Assessing prognosis in HCV-induced early-stage liver cirrhosis: An integrated model based on CX3CR1-associated immune infiltration genes

Assessing prognosis in HCV-induced early-stage liver cirrhosis: An integrated model based on CX3CR1-associated immune infiltration genes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Assessing prognosis in HCV-induced early-stage liver cirrhosis: An integrated model based on CX3CR1-associated immune infiltration genes Haozheng Cai, Jing Zhang, Chuwen Chen, Junyi Shen, Xiaoyun Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4336291/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Chemokine (C-X3-C motif) Receptor 1 (CX3CR1) is a chemokine receptor that functions primarily by mediating the chemotaxis and adhesion of immune cells. However, the role of CX3CR1 in hepatitis C virus (HCV)-induced early-stage liver cirrhosis remains unexplored. GSE15654 retrieved from the GEO. Cox regression model, CIBERSOT and LASSO technique was utilized to identify CX3CR1-associated prognostic genes. Surgical resection samples were collected for verification. High expression of CX3CR1 in the liver was linked to worse prognosis in individuals with HCV-induced early-stage liver cirrhosis. CX3CR1-associated immune infiltration genes(IIGs), namely ACTIN4, CD1E, TMCO1, LOC400499, MTHFD2, and WSF1, were identified, showing specific expression in the livers of individuals with post-hepatic cirrhosis and liver failure compared to HC. Notably, high infiltration of plasma cells and low infiltration of monocytes were predictive of poor prognosis in early-stage cirrhosis. The combined risk model predicted that high expression of CX3CR1-associated IIGs and increased infiltration of plasma cells were associated with unfavorable prognosis in individuals with HCV-induced early-stage liver cirrhosis. Elevated expression of CX3CR1 is a risk factor for individuals with HCV-induced early-stage liver cirrhosis. The developed combined risk model effectively predicted the prognosis of such individuals. Health sciences/Diseases/Infectious diseases/Hepatitis/Viral hepatitis/Hepatitis c Health sciences/Biomarkers/Predictive markers HCV Liver fibrosis CX3CR1 Immune Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Liver cirrhosis is a common physiological outcome of chronic fibrotic liver disease caused by various factors 1,2 . It involves the replacement of normal hepatic architecture with regenerative hepatic nodules 3 . Hepatitis C virus (HCV) affects around 71 million individuals and is a major cause of liver cirrhosis 4,5 . Liver cirrhosis claims approximately 1 million lives annually worldwide, ranking it as the 11th leading cause of mortality 2 . Diagnosing liver fibrosis during the asymptomatic compensatory period remains challenging 6,7 , leading many individuals to miss early treatment opportunities. In addition, there are no effective methods to reverse cirrhosis 8 . Thus, there is an urgent need for new diagnostic techniques to accurately detect early-stage liver fibrosis and identify effective therapeutic targets to slow its progression. Chemokine (C-X3-C motif) Receptor 1 (CX3CR1) is predominantly expressed in monocytes, macrophages, a subset of NK cells, and terminally differentiated cytotoxic T cells 9 . Its primary function involves mediating the chemotaxis and adhesion of immune cells by binding with its unique ligand CX3CL1 9 . Several studies have demonstrated that increased expression of CX3CR1 contributes to the deterioration of obstruction-induced kidney fibrosis 8 and is correlated with a worse prognosis in individuals with idiopathic pulmonary fibrosis 10 . However, regarding liver cirrhosis, the role of CX3CR1 is controversial. While one study showed significantly increased expression of CX3CR1 in end-stage liver fibrosis induced by chronic hepatitis C 11 , another study suggested that overexpression of CX3CR1 could alleviate liver inflammation and fibrosis in the carbon tetrachloride (CCl4)-induced fibrosis model 12 . Presently, no research has confirmed the involvement of CX3CR1 in predicting the prognosis of liver cirrhosis. Therefore, the primary objective of this research was to determine the link between CX3CR1 and the prognosis of individuals with early hepatic fibrosis induced by HCV. Liver fibrosis occurs through iterative cycles of tissue injury, inflammation, and repair, in a microenvironment of cytokines and chemokines, where the interaction among the innate and adaptive immune systems and stromal cells leads to hepatic stellate cell (HSC) activation and extracellular matrix (ECM) accumulation, ultimately results in liver cirrhosis 13,14 . The infiltration pattern of immune cells in the liver significantly influences the advancement of liver fibrosis. 15 . As mentioned earlier, CX3CR1 is a key mediator of immune cell migration during organ fibrosis. Some studies have indicated that the level of CX3CR1 in M1 macrophages in BALF is significantly higher in mice with interstitial lung disease 10,16 , and inhibiting the expression of CX3CR1 in BALF reduces M1 macrophage infiltration 16 . In peritoneal fibrosis, CX3CR1 mediates the expression of CX3CL1 and TGF-β on the peritoneal mesothelium, promoting the development of peritoneal fibrosis 17 . However, the association between CX3CR1 and immune cell infiltration remains unclear in HC-induced early-stage liver fibrosis. In the current research, the prognostic value of CX3CR1 was innovatively explored in individuals with chronic HCV-induced early-stage liver cirrhosis, and the link between CX3CR1 and immune cell infiltration was analyzed using bioinformatic analysis. An integrated model was developed for predicting the prognosis of these individuals based on the expression of CX3CR1, CX3CR1-associated immune infiltration genes (IIGs), and immune cell infiltration. Furthermore, clinical samples were collected from healthy liver donors, individuals with compensated cirrhosis, and those with acute-on-chronic liver failure to verify the findings. In summary, the research proposes that CX3CR1 can potentially be targeted to prevent and address the progression of liver cirrhosis in the future. Results High expression of CX3CR1 predicts poor prognosis in individuals with liver cirrhosis: Based on the expression of CX3CR1, 216 individuals were divided into two categories using maximally selected rank statistics, yielding 186 cases in the high-risk and 30 cases in the low-risk category (Figs. 2 A and 2 B). The survival analysis revealed that individuals in the high-risk category had a shorter survival period in comparison to the low-risk category ( P = 0.011) (Fig. 2 C). The clinical details of Model 1 are shown in Table 2 . Table 2 Clinical information of patients in different model. Model 1 High-risk group (96) Low-risk group (120) Survival 47(48.96%) 103(85.83) Survival time(days, mean ± s.d.) 2978.14 ± 1473.80 3689.12 ± 1497.24 platelet < 100,000/mm 3 51(53.12%) 48(40.00%) HCC 34(35.42%) 31(25.83%) Child-Pugh class B/C 43(44.79%) 23(19.17%) Model 2 High-risk group (96) Low-risk group (120) Survival 47(48.96%) 103(85.83) Survival time(days, mean ± s.d.) 2978.14 ± 1473.80 3689.12 ± 1497.24 platelet < 100,000/mm 3 51(53.12%) 48(40.00%) HCC 34(35.42%) 31(25.83%) Child-Pugh class B/C 43(44.79%) 23(19.17%) Model 3 High-risk group ( 23 ) Low-risk group (193) Survival 9(39.13%) 141(73.06%) Survival time(days, mean ± s.d.) 2593.35 ± 1458.58 3466.06 ± 1509.72 platelet < 100,000/mm 3 11(47.82%) 88(45.60%) HCC 11(47.82%) 54(27.98%) Child-Pugh class B/C 13(56.52%) 53(27.46%) Combined model High-risk group (96) Low-risk group (120) Survival 62(51.24%) 88(92.63%) Survival time(days, mean ± s.d.) 3407.96 ± 1449.39 3787.31 ± 1526.00 platelet < 100,000/mm 3 64(52.89%) 35(36.84%) HCC 44(36.36%) 21(22.11%) Child-Pugh class B/C 50(41.32%) 16(16.84%) CX3CR1-associated genes and their function in liver cirrhosis: From a pool of 18393 genes, 2,114 genes were identified using univariate Cox regression analysis. Subsequently, 21 genes linked to CX3CR1 expression were isolated from the initial 2114 genes using Pearson correlation analysis, as depicted in the correlation heatmap (Fig. 2 D). Functional analysis revealed that CX3CR1-associated genes were prominently associated with endoplasmic reticulum (ER) calcium ion homeostasis and ER overload response within the biological process. Cellular component analysis showed that CX3CR1-associated genes were mainly enriched in the integral component of the ER membrane. Molecular function analysis indicated their substantial association with protein N-terminus binding (Fig. 2 E). Furthermore, KEGG pathway analysis revealed that CX3CR1-associated genes were enriched in amoebiasis, extracellular matrix (ECM)–receptor interaction, and related processes (Fig. 2 F). CX3CR1-associated immune infiltration genes and their prognostic correlation in individuals with liver cirrhosis: In the discovery dataset, CIBERSORT was employed for the identification of the composition of 22 immune cell types. Using Pearson correlation analysis, a CX3CR1-associated gene correlated with immune cell infiltration was identified (Fig. 3 A). Further, univariate Cox regression and LASSO Cox regression analyses were employed to filter genes, identifying 11 optimal prognostic genes known as CX3CR1-associated IIGs (Figs. 3 B and 3 C). Based on the expression and corresponding coefficients of 11 CX3CR1-associated IIGs, risk score 2 was computed utilizing the following equation: expression of ACTIN4 × 1.106 + expression of BRWD1 × (− 0.477) + expression of CD1E × 0.328 + expression of HYPK × (− 0.951) + expression of ITGB7 × (− 0.474) + expression of LOC400499 × 0.234 + expression of MTHFD2 × 0.173 + expression of THBS2 × 0.335 + expression of TMCO1 × (− 0.629) + expression of TMPRSS13 × (− 1.376) + expression of WES1 × (− 0.382). Applying risk score 2, the maximally selected rank method divided individuals into high-risk (96 individuals) and low-risk categories (120 individuals) (Fig. 3 D). The cut-off value was − 9.49 (Fig. 3 E). The forest plot of 11 CX3CR1-associated IIGs is shown in Fig. 3 F. Survival analysis demonstrated that the high-risk category had shorter survival times in comparison to the low-risk category ( P < 0.0001) (Fig. 3 G). The clinical information of Model 2 is shown in Table 2 . CX3CR1-associated immune infiltration cells and their prognostic significance in individuals with liver cirrhosis: Based on CIBERSORT outcomes, immune cells exhibiting correlation with the expression of CX3CR1 were retained (Fig. 3 A). Using univariate Cox regression and LASSO Cox regression analyses, immune cell types were further refined (Figs. 4 A and 4 B). Among these, three immune cell types were identified as CX3CR1-associated immune infiltration cells. Formulating risk model 3 involved the following formula: Plasma cells × 4.346 + T follicular helper cells × 9.680 + Monocytes × (− 6.143). Based on the risk score 3, individuals were classified into high-risk (23 individuals) and low-risk categories (193 individuals), with a cut-off value of 0.54 (Figs. 4 C and 4 D). The high-risk category exhibited a poor prognosis in comparison to the low-risk category ( P < 0.0001) (Fig. 4 E). The clinical information of Model 3 is shown in Table 2 . Integrated prognostic model based on risk scores 1–3 for individuals with liver cirrhosis: To predict patient prognosis, an integrated model was constructed using multivariate Cox regression analysis based on risk models 1–3 (Figs. 5 A and 5 B). The combined risk score was derived as follows: Combined risk score = risk score 1 × 0.6264 + risk score 2 × 0.9714 + risk score 3 × 0.3491. According to the maximally selected rank statistics, 121 individuals were placed in the high-risk and 95 in the low-risk category (Figs. 5 C and 5 D). Individuals in the high-risk category exhibited a notably shorter overall survival period in comparison to the low-risk category ( P < 0.0001) (Fig. 5 G). Individuals in the high-risk category demonstrated higher plasma cell infiltration and lower monocyte infiltration in comparison to the low-risk category (Figs. 5 E and 5 F). The clinical information of the combined Model is given in Table 2 . Preliminary validation of clinical samples: Lower expression levels of ACTN4, TMCO1, CD1E, and WFS1 were observed in the liver tissues of patients compared to normal liver donors. Conversely, the expression levels of LOC400499 and MTHFD2 were elevated in individuals with liver failure in comparison to those with hepatocirrhosis (Fig. 6 A). Furthermore, Pearson correlation analysis showed a negative relationship between the expression of BRWD1 and the aspartate aminotransferase levels in patients (Fig. 6 B and 6 C). In addition, the findings demonstrated a positive relationship between the expression of LOC400499 and the levels of total bilirubin (TB) and direct bilirubin (DB) (Figs. 6 D and 6 E). Similarly, the expression of MTHFD2 exhibited positive correlations with TB, DB, and indirect bilirubin levels (Figs. 6 F and 6 G). Discussion CX3CR1, the sole receptor for CX3CL1, has been established as pivotal in prior research. The CX3CR1–CX3CL1 axis has been highlighted for its substantial role in liver fibrosis and cirrhosis progression. This study is the first to delineate the significance of CX3CR1 within a retrospective cohort of HCV-induced cirrhosis. The findings showed that high expression of CX3CR1 serves as a risk factor for the prognosis of individuals with HCV-induced cirrhosis. A series of genes and immune cells correlated with the expression of CX3CR1 were identified in the discovery dataset, leading to the successful establishment of the prognosis model. Furthermore, this study was supported by preliminary validation using clinical specimens. The present study not only advances the understanding of therapeutic targets but also furnishes potential prognostic markers for individuals with HCV-induced liver fibrosis and cirrhosis. At present, the significance of CX3CR1 in liver fibrosis remains controversial. A previous investigation reported that the expression level of CX3CR1 was increased among individuals with more severe liver fibrosis 11 , which is similar to this study showing poor prognosis in individuals with cirrhosis with higher expression of CX3CR. Furthermore, serum levels of CX3CL1 in individuals with liver cirrhosis were considerably higher than those in healthy donors, positively correlating with their Child-Pugh score 18 . However, other studies emphasized that CX3CR1 plays an important role in protecting the body from inflammatory damage. For example, in the mice model of schistosomiasis hepatic cirrhosis, CX3CR1 expression was upregulated after Schistosoma japonicum cercariae infection 19 . In addition, CX3CR1 deficiency mitigated acute inflammation by fostering M2 macrophage polarization 20 . The present study found a set of genes correlated with the expression of CX3CR1 and immune cell infiltration, including ACTN4, BRWD1, CD1E, HYPK, ITGB7, LOC400499, MTHFD2, THBS2, TMCO1, TMPRSS13, and WSF1. During validation, ACTIN4, CD1E, TMCO1, and WSF1 exhibited distinct expression in disease conditions. In contrast, LOC400499 and MTHFD2 showed elevated expression in liver failure tissues compared to hepatocirrhosis. In addition, expressions of LOC400499 and MTHFD2 were positively related to liver function, while the expression of BRWD1 was negatively correlated with liver function indices. ACTN4, also known as alpha-actinin 4, is critically involved in the pathogenesis of hepatocellular carcinoma (HCC), promoting cancer cell invasion, metastasis, and signaling regulation 21–23 . Although the role of ACTN4 in liver fibrosis is underreported at present, it's been associated with kidney fibrosis and glomerulosclerosis progression. Notably, decreased expression of ACTN4 could delay kidney fibrosis development 24–26 . Furthermore, ACTN4 interaction with TRIP13 in HCC activates the AKT/mTOR pathway, triggering EMT 23 , a process significant to the progression of liver fibrosis. Thus, this collective evidence suggests that ACTN4 is a prognostic risk factor for individuals with liver fibrosis. BRWD1, a bromodomain and WD repeat-containing protein 1 holds a predicted molecular weight of 263 KD and encompasses tandem BROMO domains and a WD40 repeat sequence 27 . Previously conducted studies have shown that the expression of BRWD1 is correlated with the development and maturation of sperm and eggs 28 . Meanwhile, the loss of BRWD1 has been correlated with the incidence of Down syndrome and hypogammaglobulinemia 29,30 . However, no research has discussed the role of BRWD1 in liver or fibrosis diseases. This study was the first to demonstrate that BRWD1 might exert a protective role in individuals with liver cirrhosis, possibly through the activation of specific signaling pathways. Further investigations need to be conducted in the future for more detailed information. CD1E, a member of the CD1 family, is a membrane-associated protein located in the Golgi compartment of immature human dendritic cells. It is subsequently transported to lysosomes, where it is cleaved into soluble form. CD1E is involved in glycolipid antigen processing 31 . Expression of CD1E is also detected on T cell membranes, facilitating glycolipid antigen presentation on the cell surface 32 . Studies involving CD1E have been reported in conditions such as HCC, chronic lung allograft dysfunction, and multiple sclerosis 33–35 . High expression of CD1E indicated poor prognosis in individuals with HCC due to altered lipid microenvironment 35 . This study suggests that CD1E could be a prognostic risk factor in individuals with cirrhosis. While no studies reported a link between CD1E expression and fibrosis, the progression of liver fibrosis is closed associated with the innate and adaptive immune responses mediated by T cells and dendritic cells. However, the precise mechanism necessitates further research. TMCO1, a product of transmembrane and coiled-coil domains 1, belongs to the DUF841 superfamily 36 . This multifunctional eukaryotic protein plays a crucial role in preventing excessive intracellular Ca 2+ accumulation and maintaining ER calcium homeostasis 37 . TMCO1 dysfunction is linked to various human diseases, including dysmorphism, mental retardation, glaucoma, tumorigenesis, gliomas, and osteoporosis 38,39 . This study suggested that high expression of TMCO1 serves as a protective factor in individuals with cirrhosis. Emerging evidence shows the protective role of calcium homeostasis in liver fibrosis 40 . Hence, it is speculated that decreased expression of TMCO1 might disrupt the Ca 2+ balance in the liver, exacerbating liver fibrosis. Nonetheless, this hypothesis necessitates further validation. The transmembrane protein wolframin, encoded by the nuclear gene WSF1, resides in the ER membrane. WSF1 mutations are associated with Wolfram syndrome or type 2 diabetes mellitus 41 . Currently, no studies have explored the role of WSF1 in liver or fibrosis disease. Although this study shows a protective role for WSF1 in individuals with cirrhosis, substantial research is required for confirmation. Moreover, this research demonstrated a positive relationship between the expression of LOC400499 and MTHFD2 and the clinical signature of patients. The LOC400499 gene remains unidentified; however, it is suggested that LOC400499 might be a prognostic risk factor for the progression of liver fibrosis in patients. Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) is a key enzyme in one-carbon metabolism. A study linked the high expression of MTHFD2 to lung fibrosis development in mice 42 . In addition, MTHFD2 is overexpressed in HCC, and its deficiency showed potential antitumor effects in various cancers 43 . In HCC, Wnt/beta-catenin signaling pathway regulates the expression of MTHFD2. Suppressed expression of MTHFD2 considerably attenuated the malignant phenotype of tumor cells induced by the activation of the Wnt/beta-catenin signaling pathway 44 . In summary, activation of the Wnt/beta-catenin signaling pathway in liver fibrosis drives the upregulation of MTHFD2, accelerating fibrosis development. Further research is imperative to substantiate this perspective in the future. The innate and adaptive immune systems perform a critical function in the process of liver fibrosis, promoting the activation of hepatic stellate cells and subsequent ECM deposition. The present study characterized the immune cell composition linked to the expression of CX3CR1-associated genes and its prognostic value in individuals with HCV-induced early-stage liver cirrhosis. Monocytes recruited from the peripheral blood through the CX3CR1–CX3CL1 axis differentiate into macrophages 45 that centrally govern the progression and regression of liver cirrhosis through their phenotypic changes. The combined model revealed that higher monocyte infiltration predicts a better prognosis. Previous studies have reported that CX3CR1 can limit liver cirrhosis by controlling the differentiation and survival of intrahepatic monocytes 12,46 . Consequently, inhibiting monocyte-to-proinflammatory macrophage differentiation might emerge as a viable strategy to reverse cirrhosis. Furthermore, this study showed that infiltration of plasma cells in injured livers was correlated with a worse prognosis. Some investigations have reported that increased infiltration of antibody-secreting B cells in cirrhosis was associated with autoimmune dysfunction 47 , and deleting B cells in the livers of CCl4-induced cirrhotic mice attenuated fibrogenesis 48 . In addition, infiltration of follicular helper T cells was found to be a prognostic risk factor for individuals with liver cirrhosis. Although its role in recruiting eosinophils during schistosomiasis infection has been reported, scarce attention has been paid to its role in liver cirrhosis pathogenesis 49 . There are several limitations to this study. First, the analysis was based on a retrospective cohort with individuals exclusively suffering from chronic HCV infection, potentially limiting the direct applicability of these findings to cirrhosis resulting from other causes such as chronic HBV infection or non-alcoholic fatty liver disease. Second, the clinical sample size was relatively small, preventing the verification of all gene expressions. Third, this study was unable to assess the relationship between liver immune infiltration and systemic immune status in individuals with cirrhosis due to the absence of fresh samples from patients. In future studies, it is intended to expand the sample size and investigate the mechanism underlying CX3CR1-associated genes in liver fibrosis. In addition, the aim is to compare data from HC, individuals who are decompensated, and those with acute-on-chronic liver failure with individuals in the early stage to precisely delineate the role of CX3CR1 and its associated immune cells in liver cirrhosis pathogenesis. In summary, the current research explored the prognostic value of CX3CR1 in individuals with chronic HCV infection and examined its correlation with immune cell infiltration using bioinformatic analyses. An integrated prognostic model based on the expression of CX3CR1, CX3CR1-associated IIGs, and immune infiltration pattern was constructed to predict patient outcomes. Furthermore, clinical samples collected from healthy liver donors, individuals with post-hepatic cirrhosis, and those with acute-on-chronic liver failure were verified using the bioinformatics findings. In conclusion, these findings suggest that CX3CR1 could emerge as a novel target for mitigating and managing the progression of liver cirrhosis. Material and methods The bioinformatic analysis flowchart and methodologies employed in the research are given in Fig. 1 . Dataset preparation: The microarray expression dataset GSE15654 was acquired from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/gds ) 50 . This dataset encompassed 216 individuals with HCV-related early-stage cirrhosis from an Italian center, with a median follow-up duration of 10 years. Using the platform annotation file, probes within the dataset were converted into corresponding gene symbols. Duplicate probes were excluded from the dataset, retaining the maximum value, before performing log2 normalization. Identification of CX3CR1 as a risk factor in individuals with liver cirrhosis: The expression matrix of CX3CR1 was extracted, and the maximally selected rank statistics were employed to divide the individuals into high and low-risk groups (categories). The algorithm was implemented using the R packages “survival” and “survminer.” Combined with the clinical information, the Kaplan–Meier method was utilized to create risk model 1. Identification of CX3CR1-associated genes in individuals with liver cirrhosis: To gain further clarity regarding genes associated with disease progression, univariate Cox analysis was employed across the whole genome, retaining genes with P 0.2 and P < 0.05). Subsequently, gene ontology functional annotation (GO) and Kyoto Encyclopedia of Genes and Genomes analyses (KEGG) were employed to uncover the functional roles and signaling pathways of the CX3CR-associated genes. The GO and KEGG analyses were performed using “DAVID” available at https://david.ncifcrf.go . Adjusted P < 0.05 was considered statistically significant. Identification of immune infiltration patterns in individuals with liver cirrhosis: CIBERSORT analysis was employed to determine the composition of 22 immune cell types in individuals with liver cirrhosis. Using Pearson correlation analysis, genes linked to immune cell infiltration were selected from CX3CR1-associated genes (| Pearson R | > 0.2 and P < 0.05). Subsequently, these genes were identified as CX3CR1-associated IIGs. The least absolute shrinkage and selection operator (LASSO), a type of shrinkage-based linear regression, was employed to filtrate the optimal gene set. Multivariate Cox regression analysis was conducted to determine corresponding coefficients. The risk score for CX3CR1-associated IIGs was calculated as Score =∑i 1n (Coefi ∗ the expression of a relative gene). A coefficient exceeding 0 indicated a risk factor, while values below 0 indicated a protective factor. Using the risk score, maximally selected rank statistics were utilized to categorize individuals into two clusters. Survival curves were generated for predicting the survival differences between the two clusters (risk model 2). Identification of CX3CR1-associated immune infiltration cells in individuals with liver cirrhosis: Immune cells that exhibited correlation with CX3CR1-associated genes were isolated using Pearson correlation analysis. These cells were then designated as CX3CR1-associated IIGs. Subsequently, individuals were divided into high- and low-immune infiltration clusters as per the infiltration levels of the identified immune cells, employing maximally selected rank statistics. The survival curve was utilized to ascertain the impact of CX3CR1-associated immune infiltration cells on the survival duration of individuals with liver cirrhosis (risk model 3). Integrated prognostic model for individuals with liver cirrhosis: Based on the risk scores of CX3CR1, CX3CR1-associated IIGs, and CX3CR1-associated immune infiltration cells, an integrated prognostic model for liver cirrhosis was developed. This model entailed calculating the combined risk score as Score =∑i 1n (Coefi ∗corresponding risk score), with coefficients established using multivariate Cox regression analysis. Employing the combined risk score, maximally selected rank statistics were used to classify individuals into high- and low-risk groups. The Kaplan–Meier method was then applied to create a survival curve. Validation of clinical patients: To verify the aforementioned bioinformatic findings, 10 surgical resection specimens, including 3 healthy controls (HC), 4 individuals with post-hepatic cirrhosis, and 3 with liver failure, were collected for gene expression testing. The research was approved by the institutional review board (Ethics Committee) of the West China Hospital of Sichuan University(2023 − 288). RNA was extracted from the liver tissues using TRIzol reagent, followed by quantitative reverse transcriptase-PCR (qRT-PCR) to assess the expression of CfX3CR1-associated IIGs. Each gene was evaluated three times, and the mean cycle threshold data was computed. Eq. 2 −△CT was used to ascertain relative gene expression. PCR primer sequences are listed in Table 1 . Table 1 PCR primer sequences Gene Bidirectional primer sequence Product length(bp) β-actin(H) F:5'GTGGCCGAGGACTTTGATTG3' R:5'CCTGTAACAACGCATCTCATATT3' 73 ACTN4 F:5'TGATCTGGACCATCATCCTTAG3' R:5'TTCTGCACATTGACGTTCTTAT3' 127 BRWD1 F:5'CTCTCATCGAGTCGGAGCTGT3' R:5'CAGTCCAATCTCTTCGGCAAC3' 127 CD1E F:5'TGAAGAAGTGGAAGACACGC3' R:5'AAAATCTCTGGAAGATGGGG3' 191 HYPK F:5'ATTGCCCTAACCAACTGATGC3' R:5'CCAGATGTACCTTGAATACTGTTGA3' 297 ITGB7 F:5'CTTTGCCAATGGTCCTTGTTTT3' R:5'ACGCGGTGAAGTTCAGTTGC3' 207 LOC400499 F:5'CCTATCATTTTTCCACCAACACC3' R:5'AAGACCACTCCCCTCCACCA3' 65 MTHFD2 F:5'CAAGTCACTCCTATGTCCTCAAC3' R:5'CCTTCTCTCATCAATATGCTCTG3' 191 THBS2 F:5'GGACGAGCCCTTCTACGA3' R:5'TTGCTGGCAACCCTTCTT3' 160 TMCO1 F:5'TTTTACTGCCCTAATGGGAATG3' R:5'CGATGAGACAGTCCTTGGATGTA3' 102 TMPRSS13 F:5'CTGTTCGCTGTGACGGGGT3' R:5'TTTCCTGGATGGTGGAGTTGTAT3' 270 WFS1 F:5'CCCAAGAAGAAGAAGCAGGTG3' R:5'CCCTTGGCGTACTTCTTAGTGAT3' 206 PCR: polymerase chain reaction; ACTN4: alpha-actinin 4, BRWD1: bromodomain and WD repeat-containing protein 1, HYPK: Huntingtin yeast partner K, MTHFD2: Methylenetetrahydrofolate dehydrogenase 2, THBS2: ECM protein thrombospondin-2, TMCO1: transmembrane and coiled-coil domains 1, TMPRSS13: transmembrane protease serine 13. Statistical analysis: All analyses were executed using R 4.0.2 and Graphpad Prism 9.0. The maximally selected rank statistics algorithm and survival plots were generated using the R packages “survival” and “survminer.” CIBERSORT analysis was conducted at https://cibersort.stanford.edu/ . The LASSO and Cox regression models were developed using the R package “glmnet.” PCR results were presented as mean ± standard deviation. Continuous data were compared utilizing the unpaired t-test. Declarations Acknowledgements None. Author contributions HC and JZ collected the literatures and drafted the initial manuscript. TW, CW and HC revised the manuscript and edited the language. WT conceptualized and guaranteed the review.CL, JS and XZ designed the figures and tables. WP, JZ and HL formatted the references and whole manuscript. All authors contributed to the article and approved the submitted version. HC and JZ contributed to this paper equally. Competing Interests: The authors have no conflicts of interest to disclose. Data availability: The datasets generated and/or analysed during the current study are available are publicly available. The data can be found here: Gene Expression Omnibus GEO, available at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15654. Ethics declarations and approval for human experiments: The experimental protocol was established according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of West China Hospital, Sichuan University(2023-288). Written informed consent was obtained from individual participants or their guardian. Consent to publish Not applicable. Funding This study was supported by grants from the National Natural Science Foundation of China (No. 82070625, No. 82100650), and Technological Supports Project of Sichuan Province (No.2022YFS0257). References Huang, D. Q. et al. 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Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 13 Sep, 2024 Reviews received at journal 12 Sep, 2024 Reviewers agreed at journal 06 Sep, 2024 Reviewers agreed at journal 16 Jul, 2024 Reviews received at journal 15 Jul, 2024 Reviewers agreed at journal 05 Jul, 2024 Reviewers invited by journal 05 Jul, 2024 Editor assigned by journal 05 Jul, 2024 Editor invited by journal 03 May, 2024 Submission checks completed at journal 30 Apr, 2024 First submitted to journal 28 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4336291","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":299401502,"identity":"1df61da8-11d0-4cda-9282-e804a5493496","order_by":0,"name":"Haozheng Cai","email":"","orcid":"","institution":"West China Hospital, Si Chuan University","correspondingAuthor":false,"prefix":"","firstName":"Haozheng","middleName":"","lastName":"Cai","suffix":""},{"id":299401504,"identity":"8abe5e8b-078e-4901-b765-d5c86c272921","order_by":1,"name":"Jing Zhang","email":"","orcid":"","institution":"West China Hospital, Si Chuan University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Zhang","suffix":""},{"id":299401507,"identity":"040c13d5-3e86-4eaf-a896-396577f42049","order_by":2,"name":"Chuwen Chen","email":"","orcid":"","institution":"West China Hospital, Si Chuan University","correspondingAuthor":false,"prefix":"","firstName":"Chuwen","middleName":"","lastName":"Chen","suffix":""},{"id":299401510,"identity":"67fd92e7-0597-495a-b7bf-018554037b14","order_by":3,"name":"Junyi Shen","email":"","orcid":"","institution":"West China Hospital, Si Chuan University","correspondingAuthor":false,"prefix":"","firstName":"Junyi","middleName":"","lastName":"Shen","suffix":""},{"id":299401513,"identity":"978991eb-05d4-44d6-b29e-3139de504117","order_by":4,"name":"Xiaoyun Zhang","email":"","orcid":"","institution":"West China Hospital, Si Chuan University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyun","middleName":"","lastName":"Zhang","suffix":""},{"id":299401516,"identity":"b0020f74-e4c1-4f99-a031-75710f1a88f5","order_by":5,"name":"Wei Peng","email":"","orcid":"","institution":"West China Hospital, Si Chuan University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Peng","suffix":""},{"id":299401518,"identity":"d663a3f6-0fb2-4aad-94d3-c378ea0a8aa5","order_by":6,"name":"Chuan Li","email":"","orcid":"","institution":"West China Hospital, Si Chuan University","correspondingAuthor":false,"prefix":"","firstName":"Chuan","middleName":"","lastName":"Li","suffix":""},{"id":299401519,"identity":"8800efcc-7de7-4a55-af04-6c38c1427755","order_by":7,"name":"Haopeng Lv","email":"","orcid":"","institution":"ChengDu Shi Xinjin Qu Renmin Yiyuan: People's Hospital of Xinjin District","correspondingAuthor":false,"prefix":"","firstName":"Haopeng","middleName":"","lastName":"Lv","suffix":""},{"id":299401521,"identity":"0a21e26a-d723-4e62-b622-fde151af4be3","order_by":8,"name":"Tianfu Wen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3QsQrCMBCA4SuFuARdU8R3SCnURfRVWgSnPoCDSEDQSWcFH6KPEAiYpThXFKxLXRwUQZzU6CgS6+aQf7rhPjgOwGT61wKACpTYa7ZYAWAxRRyG+S8EnoQEBQmVY5llvQ2hq/3ujKFRi7mdZ1qSLEMWLHJC15FXxdDxYo7qVEf8NHJZgERfEWRjEGHMMSJasj0ochPqMJkrci9AUuyycKhICr4i/DtpJZE7DSeCOEnkOXPa9mYC+VrijBJ6ul4EKUu5Ox66zdpEDnIteev5KvuHfZPJZDJ97gFRf0zkY4RhlAAAAABJRU5ErkJggg==","orcid":"","institution":"West China Hospital, Si Chuan University","correspondingAuthor":true,"prefix":"","firstName":"Tianfu","middleName":"","lastName":"Wen","suffix":""}],"badges":[],"createdAt":"2024-04-28 05:26:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4336291/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4336291/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-80422-1","type":"published","date":"2025-01-13T15:57:37+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":56099888,"identity":"ccf76eb4-c853-4feb-86ad-12c948fd292d","added_by":"auto","created_at":"2024-05-08 14:27:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":361163,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow-chart and methodologies of the research.\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4336291/v1/4a94d75c1d84c67f05308f95.jpg"},{"id":56099889,"identity":"0a604ccf-0a15-4068-ac18-854fc6335ad9","added_by":"auto","created_at":"2024-05-08 14:27:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":319057,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Histogram based on maximally selected rank grouping in model1. (B) Grouping cut-off value based on maximally selected rank. (C) Kaplan-Meier diagram of the total survival rate of Model1. (D) Heatmap showed the correlations between CX3CR1 and genes which screened by univariate Cox analysis from whole genome. (E) Histogram showed the GO function enrichment of CX3CR1 related genes. (F) Bubble diagram of KEGG pathway of CX3CR1 related genes.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4336291/v1/74d77ba1c6b461ead78a382d.jpg"},{"id":56099890,"identity":"f31b0328-d1c4-40bd-90b9-035978beab64","added_by":"auto","created_at":"2024-05-08 14:27:42","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":333480,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The correlations between the CX3CR1-related genes and immune cell compositions. (*** means P \u0026lt; 0.01, ** means P \u0026lt; 0.05, and * means P \u0026lt; 0.1). (B) LASSO coefficient profiles of 11 CX3CR1-related immune infiltration genes. (C) Three-fold cross-validation of lasso analysis. Error bars represented the SE. The dotted vertical lines showed the optimal values. (D) Histogram based on maximally selected rank grouping in model2. (E) Grouping cut-off value based on maximally selected rank. (F) Forest plot of 11 CX3CR1-related immune infiltration genes with P \u0026lt; 0.05 by univariate Cox regression. (G) Kaplan-Meier diagram of the total survival rate of Model2.\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4336291/v1/cd0ebd8870ef8f827250cfd3.jpg"},{"id":56099893,"identity":"a1e9c15f-f712-4852-9a55-406025fdec92","added_by":"auto","created_at":"2024-05-08 14:27:43","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":230372,"visible":true,"origin":"","legend":"\u003cp\u003e(A) LASSO coefficient profiles of 3 CX3CR1-related immune infiltration cells. (B) Three-fold cross-validation of lasso analysis. (C) Histogram based on maximally selected rank grouping in model3. (D) Grouping cut-off value based on maximally selected rank. (E) Kaplan-Meier diagram of the total survival rate of model3.\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4336291/v1/b5ed481060284c0148011042.jpg"},{"id":56099894,"identity":"95d25324-6b39-4f6d-aa4b-7d07ec7c086d","added_by":"auto","created_at":"2024-05-08 14:27:43","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":199890,"visible":true,"origin":"","legend":"\u003cp\u003e(A) LASSO coefficient profiles of 3 risk models. (B) Three-fold cross-validation of lasso analysis. (C) Histogram based on maximally selected rank grouping in combined model. (D) Grouping cut-off value based on maximally selected rank. (E) The infiltration of plasma cells in two clusters based on the combined risk model. (F) The infiltration of monocytes in two clusters based on the combined risk model (G) Kaplan-Meier diagram of the total survival rate of combined risk model.\u003c/p\u003e","description":"","filename":"figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4336291/v1/0abc1fd1e029a9028243c784.jpg"},{"id":56100760,"identity":"67ef8582-1180-470a-a7b8-71f393398020","added_by":"auto","created_at":"2024-05-08 14:35:43","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":218433,"visible":true,"origin":"","legend":"\u003cp\u003e(A)The expression of ACTN4, TMCO1, CD1E, WFS1 between liver donor and patients. The expression of LOC400499 and MTHFD2 between liver failure and hepatiocirrhosis patients. ** means P \u0026lt; 0.01, * means P \u0026lt; 0.05. (B)-(G) Pearson analysis showed the correlation between gene expression and clinical features.\u003c/p\u003e","description":"","filename":"figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4336291/v1/07b896bd0ff98b7a5f5915ff.jpg"},{"id":74284639,"identity":"a27a7edf-3b2f-4e75-9ded-9a1ad58c4aa0","added_by":"auto","created_at":"2025-01-20 16:10:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2757911,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4336291/v1/c1e4743f-f045-4b21-95dd-ed2ed96853cb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing prognosis in HCV-induced early-stage liver cirrhosis: An integrated model based on CX3CR1-associated immune infiltration genes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver cirrhosis is a common physiological outcome of chronic fibrotic liver disease caused by various factors \u003csup\u003e1,2\u003c/sup\u003e. It involves the replacement of normal hepatic architecture with regenerative hepatic nodules \u003csup\u003e3\u003c/sup\u003e. Hepatitis C virus (HCV) affects around 71\u0026nbsp;million individuals and is a major cause of liver cirrhosis \u003csup\u003e4,5\u003c/sup\u003e. Liver cirrhosis claims approximately 1\u0026nbsp;million lives annually worldwide, ranking it as the 11th leading cause of mortality \u003csup\u003e2\u003c/sup\u003e. Diagnosing liver fibrosis during the asymptomatic compensatory period remains challenging \u003csup\u003e6,7\u003c/sup\u003e, leading many individuals to miss early treatment opportunities. In addition, there are no effective methods to reverse cirrhosis\u003csup\u003e8\u003c/sup\u003e. Thus, there is an urgent need for new diagnostic techniques to accurately detect early-stage liver fibrosis and identify effective therapeutic targets to slow its progression.\u003c/p\u003e \u003cp\u003eChemokine (C-X3-C motif) Receptor 1 (CX3CR1) is predominantly expressed in monocytes, macrophages, a subset of NK cells, and terminally differentiated cytotoxic T cells \u003csup\u003e9\u003c/sup\u003e. Its primary function involves mediating the chemotaxis and adhesion of immune cells by binding with its unique ligand CX3CL1 \u003csup\u003e9\u003c/sup\u003e. Several studies have demonstrated that increased expression of CX3CR1 contributes to the deterioration of obstruction-induced kidney fibrosis \u003csup\u003e8\u003c/sup\u003e and is correlated with a worse prognosis in individuals with idiopathic pulmonary fibrosis \u003csup\u003e10\u003c/sup\u003e. However, regarding liver cirrhosis, the role of CX3CR1 is controversial. While one study showed significantly increased expression of CX3CR1 in end-stage liver fibrosis induced by chronic hepatitis C \u003csup\u003e11\u003c/sup\u003e, another study suggested that overexpression of CX3CR1 could alleviate liver inflammation and fibrosis in the carbon tetrachloride (CCl4)-induced fibrosis model \u003csup\u003e12\u003c/sup\u003e. Presently, no research has confirmed the involvement of CX3CR1 in predicting the prognosis of liver cirrhosis. Therefore, the primary objective of this research was to determine the link between CX3CR1 and the prognosis of individuals with early hepatic fibrosis induced by HCV.\u003c/p\u003e \u003cp\u003eLiver fibrosis occurs through iterative cycles of tissue injury, inflammation, and repair, in a microenvironment of cytokines and chemokines, where the interaction among the innate and adaptive immune systems and stromal cells leads to hepatic stellate cell (HSC) activation and extracellular matrix (ECM) accumulation, ultimately results in liver cirrhosis \u003csup\u003e13,14\u003c/sup\u003e. The infiltration pattern of immune cells in the liver significantly influences the advancement of liver fibrosis. \u003csup\u003e15\u003c/sup\u003e. As mentioned earlier, CX3CR1 is a key mediator of immune cell migration during organ fibrosis. Some studies have indicated that the level of CX3CR1 in M1 macrophages in BALF is significantly higher in mice with interstitial lung disease \u003csup\u003e10,16\u003c/sup\u003e, and inhibiting the expression of CX3CR1 in BALF reduces M1 macrophage infiltration \u003csup\u003e16\u003c/sup\u003e. In peritoneal fibrosis, CX3CR1 mediates the expression of CX3CL1 and TGF-β on the peritoneal mesothelium, promoting the development of peritoneal fibrosis \u003csup\u003e17\u003c/sup\u003e. However, the association between CX3CR1 and immune cell infiltration remains unclear in HC-induced early-stage liver fibrosis.\u003c/p\u003e \u003cp\u003eIn the current research, the prognostic value of CX3CR1 was innovatively explored in individuals with chronic HCV-induced early-stage liver cirrhosis, and the link between CX3CR1 and immune cell infiltration was analyzed using bioinformatic analysis. An integrated model was developed for predicting the prognosis of these individuals based on the expression of CX3CR1, CX3CR1-associated immune infiltration genes (IIGs), and immune cell infiltration. Furthermore, clinical samples were collected from healthy liver donors, individuals with compensated cirrhosis, and those with acute-on-chronic liver failure to verify the findings. In summary, the research proposes that CX3CR1 can potentially be targeted to prevent and address the progression of liver cirrhosis in the future.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHigh expression of CX3CR1 predicts poor prognosis in individuals with liver cirrhosis:\u003c/h2\u003e \u003cp\u003eBased on the expression of CX3CR1, 216 individuals were divided into two categories using maximally selected rank statistics, yielding 186 cases in the high-risk and 30 cases in the low-risk category (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The survival analysis revealed that individuals in the high-risk category had a shorter survival period in comparison to the low-risk category (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The clinical details of Model 1 are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical information of patients in different model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-risk group (96)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow-risk group (120)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47(48.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103(85.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival time(days, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;s.d.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2978.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1473.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3689.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1497.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eplatelet\u0026thinsp;\u0026lt;\u0026thinsp;100,000/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51(53.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(40.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(35.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(25.83%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild-Pugh class B/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43(44.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(19.17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-risk group (96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow-risk group (120)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47(48.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e103(85.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival time(days, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;s.d.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2978.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1473.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3689.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1497.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eplatelet\u0026thinsp;\u0026lt;\u0026thinsp;100,000/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51(53.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(40.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(35.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(25.83%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild-Pugh class B/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43(44.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(19.17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-risk group (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow-risk group (193)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(39.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141(73.06%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival time(days, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;s.d.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2593.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1458.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3466.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1509.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eplatelet\u0026thinsp;\u0026lt;\u0026thinsp;100,000/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(47.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88(45.60%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(47.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54(27.98%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild-Pugh class B/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(56.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53(27.46%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-risk group (96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow-risk group (120)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62(51.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88(92.63%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival time(days, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;s.d.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3407.96\u0026thinsp;\u0026plusmn;\u0026thinsp;1449.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3787.31\u0026thinsp;\u0026plusmn;\u0026thinsp;1526.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eplatelet\u0026thinsp;\u0026lt;\u0026thinsp;100,000/mm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64(52.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(36.84%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(36.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(22.11%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild-Pugh class B/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50(41.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(16.84%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCX3CR1-associated genes and their function in liver cirrhosis:\u003c/h2\u003e \u003cp\u003eFrom a pool of 18393 genes, 2,114 genes were identified using univariate Cox regression analysis. Subsequently, 21 genes linked to CX3CR1 expression were isolated from the initial 2114 genes using Pearson correlation analysis, as depicted in the correlation heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Functional analysis revealed that CX3CR1-associated genes were prominently associated with endoplasmic reticulum (ER) calcium ion homeostasis and ER overload response within the biological process. Cellular component analysis showed that CX3CR1-associated genes were mainly enriched in the integral component of the ER membrane. Molecular function analysis indicated their substantial association with protein N-terminus binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Furthermore, KEGG pathway analysis revealed that CX3CR1-associated genes were enriched in amoebiasis, extracellular matrix (ECM)\u0026ndash;receptor interaction, and related processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCX3CR1-associated immune infiltration genes and their prognostic correlation in individuals with liver cirrhosis:\u003c/h2\u003e \u003cp\u003eIn the discovery dataset, CIBERSORT was employed for the identification of the composition of 22 immune cell types. Using Pearson correlation analysis, a CX3CR1-associated gene correlated with immune cell infiltration was identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Further, univariate Cox regression and LASSO Cox regression analyses were employed to filter genes, identifying 11 optimal prognostic genes known as CX3CR1-associated IIGs (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Based on the expression and corresponding coefficients of 11 CX3CR1-associated IIGs, risk score 2 was computed utilizing the following equation: expression of ACTIN4 \u0026times; 1.106\u0026thinsp;+\u0026thinsp;expression of BRWD1 \u0026times; (\u0026minus;\u0026thinsp;0.477)\u0026thinsp;+\u0026thinsp;expression of CD1E \u0026times; 0.328\u0026thinsp;+\u0026thinsp;expression of HYPK \u0026times; (\u0026minus;\u0026thinsp;0.951)\u0026thinsp;+\u0026thinsp;expression of ITGB7 \u0026times; (\u0026minus;\u0026thinsp;0.474)\u0026thinsp;+\u0026thinsp;expression of LOC400499 \u0026times; 0.234\u0026thinsp;+\u0026thinsp;expression of MTHFD2 \u0026times; 0.173\u0026thinsp;+\u0026thinsp;expression of THBS2 \u0026times; 0.335\u0026thinsp;+\u0026thinsp;expression of TMCO1 \u0026times; (\u0026minus;\u0026thinsp;0.629)\u0026thinsp;+\u0026thinsp;expression of TMPRSS13 \u0026times; (\u0026minus;\u0026thinsp;1.376)\u0026thinsp;+\u0026thinsp;expression of WES1 \u0026times; (\u0026minus;\u0026thinsp;0.382). Applying risk score 2, the maximally selected rank method divided individuals into high-risk (96 individuals) and low-risk categories (120 individuals) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The cut-off value was \u0026minus;\u0026thinsp;9.49 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). The forest plot of 11 CX3CR1-associated IIGs is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eF. Survival analysis demonstrated that the high-risk category had shorter survival times in comparison to the low-risk category (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). The clinical information of Model 2 is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCX3CR1-associated immune infiltration cells and their prognostic significance in individuals with liver cirrhosis:\u003c/h2\u003e \u003cp\u003eBased on CIBERSORT outcomes, immune cells exhibiting correlation with the expression of CX3CR1 were retained (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Using univariate Cox regression and LASSO Cox regression analyses, immune cell types were further refined (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Among these, three immune cell types were identified as CX3CR1-associated immune infiltration cells. Formulating risk model 3 involved the following formula: Plasma cells \u0026times; 4.346\u0026thinsp;+\u0026thinsp;T follicular helper cells \u0026times; 9.680\u0026thinsp;+\u0026thinsp;Monocytes \u0026times; (\u0026minus;\u0026thinsp;6.143). Based on the risk score 3, individuals were classified into high-risk (23 individuals) and low-risk categories (193 individuals), with a cut-off value of 0.54 (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The high-risk category exhibited a poor prognosis in comparison to the low-risk category (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). The clinical information of Model 3 is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIntegrated prognostic model based on risk scores 1\u0026ndash;3 for individuals with liver cirrhosis:\u003c/h2\u003e \u003cp\u003eTo predict patient prognosis, an integrated model was constructed using multivariate Cox regression analysis based on risk models 1\u0026ndash;3 (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The combined risk score was derived as follows: Combined risk score\u0026thinsp;=\u0026thinsp;risk score 1 \u0026times; 0.6264\u0026thinsp;+\u0026thinsp;risk score 2 \u0026times; 0.9714\u0026thinsp;+\u0026thinsp;risk score 3 \u0026times; 0.3491. According to the maximally selected rank statistics, 121 individuals were placed in the high-risk and 95 in the low-risk category (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Individuals in the high-risk category exhibited a notably shorter overall survival period in comparison to the low-risk category (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eG). Individuals in the high-risk category demonstrated higher plasma cell infiltration and lower monocyte infiltration in comparison to the low-risk category (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eE and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). The clinical information of the combined Model is given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePreliminary validation of clinical samples:\u003c/h2\u003e \u003cp\u003eLower expression levels of ACTN4, TMCO1, CD1E, and WFS1 were observed in the liver tissues of patients compared to normal liver donors. Conversely, the expression levels of LOC400499 and MTHFD2 were elevated in individuals with liver failure in comparison to those with hepatocirrhosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Furthermore, Pearson correlation analysis showed a negative relationship between the expression of BRWD1 and the aspartate aminotransferase levels in patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). In addition, the findings demonstrated a positive relationship between the expression of LOC400499 and the levels of total bilirubin (TB) and direct bilirubin (DB) (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eD and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Similarly, the expression of MTHFD2 exhibited positive correlations with TB, DB, and indirect bilirubin levels (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eF and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eCX3CR1, the sole receptor for CX3CL1, has been established as pivotal in prior research. The CX3CR1\u0026ndash;CX3CL1 axis has been highlighted for its substantial role in liver fibrosis and cirrhosis progression. This study is the first to delineate the significance of CX3CR1 within a retrospective cohort of HCV-induced cirrhosis. The findings showed that high expression of CX3CR1 serves as a risk factor for the prognosis of individuals with HCV-induced cirrhosis. A series of genes and immune cells correlated with the expression of CX3CR1 were identified in the discovery dataset, leading to the successful establishment of the prognosis model. Furthermore, this study was supported by preliminary validation using clinical specimens. The present study not only advances the understanding of therapeutic targets but also furnishes potential prognostic markers for individuals with HCV-induced liver fibrosis and cirrhosis.\u003c/p\u003e \u003cp\u003eAt present, the significance of CX3CR1 in liver fibrosis remains controversial. A previous investigation reported that the expression level of CX3CR1 was increased among individuals with more severe liver fibrosis \u003csup\u003e11\u003c/sup\u003e, which is similar to this study showing poor prognosis in individuals with cirrhosis with higher expression of CX3CR. Furthermore, serum levels of CX3CL1 in individuals with liver cirrhosis were considerably higher than those in healthy donors, positively correlating with their Child-Pugh score \u003csup\u003e18\u003c/sup\u003e. However, other studies emphasized that CX3CR1 plays an important role in protecting the body from inflammatory damage. For example, in the mice model of schistosomiasis hepatic cirrhosis, CX3CR1 expression was upregulated after \u003cem\u003eSchistosoma japonicum cercariae\u003c/em\u003e infection \u003csup\u003e19\u003c/sup\u003e. In addition, CX3CR1 deficiency mitigated acute inflammation by fostering M2 macrophage polarization \u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe present study found a set of genes correlated with the expression of CX3CR1 and immune cell infiltration, including ACTN4, BRWD1, CD1E, HYPK, ITGB7, LOC400499, MTHFD2, THBS2, TMCO1, TMPRSS13, and WSF1. During validation, ACTIN4, CD1E, TMCO1, and WSF1 exhibited distinct expression in disease conditions. In contrast, LOC400499 and MTHFD2 showed elevated expression in liver failure tissues compared to hepatocirrhosis. In addition, expressions of LOC400499 and MTHFD2 were positively related to liver function, while the expression of BRWD1 was negatively correlated with liver function indices.\u003c/p\u003e \u003cp\u003eACTN4, also known as alpha-actinin 4, is critically involved in the pathogenesis of hepatocellular carcinoma (HCC), promoting cancer cell invasion, metastasis, and signaling regulation \u003csup\u003e21\u0026ndash;23\u003c/sup\u003e. Although the role of ACTN4 in liver fibrosis is underreported at present, it's been associated with kidney fibrosis and glomerulosclerosis progression. Notably, decreased expression of ACTN4 could delay kidney fibrosis development \u003csup\u003e24\u0026ndash;26\u003c/sup\u003e. Furthermore, ACTN4 interaction with TRIP13 in HCC activates the AKT/mTOR pathway, triggering EMT \u003csup\u003e23\u003c/sup\u003e, a process significant to the progression of liver fibrosis. Thus, this collective evidence suggests that ACTN4 is a prognostic risk factor for individuals with liver fibrosis. BRWD1, a bromodomain and WD repeat-containing protein 1 holds a predicted molecular weight of 263 KD and encompasses tandem BROMO domains and a WD40 repeat sequence \u003csup\u003e27\u003c/sup\u003e. Previously conducted studies have shown that the expression of BRWD1 is correlated with the development and maturation of sperm and eggs \u003csup\u003e28\u003c/sup\u003e. Meanwhile, the loss of BRWD1 has been correlated with the incidence of Down syndrome and hypogammaglobulinemia \u003csup\u003e29,30\u003c/sup\u003e. However, no research has discussed the role of BRWD1 in liver or fibrosis diseases. This study was the first to demonstrate that BRWD1 might exert a protective role in individuals with liver cirrhosis, possibly through the activation of specific signaling pathways. Further investigations need to be conducted in the future for more detailed information. CD1E, a member of the CD1 family, is a membrane-associated protein located in the Golgi compartment of immature human dendritic cells. It is subsequently transported to lysosomes, where it is cleaved into soluble form. CD1E is involved in glycolipid antigen processing \u003csup\u003e31\u003c/sup\u003e. Expression of CD1E is also detected on T cell membranes, facilitating glycolipid antigen presentation on the cell surface \u003csup\u003e32\u003c/sup\u003e. Studies involving CD1E have been reported in conditions such as HCC, chronic lung allograft dysfunction, and multiple sclerosis \u003csup\u003e33\u0026ndash;35\u003c/sup\u003e. High expression of CD1E indicated poor prognosis in individuals with HCC due to altered lipid microenvironment \u003csup\u003e35\u003c/sup\u003e. This study suggests that CD1E could be a prognostic risk factor in individuals with cirrhosis. While no studies reported a link between CD1E expression and fibrosis, the progression of liver fibrosis is closed associated with the innate and adaptive immune responses mediated by T cells and dendritic cells. However, the precise mechanism necessitates further research. TMCO1, a product of transmembrane and coiled-coil domains 1, belongs to the DUF841 superfamily \u003csup\u003e36\u003c/sup\u003e. This multifunctional eukaryotic protein plays a crucial role in preventing excessive intracellular Ca\u003csup\u003e2+\u003c/sup\u003e accumulation and maintaining ER calcium homeostasis \u003csup\u003e37\u003c/sup\u003e. TMCO1 dysfunction is linked to various human diseases, including dysmorphism, mental retardation, glaucoma, tumorigenesis, gliomas, and osteoporosis \u003csup\u003e38,39\u003c/sup\u003e. This study suggested that high expression of TMCO1 serves as a protective factor in individuals with cirrhosis. Emerging evidence shows the protective role of calcium homeostasis in liver fibrosis \u003csup\u003e40\u003c/sup\u003e. Hence, it is speculated that decreased expression of TMCO1 might disrupt the Ca\u003csup\u003e2+\u003c/sup\u003e balance in the liver, exacerbating liver fibrosis. Nonetheless, this hypothesis necessitates further validation. The transmembrane protein wolframin, encoded by the nuclear gene WSF1, resides in the ER membrane. WSF1 mutations are associated with Wolfram syndrome or type 2 diabetes mellitus\u003csup\u003e41\u003c/sup\u003e. Currently, no studies have explored the role of WSF1 in liver or fibrosis disease. Although this study shows a protective role for WSF1 in individuals with cirrhosis, substantial research is required for confirmation. Moreover, this research demonstrated a positive relationship between the expression of LOC400499 and MTHFD2 and the clinical signature of patients. The LOC400499 gene remains unidentified; however, it is suggested that LOC400499 might be a prognostic risk factor for the progression of liver fibrosis in patients. Methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) is a key enzyme in one-carbon metabolism. A study linked the high expression of MTHFD2 to lung fibrosis development in mice\u003csup\u003e42\u003c/sup\u003e. In addition, MTHFD2 is overexpressed in HCC, and its deficiency showed potential antitumor effects in various cancers\u003csup\u003e43\u003c/sup\u003e. In HCC, Wnt/beta-catenin signaling pathway regulates the expression of MTHFD2. Suppressed expression of MTHFD2 considerably attenuated the malignant phenotype of tumor cells induced by the activation of the Wnt/beta-catenin signaling pathway\u003csup\u003e44\u003c/sup\u003e. In summary, activation of the Wnt/beta-catenin signaling pathway in liver fibrosis drives the upregulation of MTHFD2, accelerating fibrosis development. Further research is imperative to substantiate this perspective in the future.\u003c/p\u003e \u003cp\u003eThe innate and adaptive immune systems perform a critical function in the process of liver fibrosis, promoting the activation of hepatic stellate cells and subsequent ECM deposition. The present study characterized the immune cell composition linked to the expression of CX3CR1-associated genes and its prognostic value in individuals with HCV-induced early-stage liver cirrhosis. Monocytes recruited from the peripheral blood through the CX3CR1\u0026ndash;CX3CL1 axis differentiate into macrophages \u003csup\u003e45\u003c/sup\u003e that centrally govern the progression and regression of liver cirrhosis through their phenotypic changes. The combined model revealed that higher monocyte infiltration predicts a better prognosis. Previous studies have reported that CX3CR1 can limit liver cirrhosis by controlling the differentiation and survival of intrahepatic monocytes \u003csup\u003e12,46\u003c/sup\u003e. Consequently, inhibiting monocyte-to-proinflammatory macrophage differentiation might emerge as a viable strategy to reverse cirrhosis. Furthermore, this study showed that infiltration of plasma cells in injured livers was correlated with a worse prognosis. Some investigations have reported that increased infiltration of antibody-secreting B cells in cirrhosis was associated with autoimmune dysfunction \u003csup\u003e47\u003c/sup\u003e, and deleting B cells in the livers of CCl4-induced cirrhotic mice attenuated fibrogenesis \u003csup\u003e48\u003c/sup\u003e. In addition, infiltration of follicular helper T cells was found to be a prognostic risk factor for individuals with liver cirrhosis. Although its role in recruiting eosinophils during schistosomiasis infection has been reported, scarce attention has been paid to its role in liver cirrhosis pathogenesis \u003csup\u003e49\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThere are several limitations to this study. First, the analysis was based on a retrospective cohort with individuals exclusively suffering from chronic HCV infection, potentially limiting the direct applicability of these findings to cirrhosis resulting from other causes such as chronic HBV infection or non-alcoholic fatty liver disease. Second, the clinical sample size was relatively small, preventing the verification of all gene expressions. Third, this study was unable to assess the relationship between liver immune infiltration and systemic immune status in individuals with cirrhosis due to the absence of fresh samples from patients. In future studies, it is intended to expand the sample size and investigate the mechanism underlying CX3CR1-associated genes in liver fibrosis. In addition, the aim is to compare data from HC, individuals who are decompensated, and those with acute-on-chronic liver failure with individuals in the early stage to precisely delineate the role of CX3CR1 and its associated immune cells in liver cirrhosis pathogenesis.\u003c/p\u003e \u003cp\u003eIn summary, the current research explored the prognostic value of CX3CR1 in individuals with chronic HCV infection and examined its correlation with immune cell infiltration using bioinformatic analyses. An integrated prognostic model based on the expression of CX3CR1, CX3CR1-associated IIGs, and immune infiltration pattern was constructed to predict patient outcomes. Furthermore, clinical samples collected from healthy liver donors, individuals with post-hepatic cirrhosis, and those with acute-on-chronic liver failure were verified using the bioinformatics findings. In conclusion, these findings suggest that CX3CR1 could emerge as a novel target for mitigating and managing the progression of liver cirrhosis.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eThe bioinformatic analysis flowchart and methodologies employed in the research are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDataset preparation:\u003c/h2\u003e \u003cp\u003eThe microarray expression dataset \u003cb\u003eGSE15654\u003c/b\u003e was acquired from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gds\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gds\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e50\u003c/sup\u003e. This dataset encompassed 216 individuals with HCV-related early-stage cirrhosis from an Italian center, with a median follow-up duration of 10 years. Using the platform annotation file, probes within the dataset were converted into corresponding gene symbols. Duplicate probes were excluded from the dataset, retaining the maximum value, before performing log2 normalization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of CX3CR1 as a risk factor in individuals with liver cirrhosis:\u003c/h2\u003e \u003cp\u003eThe expression matrix of CX3CR1 was extracted, and the maximally selected rank statistics were employed to divide the individuals into high and low-risk groups (categories). The algorithm was implemented using the R packages \u0026ldquo;survival\u0026rdquo; and \u0026ldquo;survminer.\u0026rdquo; Combined with the clinical information, the Kaplan\u0026ndash;Meier method was utilized to create risk model 1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of CX3CR1-associated genes in individuals with liver cirrhosis:\u003c/h2\u003e \u003cp\u003eTo gain further clarity regarding genes associated with disease progression, univariate Cox analysis was employed across the whole genome, retaining genes with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Pearson correlation analysis was conducted to identify genes related to CX3CR1 (| Pearson R | \u0026gt; 0.2 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subsequently, gene ontology functional annotation (GO) and Kyoto Encyclopedia of Genes and Genomes analyses (KEGG) were employed to uncover the functional roles and signaling pathways of the CX3CR-associated genes. The GO and KEGG analyses were performed using \u0026ldquo;DAVID\u0026rdquo; available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.go\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.go\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of immune infiltration patterns in individuals with liver cirrhosis:\u003c/h2\u003e \u003cp\u003eCIBERSORT analysis was employed to determine the composition of 22 immune cell types in individuals with liver cirrhosis. Using Pearson correlation analysis, genes linked to immune cell infiltration were selected from CX3CR1-associated genes (| Pearson R | \u0026gt; 0.2 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subsequently, these genes were identified as CX3CR1-associated IIGs. The least absolute shrinkage and selection operator (LASSO), a type of shrinkage-based linear regression, was employed to filtrate the optimal gene set. Multivariate Cox regression analysis was conducted to determine corresponding coefficients. The risk score for CX3CR1-associated IIGs was calculated as Score =\u0026sum;i 1n (Coefi \u0026lowast; the expression of a relative gene). A coefficient exceeding 0 indicated a risk factor, while values below 0 indicated a protective factor. Using the risk score, maximally selected rank statistics were utilized to categorize individuals into two clusters. Survival curves were generated for predicting the survival differences between the two clusters (risk model 2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of CX3CR1-associated immune infiltration cells in individuals with liver cirrhosis:\u003c/h2\u003e \u003cp\u003eImmune cells that exhibited correlation with CX3CR1-associated genes were isolated using Pearson correlation analysis. These cells were then designated as CX3CR1-associated IIGs. Subsequently, individuals were divided into high- and low-immune infiltration clusters as per the infiltration levels of the identified immune cells, employing maximally selected rank statistics. The survival curve was utilized to ascertain the impact of CX3CR1-associated immune infiltration cells on the survival duration of individuals with liver cirrhosis (risk model 3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eIntegrated prognostic model for individuals with liver cirrhosis:\u003c/h2\u003e \u003cp\u003eBased on the risk scores of CX3CR1, CX3CR1-associated IIGs, and CX3CR1-associated immune infiltration cells, an integrated prognostic model for liver cirrhosis was developed. This model entailed calculating the combined risk score as Score =\u0026sum;i 1n (Coefi \u0026lowast;corresponding risk score), with coefficients established using multivariate Cox regression analysis. Employing the combined risk score, maximally selected rank statistics were used to classify individuals into high- and low-risk groups. The Kaplan\u0026ndash;Meier method was then applied to create a survival curve.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eValidation of clinical patients:\u003c/h2\u003e \u003cp\u003eTo verify the aforementioned bioinformatic findings, 10 surgical resection specimens, including 3 healthy controls (HC), 4 individuals with post-hepatic cirrhosis, and 3 with liver failure, were collected for gene expression testing. The research was approved by the institutional review board (Ethics Committee) of the West China Hospital of Sichuan University(2023\u0026thinsp;\u0026minus;\u0026thinsp;288). RNA was extracted from the liver tissues using TRIzol reagent, followed by quantitative reverse transcriptase-PCR (qRT-PCR) to assess the expression of CfX3CR1-associated IIGs. Each gene was evaluated three times, and the mean cycle threshold data was computed. Eq.\u0026nbsp;2\u003csup\u003e\u0026minus;△CT\u003c/sup\u003e was used to ascertain relative gene expression. PCR primer sequences are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePCR primer sequences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBidirectional primer sequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProduct length(bp)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ-actin(H)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:5'GTGGCCGAGGACTTTGATTG3'\u003c/p\u003e \u003cp\u003eR:5'CCTGTAACAACGCATCTCATATT3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACTN4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:5'TGATCTGGACCATCATCCTTAG3'\u003c/p\u003e \u003cp\u003eR:5'TTCTGCACATTGACGTTCTTAT3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRWD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:5'CTCTCATCGAGTCGGAGCTGT3'\u003c/p\u003e \u003cp\u003eR:5'CAGTCCAATCTCTTCGGCAAC3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD1E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:5'TGAAGAAGTGGAAGACACGC3'\u003c/p\u003e \u003cp\u003eR:5'AAAATCTCTGGAAGATGGGG3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHYPK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:5'ATTGCCCTAACCAACTGATGC3'\u003c/p\u003e \u003cp\u003eR:5'CCAGATGTACCTTGAATACTGTTGA3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eITGB7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:5'CTTTGCCAATGGTCCTTGTTTT3'\u003c/p\u003e \u003cp\u003eR:5'ACGCGGTGAAGTTCAGTTGC3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOC400499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:5'CCTATCATTTTTCCACCAACACC3'\u003c/p\u003e \u003cp\u003eR:5'AAGACCACTCCCCTCCACCA3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTHFD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:5'CAAGTCACTCCTATGTCCTCAAC3'\u003c/p\u003e \u003cp\u003eR:5'CCTTCTCTCATCAATATGCTCTG3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTHBS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:5'GGACGAGCCCTTCTACGA3'\u003c/p\u003e \u003cp\u003eR:5'TTGCTGGCAACCCTTCTT3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMCO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:5'TTTTACTGCCCTAATGGGAATG3'\u003c/p\u003e \u003cp\u003eR:5'CGATGAGACAGTCCTTGGATGTA3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMPRSS13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:5'CTGTTCGCTGTGACGGGGT3'\u003c/p\u003e \u003cp\u003eR:5'TTTCCTGGATGGTGGAGTTGTAT3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWFS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:5'CCCAAGAAGAAGAAGCAGGTG3'\u003c/p\u003e \u003cp\u003eR:5'CCCTTGGCGTACTTCTTAGTGAT3'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePCR: polymerase chain reaction; ACTN4: alpha-actinin 4, BRWD1: bromodomain and WD repeat-containing protein 1, HYPK: Huntingtin yeast partner K, MTHFD2: Methylenetetrahydrofolate dehydrogenase 2, THBS2: ECM protein thrombospondin-2, TMCO1: transmembrane and coiled-coil domains 1, TMPRSS13: transmembrane protease serine 13.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis:\u003c/h2\u003e \u003cp\u003eAll analyses were executed using R 4.0.2 and Graphpad Prism 9.0. The maximally selected rank statistics algorithm and survival plots were generated using the R packages \u0026ldquo;survival\u0026rdquo; and \u0026ldquo;survminer.\u0026rdquo; CIBERSORT analysis was conducted at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cibersort.stanford.edu/\u003c/span\u003e\u003cspan address=\"https://cibersort.stanford.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The LASSO and Cox regression models were developed using the R package \u0026ldquo;glmnet.\u0026rdquo; PCR results were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. Continuous data were compared utilizing the unpaired t-test.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHC and JZ collected the literatures and drafted the initial manuscript. TW, CW and HC revised the manuscript and edited the language. WT conceptualized and guaranteed the review.CL, JS and XZ designed the figures and tables. WP, JZ and HL formatted the references and whole manuscript. All authors contributed to the article and approved the submitted version. HC and JZ contributed to this paper equally.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available are publicly available. The data can be found here: Gene Expression Omnibus GEO,\u003c/p\u003e\n\u003cp\u003eavailable at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE15654.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations and approval for human experiments:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental protocol was established according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of West China Hospital, Sichuan University(2023-288).\u0026nbsp;Written informed consent was obtained from individual participants or their guardian.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the National Natural Science Foundation of China (No. 82070625, No. 82100650), and Technological Supports Project of Sichuan Province (No.2022YFS0257).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHuang, D. 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However, the role of CX3CR1 in hepatitis C virus (HCV)-induced early-stage liver cirrhosis remains unexplored. GSE15654 retrieved from the GEO. Cox regression model, CIBERSOT and LASSO technique was utilized to identify CX3CR1-associated prognostic genes. Surgical resection samples were collected for verification. High expression of CX3CR1 in the liver was linked to worse prognosis in individuals with HCV-induced early-stage liver cirrhosis. CX3CR1-associated immune infiltration genes(IIGs), namely ACTIN4, CD1E, TMCO1, LOC400499, MTHFD2, and WSF1, were identified, showing specific expression in the livers of individuals with post-hepatic cirrhosis and liver failure compared to HC. Notably, high infiltration of plasma cells and low infiltration of monocytes were predictive of poor prognosis in early-stage cirrhosis. The combined risk model predicted that high expression of CX3CR1-associated IIGs and increased infiltration of plasma cells were associated with unfavorable prognosis in individuals with HCV-induced early-stage liver cirrhosis. Elevated expression of CX3CR1 is a risk factor for individuals with HCV-induced early-stage liver cirrhosis. The developed combined risk model effectively predicted the prognosis of such individuals.\u003c/p\u003e","manuscriptTitle":"Assessing prognosis in HCV-induced early-stage liver cirrhosis: An integrated model based on CX3CR1-associated immune infiltration genes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-08 14:27:37","doi":"10.21203/rs.3.rs-4336291/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-13T12:07:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-12T14:28:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168007862337289730812804177031955683840","date":"2024-09-06T15:11:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172842315877771063352281006337733170280","date":"2024-07-16T08:22:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-15T21:57:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100095396110838444416081648050086707427","date":"2024-07-05T16:10:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-05T07:21:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-05T07:20:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-03T18:01:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-30T14:08:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-04-28T05:21:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"750239f9-e14f-41e7-8ff0-1068b17862be","owner":[],"postedDate":"May 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":31586353,"name":"Health sciences/Diseases/Infectious diseases/Hepatitis/Viral hepatitis/Hepatitis c"},{"id":31586355,"name":"Health sciences/Biomarkers/Predictive markers"}],"tags":[],"updatedAt":"2025-01-20T16:02:53+00:00","versionOfRecord":{"articleIdentity":"rs-4336291","link":"https://doi.org/10.1038/s41598-024-80422-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-13 15:57:37","publishedOnDateReadable":"January 13th, 2025"},"versionCreatedAt":"2024-05-08 14:27:37","video":"","vorDoi":"10.1038/s41598-024-80422-1","vorDoiUrl":"https://doi.org/10.1038/s41598-024-80422-1","workflowStages":[]},"version":"v1","identity":"rs-4336291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4336291","identity":"rs-4336291","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}