Monocytes Subsets Altered Distribution and Dysregulated Plasma hsa-miR-21-5p and hsa- miR-155-5p in HCV-Linked Liver Cirrhosis Progression to Hepatocellular Carcinoma

doi:10.21203/rs.3.rs-2626454/v1

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Monocytes Subsets Altered Distribution and Dysregulated Plasma hsa-miR-21-5p and hsa- miR-155-5p in HCV-Linked Liver Cirrhosis Progression to Hepatocellular Carcinoma

https://doi.org/10.21203/rs.3.rs-2626454/v1

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Cirrhosis-associated immune dysfunction (CAID) is an immunological perturbation that develops on top of liver cirrhosis (LC). Immune perturbation directs LC progression to hepatocellular carcinoma (HCC). Innate immune cells, in particular, monocytes, play key roles in inflammation and tumorigenesis. MicroRNAs (miRs) have been regarded as master regulators of the immune networks. We aim to investigate the altered monocytes subsets distribution in LC and subsequent HCC in association with the expression level of plasma homo sapiens (hsa)-miR-21-5p and hsa-miR-155-5p. A step toward non-protein coding (nc) RNA precision medicine based on the immune perturbation, manifested as altered monocytes distribution, on top of LC and HCC. Subjects and Methods: Seventy-nine patients diagnosed with chronic hepatitis C virus (CHCV) infection with LC were enrolled in the current study. Patients were sub-classified into LC group without HCC (n=40), LC with HCC (n=39), and 15 apparently healthy controls. Monocyte subsets frequencies were assessed by flow-cytometry. Real-time quantitative PCR was used to measure plasma hsa-miR-21-5p and hsa-miR-155-5p expression. Results: hsa-miR-21-5p correlated with intermediate monocytes (r=0.30, p=0.007), while hsa-miR-155-5p negatively correlated with nonclassical monocytes (r= -0.316, p=0.005). ROC curve analysis revealed that combining intermediate monocytes frequency and hsa-miR-21 yielded sensitivity= 79.5%, specificity= 75%, and AUC= 0.84. In comparison, AFP yielded a lower sensitivity = 69% and 100% specificity with AUC= 0.85. Logistic regression analysis proved that up-regulation of intermediate monocytes frequency and hsa-miR-21-5p were independent risk factors for LC progression to HCC, after adjustment for co-founders. Conclusion: Monocyte subsets differentiation in HCC was linked to hsa-miR-21-5p and hsa-miR-155-5p. Combined up-regulation of intermediate monocytes frequency and hsa-miR-21-5p expression could be considered a sensitive indicator of LC development to HCC. Circulating intermediate monocytes and hsa-miR-21-5p were independent risk factors for HCC evolution, clinically and in silicoproofed.

HCC

hsa-miR-21-5p

hsa-miR-155-5p

liver cirrhosis

HCV

intermediate and nonclassical monocytes

in silico databases

Innate immunity activation and inflammation play key roles in liver disease development [1]. The dynamic spectrum of immunological perturbations that develop in cirrhotic patients is referred to as cirrhosis-associated immune dysfunction (CAID) [2]. This starts with systemic inflammation exacerbating clinical manifestations of cirrhosis, followed by immunodeficiency [3]. The intensity of CAID has important consequences on cirrhosis progression and correlates with the severity of liver insufficiency and organ failure [2]. Innate immune cells, in particular monocytes, are pivotal effector and target cells in CAID [4]. Circulating monocytes as they move through the liver, contribute to the generation of damage-associated molecular patterns, which act as continual inflammatory stimuli, causing systemic perturbations and the release of inflammatory cytokines [3]. Damage-associated molecular patterns (DAMPs) drive the progression of cirrhosis via perpetuating inflammation [5].

Liver disease is thought to be responsible for around 2 million deaths worldwide [6]. Hepatitis C virus (HCV) is a human hepatotropic pathogen that infects 58 million people globally, with a high mortality rate reaching 290,000 deaths annually [7]. In half of the cases, patients fail to clear the virus spontaneously and acute HCV infection progresses to chronic hepatitis C virus (CHCV) [8]. CHCV infection could prompt liver cirrhosis (LC) and hepatocellular carcinoma (HCC) [9]. HCC normally develops in the setting of cirrhosis and the process of tumorigenesis is further promoted by HCV infection [10]. In Egypt, about 80% of the patients with HCC have underling CHCV [11].

HCC prognosis varies greatly according to tumor stage at the time of diagnosis, so identifying cirrhotic HCC during liver cirrhosis stage is pivotal for improving the clinical outcomes of cirrhotic HCC patients [12].

MicroRNAs (miRs) are non-protein coding RNA which play a vital role in regulating gene expression at various levels of transcription, translation, and protein function [13]. Disturbed expression of miRs has been associated with the clinicopathological features of cirrhosis [14], and development of HCC [15]. Few miRs have been regarded as master immune regulators of multiple cellular processes in HCC [16].

Homo sapiens (hsa)-miR-155-5p is a crucial regulator that controls cellular pro-inflammatory activities [17], and has been involved in both HCC and CHCV [18]. Hsa-miR-21-5p is also a key molecular marker regulating different immune networks [19], and its overexpression in plasma was shown to have a potential value as a screening marker for HCC [20]. In our recent work, we found that both hsa-miR-21-5p and hsa-miR-155-5p plasma levels were shown to be related to the progression of LC to HCC, and showed potential diagnostic value in patients without elevated alpha feto protein (AFP) [21].

Monocytes subsets have different functional characteristics and roles during inflammation and/or malignancy [22]. The monocyte cluster of differentiation 14 (CD14) [23] is identified by Uniport and GeneCards in silico databases. CD14 is involved in mediating the innate immune response, on chromosome 5 reverse strand, activating the nuclear factor kappa-B cell (NF-_KB), few cytokine secretions and the inflammatory response [24], as identified via the curated database SIGnaling Network Open Resource; Signor3.0 [25].

Fc gamma receptor IIIa (FCGR3A: FcγRIII) or CD16A, on chromosome 1 reverse strand [26], is expressed on some monocytes surface, but, is more related to natural killer (NK) cells within tissues [27]; as identified by Gene-NCBI and the Human Universal Single Cell Hub (HUSCH) databases [28].

Peripheral blood monocytes are subclassified according to expression of CD14 and CD16A into three subsets. The first subset is classical monocytes (CD14^high CD16-), that accounts for most of circulating monocytes in healthy individuals [29]. This population has been reported to increase in cases of acute inflammation and is rapidly recruited to the infection scene [30]. On the other hand, 5–10% of total blood monocytes express CD16A and are referred to as intermediate monocytes (CD14^high CD16+) which are potent producers of pro-inflammatory cytokines [31]. Finally, the non-classical monocytes (CD14^dim CD16^high) [32, 33]. This would support the potential interest of blood monocytes in monitoring LC development and its progression to HCC. In the same line, our recent research revealed an alteration of intermediate monocytes subset in LC and HCC [34].

In the setting of LC due to CHCV genotype-4 (G4) infection, and subsequent HCC, the relationship between intermediate monocytes and immune-regulatory miRs, hsa-miR-21-5p and hsa-miR-155-5p, coincidence remains to be examined.

Therefore, we aimed to investigate the clinical relevance of peripheral blood liquid biopsy monocytes subsets distribution and circulating hsa-miR-21-5p and hsa-miR-155-5p in the development of LC-linked to CHCV G4 infection, as well as their role in LC progression to HCC.

An in silico database search to provide insight on immune cells in blood and liver as well as monocyte surface-CDs activation drivers. Second, using curated databases and text-mining to identify monocytes surface antigens interacting genes and their down-stream target genes, and monocytes surface antigens targeting genes.

Study Design

case-controlled

Sample size and the study power: Based on the previous study by Hammad et al. [35], sample size estimation was performed using the G power* sample size online calculator https://riskcalc.org/samplesize/# depending on a two-sided significance level of 0.05 and power (1-beta) of 0.95. Estimatedsample size was minimum of 40 patients’ vs 15 controls to reject the null hypothesis (power) of 0.9.

Study Participants

This prospective study enrolled 79 patients with CHCV related LC divided into Group 1, with early HCC (n = 39) and Group 2, without HCC (n = 40). These groups were compared to apparently healthy subjects (Group 3, n = 15).

Patients were recruited from the National Liver Institute, Menoufia University, Menoufia, Egypt, and Al-Zahraa University Hospital, Al-Azhar University, Cairo, Egypt. Patients’ Inclusion criteria: Child Pugh scores were used to categorize cirrhotic patients [36]. A blind abdominal computed tomography (CT) scan was performed using Siemens 128 (Germany). CHCV fulfilling imaging criteria in accordance with recent recommendations were used to confirm the HCC diagnosis. The Barcelona Clinic Liver Cancer (BCLC) classification system was used to stage HCC patients [37].

Participants’ assessment

Age, gender, and medical history were retrieved (after ethical approval) from the hospital medical records. Participants underwent general clinical examination and measuring their body mass index (BMI). The control group included age and sex-matched apparently healthy blood donors in Al-Zahraa University Hospital Blood Bank, who were informed and asked to join the study. They were enrolled only after negative viral hepatitis screening and normal results were reported from check-up laboratory tests.

Patients’ Exclusion criteria

Patients with a history of alcoholism or autoimmune disease, acute or chronic HBV (as determined by serology), HCC not mediated by CHCV, and patients who were undergoing any type of radiation or chemotherapy for a malignancy other than HCC.

Blood Sampling:

Peripheral blood samples (4 mL) were collected on EDTA tubes and centrifuged for 10 minutes at 1900xg, after which the plasma was carefully withdrawn and centrifuged again for 10 minutes at 16,000xg at 4°C to remove additional cellular nucleic acids attached to cell debris. The supernatant was then transferred to microcentrifuge vials and stored at -80^oC until RNA extraction and routine lab analysis. Additionally, one ml fresh blood sample on EDTA was used for the flow cytometry (FC) assay.

Assays and Analysis:

FC Assay

A volume of 50 µl blood was incubated with 5 µl CD14-PE-conjugated Ab (cat. no. A07764, lot. no.25, BD Biosciences) and 5 µl CD16-FITC-conjugated Ab (cat. no. P59232AA, lot no.200105, Immunotech; Beckman Coulter, Marseille, France), 5 µl CD45-PerCP-conjugated anti-human (cat. no. 345809, lot no. 6039924, BD Biosciences, USA), and incubated for 20 min. Red blood cells were lysed. Samples were washed and suspended in phosphate buffer saline for using FACSCalibur (Biosciences, San Jose, USA). The gating strategy is displayed in Fig. 1. The initial gate was taken on dot-plot graph using forward scatter (FS)/CD45-PerCP and total monocytes-were defined. Total monocytes subsets-were selected on quadrant plot, using CD14-PE and CD16-FITC for subsets determination.

RNA extraction and qRT-PCR

Plasma mature hsa-miR-21-5p and hsa-miR-155-5p were extracted from 200 µl of stored plasma using miRNeasy commercial kit (Cat. NO. 217004, Qiagen, Germany), according to the manufacturer’s protocol. Purity of extracted RNA was tested spectrophotometrically at 260/280 nm. Synthesis of complementary DNA (cDNA) was carried out using miRCURY LNA RT Kit (Cat. No. 339340, Qiagen, Germany) according to the manufacturer’s instructions. Hsa-miR-21-5p and hsa-miR-155-5p expression were determined using miRCURY LNA SYBR Green PCR Kit (Cat. No. 339345, Qiagen, Germany), following manufacturer’s protocol, utilizing a real-time PCR quaint studio 5 system (Applied Biosystem, USA). An internal housekeeping endogenous control, miR SNORD68, was employed.

The qRT-PCR cycling conditions were as follows: 95°C for two min, then 40 cycles, each of 10 seconds at 95°C, 60 seconds at 56°C, and 30 seconds at 70°C. ∆ cycle threshold (Ct) was calculated by subtracting the Ct values of SNORD68 from the Ct values of the target miRs in all samples. Fold change was calculated using 2^−∆∆Ct for relative quantification.

Clinical Chemistry Lab. Analysis

A complete blood count (CBC) was performed by a full automated haematology analyzer (Sysmex, KX21N, Kobe, Japan). Using a chemistry autoanalyzer device (Cobas Integra 400 Plus, Roche Diagnostics, Germany), following the manufacturer's instructions, routine biochemical analysis of serum albumin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), bilirubin (total and direct), alkaline phosphatase (ALP), gamma GT (GGT), total cholesterol (TC), triacyclglycerol (TAG), and high-density lipoprotein-cholesterol (HDL-C). To measure plasma AFP, electro-chemiluminescence immunoassay (ECLIA) using a Cobas 6000, e601 module (Roche Diagnostics, Germany) was used. Finally, blood insulin was measured using the enzyme immunoassay (Hyperion Inc, Miami, FL).

Inflammatory and Resistance Ratios:Indices

TAG/HDL-C ratio with a cut-off value of more than the apparently healthy control group is set diagnostic for insulin resistance (IR) [38]. IR is considered positive in obese, diabetic, and dyslipidemic patients, and having insulin levels of 18 mU/mL or more after glucose/meal [39].

In silico Database Analysis

Identification of immune cells

To visualize closely related immune cells from release of the human peripheral blood mononuclear cell single cells or liver immune cell using Uniform Manifold Approximation and Projection (UMAP) [40]. This was performed using the Human Universal Single Cell Hub (HUSCH) a scRNA-seq database http://husch.comp-genomics.org/#/info_tissue/ (accessed on Dec. 13th, 2022).

Curated databases for prediction of monocytes surface antigens activation

Searched in relation to diseases pathogenesis and related metabolic and molecular pathways using the SIGnaling https://signor.uniroma2.it/ Network Open Resource (SIGNOR3.0) new release October 16th, 2022 (accessed on November 29th, 2022).

Prediction of monocytes surface antigens gene-gene interactions and pathways by Bioinformatics Analysis

Monocytes surface antigens CD14 and CD16A:FCGR3A top interacting genes to be predicted via gene-interaction at University of California Santa Cruiz (UCSC) [41]. Genomics institute http://genome.ucsc.edu/index.html (accessed on November 29th, 2022).

STATISTICAL ANALYSIS

Data were tested for normality using Shapiro-Wilk online calculator [Internet]. Statistics Kingdom 2017 (cited October, 2022). Available from: https://www.statskingdom.com/shapiro-wilk-test-calculator.html (Date launched Nov. 2017, last update June 2022, and validated with R software).

Normally distributed variables are presented as mean ± S.D and analyzed using two samples independent Students’ t-test for comparison. For not-normally distributed variables, data is presented as median (interquartile range) as 1st-3rd quartiles: 25th-75th quartiles. Mann-Whitney (U) was conducted to compare between any two independent not-normally distributed groups. Qualitative data are presented as frequencies (n) and percentages (%). SPSS v17 (Chicago, IL, USA) software was used for analysis.

Student’s t-test and the Chi-square χ2 test were used to compare quantitative and qualitative normally distributed variables between the patients and control groups, respectively. Spearman's rho correlation test was used to assess the association between quantitative non-parametric variables. Receiver operating characteristic (ROC) curve was performed to detect the best cut-off, sensitivities (SNs), specificities (SPs), with an area under the curve (AUC) calculated range from 0 to 1, where the higher the AUC, the better is the parameter in classifying the outcomes correctly.

Logistic regression was performed to determine the independent association of the studied miRs and other parameters with LC progression to HCC. The significance level is set at value < 0.05 for p and the confidence level or confidence interval (C.I) as 95% and 5%, respectively.

The study participants’ demographic and biochemical analysis data are shown in Table 1. The HCC group showed a significant increase in circulating intermediate monocytes when compared to LC group. Also, the HCC group showed significant up-regulation of plasma hsa-miR-21-5p expression compared to LC group (median = 27.66-fold change vs 8.61-fold change from average expression, p < 0.001) and the control (p < 0.001). In addition, hsa-miR-155-5p expression was significantly higher in HCC patients in comparison to the cirrhotic patients (median = 3.18-fold change vs 1.81-fold change, p = 0.001) as well as to the control subjects (p = 0.001).

Table 1

Study participants’ demographic and clinical characteristics (unit) in liver cirrhosis group (n = 40) and HCC group (n = 39) compared to each other and to the apparently healthy control participants (n = 15)
	Groups, n			Significance
Parameter (Unit)	HCC, 39	LC, 40	Control, 15	P1	P2	P3
Gender M/F	27/12	28/12	11/4	NS	NS	NS
Age (years)	61.0(56.0–67.0)	58.5(54.25 -65.0)	58.0(55.0–60.0)	NS	NS	NS
BMI (Kg/m²)	29.0(27.0–31.0)	29.9(27.55–33.2)	27.1(26.7–27.8)	NS	0.008*	0.008*
D.M Yes/No	15/24	24/16	0/15	NS	0.005*	< 0.001*
s. Insulin (mIU/L)	25.0(15.7–42.5)	13.5(5.98–20.37)	8.5(5.9–10.7)	0.001*	< 0.001*	NS
s. Albumin (mg/dl)	3.4(2.9–4.1)	2.7(2.12–3.65)	3.3(3.2–3.8)	0.009*	NS	0.035*
AST (U/L)	77.0(62.0 -105.0)	72.0(62.0–78.0)	33.0(30.0–43.0)	NS	< 0.001*	< 0.001*
ALT (U/L)	51.0(42.0–65.0)	57.0(50.0–63.7)	28.0(24.0–38.0)	NS	< 0.001*	< 0.001*
Total Bilirubin (mg/dl)	1.2(0.9- 2.0)	1.5(1.0–3.07)	0.80(0.6–1.0)	NS	< 0.001*	< 0.001*
Direct Bilirubin (mg/dl)	0.70(0.40–1.2)	0.8(0.4–1.95)	0.30(0.18–0.42)	NS	< 0.001*	< 0.001*
ALP (U/L)	110.0(82.0–155.0)	120.0(99.8–132.8)	46.0 (39.0–58.0)	NS	< 0.001*	< 0.001*
GGT (U/L)	60.0(53.0–77.0)	67.0 (55.3–83.5)	19.0(17.0–23.0)	NS	< 0.001*	< 0.001*
TC (mg/dl)	162.0(122.0 -220)	147.0(112–181)	155.0(152.0–162)	NS	NS	NS
TAG (mg/dl)	133.0(94.0 -193.0)	115.0(76.8–147)	115.0(99.0–123)	NS	NS	NS
HDL-C (mg/dl)	34.0(26.0–40.0)	36.5(30.5–41.75)	47.0(43.0–51.0)	NS	< 0.001*	< 0.001*
TAG/HDL-C ratio	4.1(2.6–6.7)	3.3(2.34–4.59)	2.35(2.2–2.7)	NS	< 0.001*	0.01*
NLR	2.5(2.0–3.9)	2.1(1.38–3.49)	0.93(0. 33–1.42)	NS	< 0.001*	0.001*
PLR	111.4(72.5–240.0)	74.5(47.8-150.13)	128.4(82.1 -154.8)	NS	NS	NS
LMR	2.6(1.4–4.25)	2.8(1.60–3.92)	5.5(4.1–6.0)	NS	0.001*	< 0.001*
INR	1.2(1.1–1.37)	1.5(1.30–2.06)	1.1(1.0–1.30)	< 0.001*	NS	< 0.001*
AFP (ng/mL)	80 (13–305)	7.4 (4.5–10.37)	3.2 (2.7–6.8)	< 0.001*	< 0.001*	0.002*
Total monocytes %	6.7(5.1–9.38)	6.0(4.9–7.5)	3.0(2.6–3.47)	NS	< 0.001*	< 0.001*
Classical monocytes %	4.5(3.5–6.5)	4.0(3.23–5.5)	1.9(1.4–2.45)	NS	< 0.001*	< 0.001*
Intermediate monocytes %	1.16(0.9–1.90)	0.6(0.48–0.98)	0.15(0.10–0.30)	< 0.001*	< 0.001*	< 0.001*
Non-classical monocytes %	0.56(0.2–0.90)	0.5(0.24–0.94)	0.24(0.14–0.3)	NS	0.017*	0.010*
hsa-miR-21-5p fold change	27.6(6.9–69.5)	8.6(3.9–11.3)	0.96(0.94–1.0)	< 0.001*	< 0.001*	< 0.001*
hsa-miR-155-5p fold change	3.1(1.7–8.12)	1.8(0.76–2.2)	1.07(0.9–1.69)	0.001*	0.001*	NS
Data are median (inter quartile range(1st -3rd quartile), statistics were computed using SPSS software, Mann–Whitney test was used (non-parametric data), p1 for comparison between HCC & liver cirrhosis groups, p2 for comparison between HCC & control, p3 for comparison between liver cirrhosis & control, * statistical significance p-value < 0.05, NS, non-significant. [ALT, alanine aminotransferase; AST, aspartate aminotransferase, AFP, alpha fetoprotein, BMI, Body mass index; HCC, hepatocellular carcinoma; HDL, high-density lipoprotein; GGT, gamma glutamyl transferase; LC, liver cirrhosis; PLR, platelet-to-lymphocyte ratio; NLR, neutrophil-to-lymphocyte ratio; LMR, lymphocyte-to-monocyte ratio; TC, total cholesterol; TAG, triacylglycerol.]

The pathological characteristics of the HCC cases are shown in Table 2. According to child score data of all study patients (n = 79) presented in Table 3, hsa-miR-21-5p and hsa-miR-155-5p fold changes showed significant upregulation in patients with early LC score A when compared to more advanced cases with child score B and C. However, according to BCLC stage data in HCC patients (n = 39), no significant difference was detected between the HCC group with BCLC stage A and the more advanced BCLC stages concerning all study parameters (supplementary Table S1).

Table 2

Pathological characteristics of the studied HCC cases (n = 39) and LC cases (n = 40)
	Groups, n (%)
Pathology	HCC, 39 (100%)		LC, 40 (100%)	Statistics test, P-value
Ascites				X² = 7.63, 0.05*
No	24 (61.5%)	16 (40.0%)
Yes	15 (38.5%)	24 (60.0%)
Liver size^$	16.2(14–18)	12.6(10.47–14.27)		U test = 223.0, < 0.001*
Spleen size^$	16.8(15.25–17.5)	15.5(13.42–20.95)		U-test = 719, NS
Largest liver mass size				N.A
≤ 3.00 cm	8 (20.5%)	0 (0.0%)
> 3.00 cm	31 (79.5%)	0 (0.0%)
Subclassifications
Liver disease Child Score				X² = 8.9, 0.012*
A = Least severe	24 (61.5%)	12 (30.0%)
B = Moderately severe	10 (25.6%)	14 (35.0%)
C = Most severe	5 (12.8%)	14 (35.0%)
BCLC classification				N.A
A = Early stage	12 (30.8%)	-
B = Intermediate stage	10 (25.6%)	-
C = Advanced stage	12 (30.8%)	-
D = Terminal stage	5 (12.8%)	-
Total	39 (100%)	-
Data are number (%), ^$median (inter quartile range (1st -3rd quartile)), statistics were computed using SPSS software, * statistical significance p-value < 0.05. [NS, non-significant, N.A, not applicable; HCC, hepatocellular carcinoma; LC, liver cirrhosis; BCLC, Barcelona Clinic Liver Cancer.]

Table 3

Monocytes subsets frequencies, hsa-miR-21-5p and hsa-miR-155-5p fold changes in all study cases (n = 79) with liver cirrhosis background according to Child score
Group, n	LC with and without HCC, 79
	Child score, n
Parameter (Unit)	A, 36	B & C, 43	P value
Total monocytes %	6.8(5.08–8.5)	6.0(5.0–8.1)	NS
Classical monocytes %	4.8(3.6–6.2)	3.9(3.3–5.9)	NS
Intermediate monocytes %	1.0(0.61–1.5)	0.90(0.60–1.3)	NS
Non-classical monocytes %	0.53(0.24–0.94)	0.50(0.21–0.77)	NS
hsa-miR-21-5p fold change	20.3(9.9–39.5)	7.8(3.9–14.2)	0.002*
hsa-miR-155-5p fold change	2.5(1.6–6.9)	1.8(0.76–2.6)	0.014*
Data are median (inter quartile range (1st -3rd quartile)), statistics were computed using SPSS software, * statistical significance p-value < 0.05. [NS, non-significant; HCC, hepatocellular carcinoma; LC, liver cirrhosis.]

Correlation Studies between the investigated monocytes subsets with various biomarkers in all cases (n = 79) is presented in Table 4. Nonclassical monocytes was negatively correlated to hsa-miR-155-5p (r= -0.316, p = 0.005). The frequency of intermediate monocytes was positively correlated with hsa-miR-21-5p (r = 0.30, p = 0.007). A positive correlation was detected between intermediate monocytes % and insulin resistance (r = 0.266, p = 0.042), AFP (r = 0.258, p = 0.022), AST (r = 0.224, p = 0.047), and negative significant correlation with HDL (r= -0.225, p = 0.046). Other non-significant correlations are presented in the supplementary Table S2.

Table 4

Spearman's correlation coefficient among investigated frequency of monocytes subsets in all post-CHCV patients (n = 79)
	post-CHCV G4 patients (n = 79)
Monocytes %	Total		Classical		Intermediate		Non-classical
Parameter	r	p-value	r	p-value	r	p-value	r	p-value
Insulin resistance	0.015	NS	-0.012	NS	0.266	0.042*	0.002	NS
AFP (ng/mL)	0.106	NS	0.078	NS	0.258	0.022*	0.044	NS
AST (U/L)	0.029	NS	-0.075	NS	0.224	0.047*	0.081	NS
HDL-C (mg/dL)	0.059	NS	0.000	NS	0.225	0.046*	0.191	NS
hsa-miR-21-5p	0.066	NS	0.051	NS	0.300	0.007*	0.195	NS
hsa-miR-155-5p	0.093	NS	0.013	NS	0.181	NS	0.316	0.005*
Spearman correlation coefficient (r) was calculated using SPSS software, *significant correlation at p < 0.05 level (2-tailed). [NS; nonsignificant, AST, aspartate aminotransferase, AFP, alpha feto protein, HDL, high-density lipoprotein.]

Correlation studies between the inflammatory indices with various biomarkers in all cases (n = 79) is presented in the supplementary Table S3, where significant negative correlation was shown between Absolute monocytic count (AMC) and hsa-miR-155-5p fold changes (r= -0.233, p = 0.039).

The discriminative ability of the studied parameters to differentiate LC cases from healthy control and to differentiate HCC cases from LC cases, calculated from the ROC curve. As depicted in Table 5 and Fig. 2, according to the ROC curves the discriminative power to differentiate HCC from LC cases after combining hsa-miR-21-5p and hsa-miR-155-5p yielded SN = 84.6%, SP = 45%, and AUC = 0.80. While a better specificity was achieved after combining hsa-miR-155-5p and frequency of intermediate monocytes which yielded SN = 76.9%, SP = 75%, and AUC = 0.766. Moreover, combining hsa-miR-21-5p and frequency of intermediate monocytes yielded SN = 79.5%, SP = 75%, and AUC = 0.844. In comparison, AFP yielded a lower SN = 69% and 100% SP with AUC = 0.85.

Table 5

The discriminative ability of the studied markers to differentiate liver cirrhosis cases from healthy controls and HCC from liver cirrhosis cases
			%
Variable	Cut-off	AUC	SN	SP	P-value
Discriminative ability to differentiate LC cases from healthy controls
Intermediate monocytes %	> 0.38	0.927	82.5	86.7	< 0.001*
hsa-miR-21-5p fold change	> 1.55	0.977	97.5	100	< 0.001*
hsa-miR-155-5p fold change	> 1.17	0.618	67.5	60	NS
hsa-miR-21-5p + hsa-miR-155-5p	-	0.975	97.5	100	< 0.001*
Intermediate monocytes %+hsa-miR-21-5p	-	1.00	100	100	< 0.001*
Intermediate monocytes %+ hsa-miR-155-5p	-	0.935	90	86.7	< 0.001*
AFP (ng/mL)	> 5.9	0.777	62.5	73.3	0.002*
NLR	> 1.14	0.805	80.0	73.3	0.001*
LMR	< 5.09	0.813	90.0	66.7	< 0.001*
Discriminative ability to differentiate HCC cases from LC
Intermediate monocytes %	0.65	0.741	87.2	52.5	< 0.001*
hsa-miR-21-5p fold change	> 7.3	0.8	74	45	< 0.001*
hsa-miR-155-5p fold change	> 1.8	0.7	72	48	< 0.01*
hsa-miR-21-5p + hsa-miR-155-5p	-	0.807	84.6	45	< 0.001*
Intermediate monocytes %+hsa-miR-21-5p	-	0.844	79.5	75	< 0.001*
Intermediate monocytes %+ hsa-miR-155-5p	-	0.766	76.9	75	< 0.001*
AFP (ng/mL)	> 23.3	0.85	69	100	< 0.001*
*significance at p < 0.05 level (2-tailed). [AFP, alpha feto protein; AUC, area under the curve, SN, sensitivity, SP, specificity.]

Logistic Regression Analysis as depicted in Table 6, proved that the circulating classical, intermediate monocytes frequencies and hsa-miR-21-5p were independent risk factors for LC progression to HCC after adjustment for confounders (age, BMI, RBS, AFP, and lipids).

Table 6

Logistic regression analysis using frequency of monocytes subsets, hsa-miR-21-5p and hsa-miR-155-5p expression level-fold change, and the inflammatory ratios as predictors of liver cirrhosis progression to HCC (n = 79) after adjustment for the confounders (AFP, age, BMI, lipids, and RBS) *significant p-value < 0.05. [BMI, Body mass index; C.I., confidence interval; HCC, hepatocellular carcinoma; HDL, high-density lipoprotein; OR, odds ratio; RBS, random blood sugar; TAG, triacyl glycerol; TC, total cholesterol.]
			95% C. I
Variables	P value	OR
Variables	P value	OR	Lower	Upper
Total monocytes %	0.032*	0.273	0.083	0.894
Classical monocytes %	0.026*	4.310	1.192	15.585
Intermediate monocytes %	0.045*	6.721	1.047	43.146
Non-classical monocytes %	NS	1.509	0.736	3.093
hsa-miR-21-5p fold change	0.001*	1.183	1.069	1.309
hsa-miR-155-5p fold change	NS	0.990	0.952	1.030
AFP (ng/mL)	0.016*	1.182	1.032	1.353
Age (years)	0.029*	1.164	1.015	1.335
BMI (Kg/m²)	0.050*	0.806	0.649	1.000
TAG (mg%)	NS	1.004	0.982	1.025
TC (mg%)	NS	1.000	0.981	1.021
RBS (mg%)	NS	0.988	0.974	1.003

*significant p-value <0.05. [BMI, Body mass index; C.I., confidence interval; HCC, hepatocellular carcinoma; HDL, high-density lipoprotein; OR, odds ratio; RBS, random blood sugar; TAG, triacyl glycerol; TC, total cholesterol.]

In silico Databases Analysis for Identification of Immune cells from blood and liver

Figure 3 addresses blood and liver immune cells pattern of hematopoietic cell clustering. Blood immune cells annotation details Dataset: 21, CellNumber: 483286, Celltype: B, CD4⁺ T cells, CD8⁺ T cells dendritic cells (DC), Mast, Megakaryocyte, CD14⁺ monocytes, FCGR3A⁺ monocytes, Myofibroblast, Neutrophil, NK, Plasma, and T-regulatory (Treg) cells (http://husch.comp-genomics.org/#/info_tissue/Blood)

Liver immune cells annotation details Dataset: 7, CellNumber: 59993, Celltype: B, CD4T, CD8T, Cholangiocyte, DC, Endothelial, Epithelial, Erythrocyte, Hepatic Oval, Hepatocyte, Kupffer, Mast, Mesenchymal, Mono/Macro, Muscle, Neutrophil, NK, Plasma, Portal Endothelial, Smooth Muscle, and Treg (http://husch.comp-genomics.org/#/info_tissue/Liver) studied by the Human Universal Single Cell Hub (HUSCH) (accessed on Dec. 13th, 2022). [UMAP, Uniform Manifold Approximation and Projection.]

Monocyte Activation Pathway Bioinformatics

SIGNOR3.0 searched in relation to diseases pathogenesis pathways (accessed on November 29th, 2022). Where, the transcription activator regulator; SPI1 (involved in blood cells differentiation and activation) up-regulates the monocyte differentiation antigen CD14 expression via transcriptional regulation https://signor.uniroma2.it/relation_result.php?id=P08571. However, hsa-miR-155 down-regulates SPI1 via post-transcriptional repression

https://signor.uniroma2.it/relation_result.php?id=P17947&organism=human

Monocytes Surface Antigens Gene-Gene Interactions and Pathways from Curated Databases and Text-mining (Fig. 4) (accessed on November 29th, 2022)

Via gene-interaction on UCSC genomics institute

http://genome.ucsc.edu/cgi-bin/hgGeneGraph?gene=CD14&1=OK&supportLevel=text&geneCount=25&geneCount=25&geneAnnot=drugbank&1=OK&lastGene=MIR21

and

http://genome.ucsc.edu/cgi-bin/hgGeneGraph?supportLevel=text&geneCount=25&geneAnnot=drugbank&1=OK&lastGene=MIR21&gene=FCGR3A for monocytes surface antigens CD14 and CD16A:FCGR3A, respectively.

The monocytes surface antigen CD14 top interacting genes are TLRs, cytokines IL-6, IL-1Beta, TNF, interferon-gamma, and the NF-_KB, targeted by anti-TNF drugs and anti-cytokines therapy (DrugBank). However, the surface antigen CD16A:FCGR3A top interacting genes are interferon-gamma, mitogen-activated protein 3 kinase 11 (MAP3K11), PIK3R1 and R2, targeted by caffeine, isoprenaline, and glucosamine (DrugBank).

Do monocytes have crucial role in the cirrhotic inflammatory milieu?

A question to be answered in relation to non-protein coding-epigenetics in peripheral blood. The current study results showed no discernible difference between the HCC and LC groups, in terms of the frequency of circulating total, classical, and non-classical monocytes. Blood inflammation indices are cost-effective and easily available, but, unfortunately, non-specific to tumor [42]. In the current study, significant differences were seen in neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) between LC patients and controls as well as between HCC patients and controls, while no differences were detected between LC and HCC groups. Zhu and Zhou [42] documented NLR ability to distinguish LC from healthy controls, but could not discriminate cirrhotic HCC from LC. Also, LMR was suggested to be useful to determine the outcome of cirrhotic patients by Hsu et al. [43]. In contrast, Du et al. [44] found that elevated NLR is associated with HCC development in cirrhotic patients with HBV who underwent splenectomy for hypersplenism. Yilma et al. [45] suggested that NLR was a component of HCC development and recurrence risk models in the context of CHCV. Currently, PLR showed no significant differences among the three groups, which disagrees with Catanzaro et al. [46] who inferred PLR to be used as a predictive marker for LC.

Our correlation data results revealed a positive correlation between the frequency of intermediate monocytes% and insulin resistance (IR). Keeping in mind that the IR is a state of chronic low-level inflammation [47] and intermediate monocyte population has already been related to some inflammatory disease [48], via higher prevalence of the monocyte surface activation inflammatory marker CD14 [30]. Accordingly, it has been shown that experimental inhibition of monocyte recruitment to the liver through blocking the C-C motif chemokine receptor 2 (CCR2), ameliorates both the IR and hepatic inflammation [49].

Also, intermediate monocytes% showed significant correlation with AFP (r=0.258, p=0.022). Our results agree with Kong et al. [50] that AFP level is associated with monocyte activation and its phagocytosis ability. On the other hand, it was claimed that the immunomodulating properties of tumor-derived AFP (tAFP) could induce immune-escape through inhibiting monocyte-derived dendritic cells (DC) function [51]. Interestingly, Munson et al. observed the in vitro tAFP ability to supress monocyte function, rather than frequency, via supressing their ability to produce TNFα and IL-1β [52].

Intermediate monocytes% showed significant negative correlation with HDL-C (r= -0.225, p=0.046) agreeing with Rogacev et al. [53]. Idzkowska et al. [54] provided evidence that inflammation could induce lipid dysregulation mainly through the modulation of monocytes recruitment and activation. Monocytes priming was demonstrated in dyslipidaemic LC or HCC patients [55]. Therefore, monocytes may present one important component, via atherosclerosis development, during the liver fibrotic stage preceding or early during LC.

According to Martín-Sierra et al. [56] intermediate-monocyte subsets through their pro-inflammatory role are related to HCC tumorigenicity in CHCV-G4 Egyptian patients. Since hsa-miR-21-5p over-expression, in the disease groups, was positively correlated with intermediate-monocytes frequency%, therefore, we can hypothesize that up-regulated hsa-miR-21-5p may be involved in monocytes differentiation [57].

It is noteworthy to mention, that we have studied adipokines single nucleotide polymorphisms (SNPs), several apoptosis/autophagy genes and their role on IR or inflammation, immunity, and carcinogenesis or the reverse, as well as several Ils or vitamin D SNPs influence on various types of cancers, common in Egypt, as an attempt of study “Cancer Genetics in The Egyptian Population” [58], [59], [60], [61], [62]. However, nowadays, we are into epigenetics.

According to the Mattoscio et al. [63] non-classical monocytes exert anti-tumoral properties manifested as cytotoxicity, preventing metastasis, autophagy, NK cells recruitment, and Treg suppression. Correlation results obtained in the study between hsa-miR-21-5p and/or hsa-miR-155-5p and the monocyte subsets, suggest a co-incidence and/or an interaction. However, significant reduction in the non-classical monocyte subsets frequency% will be manifested as lack of their anti-tumoral impact, therefore, unfortunately, HCC development commences.

Regarding ROC curve analysis, hsa-miR-21-5p was shown to be superior to hsa-miR-155-5p as a plasma molecular marker for identifying LC cases from healthy cases and combining both hsa-miR-21-5p + hsa-miR-155-5p provided an accepted discriminative sensitivity for HCC cases identification from LC [34]. Moreover, logistic regression analysis proved that circulating classical and intermediate monocytes frequencies% and hsa-miR-21-5p were independent predictors of HCC progression from cirrhotic background after adjustment for the confounders (age, BMI, RBS, AFP, and lipids).

Monocyte activation pathway informatics (SIGNOR3.0) revealed that the transcription factor SPI1 could up-regulate CD14 expression on monocytes surface. hsa-miR-155 could post-transcriptionally down-regulate SPI. This may explain our finding of hsa-miR-155-5p negative correlation to non-classical monocytes subtypes characterized with dim CD14 expression, which could be claimed from one side to the indirect suppressive effect of the up regulated hsa-miR-155-5p on monocytes surface-expressed CD14, mediated via SPI1.

Kyoto Encyclopedia of Genes and Genomes KEGG targeted pathways search for MiR21 and MIR155 genes Clusters/Heatmap using DIANA TOOLS Mirpath reverse search and genes that share domains determined via GenesLikeMe. MiR21 and MIR155 genes are related to each other. MIR155 gene is related to and is targeted with genes involved in inflammation NF-_kB, STAT3, IL-6, TNF, MIR21, MAPK8, and TLR4.

In Summary, per, we now fulfilled our objectives and the story almost reached the end, proving the clinical relevance of measuring and quantifying peripheral blood, as liquid biopsy, monocytes subsets distribution frequency% and circulating hsa-miR-21-5p and hsa-miR-155-5p fold-expression in liver cases diagnosis. Moreover, for the story to end “happily-ever-after” the treatment of choice should be identified “healthy-ever-after” for “Better Health” SDG #3.

Drugs for MIR21 and MIR155 genes - from GeneCards, DrugBank, PharmGKB, DGIdb, IUPHAR, and Novoseek are cisplatin and Cobomarsen, respectively. Drugs targeting MiR21 and MIR155 down-stream related/interacting genes and the monocytes surface antigen CD14 gene are lovastatin or anti-inflammatory, anti-cytokine; anti-TNF-alpha, caffeine, and glucosamine.

Limitation

The current study did not include the predictive survival role of the investigated miRs panel; hsa-miR-21-5p and hsa-miR-155-5p in the CHCV G4-linked to HCC patients’ cohort (a prospective study is prepared by our group, currently).

Strength(s)-related to the current research

Up to our knowledge, this study is the first to describe the diagnostic utility of hsa-miR-21-5p or hsa-miR-155-5p and as panel, in combination with AFP, for an enhanced and, hopefully, early diagnosis of clinical CHCV G4-related HCC and LC. hsa-miR-21-5p/hsa-miR-199a-5p ratios are proved, clinically, in the current study, as diagnostic for AFP-negative HCC cases [21].

Recommendations

Considering hsa-miR-21-5p and/or hsa-miR-155-5p as potential precision nc-epigenetic therapeutic target(s) for CHCV-G4 related HCC and/or LC treatment, based on blood monocytes sub-classification, after proofing the mechanism experimentally.

Sustainability Plan

Blocking hsa-miR-21-5p and/or hsa-miR-155-5p target genes obtained from gene-gene interaction network algorithms and KEGG pathways in silico curated databases. These genes if being targeted, will present a promising future potential treatment option(s); treatment-based on ncRNA, a step-toward precision health.

Linking hsa-miR-21-5p and/or hsa-miR-155-5p to altered-monocytes distribution on top of LC and HCC, for finding better treatment option(s), based on the immune cells’ perturbation in blood. Altered intermediate monocytes frequency is linked to dysregulated lipid metabolism and insulin resistance in LC patients. Circulating classical monocytes, intermediate monocytes frequencies and hsa-miR-21-5p were shown to be independent risk factors for HCC evolution.

AFP, alpha-fetoprotein; ALP, alkaline phosphatase; ALT, alanine aminotransferase; AMC, Absolute monocytic count; AST, aspartate aminotransferase; AUC, area under the curve; BCL2, B-cell lymphoma 2; BCLC, Barcelona Clinic Liver Cancer; BMI, body mass index; C.I, confidence level or interval; CAID, Cirrhosis-associated immune dysfunction; CBC, Complete blood count; CCR2, chemokine receptor C-C motif chemokine receptor 2; Ct, cycle threshold; CL2, chemokine (C-C motif) ligand 2; CD, cluster of differentiation; cDNA, complementary DNA; CHCV, chronic hepatitis C virus; CT, computed tomography; CYR61, Cysteine-rich angiogenic inducer 61; DAMPs, Damage-associated molecular patterns; DN, dendritic cells, D.M, Diabetes Mellitus; ECLIA, electro-chemiluminescence immunoassay; EDTA, Ethylenediaminetetraacetic acid; EGF, epidermal growth factor; ELISA, enzyme-linked immunosorbent test; FC, flow-cytometry; FCGR3A, flow-cytometry gamma receptor IIIa; FS, forward scatter; G4, genotype 4; GGT, gamma glutamyl transferase; HBV, Hepatitis B virus; HCC, Hepatocellular carcinoma; HCV, Hepatitis C virus; HDL, high-density lipoprotein; HIF-1, hypoxia-induced factor-1; hsa, homo sapien; HUSCH, Human Universal Single Cell Hub; I.C, informed consent; IGF1, Insulin-like growth factor 1; IL-6, InterLeukin-6; INR, International normalized ratio; INS, Insulin; IR, insulin resistance; KEGG, Kyoto Encyclopedia of Genes and Genomes; LC, liver cirrhosis; LMR, lymphocyte-to-monocyte ratio; LN, lymph node; MAPK8, Mitogen-activated protein kinase 8; MAP3K11, mitogen-activated protein 3 kinase 11; miRs, MicroRNAs; nc, non-protein coding; NF-_kB, nuclear factor kappa B cell, NK, natural killer; NLR, Neutrophil-to-lymphocyte ratio; OR, odds ratio; PI3K, phosphatidyl inositol 3kinases; PLR, platelets-to-lymphocytes ratio; PV, portal vein; qRT-PCR, quantitative real-time PCR; RBS, Random blood sugar; REC, Research Ethics Committee; ROC, Receiver operating characteristic; SMAD2, Suppressor of Mothers Against Decapentaplegic 2; SN, sensitivities; SNORD68, Small Nucleolar RNA, C/D Box 68; SNPs, single nucleotide polymorphisms, SP, specificities; SPSS, Statistical Package for Social Science software; STAT3, Signal transducer and activator of transcription 3; TAG, triacylglycerol; tAFP, tumor-derived AFP; TC, total cholesterol; TLR4, Toll like receptor 4; TP, Tumor protein; Treg, T-regulatory; UCSC, University of California Santa Cruiz; UMAP, Uniform Manifold Approximation and Projection.

Full list of author information is available at the end of the article.

Acknowledgment

Prof. Dr. Mahmoud Seddik; Al-Azhar University Vice President for Post-Graduate Studies and Research, Cairo, Egypt, for work facilitation.

Author contributions

Conceptualization by R.H., C.L. and N.M.H., Methodology by R.H., M.A.E. and N.M.H., In silico/Bioinformatics Databases/Software analysis by N.M.H., Resources by R.B.A., H.G.K, F.K., and S.Z., Validation by N.M.H. and S.Z., Formal Analysis by R.H., C.L. and N.M.H., Investigation by A.A.E., Data Curation by O.I.A-E., F.A, S.G.A.-H. and N.M.H., Writing – Original Draft Preparation by R.H., E.A., C.L., S.G.A.-H. and N.M.H., Ethical paper work by A.A.E., Rewriting – Review & Editing by R.H., S.K., U.S., C.L. and N.M.H., Visualization by R.H. and N.M.H., Supervision by N.M.H. and C.L., Project Administration by R.H. and N.M.H., and finally, Funding Acquisition by all authors.

Funding

Research reported in this manuscript was self-supported.

Data availability statement

The original contributions presented in the study are included in the manuscript. Further inquiries can be directed to the corresponding author.

Ethical Approval

Institutional Review Board Statement: The study was conducted between November 2021 to July 2022, after obtaining approval from the Research Ethics Committee (REC) of Faculty of Medicine for Girls, Al-Azhar University, Cairo, Egypt (Approval #: No.2022121641). Participants were informed about the aim of the study and provided their written Informed Consent (I.C.) statement before enrolment in the study. The study was carried out in adherence to the Declaration of Helsinki Guidelines in 2013.

Consent for publication

All authors have read the manuscript in its final form and approved publication. All authors approve the journal’s authorship agreement.

Conflicts-of-interest

Authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Author details

¹Clinical Pathology Department, Faculty of Medicine (for Girls), Al-Azhar University, Nasr City, 11884, Cairo, Egypt. ²Hepatology and Gastroenterology Department, National Liver Institute, Menoufia University, Menoufia, 35211, Egypt. ³Biochemistry Department, Faculty of Pharmacy, Ain Shams University, Abassia, 11566, Cairo, Egypt. ⁴Hepatology, Gastroenterology and Infectious Diseases Department Faculty of Medicine (for Girls), Al-Azhar University, Nasr City, 11884, Cairo, Egypt. ⁵Molecular Biology, Zoology and Entomology Department, Faculty of Science (for Girls), Al-Azhar University, Egypt. ⁶Pharmacology Department, Faculty of Medicine (for Girls), Al-Azhar University, Nasr City, 11884, Cairo, Egypt. ⁷Internal Medicine Department, Faculty of Medicine (Girls), Al-Azhar University, Egypt. ⁸Community Medicine and Public Health, Faculty of Medicine, Al-Azhar University, Nasr City, 11884, Cairo, Egypt. ⁹Institute of Clinical Immunology, University Medical Center Leipzig, Johannisallee 30, 04103 Leipzig, Germany. ¹⁰DHGS German University of Health and Sport, Berlin, Germany. ¹¹Cytometry Unit,Immunology Laboratory, Saint-Etienne University Hospital, France.

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Monocytes Subsets Altered Distribution and Dysregulated Plasma hsa-miR-21-5p and hsa- miR-155-5p in HCV-Linked Liver Cirrhosis Progression to Hepatocellular Carcinoma

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Abstract

Figures

Introduction

Patients And Methods

Blood Sampling:

Assays and Analysis:

RNA extraction and qRT-PCR

Clinical Chemistry Lab. Analysis

Inflammatory and Resistance Ratios:Indices

Identification of immune cells

Curated databases for prediction of monocytes surface antigens activation

STATISTICAL ANALYSIS

Results

Monocyte Activation Pathway Bioinformatics

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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