Expressional and prognostic value of cytokine receptor-like factor 3 in liver hepatocellular carcinoma patients via integrated bioinformatics analyses and experiments

Abstract

Background:

Liver hepatocellular carcinoma is a highly prevalent and lethal malignancy. The orphan cytokine receptor-like factor 3, although evolutionarily conserved and implicated in hematopoiesis and neuroprotection, remains poorly characterized in liver hepatocellular carcinoma.

Objective:

To investigate the expression pattern of cytokine receptor-like factor 3 in liver hepatocellular carcinoma and its association with clinicopathological characteristics, prognosis, and potential biological functions through bioinformatics analysis.

Methods:

Cytokine receptor-like factor 3 mRNA and protein expression in liver hepatocellular carcinoma were evaluated using the cancer genome atlas, human protein atlas, immunohistochemistry, and qPCR. The association of cytokine receptor-like factor 3 expression with prognosis and clinicopathological features was assessed using Kaplan–Meier survival analysis, Cox regression, logistic regression, and receiver operating characteristic curves. Potential functional pathways associated with cytokine receptor-like factor 3 were explored using gene ontology, Kyoto encyclopedia of genes and genomes, gene set enrichment analysis, and ssGSEA.

Results:

Cytokine receptor-like factor 3 expression was significantly elevated in liver hepatocellular carcinoma tissues and correlated with poorer overall survival, disease-specific survival, and progression-free interval. High cytokine receptor-like factor 3 expression was associated with advanced T stage, pathologic stage, histologic grade, and elevated alpha-fetoprotein levels, and emerged as an independent prognostic factor. Receiver operating characteristic curve analysis suggested a potential diagnostic value for cytokine receptor-like factor 3 in distinguishing liver hepatocellular carcinoma tissues from normal tissues. Enrichment analysis indicated that genes correlated with cytokine receptor-like factor 3 are enriched in pathways such as PI3K/Akt, Wnt, and JAK/STAT, as well as immune-related processes. Notably, cytokine receptor-like factor 3 expression showed a strong positive correlation with Th2 cell infiltration.

Conclusions:

This study reveals that cytokine receptor-like factor 3 is overexpressed in liver hepatocellular carcinoma and is associated with poor patient prognosis and immune infiltration. These findings suggest that cytokine receptor-like factor 3 may serve as a promising candidate prognostic biomarker and warrants further investigation as a potential immunotherapeutic target in liver hepatocellular carcinoma. The mechanistic hypotheses generated here provide a foundation for subsequent experimental validation.

Keywords

liver hepatocellular carcinoma overall survival prognosis diagnosis

Introduction

With more than 800,000 fatalities each year, liver hepatocellular carcinoma (LIHC) is the fourth most common cause of cancer-related mortality worldwide.^1,2 It has been shown that developing countries have a higher prevalence of liver illnesses.³ LIHC primarily arises in the context of chronic liver conditions, including hepatitis B and C virus infections, metabolic-associated fatty liver disease (formerly known as nonalcoholic fatty liver disease), and cirrhosis.⁴ Currently, treatment outcomes for LIHC patients remain suboptimal, largely due to the high heterogeneity of the disease. This heterogeneity develops through tumor-initiating stem cells that undergo genetic and epigenetic changes, interact with alterations in the tumor microenvironment, and differentiate into diverse drug-resistant tumor cell populations, thereby affecting therapeutic efficacy.⁵ Tumor heterogeneity refers to qualitative differences between tumors of the same type across different patients, as well as variations within a single tumor, including differences in genotype, phenotype, and function among regions and cells within the same patient.^5
–7 Although significant progress has been made in the genomics and molecular classification of LIHC over the past decade, its heterogeneity has not been fully resolved due to the complexity of its regulatory mechanisms.^8
–12 Currently, the clinical management of LIHC includes multiple therapeutic strategies, such as chemotherapy, immunotherapy, natural product-based treatments, and nanotechnology-based approaches. Despite these options, a definitive cure for LIHC remains elusive.¹³ The integration of diverse tumor biomarkers, tailored to specific clinical contexts, is critical for accurate diagnosis, monitoring of treatment response, and prognostic assessment of primary liver cancer.¹⁴ Therefore, the identification and validation of novel biomarkers continue to be essential for advancing these objectives.¹⁵

Currently used biomarkers for LIHC include alpha-fetoprotein (AFP), its isoform lectin-reactive AFP (AFP-L3), and des-gamma-carboxy prothrombin (DCP), also known as vitamin K-dependent protein induced by absence or antagonist-II.^16
–18 However, the complex pathogenesis and heterogeneity of LIHC present significant challenges for its early detection. Consequently, only 20%–30% of LIHC patients are eligible for curative treatments, primarily due to the lack of reliable early-detection methods. This underscores the critical need for more dependable and precise biomarkers for LIHC.¹⁹

The cytokine receptor-like factor (CRLF) family consists of three members: CRLF1, CRLF2, and CRLF3. Evidence suggests that CRLF proteins are involved in the pathogenesis of various neoplastic conditions.^20
–23 CRLF3, in particular, has been linked to multiple human diseases and is known to play roles in vertebrate hematopoiesis and neuroprotection in insects.²⁴ T studies have shown that CRLF3 is essential for embryonic hematopoiesis during the early stages of zebrafish development.²⁵ In teleost fish, CRLF3 promotes the degradation of TBK1, thereby negatively regulating antiviral immunity.²⁶ However, the precise molecular mechanisms by which CRLF3 contributes to LIHC remain unclear.

In this study, we analyzed the transcriptional and protein expression patterns of CRLF3 using the cancer genome atlas (TCGA) and human protein atlas (HPA) databases, and validated its expression in LIHC and adjacent normal tissues through immunohistochemistry (IHC) and qPCR. To further investigate the potential biological roles of CRLF3, we performed functional enrichment analyses using gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and gene set enrichment analysis (GSEA). This study aims to investigate the potential mechanisms by which CRLF3 may be involved in LIHC, with particular emphasis on its association with immune infiltration. Additionally, bioinformatics analyses were conducted to assess the diagnostic and prognostic significance of CRLF3 in LIHC patients.

Materials and methods

Downloading and processing data from public databases

The dataset used in this study consisted of RNA-seq gene expression data from TCGA, including 374 LIHC tissues and 50 normal liver tissues. CRLF3 protein expression levels in LIHC and normal liver tissues were analyzed using the HPA database (https://www.proteinatlas.org). Additionally, CRLF3 expression in LIHC was evaluated using the HCCDB database (http://lifeome.net/database/hccdb). The prognostic value of CRLF3 in LIHC was assessed in terms of overall survival (OS) using the Kaplan–Meier plotter (kmplot.com/analysis).

Functional enrichment analysis

The screened genes were subjected to functional enrichment analysis using the GO and KEGG databases.

GSEA evaluates the distribution patterns of genes within a predefined gene set across a ranked list of genes sorted by their association with a particular phenotype, in order to determine their contribution to that phenotype.²⁷ After converting the input data to standardized gene IDs, GSEA was performed using the clusterProfiler R package, with significance thresholds set at p adjust <0.05 and q-value <0.25.

Protein–protein interaction (PPI) network analysis

PPI network analysis was performed using the STRING database, with a minimum required interaction score set at 0.400.

Screening of differentially expressed genes (DEGs)

DEGs were identified using the DESeq2 R package, with significance thresholds set at adjusted p-value <0.05 and |log2(fold change)| > 1.²⁸ The raw counts matrix was first analyzed for variance using DESeq2 according to the standard protocol and subsequently normalized using the variance stabilizing transformation method provided by the DESeq2 package.

Immune cell infiltration of single-sample GSEA (ssGSEA)

The ssGSEA method was employed to quantify the infiltration levels of 24 immune cell types in LIHC tumor samples. Spearman’s correlation test was used to assess the relationship between CRLF3 expression and immune cell abundance.²⁹ Additionally, the Wilcoxon rank-sum test was applied to compare immune cell infiltration between high and low CRLF3 expression groups.

Patients and samples

This study is an exploratory investigation that integrates systematic bioinformatics analyses with preliminary validation using clinical samples. All human tissue samples were obtained from the Department of Pathology at Hangzhou Xixi Hospital, comprising 25 tumor samples and 25 adjacent normal tissue samples. The study started in 2024 and ran for a duration of 10 months. Inclusion Criteria: Confirmed Diagnosis: Patients with LIHC confirmed by postoperative pathological examination. Surgical Type: Patients who underwent radical hepatectomy without prior anticancer treatment, including chemotherapy, radiotherapy, targeted therapy, or immunotherapy. Clinical Records: Availability of detailed clinicopathological information, including at minimum age, sex, tumor size, and differentiation grade. Exclusion Criteria: Pathology Type Mismatch: Exclusion of patients with mixed LIHC, intrahepatic cholangiocarcinoma, metastatic liver cancer, or other non-LIHC pathological types. Incomplete Clinical Information: Patients lacking essential clinicopathological data or follow-up information. Preoperative Treatment: Patients who received any antitumor therapy prior to surgery that could affect tumor antigen expression. Sample Quality Issues: Tissue sections unsuitable for accurate immunohistochemical analysis due to improper fixation, severe fading, damage, or insufficient tumor cell content.

Quantitative real-time polymerase chain reaction (qRT-PCR)

Total RNA was extracted from LIHC and adjacent normal tissues using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). Complementary DNA was synthesized using the PrimeScript RT reagent kit (Thermo Fisher Scientific, Waltham, MA, USA). qRT-PCR was performed using SYBR Green assays (Vazyme, Nanjing, China) on a StepOnePlus real-time PCR system (Life Technologies, Carlsbad, CA, USA). The primer sequences for CRLF3 were as follows: forward 5′–GAAAGTGCATCACAGACAAGGG–3′ and reverse 5′–TCTGGCAGTCATCTAGTGGTTT–3′. GAPDH was used as the internal control. Relative CRLF3 expression levels were calculated using the 2^−ΔΔCT method.

IHC and scoring analyses

IHC was performed to assess CRLF3 protein expression and its prognostic significance. LIHC tissues and paired adjacent normal tissues were stained with anti-human CRLF3 antibody (1:150; Rabbit; DF8931; Affinity, USA), followed by detection using a two-step universal kit (mouse/rabbit ultrasensitive polymer assay system; PV-800; ZSGB-BIO, Beijing, China). Stained samples were observed under a microscope and photographed for further analysis. Each LIHC sample was evaluated based on staining intensity and the percentage of positively stained cells. Staining intensity was scored as negative (0), mild (1), moderate (2), or strong (3). The proportion of positive cells was scored as ⩽25% (1), >25% and ⩽50% (2), >50% and ⩽75% (3), or >75% (4). The sum of these two scores yielded the final IHC score for each sample. Immunohistochemical scoring was further validated using ImageJ software. A paired t-test was employed to compare CRLF3 expression between LIHC tissues and matched nontumor tissues.

Single cell portal database analysis

The Single Cell Portal database (https://singlecell.broadinstitute.org/single_cell) is an online resource for the analysis and exploration of single-cell sequencing data.

Ethics approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Hangzhou Xixi Hospital, Hangzhou Sixth People’s Hospital, and Hangzhou Xixi Hospital affiliated with Zhejiang Chinese Medical University [Approval No. (2024)11]. Written informed consent was obtained from all participants.

Statistical analysis

All statistical analyses were performed using R software (version 4.2.1). The Wilcoxon rank-sum test and paired-samples t-test were applied to compare CRLF3 expression between tumor and normal tissues. Univariate and multivariate analyses were conducted to evaluate the impact of clinical factors on OS. A p-value <0.05 was considered statistically significant.

Results

Protein and mRNA expression levels of CRLF3 in patients with LIHC

We initially assessed the expression levels and prognostic associations of CRLF family members in LIHC and corresponding adjacent tissues. CRLF1, CRLF2, and CRLF3 were all significantly upregulated in LIHC tissues (Figure 1(a)). Notably, CRLF3 showed both high expression in LIHC tissues and a significant correlation with poor OS (Figure 1(b)). Furthermore, CRLF3 expression was elevated in LIHC tissues compared with paired adjacent or normal tissues (Figure 1(c) and (d)), leading us to select CRLF3 as the primary focus of this study. CRLF3 protein expression was also higher in LIHC tissues than in normal tissues, as shown in the HPA database (Figure 1(e)), and these findings were further validated by immunohistochemical staining (Figure 1(f)). Receiver operating characteristic (ROC) curve analysis suggested a potential diagnostic value for CRLF3, yielding an area under the curve (AUC) of 0.823 (Figure 1(g)).

Figure 1.

The expression of CRLFs in liver hepatocellular carcinoma tissues.

CRLF3 expression was also upregulated across multiple cancer types, suggesting a potential broad role in tumor biology (Figure 1(h)). Using the HCCDB database, we performed a comprehensive analysis of nine independent HCC cohorts. This analysis consistently showed a significant increase in CRLF3 mRNA expression in HCC tissues compared with adjacent normal tissues (Figure 1(i)).

Association between CRLF3 expression and clinicopathologic characteristics in LIHC

The 374 LIHC samples were divided into high and low CRLF3 mRNA expression groups. Analysis revealed significant associations between CRLF3 expression and several clinicopathological parameters, including pathologic stage (p = 0.046), age (p = 0.026), histologic grade (p < 0.001), and AFP levels (p < 0.001). No significant associations were observed between CRLF3 expression and TNM stage, gender, or BMI (Table 1).

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%)

CRLF3: cytokine receptor-like factor 3; LIHC: liver hepatocellular carcinoma.

Logistic regression analysis revealed significant associations between CRLF3 expression and several clinical variables, including T stage (T2–T4 vs T1; OR = 1.701, p = 0.011), pathologic stage (Stages II–IV vs Stage I; OR = 1.621, p = 0.025), age (>60 vs ⩽60; OR = 0.629, p = 0.026), and AFP levels (>400 vs ⩽400; OR = 3.562, p < 0.001) (Table 2). Consistently, both Welch’s t-test and Student’s t-test analyses demonstrated significant correlations between CRLF3 mRNA expression and clinical parameters such as AFP concentration, histologic grade, pathologic T stage, and overall pathologic stage (Figure 2(a)–(f)).

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 and T3 and 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 stages (Stage II and Stage III and Stage IV vs Stage I)	350	1.621 (1.063–2.472)	0.025
Histological type (Hepatocellular carcinoma and Hepatocholangiocarcinoma (mixed) vs Fibrolamellar carcinoma)	374	0.497 (0.045–5.532)	0.570
Histologic grades (G2 and G3 and 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 and 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 and 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 and severe vs none)	237	1.250 (0.749–2.087)	0.393

AFP: alpha-fetoprotein; CRLF3: cytokine receptor-like factor 3.

Figure 2.

The box plots show different characteristics. According to the expression level of CRLF3 mRNA. (a) AFP, (b) Age, (c) Gender, (d) histologic grade, (e) pathologic T stage, (f) Pathologic stage. Data are mean ± SEM. *p < 0.05; **p < 0.01;***p < 0.001.

Prognostic value of CRLF3 expression in LIHC

We next evaluated the association between CRLF3 mRNA expression and clinical outcomes in LIHC. High CRLF3 expression was significantly associated with poorer (OS; [HR] = 1.51, p = 0.021), disease-specific survival (DSS; HR = 1.62, p = 0.034), and progression-free interval (HR = 1.39, p = 0.025) (Figure 3(a)–(c)). Subgroup analysis based on clinicopathological characteristics revealed that CRLF3 overexpression remained significantly associated with poorer OS in patients with advanced disease, including those at pathologic T3–T4 stages (HR = 1.75, p = 0.046) and pathologic Stages III–IV (HR = 1.85, p = 0.037) (Figure 3(d)–(k)). In contrast, no significant association was observed between CRLF3 expression and AFP levels in LIHC patients (Figure 3(l)–(m)).

Figure 3.

Correlations between CRLF3 and prognosis in LIHC. (a) OS, (b) DSS,(c) PFI, (d) Age: ⩽60, (e) Gender: Female, (f) T stage: T1 andT2, (g) Pathologic stage: Stages I and II, (h) Age: >60, (i) Gender: Male, (j) T stage: T3 and T4, (k) Pathologic stage: Stages III–IV, (l) AFP (ng/ml): >400, (m) AFP (ng/ml): ⩽400.

Univariate and multivariate Cox regression analyses demonstrated that high CRLF3 expression was significantly associated with poorer (OS; HR = 1.507, p = 0.021) and (DSS; HR = 1.622, p = 0.034). Multivariate analysis further confirmed that elevated CRLF3 expression serves as an independent prognostic factor in patients with LIHC (Tables 3 and 4). These findings suggest that CRLF3 may serve as a potential prognostic biomarker for LIHC patients.

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 and T3 and 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
Stages III and IV	90	Reference		Reference
Stages I and II	259	0.399 (0.275–0.579)	<0.001	0.489 (0.305–0.785)	0.003
Histologic grade	368
G1 and G2	233	Reference
G3 and 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

AFP: alpha-fetoprotein; CRLF3: cytokine receptor-like factor 3; OS: overall survival.

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 and T3 and 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
Stages III and IV	87	Reference		Reference
Stages I and II	254	0.263 (0.162–0.427)	<0.001	0.330 (0.175–0.622)	<0.001
Histologic grade	360
G1 and G2	227	Reference
G3 and 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

AFP: alpha-fetoprotein; CRLF3: cytokine receptor-like factor 3; DSS: disease-specific survival.

Predicted protein interaction network of CRLF3

To explore potential molecular associations of CRLF3 in LIHC, we constructed a predicted PPI network using the STRING database (Figure 4(a)). Network analysis identified several potential interacting partners of CRLF3 based on database predictions. The top-ranked interactors, based on composite confidence scores, included UTP6 (score = 0.802), TEFM (score = 0.709), and ATAD5 (score = 0.631). According to existing annotations, these proteins are involved in key cellular processes: UTP6 in ribosome biogenesis, TEFM in mitochondrial transcription and energy metabolism, and ATAD5 in DNA replication and genome stability. These predicted interactions suggest that CRLF3 may be functionally linked to fundamental processes governing cell growth, proliferation, and survival, although this remains to be experimentally validated.

Figure 4.

Protein–protein interaction (PPI) network of CRLF3 and its related proteins constructed using the STRING database. CRLF3 interaction network. (a) Network mapping of protein–protein interactions was obtained using STRING v10.5. (b) Correlations between UTP6 and prognosis in LIHC. (c) Correlations between TEFM and prognosis in LIHC. (d) Correlations between ATAD5 and prognosis in LIHC.

To assess the potential clinical relevance of these predicted CRLF3-interacting proteins, we analyzed their prognostic associations in LIHC using the Kaplan–Meier plotter database (Figure 4(b)–(d)). Survival analysis showed that high expression of most of these proteins, including UTP6, TEFM, and ATAD5, was significantly associated with poorer OS in LIHC patients. These findings suggest a potential coordinated prognostic role for this predicted network, but experimental studies are needed to confirm whether CRLF3 functionally interacts with these proteins to influence LIHC progression.

Functional enrichment analysis

To further explore the potential biological processes and pathways associated with CRLF3, we performed GO, KEGG, and GSEA analyses using genes correlated with CRLF3 expression. GO enrichment analysis showed that genes correlated with CRLF3 were significantly enriched in immune-related processes (Figure 5(a)). KEGG pathway analysis indicated that these genes were predominantly enriched in four signaling pathways known to be closely linked to cancer progression (Figure 5(a)). GSEA further revealed significant enrichment of CRLF3-correlated genes in multiple pathways, including PI3K–Akt signaling, Wnt signaling, 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 (Figure 5(b)–(g)). These enrichment results suggest potential functional links between CRLF3 and these pathways, providing hypotheses for future experimental investigation.

Figure 5.

Functional enrichment analysis of CRLF3 in LIHC. (a) GO functional enrichment and KEGG pathways. (b–g) GSEA revealed the enrichment of five signaling pathways in individuals with LIHC who have high expression of the CRLF3 gene.

Correlation between CRLF3 and immune cell infiltration

The association between CRLF3 expression and the infiltration levels of 24 immune cell types in LIHC was analyzed using the ssGSEA method (Figure 6(a)). CRLF3 expression was significantly positively correlated with the infiltration levels of T helper cells (R = 0.515, p < 0.001) and Th2 cells (R = 0.414, p < 0.001) (Figure 6(b) and (c)).

Figure 6.

Relationship between CRLF3 expression and immune infiltration in LIHC tumor microenvironment. (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’s correlation validation of CRLF3 with T helper cells. (d–e) correlation between levels of Th2 and T helper cell infiltration and CRLF3 expression. (d) T helper cells. (e) Th2 cells. (f) Validation of CRLF3 expression in LIHC immune cell subpopulations using single-cell datasets.

The Wilcoxon rank-sum test was used to compare differences in immune cell infiltration between high and low CRLF3 expression groups. The results confirmed that T helper cells and Th2 cells were significantly more abundant in the high CRLF3 expression group (Figure 6(d) and (e)). Additionally, we examined CRLF3 expression across immune cell subpopulations using single-cell datasets (Figure 6(f)).

Discussion

The CRLF3 gene encodes the protein cytokine receptor-like factor 3 in humans. Phylogenetic studies suggest that CRLF3 may have originated from a common ancestor of Cnidaria and Bilateria, coinciding with the emergence of the nervous system.^30,31 The CRLF3 protein is highly conserved across metazoans and features a characteristic cytokine receptor homology domain (CHD). Previous studies have shown that CRLF3 plays critical roles in various developmental and homeostatic processes, particularly in blood and immune cell function. For instance, CRLF3 has been reported to be essential for the initiation of hematopoiesis during early embryonic development in zebrafish.²⁵ Additionally, dysregulation of CRLF3 expression has been observed in cutaneous squamous cell carcinoma, suggesting a potential role in the disease’s pathogenesis, progression, and possible diagnostic applications.³² However, the prognostic significance and potential biological functions of CRLF3 in LIHC remain largely unexplored. Therefore, the primary objective of this study is to investigate the expression pattern of CRLF3 in LIHC, evaluate its prognostic relevance, and explore its potential associated biological pathways using bioinformatics analysis.

In this study, we first examined the expression levels of the CRLF family in LIHC, revealing that members of this family were significantly upregulated in tumor tissues compared to normal tissues. Through experimental validation and bioinformatics analysis, we confirmed that CRLF3 is highly expressed in LIHC tissues compared to normal tissues. We then assessed the mRNA and protein expression levels of CRLF3 in LIHC tissues and corresponding normal tissues, consistently demonstrating its upregulation. ROC curve analysis suggested that CRLF3 may have diagnostic potential for LIHC. Univariate and multivariate Cox regression analyses were performed to evaluate the association between CRLF family members and OS, which identified CRLF3 as a candidate of interest. Kaplan–Meier survival analysis showed that LIHC patients with elevated CRLF3 expression had significantly poorer prognosis. Furthermore, multivariate Cox regression analysis confirmed that high CRLF3 expression was an independent risk factor for poorer OS in LIHC patients. In summary, our findings suggest that CRLF3 may serve as a potential diagnostic and prognostic biomarker for LIHC.

To further explore potential molecular associations of CRLF3, we identified several genes predicted to interact with it based on STRING database analysis. Our analysis suggested that CRLF3 may be associated with UTP6, TEFM, and ATAD5 at the level of potential protein–protein interactions. Notably, these genes have been independently implicated in cancer biology by previous studies. For instance, UTP6 has been reported to play regulatory roles in multiple cancer types; its hypermethylation and downregulation are linked to stem cell-like properties, chemoradiotherapy resistance, and prognosis in rectal cancer.³³ Similarly, elevated TEFM expression has been shown to promote hepatocellular carcinoma growth and metastasis through activation of the ROS/ERK signaling pathway.³⁴ Genetic and functional defects in ATAD5 have been shown to increase cancer susceptibility in mammals,³⁵ and ATAD5 plays a critical role in various DNA repair pathways.³⁶ As the human homolog of the yeast protein Elg1, ATAD5 is involved in the deubiquitination of PCNA.³⁷ Taken together, these observations suggest a potential functional link between CRLF3 and cancer-related processes mediated by its predicted interacting partners. However, as these interactions are based on computational predictions, experimental studies are needed to validate whether CRLF3 indeed functionally interacts with UTP6, TEFM, and ATAD5, and to determine the biological significance of such interactions in LIHC progression.

GSEA analysis revealed that genes correlated with CRLF3 expression were significantly enriched in multiple cellular processes and signaling pathways, 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. These pathways have well-established roles in cancer biology. Dysregulation of the PI3K/AKT/mTOR pathway is commonly observed in LIHC, where it regulates metabolic processes involving glucose, lipids, amino acids, pyrimidines, and oxidative reactions in the liver.^38,39 The Wnt signaling pathway plays a critical role in cell fate determination, proliferation, and the establishment of cell polarity.⁴⁰ Disruption of WNT/β-catenin signaling has been implicated in LIHC development, further underscoring its relevance in this malignancy.⁴¹ FcεRI-mediated NF-κB activation regulates immune-inflammatory responses and has been shown to influence cancer progression.⁴² Interactions between immune cells and microRNAs within the tumor microenvironment (TME) contribute to key tumor-related processes, including proliferation, invasion, and immune evasion, and can either promote or suppress tumor development.⁴³ Moreover, activation of the JAK/STAT pathway has been shown to drive the onset and progression of various diseases, including inflammatory disorders, lymphomas, leukemias, and solid tumors.^44,45

Collectively, these enrichment results suggest that CRLF3 may be functionally linked to multiple cancer-related pathways, particularly those involved in cell proliferation, metabolism, and immune regulation. However, as these findings are based on computational predictions, experimental studies are needed to validate the precise role of CRLF3 in these pathways and its functional significance in LIHC progression.

The TME encompasses the local milieu surrounding the tumor, including adjacent blood vessels, immune cells, and other components of the immune system.⁴⁶ Previous studies have suggested that immune cells can exert either antitumor or protumor effects through the secretion of cytokines, chemokines, and other factors, playing a critical role in tumor initiation and progression.⁴⁷ Recent research has highlighted the significant association between immune cell infiltration and liver carcinogenesis, as well as its potential implications for prognosis and treatment in LIHC.^48,49 In this study, ssGSEA was employed to quantify the infiltration levels of 24 specific immune cell types within the LIHC tumor microenvironment. Our analysis revealed a strong correlation between CRLF3 expression and T helper cells, particularly Th2 cells. Notably, elevated Th2 cell infiltration has been linked to more advanced tumor stages and poorer therapeutic responses in previous studies.^50,51 These findings suggest a potential association between CRLF3 and Th2 cell-mediated immune responses in the LIHC microenvironment. However, it is important to note that these results are based on computational predictions and correlation analyses. Therefore, further experimental studies are needed to validate the functional relationship between CRLF3 and immune cell infiltration, and to elucidate the underlying mechanisms.

Conclusions

In conclusion, our study demonstrates that CRLF3 is overexpressed in LIHC tissues and its elevated expression is significantly associated with poor prognosis and survival in LIHC patients. Bioinformatics analysis suggests that CRLF3 may be functionally linked to multiple signaling pathways, including PI3K/Akt, Wnt, FcεRI-mediated NF-κB activation, activation of the intestinal immune network for IgA synthesis, interactions between immune cells and microRNAs, and JAK/STAT signaling within the tumor microenvironment. Additionally, CRLF3 expression showed a significant correlation with T helper cell infiltration, particularly Th2 cells, in LIHC. These findings provide a basis for further investigation into the potential mechanisms by which CRLF3 may contribute to LIHC progression. Despite the comprehensive methodology employed in this study, several limitations should be acknowledged. First, our analysis relied on retrospective data from public databases (TCGA and GEO), which may carry inherent biases despite standardization and batch correction. Second, although CRLF3 overexpression in LIHC tissues was validated via qPCR and immunohistochemistry, functional experiments directly examining its role in LIHC progression were not performed. Such experimental validation is crucial for establishing causality, but due to constraints, it has not yet been completed. Third, our clinical sample size was moderate and sourced from a single center, potentially limiting the generalizability of our findings. Future studies involving larger, multicenter cohorts and mechanistic experiments, such as CRLF3 knockdown or overexpression, are needed to validate and extend these results. Overall, our findings provide a valuable foundation for further investigation into the potential role of CRLF3 in LIHC progression.

Supplemental Material

sj-xlsx-1-smo-10.1177_20503121261440458 – Supplemental material for Expressional and prognostic value of cytokine receptor-like factor 3 in liver hepatocellular carcinoma patients via integrated bioinformatics analyses and experiments

Supplemental material, sj-xlsx-1-smo-10.1177_20503121261440458 for Expressional and prognostic value of cytokine receptor-like factor 3 in liver hepatocellular carcinoma patients via integrated bioinformatics analyses and experiments by Xingxing Wang, Zhen Huang, Lili Huang, Yi Wang, Congxiang Huang, Xiaoying Zhang and Xiantu Zhang in SAGE Open Medicine

Footnotes

Acknowledgements

We express our gratitude to Xiantu Zhang and Xiaoying Zhang for their valuable support and encouragement for this article.

ORCID iDs

Xingxing Wang

Zhen Huang

Lili Huang

Yi Wang

Congxiang Huang

Xiaoying Zhang

Xiantu Zhang

Author contributions

The project was conceived by XTZ and XYS. Data were analyzed by XXW, who subsequently authored the manuscript. The data were analyzed by ZH, LLH, YW, and CXH. The final manuscript was read and approved by all of the authors.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The article contains the datasets that provide support for the conclusions drawn. The dataset used in this study was created by downloading and collating RNAseq data from the TCGA-LIHC (hepatocellular liver cancer) project STAR process from the TCGA database () and extracting the data in TPM format.

Supplemental material

Supplemental material for this article is available online.

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