Determination of both the expression and serum levels of epidermal growth factor (EGF) and transforming growth factor-β1 (TGFβ1) genes in SARS-CoV-2

doi:10.21203/rs.3.rs-4869027/v1

Aim: We aimed to evaluate the effects of both the expression and serum levels of Epidermal growth factor (EGF) and Transforming growth factor-β1 (TGF-β1) genes that can lead to fibrosis SARS-CoV-2, which has different degrees of cellular damage as mild, moderate, severe, and critical illness.

Method: Totally 45 individuals (male:21(46.67%); female:24(53.33%)) with COVID-19 infection were included in this study. Four groups were constituted as mild (n=16)], moderate(n=10), severe(n=10), and critical(n=9) according to the severity of the disease. Blood samples were drawn from the patients, and hemograms, EGF and TGFβ1 gene expression, and serum levels were evaluated.

Results: The mean age of individuals was 57.311±18.383 (min:28, max:94). Significant differences were found among the groups for PLT (χ²=9.955; p=0.019), CRP (χ²=7.693; p=0.053), Ferritin (χ²=22.196; p<0.001), D-dimer (χ²=21.982; p=0.000), LDH (χ²=21.807; p<0.001) and all these parameters (exclude PLT in severe groups) was increased depending on the severity of the disease. Additionally, significant differences were detected for EGF (χ²=29.528; p<0.001), TGFB1 (χ²=28.981; p<0.001) expression (that increased depending on the disease severity), and EGF(χ²=7.84; p=0.049), TGFB1 (χ²=17.451; p=0.001) serum concentration levels (that decreased depending on the disease severity).

Conclusion: This study found statistically significant differences for both EGF 2^-ΔΔCt. TGFβ1 2^-ΔΔCt and EGF, TGFβ1 serum concentration values among all patient groups. As disease severity increased, EGF 2^-ΔΔCt. TGFβ1 2^-ΔΔCtlevels increased, while EGF and TGFβ1 serum concentration levels decreased. Perhaps this study will be useful in managing COVID-19 infection severity and pulmonary fibrosis cases secondary to COVID-19.

Infectious Diseases

Cellular damage

Epidermal growth factor

infection

pulmonary fibrosis

SARS-CoV-2

Transforming growth factor-β1

The rapid spread of coronavirus disease 19 (COVID-19) has prompted significant shifts within the medical and scientific communities in a remarkably short period. Despite the wealth of research on the epidemiology and immediate clinical symptoms of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, the long-term effects of this disease are only beginning to emerge [1]. While COVID-19 affects multiple systems, it primarily targets the lungs, leading to pneumonia and, in severe cases, acute respiratory distress syndrome (ARDS) [2, 3].

Concerns about potential chronic pulmonary complications of COVID-19 infection, particularly pulmonary fibrosis, have been raised early in the pandemic due to its association with ARDS [4]. Recent studies indicate an increased risk of pulmonary fibrosis following severe COVID-19 infection, particularly among patients with comorbidities like diabetes, hypertension, or coronary disease. Furthermore, inflammation appears to contribute to permanent lung structural changes, including fibrosis [4–7].

Transforming growth factor-β1 (TGF-β1) plays a pivotal role in fibrogenesis, with the nucleocapsid protein of SARS-CoV-1 directly enhancing its activity. Given the similarity between the nucleocapsid proteins of SARS-CoV-2 and SARS-CoV-1, this mechanism may also contribute to lung fibrosis. Additionally, angiotensin II, which accumulates in the lungs due to virus-induced downregulation of ACE-2, upregulates TGF-β1 along with connective tissue growth factor [8].

TGF-β1 activation stimulates fibroblasts to transform into myofibroblasts, driving fibrosis. Numerous studies have identified TGF-β1 as a key pro-fibrotic factor that triggers Epithelial-mesenchymal transition (EMT) in pulmonary fibrosis, primarily through Smad-dependent or Smad-independent pathways [8–12].

Epidermal growth factor (EGF), a polypeptide growth factor, mediates its biological effects through the transmembrane protein EGF receptor (EGFR). It belongs to the group I EGF family, which includes transforming growth factor-α (TGF-α), heparin-binding EGF (HB-EGF), betacellulin, amphiregulin, epiregulin, and epigen [13].

Biological effects of EGF are mediated through the transmembrane protein EGF receptor (EGFR).

EGFR (ErbB1) belongs to a structurally related family of tyrosine kinase receptors [14]. The EGFR signaling pathway is known to play a role in various processes, including inflammation and fibroblast proliferation in fibrosis [15].

We aimed to evaluate the effects of both the expression and serum levels of EGF and TGFβ1 genes that can lead to fibrosis SARS-CoV-2, which has different degrees of cellular damage as mild, moderate, severe, and critical illness. Perhaps this study will be useful in managing pulmonary fibrosis cases secondary to COVID-19 infection.

Patients and Groups

Totally 45 individuals (male:21(46.67%); female:24(53.33%)) with COVID-19 infection were included in this study. Four groups were constituted as mild (n = 16)], moderate(n = 10), severe(n = 10), and critical(n = 9) according to the severity of the disease. The classification of the disease severity was performed according to the WHO classification system (WHO/2019-nCoV/clinical/2021.1). Pregnant women and individuals under 18 years were excluded from the study. The current study was approved by the Ministry of Health and Local Ethics Committee (Düzce University Local Ethic Committee, Date/Number 11.05.2020/89).

Detection of Blood Parameters and Biomarkers

This study employed various laboratory techniques to detect several blood parameters and biomarkers. Hemogram parameters, including hemoglobin (HB), platelets (PLT), and lymphocytes, were analyzed using a Beckman Coulter LH 780 Analyzer. C-reactive protein (CRP) levels were determined using original commercial kits with a Roche Cobas Integra 400 plus autoanalyzer. D-dimer levels were measured using a Cobas Roche t511 analyzer, while a Roche Cobas 702 auto-analyzer was employed to measure serum LDH and ferritin levels.

The presence of SARS-CoV-2 was detected using a SARS-CoV-2 RTqPCR Detection Kit (Bioeksen, Turkey). Polymerase chain reactions (PCR) were performed using a Real-Time PCR analyzer (Anatolia Gene Works, Turkey).

Detection of Human EGF and TGFB1 Serum Concentrations via Enzyme-Linked Immunosorbent Assay (ELISA)

Serum EGF and TGFB1 levels of individuals participating in the study were determined using an Enzyme-Linked Immunosorbent Assay (ELISA) based on biotin double antibody sandwich technology. This process utilized Human EGF and TGFB1 kits from Bioassay Technology Laboratory.

Following each washing step with a Bio-Tek Instruments E.L.X. 50 Strip Washer, the optical density (OD value) of each well was measured using a 450 nm wavelength microplate reader (Bio-Tek Instruments ELX 800 Absorbance Microplate Reader). Results were obtained by substituting the absorbance values from the device into the calibration graph.

RNA Isolation and cDNA Synthesis

RNA was isolated from peripheral blood samples of individuals using RiboEx (Catalog No: 301-001) and Hybrid-R (Catalog No: 305 − 101) kits, following the manufacturer's instructions. The extracted RNA was stored in RNase-free water at -20°C until further analysis. cDNA was synthesized from the isolated RNA using the WizScript™ cDNA Synthesis Kit (High Capacity) (Catalog No: W2211).

The reverse transcription reaction employed for cDNA synthesis followed these conditions: step 1 (25°C for 10 min), step 2 (37°C for 120 min), step 3 (85°C for 5 min), and step 4 (4°C, ~).

Relative Gene Expressions of EGF and TGFB1 Gene by Real-Time qPCR

EGF, TGFB1, and the reference gene (ACTB) expression levels were determined for each cDNA sample using Applied Biosystems 7500 and ViiA7 Real-Time PCR Systems. The PCR was carried out using the WizPure™ 2X qPCR Master (SYBR) (Catalog No: W1711) kit in a final volume of 20 µL.

The Real-Time qPCR cycle conditions included an initial denaturation at 95°C for 600 seconds, followed by 40 cycles of denaturation at 95°C for 15 seconds, and 40 cycles of annealing at 60°C for 60 seconds. ACTB served as the reference gene for quantifying mRNA expressions, which were normalized to the control group. Fold change calculations were performed by processing ^ΔΔCt values as ^2−ΔΔCt

Statistical analysis

The Statistical analysis was performed via the Statistical Package for Social Sciences (I.B.M. Corp., Armonk, NY, U.S.A.) for Windows 23.0. After the Shapiro-Wilk test, non-parametric tests were carried out because the data were not normally distributed (P < 0.05). The descriptive statistics [number, mean, standard deviation (S.D.), percentage] were performed for each variable. Also, the Kruskal-Wallis and polynomial regression tests were performed to compare and correlate the groups. For statistical significance level, p < 0.05 was accepted.

The mean age of individuals was 57.311±18.383(min:28, max:94). Statistically significant differences were found among the groups for age, and the severity of the disease was increased depending on the age. (χ²=21.414; p<0.001). Significant differences were found among the groups for PLT (χ²=9.955; p=0.019), CRP (χ²=7.693; p=0.053), Ferritin (χ²=22.196; p<0.001), D-dimer (χ²=21.982; p=0.000), LDH (χ²=21.807; p<0.001) and all these parameters (exclude PLT in severe groups) was increased depending on the severity of the disease. Also, significant differences were detected among the groups for SpO2 saturation that decreased depending on the severity of the disease (χ²=39.389; p<0.001) (Table 1). Additionally, significant differences were detected for EGF (χ²=29.528; p<0.001) (Table 1, Figure 1b), TGFB1 (χ²=28.981; p<0.001) (Table 1, Figure 2b) expression (that increased depending on the disease severity) and EGF(χ²=7.84; p=0.049) (Table 1, Figure 1c), TGFB1 (χ²=17.451; p=0.001) (Table 1, Figure 2c) serum concentration levels (that decreased depending on the disease severity).

When the binary comparison of the groups to be considered, statistically significant differences were detected between mild and severe, mild and critical, moderate and severe, moderate and critical for both EGF and TGFB1 expression levels (p<0.05); between mild and critical for EGF serum concentration levels (p<0.05); between mild and severe, mild and critical, moderate and critical for TGFB1 serum concentration levels (p<0.05); between mild and moderate, mild and severe, mild and critical, moderate and critical, severe and critical for Ferritin (p<0.05). Also significant differences were detected between mild and severe, mild and critical, moderate and critical for D-dimer (p<0.05); between mild and critical, moderate and critical, severe and critical for LDH(p<0.05); between mild and moderate, mild and severe, mild and critical, moderate and severe, moderate and critical, severe and critical for SpO2 (p<0.05); between mild and moderate, mild and critical for CRP (p<0.05); between mild and moderate, mild and severe for platelet (p<0.05); between mild and severe for Hb (p<0.05) (Table 2).

The differences were meaningful among the groups for additional disease (χ²=17.117; p<0.001), hypertension (χ²=13.46; p=0.004), chronic respiratory disease (χ²=8.75; p=0.033), and Last situation of the patients (χ²=13.388; p=0.004) (Table 3).

When the polynomial regression test was carried out, significant relation between EGF expression levels and age, EGF expression levels and CRP, EGF expression levels and Ferritin, EGF expression levels and D-dimer, EGF expression levels and LDH, EGF expression levels and SpO2, EGF expression levels and EGF serum concentration levels, EGF expression levels and TGFB1 expression levels, EGF expression levels and TGFB1 serum concentration levels, EGF serum concentration levels and age, EGF serum concentration levels and Hb, EGF serum concentration levels and Ferritin, EGF serum concentration levels and and SpO2, EGF serum concentration levels and TGFB1 serum concentration levels (p<0.05) (Table 4, Figure 3, 5).

Additionally, there is significant relation between TGFB1 expression levels and age, TGFB1 expression levels and CRP, TGFB1 expression levels and Ferritin, TGFB1 expression levels and D-dimer, TGFB1 expression levels and LDH, TGFB1 expression levels and SpO2, TGFB1 expression levels and EGF expression levels, TGFB1 expression levels and TGFB1 concentration levels, TGFB1 serum concentration levels and age, TGFB1 serum concentration levels and Hb, TGFB1 serum concentration levels and CRP, TGFB1 serum concentration levels and Ferritin, TGFB1 serum concentration levels and LDH, TGFB1 serum concentration levels and SpO2, TGFB1 serum concentration levels and EGF serum concentration levels (p<0.05) (Table 5, Figure 4, 6).

This study found a statistically significant difference for both EGF 2^−ΔΔCt TGFβ1 2^−ΔΔCt and EGF, TGFβ1 serum concentration values among all patient groups. As disease severity increased, EGF 2^−ΔΔCt_, TGFβ 12^−ΔΔCt levels increased, while EGF and TGFβ1 serum concentration levels decreased.

Research findings indicate that a decrease in hemoglobin [16, 17] and lymphocyte levels [18] is associated with an increase in LDH [19, 20], D-dimer [21–23], and ferritin [24, 25] levels, correlating with the severity of the disease. Statistical analyses across all groups revealed significant differences for Ferritin (p < 0.001), D-dimer (p < 0.001), LDH (p < 0.001), and SpO2 (p < 0001). In our investigation, we observed a non-significant trend toward increased disease severity with decreasing lymphocyte values, while no statistically significant association was found with hemoglobin levels.

Cellular infection by SARS-CoV-2 relies on ACE-2 receptors, which also regulate the Renin-Angiotensin System (RAS). The virus has a preference for infecting type II pneumocytes due to their elevated ACE-2 expression compared to type I pneumocytes. However, the interaction between the S-Spike protein and ACE-2 can lead to ACE-2 downregulation, causing Ang II accumulation. This activates the ACE-AngII-AT1 axis, resulting in adverse effects such as vasoconstriction, inflammation, and fibrosis [26–28].

Ang II-induced collagen expression depends on TGF-β1, and increased Ang II activity usually leads to TGF-β1 upregulation [29]. Despite studies investigating the correlation between TGF-β1 and COVID-19 severity, findings remain inconclusive. TGF-β1 influences immune cell development, differentiation, tolerance induction, and homeostasis, with pleiotropic effects contributing to either immune response or tolerance establishment. While previous research has implicated TGF-β1 in various pathological processes, its role in COVID-19 progression and outcome remains inadequately explored [30].

A study by Caroline et al. [31], revealed that COVID-19 patients exhibit more extensive diffuse alveolar damage and fibrosis in the alveolar septa compared to H1N1 patients. This was accompanied by a greater density of Collagen I and III, indicating a more pronounced fibrotic response. Notably, COVID-19 patients displayed elevated expression of several tissue biomarkers, including ACE-2, AKT-1, CD44v6, IL-4, MMP-9, α-SMA, Sphingosine-1, and TGF-β1. This heightened immunoexpression, particularly of TGF-β1, suggests a potential role of TGF-β1 pathways in the development of pulmonary fibrosis associated with COVID-19.

In another study by Laloğlu et al. [32], TGF-β1 serum levels increased with COVID-19 severity on the first and seventh days, but decreased by the seventh day in severe and critical patients. Karadeniz et al. [33] found no significant difference in TGF-β1 concentrations between COVID-19 patients and healthy controls. Ghazavi et al. [34] reported higher serum TGF-β1 concentrations among COVID-19 patients in a prospective case-control study.According to our results, TGF-β1 gene expression begins to increase to respond to disease when the virus is encountered. It may be said that the severity of the disease probably decreases depending on the TGF-β1 gene expression capacity increase. Although the TGF-B1 gene expression level increases depending on the severity of the disease in order to respond to the disease, could the decrease in serum concentration levels be due to the decrease in the translation rate of TGF-B1 mRNA due to the predominance of viral infection? Increasing the TGF-B1 serum concentration level due to increased translation of TGF-B1 mRNA may reduce the severity of the disease. To examine the daily change of TGF-β1 level in other planned studies, It may be useful to explain changes in the pathogenesis of the disease.

Snezana Zivancevic-Simonovic and her colleagues [30] discovered that the concentration of TGF-β1 was lower in individuals who succumbed to COVID-19 compared to those who survived. Furthermore, their correlation analysis revealed a robust positive connection between TGF-β1 levels in patients' serum and platelet counts. Diminished serum levels of TGF-β1 were linked to unfavorable outcomes in COVID-19 cases. They concluded that both TGF-β1 levels and platelet counts exhibited a significant association with adverse disease outcomes in severely affected COVID-19 patients. In our investigation, we observed a decline in TGF-β1 serum levels with increasing disease severity, coupled with elevated platelet values. This trend was noticeable across mild, moderate, and severe cases. Critically ill patients exhibited lower platelet values compared to those with severe disease. It's important to note that all severely ill patients in our study were undergoing steroid treatment, which could also impact platelet values; however, this aspect was not explored in the study data, representing a potential limitation.

The analysis of soluble cytokines and chemokines associated with COVID-19 ARDS and their correlation with mortality and disease progression has been extensively discussed. While the connection between TGF-β1 and SARS-CoV-2 has been explored, the relationship between EGF and SARS-CoV-2 is less investigated. Previous suggestions indicate that acute lung injury triggers growth factor responses that initiate repair mechanisms for restoring lung integrity. EGF, in particular, plays a role in regulating bronchial and alveolar epithelial repair post-lung injury, enhancing lung fluid clearance by influencing the permeability of the alveolar epithelial junction. Furthermore, EGF gene polymorphisms have been associated with the risk of ARDS [35–38].

In a study by Balnis et al. [39] that evaluated 41 patients who needed mechanical ventilators due to severe ARDS, Decreased EGF levels were associated with mortality on day 45. The strength of our study was that we also looked at EGF expression levels. Our study found that EGF gene expression levels increased depending on disease severity, whereas EGF serum levels decreased as disease severity increased.

EGF expression levels may increase with disease severity as a response to viral infection and lung reorganization. We think that the serum level decreases due to decreased translation levels of EGF mRNA, and it is insufficient to prevent the severe course of the disease. Increasing EGF serum concentration level due to increased translation of EGF mRNA may reduce the severity of the disease.

There are studies on EGF levels that contradict our study. In the study conducted by Marija Petrushevska and her colleagues [40] in 14 severe COVID-19 infections and 20 control groups, They found EGF levels to be higher in patients with COVID-19 infection. In this study, since they do not determine the EGF level according to disease severity, it will be difficult to explain its place in disease pathogenesis. It is likely that they will find lower EGF levels in healthy individuals, but reporting the correlation with severity will contribute more to pathogenesis.

Our study has some limitations. First, it is a single center with a limited number of patients. Not examining our patients' treatment is among our study's limitations.

In conclusion, considering the prevalence and characteristics of the COVID-19 epidemic, it is highly likely that SARS-CoV-2 will become an endemic virus. Vaccines have proven to be effective agents in controlling the disease, but on the other hand, there are now serious concerns about post-COVID-19 sequelae, especially those related to pulmonary fibrosis. This study is planned to shed light on further studies to reveal the effects of EGF and TGF-β1 on COVID-19-related fibrosis and the severity of the disease. However, more in-depth research is needed to understand these mechanisms fully. In particular, future studies focusing on the interactions of these factors at the cellular level and their roles in specific signaling pathways may further increase knowledge in this field. Our findings showing that EGF and TGF-β1 levels influence the severity of COVID-19 disease highlight the potential of these factors to modulate the course of infection. In this context, understanding the effects of these factors on clinical outcomes and disease course may be important in guiding future treatment strategies.

Acknowledgements:

The Düzce University coordinatorship of scientific research projects supported this study. (DÜBAP:2020.04.03.1146).

Conflict of interest: All author state that there is no conflict of interest.

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Table 1 The Hemogram values, EGF 2^-ΔΔCt. TGFβ1 2^-ΔΔCt and EGF, TGFβ1 concentration levels of each group

	Groups
	Mild (n=16)	Moderate (n=10)	Severe (n=10)	Critical (n=9)	Χ²/p
Age (years)	41.125±15.297	57.3±12.311	69.7±11.373	72.333±11.587	21.414/0.000*
HB	12.994±1.323	12.76±1.974	12.1±1.352	12.71±1.02	3.271/0.352
PLT	201.5±40.135	282.1±132	310.3±90,532	237.778±127.694	9.955/0.019*
Lymphocyte	687.5±116.3	662±309.365	590.4±200.437	564±240.269	2.866/0.413
CRP	2.6584±1,884	6,928±5.197	7.339±8,349	8,14±6.277	7.693/0.053*
Ferritin	216.75±61.623	497.5±273.618	664±421.937	1146.111±470.134	22.196/0.000*
D-dimer	0.359±0.14	0.748±0.544	1.156±1.103	6.336±7.114	21.982/0.000*
LDH	311.688±104.52	310.6±89.077	361.4±95.336	721.111±220.898	21.807/0.000*
SpO2	95.561±1.151	90.3±2.908	81.8±5.181	68.3±7.416	39.389/0.000*
*EGF* 2^-ΔΔCt	1.227±0.8	1.661±1.621	4.6±2.259	7.827±3.802	29.528/0.000*
*EGF* conc.	24.759±15.329	17.052±4.72	14.776±6.059	13.314±3.481	7.84/0.049*
*TGFβ1* 2^-ΔΔCt	1.296±1.136	1.927±0.981	4.248±2.345	10.28±5.934	28.981/0.000*
*TGFβ1* conc.	192.332±213.327	129.772±105.792	90.949±21.962	68.561±14.589	17.451/0.001*

EGF: Epidermal Growth Factor TGFβ1: Transforming Growth Factor Beta 1 Conc: Concentration HB: Hemoglobin PLT: Platelet CRP: C-reactive protein LDH: Lactate dehydrogenase SpO2: Oxygen saturation level in the blood *: Statistically significant

Table 2 Binary comparison of the groups for EGF 2^-ΔΔCt, EGF, TGFB1 2^-ΔΔCt, TGFB1 levels and blood parameters

	Grps	Mild		Moderate		Severe		Critical
*EGF* 2^-ΔΔCt		χ2	P	χ2	P	χ2	P	χ2	P
	Mild	-	-	2.5	0.114	14.003	<0.001*	16.615	<0.001*
	Moderate	2.5	0.114	-	-	7.823	0.005*	13.5	<0.001*
	Severe	14.003	<0001*	7.823	0.005*	-	-	4.686	0.030*
	Critical	16.615	<0.001*	13.5	<0.001*	4.686	0.030*	-	-
		Mild		Moderate		Severe		Critical
		χ2	P	χ2	P	χ2	P	χ2	P
EGF Conc	Mild	-	-	1.112	0.292	3.505	0.061	5.791	0.016*
	Moderate	1.112	0.292	-	-	0.692	0.406	3.527	0.060
	Severe	3.505	0.061	0.692	0.406	-	-	0.240	0.624
	Critical	5.791	0.016*	3.527	0.060	0.240	0.624	-	-
		Mild		Moderate		Severe		Critical
		χ2	P	χ2	P		χ2	P	χ2
*TGFB1* 2^-ΔΔCt	Mild	-	-	3.803	0.051*	13.225	<0.001*	15.705	<0.001*
	Moderate	3.803	0.051*	-	-	7.823	0.005*	11.760	0.001*
	Severe	13.225	<0.001*	7.823	0.005*	-	-	7.707	0.006*
	Critical	15.705	<0.001*	11.760	0.001*	7.707	0.006*	-	-
		Mild		Moderate		Severe		Critical
		χ2	P	χ2	P	χ2	P	χ2	P
*TGFB1* Conc	Mild	-	-	0.757	0.384	6.541	0.011*	15.271	<0.001*
	Moderate	0.757	0.384	-	-	0.691	0.406	4.507	0.034*
	Severe	6.541	0.011*	0.691	0.406	-	-	6.407	0.011*
	Critical	15.271	<0.001*	4.507	0.034*	6.407	0.011*	-	-
		Mild		Moderate		Severe		Critical
		χ2	P	χ2	P	χ2	P	χ2	P
Ferritin	Mild	-	-	4.903	0.027*	7.511	0.006*	16.615	<0.001*
	Moderate	4.903	0.027*	-	-	0.823	0.364	8.409	0.004*
	Severe	7.511	0.006*	0.823	0.364	-	-	4.167	0.041*
	Critical	16.615	<0.001*	8.409	0.004*	4.167	0.041*	-	-
		Mild		Moderate		Severe		Critical
		χ2	P	χ2	P	χ2	P	χ2	P
D-dimer	Mild	-	-	2.597	0.107	15.835	<0001*	15.307	<0001*
	Moderate	2.597	0.107	-	-	1.465	0.226	4.873	0.027*
	Severe	15.835	<0001*	1.465	0.226	-	-	2.162	0.141
	Critical	15.307	<0001*	4.873	0.027*	2.162	0.141	-	-
		Mild		Moderate		Severe		Critical
		χ2	P	χ2	P	χ2	P	χ2	P
LDH	Mild	-	-	0.011	0.916	3.211	0.073	15.260	<0.001*
	Moderate	0.011	0.916	-	-	1.202	0.273	13.5	<0001*
	Severe	3.211	0.073	1.202	0.273	-	-	12.327	<0.001*
	Critical	15.260	<0.001*	13.5	<0001*	12.327	<0.001*	-	-
		Mild		Moderate		Severe		Critical
		χ2	P	χ2	P	χ2	P	χ2	P
SpO2	Mild	-	-	16.047	<0.001*	18.214	<0001*	17.075	<0001*
	Moderate	16.047	<0.001*	-	-	12.487	<0.001*	13.619	<0001*
	Severe	18.214	<0001*	12.487	<0.001*	-	-	11.336	0.001*
	Critical	17.075	<0001*	13.619	<0001*	11.336	0.001*	-	-
		Mild		Moderate		Severe		Critical
		χ2	P	χ2	P	χ2	P	χ2	P
CRP	Mild	-	-	3.805	0.051*	3.028	0.082	5.931	0.015*
	Moderate	3.805	0.051*	-	-	0.280	0.597	0.167	0.683
	Severe	3.028	0.082	0.280	0.597	-	-	0.376	0.540
	Critical	5.931	0.015*	0.167	0.683	0.376	0.540	-	-
		Mild		Moderate		Severe		Critical
		χ2	P	χ2	P	χ2	P	χ2	P
Platelet	Mild	-	-	4.671	0.031*	9.841	0.002*	0.080	0.777
	Moderate	4.671	0.031*	-	-	2.063	0.151	0.667	0.414
	Severe	9.841	0.002*	2.063	0.151	-	-	1,5	0.321
	Critical	0.080	0.777	0.667	0.414	1,5	0.321	-	-
		Mild		Moderate		Severe		Critical
		χ2	P	χ2	P	χ2	P	χ2	P
Hb	Mild	-	-	0.137	0.712	3.712	0.054*	1.104	0.293
	Moderate	0.137	0.712	-	-	0.575	0.448	0.137	0.711
	Severe	3.712	0.054*	0.575	0.448	-	-	0.334	0.563
	Critical	1.104	0.293	0.137	0.711	0.334	0.563	-	-

EGF: Epidermal Growth Factor TGFβ1: Transforming Growth Factor Beta 1 Conc: Concentration HB: Hemoglobin CRP: C-reactive protein LDH: Lactate dehydrogenase SpO2: Oxygen saturation level in the blood *: Statistically significant

Table 3: The sex, additional diseases and last situation of each group

	Groups
	Mild (n=16)	Middle (n=10)	Severe (n=10)	Critical (n=9)	Χ²/p
Sex(M/F)	4(25%)/12(75%)	5(50%)/5(50%)	5(50%)/5(50%)	7(77.8%)/2(27.2%)	6.607/0.086
AD(Y/N)	4(125%)/12(75%)	8(80%)/2(20%)	9(90%)/1(10%)	8(88.9%)/1(11.1%)	17.117/0.000*
DM(Y/N)	3(18.8%)/13(81.3%)	5(50%)/5(50%)	2(20%)/8(80%)	3(33.3%)/6(66.7%)	3.441/0.328
HT(Y/N)	2(12.5%)/14(87.5%)	5(50%)/5(50%)	2(20%)/8(80%)	3(33.3%)/6(66.7%)	13.46/0.004*
CVA(Y/N)	0(0%)/16(100%)	1(10%)/9(90%)	0(0%)/10(100%)	2(22.2%)/7(77.8%)	5.536/0.137
CRD(Y/N)	0(0%)16(100%)	1(10%)/9(90%)	4(40%)/6(60%)	1(11.1%)/8(88.9%)	8.75/0.033*
CRF(Y/N)	0(0%)16(100%)	1(10%)/9(90%)	0(0%)/10(100%)	0(0%)/9(100%)	3.58/0.311
Malig(Y/N)	0(0%)16(100%)	1(10%)/9(90%)	0(0%)/10(100%)	2(22.2%)/7(77.8%)	11.869/0.221
CVD(Y/N)	3(18.8%)/13(81.2%)	3(30%)/7(70%)	2(20%)/8(80%)	1(11.1%)/8(88.9%)	1.085/0.781
Last Sit(Ex/Dis)	0(0%)/16(100%)	1(10%)/9(90%)	0(0%)/10(100%)	4(44.4%)/5(55.6%)	13.388/0.004*

M:Male F:Female AD: Additional Disease Y:Yes N:No DM: Diabetes Mellitus HT: Hypertension CVA: Cerebrovascular accident CRF: Chronic renal Failure Malig: Malignancy

*: Statistically significant CVD: Cardiovascular disease CRD: Chronic respiratory disease Sit:Situation Ex:Exitus Dis: discharged

Table 4 Model Summary and Parameter Estimates for between both EGF 2^-ΔΔCt and EGF Concentration levels and hemogram parameters

		Model Summery					Parameter Estimates
Variable	*Equation*	R²	F	*df1*	*df2*	*sig*	Constant	b1	b2	b3
*EGF* 2^-ΔΔCt and Age	Linear	,209	11,358	1	43	,002	48,780	2,514
	Log	.	.	.	.	.	.	.
	Cubic	,317	6,337	3	41	,001	44,288	4,287	,099	-,018
*EGF* 2^-ΔΔCt and PLT	Linear	,081	3,791	1	43	,058	220,953	8,809
	Log	.	.	.	.	.	.	.
	Cubic	,143	2,276	3	41	,094	230,624	12,047	-2,388	,155
*EGF* 2^-ΔΔCt and CRP	Linear	,081	3,769	1	43	,059	4,071	,493
	Log	.	.	.	.	.	.	.
	Cubic	,086	1,287	3	41	,292	4,015	,331	,058	-,003
*EGF* 2^-ΔΔCt and Ferritin	Linear	,403	28,997	1	43	,000	269,386	86,946
	Log	.	.	.	.	.	.	.
	Cubic	,429	10,272	3	41	,000	217,881	104,126	2,099	-,254
*EGF* 2^-ΔΔCt and D-dimer	Linear	,387	27,091	1	43	,000	-,612	,716
	Log	.	.	.	.	.	.	.
	Cubic	,497	13,502	3	41	,000	,269	,427	-,037	,004
*EGF* Conc. and LDH	Linear	,180	9,419	1	43	,004	316,032	26,037
	Log	.	.	.	.	.	.	.
	Cubic	,228	4,028	3	41	,013	315,264	4,284	6,821	-,370
*EGF* 2^-ΔΔCt and SPO2	Linear	,587	61,002	1	43	,000	94,511	-2,541
	Logarithmic	.	.	.	.	.	.	.
	Cubic	,618	22,078	3	41	,000	95,036	-2,075	-,219	,013
*EGF* *2^-ΔΔCt and EGF* Conc.**	Linear	,079	3,669	1	43	,062	21,628	-,910
	Logarithmic	.	.	.	.	.	.	.
	Cubic	,092	1,384	3	41	,261	22,761	-1,539	,031	,001
*EGF* *2^-ΔΔCt and TGFB1* 2^-ΔΔCt**	Linear	,706	103,029	1	43	,000	,088	1,120
	Logarithmic	.	.	.	.	.	.	.
	Cubic	,786	50,107	3	41	,000	1,123	-,314	,294	-,013
*EGF* *2^-ΔΔCt and TGFB1* Conc.**	Linear	,051	2,301	1	43	,137	163,887	-9,649
	Logarithmic	.	.	.	.	.	.	.
	Cubic	,079	1,175	3	41	,331	164,251	2,050	-3,661	,199
*EGF* Conc. And Age	Linear	,266	15,596	1	43	,000	73,514	-,874
	Logarithmic	,248	14,209	1	43	,000	112,008	-19,536
	Cubic	,297	5,776	3	41	,002	52,274	2,485	-,145	,002
*EGF* Conc. And Hb	Linear	,134	6,662	1	43	,013	11,779	,049
	Logarithmic	,205	11,119	1	43	,002	8,762	1,402
	Cubic	,220	3,853	3	41	,016	8,972	,379	-,010	8,705
*EGF* Conc. And PLT	Linear	,057	2,585	1	43	,115	292,925	-2,270
	Logarithmic	,071	3,296	1	43	,076	415,613	-58,851
	Cubic	,071	1,040	3	41	,385	354,172	-8,770	,167	-,001
*EGF* Conc. And Ferritin	Linear	,085	4,004	1	43	,052	792,706	-12,315
	Logarithmic	,069	3,201	1	43	,081	1283,929	-256,995
	Cubic	,143	2,286	3	41	,093	-55,425	116,456	-5,361	,059
*EGF* Conc. And SPO2	Linear	,107	5,156	1	43	,028	79,690	,334
	Logarithmic	,104	5,010	1	43	,030	64,503	7,638
	Cubic	,133	2,088	3	41	,117	89,382	-1,269	,072	-,001
*EGF* Conc. *And TGFB1* Conc.**	Linear	,318	20,078	1	43	,000	-6,763	7,439
	Logarithmic	,297	18,124	1	43	,000	-333,995	166,135
	Cubic	,489	13,087	3	41	,000	413,185	-57,670	2,761	-,031

EGF: Epidermal Growth Factor TGFβ1: Transforming Growth Factor Beta 1 Conc: Concentration HB: Hemoglobin PLT: Platelet LDH: Lactate dehydrogenase SpO2: Oxygen saturation level in the blood *: Statistically significant

Table 5 Model Summary and Parameter Estimates for between both TGFB1 2^-ΔΔCt and TGFB1 Concentration levels and hemogram parameters

		Model Summery					Parameter Estimates
Variable	*Equation*	R²	F	*df1*	*df2*	*sig*	Constant	b1	b2	b3
*TGFB1* 2^-ΔΔCt and Age	Linear	,191	10,173	1	43	,003	50,296	1,804
	Log	,235	13,203	1	43	,001	50,067	8,609
	Cubic	,256	4,700	3	41	,007	42,837	6,449	-,439	,010
*TGFB1* 2^-ΔΔCt and PLT	Linear	,005	,214	1	43	,646	244,493	1,633
	Log	,010	,453	1	43	,505	242,259	10,203
	Cubic	,013	,173	3	41	,914	240,718	1,821	,411	-,024
*TGFB1* 2^-ΔΔCt and CRP	Linear	,060	2,749	1	43	,105	4,502	,319
	Log	,107	5,154	1	43	,028	4,200	1,835
	Cubic	,142	2,254	3	41	,096	3,335	,716	,028	-,003
*TGFB1* 2^-ΔΔCt and Ferritin	Linear	,237	13,348	1	43	,001	369,973	49,996
	Log	,360	24,188	1	43	,000	341,041	265,451
	Cubic	,422	9,993	3	41	,000	143,890	167,698	-6,542	-,012
*TGFB1* 2^-ΔΔCt and D-dimer	Linear	,266	15,621	1	43	,000	,084	,446
	Log	,207	11,258	1	43	,002	,392	1,695
	Cubic	,450	11,164	3	41	,000	1,393	-,935	,242	-,009
*TGFB1* Conc. and LDH	Linear	,207	11,239	1	43	,002	322,850	20,965
	Log	,201	10,828	1	43	,002	329,522	88,962
	Cubic	,290	5,594	3	41	,003	291,283	27,313	2,037	-,134
*TGFB1* 2^-ΔΔCt and SPO2	Linear	,574	57,929	1	43	,000	93,218	-1,885
	Logarithmic	,593	62,635	1	43	,000	92,831	-8,251
	Cubic	,684	29,514	3	41	,000	96,765	-3,531	,044	,003
*TGFB1* *2^-ΔΔCt and EGF* 2^-ΔΔCt**	Linear	,706	103,029	1	43	,000	,944	,630
	Logarithmic	,645	78,048	1	43	,000	1,211	2,593
	Cubic	,742	39,227	3	41	,000	,510	,772	,012	-,001
*TGFB1* *2^-ΔΔCt and TGFB* Conc**	Linear	,058	2,647	1	43	,111	161,204	-7,729
	Logarithmic	,098	4,696	1	43	,036	167,647	-43,380
	Cubic	,107	1,637	3	41	,196	218,789	-46,488	4,234	-,112
*TGFB1* Conc. And Age	Linear	,199	10,655	1	43	,002	64,820	-,057
	Logarithmic	,268	15,760	1	43	,000	133,101	-16,326
	Cubic	,303	5,955	3	41	,002	84,946	-,327	,001	-3,754
*TGFB1* Conc. And Hb	Linear	,049	2,233	1	43	,142	12,391	,002
	Logarithmic	,090	4,238	1	43	,046	9,229	,745
	Cubic	,129	2,016	3	41	,127	10,590	,029	-7,848	5,530
*TGFB* Conc. And PLT	Linear	,019	,828	1	43	,368	263,880	-,099
	Logarithmic	,021	,926	1	43	,341	370,396	-25,753
	Cubic	,027	,376	3	41	,771	301,008	-,624	,001	-9,361
*TGFB1* Conc. And CRP	Linear	,033	1,484	1	43	,230	6,715	-,007
	Logarithmic	,090	4,242	1	43	,046	19,590	-2,983
	Cubic	,128	2,014	3	41	,127	14,407	-,119	,000	-2,176
*TGFB1* Conc. And Ferritin	Linear	,079	3,693	1	43	,061	682,425	-,900
	Logarithmic	,157	8,017	1	43	,007	2009,323	-311,256
	Cubic	,192	3,257	3	41	,031	1295,675	-9,520	,023	-1,500
*TGFB1* Conc. And LDH	Linear	,035	1,562	1	43	,218	439,605	-,269
	Logarithmic	,093	4,414	1	43	,042	902,982	-107,406
	Cubic	,149	2,399	3	41	,082	742,681	-4,701	,013	-8,942
*TGFB1* Conc. And SPO2	Linear	,090	4,248	1	43	,045	82,841	,023
	Logarithmic	,174	9,038	1	43	,004	49,099	7,925
	Cubic	,231	4,110	3	41	,012	65,176	,277	-,001	4,844
*TGFB1* Conc. *And EGF* Conc.**	Linear	,318	20,078	1	43	,000	12,927	,043
	Logarithmic	,450	35,228	1	43	,000	-39,430	12,487
	Cubic	,548	16,574	3	41	,000	,478	,193	,000	8,787

EGF: Epidermal Growth Factor TGFβ1: Transforming Growth Factor Beta 1 Conc: Concentration HB: Hemoglobin PLT: Platelet CRP: C-reactive protein LDH: Lactate dehydrogenase SpO2: Oxygen saturation level in the blood *: Statistically significant

The authors declare no competing interests.

Determination of both the expression and serum levels of epidermal growth factor (EGF) and transforming growth factor-β1 (TGFβ1) genes in SARS-CoV-2

Status:

Version 1

Abstract

Figures

Introduction

Methods

Patients and Groups

Detection of Blood Parameters and Biomarkers

Detection of Human EGF and TGFB1 Serum Concentrations via Enzyme-Linked Immunosorbent Assay (ELISA)

RNA Isolation and cDNA Synthesis

Relative Gene Expressions of EGF and TGFB1 Gene by Real-Time qPCR

Statistical analysis

Results

Discussion

Declarations

Acknowledgements:

References

Tables

Additional Declarations

Status:

Version 1