
==== Front
Virus Res
Virus Res
Virus Research
0168-1702
1872-7492
Elsevier

S0168-1702(24)00150-3
10.1016/j.virusres.2024.199457
199457
Article
New insights into potential biomarkers and their roles in biological processes associated with hepatitis C-related liver cirrhosis by hepatic RNA-seq-based transcriptome profiling
Nasr Azadani Hossein a
Nassiri Toosi Mohssen b
Shahmahmoodi Shohreh ac
Nejati Ahmad a
Rahimi Hamzeh d
Farahmand Mohammad e
Keshavarz Abolfazl a
Ghorbani Motlagh Fatemeh a
Samimi-Rad Katayoun ksamimirad@sina.tums.ac.ir
a⁎
a Department of Virology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
b Liver Transplantation Research Center, Imam-Khomeini Hospital, Tehran University of Medical Sciences (TUMS), Tehran, Iran
c Food Microbiology Research Center, Tehran University of Medical Sciences, Tehran, Iran
d Department of Molecular Medicine, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
e Pediatric Infectious Disease Research Center, Tehran University of Medical Sciences, Tehran, Iran
⁎ Corresponding author at: Department of Virology, School of Public Health, Tehran University of Medical Sciences, Enqelab Square, P.O. Box 1417613151,Tehran, Iran. ksamimirad@sina.tums.ac.ir
10 9 2024
11 2024
10 9 2024
349 1994577 5 2024
19 8 2024
20 8 2024
© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Highlights

• Hepatic RNA-seq profiling figured out HCV-related cirrhosis pathogenesis and diagnostic biomarkers.

• LTB, ISLR, ZAP70, MOXD1, KLRB1, and Slitrk3, were identified as potential biomarkers.

• Inflammation-enhancing pathways, cytokine-cytokine receptor interaction, NF-κB signaling, and Rheumatoid arthritis, were highlighted in HCV-related cirrhosis.

• Neural DEGs, MOXD1 and SLITRK3, were involved in neuropsychiatric disorders of cirrhotic patients.

• TNF signaling, bile secretion, FoxO signaling, and axon guidance were highlighted in relation to neurological disorders in cirrhosis patients.

Chronic hepatitis C virus infection is a major cause of mortality due to liver cirrhosis globally. Despite the advances in recent therapeutic strategies, there is yet a high burden of HCV-related cirrhosis worldwide concerning low coverage of newly developed antiviral therapies, insufficient validity of the current diagnostic methods for cirrhosis, and incomplete understanding of the pathogenesis in this stage of liver disease. Hence we aimed to clarify the molecular events in HCV-related cirrhosis and identify a liver-specific gene signature to potentially improve diagnosis and prognosis of the disease.

Through RNA-seq transcriptome profiling of liver samples of Iranian patients with HCV-related cirrhosis, the differentially expressed genes (DEGs) were identified and subjected to functional annotation including biological process (BP) and molecular function (MF) analysis and also KEGG pathway enrichment analysis. Furthermore, the validation of RNA-seq data was investigated for seven candidate genes using qRT-PCR. Moreover, the diagnostic and prognostic power of validated DEGs were analyzed in both forms of individual DEG and combined biomarkers through receiver operating characteristic (ROC) analysis. Finally, we explored the pair-wise correlation of these six validated DEGs in a new approach.

We identified 838 significant DEGs (padj ˂0.05) enriching 375 and 15 significant terms subjected to BP and MF, respectively (false discovery rate ˂ 0.01) and 46 significant pathways (p-value ˂ 0.05). Most of these biological processes and pathways were related to inflammation, immune responses, and cellular processes participating somewhat in the pathogenesis of liver disease. Interestingly, some neurological-associated genes and pathways were involved in HCV cirrhosis-related neuropsychiatric disorders. Out of seven candidate genes, six DEGs, including inflammation-related genes ISLR, LTB, ZAP70, KLRB1, and neuronal-related genes MOXD1 and Slitrk3 were significantly confirmed by qRT-PCR. There was a close agreement in the expression change results between RNA-seq and qRT-PCR for our candidate genes except for SAA2-SAA4 (P= 0.8). High validity and reproducibility of six novel DEGs as diagnostic and prognostic biomarkers were observed. We also found several pair-wise correlations between validated DEGs.

Our findings indicate that the six genes LTB, ZAP70, KLRB1, ISLR, MOXD1, and Slitrk3 could stand as promising biomarkers for diagnosing of HCV-related cirrhosis. However, further studies are recommended to validate the diagnostic potential of these biomarkers and evaluate their capability as targets for the prevention and treatment of cirrhosis disease.

Keywords

HCV
Cirrhosis
Gene expression
Biomarkers
Neuropsychiatric disorders
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pmc1 Introduction

Hepatitis C virus (HCV) infection is a global public health concern. It is the leading cause of persistent liver disease, affecting approximately 50 million people worldwide. In the majority of cases, infection with HCV leads to chronic hepatitis, which can progress into cirrhosis and ultimately to hepatocellular carcinoma (HCC) ("Hepatitis C, Key facts," 2022; Ijaz et al., 2019). Viral hepatitis is the seventh leading cause of global mortality and the fifth in the Middle East and North Africa, mainly due to HCV (Hajarizadeh et al., 2016; Mahmud et al., 2018).

Regarding the asymptomatic nature of HCV infection in most cases and the unawareness of many patients (45 %-85 %) of their infection, the chronic form of the disease remains often undiagnosed and untreated leading to progress to liver cirrhosis (Khullar and Firpi, 2015). Moreover, in some patients who cleared the virus and achieved the sustained virologic response (SVR) after direct antiviral agents (DAA) therapy, there is still a major risk of morbidity and mortality associated with the progression of liver injury to cirrhosis and HCC (Negro, 2021).

Cirrhosis as a considerable cause of morbidity and mortality in subjects suffering from chronic liver disease was reported to be associated with 2.4 % of deaths worldwide (Huang et al., 2023). HCV and HBV could be responsible for nearly two-thirds of the global burden of cirrhosis. According to the Global Burden of Disease study in 2017, the number of people with compensated cirrhosis was almost 112 million cases in the world. (Alberts et al., 2022; "The global, regional, and national burden of cirrhosis by cause in 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017," 2020). Therefore, diagnosis of liver cirrhosis is critical since it progresses to the advanced forms known as decompensated cirrhosis and HCC along with a reduction in the rate of survival (Flamm, 2018). For this, further investigations are needed to elucidate the molecular mechanisms of HCV-induced cirrhosis, particularly after DAA therapy, as well as identify reliable screening biomarkers to diagnose and monitor cirrhosis in different stages of the disease. Besides, these biomarkers may be used as new targets for antiviral therapy due to interfering with cellular proteins involved in the virus life cycle (Yasser et al., 2021; Zeisel et al., 2015).

In addition to liver complications, chronic HCV infection has frequently been associated with a wide variety of extrahepatic manifestations, including a broad spectrum of neurological and psychiatric disorders. They have been reported in up to 50 % of chronic HCV-infected patients and include cognitive impairment, fatigue, sleep disorders, depression, anxiety, bipolar affective disorder, and schizophrenia (Dybowska et al., 2023; Fang et al., 2023). The molecular events in the pathogenesis of neuropsychiatric disorders after DAA treatment are still unclear.

Animal models and peripheral blood do not accurately reflect what happens during cirrhosis in the human liver. Therefore, to find approaches to diagnose, prevent, and treat cirrhosis, we need to focus on the major organ, the liver, where pathologic events related to the disease occur. On the other hand, the study of variations in gene expression profiles using high throughput RNA-seq (HTS) technology can discover new potential signatures for diagnosing diseases and getting a more comprehensive view of the molecular mechanisms of viral pathogenesis (Rao et al., 2018). Thus, in this study, we analyzed the gene expression changes of freshly stabilized liver tissue in Iranian patients with HCV-related cirrhosis compared with healthy controls using HTS technology. Additionally, the validation of RNA-seq results for six novel genes was performed using quantitative real-time PCR (qRT-PCR). To achieve a deeper insight into HCV-related progressive liver disease, we attempted to explore the link between the results of system biology analyses and clinical indicators.

2 Materials and methods

2.1 Clinical samples and specimen collection

This study was carried out with the approval of the Ethics Committee of Tehran University of Medical Sciences (code of ethics: IR.TUMS.SPH.REC.1397.144) in the Liver Transplantation Research Center of a national hospital affiliated with Tehran University of Medical Sciences, Tehran, Iran. Written informed consent was obtained from the participants or their legal surrogates before enrolment. The study was conducted on 26 Iranian subjects (male and female), including 13 end-stage HCV-associated liver cirrhosis patients who were undergoing liver transplantation and 13 healthy controls among brain-dead donors. The patients were identified among the subjects with histologically confirmed end-stage cirrhosis due to HCV infection through a previous diagnosis of serum HCV RNA positive, HCV antibody (anti-HCV) positive, and negative molecular tests for hepatitis B virus (HBV) or human immunodeficiency virus (HIV) coinfections. Owing to achieving the SVR, all enrolled patients were not taking any treatment at the time of sample collection. All participants (patients and controls) with a history of alcohol abuse, positive serologic or molecular test for HIV/HBV infection, and non-HCV-associated liver disease were excluded. Furthermore, liver function-associated clinical laboratory tests were measured in both patient and control groups before liver transplantation at the same standard condition. Following the liver transplantation surgery, liver biopsies were taken from removed cirrhotic livers as patient samples. Simultaneously, control samples were provided by taking a portion of biopsies from healthy donated livers in a routine histological investigation procedure. All biopsies were immediately submerged in a sufficient volume of RNAlater (Qiagen, Hilden, Germany), maintained at 4°C overnight, and subsequently stored at -80°C until RNA extraction.

2.2 RNA isolation

Liver biopsy disruption as the first step of the RNA extraction process was performed in a mortar-pestle-like system under continuous liquid nitrogen conditions without allowing the frozen samples to thaw until a completely homogenous powder was made from tissue biopsies. Subsequently, homogenization was done using RLT lysis buffer (Qiagen, Hilden, Germany) supplemented with β-mercaptoethanol, by passing at least 5 times into an RNase-free fitted blunt needle-syringe system (Qiagen, Hilden, Germany). The prepared lysate was subjected to the following steps of the RNA extraction process using RNeasy Micro Kit according to the manufacturer's protocol (Qiagen, Hilden, Germany). The concentration of isolated RNAs was measured by spectrophotometry on NanoDrop instrument (Thermo Scientific™ NanoDrop™ One Microvolume UV-Vis Spectrophotometer, Wilmington, USA), and the purity of the RNAs was evaluated by considering A260:A280 and A260:A230 ratios. The RNA integrity number (RIN) was evaluated using Agilent 2100 Bioanalyzer by the next-generation sequencing (NGS) service provider. Three qualified RNA samples (260:280 ≥ 1.9, 260:230 ≥ 1.8) from either the patient or control group were selected as sequencing groups subjected to subsequent RNA-seq analysis. The RNA samples in sequencing groups were transferred into RNA-protecting tubes, GenTegra®, and lyophilized according to the manufacturer's protocol (GenTegra LLC, California, USA). All lyophilized RNA samples were sent to the NGS service provider company (Novogen, Hong Kong).

2.3 Sequencing library preparation and next-generation sequencing

After recovery, the RNA samples with a suitable RIN value (≥7) were considered good quality and used for cDNA library preparation. The sequencing library was prepared using the Illumina® TruSeq™ RNA Sample Preparation Guide, which involved purifying the poly-A containing mRNA molecules, fragmentation of mRNA, reverse transcription, end repair, addition of a single ‘A’ base, ligation of the adapters, and purification and enrichment by PCR to create the final cDNA library. The library was paired-end sequenced using an Illumina HiSeq 2500 platform (2 × 150 bp). The RNA-seq fastq data of sequence reads generated in this study have been deposited at the NCBI Sequence Read Archive (SRA) database with the project accession number PRJNA1105650.

2.4 Quality control (QC) and alignment of sequencing data

The quality of raw sequencing reads was checked with FASTQC software. The principal aspects of QC included the evaluation of Phred quality score, sequence GC content, and adapter content. Low-quality reads including Phred quality score below 30, unusual GC content distribution across the sequences, and the presence of a significant amount of adapter in each library were trimmed and the quality of trimmed reads was assayed again. The clean reads obtained from each library were mapped against Homo sapiens reference genome hg38 using STAR software (V 2.4.0). Then, post-alignment quality control was performed to inspect alignment accuracy and mapping quality using STAR. Transcript assembly and quantification analysis were done to collect subsets of the reads corresponding to each gene using StringTie2 software.

2.5 Differential expression (DE) analysis

Differential gene expression analysis was performed to identify the difference in the expression level of each transcript across the compared groups of the study using the Deseq2 package. The expression level of each gene was reported in fragments per kb per million (FPKM). To achieve results with high confidence, the adjusted p-value (padj) ˂ 0.05 and ǀLog2fold changeǀ (ǀLog2FCǀ)≥ 1 were considered as stringent filtering criteria.

2.6 Functional annotation

The significant differentially expressed genes (DEGs) were subjected to functional annotation analyses consisting of gene ontology (GO) and pathway enrichment. The GO enrichment analysis was performed to find the over-represented GO terms in three levels of biological process (BP), molecular functions (MF), and cellular components (CC) on the DEGs using Enrichr (https://maayanlab.cloud/Enrichr/). The pathway enrichment was conducted using Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis database (https://www.genome.jp/kegg/pathway.html) to identify the key enriched pathways. The enriched GO terms and KEGG pathways with an adjusted p-value (padj value) of ˂ 0.05 were considered significant.

2.7 Validation of changes in the expression level of six genes using qRT-PCR

To validate the differential gene expression identified based on the initial HTS results, twenty RNA samples including ten patient and ten control samples were subjected to qRT-PCR validation as the following pipeline;

2.7.1 Selection of candidate DEGs and reference gene

To build a more comprehensive picture of the molecular events along with the pathogenesis of HCV-related liver cirrhosis, some criteria were considered to select the candidate DEGs including the most fold-change (ǀLog2FCǀ˃ 6) of expression, the most statistical significance (padj ˂0.005), and the correlation with the liver disease according to the previous studies. Due to considering HCV-related neuropsychiatric disorders, we also focused on the DEGs annotated to the most significantly enriched pathways which were related to neurological processes based on the system biology databases (https://www.genecards.org/ and https://asia.ensembl.org/index.html). Finally, seven candidate genes consisting of five up- and two down-regulated genes were selected to validate using qRT-PCR. The beta-actin (ACTB) was selected as the reference gene.

2.7.2 Primer design

All specific primers for selected candidate transcripts and reference gene were designed using the Primer-BLAST database (https://www.ncbi.nlm.nih.gov/tools/primer-blast/index.cgi) and synthesized by Metabion (Germany). Before starting the qRT-PCR tests on samples, all designed primers were subjected to the examination of primer efficiency by performing qRT-PCR on a foursome series of 10-fold dilutions of a cDNA synthesized from one of the patient RNA samples. Then, standard curves were analyzed and the amplification efficiency was determined using Rotor-Gene 6000 seies software, Corbett Research, Australia. The sequences of primer pairs for all candidate and reference genes are listed in Table S1.

2.7.3 qRT-PCR

1 μg of total RNA extracted from each of the tissue samples was used to synthesize cDNA by the QuantiTect Reverse Transcription Kit (Qiagen, Germany) according to the manufacturer's protocol. The concentration of synthesized cDNA samples was measured using spectrophotometry on NanoDrop One (Thermo Scientific™ NanoDrop™ One Microvolume UV-Vis Spectrophotometer, Wilmington, USA). qRT-PCR was carried out on 100ng of each cDNA template using 2x QuantiNova SYBR Green PCR Master Mix (Qiagen, Germany) with 0.7 μM/reaction forward and reverse primers. Thermal cycling was performed by the Rotor-Gene 6000 instrument (Corbett, Mortlake, New South Wales, Australia) with the following cycling program: an initial PCR activation step at 95°C for 2 min followed by 40 cycles of denaturation at 95°C for 10 sec and combined annealing-extension at 60°C for 30 sec. The results were normalized to ACTB as a reference gene (Zárybnický et al., 2019). The expression levels for genes of interest (GOIs) were computed according to the 2−ΔΔCt method. The relative mRNA quantification of each GOI was reported as a fold change to compare the expression level between patients. All reactions were performed in duplicate.

2.8 Statistical analysis

Continuous and categorical variables were presented as median (interquartile range [IQR])and n ( %), respectively. To compare differences between independent groups, we used the Wilcoxon rank-sum test, χ2 test, or Fisher's exact test where appropriate. The diagnostic performance of each investigated mRNA was evaluated through receiver operator characteristic (ROC) curve analysis. We also analyzed the correlation between validated DEGs using the Pearson correlation coefficient. Finally, the combiROC R package (Ferrari et al., 2023) which applies a binomial logistic regression method, was used to evaluate the efficacy of the DEGs as a potential combined biomarker discriminating between cirrhosis patients and controls. This method models the probability of the binary outcome (presence or absence of cirrhosis) based on the DEGs. Logistic regression optimizes the weights for each gene to maximize the likelihood of correct classification, which results in both improved diagnostic accuracy and interpretability of each biomarker's contribution. A two-sided α of less than 0.05 was considered statistically significant. Statistical analyses were done using R version 4.3.2.

3 Result

3.1 Demographic and clinical characteristics of participants

Global transcriptome sequencing was conducted on a total of six liver samples (n=3 specimens in either patient or control groups). These specimens were selected as the sequencing group from 26 participants enrolled in this study (n=13 samples in either patient or control groups) based on the highest RNA quality. All enrolled patient specimens were pathologically confirmed to have cirrhotic liver condition. The samples were taken from patients with the previously RT-PCR-confirmed infection of HCV as the causative factor for cirrhosis who underwent treatment to achieve an SVR before liver transplantation. All enrolled control samples were histologically confirmed to be healthy liver biopsies taken and shared from brain-dead donors in a routine surgical operation process for histological investigations. RNA-seq data were largely validated by RT-PCR on the remaining twenty samples consisting of ten patients and ten controls. Demographic and clinical data for all participants are summarized in Table 1. The patient group included ten males and three females whereas the control group consisted of seven males and six females. The mean age in the patient group and control group were 57.7 (54-65) and 37.9 (26-58) years old, respectively. The age difference between subjects in the two groups was statistically significant (p-value ˂ 0.001). The distribution of gender did not differ between patient and control groups. Despite the change in the serum levels of biochemical markers for hepatic function including aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase (ALP) in the patient group as compared to the control group, only the change of ALT was statistically significant (p-value= 0.023). Furthermore, the serum level of sodium increased significantly (p-value= 0.001) in patients in comparison with healthy controls, whereas the elevation in the level of potassium was marginally significant (p-value= 0.053). Consistent with liver damage in cirrhosis patients, the serum level of total bilirubin elevated significantly (p-value ˂ 0.0001) as compared to the controls. The dramatically decreased function of the liver in cirrhosis patients was reflected as a significant decrease in serum level of albumin (p-value= 0.003) and coagulation activity as represented by the elevated international normalized ratio (INR) (p-value ˂ 0.0001) and decreased platelet count (p-value ˂ 0.0001). Concerning the acute or chronic renal disease in most liver cirrhosis patients, the serum level of creatinine increased significantly as compared to the controls (p-value= 0.001). The total count of white blood cells (WBC) indicated no significant difference in comparison between the two groups. The information for alpha-fetoprotein (AFP) in healthy controls was not available.Table 1 Demographic and clinical characteristics of patients and controls.

Table 1Characteristics	Patient	Control	p-value	
Demographics				
 Gender: Male/Female	10/3	7/6	0.216	
 Total	13	13		
 Age, years	57.7 (50-65)	37.9 (26-58)	0.00078	
Clinical parameters				
 AST (IU/L)	53 (19-119)	37.8 (29-45)	0.258	
 ALT (IU/L)	30.8 (14-81)	40.9 (30-51)	0.0232	
 ALP (IU/L)	309 (144-507)	268.6 (180-478)	0.347	
 Sodium (μmol/L)	135.3 (130-144)	149.2 (134-159)	0.00132	
 Potassium (μmol/L)	4.97 (4.1-6.0)	4.39 (2.9-5.1)	0.0536	
 Total bilirubin (mg/dL)	4.65 (1.5-25.1)	0.75 (0.6-1)	0.00018	
 ALB (g/dL)	3.27 (2.8-4.3)	4.13 (3.3-5)	0.00318	
 INR	1.684 (1.6-2.0)	1.05 (1-1.2)	0.00018	
 AFP (IU/L)	15.37 (5.0-65.0)	NA		
 Serum creatinine (mg/dL)	1.41 (1.0-2.0)	0.97 (0.8-1.2)	0.00194	
 Total WBC count (per μL)	6970 (5000-13700)	7640 (5300-10200)	0.17384	
 Platelets (per μL)	118000 (68000-210000)	320500 (218000-401000)	0.00018	
NA: not applicable, AFP had not been assessed for controls.

All values are indicated as the mean with the range in brackets.

p-values ˂0.05 were considered significant and were calculated by chi-square and two-tailed Mann–Whitney U tests for gender and other parameters, respectively.

AST aspartate transaminase, IU international unit, ALT alanine transaminase, ALP alkaline phosphatase, ALB albumin, INR international normalized ratio, AFP alpha-fetoprotein, WBC white blood cells.

3.2 Transcriptomic changes in patients with HCV-induced cirrhosis

To better clarify the pathologic mechanism of HCV-induced cirrhosis and find potentially diagnostic biomarkers, we performed RNA-seq to determine the DEGs in patients with cirrhosis. The differential gene expression analysis indicated 838 DEGs between cirrhotic patients and the control group (ǀLog2FCǀ≥ 1, padj value ˂0.05) (Supplementary file 2). Of these, 666 genes were upregulated while 172 genes were downregulated in patients with cirrhosis. Gene ontology (GO) and pathway enrichment analyses were conducted to understand the functional annotation of these DEGs. There were 375 and 15 significant terms subjected to BP and MF, respectively (false discovery rate [FDR] ˂ 0.01) (Supplementary file 3). As shown in Fig. 1A, GO analysis indicated that the GO-enriched terms of the biological process are most associated with the cell structural processes and can be categorized into some groups such as cell organization, adhesion, migration, and motility. Furthermore, our GO-enriched terms of molecular function indicated in Fig. 1A, can be categorized into some groups including receptor binding, protein binding, enzyme activity, and enzyme inhibitory activity. These processes and functions may be largely subjected to molecular events in the carcinogenesis process consistent with our cirrhotic patients' clinical status. Moreover, we identified 46 significant pathways (p-value ˂0.05) (Supplementary file 3). The top 40 significant enriched pathways, including pathways in immune responses, cancer, cellular processes, and environmental information processing are shown in Fig. 1B. The GO analysis of genes revealed that most upregulated genes are included in the category of immune responses such as activation and differentiation of lymphocytes, increase in proinflammatory cytokines, disorders in cellular and humoral immunity, regulation of innate and adaptive inflammatory host defense, and cell survival. These biological processes were associated with some clinical characteristics of our patients that indicate liver injuries caused by the immune responses to HCV infection have progressed to cirrhosis due to the dysregulation of these responses and their persistence. The consequences of liver injuries in our patients were observed as clinical findings such as the significant decrease in ALT (P= 0.02), ALB (P= 0.003 and platelet counts (P= 0.0001) and the significant increase in total bilirubin (P= 0.0001) and INR (P= 0.0001). Moreover, the GO analysis of genes also showed that some downregulated and upregulated genes tended to execute neurological functions. Therefore, the biological processes in which these genes are involved, play a role in the neuropsychiatric manifestations that are reported in cirrhotic patients.Fig. 1 Functional annotation of our 838 DEGs. A. GO enrichment analysis in three levels of Biological Process (BP), Molecular Function (MF), and Cellular Component (CC). B. Pathway enrichment analysis indicates 40 top KEGG-enriched pathways (Ranking by p-value). The vertical axis lists the names of the pathways in the KEGG database and the horizontal axis shows the proportion of annotated genes in each pathway versus the total number of annotated genes.

Fig 1

3.3 Validation of changes in the expression level of seven candidate DEGs using qRT-PCR

To validate the RNA-seq results, the expression changes of seven candidate DEGs were examined by qRT-PCR using all 20 cirrhosis and control liver samples. The candidate genes were selected among the DEGs with the most fold-change of expression (ǀLog2FCǀ˃ 6) and biological function in either neurologic, inflammation-, and immunologic-related pathways or processes based on the annotation of genes in Ensembl (https://asia.ensembl.org/index.html) and GeneCards (https://www.genecards.org/) databases. These candidates included five up-regulated (ISLR, KLRB1, LTB, MOXD1, and ZAP70) and two down-regulated (SAA2-SAA4 and Slitrk3) genes. The RNA-seq results of all genes were entirely in agreement with qRT-PCR data except for SAA2-SAA4. (Fig. 2 and Table 2). The qRT-PCR results for SAA2-SAA4 in patients were in agreement with RNA-seq data however, the difference in the expression level of the gene was not statistically significant (p-value= 0.3). The results of qRT-PCR were notably consistent with the RNA-seq data (Fig. 3).Fig. 2 The expression changes of seven candidate genes, ISLR, LTB, MOXD1, KLRB1, ZAP70, SLITRK3, and SAA2-SAA4, between patients and controls using qRT-PCR. Box plots indicate the difference in delta-CT mean between the cirrhotic patients and controls. Blue boxes show the results of patients and red boxes show the results of controls. Dots indicate the result distribution of individual samples. Except for SAA2-SAA4, the expression differences for the other 6 DEGs were significant.

Fig 2

Table 2 The expression differences of candidate DEGs between patients and controls.

Table 2	Group		
Gene	Controls, N= 131	Patients, N= 131	p-value2	
ISLR	9.28 (8.38, 9.84)	5.75 (5.54, 6.36)	< 0.001	
Slitrk3	7.8 (6.8, 8.9)	14.1 (12.3, 15.1)	0.004	
LTB	9.91 (8.51, 10.35)	6.36 (5.82, 7.18)	<0.001	
MOXD1	10.73 (10.25, 11.74)	6.18 (6.01, 7.10)	< 0.001	
KLRB1	9.03 (8.73, 9.24)	6.22 (5.54, 6.66)	< 0.001	
ZAP70	10.79 (10.28, 11.64)	7.41 (7.23, 7.86)	< 0.001	
SAA2_SAA4	1.04 (0.35, 1.39)	1.46 (0.92, 1.85)	0.3	
1 The average of cycle threshold (CT) values was calculated as Median via Interquartile Range (IQR).

2 The p-values were calculated by Wilcoxon rank sum exact test.

Fig. 3 RNA-seq and qRT-PCR results of seven candidate genes. A close agreement of expression difference results of six significant DEGs (ISLR, KLRB1, LTB, ZAP70, MOXD1, and Slitrk3) between RNA-seq and qRT-PCR techniques based on Log2FC.

Fig 3

3.4 Differential expression levels and diagnostic performance for the candidate genes between the two studied groups

Using ACTB as the reference gene, we examine the relative expression difference of seven candidate genes, including ISLR, Slitrk3, KLRB1, LTB, MOXD1, ZAP70, and SAA2-SAA4 between liver cirrhosis and healthy control samples by qRT-PCR. Student's t-test was performed to identify significant validated DEGs between two groups (p-value ˂ 0.05). Additionally, as shown in Fig. 4, to evaluate the diagnostic or prognostic power of the validated transcripts for HCV-related liver cirrhosis, receiver operating characteristic (ROC) and binomial logistic regression analysis were conducted on the qRT-PCR data of all ten patients, and the area under the curve (AUC) of ROC was measured for each candidate transcript (p-value ˂ 0.05). In this regard, our findings from ROC analysis of three candidate genes including KLRB1, ZAP70, and MOXD1 revealed that they could discriminate significantly between cirrhosis and healthy subjects (AUC= 1.00, p-value ˂ 0.001) with 100 % sensitivity, 100 % specificity, and 100 % accuracy at the cut-off value (7.27, 9.01, and 9.32, respectively), positive predictive value (PPV)= 100 %, negative predictive value (NPV)= 100 %, positive likelihood ratio (+LR)= infinite, and negative likelihood ratio (-LR)= 0.0. (Fig. 4A, B, and C, respectively).Fig. 4 Receiver operating characteristic (ROC) analysis of seven candidate DEGs. Results showed significant diagnostic values for KLRB1 (A), ZAP70 (B), MOXD1 (C), ISLR (D), LTB (E), Slitrk3 (F), and no significant diagnostic value for SAA2-SAA4 (G) between cirrhotic patients and the controls.

Fig 4

The ROC analysis for ISLR transcript also showed that significant differentiation between liver cirrhosis and controls (AUC= 0.97, p-value ˂ 0.001) with 100 % sensitivity, 87 % specificity, and 94 % accuracy at a cut-off value (7.688), PPV= 91 %, NPV= 100 %, +LR= 8, and -LR= 0.0. (Fig. 4D).

Furthermore, the analysis for LTB showed that this transcript distinguished between liver cirrhosis and healthy controls (AUC= 0.96, p-value ˂ 0.001) with 80 % sensitivity, 100 % specificity, and 88 % accuracy at the cut-off value (7.26), PPV= 100 %, NPV= 80 %, +LR= infinite, and -LR= 0.2. (Fig. 4E)

Moreover, the results of ROC analysis indicated that Slitrk3 could significantly differentiate between the two studied groups (AUC= 0.89, p-value= 0.002) with 80 % sensitivity, 100 % specificity, and 89 % accuracy at the cut-off value (11.85), PPV= 100 %, NPV= 80 %, +LR= infinite, and -LR= 0.2. (Fig. 4F)

Although the fold-change of SAA2-SAA4 downregulation was appropriate in RNA-seq data in cirrhosis patients compared to the controls (Log2FC= -6.2, padj value ˂0.001), and reduction in its expression was also observed in qRT-PCR, there was no significant differential expression between the two studied groups. The ROC analysis results for this candidate gene, were not also significant (AUC= 0.65, p-value= 0.8). (Fig. 4G)

The findings of these analyses suggested that the candidate transcripts were reliably predictive and could serve as strong biomarkers with the superlative values of specificity and sensitivity for HCV-associated cirrhosis.

3.5 Using candidate DEGs as combined biomarkers between the cirrhosis patients and controls

To evaluate the efficacy of the DEGs as any possible combined biomarker discriminating between the cirrhosis patients and controls, binomial logistic regression was conducted using the combiROC R package (P ≤ 0.05). Accordingly, the analysis identified the four significant combined biomarkers between patient and control groups which contain three two-gene combinations including KLRB1 and ZAP70, ISLR and KLRB1, LTB and ZAP70, and one three-gene combination, ISLR, KLRB1, and ZAP70. All four combined biomarkers showed AUC=1 and P˂0.001 (Table 3). Our results revealed that these leading combined biomarkers could strongly distinguish between cirrhosis patients and controls.Table 3 Binomial logistic regression analysis results for six significant DEGs.

Table 3	Members	AUC	SEN	SPE	Cutoff	ACC	NPV	PPV	p-value	
Combination 1	KLRB1, ZAP70	1.0	100 %	100 %	0.5	100 %	100 %	100 %	˂ 0.001	
Combination 2	ISLR, KLRB1	1.0	100 %	100 %	0.5	100 %	100 %	100 %	˂ 0.001	
Combination 3	LTB, ZAP70	1.0	100 %	100 %	0.5	100 %	100 %	100 %	˂ 0.001	
Combination 4	ISLR-KLRB1-ZAP70	1.0	100 %	100 %	0.5	100 %	100 %	100 %	˂ 0.001	
SEN (sensitivity); SPE (specificity); ACC (accuracy); NPV (negative predictive value); PPV (positive predictive value).

3.6 Pearson's correlation analysis between possible pair-wise validated DEGs

We assessed the pair-wise correlation of validated DEGs in the cirrhosis patient group using Pearson's correlation analysis by the cor package in the R environment (where P ≤ 0.05 was considered significant). Pearson's correlation was performed to address the possible relation of the expression pattern between pair-wise DEGs and their role in pathogenesis. The analysis indicated several statistically significant positive correlations in pair-wise DEGs. They included the highest significant correlation between ZAP70 and ISLR (P˂0.0001, r= 0.94) which was followed by the positive correlation between ZAP70 and Slitrk3 (P= 0.004, r= 0.81), LTB and Slitrk3, and ISLR and Slitrk3 (P= 0.005, r= 0.8). In addition, statistically significant correlations were observed between MOXD1 and ISLR (P= 0.01, r= 0.76), ISLR and LTB (P= 0.01, r= 0.74), and ZAP70 and LTB (P= 0.01, r= 0.73). There was no significant correlation in all pair-wise comparisons of KLRB1. The values of Pearson's correlation coefficient for pair-wise DEGs are shown in Fig. 5.Fig. 5 Pearson's correlation between pair-wise DEGs. Results show several significant positive correlations among pair-wise DEGs. The correlation between pair-wise DEGs becomes stronger as the intensity of the blue color and the size of the circles increase. The numbers indicate the correlation coefficient.

Fig 5

4 Discussion

Progression of chronic hepatitis C to cirrhosis even after DAA treatment and also neuropsychiatric symptoms which are common extrahepatic manifestations in the cirrhosis stage are severe problems in the treatment of this disease (Faccioli et al., 2021; Goto et al., 2020). To overcome these problems, understanding the underlying pathogenesis of the disease is essential. Moreover, the asymptomatic nature of the acute infection, lack of optimal sensitivity and specificity of non-invasive tools for early detection of HCV-related cirrhosis, and heavy health and economic burden that are common in cirrhosis and its associated complications such as HE and neuropsychiatric disorders, reveal the need to identify stronger diagnostic and prognostic biomarkers for HCV-related cirrhosis (Liu and Chen, 2022; Nallagangula et al., 2018). Therefore, in this study, we identified the DEGs associated with HCV-related liver cirrhosis and neuropsychiatric disorders through screening the gene expression profiles by RNA-seq. These DEGs encoded factors that were involved in significant DEGs-enriched pathways to regulate biological processes particularly immune response, neurological functions, and tumor development. To the best of our knowledge, this is the first study that mainly focuses on hepatic transcriptome profiling of patients with HCV-associated cirrhosis by RNA-seq technology. Previous studies in this area are different in terms of the stage of HCV-associated liver disease (chronic, fibrosis, hepatocellular carcinoma) (Boldanova et al., 2017; Robinson et al., 2015; S.R, 2023), the sample used (peripheral blood mononuclear cells, cell culture) (Burchill et al., 2021; Hojka-Osinska et al., 2016), and the technology applied to analyze the transcriptome profile (microarray, real-time PCR) (Bièche et al., 2005; Hoshida et al., 2013; Ijaz et al., 2019). Each of these factors could be a source of variation in the results of gene expression analysis (Singh et al., 2018). Moreover, most studies investigating the gene expression signature within the liver usually compare the diseased tissue against healthy samples, either from neighboring unaffected parts of the same sample or different patients mostly taken during surgical resection (Bellodi-Privato et al., 2009; Honda et al., 2001; Robinson et al., 2015; Walters et al., 2006). Nevertheless, our control group was completely healthy liver samples obtained from individuals declared brain dead.

In recent years some studies report that alterations in immune responses, particularly T lymphocytes and inflammatory cytokines, contribute to the pathogenesis of cirrhosis by inducing inflammation and liver injuries (Tuchendler et al., 2018). In fact, during HCV infection, inflammatory processes initiated by viral proteins lead to the production of inflammatory cytokines, chemokines, and the recruitment of immune cells to hepatocytes. These events, establish the inflammatory liver microenvironment through direct signaling and recruiting further lymphocytes and monocytes to the site of inflammation, where the resident macrophages are activated, which worsens damaged parenchyma in chronic hepatitis C (CHC) and promotes liver injuries. Since HCV infection is not resolved in most cases, long-term persistent inflammation is established, which may cause the progression of liver injuries to advanced forms of liver disease such as cirrhosis (Bauer et al., 2012; Dolganiuc et al., 2004; Rios et al., 2021; Simonin et al., 2013). In this study, we observed a significant increase in the expression of ZAP70, LTB, ISLR, and KLRB1 genes in our cirrhotic patients. Considering the function of these genes in inducing inflammatory responses, we suggest the important role of these genes in the development and progression of cirrhosis. Moreover, it appears that with a significant increase in the expression of these genes, some DEGs-enriched pathways including NF-κB signaling, cytokine-cytokine receptor interaction, TNF signaling, rheumatoid arthritis, and T cell receptor signaling were affected and could induce inflammatory responses and systemic chronic inflammation (Fig. 1). In this regard, LTB, one of our validated upregulated DEGs, was enriched in three pathways including cytokine-cytokine receptor interaction, NF-κB signaling pathway, and rheumatoid arthritis. NF-κB signaling pathway was also enriched by ZAP70, the other validated upregulated DEG suggesting that this pathway could help better understand the pathogenesis of HCV-induced liver cirrhosis. Previous studies have indicated that NF-κB plays various roles in different cells, such as inflammation in Kupffer cells, the survival of hepatocytes, and the activation, inflammation, and survival, of hepatic stellate cells (HSCs). The activation of NF-κB in HSCs seems to contribute to liver fibrosis and cirrhosis through various mechanisms consisting of promoting fibrogenesis directly, antiapoptotic impacts, and secreting chemokines that recruit macrophages. Therefore, NF-κB has a crucial role in regulating the outcome of chronic liver disease including liver fibrosis, cirrhosis, and/or HCC (Chen et al., 2021; Luedde and Schwabe, 2011; Tao et al., 2022). The Ras signaling pathway was also enriched by ZAP70. Numerous studies have discovered that cirrhotic liver samples exhibit increased levels of both mRNA and activated forms of Raf1, a key proto-oncogene involved in the downstream of this pathway. Accordingly, Raf1 is functionally implicated in the cellular processes including cell protection from apoptosis and the induction of normal cell growth and differentiation. Additionally, the dysregulated expression of Raf1 drives aberrant activation of its other interconnected pathway, MAPK, possibly resulting in the promotion of liver cirrhosis (Hwang et al., 2004).

Chronic liver inflammation also is strongly associated with an increased risk of HCC by aberrant expression of cytotoxic cytokines and recruiting inflammatory cells that infiltrate the tumor microenvironment in the liver (Haybaeck et al., 2009; Simonin et al., 2013; Vainer et al., 2008). Subsequently, these cells generate tumor-enhancing immune activity by secreting inflammatory mediators and proteases which contribute to cancer development (Bauer et al., 2012). According to the function of LTB, ZAP70 and ISLR genes, it appears that they are also associated with inflammation-induced tumorigenesis by changes in the chemokines synthesis, recruitment of circulating inflammatory cells to the liver, and activation of NF-κB and lymphocytes signaling that cause hyperproliferative hepatotoxic microenvironments leading to HCC formation (Chen et al., 2020; Haybaeck et al., 2009; Li et al., 2020; Simonin et al., 2013). In addition, MOXD1 as a nervous system-related gene, also contributes to cancer development, particularly glioblastoma (GBM). MOXD1 has been reported to be highly expressed in GBM cells and its overexpression is associated with GBM grade and poor survival of patients with primary GBM (Shi et al., 2022).

Systemic inflammation that is regulated by the liver is one of the mechanisms by which HCV infection promotes the risk of neuropsychiatric disorders (Aregay et al., 2018; Choi et al., 2021). In fact, when inflammatory cytokines are secreted in large amounts in the liver, they exert systemic effects by inducing hepatocytes to produce factors that activate brain endothelium. The activated endothelium secretes mediators, which stimulate particular neuronal cells in the CNS and lead to neuropsychiatric manifestations such as fatigue, anxiety, depression, and cognitive impairments that are reported in more than 50 % of CHC patients (Adinolfi et al., 2015; Bauer et al., 2012). In this regard, Senzolo and colleagues also indicated that cognitive impairments might be associated with systemic inflammation (Senzolo et al., 2011). There is also evidence that patients with cirrhosis have significant cognitive dysfunction and show more memory errors (Adekanle et al., 2012; Ciećko-Michalska et al., 2013). Furthermore, HE, which is a brain dysfunction with a range of neuropsychiatric abnormalities and results from liver failure, is also observed in most patients with HCV-induced cirrhosis (Ciećko-Michalska et al., 2013; Ney et al., 2018). Symptoms of HE typically include changes in sleep patterns, confusion, personality change, poor concentration, and forgetfulness. The high amount of pro-inflammatory cytokines secretion in the liver during cirrhosis causes changes in the permeability of the blood-brain barrier, which in turn leads to indiscriminate brain entry of toxins produced in increased levels in the liver. These toxins can impair brain function by either direct effect or indirect pro-inflammatory response to toxins in the brain (Cheon and Song, 2021; Martínez-Esparza et al., 2015; Yan et al., 2023). On the other hand, the increased bile acids synthesized in liver cirrhosis has been found to be involved in many neurological disorders through the bile signaling pathway in the CNS (McMillin and DeMorrow, 2016; Sauerbruch et al., 2021; Yan et al., 2023). In our study the DEGs- enriched pathways including cytokine-cytokine receptor interaction, TNF signaling, and bile secretion may relate to these processes (Fig. 1). Moreover in the present study, the two genes, MOXD1 and Slitrk3 were also related to neurological processes. About 75 % of patients with cirrhosis indicate sleep and biorhythm disorders in addition to characteristic neurological symptoms of HE (Mechtcheriakov et al., 2005). According to the suggested association between MOXD1 and sleep regulation (Tsuneoka and Funato, 2021) and its upregulation level in our cirrhotic patients, MOXD1 may be involved in the sleep disorders observed in these patients. Some studies report that Slitrk3 is needed for normal functional GABAergic synapse development (Takahashi et al., 2012). Since these synapses are the main source of inhibition in the mammalian brain, they are critical in many aspects of brain physiology. Therefore, a reduction in Slitrk3 causes dysregulation in GABAergic synapse development which can lead to some neuropsychiatric disorders (Ko et al., 2015; Ramamoorthi and Lin, 2011). In this regard, studies on mice also revealed that a reduction of functional inhibitory synapses causes various neurological disorders, particularly spontaneous seizures or epilepsy (Moehler, 2006; Takahashi et al., 2012). Thus, the downregulation of Slitrk3 expression in our cirrhotic patients could also contribute to the development of neuropsychiatric manifestations reported in these patients. For example, its reduction may enhance the risk of seizures in HE, which has an incidence of 2 % to 32 % (Prabhakar and Bhatia, 2003). Importantly, recent studies suggest that the resolution of HE does not completely resolve cognitive disorders (Umapathy et al., 2014). These features show the importance of identifying the genes and their changes involved in these disorders during the cirrhosis stage, particularly those that are not normalized after the resolution of HE.

Serum amyloid A (SAA) is a mainly liver protein family including acute phase proteins SAA1 and SAA2, and constitutive protein SAA4 in humans. The gene expression and serum level of former proteins are elevated in response to acute inflammatory conditions such as infection, tissue injury, and trauma, while the SAA4 is constitutively expressed by many kinds of cells and responds minimally to inflammation (den Hartigh et al., 2023; Li et al., 2024). SAA2-SAA4 is a readthrough transcript made between neighboring SAA2 and SAA4 genes and serves as an inflammation and immune activation marker (Karlsson et al., 2021; Schulten et al., 2016). Despite all individual isoforms of SAA, the differential expression of SAA2-SAA4 in various pathological conditions has been rarely studied. Schulten et.al identified SAA2-SAA4 as a significant up-regulated DEG among the top 95 probe sets in a microarray gene expression profiling of brain metastatic samples from papillary thyroid carcinoma. The vast majority of these 95 DEGs were up-regulated and comprised several inflammatory cytokines and cytokine receptors suggesting active inflammation in this stage of the disease (Schulten et al., 2016). Conversely, Li et al. observed a significant reduction in the expression levels of both the SAA4 gene and its corresponding protein in the liver tumor tissues of patients with advanced HCC when compared to normal liver tissues (Li et al., 2024). Moreover, Yuan et. al investigated the serum level of SAA in patients with different liver disease. Their findings indicated that patients with active liver disease exhibited significantly elevated serum SAA levels compared to both healthy control subjects and those with inactive chronic hepatitis. Additionally, they concluded that inflammatory characteristics of SAA increase coordinate with active hepatitis, rather than cirrhosis (Yuan et al., 2019). Furthermore, Wu et.al evaluated the potential of SAA as a biomarker for HCC by measuring and analyzing the serum level of SAA in three groups including chronic hepatitis, cirrhosis, and HCC. Findings from this study showed that the concentration of total acute-phase SAA (A-SAA) in HCC patients was significantly higher than in chronic hepatitis and liver cirrhosis, but no significant difference was found between chronic hepatitis and cirrhosis groups. In addition, they found a significantly lower concentration of total A-SAA in cirrhosis patients when compared to advanced HCC (Wu et al., 2022). Our study revealed that the expression levels of SAA2-SAA4 were lower in patients with cirrhosis in comparison to healthy controls by both RNA-seq and qRT-PCR, however, the difference was not significant. The collective findings from the studies suggest that during the inflammation resulting from the active phases of liver disease associated with cancer progression (Diakos et al., 2014), there is an elevated expression of SAA. Conversely, in the inactive stages of the disease, including cirrhosis and advanced HCC, the expression of this gene is reduced as a result of liver failure.

Recent studies have shown that biochemical biomarkers do not have optimal sensitivity and specificity for early detection of HCV-related cirrhosis (Nallagangula et al., 2018). To determine the potentially appropriate biomarkers, we examined the diagnostic and prognostic potential of six qRT-PCR-validated DEGs including ISLR, KLRB1, ZAP70, MOXD1, LTB, and Slitrk3, both individually and in combination using ROC analysis. Our results revealed that these six genes have significant associations as valuable biomarkers with patients in the cirrhotic stage compared to the control group. Three up-regulated transcripts, including KLRB1, MOXD1, and ZAP70 were identified as statistically optimal biomarkers, exhibiting a sensitivity, specificity, and accuracy of 100 %. Three other verified DEGs, including two up-regulated transcripts LTB and ISLR, and a down-regulated transcript Slitrk3, had sufficient ROC values to effectively function as reliable biomarkers (Fig. 4). To the best of our knowledge, five genes ISLR, ZAP70, Slitrk3, KLRB1, and MOXD1 have not previously been investigated in HCV-related gene expression studies. Regarding LTB, Lowes et al reported a significant upregulation of its expression in cirrhosis patients (Lowes et al., 2003). The finding of their study confirmed the validity of our LTB results regarding its significant upregulation in our cirrhosis patients. In addition, we constructed efficient diagnostic and prognostic panels of combined biomarkers based on sensitivity, specificity, and accuracy using the newly developed R package combiROC (Ferrari et al., 2023). Our findings suggested four valuable combined biomarkers with the sensitivity, specificity, and accuracy of 100 % (Table 3). These combinations comprised four up-regulated transcripts ZAP70, KLRB1, ISLR, and LTB. Several previous studies have found co-expression between ZAP70 and KLRB1 in various pathological conditions such as cervical carcinoma (Qin et al., 2021), leiomyosarcoma (Darzi et al., 2021), pulmonary disorders (Zhang et al., 2021), and sepsis (Gong et al., 2020) suggesting a possible relevant role of these two DEGs in immune and inflammation-related disorders. Consistently, our findings indicated that ZAP70 and KLRB1 were co-enriched in several biological processes and molecular functions mostly related to immune responses and signal transductions (Fig. 1B, Supplementary file 3).

To further screen the DEGs, we conducted a novel approach by investigating all possible pair-wise correlations between six qRT-PCR-validated DEGs in our patient group. We found that there were several positive pair-wise correlations among DEGs. This may suggest the specificity and association of these positively correlative DEGs in HCV-liver cirrhosis. ZAP70 had the most number of correlations with four out of the possible existing six conditions indicating the possibly central role of ZAP70 in association with a variety of molecular functions in the process of HCV-related cirrhosis. On the contrary, no significant correlation was observed among the pair-wise DEGs including KLRB1. This may related to the complexity of cirrhotic tissues, various backgrounds of cirrhosis patients, epigenetic changes in KLRB1 in cirrhotic tissues, and the variation-prone nature of gene expression techniques used.

5 Conclusion

To the best of our knowledge, this is the first study to analyze hepatic gene expression profiling in HCV-related cirrhosis patients using RNA-seq. To validate high-throughput data, the significant differential expression of six novel identified DEGs was confirmed by qRT-PCR in patients compared to controls. These DEGs include up-regulated genes LTB, ZAP70, KLRB1, and ISLR, found to be related to immune response and inflammation, and two liver neurodevelopment-related genes MOXD1 with up-regulation and Slitrk3 with down-regulation. The reduction observed in the expression of SAA2-SAA4 by both RNA-seq and qRT-PCR was not significant. Our study revealed that qRT-PCR-confirmed DEGs could serve as potentially valuable diagnostic and prognostic biomarkers for the liver cirrhosis stage both individually and in combination. Several DEG-enriched pathways were particularly highlighted, revealing their important roles in interactions between liver and inflammatory responses in cirrhosis disease. Additionally, dysregulation of liver neuronal DEGs could potentially explain several neurological and neuropsychiatric disorders that are common in cirrhotic patients. Further investigation is recommended to analyze the expression changes of our candidate genes in CHC and HCC to reveal the trend of alterations in these genes in different stages of HCV infection.

Ethics statement

The protocol of this research was approved by the Ethical Review Committee of the School of Public Health, Tehran University of Medical Sciences (IR.TUMS.SPH.REC.1397.144).

Funding sources

This study was funded by Tehran University of Medical Sciences with No. 97-04-15-39021 .

Author statement

All authors concur with the submission, have seen a draft copy of the manuscript and are agree with its submission and publication to your Journal.

CRediT authorship contribution statement

Hossein Nasr Azadani: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Data curation. Mohssen Nassiri Toosi: Writing – review & editing, Supervision, Resources, Project administration. Shohreh Shahmahmoodi: Validation, Resources, Formal analysis. Ahmad Nejati: Validation, Methodology, Data curation. Hamzeh Rahimi: Writing – review & editing, Software, Formal analysis. Mohammad Farahmand: Software, Formal analysis, Data curation. Abolfazl Keshavarz: Visualization, Investigation. Fatemeh Ghorbani Motlagh: Investigation. Katayoun Samimi-Rad: Writing – review & editing, Writing – original draft, Supervision, Project administration, Methodology, Investigation, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix Supplementary materials

Image, application 1

Data availability

The fastq format of RNA-seq data generated in this study has been deposited at NCBI SRA database with the project accession number PRJNA1105650. Further data will be made available on request.

Acknowledgment

The authors respectfully acknowledge and keep alive the memory of the late Dr. Habib-Allah Dashti who generously provided the liver tissue samples used in this study. His invaluable contribution has greatly enhanced the quality and significance of our research.

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.virusres.2024.199457.
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