Expressional and prognostic value of CRLF3 in liver hepatocellular carcinoma patients via integrated bioinformatics analyses and experiments

Expressional and prognostic value of CRLF3 in liver hepatocellular carcinoma patients via integrated bioinformatics analyses and experiments | 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 Research Article Expressional and prognostic value of CRLF3 in liver hepatocellular carcinoma patients via integrated bioinformatics analyses and experiments 幸幸 王, Zhen Huang, Lili Huang, Cong Huang, Xiaoying Zhang, Xiantu Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3975470/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract BACKGROUND: Liver hepatocellular carcinoma (LIHC) exhibits a notable prevalence and fatality rate, posing a significant risk to human well-being. 1 . The orphan cytokine receptor-like factor 3 (CRLF3), which exhibits evolutionary conservation, has been associated with hematopoiesis in vertebrates, human diseases, and neuroprotection in insects 2,3 . However, there is a dearth of research investigating the role of CRLF3 in LIHC and the underlying mechanisms involved. METHODS: The researchers utilized the TCGA database to examine the putative regulatory association between the expression of CRLF3 mRNA and LIHC.The Human Protein Atlas (HPA) has made available visual representations of the expression patterns of the CRLF3 protein. To determine the protein expression levels of CRLF3 in LIHC and adjacent normal tissues, immunohistochemistry techniques were employed.The study employed the Kaplan-Meier method, Cox regression, and logistic regression to evaluate the association between CRLF3 mRNA expression levels and survival outcomes and prognosis. In this study, the researchers employed GO and Kyoto KEGG pathway enrichment analyses, as well as GSEA, to investigate the potential regulatory role of CRLF3. The biological function of CRLF3 was identified using the ssGSEA technique. RESULTS: The primary objective of this study is to assess the levels of expression exhibited by various members of the CRLF family in LIHC and analyze their potential influence on prognosis. The mRNA expression levels of CRLF3 exhibited a significant increase in LIHC tissues, both at the transcript and protein levels. Furthermore, research has demonstrated that patients exhibiting elevated levels of CRLF3 in LIHC experience diminished OS, DSS, and PFI. Several clinicopathologic parameters, including clinical T stage, pathologic stage, histologic grade, and AFP concentration, have been seen to exhibit associations with CRLF3 expression in LIHC. The study used multivariate survival analysis to establish that CRLF3 served as an independent predictive factor. Additional enrichment analysis was conducted, which demonstrated that the PI3K Akt, Wnt, FcεRI-mediated NF-κB activation, activation of the intestinal immune network for the IgA production, interactions between immune cells and microRNAs in the tumor microenvironment, and JAK/STAT signaling pathways exhibited significant enrichment in the group with high CRLF3 expression. The ssGSEA analysis revealed a significant positive connection between the expression of CRLF3 and the presence of T helper 2 (Th2) and T helper cells. CONCLUSIONS: Increased CRLF3 in LIHC is strongly linked to decreased survival and immune infiltration invasion. Based on the findings of our study, it is suggested that CRLF3 has the potential as a prognostic marker for unfavorable outcomes and might serve as a viable target for immunotherapeutic interventions in the management of LIHC. Cytokine Receptor-Like Factor 3 (CRLF3) Liver hepatocellular carcinoma overall survival prognosis diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction With more than 800,000 fatalities each year, liver hepatocellular carcinoma (LIHC) is the fourth most common cause of mortality in the world 4 , 5 . Based on the findings of recent research, projections indicate that the mortality rate attributed to liver cancer is expected to surpass one million individuals by the year 2030. It has been shown that developing countries have a higher prevalence of liver illnesses 6 . Currently, the clinical management of LIHC encompasses several therapeutic approaches such as chemotherapy, immunotherapy, the utilization of natural substances, and the use of nanotechnology. Despite the existence of several therapy modalities, the medical community faces a notable absence of a definitive remedy for LIHC 7 . The integration of diverse tumor biomarkers based on varying clinical situations has immense importance in the diagnosis, monitoring of treatment efficacy, and prognostic assessment of primary liver cancer 8 . Furthermore, the exploration and establishment of novel biomarkers further contribute to these objectives 9 . Alpha-fetoprotein (AFP), squamous lectin-responsive alpha-fetoprotein (AFP-L3), vitamin K insufficiency or antagonist-II (PIVKA-II), and de-gamma-carboxyprothrombinogen (DCP) are frequently observed indicators of LIHC 10 – 12 . Nevertheless, the intricate pathogenesis and diverse individual characteristics of LIHC present significant obstacles in the timely identification of this disease. Nevertheless, the eligibility for curative therapy in LIHC patients is limited to only 20–30% mostly as a result of the absence of early-detection methods. This underscores the importance of dependable and precise biomarkers 13 . The cytokine receptor-like factor family comprises three members, namely CRLF1, CRLF2, and CRLF3. There exists data indicating that the family of CRLFs plays a role in the pathogenesis of several neoplastic conditions 14 – 17 . CRLF3 has been associated with various human diseases, as well as being involved in vertebrate hematopoiesis and insect neuroprotection 2 . The study revealed that CRLF3 plays a significant role in facilitating embryonic hematopoiesis throughout the early stages of zebrafish development. 18 . In teleost fish, CRLF3 Promotes the Degradation of TBK1 to Negatively Regulate Antiviral Immunity 19 . Nevertheless, the precise molecular mechanism behind the role of CRLF3 in LIHC remains elusive. In this study, the transcriptional and protein expression of CRFL3 was found through the utilization of TCGA and HPA databases. Additionally, to comprehend the fundamental mechanisms underlying the pathophysiology of CRLF3, we have carried out additional research using GO, KEGG, and GSEA. The objective of this work was to explore the potential mechanism by which CRLF3 is implicated in LIHC, specifically by investigating the association between CRLF3 expression and immune infiltration. Furthermore, much research has been conducted to examine the clinical features and prognostic implications of CRLF3 in individuals diagnosed with LIHC. Hence, the results obtained from our research possess the capability to reveal innovative targets and methodologies for the detection and treatment of LIHC. Materials and Methods Downloading and processing data from public databases The dataset used in this study comprises gene expression RNA-seq data from the Cancer Genome Atlas (TCGA), consisting of 374 LIHC tissues and 50 normal liver tissues from TCGA database. The HPA database ( https://www.proteinatlas.org ) was used to compare CRLF3 Protein expression levels in LIHC tissues and normal liver tissues. The analysis of CRLF3 expression in LIHC was also conducted using the HCCDB database ( http://lifeome.net/database/hccdb ). The predictive value of CRLF3 in LIHC was evaluated in terms of overall survival (OS) using the Kaplan-Meier plotter (kmplot.com/analysis). Functional enrichment analysis The genes that underwent screening were subjected to analysis for functional enrichment, utilizing the Gene Ontology (GO) database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Gene Set Enrichment Analysis (GSEA) uses genes in a predefined gene set to assess trends in the distribution of genes in a table of genes sorted by phenotypic relatedness to determine their contribution to the phenotype 20 . Following the application of ID transformation to the molecules in the input data, the clusterProfiler software will be utilized to conduct Gene Set Enrichment Analysis (GSEA) ((p.adj < 0.05 & qvalue < 0.25). Protein-protein interaction (PPI) network analysis The researchers utilized the String database to perform protein-protein interaction network analysis. Minimum required interaction score:0.400. Screening of differentially expressed genes (DEGs) The DEGs were performed using the DESeq2 tool in the R programming language (p.adj 1) 21 . The initial Counts matrix underwent variance analysis using the DESeq2 tool, following the established protocol, and was afterward standardized using the VST (Variance Stabilizing Transformations) approach offered by the DESeq2 package. Immune cell infiltration of ssGSEA The ssGSEA method measures the amount of immune invasion of LIHC by 24 immune cells in tumor samples. It does this by using Spearman's correlation test 22 . To find out if there was a link between CRLF3 expression and the number of immune cells in the body, Spearman's correlation test was used. The Wilcoxon rank sum test was used to find out if there was a link between immune cell influx and high and low CRLF3 expression groups. Immunohistochemistry and scoring analyses Immunohistochemistry was conducted to determine CRLF3 protein expression and prognostic value. LIHC tissues and paired adjacent normal tissues were stained by immunohistochemistry with anti-human CRLF3 (1:150; Rabbit; DF8931; Affinity, USA), followed by Two-step universal kit (mouse/rabbit ultrasensitive polymer assay system) (PV-800;ZSGB-BIO, Beijing, China). Then, the samples were observed under a microscope and photographed for further analysis. Each LIHC sample was evaluated based on staining intensity and positively stained cell percentage. The stainingintensity (negative, 0; mild, 1; moderate, 2; and strong,3) and percentage area of positive cells (≤ 25, 1; >25 and ≤ 50%, 2; >50 and ≤ 75%, 3; and > 75%, 4) were quanti-fied, respectively. The scores of the two groups were summed to obtain the final IHC scores. The results of immunohistochemical scoring were also validated using ImageJ software.A paired t-test was used to compare CRLF3 expression between LIHC tissues and the paired non-tumor tissues. Statistical analysis R software (version 4.2.1) was used for all statistical studies. The Wilcoxon rank sum test and the Paired samples t-test were used to see how CRLF3 levels changed in tumor and healthy tissues. We used both univariate and multivariate analysis to find out how the clinical factors affected Overall Survival. A p-value less than 0.05 is indicative of statistical significance. Results Protein and mRNA expression levels of CRLF3 in patients with LIHC Initially, we conducted an assessment of the expression levels and predictive significance of members belonging to the CRLF family in LIHC, as well as in corresponding neighboring samples.CRLF1, CRLF2, and CRLF3 were observed to exhibit a statistically significant upregulation in LIHC. (Fig. 1 A). However, it should be noted that CRLF3 exhibits not only a high level of expression in LIHC but also a significant correlation with poor overall survival. (Fig. 1 B). In addition, CRLF3 expression was increased in LIHC tissues when compared with paired adjacent tissues or normal tissues (Fig. 1 C). As a result, CRLF3 was selected as the focal point of the study. The expression of CRLF3 protein was shown to be elevated in LIHC tissues in comparison to normal tissues, as observed in the HPA database. (Fig. 1 D). The results of the HPA database were also verified by immunohistochemical staining (Fig. 1 E).The diagnostic capability of CRLF3 was found to be significant based on the examination of the receiver operating characteristic (ROC), as shown by an area under the curve (AUC) value of 0.823 (Fig. 1 F). The expression of CRLF3 is upregulated in various types of malignancies, suggesting its potential involvement in the pathogenesis of cancer. (Fig. 1 G). By utilizing the HCCDB database, a comprehensive analysis was conducted on nine distinct hepatocellular carcinoma (HCC) cohorts. The examination revealed a noteworthy elevation in the mRNA expression levels of CRLF3 within HCC tissues, as compared to the adjacent tissues. (Fig. 1 H). Association between CRLF3 expression and clinicopathologic characteristics in LIHC The 374 samples were categorized into two groups according to the levels of CRLF3 mRNA expression. The examination of the two sets of samples yielded noteworthy findings about the correlation between CRLF3 expression and several parameters, such as pathologic stage ( P = 0.046), Age ( P = 0.026), Histologic grad ( P < 0.001), and AFP ( P < 0.001) concentration. There was no observed connection between TNM stage, gender, and BMI. (Table 1 ). The findings from the logistic regression analysis demonstrated a statistically significant relationship between the expression of CRLF3 and many variables., including T stage (T2&T3&T4 vs. T1) (OR = 1.701, P = 0.011), pathologic stage (Stage II&Stage III&Stage IV vs. Stage I) (OR = 1.621, P = 0.025), Age((> 60 vs. Age 400 vs. AFP < = 400) (OR = 3.562, P < 0.001). (Table 2 ). The findings from both the Welch t-test and t-test analyses revealed a statistically significant correlation between the levels of CRLF3 mRNA expression and several clinical variables., including AFP concentration, histologic grade, pathologic T stage, and pathologic stage (Fig. 2 A–F). Table 1 Relationship between the clinicopathologic features of LIHC and the expression of CRLF3 Characteristics Low expression of CRLF3 High expression of CRLF3 P value n 187 187 Pathologic T stage, n (%) 0.089 T1 103 (27.8%) 80 (21.6%) T2 41 (11.1%) 54 (14.6%) T3 34 (9.2%) 46 (12.4%) T4 6 (1.6%) 7 (1.9%) Pathologic N stage, n (%) 0.625 N0 127 (49.2%) 127 (49.2%) N1 1 (0.4%) 3 (1.2%) Pathologic stage, n (%) 0.046 Stage I 98 (28%) 75 (21.4%) Stage II 40 (11.4%) 47 (13.4%) Stage III 35 (10%) 50 (14.3%) Stage IV 4 (1.1%) 1 (0.3%) Gender, n (%) 0.097 Male 134 (35.8%) 119 (31.8%) Female 53 (14.2%) 68 (18.2%) Age, n (%) 0.026 60 109 (29.2%) 87 (23.3%) BMI, n (%) 0.851 25 83 (24.6%) 77 (22.8%) Histologic grade, n (%) < 0.001 G1 36 (9.8%) 19 (5.1%) G2 100 (27.1%) 78 (21.1%) G3 45 (12.2%) 79 (21.4%) G4 4 (1.1%) 8 (2.2%) AFP(ng/ml), n (%) < 0.001 400 19 (6.8%) 46 (16.4%) Table 2 The logistic regression analysis shows the relationship between clinicopathological characteristics and CRLF3 expression. Characteristics Total (N) OR (95% CI) P value Pathologic T stage (T2&T3&T4 vs. T1) 371 1.701 (1.128–2.564) 0.011 Pathologic N stage (N1 vs. N0) 258 3.000 (0.308–29.225) 0.344 Pathologic M stage (M1 vs. M0) 272 0.349 (0.036–3.394) 0.364 Pathologic stage (Stage II&Stage III&Stage IV vs. Stage I) 350 1.621 (1.063–2.472) 0.025 Histological type (Hepatocellular carcinoma&Hepatocholangiocarcinoma (mixed) vs. Fibrolamellar carcinoma) 374 0.497 (0.045–5.532) 0.570 Histologic grade (G2&G3&G4 vs. G1) 369 2.098 (1.154–3.817) 0.015 Tumor status (With tumor vs. Tumor free) 355 1.465 (0.961–2.235) 0.076 Residual tumor (R1&R2 vs. R0) 345 1.681 (0.636–4.443) 0.295 Gender (Female vs. Male) 374 1.445 (0.934–2.234) 0.098 Race (Black or African American&White vs. Asian) 362 0.965 (0.637–1.462) 0.867 Age (> 60 vs. 400 vs. <= 400) 280 3.562 (1.955–6.490) < 0.001 Vascular invasion (Yes vs. No) 318 1.517 (0.953–2.413) 0.079 Adjacent hepatic tissue inflammation (Mild&Severe vs. None) 237 1.250 (0.749–2.087) 0.393 Table 3 Cox proportional risk models with univariate and multivariate variables were used to analyze the factors influencing OS. Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Pathologic T stage 370 T2&T3&T4 187 Reference Reference T1 183 0.470 (0.328–0.675) < 0.001 0.726 (0.449–1.173) 0.191 Pathologic stage 349 Stage III&Stage IV 90 Reference Reference Stage I&Stage II 259 0.399 (0.275–0.579) < 0.001 0.489 (0.305–0.785) 0.003 Histologic grade 368 G1&G2 233 Reference G3&G4 135 1.091 (0.761–1.564) 0.636 Gender 373 Male 252 Reference Female 121 1.261 (0.885–1.796) 0.200 Age 373 60 196 1.205 (0.850–1.708) 0.295 BMI 336 25 159 0.798 (0.550–1.158) 0.235 AFP(ng/ml) 279 400 64 1.075 (0.658–1.759) 0.772 Vascular invasion 317 No 208 Reference Yes 109 1.344 (0.887–2.035) 0.163 CRLF3 373 Low 187 Reference Reference High 186 1.507 (1.065–2.133) 0.021 1.466 (1.015–2.116) 0.041 Table 4 Cox proportional risk models with univariate and multivariate variables were used to analyze the factors influencing DSS. Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Pathologic T stage 362 T2&T3&T4 182 Reference Reference T1 180 0.353 (0.218–0.573) < 0.001 0.680 (0.344–1.343) 0.267 Pathologic stage 341 Stage III&Stage IV 87 Reference Reference Stage I&Stage II 254 0.263 (0.162–0.427) < 0.001 0.330 (0.175–0.622) < 0.001 Histologic grade 360 G1&G2 227 Reference G3&G4 133 1.086 (0.683–1.728) 0.726 Gender 365 Male 247 Reference Female 118 1.230 (0.780–1.937) 0.373 Age 365 60 191 0.846 (0.543–1.317) 0.458 BMI 329 25 154 0.826 (0.512–1.330) 0.431 AFP(ng/ml) 275 400 61 0.867 (0.450–1.668) 0.668 Vascular invasion 309 No 204 Reference Yes 105 1.277 (0.707–2.306) 0.418 CRLF3 365 Low 183 Reference Reference High 182 1.622 (1.037–2.537) 0.034 1.671 (1.024–2.727) 0.040 The predictive importance of the CRLF3 gene in LIHC Subsequently, an investigation was conducted to assess the potential correlation between the mRNA expression level of CRLF3 in LIHC and its associated clinical outcome. The findings of our investigation demonstrated a noteworthy association between heightened CRLF3 expression and worse OS (hazard ratio [HR] = 1.51, p-value = 0.021), DSS (HR = 1.62, p-value = 0.034), and reduced PFI (HR = 1.39, p-value = 0.025). (Fig. 3A–C). Cox regression analysis revealed that the overexpression of CRLF3 mRNA had a substantial influence on OS in LIHC who were at Pathologic T3&T4 stage (HR = 1.75, P = 0.046) and Pathologic Stage III&Stage IV (HR = 1.85, P = 0.037). (Fig. 3D–K). Univariate and multivariate COX regression models indicated a significant correlation between CRLF3 expression and unfavorable OS ([HR] = 1.507, p-value = 0.021) as well as disease-specific survival (HR = 1.622, p-value = 0.034). The results of the multivariate analysis revealed that elevated expression of CRLF3 served as a significant independent prognostic indicator in individuals diagnosed with LIHC (Tables 3 and 4 ). According to our research, CRLF3 may be used as a prognostic marker for OS and DSS in people who have been diagnosed with LIHC. Establishing a protein-protein interaction network The protein interaction networks were extracted from the STRING database (Fig. 4 A), while the identification of differentially expressed genes (DEGs) was visualized using volcano plots (Fig. 4 B). The ATAD5 gene was subjected to screening by identifying the overlapping genes from the STRING database that interact with differentially expressed genes (DEGs (Fig. 4 C). Overall, a notable positive association was seen between the levels of CRLF3 expression and the overall expression of ATAD5 (r = 0.770, P < 0.001) (Fig. 4 D). Functional enrichment analysis To conduct a more comprehensive investigation into the cellular molecular pathways, the studies of GO, KEGG, and GSEA were carried out. The analysis of gene ontology enrichment reveals a significant association between CRLF3 and immune-related processes. (Fig. 5 A). The KEGG analysis demonstrated that the discovered genes displayed notable enrichment mostly in four signaling pathways that are intimately linked to the progression of cancer (Fig. 5 A). The GSEA analysis demonstrated that the DEGs exhibited a notable enrichment in various pathways, including but not limited to PI3K Akt, Wnt, FcεRI-mediated NF-κB activation, activation of the intestinal immune network for IgA production, interactions between immune cells and microRNAs in the tumor microenvironment, and JAK/STAT signaling pathways (Fig. 5 B-G). Correlation between CRLF3 and immune cell infiltration The assessment of the correlations between the proportional representation of 24 immune cells and the expression of CRLF3 in liver hepatocellular carcinoma (LIHC) was performed utilizing the ssGSEA technique (Fig. 6 A). The expression of CRLF3 exhibited a substantial linear correlation with the degree of infiltration of T helper cells (R = 0.515, P < 0.001) and Th2 cells (R = 0.414, P < 0.001) (Fig. 6 B-C). The Wilcoxon Rank-sum test was employed to ascertain the presence of immune cell enrichment in the groups categorized by high and low expression of CRLF3. The study's findings revealed that there was an elevation in the concentration of T helper cells and Th2 cells within the group exhibiting high expression of CRLF3. Discussion With an estimated prevalence exceeding 1 million cases by the year 2025, LIHC presents a significant threat to human well-being owing to its elevated occurrence and fatality rate. The primary risk factors associated with the development of liver cancer, namely LIHC, encompass infections caused by the hepatitis B and C viruses 23 . The management of LIHC continues to provide significant challenges, mostly relying on the timely identification of the disease. AFP is presently the prevailing biomarker employed for the early detection of liver cancer 24 . The expression of AFP is elevated in certain individuals diagnosed with hepatitis, germ cell tumors, and gastric cancer 25 – 27 . Hence, it is of utmost significance to ascertain biomarkers that can monitor LIHC. The CRLF3 gene encodes the protein known as cytokine receptor-like factor 3 in humans. Phylogenetic investigations have provided evidence indicating that the emergence of CRLF3 can be traced back to a shared ancestor of Cnidaria and Bilateria, coinciding with the genesis of the nervous system 28 , 29 . The protein CRLF3 is highly conserved throughout metazoans and possesses a characteristic cytokine receptor homology domain (CHD). CRLF3 is known to have significant involvement in numerous developmental and homeostatic mechanisms, with a particular emphasis on blood and immune cell functions. Moreover, prior research has provided evidence indicating that CRLF3 plays a crucial role in the start of hematopoiesis in the early embryonic phases of zebrafish 18 . The dysregulation of CRLF3 expression has been observed in cutaneous squamous cell carcinoma, thereby enhancing our comprehension of the etiology and advancement of this condition, as well as diagnostic approaches 30 . The prognostic importance of CRLF3 in LIHC has not yet been thoroughly investigated in the current literature. Therefore, the main aim of this work is to examine the prognostic importance of CRLF3 expression in liver hepatocellular carcinoma (LIHC) and understand the underlying regulatory mechanisms involved. Initially, an examination was conducted on the expression levels of the CRLF3 family. It was observed that the members of the CRLF3 family had a significantly elevated expression level in comparison to that of normal tissues. Univariate and multivariate Cox regression studies were conducted on members of the CRLF family with OS. CRLF3 was included in the study based on the obtained results. Following this, a study was undertaken to analyze the mRNA and protein expression levels of CRLF3 in both liver hepatocellular carcinoma (LIHC) tissues and normal tissues. The results of a Kaplan-Meier analysis revealed that individuals with liver LIHC who exhibited elevated expression levels of CRLF3 experienced a poorer prognosis. In addition, the findings of a multivariate Cox regression analysis revealed that elevated expression of CRLF3 was identified as an autonomous risk factor linked to reduced OS among patients diagnosed with LIHC. The receiver operating characteristic (ROC) analysis demonstrated that CRLF3 exhibits a substantial diagnostic utility. To further our comprehension of the molecular process underlying CRLF3, we conducted an analysis aimed at identifying the genes that interact with CRLF3 and exhibit differential expression (referred to as DEG genes). Based on our research, it can be inferred that there exists a close association between CRLF3 and ATAD5. ATAD5, the human counterpart of the yeast protein Elg1, is involved in the process of PCNA deubiquitination 31 . Genetic and functional defects in ATAD5 contribute to cancer susceptibility in mammals 32 , and ATAD5 plays a very important role in different DNA repairs 33 . ATAD5, the human counterpart of the yeast protein Elg1, is involved in the process of PCNA deubiquitination 34 . Therefore, it is plausible that ATAD5 could potentially exert a pivotal regulatory function in the progression of LIHC. The GSEA analysis revealed that CRLF3 has a role in various cellular processes, including PI3K Akt, Wnt, FcεRI-mediated NF-κB activation, activation of the intestinal immune network for IgA synthesis, interactions between immune cells and microRNAs in the tumor microenvironment, and JAK/STAT signaling pathways. The dysregulation of the PI3K/AKT/mTOR signaling pathway is a prevalent occurrence in LIHC. The protein in question assumes a regulatory function in the metabolic processes of glucose, lipids, amino acids, pyrimidines, and oxidative reactions in the liver 35 , 36 . The Wnt signaling pathway is of significant importance in the processes of cell fate determination, proliferation, and the formation of cell polarity 37 . The disruption of the WNT/β-catenin signaling pathway has been observed in the development of LIHC, suggesting its substantial regulatory involvement in the pathogenesis of this specific malignancy 38 . The signaling system responsible for NF-κB activation through FcεRI can effectively regulate the immune-inflammatory response, hence exerting a significant influence on the advancement of cancer 39 . Interactions between immune cells and microRNAs in the tumor microenvironment signaling pathway have a role in various biological processes of tumor development, such as proliferation, invasion, and evasion. These interactions can either promote or suppress tumor progression 40 . Research has demonstrated that the initiation and advancement of certain diseases, such as inflammatory disorders, lymphomas, leukemias, and solid tumors, are facilitated by the activation of the JAK/STAT pathway 41 , 42 . The findings of this research suggest that CRLF3 possesses the capacity to regulate the progression of cancer through its influence on many signaling pathways. The tumor microenvironment (TME) encompasses the milieu encompassing the tumor, comprising adjacent blood vessels, immune system components, and immune cells. 43 . According to a recent mechanistic study, it has been proposed that immune cells can exert either anti-tumor or pro-tumor effects through the secretion of various cytokines, chemokines, and other substances. These factors play a crucial role in determining the initiation and progression of tumors 44 . In recent years, several studies have provided insights into the substantial role played by immune cell infiltration in the progression of liver carcinogenesis, and its potential implications for the prognostication and treatment of LIHC 45 , 46 . Following that, the ssGSEA technique was employed to measure the amounts of infiltration of 24 specific immune cell types within the tumor microenvironment of LIHC. Our research revealed a strong correlation between CRLF3 and T helper and Th2 cells. Moreover, increased circulating Th2 cells are associated with a more advanced tumor stage and poorer treatment response 47 , 48 . The aforementioned research indicates that CRLF3 is involved in the advancement of LIHC through its regulation of immune cell infiltration inside the tumor microenvironment. In conclusion, our research indicates that CRLF3 potentially serves as a mediator in the diagnosis, prognosis, and survival of LIHC. The progression of LIHC is influenced by CRLF3 through many signaling pathways, including PI3K Akt, Wnt, FcεRI-mediated NF-κB activation, activation of the intestinal immunity network for IgA synthesis, interactions between immune cells and microRNAs, and JAK/STAT signaling pathways within the tumor microenvironment. Moreover, it is plausible that CRLF3 may have a significant impact on the modulation of T helper cells. Undoubtedly, this paper exhibits numerous shortcomings. We have only validated the expression levels of CRLF3 in tumour tissues and paracancerous normal tissues, and the lack of experimental validation limits our understanding of the underlying molecular mechanisms. Therefore, further data collection and in-depth investigations are required to address this matter. In summary, our findings offer a solid foundation for further investigations into the mechanistic cascade of CRLF3 in the progression of LIHC. Declarations Ethics approval and consent to participate The study was also in accordance with the Helsinki Declaration and approved by the Ethics Committee of the Affiliated Hangzhou Xixi Hospital , Zhejiang University of Chinese Medicine[No.(2024)11]. Consent for publication Not applicable. Availability of data and material The article contains the datasets that provide support for the conclusions drawn. Competing interests The authors assert that they do not possess any conflicting interests. Funding None. Authors' contributions The project was conceived by XTZ ，XYS and CXH. Data was analyzed by XXW, who subsequently authored the manuscript. The data was analyzed by ZH and LLH. The final manuscript was read and approved by all of the authors. Acknowledgments We express our gratitude to Xiantu Zhang and Congxiang Huang for their valuable support and encouragement to this article. References Oh, J. H. & Jun, D. W. The latest global burden of liver cancer: A past and present threat. Clin Mol Hepatol 29 , 355-357, doi:10.3350/cmh.2023.0070 (2023). Knorr, D. Y. et al. The cytokine receptor CRLF3 is a human neuroprotective EV-3 (Epo) receptor. Front Mol Neurosci 16 , 1154509, doi:10.3389/fnmol.2023.1154509 (2023). Bennett, C. et al. CRLF3 plays a key role in the final stage of platelet genesis and is a potential therapeutic target for thrombocythemia. Blood 139 , 2227-2239, doi:10.1182/blood.2021013113 (2022). Arnold, M. et al. Global Burden of 5 Major Types of Gastrointestinal Cancer. Gastroenterology 159 , 335-349 e315, doi:10.1053/j.gastro.2020.02.068 (2020). Chon, Y. E. et al. Hepatocellular carcinoma incidence is decreasing in Korea but increasing in the very elderly. Clin Mol Hepatol 29 , 120-134, doi:10.3350/cmh.2021.0395 (2023). Starley, B. Q., Calcagno, C. J. & Harrison, S. A. Nonalcoholic fatty liver disease and hepatocellular carcinoma: a weighty connection. Hepatology 51 , 1820-1832, doi:10.1002/hep.23594 (2010). Anwanwan, D., Singh, S. K., Singh, S., Saikam, V. & Singh, R. Challenges in liver cancer and possible treatment approaches. Biochim Biophys Acta Rev Cancer 1873 , 188314, doi:10.1016/j.bbcan.2019.188314 (2020). Yang, Y., Zhao, W., Du, J. & Wang, Y. Prognostic impact of MICALL1 and associates with immune infiltration in liver hepatocellular carcinoma patients. Cancer Biomark 37 , 147-160, doi:10.3233/CBM-220370 (2023). Gao, Y. X. et al. Progress and prospects of biomarkers in primary liver cancer (Review). Int J Oncol 57 , 54-66, doi:10.3892/ijo.2020.5035 (2020). Hanif, H. et al. Update on the applications and limitations of alpha-fetoprotein for hepatocellular carcinoma. World J Gastroenterol 28 , 216-229, doi:10.3748/wjg.v28.i2.216 (2022). Park, S. J. et al. Usefulness of AFP, AFP-L3, and PIVKA-II, and their combinations in diagnosing hepatocellular carcinoma. Medicine (Baltimore) 96 , e5811, doi:10.1097/MD.0000000000005811 (2017). Tayob, N., Kanwal, F., Alsarraj, A., Hernaez, R. & El-Serag, H. B. The Performance of AFP, AFP-3, DCP as Biomarkers for Detection of Hepatocellular Carcinoma (HCC): A Phase 3 Biomarker Study in the United States. Clin Gastroenterol Hepatol 21 , 415-423 e414, doi:10.1016/j.cgh.2022.01.047 (2023). Chen, F., Wang, J., Wu, Y., Gao, Q. & Zhang, S. Potential Biomarkers for Liver Cancer Diagnosis Based on Multi-Omics Strategy. Front Oncol 12 , 822449, doi:10.3389/fonc.2022.822449 (2022). Li, Y. et al. miR-3065-3p promotes stemness and metastasis by targeting CRLF1 in colorectal cancer. J Transl Med 19 , 429, doi:10.1186/s12967-021-03102-y (2021). Yu, S. T. et al. CRLF1 promotes malignant phenotypes of papillary thyroid carcinoma by activating the MAPK/ERK and PI3K/AKT pathways. Cell Death Dis 9 , 371, doi:10.1038/s41419-018-0352-0 (2018). Juarez-Velazquez, M. D. R. et al. High occurrence of CRLF2 abnormalities in Mexican children with B-cell acute lymphoblastic leukemia. Cytokine 155 , 155896, doi:10.1016/j.cyto.2022.155896 (2022). Roll, J. D. & Reuther, G. W. CRLF2 and JAK2 in B-progenitor acute lymphoblastic leukemia: a novel association in oncogenesis. Cancer Res 70 , 7347-7352, doi:10.1158/0008-5472.CAN-10-1528 (2010). Taznin, T., Perera, K., Gibert, Y., Ward, A. C. & Liongue, C. Cytokine Receptor-Like Factor 3 (CRLF3) Contributes to Early Zebrafish Hematopoiesis. Front Immunol 13 , 910428, doi:10.3389/fimmu.2022.910428 (2022). Yan, X., Zheng, W., Geng, S., Zhou, M. & Xu, T. Cytokine Receptor-Like Factor 3 Negatively Regulates Antiviral Immunity by Promoting the Degradation of TBK1 in Teleost Fish. J Virol 97 , e0179222, doi:10.1128/jvi.01792-22 (2023). Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102 , 15545-15550, doi:10.1073/pnas.0506580102 (2005). Wang, L., Feng, Z., Wang, X., Wang, X. & Zhang, X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. Bioinformatics 26 , 136-138, doi:10.1093/bioinformatics/btp612 (2010). Bindea, G. et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 39 , 782-795, doi:10.1016/j.immuni.2013.10.003 (2013). Vogel, A., Meyer, T., Sapisochin, G., Salem, R. & Saborowski, A. Hepatocellular carcinoma. Lancet 400 , 1345-1362, doi:10.1016/S0140-6736(22)01200-4 (2022). Johnson, P., Zhou, Q., Dao, D. Y. & Lo, Y. M. D. Circulating biomarkers in the diagnosis and management of hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 19 , 670-681, doi:10.1038/s41575-022-00620-y (2022). Dambadarjaa, D. et al. Hepatitis B, C, and D Virus Infections and AFP Tumor Marker Prevalence Among the Elderly Population in Mongolia: A Nationwide Survey. J Prev Med Public Health 55 , 263-272, doi:10.3961/jpmph.21.573 (2022). El-Bahrawy, M. Alpha-fetoprotein-producing non-germ cell tumours of the female genital tract. Eur J Cancer 46 , 1317-1322, doi:10.1016/j.ejca.2010.01.028 (2010). Tatli, A. M. et al. Alpha-fetoprotein (AFP) elevation gastric adenocarcinoma and importance of AFP change in tumor response evaluation. Asian Pac J Cancer Prev 16 , 2003-2007, doi:10.7314/apjcp.2015.16.5.2003 (2015). Hahn, N. et al. The Orphan Cytokine Receptor CRLF3 Emerged With the Origin of the Nervous System and Is a Neuroprotective Erythropoietin Receptor in Locusts. Front Mol Neurosci 12 , 251, doi:10.3389/fnmol.2019.00251 (2019). Liongue, C. & Ward, A. C. Evolution of Class I cytokine receptors. BMC Evol Biol 7 , 120, doi:10.1186/1471-2148-7-120 (2007). Dang, C. et al. Identification of dysregulated genes in cutaneous squamous cell carcinoma. Oncol Rep 16 , 513-519 (2006). Scholes, D. T., Banerjee, M., Bowen, B. & Curcio, M. J. Multiple regulators of Ty1 transposition in Saccharomyces cerevisiae have conserved roles in genome maintenance. Genetics 159 , 1449-1465, doi:10.1093/genetics/159.4.1449 (2001). Bell, D. W. et al. Predisposition to cancer caused by genetic and functional defects of mammalian Atad5. PLoS Genet 7 , e1002245, doi:10.1371/journal.pgen.1002245 (2011). Kim, S. et al. ATAD5 restricts R-loop formation through PCNA unloading and RNA helicase maintenance at the replication fork. Nucleic Acids Res 48 , 7218-7238, doi:10.1093/nar/gkaa501 (2020). Rao, C. V., Asch, A. S. & Yamada, H. Y. Frequently mutated genes/pathways and genomic instability as prevention targets in liver cancer. Carcinogenesis 38 , 2-11, doi:10.1093/carcin/bgw118 (2017). Tian, L. Y., Smit, D. J. & Jucker, M. The Role of PI3K/AKT/mTOR Signaling in Hepatocellular Carcinoma Metabolism. Int J Mol Sci 24 , doi:10.3390/ijms24032652 (2023). Mohseni, A. H., Casolaro, V., Bermudez-Humaran, L. G., Keyvani, H. & Taghinezhad, S. S. Modulation of the PI3K/Akt/mTOR signaling pathway by probiotics as a fruitful target for orchestrating the immune response. Gut Microbes 13 , 1-17, doi:10.1080/19490976.2021.1886844 (2021). Hu, H. H., Cao, G., Wu, X. Q., Vaziri, N. D. & Zhao, Y. Y. Wnt signaling pathway in aging-related tissue fibrosis and therapies. Ageing Res Rev 60 , 101063, doi:10.1016/j.arr.2020.101063 (2020). West, C. A., Black, A. P. & Mehta, A. S. in Hepatocellular Carcinoma: Translational Precision Medicine Approaches (ed Y. Hoshida) 93-107 (2019). Inoue, J., Gohda, J., Akiyama, T. & Semba, K. NF-kappaB activation in development and progression of cancer. Cancer Sci 98 , 268-274, doi:10.1111/j.1349-7006.2007.00389.x (2007). Xing, Y. et al. Tumor Immune Microenvironment and Its Related miRNAs in Tumor Progression. Front Immunol 12 , 624725, doi:10.3389/fimmu.2021.624725 (2021). Owen, K. L., Brockwell, N. K. & Parker, B. S. JAK-STAT Signaling: A Double-Edged Sword of Immune Regulation and Cancer Progression. Cancers (Basel) 11 , doi:10.3390/cancers11122002 (2019). Chen, J. et al. Aberrant JAK-STAT signaling-mediated chromatin remodeling impairs the sensitivity of NK/T-cell lymphoma to chidamide. Clin Epigenetics 15 , 19, doi:10.1186/s13148-023-01436-6 (2023). Arneth, B. Tumor Microenvironment. Medicina (Kaunas) 56 , doi:10.3390/medicina56010015 (2019). Lu, C., Liu, Y., Ali, N. M., Zhang, B. & Cui, X. The role of innate immune cells in the tumor microenvironment and research progress in anti-tumor therapy. Front Immunol 13 , 1039260, doi:10.3389/fimmu.2022.1039260 (2022). Lu, Y. et al. Resident Immune Cells of the Liver in the Tumor Microenvironment. Front Oncol 12 , 931995, doi:10.3389/fonc.2022.931995 (2022). Meng, L. et al. Profiles of immune infiltration in the tumor microenvironment of hepatocellular carcinoma. J Gastrointest Oncol 12 , 1152-1163, doi:10.21037/jgo-21-291 (2021). Momiyama, K., Nagai, H. & Sumino, Y. Changes of host immunity in relation to efficacy in liver cirrhosis patients with advanced hepatocellular carcinoma treated by intra-arterial chemotherapy. Cancer Chemother Pharmacol 64 , 271-277, doi:10.1007/s00280-008-0866-8 (2009). Bian, J. et al. T lymphocytes in hepatocellular carcinoma immune microenvironment: insights into human immunology and immunotherapy. Am J Cancer Res 10 , 4585-4606 (2020). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3975470","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274444479,"identity":"5cd589ba-d47c-479a-a3b3-9c83028573f8","order_by":0,"name":"幸幸 王","email":"","orcid":"","institution":"Affiliated Hangzhou Xixi Hospital， Zhejiang University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"幸幸","middleName":"","lastName":"王","suffix":""},{"id":274444480,"identity":"b1138a61-cb7b-4c41-8a54-a630755a540e","order_by":1,"name":"Zhen Huang","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Huang","suffix":""},{"id":274444481,"identity":"af23d828-eb59-4a70-8c45-2e41579941cd","order_by":2,"name":"Lili Huang","email":"","orcid":"","institution":"Affiliated Hangzhou Xixi Hospital， Zhejiang University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Huang","suffix":""},{"id":274444482,"identity":"31d3c81a-264a-48ca-afcc-218716c6da26","order_by":3,"name":"Cong Huang","email":"","orcid":"","institution":"Affiliated Hangzhou Xixi Hospital， Zhejiang University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Huang","suffix":""},{"id":274444483,"identity":"78b2296c-b2ad-405e-813b-b9fee7cdd6c1","order_by":4,"name":"Xiaoying Zhang","email":"","orcid":"","institution":"The First People’s Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoying","middleName":"","lastName":"Zhang","suffix":""},{"id":274444484,"identity":"12cce1c3-ad97-4424-88fc-68e0ebe33eb0","order_by":5,"name":"Xiantu Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACCRCRYCAhx8befODAhx/EavlQYWHMx3Ms8eDMHiK1MM44U5E4TyLH+DAHGxFaJGekP/vM2ybB2MaQ8+EwAw+DPL/YAfxapHkOJM8GamFmYzi74XCBBYPhzNkJ+LXIsTccZgZqYWNj7N1weAYPMChuE9LCzNgM0sLDxszz4DAPGxFapNmbmYHelwBaw8NAnBbJnmPMwECWMGDjYTMABrIEYb9I3Eh/DDS5rn7+/MePP3z4YSPPL01AC4YRpCkfBaNgFIyCUYAdAABzbT9BbUsrawAAAABJRU5ErkJggg==","orcid":"","institution":"Affiliated Hangzhou Xixi Hospital， Zhejiang University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Xiantu","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-02-21 12:44:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3975470/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3975470/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51766236,"identity":"173365a3-77b1-4b21-9991-eaeddb5260af","added_by":"auto","created_at":"2024-02-28 18:51:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1289481,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression of CRLFs in liver hepatocellular carcinoma tissues.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The mRNA expression levels of each member of the CRLF family in liver hepatocellular carcinoma tissues. (B) Multivariate and Univariate Cox regression analysis of CRLF family members according to OS. (C) The mRNA expressions of CRLF3 in LIHC samples and normal liver tissue. (D) The protein expression levels of CRLF3 in LIHC tissues and normal tissues were analyzed using the HPA database. (E) )Immunohistochemistry detecting the expression of CRLF3 in LIHC and paired adjacent normal samples (Normal group: n = 20; Tumor group:n = 20) . (F) The ROC curve for CRLF3 in normal and LIHC samples. (G) Expression levels of CRLF3 across several types of cancer. (H) In the Heptromic cohort, the comparison was made between normal liver samples and LIHC samples. Data are mean ± SEM. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01;***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Onlinefigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3975470/v1/93369836b8b58a6c16aa50c1.jpg"},{"id":51766232,"identity":"0b58f95d-9b89-4ad8-8d57-10248245dfa5","added_by":"auto","created_at":"2024-02-28 18:51:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":180386,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe box plots show different characteristics. according to the expression level of CRLF3 mRNA.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) AFP, (B) Age, (C) Gender, (D) histologic grade, (E) pathologic T stage, (F) Pathologic stage. Data are mean ± SEM. *p \u0026lt; 0.05; **p \u0026lt; 0.01;***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Onlinefigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3975470/v1/9640ca0113f64f93fe4cc421.jpg"},{"id":51766231,"identity":"16eeee38-f8ba-43a2-ad19-7624eb561aae","added_by":"auto","created_at":"2024-02-28 18:51:29","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":413058,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations between CRLF3 and prognosis in LIHC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) OS, (B) DSS,(C) PFI, (D) Age: \u0026lt;= 60, (E) Gender: Female, (F) T stage: T1\u0026amp;T2, (G) Pathologic stage: Stage I \u0026amp; II, (H) Age: \u0026gt; 60, (I) Gender: Male, (J) T stage: T3\u0026amp;T4, (K) Pathologic stage: Stage III–IV.\u003c/p\u003e","description":"","filename":"Onlinefigure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3975470/v1/88ea216dc7964cd991802cd1.jpg"},{"id":51767195,"identity":"cbcd6114-296d-4d6a-84a0-71faf874c24f","added_by":"auto","created_at":"2024-02-28 18:59:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":299064,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExamining the connection between CRLF3-interacting genes and DEG genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) The protein-protein interactions were mapped using the STRING v10.5 software. (B) Volcano plots have been utilized for the visualization of differential gene expression data. (C) The present study investigates the relationship between genes that interact with CRLF3 and differentially expressed genes (DEGs) of CRLF3, utilizing Wayne diagrams. (D) The association between CRLF3 and ATAD5 was established through the utilization of a scatter plot.\u003c/p\u003e","description":"","filename":"Onlinefigure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3975470/v1/a15d0388725352f47bfa34c5.jpg"},{"id":51766233,"identity":"3e2be0a6-85d1-4e4a-867c-709b9dd6e05c","added_by":"auto","created_at":"2024-02-28 18:51:29","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":260612,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis of CRLF3 in LIHC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) GO functional enrichment and KEGG pathways. (B-I) GSEA revealed the enrichment of five signaling pathways in individuals with LIHC who have high expression of the CRLF3 gene.\u003c/p\u003e","description":"","filename":"Onlinefigure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3975470/v1/8e01a84f8bbad316f2bc2547.jpg"},{"id":51766234,"identity":"8857cd24-650f-4265-8444-bc5db60d8a24","added_by":"auto","created_at":"2024-02-28 18:51:29","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":319239,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between CRLF3 expression and immune infiltration in LIHC tumor microenvironment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) correlation between the 24 immunological cells' respective abundances. (B-C) correlation between CRLF3 expression levels and T helper and Th2 cell infiltration levels. (B) T helper cells. (C) Th2 cells. Spearman correlation validation of CRLF3 with T helper cells. (D-E) correlation between levels of Th2 and T helper cell infiltration and CCDC68 expression, both high and low. (D) T helper cells. (E) Th2 cells.\u003c/p\u003e","description":"","filename":"Onlinefigure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3975470/v1/bca44e6ae790d61792e0bf69.jpg"},{"id":76921287,"identity":"9051a4d8-51b0-4a62-bcc5-f2d698d7d686","added_by":"auto","created_at":"2025-02-22 11:16:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4410917,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3975470/v1/a1b749e0-637c-48df-b3f9-3f3a92618d17.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Expressional and prognostic value of CRLF3 in liver hepatocellular carcinoma patients via integrated bioinformatics analyses and experiments","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith more than 800,000 fatalities each year, liver hepatocellular carcinoma (LIHC) is the fourth most common cause of mortality in the world \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Based on the findings of recent research, projections indicate that the mortality rate attributed to liver cancer is expected to surpass one million individuals by the year 2030. It has been shown that developing countries have a higher prevalence of liver illnesses\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Currently, the clinical management of LIHC encompasses several therapeutic approaches such as chemotherapy, immunotherapy, the utilization of natural substances, and the use of nanotechnology. Despite the existence of several therapy modalities, the medical community faces a notable absence of a definitive remedy for LIHC\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The integration of diverse tumor biomarkers based on varying clinical situations has immense importance in the diagnosis, monitoring of treatment efficacy, and prognostic assessment of primary liver cancer\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Furthermore, the exploration and establishment of novel biomarkers further contribute to these objectives\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlpha-fetoprotein (AFP), squamous lectin-responsive alpha-fetoprotein (AFP-L3), vitamin K insufficiency or antagonist-II (PIVKA-II), and de-gamma-carboxyprothrombinogen (DCP) are frequently observed indicators of LIHC\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the intricate pathogenesis and diverse individual characteristics of LIHC present significant obstacles in the timely identification of this disease. Nevertheless, the eligibility for curative therapy in LIHC patients is limited to only 20\u0026ndash;30% mostly as a result of the absence of early-detection methods. This underscores the importance of dependable and precise biomarkers\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe cytokine receptor-like factor family comprises three members, namely CRLF1, CRLF2, and CRLF3. There exists data indicating that the family of CRLFs plays a role in the pathogenesis of several neoplastic conditions\u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. CRLF3 has been associated with various human diseases, as well as being involved in vertebrate hematopoiesis and insect neuroprotection\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The study revealed that CRLF3 plays a significant role in facilitating embryonic hematopoiesis throughout the early stages of zebrafish development.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In teleost fish, CRLF3 Promotes the Degradation of TBK1 to Negatively Regulate Antiviral Immunity\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the precise molecular mechanism behind the role of CRLF3 in LIHC remains elusive.\u003c/p\u003e \u003cp\u003eIn this study, the transcriptional and protein expression of CRFL3 was found through the utilization of TCGA and HPA databases. Additionally, to comprehend the fundamental mechanisms underlying the pathophysiology of CRLF3, we have carried out additional research using GO, KEGG, and GSEA. The objective of this work was to explore the potential mechanism by which CRLF3 is implicated in LIHC, specifically by investigating the association between CRLF3 expression and immune infiltration. Furthermore, much research has been conducted to examine the clinical features and prognostic implications of CRLF3 in individuals diagnosed with LIHC. Hence, the results obtained from our research possess the capability to reveal innovative targets and methodologies for the detection and treatment of LIHC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDownloading and processing data from public databases\u003c/h2\u003e \u003cp\u003eThe dataset used in this study comprises gene expression RNA-seq data from the Cancer Genome Atlas (TCGA), consisting of 374 LIHC tissues and 50 normal liver tissues from TCGA database. The HPA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to compare CRLF3 Protein expression levels in LIHC tissues and normal liver tissues. The analysis of CRLF3 expression in LIHC was also conducted using the HCCDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://lifeome.net/database/hccdb\u003c/span\u003e\u003cspan address=\"http://lifeome.net/database/hccdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The predictive value of CRLF3 in LIHC was evaluated in terms of overall survival (OS) using the Kaplan-Meier plotter (kmplot.com/analysis).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eThe genes that underwent screening were subjected to analysis for functional enrichment, utilizing the Gene Ontology (GO) database and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.\u003c/p\u003e \u003cp\u003eGene Set Enrichment Analysis (GSEA) uses genes in a predefined gene set to assess trends in the distribution of genes in a table of genes sorted by phenotypic relatedness to determine their contribution to the phenotype\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Following the application of ID transformation to the molecules in the input data, the clusterProfiler software will be utilized to conduct Gene Set Enrichment Analysis (GSEA) ((p.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 \u0026amp; qvalue\u0026thinsp;\u0026lt;\u0026thinsp;0.25).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eProtein-protein interaction (PPI) network analysis\u003c/h2\u003e \u003cp\u003eThe researchers utilized the String database to perform protein-protein interaction network analysis. Minimum required interaction score:0.400.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eScreening of differentially expressed genes (DEGs)\u003c/h2\u003e \u003cp\u003eThe DEGs were performed using the DESeq2 tool in the R programming language (p.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and log2 (fold change)\u0026thinsp;\u0026gt;\u0026thinsp;1)\u003csup\u003e21\u003c/sup\u003e. The initial Counts matrix underwent variance analysis using the DESeq2 tool, following the established protocol, and was afterward standardized using the VST (Variance Stabilizing Transformations) approach offered by the DESeq2 package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eImmune cell infiltration of ssGSEA\u003c/h2\u003e \u003cp\u003eThe ssGSEA method measures the amount of immune invasion of LIHC by 24 immune cells in tumor samples. It does this by using Spearman's correlation test\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. To find out if there was a link between CRLF3 expression and the number of immune cells in the body, Spearman's correlation test was used. The Wilcoxon rank sum test was used to find out if there was a link between immune cell influx and high and low CRLF3 expression groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry and scoring analyses\u003c/h2\u003e \u003cp\u003eImmunohistochemistry was conducted to determine CRLF3 protein expression and\u003c/p\u003e \u003cp\u003eprognostic value. LIHC tissues and paired adjacent normal tissues were stained by\u003c/p\u003e \u003cp\u003eimmunohistochemistry with anti-human CRLF3 (1:150; Rabbit; DF8931; Affinity, USA), followed by Two-step universal kit (mouse/rabbit ultrasensitive polymer assay system) (PV-800;ZSGB-BIO, Beijing, China). Then, the samples were observed under a microscope and photographed for further analysis. Each LIHC sample was evaluated based on staining intensity and positively stained cell percentage. The stainingintensity (negative, 0; mild, 1; moderate, 2; and strong,3) and percentage area of positive cells (\u0026le;\u0026thinsp;25, 1; \u0026gt;25 and \u0026le;\u0026thinsp;50%, 2; \u0026gt;50 and \u0026le;\u0026thinsp;75%, 3; and \u0026gt;\u0026thinsp;75%, 4) were quanti-fied, respectively. The scores of the two groups were summed to obtain the final IHC scores. The results of immunohistochemical scoring were also validated using ImageJ software.A paired t-test was used to compare CRLF3 expression between LIHC tissues and the paired non-tumor tissues.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eR software (version 4.2.1) was used for all statistical studies. The Wilcoxon rank sum test and the Paired samples t-test were used to see how CRLF3 levels changed in tumor and healthy tissues. We used both univariate and multivariate analysis to find out how the clinical factors affected Overall Survival. A p-value less than 0.05 is indicative of statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eProtein and mRNA expression levels of CRLF3 in patients with LIHC\u003c/h2\u003e\n \u003cp\u003eInitially, we conducted an assessment of the expression levels and predictive significance of members belonging to the CRLF family in LIHC, as well as in corresponding neighboring samples.CRLF1, CRLF2, and CRLF3 were observed to exhibit a statistically significant upregulation in LIHC. (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). However, it should be noted that CRLF3 exhibits not only a high level of expression in LIHC but also a significant correlation with poor overall survival. (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). In addition, CRLF3 expression was increased in LIHC tissues when compared with paired adjacent tissues or normal tissues (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC). As a result, CRLF3 was selected as the focal point of the study. The expression of CRLF3 protein was shown to be elevated in LIHC tissues in comparison to normal tissues, as observed in the HPA database. (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD). The results of the HPA database were also verified by immunohistochemical staining (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eE).The diagnostic capability of CRLF3 was found to be significant based on the examination of the receiver operating characteristic (ROC), as shown by an area under the curve (AUC) value of 0.823 (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e\n \u003cp\u003eThe expression of CRLF3 is upregulated in various types of malignancies, suggesting its potential involvement in the pathogenesis of cancer. (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eG). By utilizing the HCCDB database, a comprehensive analysis was conducted on nine distinct hepatocellular carcinoma (HCC) cohorts. The examination revealed a noteworthy elevation in the mRNA expression levels of CRLF3 within HCC tissues, as compared to the adjacent tissues. (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eH).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation between CRLF3 expression and clinicopathologic characteristics in LIHC\u003c/h2\u003e\n \u003cp\u003eThe 374 samples were categorized into two groups according to the levels of CRLF3 mRNA expression. The examination of the two sets of samples yielded noteworthy findings about the correlation between CRLF3 expression and several parameters, such as pathologic stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046), Age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026), Histologic grad (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and AFP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) concentration. There was no observed connection between TNM stage, gender, and BMI. (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe findings from the logistic regression analysis demonstrated a statistically significant relationship between the expression of CRLF3 and many variables., including T stage (T2\u0026amp;T3\u0026amp;T4 vs. T1) (OR\u0026thinsp;=\u0026thinsp;1.701, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), pathologic stage (Stage II\u0026amp;Stage III\u0026amp;Stage IV vs. Stage I) (OR\u0026thinsp;=\u0026thinsp;1.621, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025), Age((\u0026gt;\u0026thinsp;60 vs. Age\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;60) (OR\u0026thinsp;=\u0026thinsp;0.629, P\u0026thinsp;=\u0026thinsp;0.026), and AFP (\u0026gt;\u0026thinsp;400 vs. AFP\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;400) (OR\u0026thinsp;=\u0026thinsp;3.562, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The findings from both the Welch t-test and t-test analyses revealed a statistically significant correlation between the levels of CRLF3 mRNA expression and several clinical variables., including AFP concentration, histologic grade, pathologic T stage, and pathologic stage (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;F).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRelationship between the clinicopathologic features of LIHC and the expression of CRLF3\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLow expression of CRLF3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh expression of CRLF3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathologic T stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103 (27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80 (21.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (9.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathologic N stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127 (49.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e127 (49.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathologic stage, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98 (28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47 (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134 (35.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119 (31.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;= 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99 (26.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109 (29.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 (23.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;= 25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83 (24.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77 (22.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistologic grade, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (27.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 (21.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (12.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAFP(ng/ml), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;= 400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128 (45.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 (31.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (16.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe logistic regression analysis shows the relationship between clinicopathological characteristics and CRLF3 expression.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal (N)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathologic T stage (T2\u0026amp;T3\u0026amp;T4 vs. T1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.701 (1.128\u0026ndash;2.564)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathologic N stage (N1 vs. N0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.000 (0.308\u0026ndash;29.225)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathologic M stage (M1 vs. M0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.349 (0.036\u0026ndash;3.394)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePathologic stage (Stage II\u0026amp;Stage III\u0026amp;Stage IV vs. Stage I)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.621 (1.063\u0026ndash;2.472)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistological type (Hepatocellular carcinoma\u0026amp;Hepatocholangiocarcinoma (mixed) vs. Fibrolamellar carcinoma)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.497 (0.045\u0026ndash;5.532)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistologic grade (G2\u0026amp;G3\u0026amp;G4 vs. G1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.098 (1.154\u0026ndash;3.817)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.015\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTumor status (With tumor vs. Tumor free)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.465 (0.961\u0026ndash;2.235)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidual tumor (R1\u0026amp;R2 vs. R0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.681 (0.636\u0026ndash;4.443)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (Female vs. Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.445 (0.934\u0026ndash;2.234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace (Black or African American\u0026amp;White vs. Asian)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.965 (0.637\u0026ndash;1.462)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (\u0026gt;\u0026thinsp;60 vs. \u0026lt;= 60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.629 (0.418\u0026ndash;0.947)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAFP(ng/ml) (\u0026gt;\u0026thinsp;400 vs. \u0026lt;= 400)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.562 (1.955\u0026ndash;6.490)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVascular invasion (Yes vs. No)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.517 (0.953\u0026ndash;2.413)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdjacent hepatic tissue inflammation (Mild\u0026amp;Severe vs. None)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.250 (0.749\u0026ndash;2.087)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCox proportional risk models with univariate and multivariate variables were used to analyze the factors influencing OS.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003eTotal(N)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 33.9321%;\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 33.9321%;\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 25.4889%;\"\u003e\n \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003ePathologic T stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eT2\u0026amp;T3\u0026amp;T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003e0.470 (0.328\u0026ndash;0.675)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\n \u003cp\u003e0.726 (0.449\u0026ndash;1.173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003ePathologic stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eStage III\u0026amp;Stage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eStage I\u0026amp;Stage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003e0.399 (0.275\u0026ndash;0.579)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\n \u003cp\u003e0.489 (0.305\u0026ndash;0.785)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eHistologic grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eG1\u0026amp;G2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eG3\u0026amp;G4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003e1.091 (0.761\u0026ndash;1.564)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003e1.261 (0.885\u0026ndash;1.796)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003e\u0026lt;= 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003e1.205 (0.850\u0026ndash;1.708)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003e\u0026lt;= 25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003e0.798 (0.550\u0026ndash;1.158)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eAFP(ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003e\u0026lt;= 400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003e1.075 (0.658\u0026ndash;1.759)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eVascular invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003e1.344 (0.887\u0026ndash;2.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eCRLF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003e1.507 (1.065\u0026ndash;2.133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\n \u003cp\u003e1.466 (1.015\u0026ndash;2.116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCox proportional risk models with univariate and multivariate variables were used to analyze the factors influencing DSS.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003eTotal(N)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 33.9321%;\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 33.9321%;\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 25.4889%;\"\u003e\n \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003ePathologic T stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eT2\u0026amp;T3\u0026amp;T4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003e0.353 (0.218\u0026ndash;0.573)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\n \u003cp\u003e0.680 (0.344\u0026ndash;1.343)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003ePathologic stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eStage III\u0026amp;Stage IV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eStage I\u0026amp;Stage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003e0.263 (0.162\u0026ndash;0.427)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\n \u003cp\u003e0.330 (0.175\u0026ndash;0.622)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eHistologic grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eG1\u0026amp;G2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eG3\u0026amp;G4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003e1.086 (0.683\u0026ndash;1.728)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003e1.230 (0.780\u0026ndash;1.937)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003e\u0026lt;= 60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003e0.846 (0.543\u0026ndash;1.317)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003e\u0026lt;= 25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003e0.826 (0.512\u0026ndash;1.330)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eAFP(ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003e\u0026lt;= 400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003e0.867 (0.450\u0026ndash;1.668)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e0.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eVascular invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003e1.277 (0.707\u0026ndash;2.306)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eCRLF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.2839%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 20.3911%;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 9.5583%;\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.6482%;\"\u003e\n \u003cp\u003e1.622 (1.037\u0026ndash;2.537)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 25.4889%;\"\u003e\n \u003cp\u003e1.671 (1.024\u0026ndash;2.727)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" style=\"width: 8.2839%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eThe predictive importance of the CRLF3 gene in LIHC\u003c/h2\u003e\n \u003cp\u003eSubsequently, an investigation was conducted to assess the potential correlation between the mRNA expression level of CRLF3 in LIHC and its associated clinical outcome. The findings of our investigation demonstrated a noteworthy association between heightened CRLF3 expression and worse OS (hazard ratio [HR]\u0026thinsp;=\u0026thinsp;1.51, p-value\u0026thinsp;=\u0026thinsp;0.021), DSS (HR\u0026thinsp;=\u0026thinsp;1.62, p-value\u0026thinsp;=\u0026thinsp;0.034), and reduced PFI (HR\u0026thinsp;=\u0026thinsp;1.39, p-value\u0026thinsp;=\u0026thinsp;0.025). (Fig.\u0026nbsp;3A\u0026ndash;C). Cox regression analysis revealed that the overexpression of CRLF3 mRNA had a substantial influence on OS in LIHC who were at Pathologic T3\u0026amp;T4 stage (HR\u0026thinsp;=\u0026thinsp;1.75, P\u0026thinsp;=\u0026thinsp;0.046) and Pathologic Stage III\u0026amp;Stage IV (HR\u0026thinsp;=\u0026thinsp;1.85, P\u0026thinsp;=\u0026thinsp;0.037). (Fig.\u0026nbsp;3D\u0026ndash;K).\u003c/p\u003e\n \u003cp\u003eUnivariate and multivariate COX regression models indicated a significant correlation between CRLF3 expression and unfavorable OS ([HR]\u0026thinsp;=\u0026thinsp;1.507, p-value\u0026thinsp;=\u0026thinsp;0.021) as well as disease-specific survival (HR\u0026thinsp;=\u0026thinsp;1.622, p-value\u0026thinsp;=\u0026thinsp;0.034). The results of the multivariate analysis revealed that elevated expression of CRLF3 served as a significant independent prognostic indicator in individuals diagnosed with LIHC (Tables \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). According to our research, CRLF3 may be used as a prognostic marker for OS and DSS in people who have been diagnosed with LIHC.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eEstablishing a protein-protein interaction network\u003c/h2\u003e\n \u003cp\u003eThe protein interaction networks were extracted from the STRING database (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA), while the identification of differentially expressed genes (DEGs) was visualized using volcano plots (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). The ATAD5 gene was subjected to screening by identifying the overlapping genes from the STRING database that interact with differentially expressed genes (DEGs (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). Overall, a notable positive association was seen between the levels of CRLF3 expression and the overall expression of ATAD5 (r\u0026thinsp;=\u0026thinsp;0.770, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e\n \u003cp\u003eTo conduct a more comprehensive investigation into the cellular molecular pathways, the studies of GO, KEGG, and GSEA were carried out. The analysis of gene ontology enrichment reveals a significant association between CRLF3 and immune-related processes. (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). The KEGG analysis demonstrated that the discovered genes displayed notable enrichment mostly in four signaling pathways that are intimately linked to the progression of cancer (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). The GSEA analysis demonstrated that the DEGs exhibited a notable enrichment in various pathways, including but not limited to PI3K Akt, Wnt, Fc\u0026epsilon;RI-mediated NF-\u0026kappa;B activation, activation of the intestinal immune network for IgA production, interactions between immune cells and microRNAs in the tumor microenvironment, and JAK/STAT signaling pathways (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB-G).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eCorrelation between CRLF3 and immune cell infiltration\u003c/h2\u003e\n \u003cp\u003eThe assessment of the correlations between the proportional representation of 24 immune cells and the expression of CRLF3 in liver hepatocellular carcinoma (LIHC) was performed utilizing the ssGSEA technique (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). The expression of CRLF3 exhibited a substantial linear correlation with the degree of infiltration of T helper cells (R\u0026thinsp;=\u0026thinsp;0.515, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Th2 cells (R\u0026thinsp;=\u0026thinsp;0.414, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB-C).\u003c/p\u003e\n \u003cp\u003eThe Wilcoxon Rank-sum test was employed to ascertain the presence of immune cell enrichment in the groups categorized by high and low expression of CRLF3. The study\u0026apos;s findings revealed that there was an elevation in the concentration of T helper cells and Th2 cells within the group exhibiting high expression of CRLF3.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWith an estimated prevalence exceeding 1\u0026nbsp;million cases by the year 2025, LIHC presents a significant threat to human well-being owing to its elevated occurrence and fatality rate. The primary risk factors associated with the development of liver cancer, namely LIHC, encompass infections caused by the hepatitis B and C viruses\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. The management of LIHC continues to provide significant challenges, mostly relying on the timely identification of the disease. AFP is presently the prevailing biomarker employed for the early detection of liver cancer\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The expression of AFP is elevated in certain individuals diagnosed with hepatitis, germ cell tumors, and gastric cancer \u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Hence, it is of utmost significance to ascertain biomarkers that can monitor LIHC.\u003c/p\u003e \u003cp\u003eThe CRLF3 gene encodes the protein known as cytokine receptor-like factor 3 in humans. Phylogenetic investigations have provided evidence indicating that the emergence of CRLF3 can be traced back to a shared ancestor of Cnidaria and Bilateria, coinciding with the genesis of the nervous system\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The protein CRLF3 is highly conserved throughout metazoans and possesses a characteristic cytokine receptor homology domain (CHD). CRLF3 is known to have significant involvement in numerous developmental and homeostatic mechanisms, with a particular emphasis on blood and immune cell functions. Moreover, prior research has provided evidence indicating that CRLF3 plays a crucial role in the start of hematopoiesis in the early embryonic phases of zebrafish\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. The dysregulation of CRLF3 expression has been observed in cutaneous squamous cell carcinoma, thereby enhancing our comprehension of the etiology and advancement of this condition, as well as diagnostic approaches\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The prognostic importance of CRLF3 in LIHC has not yet been thoroughly investigated in the current literature. Therefore, the main aim of this work is to examine the prognostic importance of CRLF3 expression in liver hepatocellular carcinoma (LIHC) and understand the underlying regulatory mechanisms involved.\u003c/p\u003e \u003cp\u003eInitially, an examination was conducted on the expression levels of the CRLF3 family. It was observed that the members of the CRLF3 family had a significantly elevated expression level in comparison to that of normal tissues. Univariate and multivariate Cox regression studies were conducted on members of the CRLF family with OS. CRLF3 was included in the study based on the obtained results. Following this, a study was undertaken to analyze the mRNA and protein expression levels of CRLF3 in both liver hepatocellular carcinoma (LIHC) tissues and normal tissues. The results of a Kaplan-Meier analysis revealed that individuals with liver LIHC who exhibited elevated expression levels of CRLF3 experienced a poorer prognosis. In addition, the findings of a multivariate Cox regression analysis revealed that elevated expression of CRLF3 was identified as an autonomous risk factor linked to reduced OS among patients diagnosed with LIHC. The receiver operating characteristic (ROC) analysis demonstrated that CRLF3 exhibits a substantial diagnostic utility.\u003c/p\u003e \u003cp\u003eTo further our comprehension of the molecular process underlying CRLF3, we conducted an analysis aimed at identifying the genes that interact with CRLF3 and exhibit differential expression (referred to as DEG genes). Based on our research, it can be inferred that there exists a close association between CRLF3 and ATAD5. ATAD5, the human counterpart of the yeast protein Elg1, is involved in the process of PCNA deubiquitination\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Genetic and functional defects in ATAD5 contribute to cancer susceptibility in mammals\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, and ATAD5 plays a very important role in different DNA repairs\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. ATAD5, the human counterpart of the yeast protein Elg1, is involved in the process of PCNA deubiquitination\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Therefore, it is plausible that ATAD5 could potentially exert a pivotal regulatory function in the progression of LIHC.\u003c/p\u003e \u003cp\u003eThe GSEA analysis revealed that CRLF3 has a role in various cellular processes, including PI3K Akt, Wnt, FcεRI-mediated NF-κB activation, activation of the intestinal immune network for IgA synthesis, interactions between immune cells and microRNAs in the tumor microenvironment, and JAK/STAT signaling pathways. The dysregulation of the PI3K/AKT/mTOR signaling pathway is a prevalent occurrence in LIHC. The protein in question assumes a regulatory function in the metabolic processes of glucose, lipids, amino acids, pyrimidines, and oxidative reactions in the liver \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The Wnt signaling pathway is of significant importance in the processes of cell fate determination, proliferation, and the formation of cell polarity\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. The disruption of the WNT/β-catenin signaling pathway has been observed in the development of LIHC, suggesting its substantial regulatory involvement in the pathogenesis of this specific malignancy\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The signaling system responsible for NF-κB activation through FcεRI can effectively regulate the immune-inflammatory response, hence exerting a significant influence on the advancement of cancer\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Interactions between immune cells and microRNAs in the tumor microenvironment signaling pathway have a role in various biological processes of tumor development, such as proliferation, invasion, and evasion. These interactions can either promote or suppress tumor progression\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Research has demonstrated that the initiation and advancement of certain diseases, such as inflammatory disorders, lymphomas, leukemias, and solid tumors, are facilitated by the activation of the JAK/STAT pathway\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The findings of this research suggest that CRLF3 possesses the capacity to regulate the progression of cancer through its influence on many signaling pathways.\u003c/p\u003e \u003cp\u003eThe tumor microenvironment (TME) encompasses the milieu encompassing the tumor, comprising adjacent blood vessels, immune system components, and immune cells. \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. According to a recent mechanistic study, it has been proposed that immune cells can exert either anti-tumor or pro-tumor effects through the secretion of various cytokines, chemokines, and other substances. These factors play a crucial role in determining the initiation and progression of tumors\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. In recent years, several studies have provided insights into the substantial role played by immune cell infiltration in the progression of liver carcinogenesis, and its potential implications for the prognostication and treatment of LIHC\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Following that, the ssGSEA technique was employed to measure the amounts of infiltration of 24 specific immune cell types within the tumor microenvironment of LIHC. Our research revealed a strong correlation between CRLF3 and T helper and Th2 cells. Moreover, increased circulating Th2 cells are associated with a more advanced tumor stage and poorer treatment response\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. The aforementioned research indicates that CRLF3 is involved in the advancement of LIHC through its regulation of immune cell infiltration inside the tumor microenvironment.\u003c/p\u003e \u003cp\u003eIn conclusion, our research indicates that CRLF3 potentially serves as a mediator in the diagnosis, prognosis, and survival of LIHC. The progression of LIHC is influenced by CRLF3 through many signaling pathways, including PI3K Akt, Wnt, FcεRI-mediated NF-κB activation, activation of the intestinal immunity network for IgA synthesis, interactions between immune cells and microRNAs, and JAK/STAT signaling pathways within the tumor microenvironment. Moreover, it is plausible that CRLF3 may have a significant impact on the modulation of T helper cells. Undoubtedly, this paper exhibits numerous shortcomings. We have only validated the expression levels of CRLF3 in tumour tissues and paracancerous normal tissues, and the lack of experimental validation limits our understanding of the underlying molecular mechanisms. Therefore, further data collection and in-depth investigations are required to address this matter. In summary, our findings offer a solid foundation for further investigations into the mechanistic cascade of CRLF3 in the progression of LIHC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was also in accordance with the Helsinki Declaration and approved by the Ethics Committee of the Affiliated Hangzhou Xixi Hospital , Zhejiang University of Chinese Medicine[No.(2024)11].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe article contains the datasets that provide support for the conclusions drawn.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors assert that they do not possess any conflicting interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project was conceived by XTZ\u0026nbsp;，XYS and CXH. Data was analyzed by XXW, who subsequently authored the manuscript. The data was analyzed by ZH and LLH. The final manuscript was read and approved by all of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to Xiantu Zhang and Congxiang Huang for their valuable support and encouragement to this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOh, J. H. \u0026amp; Jun, D. W. The latest global burden of liver cancer: A past and present threat. \u003cem\u003eClin Mol Hepatol\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 355-357, doi:10.3350/cmh.2023.0070 (2023).\u003c/li\u003e\n\u003cli\u003eKnorr, D. Y.\u003cem\u003e et al.\u003c/em\u003e The cytokine receptor CRLF3 is a human neuroprotective EV-3 (Epo) receptor. \u003cem\u003eFront Mol Neurosci\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 1154509, doi:10.3389/fnmol.2023.1154509 (2023).\u003c/li\u003e\n\u003cli\u003eBennett, C.\u003cem\u003e et al.\u003c/em\u003e CRLF3 plays a key role in the final stage of platelet genesis and is a potential therapeutic target for thrombocythemia. \u003cem\u003eBlood\u003c/em\u003e \u003cstrong\u003e139\u003c/strong\u003e, 2227-2239, doi:10.1182/blood.2021013113 (2022).\u003c/li\u003e\n\u003cli\u003eArnold, M.\u003cem\u003e et al.\u003c/em\u003e Global Burden of 5 Major Types of Gastrointestinal Cancer. \u003cem\u003eGastroenterology\u003c/em\u003e \u003cstrong\u003e159\u003c/strong\u003e, 335-349 e315, doi:10.1053/j.gastro.2020.02.068 (2020).\u003c/li\u003e\n\u003cli\u003eChon, Y. E.\u003cem\u003e et al.\u003c/em\u003e Hepatocellular carcinoma incidence is decreasing in Korea but increasing in the very elderly. \u003cem\u003eClin Mol Hepatol\u003c/em\u003e \u003cstrong\u003e29\u003c/strong\u003e, 120-134, doi:10.3350/cmh.2021.0395 (2023).\u003c/li\u003e\n\u003cli\u003eStarley, B. Q., Calcagno, C. J. \u0026amp; Harrison, S. A. Nonalcoholic fatty liver disease and hepatocellular carcinoma: a weighty connection. \u003cem\u003eHepatology\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 1820-1832, doi:10.1002/hep.23594 (2010).\u003c/li\u003e\n\u003cli\u003eAnwanwan, D., Singh, S. K., Singh, S., Saikam, V. \u0026amp; Singh, R. Challenges in liver cancer and possible treatment approaches. \u003cem\u003eBiochim Biophys Acta Rev Cancer\u003c/em\u003e \u003cstrong\u003e1873\u003c/strong\u003e, 188314, doi:10.1016/j.bbcan.2019.188314 (2020).\u003c/li\u003e\n\u003cli\u003eYang, Y., Zhao, W., Du, J. \u0026amp; Wang, Y. Prognostic impact of MICALL1 and associates with immune infiltration in liver hepatocellular carcinoma patients. \u003cem\u003eCancer Biomark\u003c/em\u003e \u003cstrong\u003e37\u003c/strong\u003e, 147-160, doi:10.3233/CBM-220370 (2023).\u003c/li\u003e\n\u003cli\u003eGao, Y. X.\u003cem\u003e et al.\u003c/em\u003e Progress and prospects of biomarkers in primary liver cancer (Review). \u003cem\u003eInt J Oncol\u003c/em\u003e \u003cstrong\u003e57\u003c/strong\u003e, 54-66, doi:10.3892/ijo.2020.5035 (2020).\u003c/li\u003e\n\u003cli\u003eHanif, H.\u003cem\u003e et al.\u003c/em\u003e Update on the applications and limitations of alpha-fetoprotein for hepatocellular carcinoma. \u003cem\u003eWorld J Gastroenterol\u003c/em\u003e \u003cstrong\u003e28\u003c/strong\u003e, 216-229, doi:10.3748/wjg.v28.i2.216 (2022).\u003c/li\u003e\n\u003cli\u003ePark, S. J.\u003cem\u003e et al.\u003c/em\u003e Usefulness of AFP, AFP-L3, and PIVKA-II, and their combinations in diagnosing hepatocellular carcinoma. \u003cem\u003eMedicine (Baltimore)\u003c/em\u003e \u003cstrong\u003e96\u003c/strong\u003e, e5811, doi:10.1097/MD.0000000000005811 (2017).\u003c/li\u003e\n\u003cli\u003eTayob, N., Kanwal, F., Alsarraj, A., Hernaez, R. \u0026amp; El-Serag, H. B. The Performance of AFP, AFP-3, DCP as Biomarkers for Detection of Hepatocellular Carcinoma (HCC): A Phase 3 Biomarker Study in the United States. \u003cem\u003eClin Gastroenterol Hepatol\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 415-423 e414, doi:10.1016/j.cgh.2022.01.047 (2023).\u003c/li\u003e\n\u003cli\u003eChen, F., Wang, J., Wu, Y., Gao, Q. \u0026amp; Zhang, S. Potential Biomarkers for Liver Cancer Diagnosis Based on Multi-Omics Strategy. \u003cem\u003eFront Oncol\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 822449, doi:10.3389/fonc.2022.822449 (2022).\u003c/li\u003e\n\u003cli\u003eLi, Y.\u003cem\u003e et al.\u003c/em\u003e miR-3065-3p promotes stemness and metastasis by targeting CRLF1 in colorectal cancer. \u003cem\u003eJ Transl Med\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 429, doi:10.1186/s12967-021-03102-y (2021).\u003c/li\u003e\n\u003cli\u003eYu, S. T.\u003cem\u003e et al.\u003c/em\u003e CRLF1 promotes malignant phenotypes of papillary thyroid carcinoma by activating the MAPK/ERK and PI3K/AKT pathways. \u003cem\u003eCell Death Dis\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 371, doi:10.1038/s41419-018-0352-0 (2018).\u003c/li\u003e\n\u003cli\u003eJuarez-Velazquez, M. D. R.\u003cem\u003e et al.\u003c/em\u003e High occurrence of CRLF2 abnormalities in Mexican children with B-cell acute lymphoblastic leukemia. \u003cem\u003eCytokine\u003c/em\u003e \u003cstrong\u003e155\u003c/strong\u003e, 155896, doi:10.1016/j.cyto.2022.155896 (2022).\u003c/li\u003e\n\u003cli\u003eRoll, J. D. \u0026amp; Reuther, G. W. CRLF2 and JAK2 in B-progenitor acute lymphoblastic leukemia: a novel association in oncogenesis. \u003cem\u003eCancer Res\u003c/em\u003e \u003cstrong\u003e70\u003c/strong\u003e, 7347-7352, doi:10.1158/0008-5472.CAN-10-1528 (2010).\u003c/li\u003e\n\u003cli\u003eTaznin, T., Perera, K., Gibert, Y., Ward, A. C. \u0026amp; Liongue, C. Cytokine Receptor-Like Factor 3 (CRLF3) Contributes to Early Zebrafish Hematopoiesis. \u003cem\u003eFront Immunol\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 910428, doi:10.3389/fimmu.2022.910428 (2022).\u003c/li\u003e\n\u003cli\u003eYan, X., Zheng, W., Geng, S., Zhou, M. \u0026amp; Xu, T. Cytokine Receptor-Like Factor 3 Negatively Regulates Antiviral Immunity by Promoting the Degradation of TBK1 in Teleost Fish. \u003cem\u003eJ Virol\u003c/em\u003e \u003cstrong\u003e97\u003c/strong\u003e, e0179222, doi:10.1128/jvi.01792-22 (2023).\u003c/li\u003e\n\u003cli\u003eSubramanian, A.\u003cem\u003e et al.\u003c/em\u003e Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e102\u003c/strong\u003e, 15545-15550, doi:10.1073/pnas.0506580102 (2005).\u003c/li\u003e\n\u003cli\u003eWang, L., Feng, Z., Wang, X., Wang, X. \u0026amp; Zhang, X. DEGseq: an R package for identifying differentially expressed genes from RNA-seq data. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cstrong\u003e26\u003c/strong\u003e, 136-138, doi:10.1093/bioinformatics/btp612 (2010).\u003c/li\u003e\n\u003cli\u003eBindea, G.\u003cem\u003e et al.\u003c/em\u003e Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. \u003cem\u003eImmunity\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 782-795, doi:10.1016/j.immuni.2013.10.003 (2013).\u003c/li\u003e\n\u003cli\u003eVogel, A., Meyer, T., Sapisochin, G., Salem, R. \u0026amp; Saborowski, A. Hepatocellular carcinoma. \u003cem\u003eLancet\u003c/em\u003e \u003cstrong\u003e400\u003c/strong\u003e, 1345-1362, doi:10.1016/S0140-6736(22)01200-4 (2022).\u003c/li\u003e\n\u003cli\u003eJohnson, P., Zhou, Q., Dao, D. Y. \u0026amp; Lo, Y. M. D. Circulating biomarkers in the diagnosis and management of hepatocellular carcinoma. \u003cem\u003eNat Rev Gastroenterol Hepatol\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 670-681, doi:10.1038/s41575-022-00620-y (2022).\u003c/li\u003e\n\u003cli\u003eDambadarjaa, D.\u003cem\u003e et al.\u003c/em\u003e Hepatitis B, C, and D Virus Infections and AFP Tumor Marker Prevalence Among the Elderly Population in Mongolia: A Nationwide Survey. \u003cem\u003eJ Prev Med Public Health\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 263-272, doi:10.3961/jpmph.21.573 (2022).\u003c/li\u003e\n\u003cli\u003eEl-Bahrawy, M. Alpha-fetoprotein-producing non-germ cell tumours of the female genital tract. \u003cem\u003eEur J Cancer\u003c/em\u003e \u003cstrong\u003e46\u003c/strong\u003e, 1317-1322, doi:10.1016/j.ejca.2010.01.028 (2010).\u003c/li\u003e\n\u003cli\u003eTatli, A. M.\u003cem\u003e et al.\u003c/em\u003e Alpha-fetoprotein (AFP) elevation gastric adenocarcinoma and importance of AFP change in tumor response evaluation. \u003cem\u003eAsian Pac J Cancer Prev\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 2003-2007, doi:10.7314/apjcp.2015.16.5.2003 (2015).\u003c/li\u003e\n\u003cli\u003eHahn, N.\u003cem\u003e et al.\u003c/em\u003e The Orphan Cytokine Receptor CRLF3 Emerged With the Origin of the Nervous System and Is a Neuroprotective Erythropoietin Receptor in Locusts. \u003cem\u003eFront Mol Neurosci\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 251, doi:10.3389/fnmol.2019.00251 (2019).\u003c/li\u003e\n\u003cli\u003eLiongue, C. \u0026amp; Ward, A. C. Evolution of Class I cytokine receptors. \u003cem\u003eBMC Evol Biol\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 120, doi:10.1186/1471-2148-7-120 (2007).\u003c/li\u003e\n\u003cli\u003eDang, C.\u003cem\u003e et al.\u003c/em\u003e Identification of dysregulated genes in cutaneous squamous cell carcinoma. \u003cem\u003eOncol Rep\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e, 513-519 (2006).\u003c/li\u003e\n\u003cli\u003eScholes, D. T., Banerjee, M., Bowen, B. \u0026amp; Curcio, M. J. Multiple regulators of Ty1 transposition in Saccharomyces cerevisiae have conserved roles in genome maintenance. \u003cem\u003eGenetics\u003c/em\u003e \u003cstrong\u003e159\u003c/strong\u003e, 1449-1465, doi:10.1093/genetics/159.4.1449 (2001).\u003c/li\u003e\n\u003cli\u003eBell, D. W.\u003cem\u003e et al.\u003c/em\u003e Predisposition to cancer caused by genetic and functional defects of mammalian Atad5. \u003cem\u003ePLoS Genet\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, e1002245, doi:10.1371/journal.pgen.1002245 (2011).\u003c/li\u003e\n\u003cli\u003eKim, S.\u003cem\u003e et al.\u003c/em\u003e ATAD5 restricts R-loop formation through PCNA unloading and RNA helicase maintenance at the replication fork. \u003cem\u003eNucleic Acids Res\u003c/em\u003e \u003cstrong\u003e48\u003c/strong\u003e, 7218-7238, doi:10.1093/nar/gkaa501 (2020).\u003c/li\u003e\n\u003cli\u003eRao, C. V., Asch, A. S. \u0026amp; Yamada, H. Y. Frequently mutated genes/pathways and genomic instability as prevention targets in liver cancer. \u003cem\u003eCarcinogenesis\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 2-11, doi:10.1093/carcin/bgw118 (2017).\u003c/li\u003e\n\u003cli\u003eTian, L. Y., Smit, D. J. \u0026amp; Jucker, M. The Role of PI3K/AKT/mTOR Signaling in Hepatocellular Carcinoma Metabolism. \u003cem\u003eInt J Mol Sci\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, doi:10.3390/ijms24032652 (2023).\u003c/li\u003e\n\u003cli\u003eMohseni, A. H., Casolaro, V., Bermudez-Humaran, L. G., Keyvani, H. \u0026amp; Taghinezhad, S. S. Modulation of the PI3K/Akt/mTOR signaling pathway by probiotics as a fruitful target for orchestrating the immune response. \u003cem\u003eGut Microbes\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1-17, doi:10.1080/19490976.2021.1886844 (2021).\u003c/li\u003e\n\u003cli\u003eHu, H. H., Cao, G., Wu, X. Q., Vaziri, N. D. \u0026amp; Zhao, Y. Y. Wnt signaling pathway in aging-related tissue fibrosis and therapies. \u003cem\u003eAgeing Res Rev\u003c/em\u003e \u003cstrong\u003e60\u003c/strong\u003e, 101063, doi:10.1016/j.arr.2020.101063 (2020).\u003c/li\u003e\n\u003cli\u003eWest, C. A., Black, A. P. \u0026amp; Mehta, A. S. in \u003cem\u003eHepatocellular Carcinoma: Translational Precision Medicine Approaches\u003c/em\u003e (ed Y. Hoshida) 93-107 (2019).\u003c/li\u003e\n\u003cli\u003eInoue, J., Gohda, J., Akiyama, T. \u0026amp; Semba, K. NF-kappaB activation in development and progression of cancer. \u003cem\u003eCancer Sci\u003c/em\u003e \u003cstrong\u003e98\u003c/strong\u003e, 268-274, doi:10.1111/j.1349-7006.2007.00389.x (2007).\u003c/li\u003e\n\u003cli\u003eXing, Y.\u003cem\u003e et al.\u003c/em\u003e Tumor Immune Microenvironment and Its Related miRNAs in Tumor Progression. \u003cem\u003eFront Immunol\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 624725, doi:10.3389/fimmu.2021.624725 (2021).\u003c/li\u003e\n\u003cli\u003eOwen, K. L., Brockwell, N. K. \u0026amp; Parker, B. S. JAK-STAT Signaling: A Double-Edged Sword of Immune Regulation and Cancer Progression. \u003cem\u003eCancers (Basel)\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, doi:10.3390/cancers11122002 (2019).\u003c/li\u003e\n\u003cli\u003eChen, J.\u003cem\u003e et al.\u003c/em\u003e Aberrant JAK-STAT signaling-mediated chromatin remodeling impairs the sensitivity of NK/T-cell lymphoma to chidamide. \u003cem\u003eClin Epigenetics\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 19, doi:10.1186/s13148-023-01436-6 (2023).\u003c/li\u003e\n\u003cli\u003eArneth, B. Tumor Microenvironment. \u003cem\u003eMedicina (Kaunas)\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e, doi:10.3390/medicina56010015 (2019).\u003c/li\u003e\n\u003cli\u003eLu, C., Liu, Y., Ali, N. M., Zhang, B. \u0026amp; Cui, X. The role of innate immune cells in the tumor microenvironment and research progress in anti-tumor therapy. \u003cem\u003eFront Immunol\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1039260, doi:10.3389/fimmu.2022.1039260 (2022).\u003c/li\u003e\n\u003cli\u003eLu, Y.\u003cem\u003e et al.\u003c/em\u003e Resident Immune Cells of the Liver in the Tumor Microenvironment. \u003cem\u003eFront Oncol\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 931995, doi:10.3389/fonc.2022.931995 (2022).\u003c/li\u003e\n\u003cli\u003eMeng, L.\u003cem\u003e et al.\u003c/em\u003e Profiles of immune infiltration in the tumor microenvironment of hepatocellular carcinoma. \u003cem\u003eJ Gastrointest Oncol\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 1152-1163, doi:10.21037/jgo-21-291 (2021).\u003c/li\u003e\n\u003cli\u003eMomiyama, K., Nagai, H. \u0026amp; Sumino, Y. Changes of host immunity in relation to efficacy in liver cirrhosis patients with advanced hepatocellular carcinoma treated by intra-arterial chemotherapy. \u003cem\u003eCancer Chemother Pharmacol\u003c/em\u003e \u003cstrong\u003e64\u003c/strong\u003e, 271-277, doi:10.1007/s00280-008-0866-8 (2009).\u003c/li\u003e\n\u003cli\u003eBian, J.\u003cem\u003e et al.\u003c/em\u003e T lymphocytes in hepatocellular carcinoma immune microenvironment: insights into human immunology and immunotherapy. \u003cem\u003eAm J Cancer Res\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 4585-4606 (2020).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cytokine Receptor-Like Factor 3 (CRLF3), Liver hepatocellular carcinoma, overall survival, prognosis, diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-3975470/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3975470/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBACKGROUND:\u003c/strong\u003e Liver hepatocellular carcinoma \u0026nbsp;(LIHC) exhibits a notable prevalence and fatality rate, posing a significant risk to human well-being. \u003csup\u003e1\u003c/sup\u003e. The orphan cytokine receptor-like factor 3 (CRLF3), which exhibits evolutionary conservation, has been associated with hematopoiesis in vertebrates, human diseases, and neuroprotection in insects \u003csup\u003e2,3\u003c/sup\u003e. However, there is a dearth of research investigating the role of CRLF3 in LIHC and the underlying mechanisms involved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS: \u003c/strong\u003eThe researchers utilized the TCGA database to examine the putative regulatory association between the expression of CRLF3 mRNA and LIHC.The Human Protein Atlas (HPA) has made available visual representations of the expression patterns of the CRLF3 protein. To determine the protein expression levels of CRLF3 in LIHC and adjacent normal tissues, immunohistochemistry techniques were employed.The study employed the Kaplan-Meier method, Cox regression, and logistic regression to evaluate the association between CRLF3 mRNA expression levels and survival outcomes and prognosis. In this study, the researchers employed GO and Kyoto KEGG pathway enrichment analyses, as well as GSEA, to investigate the potential regulatory role of CRLF3. The biological function of CRLF3 was identified using the ssGSEA \u0026nbsp;technique.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS:\u003c/strong\u003e The primary objective of this study is to assess the levels of expression exhibited by various members of the CRLF family in LIHC and analyze their potential influence on prognosis. The mRNA expression levels of CRLF3 exhibited a significant increase in LIHC tissues, both at the transcript and protein levels. Furthermore, research has demonstrated that patients exhibiting elevated levels of CRLF3 in LIHC experience diminished OS, DSS, and PFI. Several clinicopathologic parameters, including clinical T stage, pathologic stage, histologic grade, and AFP concentration, have been seen to exhibit associations with CRLF3 expression in LIHC. The study used multivariate survival analysis to establish that CRLF3 served as an independent predictive factor. Additional enrichment analysis was conducted, which demonstrated that the PI3K Akt, Wnt, FcεRI-mediated NF-κB activation, activation of the intestinal immune network for the IgA production, interactions between immune cells and microRNAs in the tumor microenvironment, and JAK/STAT signaling pathways exhibited significant enrichment in the group with high CRLF3 expression. The ssGSEA analysis revealed a significant positive connection between the expression of CRLF3 and the presence of T helper 2 (Th2) and T helper cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONCLUSIONS:\u003c/strong\u003e Increased CRLF3 in LIHC is strongly linked to decreased survival and immune infiltration invasion. Based on the findings of our study, it is suggested that CRLF3 has the potential as a prognostic marker for unfavorable outcomes and might serve as a viable target for immunotherapeutic interventions in the management of LIHC.\u003c/p\u003e","manuscriptTitle":"Expressional and prognostic value of CRLF3 in liver hepatocellular carcinoma patients via integrated bioinformatics analyses and experiments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-28 18:51:24","doi":"10.21203/rs.3.rs-3975470/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e11fc03c-4f0c-42c8-955b-b3c1b4f1f0a2","owner":[],"postedDate":"February 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-22T11:08:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-28 18:51:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3975470","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3975470","identity":"rs-3975470","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}