known that a cross-talk exists between the NOTCH pathway and TP53 protein function. Giovannini and coworkers found, in an animal model, that activation of the Notch3 receptor might suppress the expression of p53 due to posttranscriptional mechanism [45]. However, in our study, we did not find any relationship between the combination effect of NOTCH3, TP53 polymorphisms, and SSc risk. It is possible that these SNPs exert only a minor effect, and they are linked to other alleles.

A limitation of our study is the relatively small sample size (of lcSSc and dcSSc patients) which in part is due to the low incidence of the disease. This may bias the obtained results, which thus should be considered as preliminary to further testing. Departure from HWE, like in the case of NOTCH3 genotype, can be indicative of potential genotyping errors, population stratification, or association to the trait [4648]. In our study, the results obtained by the PCR-RFLP method were confirmed by automated Sanger sequencing, so we could exclude the genotyping errors. The population included in the study was ethnically homogeneous. Further analysis on a larger cohort can help to better understand the significance of NOTCH3 and TP53 polymorphisms in the pathobiology of SSc, including dcSSc.

5. Conclusions

In summary, the present study provides the first evidence that the CT + CC genotypes of NOTCH3 gene and PR + RR genotypes of the TP53 gene increased the risk of dcSSc development. Moreover, genotypes of CT + CC were associated with the active form of SSc suggesting the role of the NOTCH pathway in the pathogenesis of this disease.

Data Availability

The clinical data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

SZ and DK planned the study. MK and MMJ performed routine laboratory and imaging diagnostic tests. SZ and MWL performed the molecular analysis of NOTCH3 and TP53 polymorphisms. MK and MMJ performed the analysis of TP-53 antibodies. AAF, IW, and DK supervised the work. WS processed the experimental data and performed the statistical analysis. SZ and WS designed the figures. SZ and MMJ wrote the manuscript with support from AAF, DK, and IW. All authors discussed the results and commented on the manuscript.


The authors are grateful to the patients who participated in this study. The authors would like to thank Iwona Korszen-Pilecka and Sylwia Popek-Marciniec for assistance with molecular analyzes. Special thanks are due to Christiana Lucas and Georgia Lucas for proofreading and editing assistance, as well as substantive verification. This project was supported by the Medical University of Lublin, Poland (Grants MNmb 232 and DS 167).


  1. S. Barsotti, C. Bruni, M. Orlandi et al., “One year in review 2017: systemic sclerosis,” Clinical and Experimental Rheumatology, vol. 35, no. 1, pp. 3–20, 2017. View at: Google Scholar
  2. R. Lafyatis, “Transforming growth factor β-at the centre of systemic sclerosis,” Nature Reviews Rheumatology, vol. 10, no. 12, pp. 706–719, 2014. View at: Publisher Site | Google Scholar
  3. L. Chung, J. Lin, D. E. Furst, and D. Fiorentino, “Systemic and localized scleroderma,” Clinics in Dermatology, vol. 24, no. 5, pp. 374–392, 2006. View at: Publisher Site | Google Scholar
  4. S. Harrach, V. Barz, T. Pap et al., “Notch signaling activity determines uptake and biological effect of imatinib in systemic sclerosis dermal fibroblasts,” Journal of Investigative Dermatology, vol. 139, no. 2, pp. 439–447, 2019. View at: Publisher Site | Google Scholar
  5. F. A. Wollheim, “Classification of systemic sclerosis. Visions and reality,” Rheumatology, vol. 44, no. 10, pp. 1212–1216, 2005. View at: Publisher Site | Google Scholar
  6. A. Gabrielli, E. V. Avvedimento, and T. Krieg, “Scleroderma,” New England Journal of Medicine, vol. 360, no. 19, pp. 1989–2003, 2009. View at: Publisher Site | Google Scholar
  7. T. Liu, B. Hu, Y. Y. Choi et al., “Notch1 signaling in FIZZ1 induction of myofibroblast differentiation,” The American Journal of Pathology, vol. 174, no. 5, pp. 1745–1755, 2009. View at: Publisher Site | Google Scholar
  8. A. D. Lowe, N. Y. Elmadhun, T. A. Burgess et al., “Microvascular notch signaling is upregulated in response to vascular endothelial growth factor and chronic myocardial ischemia,” Circulation Journal, vol. 78, no. 3, pp. 743–751, 2014. View at: Publisher Site | Google Scholar
  9. D. Bellavia, A. F. Campese, E. Alesse et al., “Constitutive activation of NF-kappaB and T-cell leukemia/lymphoma in Notch3 transgenic mice,” The EMBO Journal, vol. 19, no. 13, pp. 3337–3348, 2000. View at: Publisher Site | Google Scholar
  10. B. D’Souza, L. Meloty-Kapella, and G. Weinmaster, “Canonical and non-canonical Notch ligands,” Current Topics in Developmental Biology, vol. 92, pp. 73–129, 2010. View at: Publisher Site | Google Scholar
  11. V. Gandhi, “p53 as a predictor for chemotherapy response in CLL cells,” Leukemia & Lymphoma, vol. 48, no. 2, pp. 219-220, 2007. View at: Publisher Site | Google Scholar
  12. J. Malcikova, S. Pavlova, K. S. Kozubik, and S. Pospisilova, “Tp53 mutation analysis in clinical practice: lessons from chronic lymphocytic leukemia,” Human Mutation, vol. 35, no. 6, pp. 663–671, 2014. View at: Publisher Site | Google Scholar
  13. P. Dumont, J. I.-J. Leu, A. C. Della Pietra, D. L. George, and M. Murphy, “The codon 72 polymorphic variants of p53 have markedly different apoptotic potential,” Nature Genetics, vol. 33, no. 3, pp. 357–365, 2003. View at: Publisher Site | Google Scholar
  14. C. E. Canman and M. B. Kastan, “Role of p53 in apoptosis,” Apoptosls-Pharmacological Implications and Therapeutic Opportunities, vol. 41, pp. 429–460, 1997. View at: Publisher Site | Google Scholar
  15. F. Kruiswijk, C. F. Labuschagne, and K. H. Vousden, “p53 in survival, death and metabolic health: a lifeguard with a licence to kill,” Nature Reviews Molecular Cell Biology, vol. 16, no. 7, pp. 393–405, 2015. View at: Publisher Site | Google Scholar
  16. J. T. Park, X. Chen, C. G. Tropè, B. Davidson, I.-M. Shih, and T.-L. Wang, “Notch3 overexpression is related to the recurrence of ovarian cancer and confers resistance to carboplatin,” The American Journal of Pathology, vol. 177, no. 3, pp. 1087–1094, 2010. View at: Publisher Site | Google Scholar
  17. Q. Yang, M. J. Khoury, J. Friedman, J. Little, and W. D. Flanders, “How many genes underlie the occurrence of common complex diseases in the population?” International Journal of Epidemiology, vol. 34, no. 5, pp. 1129–1137, 2005. View at: Publisher Site | Google Scholar
  18. A. T. Masi, G. P. Rodnan, T. A. Medsger et al., “Preliminary criteria for the classification of systemic sclerosis (scleroderma). Subcommittee for scleroderma criteria of the American Rheumatism Association Diagnostic and Therapeutic Criteria Committee,” Arthritis and Rheumatism, vol. 23, pp. 581–590, 1980. View at: Google Scholar
  19. F. van den Hoogen, D. Khanna, J. Fransen et al., “2013 classification criteria for systemic sclerosis: an American college of rheumatology/European league against rheumatism collaborative initiative,” Annals of the Rheumatic Diseases, vol. 72, no. 11, pp. 1747–1755, 2013. View at: Publisher Site | Google Scholar
  20. S. R. Johnson, B. M. Feldman, and G. A. Hawker, “Classification criteria for systemic sclerosis subsets,” The Journal of Rheumatology, vol. 34, no. 34, pp. 1855–1863, 2007. View at: Google Scholar
  21. E. C. LeRoy, C. Black, R. Fleischmajer et al., “Scleroderma (systemic sclerosis): classification, subsets and pathogenesis,” Journal of Rheumatology, vol. 15, no. 2, pp. 202–205, 1988. View at: Google Scholar
  22. G. Valentini, A. Della Rossa, S. Bombardieri et al., “European multicentre study to define disease activity criteria for systemic sclerosis. II. Identification of disease activity variables and development of preliminary activity indexes,” Annals of the Rheumatic Diseases, vol. 60, no. 6, pp. 592–598, 2001. View at: Publisher Site | Google Scholar
  23. M. Court, Michael H. Court’s (2005–2008) Online Calculator, Tuft University, Medford, MA, USA, 2012,
  24. P. Bacchetti and J. M. Leung, “Sample size calculations in clinical research,” Anesthesiology, vol. 97, no. 4, pp. 1028-1029, 2002. View at: Publisher Site | Google Scholar
  25. V. Cottin and K. K. Brown, “Interstitial lung disease associated with systemic sclerosis (SSc-ILD),” Respiratory Research, vol. 20, no. 1, p. 13, 2019. View at: Publisher Site | Google Scholar
  26. J. Collison, “Promoting apoptosis is key to reversing fibrosis,” Nature Reviews Rheumatology, vol. 14, no. 2, p. 61, 2018. View at: Publisher Site | Google Scholar
  27. S. Chabaud and V. J. Moulin, “Apoptosis modulation as a promising target for treatment of systemic sclerosis,” International Journal of Rheumatology, vol. 2011, Article ID 495792, 13 pages, 2011. View at: Publisher Site | Google Scholar
  28. S. Antoun, D. Atallah, R. Tahtouh et al., “Different TP53 mutants in p53 overexpressed epithelial ovarian carcinoma can be associated both with altered and unaltered glycolytic and apoptotic profiles,” Cancer Cell International, vol. 18, no. 1, p. 14, 2018. View at: Publisher Site | Google Scholar
  29. T. Nozoe, E. Nozoe, M. Kono, T. Ohga, and T. Ezaki, “Further evidence to demonstrate the significance of serum appearance of anti-p53 antibody as a marker for progressive potential in invasive ductal carcinoma of the breast,” The Journal of Medical Investigation, vol. 64, no. 3.4, pp. 241–244, 2017. View at: Publisher Site | Google Scholar
  30. M. Kunizaki, A. Fukuda, K. Wakata et al., “Clinical significance of serum p53 antibody in the early detection and poor prognosis of gastric cancer,” Anticancer Research, vol. 37, no. 4, pp. 1979–1984, 2017. View at: Publisher Site | Google Scholar
  31. Y. Hoshida, T. Hongyo, J.-X. Xu et al., “TP53 gene mutation, an unfavorable prognostic factor for malignant lymphomas in autoimmune diseases,” Oncology, vol. 69, no. 2, pp. 175–183, 2005. View at: Publisher Site | Google Scholar
  32. Z. A. Aozasa, D. H. Farag, and S. Eissa, “Tumor suppressor protein p53 and anti-p53 autoantibodies in pediatric rheumatological diseases,” Pediatric Allergy and Immunology, vol. 14, no. 3, pp. 229–233, 2003. View at: Publisher Site | Google Scholar
  33. J. Herkel, N. a. Kam, N. Erez et al., “Monoclonal antibody to a DNA-binding domain of p53 mimics charge structure of DNA: anti-idiotypes to the anti-p53 antibody are anti-DNA,” European Journal of Immunology, vol. 34, no. 12, pp. 3623–3632, 2004. View at: Publisher Site | Google Scholar
  34. Y. Mimura, N. Yazawa, Y. Tada, H. Ihn, and K. Tamaki, “Anti-p53 antibodies in patients with systemic sclerosis,” International Journal of Dermatology, vol. 46, no. 5, pp. 549-550, 2007. View at: Publisher Site | Google Scholar
  35. M. B. Mahmoudi, M. Abed Khojasteh, F. Alsahebfosoul et al., “Expressions of p53 and PUMA in fibroblasts of systemic sclerosis patients are normal at transcription level,” Journal of Cosmetic Dermatology, vol. 17, no. 3, pp. 549–554, 2018. View at: Publisher Site | Google Scholar
  36. A. Mahmoudi, S. A. Nam, W.-Y. Kim et al., “Notch signaling in the collecting duct regulates renal tubulointerstitial fibrosis induced by unilateral ureteral obstruction in mice,” The Korean Journal of Internal Medicine, vol. 33, no. 4, pp. 774–782, 2018. View at: Publisher Site | Google Scholar
  37. C. Kim, M. Tomcik, P. Zerr et al., “Notch signalling regulates fibroblast activation and collagen release in systemic sclerosis,” Annals of the Rheumatic Diseases, vol. 70, no. 7, pp. 1304–1310, 2011. View at: Publisher Site | Google Scholar
  38. C. Dees, P. Zerr, M. Tomcik et al., “Inhibition of Notch signaling prevents experimental fibrosis and induces regression of established fibrosis,” Arthritis & Rheumatism, vol. 63, no. 5, pp. 1396–1404, 2011. View at: Publisher Site | Google Scholar
  39. M. Noseda, G. McLean, K. Niessen et al., “Notch activation results in phenotypic and functional changes consistent with endothelial-to-mesenchymal transformation,” Circulation Research, vol. 94, no. 7, pp. 910–917, 2004. View at: Publisher Site | Google Scholar
  40. N. Kavian, A. Servettaz, C. Mongaret et al., “Targeting ADAM-17/notch signaling abrogates the development of systemic sclerosis in a murine model,” Arthritis & Rheumatism, vol. 62, no. 11, pp. 3477–3487, 2010. View at: Publisher Site | Google Scholar
  41. N. Villa, L. Walker, C. E. Lindsell, J. Gasson, M. L. Iruela-Arispe, and G. Weinmaster, “Vascular expression of Notch pathway receptors and ligands is restricted to arterial vessels,” Mechanisms of Development, vol. 108, no. 1-2, pp. 161–164, 2001. View at: Publisher Site | Google Scholar
  42. G. L. Bagnato, W. N. Roberts, A. Fiorenza et al., “Skin fibrosis correlates with circulating thyrotropin levels in systemic sclerosis: translational association with Hashimoto’s thyroiditis,” Endocrine, vol. 51, no. 2, pp. 291–297, 2016. View at: Publisher Site | Google Scholar
  43. V. D. Steen and T. A. Medsger, “Changes in causes of death in systemic sclerosis, 1972–2002,” Annals of the Rheumatic Diseases, vol. 66, no. 7, pp. 940–944, 2007. View at: Publisher Site | Google Scholar
  44. Y. Chen, S. Zheng, D. Qi et al., “Inhibition of notch signaling by a γ-secretase inhibitor attenuates hepatic fibrosis in rats,” PLoS One, vol. 7, no. 10, Article ID e46512, 2012. View at: Publisher Site | Google Scholar
  45. C. Giovannini, L. Gramantieri, P. Chieco et al., “Selective ablation of Notch3 in HCC enhances doxorubicin’s death promoting effect by a p53 dependent mechanism,” Journal of Hepatology, vol. 50, no. 5, pp. 969–979, 2009. View at: Publisher Site | Google Scholar
  46. S. Turner, L. L. Armstrong, Y. Bradford et al., “Quality control procedures for genome-wide association studies,” Current Protocols in Human Genetics, vol. 68, no. 1, p. 1, 2011. View at: Publisher Site | Google Scholar
  47. J. K. Wittke-Thompson, A. Pluzhnikov, and N. J. Cox, “Rational inferences about departures from hardy-weinberg equilibrium,” The American Journal of Human Genetics, vol. 76, no. 6, pp. 967–986, 2005. View at: Publisher Site | Google Scholar
  48. S. Zmorzynski, A. Szudy-Szczyrek, S. Popek-Marciniec et al., “ACE insertion/deletion polymorphism (rs4646994) is associated with the increased risk of multiple myeloma,” Frontiers in Oncology, vol. 9, p. 44, 2019. View at: Publisher Site | Google Scholar

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