IL20RB as a prognostic and immune-related biomarker in lung cancer: association with immune infiltration, tumor progression, and potential therapeutic targeting

Introduction

Lung cancer (LC) is currently the most aggressive cancer with the highest incidence and mortality rate worldwide (1), and it is primarily divided into two major types: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). Despite significant advancements in LC treatment over the past few decades, including surgical resection, radiotherapy, chemotherapy, and immunotherapy (2,3), the prognosis and survival rate of patients remain unsatisfactory. In recent years, immunotherapy and molecular targeted therapy have been proven to significantly improve the treatment outcome and survival time of LC patients (4-6). Although significant progress has been made in the research and development of immune checkpoint inhibitors (ICIs) (7-9), these drugs remain unsuitable for some LC patients. Therefore, in order to increase the early diagnostic rate of LCs, enhance the effect of immunotherapy and prolong the survival time of patients (10,11), it is necessary to identify some effective biomarkers to improve the overall prognosis of patients (12).

With the successful application of ICIs, the treatment outcome of LC patients has been significantly improved over the past few decades (13-15). Multiple studies have explored the roles of programmed cell death protein 1 (PD-1) and programmed death-ligand 1 (PD-L1) in immune suppression (16,17), confirming that they not only participate in tumor immune escape but can also serve as prognostic biomarkers for tumor progression or predictive biomarkers for immune response (18,19). However, PD-1 and PD-L1 targeted ICIs have significant limitations (20-22); more than half of the patients do not respond to PD-1/PD-L1 immunotherapy, suggesting that there may be other co-inhibitory signaling pathways in the tumor microenvironment in LC (23-25).

Interleukin-20 receptor subunit beta (IL20RB), as a member of the IL-20 receptor family (26,27), is a single transmembrane receptor protein that plays a significant role in regulating host defense, autoimmune responses, and tissue damage repair (28-30). IL20RB binds to IL20RA or IL22RA1 to form a functional heterodimeric receptor, primarily binding to IL19, IL20, and IL24. It has been reported that IL20 promotes the progression and metastasis of prostate cancer, oral cancer, and breast cancer (31). IL19 is highly expressed in breast cancer and is correlated with a poor clinical outcome (32). IL20RB is highly expressed in pancreatic cancer, and IL20RB proteins exert a crucial effect on promoting pancreatic cancer stem cell characteristics (33). However, the effect of IL20RB in immune infiltration in LC remains unclear. This study aims to investigate the relationship of IL20RB with the prognosis and immune infiltration in LC patients, thus providing important molecular evidence for early non-invasive diagnosis and immunotherapy of LC. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-2054/rc).

Methods

Data collection

The clinical and RNA sequencing data of 1,149 samples were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/), of which 108 LC tissue samples and 1,041 normal lung tissue samples were included into this study. In addition, the clinical parameters such as T stage, N stage, M stage, gender, age, and pathological stage were collected and analyzed. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Identification of differentially expressed immune-related genes

The mRNA expression data were analyzed using R packages such as DESeq2 and edgeR to identify differentially expressed mRNAs (DEmRNAs) that met the criteria of |log₂FC| >1 and adjusted P value <0.05. A total of 1,793 immune-related genes (IRGs) were downloaded from the ImmPort database (https://www.immport.org/home). Subsequently, the differentially expressed immune-related genes (DEIRGs) co-expressed with DEmRNAs were screened, and the results were visualized using the R package ggplot2. The least absolute shrinkage and selection operator (LASSO) regression analysis of DEIRGs was performed using the R package glmnet. The prognostic IRGs with non-zero coefficients were screened based on the optimal lambda value. A risk model was then constructed. The forest plot and volcano plot of DEIRGs and the heatmap of target genes and co-expressed mRNAs were visualized using the R package ggplot2. Meanwhile, the chromosomal localization of the hub genes was clarified by using the R package circlize.

Functional enrichment analysis

The function of IL20RB was further explored using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and gene set enrichment analysis (GSEA). The R package clusterProfiler was used for GO and KEGG enrichment analysis. Based on the median expression level of IL20RB, LC patients were divided into the low- and high-IL20RB expression groups. Then, GSEA was performed using the R package ggplot2.

Validation of prognostic immune-related gene characteristics

The expression status of IL20RB in the TCGA database was visually analyzed using the R package ggplot2. The R packages such as survival, survminer and ggplot2 were used to analyze overall survival (OS) between the high-and low-risk groups, Kaplan-Meier survival curves of subgroups, and groupings based on clinical characteristics. The prognostic value of the risk model was evaluated by univariate and multivariate Cox regression analyses and the receiver operating characteristic (ROC) curve analysis. The R packages such as survival and rms were used to draw nomograms and calibration curves to evaluate the consistency between the actual results and the predicted probabilities. The clinical utility and benefits of the prediction model were evaluated using the decision curve analysis (DCA). In addition, UALCAN database (http://ualcan.path.uab.edu/index.html) was used to analyze the protein expression level of IL20RB in LC tissue.

Immune cell infiltration analysis

Twenty-four types of immune cells in LC tissue samples were evaluated using the single-sample gene set enrichment analysis (ssGSEA) algorithm. Spearman’s correlation analysis was conducted to assess the correlation of IL20RB expression levels with immune cells and immune checkpoints. Subsequently, the correlation analysis results were visualized using the R packages such as ggplot2 and pheatmap. The Wilcoxon rank-sum test was conducted to compare the degree of immune cell infiltration between LC patients in the high-and low-IL20RB expression groups. Additionally, the presence of immune cells was assessed using ssGSEA. Finally, Xiantao Academic Online Tools were used to generate heatmap, intergroup comparison plot, and correlation heatmap, integrating information of 24 types of immune cells.

Protein-protein interaction (PPI) network

GeneMANIA (http://www.genemania.org) is a resource-rich website that contains genetic information and can analyze gene lists and prioritize genes, and it is also equipped with a high-precision prediction algorithm. GeneMANIA can be used to analyze the PPI network features of DEIRGs and IL20RB. The overlapping DEIRGs and hub gene IL20RB were respectively submitted to the STRING database (https://string-db.org/) to generate PPI network. Then the generated network data were imported into Cytoscape v3.10.3 for visual analysis.

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

Human LC cell lines (H1299, PC-9, and H1650) and human normal epithelial cells BEAS-2B were purchased from the Qingqi (Shanghai) Biotechnology Development Co., Ltd (Shanghai, China, http://www.bluefcell.com). Total RNAs were extracted from the cells using TRIzol reagent (Invitrogen, Waltham, MA, USA) according to the manufacturer’s instructions. Reverse transcription (RT) was performed using an RT reagent kit (Sangon Biotech, Shanghai, China) in accordance with the operation procedures in the instructions in the manual. RT-qPCR was conducted using a fluorescence quantitative PCR instrument and SYBR Green RT-qPCR Kit (ABclonal, Wuhan, China). In addition, the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as the internal reference gene, and relative quantitative analysis was performed using the 2^−ΔΔCT method. The forward primer of IL20RB was 5'-CTGTGTGAAGGCCCAGACAT-3', and the reverse primer of IL20RB was 5'-CTCCTTGCACCTCCACACAT-3'. The forward primer of GAPDH was 5'-CAGGAGGCATTGCTGATGAT-3', and the reverse primer of GAPDH was 5'-GAAGGCTGGGGCTCATTT-3'.

Identification of molecular drugs

Drugs related to diagnostic biomarkers were retrieved in the Comparative Toxicogenomics Database (CTD, https://ctdbase.org/) to identify potential therapeutic drugs for LC. CB-DOCK2 database was used to perform molecular docking between selected drugs and key biomarkers. Subsequently, the docking results were visualized using Pymol V3.9.2.

Statistical analysis

Data statistical analysis and visualization were performed using the Xiantao Academic Online Tools (https://www.xiantaozi.com/). Intergroup comparison was conducted to analyze the differentially expressed genes (DEGs), Wilcoxon rank-sum test was used for statistical inference, and Spearman’s correlation coefficient was used to assess the correlation among immune cells. P value <0.05 was considered statistically significant.

Results

Identification of immune-related DEmRNA

The workflow of this study is shown in Figure 1. The IRGs were downloaded from the ImmPort database. Subsequently, 108 LC samples and 1,041 normal samples were analyzed to screen out 2,739 DEGs, among which 36 belonged to DEIRGs. Then LASSO regression analysis of DEIRGs was performed. OS analysis was performed to identify immune-related potential prognostic genes using the LASSO Cox regression model (Figure 2A,2B), and 16 genes were identified as the optimal prognostic factors with predictive significance.

Figure 1 Workflow diagram of this study. PPI, protein-protein interaction; RT-qPCR, real-time quantitative polymerase chain reaction.

Figure 2 DEIRGs obtained from a public database. (A) LASSO coefficient distribution plot for the most predictive genes. (B) LASSO model parameter optimization using 100-fold cross-validation. (C) Univariate Cox proportional hazards regression analysis based on 12 prognostic genes. (D) Volcano plot showing the expression levels of DEmRNA in LCs: up-regulated mRNA is indicated in red, and down-regulated mRNA is indicated in blue. (E) Heatmap presenting the analysis results of 10 DEIRGs. (F) Venn plot illustrating the effects of immune-related genes and DEmRNA on the prognosis of LC patients. (G) Chromosomal localization of the DEIRG IL20RB. CI, confidence interval; DEIRG, differentially expressed immune-related gene; DEmRNA, differentially expressed mRNA; HR, hazard ratio; LASSO, least absolute shrinkage and selection operator; LC, lung cancer; TPM, transcripts per million.

The univariate Cox regression analysis revealed that 12 genes were closely correlated with the prognosis of patients with LCs, which included PTX3, DKK1, APLN, FGA, GREM1, IL20RB, LGR4, VGF, IL11, NPPC, CCL14, and ANOS1 (Figure 2C). A volcano plot showed that IL20RB was highly expressed in LCs (Figure 2D). After a comprehensive comparison, IL20RB was selected as the target gene. Figure 2E shows the heatmap of IL20RB and its co-expressed genes. The first five genes were positively correlated with IL20RB expression level, while the last five genes were negatively correlated with IL20RB expression level. Subsequently, the overlapping target genes between IRGs and DEmRNA related to the prognosis of LC patients were obtained using Venn overlap analysis (Figure 2F). The chromosomal location of the IL20RB gene is shown in Figure 2G.

Close correlation between high expression of IL20RB in LC and poor prognosis

The expression of IL20RB in the pan-cancer of the TCGA database is shown in Figure 3A. A visualization analysis of IL20RB expression was carried out using the TCGA database, and the results are shown in Figure 3B,3C. Survival analysis revealed that the LC patients with a higher IL20RB expression level had a poorer OS rate (Figure 3D). The expression level of IL20RB in LC tissue was significantly higher than that in normal lung tissue, and the area under the ROC curve (AUC) for its diagnostic performance was 0.875, indicating that IL20RB may be a potential diagnostic biomarker (Figure 3E).

Figure 3 Correlation between the upregulated IL20RB and poor prognosis in LCs. (A) IL20RB expression level in pan-cancer tissue samples from the TCGA database. (B,C) IL20RB expression data in LC samples from the TCGA database. (D) OS curve for IL20RB in the TCGA database. (E) ROC curve analysis for IL20RB. (F,G) Protein expression level of IL20RB in LC tissues from the UALCAN database. (H) mRNA expression levels of IL20RB in LC cell lines such as H1299, PC-9, and H1650 and human normal lung epithelial cells such as BEAS-2B detected by RT-qPCR. *, P<0.05; **, P<0.01; ***, P<0.001. AUC, area under the curve; FPR, false positive rate; LC, lung cancer; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; OS, overall survival; ROC, receiver operating characteristic; RT-qPCR, real-time quantitative polymerase chain reaction; TCGA, The Cancer Genome Atlas; TPM, transcripts per million; TPR, true positive rate; UALCAN, The University of Alabama at Birmingham Cancer Data Analysis Portal.

The analysis of the UALCAN database revealed that IL20RB protein expression level was significantly higher in LC tissue than in normal lung tissue (Figure 3F,3G). RT-qPCR experimental results further showed that IL20RB mRNA expression level was significantly higher in LC cell lines than in matched normal lung cells (Figure 3H). In summary, these results suggest that IL20RB may have a carcinogenic effect in the occurrence and development of LCs.

In addition, a nomogram tool was constructed based on independent predictors to predict 1-, 3-, and 5-year OS risk (Figure 4A). The calibration curves for 1-, 3-, and 5-year OS rates showed a good agreement between actual outcomes and predicted values, indicating that the IL20RB-based nomogram has a satisfactory predictive performance (Figure 4B-4D). Furthermore, DCA demonstrated that the risk factor-based model exhibited good consistency in predicting 1-, 3-, and 5-year OS rates (Figure 4E-4G).

Figure 4 Assessment of the prognostic significance of IL20RB in patients with LCs. (A) Nomogram for predicting 1-, 3-, and 5-year OS rates in TCGA database. (B-D) Calibration curves assessing the consistency between predicted values and actual 1-, 3-, and 5-year OS rates in the prognostic nomogram in TCGA. (E-G) DCA assessing the practicality of the nomogram in clinical decision-making. DCA, decision curve analysis; IL20RB, interleukin 20 receptor subunit beta; LC, lung cancer; OS, overall survival; TCGA, The Cancer Genome Atlas.

Correlation between IL20RB expression levels and clinical pathological parameters

The correlations between clinical pathological factors and IL20RB expression levels were analyzed using chi-square test and two-class logistic model, as shown in Tables 1,2. The chi-square test revealed that IL20RB expression level was correlated with tumor stage (T stage, P<0.001), pathological stage (P=0.004), smoking status (P=0.003), gender (P<0.001), and OS (P<0.001) in LC patients. Furthermore, logistic regression analysis indicated that IL20RB expression level was significantly correlated with tumor stage (T stage, P<0.001), gender (P<0.001), smoking status (P=0.003), and cumulative smoking years (P=0.050) in LC patients.

The Kruskal-Wallis rank-sum test showed that IL20RB expression level was correlated with clinical variables such as tumor location, anatomical tumor subtype, tumor stage (T stage), smoking status, gender, and age in LC patients (P<0.05) (Figure 5A-5I). The OS rates of different LC subgroups with a high or low IL20RB expression level are shown in Figure 5J-5O. The results showed that the pathologic stage I subgroup [hazard ratio (HR): 1.49, 95% confidence interval (CI): 1.09–2.03, P=0.01], N0 stage subgroup (HR: 1.42, 95% CI: 1.09–1.86, P=0.01), male subgroup (HR: 1.37, 95% CI: 1.07–1.75, P=0.01), age >65 years subgroup (HR: 1.33, 95% CI: 1.03–1.72, P=0.02), smoking subgroup (HR: 1.42, 95% CI: 1.45–1.76, P=0.001), and peripheral LC subgroup (HR: 1.58, 95% CI: 1.04–2.39, P=0.03) with increased IL20RB expressions were correlated with a poorer OS rate.

Figure 5 Correlation between IL20RB expression levels and clinical pathological parameters. (A-I) Correlation between IL20RB expression levels and various clinical variables in LC patients. (J-O) Poorer OS rate in LC patients in the high-IL20RB expression subgroup. *, P<0.05; **, P<0.01; ***, P<0.001. CI, confidence interval; HR, hazard ratio; LC, lung cancer; OS, overall survival; TPM, transcripts per million.

The effects of IL20RB expression and clinical-pathological parameters on OS of LC patients were investigated. Therefore, univariate and multivariate Cox regression analyses were performed. The univariate Cox regression analysis showed that the variables with P<0.05 included T stage, N stage, primary treatment outcome, and IL20RB expression level, and all of which were statistically significant. These variables and IL20RB were then included in the multivariate Cox regression model for further analysis. The results showed that IL20RB expression level (P=0.048), T stage (P<0.001), N stage (P<0.001), and primary treatment outcome (P<0.001) independently affected OS in LC patients (Table 3).

Functional enrichment analysis

GO was divided into three parts: molecular function (MF), cellular component (CC), and biological process (BP). GO enrichment analysis revealed that IL20RB was involved in 12 immune-related functions (Figure 6A). KEGG analysis indicated that IL20RB and its co-expressed mRNAs were enriched in five signaling pathways related to tumorigenesis and immune suppression (Figure 6B). In LC patients with a high IL20RB expression level, GSEA analysis revealed that the upregulated marker genes were enriched in signaling pathways related to tumorigenesis and immune responses, including processes such as stratum corneum formation and keratinization, biological development, and hair follicle development and cell differentiation (Figure 6C-6H).

Figure 6 Functional enrichment analysis of IL20RB in LCs. (A) Twelve GO functions enriched for IL20RB and its co-expressed mRNAs. (B) Five KEGG signaling pathways enriched for IL20RB and its co-expressed mRNAs. (C-H) Six signaling pathways enriched in LC patients with high IL20RB expression levels. BP, biological process; CC, cellular component; FDR, false discovery rate; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; LC, lung cancer; MF, molecular function; NES, normalized enrichment score.

Correlation between IL20RB expression and tumor immunity

The ssGSEA algorithm was used to assess the relationship between IL20RB expression level and the relative abundances of 24 types of immune cells in LCs (Figure 7A). The Wilcoxon rank-sum test was conducted to compare the enrichment of immune cells between the high-and low-IL20RB expression groups. The results showed that Th2 cells, natural killer (NK) CD56^dim cells, T central memory (Tcm) cells, and T gamma delta (Tgd) cells were significantly more enriched in the high-IL20RB expression group than in the low-IL20RB expression group. Further analysis revealed that T follicular helper (Tfh) cells, Th17 cells, eosinophils, and mast cells were significantly less enriched in the low-IL20RB expression group than in the high-IL20RB expression group (Figure 7B).

Figure 7 Relationship between IL20RB expression level and immune cells. (A) A lollipop plot showing a correlation between IL20RB expression levels and 24 types of immune infiltrating cells. The X-axis represents the correlation coefficient, and the Y-axis represents the type of immune cell. (B) Enrichment of immune cells in the high-and low-IL20RB expression groups. (C-F) Positive correlations of Th2 cells, NK CD56^dim cells, Tcm cells, and Tgd cells with IL20RB expression levels. (G-J) Negative correlations of eosinophils, Th17 cells, Tfh cells, and mast cells with IL20RB expression levels. ns, P>0.05; **, P<0.01; ***, P<0.001. aDC, activated dendritic cell; DC, dendritic cells; iDC, immature dendritic cells; NK, natural killer; pDC, plasmacytoid dendritic cells; Tcm, central memory T cells; Tem, effector memory T cells; Tfh, follicular helper T cells; Tgd, gamma delta T cells; TPM, transcripts per million; Treg, regulatory T cells.

The correlations between different immune cells and IL20RB expression levels varied as follows: Th2 cells (P<0.001, R=0.19), NK CD56^dim cells (P<0.001, R=0.175), Tcm cells (P<0.001, R=0.119), and Tgd cells (P=0.001, R=0.098) were positively correlated with IL20RB expression level (Figure 7C-7F); eosinophils (P<0.001, R=−0.355), Th17 cells (P<0.001, R=−0.418), Tfh cells (P<0.001, R=−0.474), and mast cells (P<0.001, R=−0.281) were negatively correlated with IL20RB expression level (Figure 7G-7J).

Spearman analysis showed that IL20RB expression level was positively correlated with the expression levels of IRGs such as CD44 (P<0.001, R=0.427), CD276 (P<0.001, R=0.311), human tumor necrosis factor receptor superfamily member (TNFRSF) 25 (P<0.001, R=0.153), TNFRSF18 (P<0.001, R=0.315), recombinant V-set domain containing T-cell activation inhibitor 1 (VTCN1) (P<0.001, R=0.301), and CD40 (P<0.001, R=0.11) (Figure 8A-8F). Among 24 types of immune cells, IL20RB exhibited significant expression differences in the remaining 20 types of immune cells except for activated dendritic cells (aDCs), neutrophils, NK cells, and regulatory T cells (Tregs) (Figure 8G). The correlations between 24 types of immune cells and immune checkpoints were evaluated using the ssGSEA method, and a correlation heatmap was drawn (Figure 8H). The correlation heatmap among the 24 types of immune cells in LC samples is shown in Figure 8I.

Figure 8 Relationship between IL20RB expression level and immune-related genes. (A-F) Positive correlation of immune-related genes such as CD44, CD276, TNFRSF25, TNFRSF18, VTCN1, and CD40 with IL20RB expression level. (G) Significant difference in IL20RB expression level among immune cells. (H) Heatmap of correlation between immune cells and immune checkpoints. (I) Heatmap of correlation among 24 types of immune cells in LC samples. *, P<0.05; **, P<0.01; ***, P<0.001. aDC, activated dendritic cell; DC, dendritic cells; iDC, immature dendritic cells; LC, lung cancer; NK, natural killer; pDC, plasmacytoid dendritic cells; Tcm, central memory T cells; Tem, effector memory T cells; Tfh, follicular helper T cells; Tgd, gamma delta T cells; TPM, transcripts per million; Treg, regulatory T cells.

PPIs of IL20RB in LC patients

To further understand the PPI relationships between IL20RB and its related genes in LCs, PPI network of DEIRGs was jointly constructed using STRING database, Cytoscape v3.10.3 software and GeneMANIA database.

PPI relationships among DEIRGs are presented in Figure 9A,9B. GeneMANIA visualization results showed that these 36 DEIRGs were mainly involved in functions such as positive regulation of myeloid cell migration, humoral immune responses, secretion processes, tissue remodelling regulation, and leukocyte migration. The PPI relationships between IL20RB and its related molecules are shown in Figure 9C,9D. GeneMANIA analysis further showed that IL20RB and its related molecules mainly participated in functions such as immune receptor activity, phosphoinositide 3-kinase activity, negative regulation of peptide tyrosine phosphorylation, lipid phosphorylation, and STAT-mediated receptor signaling pathways.

Figure 9 PPI of IL20RB in LC patients. (A,B) PPI relationship among 36 DEIRGs. (C,D) PPI relationships between IL20RB and its associated molecules. DEIRGs, differentially expressed immune-related genes; LC, lung cancer; PPI, protein-protein interaction.

Prediction of targeted drugs for core diagnostic biomarkers

Drugs related to IL20RB were further predicted using the CTD database, and the interaction relationships between drugs and biomarkers were extracted. The drug screening results showed that quercetin (D011794) from the CTD database could act on the DEIRG IL20RB. Subsequently, molecular docking simulations between the drugs and the predicted molecular targets were performed and visualized using Pymol V3.9.2 software (Figure 10).

Figure 10 Prediction of core diagnostic biomarker-targeted drugs. Action of quercetin on the DEIRG IL20RB. DEIRG, differentially expressed immune-related gene.

Discussion

LC remains the leading cause of cancer-related deaths worldwide, characterized by high incidence and strong invasiveness. Despite significant advances in surgical resection, radiotherapy, chemotherapy, and immunotherapy over the past few decades, the prognosis of LC patients remains poor, with a low survival rate (34,35). The developments of immunotherapy and targeted therapy show potential for improving treatment outcomes and extending survival time. However, since some LC patients are not suitable for ICIs, there is an urgent need to develop effective biomarkers to achieve early diagnosis and prolong patient survival time (36,37).

IL20RB is a member of IL-20 receptor family. This receptor is highly expressed in peripheral blood immune cells of patients with certain immune-related diseases and participates in disease progression by regulating the maturation process of immune cells. For example, the expression level of IL20RB in the peripheral blood of patients with rheumatoid arthritis (RA) is elevated, which induces monocytes to highly express monocyte chemoattractant protein (MCP-1), promoting their aggregation around osteoclasts and thereby accelerating the progression of RA (38). Additionally, IL20RB expression level is significantly increased in patients with inflammatory bowel disease (IBD), thus promoting macrophage activation and accelerating the progression of IBD (39). All the above-mentioned studies have shown that IL20RB plays a significant role in immune-related diseases by regulating the activation of immune cells.

The clinical and RNA data were obtained from LC patients in the TCGA database, and the IRGs were downloaded from the ImmPort database (40,41). Subsequently, IL20RB was identified as the target gene using the bioinformatics method. Further survival analysis revealed that a higher IL20RB expression is correlated with a lower OS, suggesting its potential as a prognostic biomarker. Functional enrichment analysis indicated that IL20RB is involved in tumor formation and immune suppression-related signaling pathways. These findings highlight both the prognostic importance of IL20RB in LC and its potential as a therapeutic target. IL20RB affects LC progression by regulating multiple key signaling pathways associated with tumorigenesis and immune escape, which include the JAK-STAT signaling pathway, NF-κB signaling pathway, and TGF-β signaling pathway.

IL20RB contributes to the formation of a multifaceted immunosuppressive network by activating the JAK-STAT3 pathway, recruiting and expanding myeloid-derived suppressor cells, reducing CD8⁺ T/NK cell infiltration, and upregulating immunosuppressive molecules. These mechanisms have been validated in previous models of malignant tumors such as pancreatic cancer and breast cancer. Highly expressed IL20RB shapes the immunosuppressive microenvironment; its core mechanism involves suppressing cytotoxic T cell function: down-regulating CXCL9/CXCL10, reducing CD8⁺ T cell recruitment, and directly inhibiting their activation, proliferation, and secretion of perforin/granzyme B. Our study revealed significant positive correlations between the expression level of IL20RB and the expression levels of multiple immune-related genes, such as CD44, CD276, TNFRSF25, TNFRSF18, VTCN1, and CD40, indicating IL20RB serves as a pivotal hub connecting tumor cells with the immune microenvironment. However, the specific mechanisms of IL20RB in LCs remain limited due to the lack of functional experimental validation. We will continue to validate the underlying regulatory pathways of these correlations through functional experiments (e.g., whether JAK1/STAT3 signaling pathway is involved) and explore the targeted strategies that IL20RB is combined with checkpoints such as CD276 and VTCN1, thus providing new targets for improving immunotherapy efficacy.

IL20RB expression level is positively correlated with immune cells such as Th2 cells and CD56^dim NK cells, but negatively correlated with eosinophils and Th17 cells, indicating that IL20RB plays a role in shaping the tumor microenvironment. Changes in immune cell-related parameters not only reflect the complex interactions between immune responses and cancer progression but also highlight the potential effect of IL20RB in regulating immune cell infiltration, and this effect is of great significance for cancer treatment. IL20RB expression level is correlated with clinical and pathological parameters such as tumor stage and smoking status, and its expression level is significantly correlated with patient prognosis. In addition, IL20RB can exert an effect in mediating the activity of immune receptors and subsequent signaling responses, indicating that it may become an important target for immunotherapy intervention (42,43).

Quercetin (D011794) is a flavonoid compound widely present in fruits and vegetables, and has attracted much attention due to its various biological activities involving anti-inflammation, anti-oxidation and anti-cancer (44,45). A recent study has shown that quercetin can regulate signaling pathways involved in cancer progression, including PI3K/Akt and MAPK signaling pathways, thereby inhibiting cancer cell proliferation and inducing cell apoptosis (46). The binding of quercetin to the IL20RB may offer new insights into overcoming immune evasion in LCs. Given that IL20RB plays a key role in promoting tumor progression and immune suppression, quercetin may affect the tumor immune microenvironment by regulating IL20RB-related signaling pathways. In summary, this study elucidates the important roles of IL20RB as a prognostic biomarker for LCs. The high expression level of IL20RB in LC tissues is correlated with poorer OS, highlighting the significance of high IL20RB expression level as a clinical prognostic indicator. Through comprehensive analyses including differential expression analysis, survival analysis, and gene functional enrichment analysis, it was found that IL20RB is significantly involved in immune-related signaling pathways and the progression of tumors. IL20RB-targeted therapy may become an effective clinical treatment strategy, which is expected to significantly improve the prognosis and treatment effect of LC patients.

This study identified IL20RB as an important prognostic biomarker in LCs. However, its specific molecular mechanisms in LC progression and immune regulation remain unclear and need to be confirmed by in vivo and in vitro experiments. In addition, this study focused on a limited number of IRGs, potentially overlooking the effects of other key signaling pathways on tumor behaviors. Finally, IL20RB-targeted quercetin shows promising therapeutic potential, but its efficacy and safety in LC patients still need to be further evaluated through clinical trials. Overcoming these limitations will help deepen our understanding of the effect and therapeutic potential of IL20RB in LCs.

Conclusions

This study shows that IL20RB is correlated with tumor immunity and can be used as a potential target for LC immunotherapy. This biomarker may play a role in the prediction and diagnosis of LCs. Future studies will further explore the specific mechanism of action of IL20RB in the functions of LC cells.

Acknowledgments

The authors extend their sincere gratitude to all the participants who took part in this study, especially expressing their deep appreciation to Hanzhen Ji, a medical translator at Nantong Third People’s Hospital, for her professional and meticulous editing and proofreading of the manuscript. Her linguistic expertise and careful attention to detail have greatly improved the quality and clarity of the paper.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-2054/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-2054/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-2054/prf

Funding: This study was supported by Beijing Huikang Ren’ai Public Welfare Foundation (No. HKRA2025010329) and Nantong Municipal Science and Technology Bureau’s Municipal Science and Technology Program (No. MS22021045).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-2054/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 and its subsequent amendments.

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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