REG1B Modulates IL-6 Secretion to Influence Anti-Colorectal Cancer Immune Responses

DOI:https://doi-004.org/6812/17684693066281

REG1B Modulates IL-6 Secretion to Influence Anti-Colorectal Cancer Immune Responses

Liuhui Ge¹, Xiuting Ma¹, Tian’ai Yin², Zhilin Sun³, Hui Liu¹, Bohan Dong¹*

1. Department of Microbiology and Immunology, Wannan Medical College, Wuhu, Anhui 241002, P.R. China
2. School of Medical Imaging, Wannan Medical College, Wuhu, Anhui 241002, P.R. China
3. School of Clinical Medicine, Wannan Medical College, Wuhu 241000, Wuhu, Anhui 241002, P.R. China

* Corresponding author: Bohan Dong, Department of Microbiology and Immunology, Wannan Medical College, 22 Wenchang West Road, Wuhu, Anhui241002,P.R.China.E-mail:20100044@wnmc.edu.cn

Abstract                  

REG1B, as a member of the regenerating protein (REG) family, has diverse biological functions and is expressed in various types of tumor cells. However, the precise role of REG1B in tumorigenesis and tumor progression remains poorly understood. To investigate the expression characteristics of REG1B in colorectal cancer (CRC) and clarify the regulatory role of REG1B inhibition on IL-6 secretion, as well as the impact of this regulation on the anti-tumor immune capacity of immune cells. We integrated bioinformatics analysis of TCGA and GTEx datasets, comprising 455 CRC and 349 normal samples. We discovered that REG1B is significantly overexpressed in CRC tumors and correlates positively with poor overall survival (OS), establishing it as an independent prognostic factor. Notably, REG1B expression exhibited a significant negative correlation with CD8+ T cell infiltration, indicating an active role in immune exclusion. Further mechanistic studies in the CRC cell line HCT116 revealed that siRNA-mediated inhibition of REG1B robustly reduced IL-6 secretion. REG1B knockdown in HCT116 cells led to a marked enhancement of TALL-104 cell-mediated cytotoxic activity against the cancer cells in vitro. This enhanced cytotoxicity was completely reversed by exogenous IL-6 supplementation but was further potentiated by IL-6 neutralization. By the signal transduction molecules detection, we speculate that REG1B regulates IL-6 secretion via the AP-1 signaling pathway. Totally, REG1B is highly expressed in colorectal cancer and associated with poor prognosis of patients. It promotes IL-6 secretion of CRC cells, thereby suppressing CD8⁺ T cell antitumor activity. Inhibiting REG1B reverses this immunosuppression and enhances the anti-colorectal cancer immune response, suggesting REG1B is a potential target for colorectal cancer immunotherapy. Its clinical translational value warrants further investigation.

Keywords: colorectal cancer; REG1B; IL-6; AP-1; immunotherapy

Introduction

Colorectal cancer (CRC) is the third most commonly diagnosed cancer and the second leading cause of cancer-related deaths worldwide ^[1]. Despite significant advances in surgical and chemotherapeutic interventions for early-stage CRC, the limited uptake of colonoscopy screening in China results in a high proportion of patients being diagnosed at advanced stages, thus missing the optimal window for curative surgery. Recent advancements in immunotherapy, targeted therapy, and localized treatments offer new hope for improving survival in certain patients. Nevertheless, only about 15–20% of CRC patients respond to immune checkpoint inhibitors (e.g., anti-PD-1/PD-L1 antibodies) ^[2], underscoring the urgent need to identify novel immunotherapeutic targets.

The regenerating protein (REG) family comprises secretory proteins with diverse biological activities, first identified during investigations into pancreatic regeneration ^[3]. Subsequent studies have established their extensive involvement in cell proliferation, the regulation of apoptosis, tissue repair, and disease progression ^[4,5]. Regenerating Gene Family Member 1 Beta (REG1B), a key member of the REG family, functions primarily within the digestive system ^[6]. Recent reports have highlighted the upregulation of REG1B in intestinal inflammatory diseases and colorectal carcinoma ^[7-9], its role in modulating the CRC immune microenvironment, particularly its potential influence via interleukin-6 (IL-6), remains largely uninvestigated.

To elucidate the impact of REG1B on anti-CRC immunity, this study integrated bioinformatic analyses of TCGA and GTEx datasets with in vitro experiments to investigate how REG1B inhibition affects IL-6 secretion and subsequent immune cell cytotoxicity. By characterizing these anti-CRC immune responses, this work aims to provide experimental evidence supporting the development of novel immunotherapeutic targets for CRC.

1 Materials and Methods

1.1 Acquisition of Raw Data

Transcriptomic data (RNA-seq) and corresponding clinical information for 496 CRC samples (including 455 tumor samples and 41 adjacent normal samples) were downloaded from the TCGA database (https://portal.gdc.cancer.gov/). Transcriptomic data and clinical metadata for 308 normal colorectal tissue samples were obtained from the GTEx database (https://gtexportal.org/).

1.2 Data Preprocessing and Screening of Differentially Expressed Genes (DEGs)

R software (RStudio 4.3.0) was used to integrate the transcriptomic data (in transcripts per million, TPM format) from TCGA and GTEx. Samples were categorized into two groups: TCGA tumor tissues (TP group) and GTEx normal tissues (NT group). A bar chart was generated to visualize the initial sample distribution. The TPM values before and after correction were log₂-transformed (log₂(TPM + 1)) and plotted as density distributions. The ComBat algorithm (implemented in the sva R package) was applied to eliminate batch effects, ensuring comparability between datasets from different sources. Differential expression analysis was performed using the limma R package. DEGs were defined with the threshold: |log₂(fold change)| > 1 and Benjamini-Hochberg-adjusted P-value (padj) < 0.05.

1.3 GO Functional Enrichment and KEGG Pathway Enrichment Analyses

GO functional enrichment and KEGG pathway enrichment analyses were conducted for the identified DEGs using the org.Hs.eg.db annotation database. The Benjamini-Hochberg method was used for multiple-testing correction. Terms with padj < 0.05 and q-value < 0.05 were considered significantly enriched.

1.4 Correlation Analysis, Survival Analysis, and Multivariate Cox Regression Analysis of Core Genes

The differential expression of REG1B and IL6 was validated using the method described in Section 1.1.2. Normality testing was performed on the gene expression profiles. Pearson correlation coefficients were calculated for normally distributed data, while Spearman correlation coefficients were used for non-normally distributed data (via the corrplot R package).

Kaplan-Meier survival curves were constructed using the survfit( ) function from the survival R package, based on “survival time-survival outcome” data and grouping information (high/low expression, defined by the median expression level). Log-rank tests were used to assess differences in survival, with P < 0.05 considered statistically significant.

To evaluate whether REG1B and IL-6 are independent prognostic factors for CRC, clinical covariates (age, gender, TNM stage) were extracted from TCGA clinical data. Univariate and multivariate Cox proportional hazards regression models were constructed to calculate hazard ratios (HR) and 95% confidence intervals (CI). Statistical significance was set at P < 0.05.

1.5 Immune Infiltration Analysis

The CIBERSORT deconvolution algorithm (https://cibersort.stanford.edu/) was used to estimate the relative abundance of 22 innate and adaptive immune cell subsets in CRC tumor and normal tissues. To ensure the reliability of estimates, the ESTIMATE R package was first used to calculate the tumor purity of all samples, which was then used for correction. The number of permutations was set to 1000, and only samples with a CIBERSORT P-value < 0.05 were retained (389 tumor samples and 275 normal samples).

Stacked bar charts were generated using the ggplot2 R package to visualize the relative abundance of immune cells in the two groups. Wilcoxon rank-sum tests were used to compare differences in immune cell infiltration between groups. Additionally, the correlations between REG1B/IL6 expression and immune cell abundance were analyzed, and correlation heatmaps were plotted.

 

1.6 Cell Lines and Main Reagents

The human CRC cell line HCT116 was purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China), and the human T cell leukemia cell line TALL-104 was obtained from Shanghai Yaji Biotechnology Co., Ltd. (Shanghai, China).

Key reagents included: siRNA-mate plus transfection kit (Shanghai GenePharma Co., Ltd., Shanghai, China); rabbit anti-human REG1B antibody (Hangzhou Huaan Biotechnology Co., Ltd., Hangzhou, China); IL-6 neutralizing antibody (Shanghai Aladdin Biochemical Technology Co., Ltd., Shanghai, China); recombinant human IL-6 protein and human IL-6 ELISA kit (Shanghai Yamei Biotechnology Co., Ltd., Shanghai, China); CCK-8 kit (Hefei Baisha Biotechnology Co., Ltd., Hefei, China); RNA Easy Fast Total RNA Extraction Kit (Tiangen Biochemical Technology (Beijing) Co., Ltd., Beijing, China); PrimeScript™ RT Master Mix (reverse transcription reagent) and TB Green® Premix Ex Taq™ II (high-specificity qPCR reagent) (Takara Bio Inc., Beijing, China); RPMI-1640 medium, DMEM high-glucose medium, and fetal bovine serum (FBS) (Nanjing Vicent Biotechnology Co., Ltd., Nanjing, China).

1.7 siRNA Transfection

Four pairs of siRNAs targeting REG1B and one pair of negative control (NC) siRNA were designed and synthesized based on the REG1B gene sequence in GenBank. The sequences were as follows:

siREG1B-91: Sense 5′-ACCAACUCGUUCUUCAUGCTT-3′, Antisense 5′-GCAUGAAGAACGAGUUGGUTT-3′

siREG1B-162: Sense 5′-AGAGCUGCCUAAUCCCCGATT-3′, Antisense 5′-UCGGGGAUUAGGCAGCUCUTT-3′

siREG1B-454: Sense 5′-GACACUGGAUCCCCGAGCATT-3′, Antisense 5′-UGCUCGGGGAUCCAGUGUCTT-3′

siREG1B-530: Sense 5′-AGGAUGAAUCUUGUGAGAATT-3′, Antisense 5′-UUCUCACAAGAUUCAUCCUTT-3′

Negative control (siNC): Sense 5′-UUCUCCGAACGUGUCACGUTT-3′, Antisense 5′-ACGUGACACGUUCGGAGAATT-3′

HCT116 cells were seeded in 24-well plates at a density of 2.5×10⁵ cells per well. Transfection was performed when the cell confluence reached approximately 60%. siRNA was mixed with Buffer, followed by the addition of siRNA-mate plus transfection reagent. The mixture was gently pipetted, added to the cells, and the plate was swirled to ensure uniform distribution. After 12 h of incubation, the medium was replaced, and the cells were cultured for further experiments.

1.8 Western Blot Analysis

Total protein was extracted from cells 48 h after transfection. Protein samples were separated by 12% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to polyvinylidene difluoride (PVDF) membranes. Membranes were blocked with 5% non-fat milk for 1 h at room temperature, then incubated with primary antibody (anti-REG1B, 1:1000 dilution) overnight at 4 °C. After washing, membranes were incubated with secondary antibody (1:5000 dilution) for 1–2 h at room temperature. Protein bands were visualized using enhanced chemiluminescence (ECL) reagents, and quantitative analysis was performed using a gel imaging system.

1.9 ELISA for Cytokine Detection

Cell culture supernatants were collected 48 h after transfection. IL-6 levels in the supernatants were measured using a human IL-6 ELISA kit according to the manufacturer’s instructions. Absorbance was read at 450 nm using a microplate reader, and IL-6 concentrations were calculated based on standard curves.

1.10 Cytotoxicity Assay

HCT116 cells transfected with siNC or siREG1B-454 were co-cultured with TALL-104 cells at ratios of 1:1, 1:5, 1:10, and 1:20 for 24 h. The co-cultured TALL-104 cells were collected and designated as TALL-104-siNC and TALL-104-siREG1B, respectively.

HCT116 cells (target cells) were seeded in 96-well plates at a density of 1×10⁴ cells per well. After cell adhesion, TALL-104-siNC or TALL-104-siREG1B cells (effector cells) were added at effector-to-target (E: T) ratios of 1:1, 5:1, 10:1, and 20:1. After 48 h of co-culture, the CCK-8 assay was used to detect HCT116 cell viability. Absorbance at 450 nm was measured, and the cytotoxicity rate was calculated using the following formula: Cytotoxicity rate (%) = [1 – (OD value of experimental group – OD value of effector cell control group) / OD value of target cell control group] × 100%

1.11 Exogenous IL-6 Supplementation and IL-6 Neutralization Experiments

Exogenous IL-6 supplementation: Recombinant human IL-6 was added to the culture medium of siREG1B-454-transfected HCT116 cells to match the IL-6 concentration in the medium of siNC-transfected cells. In vitro cytotoxicity assays were then performed as described above.

IL-6 neutralization: IL-6 neutralizing antibody (Siltuximab) was added to the culture medium of siREG1B-454-transfected HCT116 cells to a final concentration of 300 pg/mL. In vitro cytotoxicity assays were subsequently conducted.

1.12 Quantitative Real-Time PCR (qPCR)

Total RNA was extracted from siNC- and siREG1B-454-transfected HCT116 cells (48 h post-transfection) using the RNA Easy Fast Total RNA Extraction Kit. Complementary DNA (cDNA) was synthesized via reverse transcription using PrimeScript™ RT Master Mix. qPCR was performed using TB Green® Premix Ex Taq™ II on a real-time PCR system.

The reaction system (20 μL total volume) included: 10 μL TB Green Mix, 1 μL cDNA, 0.5 μL forward primer, 0.5 μL reverse primer, and 8 μL nuclease-free water. The reaction conditions were: 95 °C for 30 s (pre-denaturation), followed by 40 cycles of 95 °C for 10 s (denaturation) and 60 °C for 30 s (annealing/extension). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as the internal reference gene. The relative expression levels of target genes were calculated using the 2^⁻ΔΔCt method.

1.13 Statistical Analysis

All data are presented as the mean ± standard deviation (x ± s) and were derived from three independent biological replicates. Statistical analyses were performed using SPSS 26.0 software (IBM Corp., Armonk, NY, USA). One-way analysis of variance (ANOVA) was used for comparisons among multiple groups. Student’s t-test was applied for comparisons between two groups with homogeneous variances, while the rank-sum test was used for groups with heterogeneous variances. A P-value < 0.05 was considered statistically significant.

2 Results

2.1 Bioinformatics Analysis Results

2.1.1 Data Preprocessing and DEG Screening

A total of 763 samples were included in the subsequent analysis. The sample distribution revealed that 455 samples (56.6%) were CRC tumor tissues (TP group) and 308 samples (43.4%) were normal colorectal tissues (NT group) (Figure 1A). As shown in the TPM density distribution plot, the overlap between the gene expression curves of the TP and NT groups significantly increased following batch effect correction using the ComBat algorithm, indicating the effective elimination of technical variations (Figure 1B).

Principal component analysis (PCA) further demonstrated that tumor and normal samples clustered more distinctly after correction (Figures 1C, D), confirming both the reliability of the data sources and the efficacy of the normalization process. Differential expression analysis of the normalized matrix identified 600 DEGs, comprising 381 upregulated genes (e.g., REG1B, IL6, NOD2) and 219 downregulated genes (e.g., TMMO6, TREX1) in CRC tissues. These expression patterns and their statistical significance were visualized via a heatmap (Figure 1E) and a volcano plot (Figure 1F), respectively.

Figure 1. Sample distribution, batch effect correction, and analysis of differentially expressed genes (DEGs)

(A) Distribution of samples across the study groups. (B) Density plot of log₂(TPM+1) values for the CRC tumor tissue (TP) and normal tissue (NT) following batch effect correction. (C, D) Principal component analysis (PCA) plots illustrating the clustering of merged TCGA-COAD tumor samples (n=455, blue) and GTEx normal samples (n=308, red) before (C) and after (D) batch effect removal. (E) Heatmap depicting the expression profiles of 600 DEGs. Rows represent DEGs and columns represent samples; red and blue indicate up- and downregulated expression, respectively. The color scale on the right denotes standardized expression values. (F) Volcano plot of DEGs. The x-axis represents log₂(fold change, FC) and the y-axis represents -log₁₀(adjusted P-value, padj). Red and blue dots signify significantly upregulated and downregulated DEGs ( and ), respectively, while gray dots represent genes without significant differential expression.

2.1.2 GO and KEGG Enrichment Analyses

To explore the biological functions and signaling pathways associated with DEGs in CRC pathogenesis, GO and KEGG enrichment analyses were performed.

GO functional enrichment analysis revealed that in the Biological Process (BP) category, DEGs were significantly enriched in immune-related processes (e.g., immune response, inflammatory response) and the regulation of cell proliferation and differentiation processes. Within the Cellular Component (CC) category, DEGs were primarily localized to the cell membrane and cytoplasm. Regarding Molecular Function (MF), the DEGs exhibited significant enrichment in cytokine activity and receptor binding (Figure 2A).

KEGG pathway enrichment analysis revealed that DEGs were significantly enriched in several key pathways, including the PPAR signaling pathway and systemic lupus erythematosus (Figure 2B).

Figure 2. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs.

(A) Bubble plot highlighting GO functional enrichment. The x-axis denotes the Gene Count (number of DEGs enriched in each term), and the y-axis represents the names of GO terms. Bubble size is proportional to the Gene Count, while the color gradient indicates the P-value (darker colors signify higher statistical significance). (B) Bubble plot showing KEGG pathway enrichment. The x-axis represents the Gene Ratio (the proportion of DEGs enriched in the pathway relative to the total number of genes in that pathway), and the y-axis represents KEGG pathway names. Bubble size corresponds to the Gene Ratio, and the color intensity reflects the P-value (padj), with darker colors representing smaller padj values.

2.1.3 REG1B and IL6 Are Highly Expressed in CRC, Positively Correlated, and Associated with Poor Prognosis

Combined analysis of REG1B/IL6 expression and clinical characteristics was performed across 763 samples using Wilcoxon rank-sum tests. The results showed that the expression levels of REG1B and IL6 were significantly elevated in CRC tumor tissues compared to normal tissues (P < 0.05; Figure 3A). Spearman correlation analysis revealed a significant positive correlation between REG1B and IL6 expression within CRC tumor samples (Spearman ρ = 0.274, P = 3.3e-09) (Figure 3B).

To evaluate the prognostic significance of these markers, CRC samples were stratified into high- and low-expression groups based on median values. Kaplan-Meier survival analysis indicated that patients with low REG1B expression had significantly longer overall survival (OS) than those in the high-expression group (P = 0.039; Figure 3C). Similarly, patients in the low-IL6 expression group exhibited superior OS compared to the high-expression group (P = 0.022; Figure 3D). Furthermore, multivariate Cox regression analysis was performed to adjust for potential confounding clinical variables, including age, gender, and TNM stage. Following adjustment, high REG1B expression remained an independent risk factor for shortened OS (HR = 1.657, 95% CI: 1.067–2.572, P = 0.024; Figure 3E), reinforcing the prognostic value of REG1B in CRC.

Figure 3. Expression profiles of REG1B and IL6 in CRC and their clinical significance.

(A) Box plots showing the expression levels of REG1B (left) and IL6 (right) in CRC tumor tissues (n=455, blue) compared to normal tissues (n=349, red). The x-axis denotes the sample type, and the y-axis represents the gene expression level in Transcripts Per Million (TPM). Statistical significance was assessed using the Wilcoxon rank-sum test (***P < 0.001). (B) Scatter plot illustrating the Spearman correlation between REG1B and IL6 expression in CRC tumor samples (Spearman ρ = 0.274, P = 3.3e-09). (C) Kaplan-Meier survival curves for CRC patients stratified by REG1B expression. The red curve represents the REG1B high-expression group (n=228), and the blue curve represents the REG1B low-expression group (n=227). Log-rank test: HR = 1.44 (95% CI: 1.02–2.03), P = 0.039. (D) Kaplan-Meier survival curves for CRC patients stratified by IL6 expression. Log-rank test: HR = 1.28 (95% CI: 1.03–1.58), P = 0.022. (E) Forest plot of multivariate Cox regression analysis. The plot shows hazard ratios (HR), 95% confidence intervals (95% CI), and P-values for genes (including REG1B) associated with overall survival (OS) in CRC patients. REG1B high expression was an independent risk factor for shortened OS (HR = 1.657, 95% CI: 1.067–2.572, P = 0.024).

2.1.4 Immune Cell Infiltration and Its Correlation with REG1B

Evaluation of immune cell infiltration in 455 CRC samples using the CIBERSORT algorithm showed that CD4⁺ T cells and macrophages were the most abundant immune cell subsets within the CRC microenvironment, whereas B cells and CD8⁺ T cells were less infiltrated (Figure 4A). Comparative analysis between tumor and normal tissues revealed that the infiltration proportions of M0 macrophages, CD4⁺ T cells, and regulatory T cells (Tregs) were significantly elevated in tumor samples ( P < 0.05 for all; Figure 4B). Furthermore, correlation analysis indicated that while REG1B expression showed no significant association with CD4⁺ T cell infiltration, it was significantly negatively correlated with the infiltration of CD8⁺ T cells (Figure 4C). This suggests that high REG1B expression may be linked to a suppressed cytotoxic T-cell presence in the CRC tumor microenvironment.

Figure 4. Immune cell infiltration in CRC and normal colorectal samples, and its correlation with REG1B

(A) Stacked bar chart showing the relative abundance of immune cell subsets in CRC samples. The color legend on the right corresponds to 22 innate and adaptive immune cell subsets. (B) Violin plots comparing immune cell infiltration between tumor and normal tissues. Statistical significance was determined by the Wilcoxon rank-sum test (***P < 0.001, **P < 0.01, ns = not significant). (C) Scatter plots of correlation analysis between REG1B expression and infiltration levels of CD4⁺ T cells (left) and CD8⁺ T cells (right) in CRC samples, adjusted for tumor purity. Correlation coefficients (Rho) and P-values are indicated: REG1B vs. CD4⁺ T cells (Rho = -0.078, P = 1.16e-01); REG1B vs. CD8⁺ T cells (Rho = -0.122, P = 4.52e-02).

2.1.5 Inhibition of REG1B Downregulates IL-6 Secretion and the Expression of IL-6 Pathway Genes

To investigate the regulatory role of REG1B in IL-6 secretion, siRNA-mediated knockdown of REG1B was performed in HCT116 cells, with protein levels assessed via Western blot was used to verify the transfection efficiency. All four pairs of REG1B-targeting siRNAs effectively reduced REG1B protein expression compared to the negative control (siNC) group; among these, siREG1B-454 exhibited the most potent inhibitory effect (Figures 5A, B). Subsequently, ELISA results showed that IL-6 secretion in the culture supernatants of all siRNA-transfected groups was significantly lower than that in the parental HCT116 and siNC groups, with the siREG1B-454 group showing the most pronounced reduction (Figure 5C, P < 0.01). To explore the potential mechanism underlying this regulation, qPCR was utilized to quantify the expression of key genes in IL-6-related signaling pathways (e.g., NF-κB). The results showed that compared to the siNC group, the siREG1B-454 group had significantly downregulated mRNA levels of TNF-α, JUN, and SOCS3 (Figure 5D, P < 0.05).

2.1.6 Downregulation of REG1B Attenuates IL-6-Mediated Immunosuppression

Based on the aforementioned findings, HCT116 cells transfected with siREG1B-454 or siNC were selected for co-culture with TALL-104 cells. The TALL-104 effector (E) cells were incubated with HCT116 target (T) cells at E:T ratios of 1:1, 5:1, 10:1, and 20:1 to evaluate cytotoxic activity. The results showed that TALL-104-mediated cytotoxicity increased in an E:T ratio-dependent manner in both groups. Notably, at E:T ratios of 10:1 and 20:1, the cytotoxicity against siREG1B-454-transfected HCT116 cells was significantly higher than that against the siNC-transfected group (Figure 5E, P < 0.01).

To confirm the functional role of IL-6 in immune cytotoxicity, IL-6 functional rescue experiments were performed. Exogenous supplementation of recombinant human IL-6 in the culture medium of siREG1B-454-transfected HCT116 cells significantly reduced the cytotoxicity of TALL-104 cells. Conversely, adding IL-6 neutralizing antibody (Siltuximab) to the culture medium of siNC-transfected HCT116 cells significantly enhanced the cytotoxicity of TALL-104 cells (Figure 5E).

Figure 5. Effects of REG1B knockdown on IL-6 secretion and the cytotoxic activity of co-cultured TALL-104 cells.

(A) Western blot analysis of REG1B protein levels in HCT116 cells transfected with negative control siRNA (siNC) or four candidate REG1B-targeting siRNAs. GAPDH was used as a loading control. (B) Densitometric quantification of REG1B protein levels normalized to GAPDH (n = 3, mean ± SD; P < 0.05, P < 0.01 vs. siNC). (C) Enzyme-linked immunosorbent assay (ELISA) results showing IL-6 concentrations in the culture supernatants of HCT116 cells in different groups (***P < 0.001 vs. siNC group). (D) Relative mRNA expression of IL-6 signaling-related genes (IRF1, RELA, JUN, FOS, SOCS3, NFKB1, and TNF-α) determined by qPCR. Results were normalized to GAPDH (*P < 0.05, **P < 0.01 vs. siNC group). (E) Cytotoxicity of TALL-104 effector cells against HCT116 target cells at varying E:T ratios. Experimental conditions included baseline TALL-104 activity, siNC control, REG1B knockdown (siREG1B-454), IL-6 neutralization (Siltuximab), and IL-6 supplementation (Recombinant IL-6). (*p<0.05; **p<0.01; ***p<0.001).

3 Discussion

In 2024, China recorded 333,800 new CRC cases and 158,800 CRC-related deaths, ranking second in incidence and fourth in mortality among all malignancies, with an annual incidence growth rate of 2% ^[10]. The treatment of CRC, especially for advanced patients and those unresponsive to immunotherapy, remains a major challenge. Previous studies have confirmed upregulated REG1B expression in intestinal inflammatory diseases and colorectal carcinoma ^[7-9]. REG1B is frequently aberrantly expressed in gastrointestinal tumors, where it promotes cell proliferation, inhibits apoptosis, and modulates the tumor microenvironment ^[11,12]. However, the role of REG1B in anti-CRC immunity remains elusive. Therefore, investigating the expression profile of REG1B and its impact on anti-CRC immune responses is of paramount significance.

To clarify the role of REG1B in CRC, this study first integrated TCGA and GTEx datasets for comprehensive bioinformatics analysis. Our findings confirmed that REG1B is highly expressed in CRC tissues, aligning with established reports of its aberrant expression of REG1B in various gastrointestinal tumors ^[7-9]. Additionally, high REG1B expression was significantly associated with shortened OS in CRC patients, and multivariate Cox regression analysis further supported REG1B as an independent prognostic biomarker for CRC. Further analysis revealed that IL6 is also highly expressed in CRC tissues and positively correlated with REG1B expression.

As a downstream molecule of the oncogenic Ras signaling pathway, IL-6 plays a critical role in tumor initiation and progression ^{[13- 17]}. It impairs anti-tumor immune responses by recruiting and activating immunosuppressive cells and interfering with antigen presentation, thereby facilitating tumor immune escape ^{[18 – 20]}. Hailemichael et al. used clinical samples and animal models to demonstrate that blocking IL-6 can enhance tumor immune cytotoxicity and reduce the cytotoxic side effects of treatment ^[21]. Notably, the IL-6 trans-signaling pathway is also involved in the pathogenesis of other inflammatory-related diseases. For example, in sepsis-induced acute kidney injury, inhibition of PRMT1 alleviates tissue damage by blocking IL-6 trans-signaling pathway ^[22], which further confirms the conserved regulatory role of IL-6-related pathways in disease progression and supports the potential value of targeting IL-6 signaling for therapeutic intervention ^[22]. Furthermore, previous research in esophageal cancer reported that REG1B overexpression significantly upregulates IL-6 ^[23], suggesting that the “REG1B-IL-6 regulatory axis” may be conserved across gastrointestinal tumors and providing indirect evidence for REG1B-mediated modulation of the tumor immune microenvironment via IL-6.

GO and KEGG enrichment analyses further revealed that REG1B and its associated DEGs are significantly enriched in immune-related functions (e.g., positive regulation of cytokine secretion, humoral immune response) and tumor-related pathways (e.g., PPAR signaling pathway). Immune cell infiltration analysis showed high CD4⁺ T cell infiltration and low CD8⁺ T cell infiltration in CRC tissues. Correlation analysis demonstrated a negative correlation between REG1B expression and CD8⁺ T cell infiltration, suggesting that REG1B may inhibit antitumor immune responses by reducing CD8⁺ T cell infiltration. Therefore, inhibiting REG1B to reduce abnormally elevated IL-6 levels in CRC cells and the tumor microenvironment could alleviate the immunosuppressive effect on CD8⁺ T cells and enhance anti-CRC immune responses, providing a novel immunotherapeutic strategy for CRC patients.

To test this hypothesis, we utilized RNA interference to successfully knock down REG1B expression in HCT116 cells. Western blot and ELISA results confirmed that REG1B inhibition significantly diminished IL-6 secretion. Furthermore, co-culturing REG1B-silenced HCT116 cells with TALL-104 cells enhanced the cytotoxicity of TALL-104 cells against HCT116 cells. Exogenous IL-6 supplementation reduced the cytotoxicity of TALL-104 cells, while IL-6 neutralization further enhanced cytotoxicity. These results confirm that REG1B modulates the antitumor activity of CD8⁺ T cells by regulating IL-6 secretion, with IL-6 playing a critical mediating role.

To further investigate the molecular mechanisms by which REG1B regulates IL-6, we assessed the expression of key components within classical IL-6-related signaling pathways. qPCR analysis revealed that REG1B inhibition significantly downregulated the transcriptional levels of TNF-α and JUN, while the expression of other core pathway genes, such as NF-κB1 and STAT3, remained unchanged.

Some studies have established that c-Jun, encoded by the JUN gene, forms the AP-1 transcription factor complex to drive IL6 transcription by binding directly to its promoter ^{[24, 25]}. TNF-α can further enhance c-Jun activity via the JNK signaling cascade^[26]. Our results suggest that REG1B may act through c-Jun to promote the formation of AP-1 complex binding the IL6 promoter, ultimately leading to high IL-6 expression, which inhibits the antitumor activity of CD8⁺ T cells. TNF-αmay strengthen this process. However, as these findings are primarily based on transcriptional analysis, the precise biochemical interactions and phosphorylation events involved remain to be further elucidated in future studies.

In conclusion, this study demonstrates that REG1B is highly expressed in CRC and is associated with poor patient prognosis. Silencing REG1B attenuates IL-6-mediated immunosuppression, thereby revitalizing the anti-tumor immune response of CD8⁺ T cells. These findings provide a compelling theoretical foundation for the development of REG1B as a novel therapeutic target in CRC immunotherapy.

 

Acknowledgements

Not applicable.

Funding 

This work was supported by Natural Science Research Project of Anhui Provincial Department of Education (Major Projects) (2023AH040263); Natural Science Research Project of Anhui Provincial Department of Education (KJ2019A0415), Anhui Province University Outstanding Young Talent Support Program (Key Program) (gxyqZD2019040); Project of Outstanding Talents of Wannan Medical College (wyqnyx202001).

Author contributions

Liu-hui Ge and Bo-han Dong contributed to the study concept and design. Liu-hui Ge, Xiu-ting Ma, Tian-ai Yin, and Zhi-lin Sun contributed to data acquisition and statistical analysis. Liu-hui Ge drafted the manuscript; Hui Liu and Bo-han Dong provided conceptual advice; All authors commented on versions of the manuscript and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Data availability statement

The datasets generated and analyzed during this study are available in the TCGA database (https://portal.gdc.cancer.gov) and the GTEx database (https://gtexportal.org).

 

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

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