IL23R as an indicator of immune infiltration and poor prognosis in intrahepatic cholangiocarcinoma: a bioinformatics analysis

Introduction

Originating from biliary system, cholangiocarcinoma (CCA) is the second most common primary liver tumor (1). CCA can be divided into 3 types: intrahepatic CCA (CHOL), perihilar CCA, and distal CCA (1). Among these, CHOL is the least common type of CCA (2,3). Although the incidence of CHOL at only 10–15% is very low compared to, for example, hepatocellular carcinoma, its prognosis is very poor (4,5). Due the concealed clinical features, difficult diagnosis, and limited treatment strategy for CHOL, the mortality rate of CHOL is increasing worldwide (6). Therefore, timely diagnosis and treatment is particularly important. Moreover, an effective staging system of CHOL is essential to providing prognostic information and guiding therapy. Unfortunately, most existing staging systems are deficient in treatment selection and lack prognostic accuracy (3). Therefore, reliable prognostic indicators are urgently needed to enable optimal treatment allocation and prediction.

Interleukin 23 receptor (IL23R) is the receptor for IL-23 binding, and IL-23 binds to receptors composed of IL-12R β2 and IL23R (7). Studies have revealed that IL23R performs an essential role in tumorigenesis, especially in the immune tumor microenvironment (TME) (8-10). It was reported that intratumoral regulatory T cells (Tregs) became unstable due to antibody-mediated IL23R blockade, and this process could enhance immunotherapy (10). In studies related to the treatment of laryngeal cancer, IL23R was demonstrated to be a marker of the immunoediting process, as IL-23/IL23R can reduce the activation of signal transducer and activator of transcription 3 (STAT3), providing potential immunotherapy in the preservation of laryngeal function and improving survival (9). Research also indicates that IL23R is related to the prognosis of gastric cancer and non-small cell lung carcinoma (11,12). IL23R is also associated with other immune-related diseases, such as ankylosing spondylitis, rheumatoid arthritis, psoriasis, and inflammatory bowel disease (13-15). In a recent study, it was reported that a higher baseline level of IL-23 could improve the clinical results of atezolizumab combined with cobimetinib in the treatment of advanced biliary tract cancers (16). However, no studies have focused on the role of IL23R in CHOL or its relation to the immune TME.

Circular RNA (circRNA) as a competitive endogenous regulator, plays a key role in the occurrence and development of cancer. circRNA is formed by reverse splicing, which has the characteristics of covalent loop closure, high conservatism, and stability (17,18). Multiple studies have shown that circRNAs are abnormally expressed in CHOL and regulate the progression of tumor cells. For example, the low expression of circRTN4IP1 was found to inhibit the proliferation of CHOL through the miR-541-5p-HIF1A axis (19). circHMGCS1-016 was shown to be involved in CHOL development and immune tolerance through the miR-1236-3p-CD73 and GAL-8 axis (20). Furthermore, circACTN4 was shown to activate FZD7 transcription through miR-424-5p and its interaction with YBX1 to promote CHOL proliferation and metastasis (21). Therefore, greater attention should be paid to the role of circRNA in CHOL and to the regulatory network of competing endogenous RNA (ceRNA) in CHOL. However, no research concerning the role of the circRNA-miRNA-IL23R regulatory network in CHOL exists.

For patients with CHOL, the prognosis is often poor and the treatment is inadequate. The existing literature that higher baseline level of IL-23 could improve the clinical outcomes in the treatment of advanced biliary tract cancers. IL23R as the receptor of IL-23 is underexplored. In addition, research indicates that some circRNAs are abnormally expressed in CHOL and may affect its progression. Further study on the role of circRNAs in CHOL may provide improved guidance for the diagnosis and treatment of patients with CHOL. In this study, we thus aimed to characterize the expression level of IL23R using bioinformatics methods and to explore the relationship between IL23R and circRNA. We found that the expression level of IL23R was related to the overall survival (OS) of patients with CHOL and observed a relationship between IL23R and the infiltration level of immune cells. We hope that our findings offer insights into the emergence and progression of CHOL and aid in identifying new therapeutic targets for effective treatment (Figure 1). We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-455/rc).

Figure 1 IL23R as an immune infiltration and prognostic signature of poor prognosis in intrahepatic cholangiocarcinoma. The image elements are from Vecteezx (http://www.vecteezy.com/) and SMART SERVIER (https://smart.servier.com/). CHOL, intrahepatic cholangiocarcinoma; IL23R, interleukin 23 receptor; circRNA, circular RNA; miRNA, microRNA; mRNA, messenger RNA; PPI, protein-protein interaction; PI3K, phosphoinositide 3-kinase; Akt, serine/threonine kinase; TME, tumor microenvironment.

Methods

Data acquisition and preprocessing

Microarray RNA expression data were obtained from Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/; RRID: SCR_005012), specifically, the GSE181523, GSE32957, and GSE61850 datasets. The GSE181523 dataset included 7 CHOL samples and 7 nontumor tissue samples, the GSE32957 dataset included 16 CHOL samples and 5 nontumor tissue samples, and the GSE61850 dataset included 5 CHOL samples and 5 adjacent normal tissue samples. Differential expression analyses were performed using R version 4.1.2. (The R Foundation for Statistical Computing; RRID: SCR_001905), specifically, the “limma”, “pheatmap”, and “ggplot2” package. The |log₂ fold change (FC)| >1 and adjusted P value <0.05 were set as thresholds. We used the CircBank database (http://www.circbank.cn/index.html) (22) to identify the microRNA (miRNA) response elements (MREs). Targetscan (https://www.targetscan.org/vert_80/; RRID: SCR_010845) (23) was used to obtain the target genes. The intersection of MREs and differentially expressed (DE) miRNAs and the intersection of DE messenger RNA (DEmRNA) and the miRNA target gene (tar-DEmRNA) were obtained using an online Venn diagram tool (http://bioinformatics.psb.ugent.be/webtools/Venn/). The circRNA-miRNA-mRNA network was established with Cytoscape v. 3.9.0 software (RRID: SCR_003032). The data for OS and disease-free survival (DFS) of patients with CHOL in The Cancer Genome Atlas (TCGA) were obtained from the University of California, Santa Cruz (UCSC) Xena website (https://xenabrowser.net/datapages/; RRID: SCR_018938). The demographic information related to the patients with CHOL from TCGA is provided in Table 1. Patients with CHOL range in age from 29 to 82 years old. For comparison, the information of normal samples from TCGA is provided in Table 2. The normal samples ranged in age from 52 to 82 years old. This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and was approved by Ethics Committee of Guangdong Provincial People’s Hospital (No. KY-Q-2022-321-01). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Functional enrichment analysis

Gene Ontology (GO) (RRID: SCR_002811) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (RRID: SCR_012773) were used to conduct functional enrichment analyses (24,25) via The Database for Annotation, Visualization and Integrated Discovery (DAVID) analysis tool (https://david.ncifcrf.gov/home.jsp; RRID: SCR_001881) (26,27). GO annotations included cellular component, molecular function, and biological process. A false discovery rate (FDR) <0.05 was set as the statistical significance threshold. In addition, gene set enrichment analysis (GSEA) (http://www.gsea-msigdb.org/gsea/index.jsp; RRID: SCR_003199) software (28-31) was used to verify the functional enrichment of the tar-DEmRNAs.

Survival analysis

The DriverDBv3 (http://driverdb.tms.cmu.edu.tw/; RRID: SCR_007736) database and Gene Expression Profiling Interactive Analysis (GEPIA) website (http://gepia.cancer-pku.cn/index.html; RRID: SCR_018294) were used to perform survival analyses. DriverDBv3 is a database for human cancer driver gene research (32), while GEPIA is a web server for analyzing the RNA sequencing expression data (33). R version 4.1.2. and GraphPad Prism v. 8.0 (GraphPad Software; RRID: SCR_002798) were used to verify the relationship between IL23R and OS or DFS using the log-rank (Mantel-Cox) test. A P value <0.05 indicated statistical significance.

Immune cell infiltration analysis

The Venn diagram online tool was used to identify the intersection of tar-DEmRNA genes and immune-related genes from the Immunology Database and Analysis Portal (ImmPort) database (https://www.immport.org/home; RRID: SCR_012804) (34). Immune infiltrate abundance were acquired from TIMER2.0 (http://timer.comp-genomics.org/) via multiple immune deconvolution methods (35-37), including TIMER, EPIC, MCP-COUNTER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, XCELL, and TIDE methods. The immune cells included CD4⁺ T cells, CD8⁺ T cells, Tregs, B cells, neutrophils, monocytes, macrophages, dendritic cells (DCs), natural killer (NK) cells, mast cells, cancer-associated fibroblasts, common lymphoid progenitors, common myeloid progenitors, endothelial cells, eosinophils, granulocyte-monocyte progenitors, hematopoietic stem cells, follicular helper T cells, gamma delta T cells, NK T cells, and myeloid-derived suppressor cells (MDSCs).

Real-time quantitative polymerase chain reaction (RT-qPCR)

Total IL23R RNA was extracted using TRIzol (Invitrogen, Thermo Fisher Scientific, Carlsbad, CA, USA) and reverse transcribed into complement DNA (cDNA) according to instruction of the Transcriptor cDNA Synth Kit (Roche, Basel, Swiss). RT-qPCR was conducted using the SYBR Green PCR kit (Qiagen, Hilden, Germany) and according to the manufacturer’s instruction. The relative gene expression changes were analyzed with the 2^−ΔΔCt method. The forward primer for IL23R was 5'-GCTCGGATTTGGTATAAAGG-3', and the reverse primer for IL23R was 5'-ACTTGGTATCTATGTAGGTAGG-3'. The forward primer for β-actin was 5'-TCCCTGGAGAAGAGCTACGA-3', and the reverse primer for β-actin was 5'-AGCACTGTGTTGGCGTACAG-3'.

Statistical analysis

GraphPad Prism 8.0 and R version 4.1.2. were used to analyze and visualize the data. The Student’s t-test was used to compare continuous variables between 2 groups. A P value <0.05 was considered statistically significant.

Results

Identification and preprocessing of DEcircRNA, DEmiRNA, and DEmRNA

To screen the DEcircRNA, DEmiRNA, and DEmRNA, we downloaded GSE181523, GSE32957, and GSE61850 datasets, consisting of 14 samples, 21 samples, and 10 samples, respectively. By using R software, we identified 69 upregulated and 104 downregulated circRNAs (Figure 2A,2B), 30 upregulated and 28 downregulated miRNAs were screened (Figure 2C,2D), and 2,234 upregulated and 2,203 downregulated mRNAs were confirmed (Figure 2E,2F). All DERNAs were visualized using volcano plots and heatmaps. The specific information of DEcircRNAs, DEmiRNAs, and DEmRNAs are shown in the online tables (available at https://cdn.amegroups.cn/static/public/10.21037tcr-23-455-1.pdf, https://cdn.amegroups.cn/static/public/10.21037tcr-23-455-2.pdf, and https://cdn.amegroups.cn/static/public/10.21037tcr-23-455-3.pdf).

Figure 2 Identification of DEcircRNA, DEmiRNA, and DEmRNA. (A,B) The volcano plot and heatmap of circRNAs from the GSE181523 dataset, consisting of 173 DEcircRNAs. (C,D) The volcano plot and heatmap of miRNAs from the GSE32957 dataset, consisting of 58 DEmiRNAs. (E,F) The volcano plot and heatmap of mRNAs from GSE61850 dataset, consisting of 4,437 DEmRNAs. Down: downregulation with significance; Not: no significant; Up: upregulation with significance. FDR, false discovery rate; FC, fold change; DE, differentially expressed; circRNA, circular RNA; miRNA, microRNA; mRNA, messenger RNA.

After the DEcircRNAs were screened, we used CircBank to obtain the MREs that bond to circRNAs. After removing duplicate data, we identified 805 MREs, which are listed in the online table (available at https://cdn.amegroups.cn/static/public/10.21037tcr-23-455-4.pdf). hsa-miR-34c-5p, hsa-miR-516a-3p, hsa-miR-513a-3p, hsa-miR-1261, hsa-miR-512-3p, hsa-miR-199b-5p, and hsa-miR-1183 (Figure 3A), all of which are both MREs and DEmiRNAs, were screened with Venn diagram. We further used Targetscan to obtain the target genes (targetgene), and 1,864 mRNAs which were both target genes and DEmRNAs (tar-DEmRNA) (Figure 3B) were screened via Venn diagram.

Figure 3 Functional enrichment analysis of targeted DEmRNAs. (A) Venn diagram of 7 miRNAs that were both miRNA response elements and DEmiRNAs. (B) Venn diagram of 1,864 mRNAs that were both target genes and DEmRNAs. (C) Bubble plot of the top 10 most enriched Gene Ontology terms. (D) Bubble plot of the top 10 most enriched KEGG terms. (E) Verification of the functional enrichment of targeted DEmRNAs via GSEA software. MRE, miRNA response element; DE, differentially expressed; miRNA, microRNA; mRNA, messenger RNA; targetgene, target gene; FDR, false discovery rate; PI3K, phosphoinositide 3-kinase; Akt, serine/threonine kinase; ECM, extracellular matrix; PPAR, peroxisome proliferator activated receptor; KEGG, Kyoto Encyclopedia of Genes and Genomes; HCM, hypertrophic cardiomyopathy; GSEA, Gene Set Enrichment Analysis.

Functional enrichment analysis of tar-DEmRNAs

We used the DAVID website to explore the functional enrichment of tar-DEmRNAs. There were 129 cellular component-related terms, 140 molecular function-related terms, and 456 biological process-related terms. We screened out 77 GO terms with statistical significance, including cytosol, plasma membrane, cytoplasm, integral component of membrane, and extracellular exosome, among others. There are 66 KEGG pathway terms, of which 22 were statistically significant, including metabolic pathways, pathways in cancer, phosphoinositide 3-kinase (PI3K)-serine/threonine kinase (Akt) signaling pathway, human papillomavirus infection, and focal adhesion, among others. Figure 3C,3D lists the top 10 most enriched terms, with their specific information being shown in the online table (available at https://cdn.amegroups.cn/static/public/10.21037tcr-23-455-5.pdf), including name of pathways, ID of pathways, number of enriched genes, proportion of enriched genes, P value, name of enriched genes. We verified the functional enrichment of tar-DEmRNAs using GSEA software, with the complete information being available in the online table (available at https://cdn.amegroups.cn/static/public/10.21037tcr-23-455-6.pdf). As shown in Figure 3E (28-31), according to KEGG analyses, CHOL samples, termed “carcinoma”, were enriched in cancer pathway, cell cycle, ribosome, extracellular matrix (ECM)-receptor interaction, endocytosis, small-cell lung cancer, focal adhesion, gap junction, hypertrophic cardiomyopathy (HCM), oocyte meiosis, pathogenic Escherichia coli infection, and axon guidance as compared with healthy samples.

Relation of IL23R to CHOL immune infiltration abundance

We obtained the immune-related genes (imgene) using the ImmPort database. The Venn diagram online was then used to determine the intersection of tar-DEmRNA genes and imgenes, with 148 genes (im-tar-DEmRNA) being selected (Figure 4A). We found that among 148 im-tar-DEmRNAs, a low expression of IL23R was associated with poor prognosis in CHOL. We analyzed the relationship between IL23R expression and the immune cell infiltration in CHOL using TIMER2.0 database. As seen in Figure 4B, the expression of IL23R in CHOL was negatively correlated with the proportion of tumor cells (Rho =−0.418; P=1.12e−02) (35-37). IL23R expression had a positive correlation with the following immune cell types: B cells, according to the EPIC (Rho =0.398; P=1.77e−02), TIMER (Rho =0.363; P=3.19e−02) and XCELL (Rho =0.369; P=2.91e−02) methods; CD4⁺ effector memory T cells, according to the XCELL (Rho =0.373; P=2.73e−02) method; CD4⁺ T cells according to the EPIC (Rho =0.344; P=4.32e−02) method, myeloid DCs according to the MCPCOUNTER (Rho =0.347; P=4.08e−02) method; activated myeloid DCs according to the XCELL (Rho =0.386; P=2.20e−02) method; monocytes according to the CIBERSORT-ABS (Rho =0.463; P=5.11e−03) and CIBERSORT (Rho =0.432; P=9.48e−03) methods; M2 macrophages according to the CIBERSORT-ABS (Rho =0.356; P=3.59e−02) and QUANTISEQ (Rho =0.435; P=8.98e−03) methods; macrophages, according to the TIMER (Rho =0.438; P=8.44e−03) method; and activated mast cells according to the CIBERSORT-ABS (Rho =0.357; P=3.51e−02) method. Meanwhile, IL23R expression was negatively associated with following immune cell types: MDSCs, according to the TIDE (Rho =−0.528; P=1.11e−03) method; neutrophils, according to the CIBERSORT (Rho =−0.396; P=1.86e−02) and CIBERSORT-ABS (Rho =−0.377; P=2.58e−02) methods; and CD4⁺ central memory T cells, according to the XCELL (Rho =−0.339; P=4.65e−02) method (Figure 4C) (35-37).

Figure 4 IL23R was related to CHOL immune infiltration abundance. (A) Venn diagram of mRNAs that both targeted differentially expressed mRNAs and immune-related genes. (B) The expression of IL23R in CHOL was negatively correlated with the proportion of tumor cells. IL23R expression had a positive correlation with B cells according to the EPIC, TIMER, and XCELL methods; CD4⁺ effector memory T cells according to the XCELL method; CD4⁺ T cells according to the EPIC method; myeloid DCs according to the MCPCOUNTER method; activated myeloid DCs according to the XCELL method; monocytes according to the CIBERSORT-ABS and CIBERSORT methods; M2 macrophages according to the CIBERSORT-ABS and QUANTISEQ methods; macrophages according to the TIMER method; and activated mast cells according to the CIBERSORT-ABS method. (C) IL23R expression was negatively associated with MDSC according to the TIDE method, neutrophils according to the CIBERSORT and CIBERSORT-ABS methods, and CD4⁺ central memory T cells according to the XCELL method. According to the on the TIMER2.0 database, there was no linear relationship between the infiltration level of CD4⁺ central memory T cells and the expression of IL23R, which led to the inconsistency between the slope of the line and the Rho value. (B,C) were downloaded from TIMER2.0 database, and no permission was needed. imgene, immune-related gene; tar-DEmRNA, the intersection mRNA of miRNA target gene and DEmRNA; DE, differentially expressed; mRNA, messenger RNA; miRNA, microRNA; IL23R, interleukin 23 receptor; TPM, transcripts per million; CHOL, intrahepatic cholangiocarcinoma; DCs, dendritic cells; MDSC, myeloid-derived suppressor cell.

Construction of circRNA-miRNA-IL23R and protein-protein interaction networks for IL23R

MREs were identified using the CircBank database, and the target genes of miRNA were obtained using Targetscan. Through intersection, the miRNAs corresponding to the target gene of IL23R, namely hsa-miR-512-3p and hsa-miR-1183, were identified, as were the relevant circRNAs corresponding to the MREs of hsa-miR-512-3p and hsa-miR-1183. The circRNA-miRNA-mRNA interactions were integrated (Figure 5A) using Cytoscape, which provided the links among the circRNAs (hsa_circ_0050898, hsa_circ_0037100, and hsa_circ_0016956), the miRNAs (hsa-miR-512-3p, and hsa-miR-1183), and IL23R. Removing the unconnected nodes, we analyzed the 148-im-tar-DEmRNA PPI network using the Search Tool for the Retrieval of Interacting Protein (STRING; https://cn.string-db.org/cgi/input?sessionId=bcgnSYBYHI9T&input_page_active_form=multiple_identifiers; RRID: SCR_005223) website. Figure 5B shows the protein network directly related to IL23R, while the other PPI networks are shown in the online table (available at https://cdn.amegroups.cn/static/public/10.21037tcr-23-455-7.pdf).

Figure 5 Construction of the circRNA-miRNA-IL23R and protein-protein interaction network of IL23R. (A) Interactions of circRNA-miRNA-IL23R. The blue patterns represent circRNA, the green patterns represent miRNA, and the red patterns represent mRNA. (B) Protein-protein interaction network between IL23R and the other directly related mRNAs. IL23R, interleukin 23 receptor; circRNA, circular RNA; miRNA, microRNA; mRNA, messenger RNA.

A low expression of IL23R was associated with poor prognosis in CHOL

The GEPIA database and DriverDBv3 database were used to perform prognostic analysis. As the cutoff value was set to the median in the GEPIA database, the OS [hazard ratio (HR) =0.19; P=0.0042] (Figure 6A) and the DFS (HR =0.22; P=0.0052) (Figure 6B) of patients with CHOL and low IL23R expression were lower than those with a high expression of IL23R (33). In the DriverDBv3 database (32), the OS (HR =0.273; P=0.00956) (Figure 6C) and the disease-free interval (DFI) (HR =0.245; P=0.0285) (Figure 6D) of patients with CHOL and a low IL23R expression were lower than those of patients with a high expression of IL23R when the cutoff value was the median. The progression-free interval (PFI) (HR =0.298; P=0.00838) (Figure 6E) and the disease-specific survival (DSS) (HR =0.312; P=0.0254) (Figure 6F) of a low IL23R expression were also low in the DriverDBv3 database with the cutoff value equal to the median. With the cutoff values set as the mean value, the OS (HR =0.184; P=0.0118) (Figure 6G), PFI (HR =0.0985; P=0.000261) (Figure 6H), and DSS (HR =0.175; P=0.0564) (Figure 6I) of patients with CHOL and a low expression of IL23R were consistent results with those from the DriverDBv3 database. The specific information concerning the relationship of IL23R with CHOL progression is shown in the online table (available at https://cdn.amegroups.cn/static/public/10.21037tcr-23-455-8.pdf). In addition, to verify the relationship between IL23R expression and the prognosis of patients, the data of OS and DFS in patients with CHOL from TCGA were obtained via the UCSC Xena website. As shown in the Figure 6J and Figure 6K, the OS (P=0.0348) and PFI (P=0.0294) of patients with CHOL and a low expression of IL23R were shorter, which confirmed the results from the GEPIA database and DriverDBv3 database.

Figure 6 Low expression of IL23R was associated with poor prognosis in CHOL. (A) The OS (HR =0.19, P=0.0042) and (B) the disease-free survival (HR =0.22; P=0.0052) in patients with CHOL and high or low IL23R expression (GEPIA, cutoff value: median). (C) The OS (HR =0.273; P=0.00956), (D) the DFI (HR =0.245; P=0.0285), (E) the PFI (HR =0.298, P=0.00838), and (F) the DSS (HR =0.312, P=0.0254) in patients with CHOL with high or low IL23R expression (DriverDBv3, cutoff value: median). (G) The OS (HR =0.184, P=0.0118), (H) the PFI (HR =0.0985; P=0.000261), and (I) the DSS (HR =0.175; P=0.0564) in patients with CHOL with a high or low IL23R expression (DriverDBv3, cut-off value: mean). Verification of the relationship between IL23R expression and (J) the OS (P=0.0348) and (K) the PFI (P=0.0294) of patients with CHOL in TCGA obtained from the UCSC Xena website. (A,B) are downloaded from the GEPIA database. (C-I) are downloaded from the DriverDBv3 database. Permission for use of these figures is not required. IL23R, interleukin 23 receptor; HR, hazard ratio; CHOL, intrahepatic cholangiocarcinoma; OS, overall survival; DFI, disease-free interval; PFI, progression-free interval; DSS, disease-specific survival; GEPIA, Gene Expression Profiling Interactive Analysis.

Verification of IL23R expression level in CHOL

RT-qPCR was performed to explore the expression level of IL23R in CHOL, and the IL23R RNA expression in CHOL tissue was found to be lower than that in healthy liver tissue (Figure 7).

Figure 7 IL23R had a low expression in CHOL. The expression of IL23R in CHOL tissue was lower than that in healthy liver tissue performed according to RT-qPCR. IL23R, interleukin 23 receptor; mRNA, messenger RNA; CHOL, intrahepatic cholangiocarcinoma; RT-qPCR, real-time quantitative polymerase chain reaction.

Discussion

CHOL is difficult to diagnose and differentiate (38), and compared to patients with other types of CCA, those with CHOL have a worse prognosis, as indicated by lower OS and cancer-specific survival rates (39). Patients with CHOL often have no symptoms at the early stage, with 70% of the patients having advanced or metastatic tumors at the time of diagnosis (5). By this time, the chance of survival for patients has declined, and there is no satisfactory treatment (40). Presently, immunotherapy is in an emerging stage of development and can also be applied in CHOL (41). However, the research on the prognosis of patients with CHOL remains still inadequate, and there is thus an urgent need to identify relevant biomarkers to improve the treatment of patients with CHOL.

In this study, the abnormal expression of circRNA, miRNA, and mRNA in CHOL was confirmed via bioinformatics analysis, with IL23R showing a low expression in CHOL. GO and KEGG functional enrichment analyses, verified by GSEA software, showed that DEmRNA in CHOL is related to cytosol, plasma membrane, cytoplasm, integral component of membrane, extracellular exosome, metabolic pathways, pathways in cancer, PI3K-Akt signaling pathway, human papillomavirus infection, and focal adhesion, among other aspects. The overexpression of IL23R via RAS/mitogen-activated protein kinase and STAT3 pathways has been proven to induce apoptosis in 293ET and HeLa cells but does not inhibit STAT1 or PI3K-Akt signaling pathways (42). There is little research on IL23R in cancer nowadays. Nevertheless, leucine zipper ATF-like transcription factor (Batf) was found to be coexpressed with IL23R in infiltrating CD4⁺ T cells of colon cancer, while Batf-dependent IL23R⁺IL-6⁺CD4⁺Th17 cells were shown to control the formation of IL-23-driven colitis-related tumors and the progression of sporadic colon tumors. Batf-dependent IL23R⁺ T cells might be a potential therapeutic target to limit the progression of colon cancer (43). At present, little is known concerning the functional mechanism of IL23R in the development of CHOL. However, it was reported that IL-23 produced by macrophages derived from liver monocytes plays a major role in the pathogenesis of primary biliary cirrhosis (44), which is one of the main risk factors of CHOL (45). These and similar findings point to productive research directions in CHOL, especially related to the PI3K-Akt signaling pathway, immune-related TME, and amino acid metabolism.

Cytokines are one of the components of the TME. IL-23 is a covalently linked heterodimeric hematopoietic cytokine, which is produced by activated DCs and macrophages (46). IL23R is a subunit of IL23A/IL-23 receptor, which is a newly discovered member of the hemopoietic receptor superfamily. It is encoded by a gene located within 150 kb of the human chromosome 1 IL12Rβ2 gene (47). Recent research demonstrated that antibody-mediated blockade of IL23R can augment the instability of intratumoral T cells and enhance immunotherapy. It was further found that blocking the expression of IL23R could increase the expression of IL-12 and interferon-γ in Tregs, leading to the recruitment of CD8⁺ T cells and further destabilizing Tregs, thus inhibiting the development of tumor (10). In another study, the proportion of interferon-γ-producing NK cells in the lungs from B16F10 tumor-bearing mice increased the inhibition of experimental lung metastasis, with the antibody blocking IL23R being more effective than the antibody neutralizing IL-23 (48). According to the bioinformatics analysis mentioned in our study, IL23R was related to the immune infiltration of CHOL cells. IL23R expression had a positive correlation with some immune infiltration-related cells, including CD4⁺ effector memory T cells and CD4⁺ T cells, and a negative associated with others, including CD4⁺ central memory T cells. Zhan et al. found that IL-23/IL-17A signaling pathway was closely related to chronic inflammation of the bile duct in primary biliary cirrhosis and the functional loss of Tregs, which led to the decrease of Foxp3 expression and mediated the tolerance loss of primary biliary cirrhosis (49). In addition, a recent study revealed that patients with CHOL and a high proportion of Foxp3⁺ Tregs in mesenchymal tissue had a longer relapse-free survival (50). According to Shi et al., CD4CD161 cells from patients with primary biliary cirrhosis have a higher expression of IL-17 after being stimulated with IL-23 and IL-1β (51). Therefore, as primary biliary cirrhosis is related to CHOL, the expression of IL23R in CHOL may exert a certain role in TME, thus affecting the immune efficacy.

As posttranscriptional regulators, circRNAs are formed by splicing exons head to tail (52). As non-coding RNAs, only a few circRNAs have been confirmed to regulate biological processes such as cell proliferation, migration, or invasion. The biological functions of circRNAs can be achieved by acting as miRNA or protein “sponges”, by regulating protein function, or by self-translation (53). Therefore, further research into circRNAs is particularly critical. In this study, we identified 69 upregulated and 104 downregulated circRNAs and 805s MRE in CHOL. In addition, 3 circRNAs (hsa_circ_0050898, hsa_circ_0037100, and hsa_circ_0016956) and 2 miRNAs (hsa-miR-512-3p and hsa-miR-1183) were integrated to form circRNA-miRNA-mRNA interactions with IL23R. Chen et al. found that hsa_circ_0050898 can promote the proliferation and metastasis of CHOL by acting as a molecular sponge of miR-424-5p and by activating FZD7 through interaction with YBX1 (21). Moreover, Wang et al. reported that USF2-mediated hsa_circ_0050898 might promote the occurrence and development of breast cancer and could thus serve as a novel target for the diagnosis and treatment of breast cancer (54). Furthermore, Mohamadzade et al. demonstrated hsa-miR-512-3p to be a cell-type-specific tumor inhibitor that can act as a potential biomarker for breast cancer diagnosis (55). However, little research has been conducted concerning the role of circRNA-miRNA-mRNA of IL23R in CHOL. Our findings suggest the possible existence of a ceRNA network of IL23R in CHOL, but more experimental studies need to be performed to verify the possible pathways.

As a positive regulator in the initiation of T helper cells, IL23R is considered to be the principal coordinator of inflammation-driven tumors (56). However, the role of IL23R in cancer is not completely clear. In studies of laryngeal cancer, bladder cancer, and non-small cell lung cancer (NSCLC), a high expression of IL23R has been associated with poor prognosis (9,12,57). A different study found EIF5A2 to be an independent prognostic indicator for CHOL. High levels of EIF5A2 can activate the PI3K/Akt/mTOR signaling pathway, thus promoting the proliferation, migration, and invasion of CHOL cells (58). Moreover, brusatol can inhibit CHOL cell proliferation and the epithelial-mesenchymal transition process through PI3K-Akt pathway and thus promote tumor cell apoptosis, providing a direction for CHOL therapy (59). It has recently been shown that a higher baseline level of IL-23 can improve the clinical results of atezolizumab combined with cobimetinib in the treatment of advanced biliary tract cancers (16). However, the prognosis value of IL23R in patients with CHOL is still unclear. Through bioinformatics analysis, we found that a low expression of IL23R was related to the poor prognosis of CHOL, which seems to be consistent with the improvement of clinical outcomes in patients with advanced biliary tract cancers treated with atezolizumab combined with cobimetinib with a high baseline level of IL-23. Additionally, the GEPIA and DriverDBv3 databases showed that patients with CHOL and a low expression of IL23R had a lower OS and DFS. However, further research is needed to determine how IL23R affects the prognosis of patients with CHOL and clarify the detailed mechanism of IL23R in this relationship.

Conclusions

This is the first study to analyze the relationship between IL23R expression in CHOL and total survival time. The low expression of IL23R resulted in the decreased survival time of patients with CHOL. IL23R can be considered a potential biomarker for the diagnosis and treatment of patients with CHOL. However, most of the results in this study were obtained via bioinformatics analysis. In the future, a series of experiments should be conducted to further verify the role of IL23R in the diagnosis or prognosis of CHOL and determine whether interfering with IL23R expression can aid in tumor treatment.

Acknowledgments

The image elements in Figure 1 were obtained from Vecteezx (https://www.vecteezy.com/) and SMART SERVIER (https://smart.servier.com/).

Funding: This work was supported by Shenzhen Science and Technology Innovation Commission Project (Nos. JCYJ20190809100005672, ZDSYS20190902092855097, and KCXFZ20200201101050887), the General Program (No. JCYJ20210324105609024), and the Shenzhen Sanming Project (No. SZSM201612041).

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-455/rc

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Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-455/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013) and was approved by Ethics Committee of Guangdong Provincial People’s Hospital (No. KY-Q-2022-321-01). Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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