Abstract
Background
Inhibin subunit beta B (INHBB), a member of the TGF-β superfamily, has emerged as a potential regulator of tumor progression and immune modulation. However, its clinical and immunological significance in colorectal cancer (CRC) remains unclear.
Methods
This study evaluated INHBB mRNA expression in 248 CRC cases using RNA in situ hybridization (RNAscope), immunohistochemistry (IHC), and single-cell RNA sequencing (scRNA-seq). Associations with clinicopathological parameters, tumor-infiltrating immune cells, and prognosis were analyzed.
Results
INHBB was primarily expressed in tumor epithelial cells and weakly in stromal cells, but was negligible in normal mucosa. High INHBB expression correlated significantly with venous invasion, lymph node metastasis, advanced TNM stage, and reduced tumor-infiltrating lymphocytes. IHC revealed marked decreases in CD4⁺, CD8⁺, and FOXP3⁺ T cells and increased CD163⁺ tumor-associated macrophages in INHBB high tumors, indicating an immunosuppressive tumor microenvironment (TME). Kaplan–Meier analysis showed that high INHBB expression was associated with worse overall survival and recurrence-free survival, but was not an independent prognostic factor in multivariate models.
Conclusion
INHBB is predominantly expressed in tumor epithelium and is associated with aggressive features, poor prognosis, and the formation of an immune-cold, macrophage-rich TME in CRC. These findings suggest that INHBB may be a potential biomarker for immune evasion and a target for novel immunotherapeutic strategies in CRC.
Keywords: INHBB, Colorectal carcinoma, Tumor microenvironment
Introduction
Colorectal carcinoma (CRC) is one of the most common gastrointestinal malignancies, with more than 1.9 million new cases diagnosed annually worldwide. It ranks as the second leading cause of cancer-related mortality worldwide after lung cancer, and remains the second leading cause of cancer death in the United States [1]. Although CRC is potentially curable with early detection and appropriate treatment, advanced-stage cases are often associated with high rates of recurrence and metastasis, resulting in a poor prognosis. Specifically, the 5-year survival rate for early-stage disease (stage I) exceeds 90%, with recurrence rates kept below 10%. In contrast, advanced cases—particularly those with metastasis or stage III or higher—exhibit recurrence rates of 20–30%, and the 5-year survival is significantly reduced to approximately 15–20% [2–4]. Therefore, in addition to conventional staging systems such as TNM classification and serum markers like CEA, the identification of more accurate prognostic indicators and the establishment of personalized therapeutic strategies have become urgent clinical needs.
One biological process gaining attention in relation to cancer invasion and metastasis is anoikis, a form of programmed cell death induced by detachment from the extracellular matrix. The acquisition of resistance to anoikis by tumor cells is closely associated with increased malignancy and metastatic potential. Analyses targeting anoikis-related genes (ARGs) are thus expected to contribute to the discovery of novel prognostic biomarkers and therapeutic targets [5]. Among these, inhibin subunit beta B (INHBB), a member of the TGF-β superfamily, is a component of activin B and inhibin B, originally described in the context of endocrine regulation. More recently, INHBB has been implicated in a variety of tumor-associated processes, including cell proliferation, differentiation, apoptosis, fibrosis, and immune response modulation [6]. Elevated INHBB expression has been reported to correlate with malignant features and poor prognosis in various cancers, including hepatocellular carcinoma, clear cell renal cell carcinoma, and head and neck cancer [6–10]. Conversely, in nasopharyngeal carcinoma, INHBB may have tumor-suppressive effects related to promoting apoptosis and suppressing metastasis [11], suggesting that its role may be context-dependent across tumor types.
In CRC, several bioinformatics analyses using public datasets such as TCGA and GEO have suggested that high INHBB expression is associated with poor overall survival (OS), increased invasiveness and metastatic potential, and the development of an immunosuppressive TME. For example, Wu et al. identified INHBB as an anoikis-related gene in colon adenocarcinoma (COAD) and reported that its high expression correlated with worse prognosis, the activation of cancer-associated fibroblasts (CAFs), and enrichment of immune-related pathways [12]. Similarly, Jia et al. and Xin et al. demonstrated that multigene prognostic models including INHBB predicted high recurrence risk and the presence of immunosuppressive features in CRC patients [13, 14]. Furthermore, elevated INHBB expression was associated with the increased infiltration of regulatory T cells (Tregs) and Th17 cells, as well as co-expression with immune checkpoint molecules such as PD-1 and CTLA-4, suggesting its potential involvement in shaping the tumor immune microenvironment [6, 15]. Although these findings suggest a role for INHBB in modulating the immune cell composition, most of the current evidence is derived from bulk RNA-seq data or immunohistochemistry (IHC), which lack spatial resolution within tumor tissues.
RNA in situ hybridization (RNA-ISH) enables the sensitive and spatially resolved detection of mRNA expression in formalin-fixed tissue sections. Recent advances in RNAscope technology allow for the quantitative assessment of gene expression at the single-cell level within a histological context [16]. In this study, we evaluated the expression of INHBB mRNA in CRC using RNA-ISH, aiming to elucidate its clinical and pathological relevance from a spatial and histological perspective.
Materials and Methods
Patients
This study included 305 cases of CRC treated surgically at Shinshu University Hospital between 2014 and 2022. All patients were monitored for a minimum follow-up period of 2 years. Tumor differentiation was assessed, with well-differentiated, moderately-differentiated, and poorly-differentiated adenocarcinomas included in the analysis. Based on previously published criteria [17], well-differentiated and moderately-differentiated adenocarcinomas were classified as low-grade, whereas poorly-differentiated adenocarcinomas were categorized as high-grade. Among these patients, 57 cases were excluded for the following reasons: 40 cases were negative for the positive control (housekeeping gene) in the TMA, and 17 cases had no tumor tissue at the primary site within a TMA. Ultimately, 248 cases of CRC were enrolled.
Clinical and pathological data, including patient age, sex, tumor differentiation, prognosis, lymph node involvement, vascular invasion, tumor-infiltrating lymphocytes (TILs), and TNM classification, were extracted from medical records. Tumor staging and differentiation were defined following the eighth edition of the Union for International Cancer Control (UICC) classification [18] and the fifth edition of the World Health Organization (WHO) classification [19]. Histological evaluation of all specimens was independently performed by two pathologists (T.U. and M.I.). TILs in tumor-infiltrating regions were scored using a four-tier system: 0 (none), 1 (mild), 2 (moderate), and 3 (marked) [20]. For subsequent analyses, TIL scores were categorized as low (scores 0 and 1) or high (scores 2 and 3).
OS was defined as the duration between the date of surgical resection and death or last follow-up. Recurrence-free survival (RFS) was defined as the time from surgical resection to disease recurrence or the last follow-up without recurrence. This study adhered to the ethical principles outlined in the Declaration of Helsinki and received approval from the Clinical Trial Review Committee of Shinshu University School of Medicine (approval number: 5836).
Histopathology and Tissue Microarray (TMA) Construction
All specimens were fixed in 10 or 20% neutral-buffered formalin and embedded in paraffin. For the construction of a TMA, blocks containing sufficient tumor tissue from the invasive frontline were selected from formalin-fixed paraffin-embedded tissue archives. Tissue cores (3-mm diameter) were punched out from each block using thin-walled stainless steel needles (Azumaya Medical Instruments Inc., Tokyo, Japan) and arrayed into a recipient paraffin block. Serial 4-µm-thick sections were cut from the TMA blocks, and one section was stained with hematoxylin and eosin for histological assessment.
Immunohistochemistry and Evaluation
IHC staining for CD4, CD8, FOXP3, and CD163 was performed on serial TMA sections to evaluate immune cell subsets. The staining was carried out using a fully automated staining system (BOND-III; Leica Biosystems, Newcastle, UK) with the following primary antibodies: CD4 (clone 4B12, ready-to-use; Leica Biosystems), CD8 (clone C8/144B, ready-to-use; Leica Biosystems), FOXP3 (clone 236A/E7, 1:100 dilution; Abcam, UK), and CD163 (clone 10D6, ready-to-use; Leica Biosystems).
The evaluation methods differed by marker. For CD4⁺, CD8⁺, and FOXP3⁺ T cells, three areas with the highest cell density were selected from each core, and cell counts per high-power field (HPF; 10 × ocular, 40 × objective) were performed. An average of the three fields was used for analysis, and the median of these counts served as the cutoff to classify cases into low and high infiltration groups. For CD163, expression was evaluated semiquantitatively using the immunoreactivity score (IRS), which was calculated by multiplying the staining intensity (SI; 0–3 scale) by the percentage of positive cells (PP; 0–4 scale), as previously described [21]. Cases were then classified into high and low CD163 expression groups based on the median IRS value.
All histological features and staining results were independently evaluated by two experienced pathologists (T.U. and M.I.).
INHBB RNA In Situ Hybridization
The detection of INHBB mRNA was performed using the RNAscope® LS 2.5 Probe – Hs-INHBB (cat. no. 435748; Advanced Cell Diagnostics, Hayward, CA, USA) according to the manufacturer’s instructions, using unstained sample tissue slides. Briefly, tissue sections were pretreated with heat and protease prior to hybridization as previously described [22]. Brown punctate dots observed in the nucleus or cytoplasm were considered positive signals. Standard Mm-PPIB (ACD-313902) was used as a positive control to ensure interpretable results.
INHBB expression was quantified under a 40 × objective lens (Olympus BX53 microscope) according to the five-grade scoring system recommended by the manufacturer: no staining (0), 1–3 dots/cell (1 +), 4–9 dots/cell (2 +), 10–15 dots/cell and/or < 10% dots in clusters (3 +), and > 15 dots/cell and/or > 10% dots in clusters (4 +). For further analysis, samples were classified into low INHBB expression (grades 0, 1 + , and 2 +) and high INHBB expression (grades 3 + and 4 +). We analyzed the association between INHBB expression and clinicopathological parameters and prognosis in patients with CRC.
Single-Cell RNA Sequencing Analysis of INHBB Expression in CRC
The single-cell RNA sequencing (scRNA-seq) analysis of INHBB expression was conducted using a publicly available dataset (Accession Number: GSE132465) obtained from the NCBI Gene Expression Omnibus (GEO) database. This dataset comprised 23 tumor samples and 10 normal samples derived from CRC tissues. Data processing and analysis were conducted using the Seurat R package (v4.1.0). Raw count matrices were normalized to a total expression of 10,000 molecules per cell and subsequently scaled. Highly variable genes across cells were identified and utilized for principal component analysis (PCA) to reduce dimensionality. To assess cellular similarities and perform clustering, the FindNeighbors and FindClusters functions were used. The visualization of cellular heterogeneity was achieved by applying uniform manifold approximation and projection (UMAP) using the RunUMAP function. The expression distribution of INHBB marker genes was assessed across the clusters, allowing for cell type annotation.
Statistical Analysis
Categorical variables were expressed as frequencies, and differences between subgroups were assessed using Fisher’s exact test. The Mann–Whitney U-test was used to compare immune cell infiltration levels (CD4⁺, CD8⁺, CD163⁺, and FOXP3⁺ cells) between the INHBB high-expression and low-expression groups. To visualize the distribution of immune cell counts, violin plots were generated using the ggplot2 package. The OS was analyzed in the entire cohort of 248 patients (stage 0–IV) and the RFS analysis was limited to 202 patients with non-metastatic (stage 0–III) disease. The OS and RFS were estimated by the Kaplan–Meier method, and between-group comparisons were performed using the log-rank test. Prognostic factors were analyzed through univariate and multivariate Cox proportional hazards regression models. Statistical significance was defined as p < 0.05. All statistical analyses were performed using EZR (Easy R), version 1.66, a graphical interface for R developed by the R, Foundation for Statistical Computing (Vienna, Austria).
Results
Association Between INHBB Expression and the Clinicopathological Features of CRC
INHBB-positive signals appeared as brown punctate dots in tumor epithelial cells and the tumor stroma (Fig. 1a–d). Representative histological images of INHBB-negative cases are shown in Fig. 1e–h. Notably, INHBB expression was not observed in histologically normal colorectal mucosa. Among the 248 CRC cases included in the tissue microarray (TMA), 210 had detectable INHBB expression. Based on the scoring criteria, 88 cases were classified as high INHBB expression (≥ 3 + score).
Fig. 1.
RNA in situ hybridization (RNA-ISH) and H&E staining for INHBB expression in colorectal carcinoma. Representative hematoxylin and eosin (H&E) staining images in cases with high (a, b) and low (e, f) INHBB expression. Corresponding RNA-ISH images in high-expression (c, d) and low-expression (g, h) cases are also shown. Low-power (a, c, e, g) and high-power (b, d, f, h) images are displayed. In high-expression cases, brown punctate INHBB mRNA signals were observed in tumor epithelial and stromal areas (d). In contrast, such signals were barely detectable in low-expression cases (h). Black scale bars = 50 μm; white scale bars = 500 μm
The relationship between INHBB expression and clinicopathological parameters is summarized in Table 1. There were no significant differences in age, sex, or tumor differentiation between the high and low expression groups. However, high INHBB expression was significantly associated with venous invasion (p = 0.005), tumor-infiltrating lymphocyte (TIL) score (p = 0.003), CD4⁺ T cells (p = 0.002), CD8⁺ T cells (p = 0.032), and CD163⁺ cells (p = 0.038). The infiltration levels of CD4⁺, CD8⁺, and FOXP3⁺ T cells tended to be lower in the high INHBB expression group, indicating a negative association. In contrast, CD163⁺ cell levels were significantly higher in tumors with high INHBB expression, suggesting a positive association. Although FOXP3⁺ T cells showed a decreasing trend in the high-expression group, the difference did not reach statistical significance (p = 0.063). Additionally, high INHBB expression was strongly correlated with lymph node metastasis (p < 0.001) and advanced pathological stage (Stage III–IV, p < 0.001).
Table 1.
INHBB expression and clinicopathological characteristics of patients with colorectal carcinoma
| Factors | n = 248 | INHBB expression | ||
|---|---|---|---|---|
| Low (n = 160) | High (n = 88) | p value | ||
| Age | 0.69 | |||
| < 70 years | 112 | 74 | 38 | |
| ≥ 70 years | 136 | 86 | 50 | |
| Sex | 0.501 | |||
| Male | 146 | 97 | 49 | |
| Female | 102 | 63 | 39 | |
| Histological grade | 0.186 | |||
| Low | 213 | 141 | 72 | |
| High | 35 | 19 | 16 | |
| Lymphatic invasion | 0.085 | |||
| Present | 122 | 72 | 50 | |
| Absent | 126 | 88 | 38 | |
| Venous invasion | 0.005 | |||
| Present | 179 | 106 | 73 | |
| Absent | 69 | 54 | 15 | |
| TIL | 0.003 | |||
| High | 122 | 90 | 32 | |
| Low | 126 | 70 | 56 | |
| CD4 | 0.002 | |||
| High | 115 | 87 | 28 | |
| Low | 128 | 72 | 56 | |
| CD8 | 0.032 | |||
| High | 125 | 89 | 36 | |
| Low | 120 | 69 | 51 | |
| FOXP3 | 0.063 | |||
| High | 124 | 87 | 37 | |
| Low | 121 | 71 | 50 | |
| CD163 | 0.038 | |||
| High | 101 | 59 | 42 | |
| Low | 138 | 99 | 39 | |
| LN metastasis | p < 0.001 | |||
| Present | 115 | 51 | 64 | |
| Absent | 133 | 109 | 24 | |
| TNM stage | p < 0.001 | |||
| 0–II | 127 | 107 | 20 | |
| III–IV | 121 | 53 | 68 | |
LN lymph node
Bold indicate statistical significance at p 0.05
Correlation Between INHBB Expression and Tumor-Infiltrating Immune Cells
To explore the immunological features associated with INHBB expression further, tumors were stratified into low and high expression groups, and the infiltration of CD4⁺, CD8⁺, FOXP3⁺ T cells, and CD163⁺ cells was assessed by immunohistochemistry. Violin plots of immune cell counts in each group are presented in Fig. 2.
Fig. 2.
Association between INHBB expression and tumor-infiltrating immune cells. Violin plots showing the distribution of CD4⁺, CD8⁺, and FOXP3⁺ T cells, and CD163⁺ cells according to INHBB expression levels. A Mann–Whitney U-test indicated the significantly lower infiltration of CD4⁺ (p = 0.005), CD8⁺ (p < 0.001), and FOXP3⁺ (p = 0.028) T cells in the high INHBB expression group. In contrast, CD163⁺ cells were significantly increased in the high-expression group (p = 0.008)
Mann–Whitney U-test analysis revealed that the numbers of CD4⁺ (p = 0.005), CD8⁺ (p < 0.001), and FOXP3⁺ (p = 0.028) T cells were significantly lower in the high INHBB expression group compared with the low expression group. Conversely, CD163⁺ cells were significantly increased in tumors with high INHBB expression (p = 0.008). Representative immunostaining patterns in tumors with low and high INHBB expression are shown in Figs. 3 and 4, respectively.
Fig. 3.
Immunohistochemical staining of CD4, CD8, FOXP3, and CD163 in a case with high INHBB expression. Representative H&E staining near TILs in a high INHBB expression case (a, b). Immunostaining for CD4⁺ (c, d), CD8⁺ (e, f), and FOXP3⁺ (g, h) T cells showed only sparse infiltration at low and high magnification. In contrast, CD163⁺ cells (i, j) were abundantly present on the TMA. Black scale bars = 50 μm; white scale bars = 500 μm
Fig. 4.
Immunohistochemical staining of CD4, CD8, FOXP3, and CD163 in a case with low INHBB expression. Representative H&E staining near TILs in a low INHBB expression case (a, b). CD4⁺ (c, d), CD8⁺ (e, f), and FOXP3⁺ (g, h) T cells were strongly stained positive. In contrast, CD163⁺ cells (i, j) were weakly expressed on the TMA. Black scale bars = 50 μm; white scale bars = 500 μm
Prognostic Impact of INHBB Expression on the Overall Survival
Kaplan–Meier analysis demonstrated that high INHBB expression was significantly associated with shorter OS compared with low INHBB expression (log-rank p = 0.004; Fig. 5). Univariate Cox proportional hazards analysis identified tumor differentiation (HR 2.25, 95% CI 1.02–4.98, p = 0.045), TIL score (HR 0.24, 95% CI 0.11–0.52, p < 0.001), FOXP3⁺ T cells (HR 0.25, 95% CI 0.12–0.51, p < 0.001), lymph node metastasis (HR 3.27, 95% CI 1.64–6.53, p < 0.001), advanced pathological stage (HR 3.85, 95% CI 1.84–8.08, p < 0.001), and high INHBB expression (HR 2.42, 95% CI 1.31–4.48, p = 0.005) as significant prognostic factors for OS (Table 2).
Fig. 5.
Kaplan–Meier survival analysis stratified by INHBB expression. Patients were stratified into two groups based on INHBB expression levels, and OS and RFS were evaluated by Kaplan–Meier analysis. The left panel shows OS (n = 248, log-rank p = 0.004) and the right panel shows RFS (n = 202, log-rank p = 0.024). High INHBB expression was significantly associated with poorer prognosis in both analyses
Table 2.
Univariate and multivariate analyses of overall survival factors in CRC
| Factors | Univariate analysis | Multivariate analysis | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| HR | 95%CI | p-value | HR | 95%CI | p-value | |||||
| Age ≥ 70 years vs. < 70 years | 0.90 | 0.49 | – | 1.66 | 0.74 | 0.95 | 0.5 | – | 1.8 | 0.882 |
| Sex: male vs. female | 1.16 | 0.63 | – | 2.15 | 0.634 | 1.14 | 0.61 | – | 2.1 | 0.681 |
| Histological grade: low vs. high | 2.25 | 1.02 | – | 4.98 | 0.045 | |||||
| Lymphatic invasion: absent vs. present | 3.52 | 1.72 | – | 7.17 | p < 0.001 | 2.27 | 1.03 | – | 4.97 | 0.041 |
| Venous invasion: absent vs. present | 1.96 | 0.87 | – | 4.41 | 0.106 | |||||
| TIL low vs. high | 0.24 | 0.11 | – | 0.52 | p < 0.001 | |||||
| CD4 low vs. high | 0.71 | 0.38 | – | 1.32 | 0.277 | |||||
| CD8 low vs. high | 0.62 | 0.33 | – | 1.16 | 0.137 | |||||
| FOXP3 low vs. high | 0.25 | 0.12 | – | 0.51 | p < 0.001 | |||||
| CD163 low vs. high | 0.80 | 0.42 | – | 1.56 | 0.517 | |||||
| LN metastasis: absent vs. present | 3.27 | 1.64 | – | 6.53 | p < 0.001 | |||||
| TNM stage: 0-II vs. III–IV | 3.85 | 1.84 | – | 8.08 | p < 0.001 | 2.63 | 1.17 | – | 5.95 | 0.02 |
| INHBB expression: low vs. high | 2.42 | 1.31 | – | 4.48 | 0.005 | 1.64 | 0.85 | – | 3.18 | 0.142 |
LN lymph node
Bold indicate statistical significance at p 0.05
Multivariate analysis demonstrated that lymphatic invasion (HR 2.27, 95% CI 1.03–4.97, p = 0.041) and advanced stage (HR 2.63, 95% CI 1.17–5.95, p = 0.02) remained independent prognostic factors, whereas INHBB expression did not retain statistical significance as an independent predictor (HR 1.64, 95% CI 0.85–3.18, p = 0.142).
Prognostic Impact of INHBB Expression on Recurrence-Free Survival
Similarly, Kaplan–Meier analysis revealed that patients with high INHBB expression had significantly worse RFS compared with those with low INHBB expression (log-rank p = 0.024; Fig. 5).
In univariate analysis, lymphatic invasion (HR 3.05, 95% CI 1.32–7.07, p = 0.009), advanced TNM stage (HR 4.65, 95% CI 1.94–11.14, p < 0.001), and high INHBB expression (HR 2.40, 95% CI 1.10–5.28, p = 0.029) were significant prognostic factors for RFS (Table 3). In multivariate analysis, only TNM stage remained an independent prognostic factor for RFS (HR 4.65, 95% CI 1.94–11.14, p < 0.001), and high INHBB expression did not reach statistical significance (HR 1.43, 95% CI 0.62–3.32, p = 0.405).
Table 3.
Univariate and multivariate analyses of recurrence-free survival factors in CRC
| Factors | Univariate analysis | Multivariate analysis | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| HR | 95%CI | P-value | HR | 95%CI | P-value | |||||
| Age ≥ 70 years vs. < 70 years | 1.33 | 0.59 | – | 3.01 | 0.493 | |||||
| Sex: male vs. female | 1.17 | 0.53 | – | 2.57 | 0.700 | |||||
| Histological grade: low vs. high | 1.46 | 0.50 | – | 4.28 | 0.488 | |||||
| Lymphatic invasion: absent vs. present | 3.05 | 1.32 | – | 7.07 | 0.009 | 1.71 | 0.68 | – | 4.34 | 0.256 |
| Venous invasion: absent vs. present | 1.63 | 0.65 | – | 4.09 | 0.294 | |||||
| TIL low vs. high | 0.85 | 0.39 | – | 1.86 | 0.681 | |||||
| CD4 low vs. high | 1.28 | 0.58 | – | 2.82 | 0.538 | |||||
| CD8 low vs. high | 1.06 | 0.48 | – | 2.32 | 0.895 | |||||
| FOXP3 low vs. high | 1.11 | 0.50 | – | 2.47 | 0.798 | |||||
| CD163 low vs. high | 1.24 | 0.57 | – | 2.73 | 0.586 | |||||
| TNM stage: 0-II vs. III | 4.65 | 1.94 | – | 11.14 | p < 0.001 | 4.65 | 1.94 | – | 11.14 | p < 0.001 |
| INHBB expression: low vs. high | 2.40 | 1.10 | – | 5.28 | 0.029 | 1.43 | 0.62 | – | 3.32 | 0.405 |
LN lymph node
Bold indicate statistical significance at p 0.05
Spatial and Single-Cell Expression Analysis of INHBB Using scRNA-seq
To investigate the cellular origin of INHBB expression, we performed scRNA-seq analysis using publicly available CRC datasets. UMAP dimensionality reduction was used to visualize cellular clusters in tumor and normal tissues (Fig. 6). In tumor tissues, distinct clusters of epithelial cells, T cells, B cells, mast cells, myeloid cells, and stromal cells were identified (Fig. 6a). Similar clusters were also observed in normal tissues (Fig. 6b).
Fig. 6.
Spatial distribution of INHBB expression based on single-cell RNA sequencing (scRNA-seq). a, b UMAP plots visualizing cell clusters in tumor (a) and normal (b) tissues. Identified clusters included epithelial cells, T cells, B cells, mast cells, myeloid cells, and stromal cells. c, d Distribution of INHBB expression. In tumor tissues (c), INHBB was primarily expressed in epithelial cells, with weak expression also seen in some stromal cell clusters. In normal tissues (d), INHBB expression was negligible in epithelial cells and was detected sporadically in stromal cells
INHBB expression was predominantly localized to the epithelial cell clusters in tumor tissues, with weak expression also detected in subsets of stromal cells (Fig. 6c). In contrast, INHBB expression in normal tissues was minimal overall, with negligible expression in epithelial cells and scattered low-level expression in stromal cells (Fig. 6d).
Discussion
In this study, we comprehensively investigated the significance of INHBB expression in CRC using RNA-ISH, immunohistochemistry, and scRNA-seq. Our findings demonstrated that high INHBB expression was significantly associated with aggressive tumor characteristics, including advanced stage and lymph node metastasis, as well as the decreased infiltration of T cells, increased tumor-associated macrophages (TAMs), and shorter OS and RFS.
The key novel finding of this study is the spatial and cellular characterization of INHBB expression using RNA-ISH and scRNA-seq. Although INHBB mRNA was virtually undetectable in normal colonic epithelium, it was predominantly expressed in tumor epithelial cells. This suggests that tumor cells themselves may actively produce and secrete INHBB, potentially contributing to the establishment of an immunosuppressive TME through autocrine and/or paracrine signaling. Furthermore, weak INHBB expression was also observed in a subset of stromal cells, likely including CAFs, supporting the notion that epithelial–stromal interactions may collaboratively promote immune evasion via INHBB, in agreement with the findings of Wu et al. [12].
IHC analysis revealed a significantly reduced intratumoral infiltration of CD4⁺ and CD8⁺ T cells in tumors with high INHBB expression. Notably, FOXP3⁺ regulatory T cells also showed a significant decrease in cell number (p = 0.028) when directly quantified. These T cell subsets play central roles in antitumor immune responses, and their diminished infiltration is indicative of an “immune-cold” or “immune-desert” TME [23–25]. In particular, the reduction of CD8⁺ cytotoxic T cells may reflect impaired cell-mediated immunity and might contribute to resistance against immune checkpoint blockade therapy.
Of particular interest, CD163⁺ TAMs—a hallmark of immunosuppressive M2 macrophages—were significantly more abundant in the high INHBB expression group. CD163 is a marker for M2-type TAMs, which produce anti-inflammatory cytokines, suppress T cell activation, and promote angiogenesis, all of which support tumor immune evasion [13, 26, 27]. These results suggest that high INHBB expression may suppress T cell infiltration and induce M2 macrophage polarization, thereby remodeling the TME into a broadly immunosuppressive state.
As a member of the TGF-β superfamily, INHBB functions as a subunit of activin B and is implicated in fibrosis and immune regulation. Recent studies have shown that activin B promotes M2 macrophage differentiation and activates CAFs, thereby exacerbating the immunosuppressive remodeling of the TME [15, 28]. The increased infiltration of CD163⁺ cells observed in our study aligns with these prior reports.
Moreover, previous bioinformatic analyses have identified correlations between high INHBB expression and reduced T cell infiltration, CAFs activation, and the elevated expression of immune checkpoint molecules such as PD-1 and CTLA-4 [12, 29, 30]. Our findings of reduced T cell presence and increased TAMs in INHBB-high tumors are consistent with these reports. Importantly, our study extends these insights by providing spatially and cell type-resolved evidence through RNA-ISH and scRNA-seq, offering mechanistic support to existing transcriptomic data.
Prognostically, INHBB high expression was significantly associated with shorter OS and RFS. Although INHBB did not emerge as an independent prognostic factor in multivariate analysis, the observed reduction in T cell infiltration and increase in M2 TAMs suggest that INHBB contributes to tumor progression and immune evasion, which may ultimately lead to poor clinical outcomes.
Collectively, our findings indicate that INHBB likely promotes tumor progression in CRC by simultaneously suppressing antitumor immune responses and fostering an immunosuppressive TME. These findings highlight the potential of INHBB as a therapeutic target and predictive biomarker in CRC.
Nonetheless, several limitations of this study warrant consideration. First, the use of TMA may not fully capture intratumoral spatial heterogeneity, especially with respect to TIL distribution and immune cell localization. Second, the observational nature of the study precludes the direct inference of causality between INHBB expression and immune modulation. Functional validation studies, such as knockdown or overexpression assays and co-culture experiments, as well as in vivo modeling, are needed to confirm the mechanistic role of INHBB. Third, we did not directly assess the expression of immune checkpoint molecules (e.g., PD-1, CTLA-4); therefore, comparisons with previous studies are based on inferred immune cell patterns rather than direct molecular evidence.
Conclusions
Our study suggests that INHBB promotes an immunosuppressive tumor microenvironment in CRC by reducing T cell infiltration and increasing M2-type macrophage recruitment. These immune alterations are associated with tumor progression and poor prognosis. Future studies should elucidate the immunoregulatory mechanisms of INHBB further and evaluate its potential as a therapeutic target. In particular, INHBB may be a predictive biomarker or combinatorial target in immunotherapeutic strategies, including immune checkpoint blockade and macrophage-targeted therapies.
Acknowledgments
We thank Masanobu Momose, Yasuyo Shimojo, Chitose Arai, Kanade Wakabayashi, Naoko Yamaoka, Saki Mukai, Daiki Ogura, Daiki Gomyo, and Tsukane Seki at Shinshu University Hospital for their excellent technical assistance. We also thank Kelly Zammit, BVSc, and J. Ludovic Croxford, PhD, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.
Abbreviations
- ARGs
Anoikis-related genes
- CRC
Colorectal carcinoma
- CAFs
Cancer-associated fibroblasts
- CEA
Carcinoembryonic antigen
- GEO
Gene Expression Omnibus
- TCGA
The Cancer Genome Atlas
- IRS
Immunoreactivity score
- ISH
In situ hybridization
- OS
Overall survival
- RFS
Recurrence-free survival
- scRNA-seq
Single-cell RNA sequencing
- TIL
Tumor-infiltrating lymphocytes
- TMA
Tissue microarray
- TME
Tumor microenvironment
Author Contributions
Hiroshi Sawaguchi participated in the design of the study, performed the pathological analysis, and drafted the manuscript. Takeshi Uehara and Mai Iwaya helped perform the pathological analysis. Takeshi Uehara performed the statistical analysis. Hiroshi Sawaguchi, Tomoyuki Nakajima, and Shotaro Komamura performed the TMA construction and RNAscope analysis. Shinsuke Sugenoya, Yugo Iwaya, and Masato Kitazawa examined the clinical data. Takeshi Uehara, Mai Iwaya, Shiho Asaka, Hiroyoshi Ota, Yuji Soejima, and Tadanobu Nagaya critically revised the draft manuscript. All authors have read and approved the manuscript.
Funding
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Data availability
All data generated and analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Competing interests
The authors declare no competing interests.
Consent for publication
Not applicable.
Clinical trial number
Not applicable.
Ethical approval and consent to participate
The ethics committee of Shinshu University School of Medicine approved this study (Approval Code: 5836) and waived the requirement for informed consent. An opt-out method was used because of the retrospective design of the study. The investigation was conducted in compliance with the Declaration of Helsinki.
Footnotes
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All data generated and analyzed during the current study are available from the corresponding author on reasonable request.