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

doi:10.21203/rs.3.rs-3975470/v1

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

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 illnesses⁶. 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 LIHC⁷. 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 cancer⁸. Furthermore, the exploration and establishment of novel biomarkers further contribute to these objectives⁹.

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 biomarkers¹³.

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 neuroprotection². 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 Immunity¹⁹. 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.

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 phenotype²⁰. 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 < 0.05 and log2 (fold change) > 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 test²². 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.

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. 1A). 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. 1B). In addition, CRLF3 expression was increased in LIHC tissues when compared with paired adjacent tissues or normal tissues (Fig. 1C). 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. 1D). The results of the HPA database were also verified by immunohistochemical staining (Fig. 1E).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. 1F).

The expression of CRLF3 is upregulated in various types of malignancies, suggesting its potential involvement in the pathogenesis of cancer. (Fig. 1G). 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. 1H).

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 < = 60) (OR = 0.629, P = 0.026), and AFP (> 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. 2A–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	78 (20.9%)	99 (26.5%)
> 60	109 (29.2%)	87 (23.3%)
BMI, n (%)			0.851
<= 25	90 (26.7%)	87 (25.8%)
> 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	128 (45.7%)	87 (31.1%)
> 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. <= 60)	373	0.629 (0.418–0.947)	0.026
AFP(ng/ml) (> 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
Characteristics	Total(N)	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	177	Reference
> 60	196	1.205 (0.850–1.708)	0.295
BMI	336
<= 25	177	Reference
> 25	159	0.798 (0.550–1.158)	0.235
AFP(ng/ml)	279
<= 400	215	Reference
> 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
Characteristics	Total(N)	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	174	Reference
> 60	191	0.846 (0.543–1.317)	0.458
BMI	329
<= 25	175	Reference
> 25	154	0.826 (0.512–1.330)	0.431
AFP(ng/ml)	275
<= 400	214	Reference
> 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. 4A), while the identification of differentially expressed genes (DEGs) was visualized using volcano plots (Fig. 4B). The ATAD5 gene was subjected to screening by identifying the overlapping genes from the STRING database that interact with differentially expressed genes (DEGs (Fig. 4C). 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. 4D).

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. 5A). 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. 5A). 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. 5B-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. 6A). 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. 6B-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.

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 viruses²³. 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 cancer²⁴. 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 zebrafish¹⁸. 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 approaches³⁰. 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 deubiquitination³¹. Genetic and functional defects in ATAD5 contribute to cancer susceptibility in mammals³², and ATAD5 plays a very important role in different DNA repairs³³. ATAD5, the human counterpart of the yeast protein Elg1, is involved in the process of PCNA deubiquitination³⁴. 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 polarity³⁷. 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 malignancy³⁸. 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 cancer³⁹. 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 progression⁴⁰. 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 tumors⁴⁴. 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.

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.

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No competing interests reported.

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

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Downloading and processing data from public databases

Functional enrichment analysis

Protein-protein interaction (PPI) network analysis

Screening of differentially expressed genes (DEGs)

Immune cell infiltration of ssGSEA

Immunohistochemistry and scoring analyses

Statistical analysis

Results

Protein and mRNA expression levels of CRLF3 in patients with LIHC

Association between CRLF3 expression and clinicopathologic characteristics in LIHC

The predictive importance of the CRLF3 gene in LIHC

Establishing a protein-protein interaction network

Functional enrichment analysis

Correlation between CRLF3 and immune cell infiltration

Discussion

Declarations

References

Additional Declarations

Status:

Version 1