Abstract
Background
Research has consistently highlighted the key role of hepatocyte nuclear factor 1β (HNF1B) in organ development and cancer, including its involvement in colon cancer via shifted-code mutations. However, the specific effects of HNF1B on cancer immunotherapy and the immune microenvironment are not fully understood. This study investigated the impact of HNF1B on colon cancer immunotherapy in depth.
Methods
We analyzed 1,374 colon adenocarcinoma samples from the TCGA and GEO datasets. Our approach involved bioinformatics to uncover how HNF1B influences immunotherapy and the immune microenvironment, with corroboration from external databases and experimental validation.
Results
HNF1B was expressed at low levels in colon adenocarcinoma and was linked to patient prognosis. CIBERSORT, TIME, and GSVA analyses revealed that HNF1B was associated with macrophage infiltration, immune checkpoints, and signaling pathways. Drug prediction suggested a negative relationship between HNF1B and EGFR-targeted therapies, implying potential resistance. Validation with external cohorts confirmed that patients with low HNF1B expression experienced less benefit from immunotherapy.
Conclusion
This study clarifies the role of HNF1B in the treatment of colon adenocarcinoma. This study provides a foundation for further in-depth mechanistic studies and proposes new directions for optimizing immunotherapy strategies for colon adenocarcinoma.
Supplementary Information
The online version contains supplementary material available at 10.1007/s00262-024-03870-8.
Keywords: Colorectal adenocarcinoma, Immunotherapy, Hepatocyte nuclear factor-1β, Tumor microenvironment, Immune checkpoint blockade
Introduction
The hepatocyte nuclear factor (HNF) family comprises a group of transcription factors that play pivotal roles in regulating liver function and a range of metabolic processes [1]. HNF3α (FOXA1) has been demonstrated to exert either tumor-suppressive or oncogenic effects in breast and prostate cancer, contingent on the cellular context and mutation status [2–5]. HNF1α and HNF1B are exemplars of the homodimer transcription factor class and exhibit analogous domains [6]. HNF1α is abundantly expressed in hepatic tissues, where it regulates metabolic genes, including glycogen synthase and glucose-6-phosphatase. The oncogenic role of HNF1B has been documented in numerous tumor types, including prostate, lung, and renal cell carcinomas. In terms of its mechanism of action, HNF1B can exhibit either oncogenic or tumor-suppressive effects, depending on the specific type of cancer in question. For example, in advanced prostate cancer, HNF1B interacts with EZH2 to regulate SLUG gene expression and the epithelial‒mesenchymal transition (EMT) process [7]. HNF1B has been recognized as a gene associated with susceptibility specific to subtypes of ovarian cancer via DNA methylation sequencing and subtype expression profiling [8]. In breast cancer, the methylation of homeobox genes, including HOXB13 and HNF1B, is markedly more pronounced than that in normal samples [9]. Pancreatic intraepithelial neoplasia development and progression are observed in HNF1B mutants, particularly when HNF1B is combined with KRAS [10].
Studies have reported that the upregulation of HNF1B in ovarian clear cell carcinoma (OCCC) activates the STAT3 and NF-κB signaling pathways, inducing immunosuppression through the production of IL-6 and IL-8. This immunosuppressive effect has been observed to synergize with PD-1 therapy [11, 12]. Furthermore, HNF1B has been shown to regulate CD44v9 expression, which in turn modulates its interactions with various molecules, such as EGFR, thereby influencing cellular proliferation, migration, and immune responses [13]. Additionally, case reports have indicated that the p.T376I mutation in the HNF1B gene is associated with the progression of autoimmune diabetes [14]. Nevertheless, the immune related function of HNF1B in colorectal adenocarcinoma (COAD) remains uncertain.
As the second most common malignancy, colorectal cancer (CRC) accounts for more than 1.9 million new cases and an estimated 900,000 deaths annually [15]. Colorectal cancer (CRC) represents the third most common form of cancer globally, preceded only by lung and breast cancer. The incidence of colorectal cancer is relatively high in developed countries and is increasing at a rapid rate in developing nations [16]. In the United States, approximately 152,000 new cases and 53,000 deaths from CRC are anticipated in 2024 [17]. Since the 1950s, the incidence of colorectal cancer (CRC) in the United States has significantly increased. In 2024, it is projected that CRC will become the leading cause of cancer-related mortality in the country, surpassing all other cancers combined. Furthermore, it is estimated that CRC will become the most frequently diagnosed cancer type in men under the age of 50 [17]. The treatment approaches for colorectal cancer are diverse and depend on the stage of the disease and the specific conditions of the patient. The most common treatment modalities include surgical intervention, radiation therapy, chemotherapy, and targeted therapy [18, 19]. The most prevalent pathological type of colon cancer is colon adenocarcinoma (COAD), accounting for more than 90% of cases [15].
In the present investigation, we employed a combination of bioinformatics and in vivo experiments to investigate the tumor-promoting function of HNF1B in colorectal cancer. Our findings indicate that HNF1B deficiency can influence the levels of immune checkpoint genes and the EMT process. Furthermore, we investigated the relationship between HNF1B and resistance to immunotherapy, which suggests a potential association with an inhibitory tumor immune microenvironment (TIME). These results might shed new light on the role of HNF1B in colorectal cancer therapy, particularly within the realm of immunotherapy, and provide a basis for future functional investigations.
Results
Variation in HNF1B expression in COAD
A pan-cancer mRNA sequencing analysis of the TCGA database was conducted to examine the expression levels of HNF1B in various cancers. The results demonstrated that HNF1B is overexpressed in specific cancers, including CHOL (cholangiocarcinoma), KIRP (kidney renal papillary cell carcinoma), STAD (stomach adenocarcinoma), and THCA (thyroid carcinoma). However, in COAD (colon adenocarcinoma), KICH (kidney chromophobe), KIRC (kidney renal clear cell carcinoma) and LUSC (lung squamous cell carcinoma), there was a notable reduction in HNF1B expression (Fig. 1A and S2 A–C). Furthermore, the expression of HNF1B in COAD samples (Table 1) was compared with that in paired normal samples from the TCGA database (N = 41, T = 471) (Fig. 1B), the GSE33113 database (N = 6, T = 102) (Fig. 1C), the GSE39582 database (N = 19, T = 443) (Fig. 1D) and the GSE44076 database (N = 50, T = 98) (Fig. 1E) from the GEO. HNF1B was significantly downregulated in the COAD samples. Moreover, the results of immunohistochemical (IHC) samples from the Human Protein Atlas (HPA) database demonstrated that HNF1B is downregulated in COAD samples stained with HPA002083 or CAB068192 antibodies (Figs. 1F and S2E).
Table 1.
Characteristics | Overall |
---|---|
Gender, n (%) | |
Female | 226 (47.3%) |
Male | 252 (52.7%) |
BMI, median (IQR) | 27.116 (23.907, 32.574) |
Age, median (IQR) | 69 (58, 77) |
Pathologic T stage, n (%) | |
T1&T2 | 94 (19.7%) |
T3 | 323 (67.7%) |
T4 | 60 (12.6%) |
Pathologic N stage, n (%) | |
N0 | 284 (59.4%) |
N1 | 108 (22.6%) |
N2 | 86 (18%) |
Decreased HNF1B expression is associated with poor prognostic outcomes in patients with colorectal cancer
Research has demonstrated a correlation between HNF1B expression in tumor tissues and clinical prognosis in several cancer types [7]. HNF1B expression levels are closely associated with the degree of histological differentiation in various diseases. Its expression has been observed to be higher in differentiated hepatocellular carcinoma (HCC) compared to non-cancerous tissues and is positively correlated with liver cancer recurrence, suggesting a poor prognosis [20]. Additionally, studies have shown that HNF1B expression is linked to the infiltration levels of different immune cell types across various cancers, including CD8 + T cells and CD4 + T cells, prompting our investigation into its potential role in immunotherapy [21, 22]. Nevertheless, its prognostic value in COAD remains to be fully established. The objective of this study was to investigate the impact of HNF1B expression on the clinical outcomes of patients with COAD. To this end, survival analysis and Cox analysis were performed using the TCGA COAD cohort. The results of our analysis indicated that patients with lower HNF1B expression levels exhibited significantly poorer overall survival (OS) than those with higher expression levels (Fig. 1G). This prognostic significance was corroborated by four additional GEO cohorts, namely, GSE39582, GSE33113, GSE 17536 and GSE17538, which consistently demonstrated that lower HNF1B expression was associated with poorer OS and RFS (Fig. 2A–G). The results of the meta-analysis for OS were statistically significant across all four cohorts (combined HR = 0.55, 95% CI = 0.44–0.68; meta-analysis P < 0.00001) (Fig. 2H). With respect to RFS, the GSE39582, GSE33113, and GSE17538 cohorts also demonstrated statistical significance in the meta-analysis (combined HR = 0.30, 95% CI = 0.20–0.47; meta-analysis P < 0.00001) (Fig. 2J). Univariable and multivariable Cox regression analyses based on the TCGA cohort demonstrated that HNF1B expression remained a significant prognostic factor even after adjusting for potential confounding factors, including age, sex, and TNM stage (Table 2). In conclusion, the level of HNF1B expression in tumor tissues serves as a prognostic indicator for COAD, with lower HNF1B levels being independently associated with reduced survival.
Table 2.
Characteristics | Univariate analysis | Multivariate analysis | ||
---|---|---|---|---|
Odds ratio (95% CI) | P value | Odds ratio (95% CI) | P value | |
Age | 1.02 (1.00–1.04) | 0.109 | ||
Gender | 1.09 (0.67–1.78) | 0.726 | ||
T | 2.90 (1.82–4.64) | < 0.001 | 1.67 (0.95–2.95) | 0.077 |
N | 2.41 (1.77–3.27) | < 0.001 | 1.19 (0.76–1.86) | 0.450 |
TNM | 2.71 (2.00–3.67) | < 0.001 | 2.11 (1.34–3.33) | 0.001 |
hnf1b | 0.70 (0.57–0.85) | 0.001 | 0.73 (0.59–0.90) | 0.004 |
Biological networks connected to HNF1B
To gain insight into the biological mechanisms through which HNF1B influences COAD progression, we conducted a series of analyses, including gene alteration, DEG analysis and functional enrichment analysis (Figs. 3A and S3A, B). In the TCGA cohort of 512 patients (Table 1), we identified genes that were both upregulated and downregulated between the high and low HNF1B expression subgroups (Fig. 3A). Gene Ontology (GO) analysis of the 145 differentially expressed genes (DEGs) revealed that they are involved primarily in DNA replication, macro autophagy and protease activity (Fig. 3B). KEGG analysis revealed that these genes are involved in autophagy, ubiquitin mediated proteolysis signal pathways (Fig. 3C). Furthermore, we conducted GSVA to gain additional insight into the biological pathways associated with HNF1B expression (Fig. 3D, E). GSVA KEGG term revealed that the DNA repair, cell cycle, and inflammatory response pathways were significantly enriched. Furthermore, GSVA GO term analysis revealed that systemic lupus erythematosus and drug metabolism enzymes were significantly enriched. In conclusion, HNF1B is associated with tumor immunity, inflammation, and oncogenic signaling pathways in COAD.
Correlation of HNF1B with four pathways involved in tumorigenesis
To further substantiate the correlations between HNF1B and the inflammatory response, autophagy, apoptosis, and DNA repair pathways in COAD, as indicated by GO, KEGG, and GSEA analyses, we examined the expression of pivotal genes in these signaling pathways in the high and low HNF1B expression subgroups from the TCGA cohort. The heatmap demonstrated that genes associated with autophagy, apoptosis, and DNA repair were predominantly downregulated in the low HNF1B expression subgroup, whereas inflammatory response genes exhibited a similar trend in the low HNF1B expression subgroup (Fig. 4A). GSVA and correlograms revealed notable differences in the activation of these pathways between the high and low HNF1B expression groups (Figs. 4B–F and S3C, D).
Correlations between immune cell infiltration patterns and HNF1B in COAD
To elucidate the relationship between HNF1B expression and immune cell infiltration in the tumor microenvironment, we employed the CIBERSORT algorithm to determine the relative proportions of infiltrating immune cells. Figure 5A shows the estimated distributions of 22 immune cell types in the high-expression and low-expression groups. Compared with the high-expression group, the low-expression group exhibited significantly greater infiltration of follicular helper T cells, M1 macrophages, M2 macrophages, and neutrophils. Conversely, the high-expression group presented a significantly lower proportion of M0 macrophages. While differences in T cell and macrophage infiltration were noted, HNF1B expression was absent in our single-cell data, possibly due to its mitochondrial localization, which may hinder detection in single-cell analyses (Figs. S5 and S6).
Furthermore, we investigated the expression of immune checkpoint genes and observed notable increases in the expression levels of BTLA, CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, and TIGIT in the high-expression group (Fig. 5C). These findings indicate that patients with high HNF1B expression may exhibit a greater response to immunotherapy. Further analysis of the tumor mutational burden (TMB) revealed that the low-expression group presented a greater TMB (Fig. 5B). Furthermore, the ESTIMATE algorithm was employed to evaluate immune and stromal cell infiltration, revealing that the low-expression group presented elevated immune, stromal, and ESTIMATE scores (Fig. 5E–G). Conversely, tumor purity was lower in the low-expression group than in the high-expression group (Fig. 5H). Collectively, these findings suggest that immunotherapy may be more advantageous for low-risk patients with an active immune response.
A comprehensive analysis of drug sensitivity
Using the CellMiner database, an investigation was conducted to determine the correlation between HNF1B expression and drug sensitivity to identify potential pharmaceutical agents for patients with low HNF1B levels. These findings indicate that patients with reduced HNF1B expression exhibit heightened sensitivity to a range of pharmacological agents. By employing processed data from the CellMiner database, we were able to estimate the disparate degrees of chemosensitivity exhibited by patients with low and high HNF1B expression. The results demonstrated that the group with low expression exhibited greater sensitivity to several chemotherapeutic agents, including TKI inhibitors (neratinib, gefitinib, lapatinib, AZD.3759, and bosutinib), EGFR inhibitors (EXEL.7647), HER2 inhibitors (BMS.599626), and ERBB inhibitors (PD183805). With respect to drugs such as erlotinib, afatinib, poziotinib, sapitinib, and ibrutinib, the low-expression group exhibited greater sensitivity than did the high-expression group, although these differences were not statistically significant (Fig. 6). Furthermore, correlation analysis revealed a positive correlation between HNF1B expression and the IC50 values of numerous drugs, with only a few, such as thyrothricin, exhibiting a negative correlation (Fig. S4). These findings suggest that high HNF1B expression may be associated with drug resistance in tumor cells.
HNF-1β and immunotherapy biomarkers have synergistic effects on cancer immunotherapy
Immunotherapy has emerged as a highly efficacious clinical strategy for the treatment of cancer. A comprehensive understanding of the mechanisms underlying the clinical response and treatment resistance is essential for further expanding the clinical benefits of immunotherapy. Among these therapeutic modalities, immune checkpoint inhibitors (ICBs) and cytokine treatments, including IFNG and IL2, play pivotal roles in cancer immunotherapy, with favorable outcomes observed in some patients. A substantial body of evidence has revealed the interference and synergistic effects associated with ICB therapies. For example, targeting of the IFNγ-PKM2-β-catenin axis has been demonstrated to prevent HPD in preclinical models, whereas TGF-β has been shown to facilitate anti-PD-L1 therapy in urothelial cancer. As previously stated, functional enrichment analysis indicated that HNF1B expression is associated with immune-related pathways in COAD and immunotherapy. Accordingly, we further investigated the role of HNF1B in immunotherapy via a comprehensive RNA-seq dataset comprising 348 urothelial cancer patients who had been treated with the anti-PD-L1 drug atezolizumab (Table S1). The term "responders" was used to describe patients who achieved a complete response (CR) or partial response (PR), whereas the term "nonresponses" was used to describe patients who exhibited stable disease (SD) or progressive disease (PD). Patients with low HNF1B expression exhibited a poor prognosis among those with metastatic urothelial cancer. Although the p value was not statistically significant when comparing the CR/PR and SD/PD groups, it indicated a potential adverse effect of HNF1B on the atezolizumab response (Fig. S7A). Moreover, the expression and prognosis of PD-1, PD-L1, CTLA-4, CD20, and IFNγ in this cohort were examined, and effects analogous to those observed for HNF1B were identified (Fig. S7B–F).
The cohort was classified into four subgroups on the basis of the expression levels of HNF1B and CD20, CTLA-4, IFNG, PD-1, and PDL1. Analysis of the HNF1B and CD20 subgroups revealed that patients with low expression of both HNF1B and CD20 presented the poorest prognosis. High HNF1B expression mitigated some of the adverse effects associated with low CD20 expression on survival outcomes. The subgroup exhibiting low HNF1B and high CD20 expression presented a prognosis comparable to that of the subgroup with high HNF1B and low CD20 expression (Fig. 7A, Table S2). Significant differences in the response to atezolizumab were observed among the four subgroups. Patients with high expression of both HNF1B and CD20 presented the greatest response to atezolizumab immunotherapy, whereas those with high HNF1B and low CD20 expression presented the lowest response, followed by the group with low expression of both markers (Fig. 7A). A comparison of the second and fourth bars, as well as the first and third bars in Fig. 7A, indicated that higher HNF1B levels enhanced the response to atezolizumab mediated by CD20. Conversely, CD20 was found to significantly influence the ability of HNF1B to enhance the response to immunotherapy (p = 0.0133 and p = 0.0013, Fig. 7A). Similar patterns were observed for CTLA-4, IFNG, PD-1, and PD-L1. These findings indicate that HNF1B and CD20, in conjunction with CTLA-4, IFNG, PD-1, and PD-L1, exert a synergistic influence on patient prognosis and the efficacy of ICB therapy (Fig. 7B–E).
Inhibition of HNF1B expression has been demonstrated to significantly promote colon adenocarcinoma cell proliferation and migration and regulate epithelial‒mesenchymal transition (EMT) and inflammatory biomarker expression
To corroborate the findings of the bioinformatic analysis, a series of in vitro experiments were conducted. The efficacy of HNF1B siRNA knockdown in the DLD-1 and HCT116 cell lines was corroborated by Western blot analysis (Fig. 8A, B). A CCK8 assay was used to determine the impact of HNF1B knockdown on the proliferation of HCT116 and DLD-1 cells. The results demonstrated a notable increase in proliferation following HNF1B knockdown (Fig. 8C, D). Colony formation assays demonstrated that HNF1B knockdown resulted in a statistically significant increase in the number of colonies formed by DLD-1 and HCT116 cells (Fig. 8E, F). Furthermore, the EdU staining results demonstrated that HNF1B knockdown markedly increased the proliferation rates of DLD-1 and HCT116 cells compared with those in the control group (Fig. 8G, H). Given the potential role of HNF1B in regulating cell migration, wound healing assays were conducted, which demonstrated that HNF1B knockdown significantly enhanced cell migration (Fig. 8I, J).
As evidenced by previous single-cell RNA seq analyses and published literature, HNF1B is expressed in tumor cells and epithelial cells (Figs. S5 and S6). Therefore, the primary mechanisms by which HNF1B affects COAD progression may involve EMT and the inflammatory response. To test this hypothesis, Western blot analyses were conducted to examine whether HNF1B influences the expression of EMT-related proteins. Our findings indicated that E-cadherin protein levels significantly decreased following HNF1B knockdown in the DLD-1 and HCT116 cell lines, confirming the role of HNF1B in the EMT process in COAD cells (Fig. S8 B, C). Moreover, qPCR analysis demonstrated that HNF1B markedly influences the transcription levels of Kras, STAT1, ACKR3, and CXCL12R (Fig. S8 D–F), substantiating our hypothesis. In conclusion, the absence of HNF1B is associated with an increased risk of COAD progression, with EMT and the inflammatory response representing the underlying mechanisms. Our results demonstrated that reduced HNF1B expression is associated with increased proliferation and migration in colorectal cancer cell lines, and it also influences the mRNA expression of downstream immune signaling molecules,
Discussion
HNF-1β is an effective and specific marker for the diagnosis of intrahepatic cholangiocarcinoma (iCCA) and the identification of the cholangiocarcinoma (CCA) component in combined hepatocellular-cholangiocarcinoma (cHCC-CCA). The absence of HNF-1β expression may be used to exclude the possibility of iCCA in cases of adenocarcinomas of unknown primary origin. HNFs play pivotal roles in the regulation of liver-specific gene expression, metabolism, development, cell growth, and a multitude of cellular functions. These genes are activated or influenced by hormones and insulin-like growth factors (IGFs), and different combinations of the four HNF factors form a network that controls the expression of liver-specific or liver-enriched genes [23]. As transcription factors, HNFs regulate liver development and differentiation and maintain liver function by controlling the expression of downstream genes. Furthermore, HNF-1B is a valuable biomarker for diagnosing pancreatic ductal adenocarcinoma (PDAC) when used in conjunction with other lineage-specific biomarkers [24]. In a genome-wide association study, HNF1B variants were identified and analyzed for their association with endometrial cancer in large case‒control studies within two prospective cohorts. Next-generation sequencing revealed HNF1B methylation in ovarian cancer (OC) patients, which was associated with specific clinicopathological characteristics [25]. Research in clear cell renal cell carcinoma (ccRCC) and chromophobe renal cell carcinoma (chRCC) indicates that while HNF1B functions as an oncogene in papillary renal cell carcinoma, it may act as a tumor suppressor [26]. This study is the first to comprehensively examine the relationship between HNF1B expression levels and immunotherapy response in colon adenocarcinoma (COAD). The potential value of HNF1B as a candidate target for tumor immune microenvironment (TIME) immunotherapy across various solid cancers was investigated. The results of our study indicate that patients with low HNF1B expression demonstrate reduced benefit from various immunotherapies, including anti-PD1/L1 and anti-CTLA4 treatments.
Furthermore, we investigated the relationship between HNF1B and established immunotherapy biomarkers, including the TMB and TIDE score. In COAD, a negative correlation was observed between HNF1B stromal component scores and estimation scores. The TIDE score is becoming increasingly recognized as a predictive indicator of immunotherapy response and survival rates. The results demonstrate that patients with low HNF1B expression in COAD exhibit elevated TIDE scores, indicating suboptimal immunotherapy efficacy and a correlation with unfavorable prognosis. In conclusion, low HNF1B expression may be associated with immunotherapy resistance.
To date, clinical trials and pharmacological interventions targeting HNF1B remain limited. However, animal studies have demonstrated that HNF1B mutations are linked to hypomagnesemia and renal magnesium excretion, and a magnesium-deficient diet in rats has been shown to upregulate HNF1B expression [27]. In a study examining HNF1B expression in ovarian clear cell tumors, significant differences in protein levels were observed between clear cell carcinoma and non-clear cell carcinoma, highlighting its potential as a molecular marker for ovarian clear cell carcinoma [28]. Elevated HNF1B expression is strongly associated with glutathione metabolism and chemotherapy resistance in ovarian clear cell carcinoma, and its abnormal expression may facilitate tumor cell survival and growth [29, 30]. In post-liver transplantation patients, Regulation of AFP expression at the transcriptional level by HNF1B may contribute to various stages of hepatocellular carcinoma progression following recurrence, suggesting that HNF1B expression could serve as a predictive marker for liver cancer recurrence [20, 31]. While clinical research on HNF1B in colorectal cancer is sparse, our findings suggest that its differential expression is correlated with survival and recurrence in COAD, offering a novel biomarker for the future diagnosis of COAD.
EGFR-TKI inhibitors constitute one of the earliest classes of targeted drugs to be developed for the treatment of cancer. However, as these drugs are employed in clinical practice and treatment progresses, additional resistance mechanisms have been identified in various tumor patients [32–34]. Previous studies have demonstrated that the downregulation of HNF1B can regulate drug resistance-related biological processes, thereby promoting cisplatin resistance in ovarian cancer [35]. Additionally, HNF1B has been linked to insulin resistance in hepatic metabolism, as evidenced by studies [36, 37]. Nevertheless, research examining its correlation with EGFR and HER2 inhibitor drug resistance remains scarce. The present study revealed that patients with low HNF1B expression presented greater sensitivity to EGFR-TKI treatment than did those with high HNF1B expression. These findings suggest that high HNF1B expression may be associated with resistance to EGFR-TKIs and HER2 inhibitors, thereby providing a direction for further research.
To elucidate the immune-related roles of HNF1B in the TIME, our study demonstrated a close relationship between HNF1B levels and stromal scores, immune scores, and ESTIMATE scores. Notably, HNF1B was found to be positively correlated with the infiltration of various immune cells, particularly M1 and M2 macrophages. Although research on the influence of HNF1B on macrophages is scarce, it has been demonstrated that SUMOylation of HNF1B in atherosclerosis facilitates its inhibition of sortilin expression, thereby reducing the lipid content in macrophages [38]. Our findings first revealed an association between HNF1B and macrophages, thereby establishing a potential link between HNF1B dysregulation and immunotherapy resistance in COAD. Furthermore, the relationships between HNF1B and various novel immune checkpoints were evaluated, and positive correlations were identified between HNF1B and key immunotherapy response effectors, including BTLA, LAG3, PDCD1, CTLA4, CD274, and TIGIT, in COAD. However, the limitation of this study was validated exclusively through bioinformatics analysis and in vitro experiments, lacking sufficient in vivo experimentation and a thorough investigation of the underlying mechanisms. Additionally, our research was limited to colorectal adenocarcinoma, without extending to other cancer types. These findings indicate that HNF1B may play a pivotal role in the tumor immune microenvironment (TIME) across a range of tumor types.
The present study revealed that low HNF1B expression is associated with a protumor role in COAD. However, further experimental data are needed to validate the bioinformatics analysis results and elucidate the related molecular pathways, including HNF1B regulation of immune expression, autophagy, and EMT mechanisms. Further research is needed to elucidate the synergistic effects of novel immune checkpoint inhibitors and HNF1B on poor patient ICB treatment response. Moreover, further validation is needed to ascertain the carcinogenic impact of HNF1B in other cancer types. Furthermore, it would be beneficial to explore the potential of corresponding targeted drugs for HNF1B.
Materials and methods
Data collection and processing
We utilized data from four independent databases for our study, including transcriptomic sequencing and clinical information from 512 COAD patients in the TCGA database (https://portal.gdc.cancer.gov/). The GSE39582 dataset was utilized to demonstrate and validate a multi-molecular signature for molecular stratification in colorectal cancer [39]. Both GSE17538 and GSE17536 datasets provide gene expression profiles of highly metastatic and invasive colorectal cancer cells, used to investigate the risks of recurrence and mortality in colorectal cancer. GSE17538 includes a training cohort and a validation cohort, whereas GSE17536 serves as an independent validation cohort [40, 41]. The GSE33113 dataset was employed to evaluate the risk of recurrence and metastasis in colorectal cancer patients following surgical resection [42]. Additionally, we downloaded pathological specimens from the publicly available pathology database HPA (https://www.proteinatlas.org/). R version 4.3.1 software was used to normalize and process the data. The RNA-seq and clinical data of the urothelial cancer cohort were obtained from IMvigor210.
Survival analysis and meta-analysis
The associations between HNF1B expression levels and clinical outcomes in patients with COAD, as well as the combined impact of HNF1B with CTLA4, PD-L1, PD-1, and IFNγ on postimmunotherapy outcomes, were analyzed via the "survival" R package in the TCGA, GEO, and IMvigor210 datasets. The log-rank test was used to assess the statistical significance of differences between survival curves. Meta-analysis was conducted via the Sangerbox online tool [43].
Immune cell infiltration and tumor microenvironment analysis
We applied the CIBERSORT algorithm in the R package to analyze the immune infiltration relationships of different genes across 22 cell types from the TCGA database. The immune score, estimate score, stromal score, and tumor purity were evaluated via the estimate algorithm in R. The tumor mutation burden (TMB) was computed via the maftools package in R. Furthermore, the tumor immune dysfunction and exclusion (TIDE) scores were retrieved from a web-based tool (http://tide.dfci.harvard.edu/).
Functional enrichment analysis
We performed differential expression gene (DEG) analysis on COAD cases with varying HNF1B expression levels via the TCGA dataset. A volcano plot was generated to display the fold changes and p values of DEGs between the high and low HNF1B expression groups. Additionally, a heatmap was used to visualize the expression patterns of genes related to the inflammatory response, DNA repair, apoptosis, and autophagy across these groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted on the identified genes via the R packages "clusterProfiler," "org.Hs.eg.db," "enrichplot," and "ggplot2".
GSVA and GSEA
We employed GSVA to assess the associations between HNF1B and various cell death pathways in the TCGA and GEO datasets. The GSVA scores indicate the degree of gene set enrichment in the samples. To explore the relationships between HNF1B and the four tumorigenesis pathways, we conducted Pearson’s correlation analysis. Additionally, hallmark term and GO term analyses were performed via GSEA software (V.4.1.0) to identify relevant signaling pathways in groups with high and low HNF1B expression.
Cell culture and transfection
The COAD cell lines DLD-1 and HCT116 were obtained from the American Type Culture Collection (ATCC). The cells were cultured according to the protocols provided on the ATCC website. In accordance with the manufacturer's instructions, DLD-1 and HCT116 cells were transfected twice with 50 nM double-stranded siRNA oligonucleotides synthesized by GenePharma (Biotech, Shanghai, China). The sequences for HNF1B siRNA were as follows: siHNF1B-1: 5’-3’ GGAAUGCAACAGGGCAGAATT, 3’-5’ UUCUGCCCUGUUGCAUUCCTT; siHNF1B-2: 5’-3’ GCUCCUCUCCUCCAAACAATT, 3’-5’ UUGUUUGGAGGAGAGGAGCTT. Cell transfections were carried out via either Lipofectamine 3000 or GP-transfect-mate (GenePharma, Shanghai, China).
CCK-8 assay
The COAD cell lines DLD-1 and HCT116 were subjected to transfection via either siRNAs specifically designed to target HNF1B or a negative control vector that served as a baseline reference. After successful transfection, these cells were carefully seeded into 96-well plates, with each well containing a uniform density of 2000 cells to ensure consistency across all experimental conditions. The cells were then allowed to incubate for 24, 48, or 72 h to assess the impact of HNF1B knockdown over time. At each of these time points, 10 μL of CCK-8 reagent was added to each well to facilitate the quantification of cell viability. This reagent interacted with the cells during a subsequent 2-h incubation period, after which the optical density was meticulously measured at 450 nm. This measurement provided critical data on the metabolic activity and viability of the cells, reflecting the effects of HNF1B suppression on cell proliferation.
EDU staining and quantification
The cells were cultured and treated following the established experimental protocol. EDU (10 μM) was added to the culture medium, which was subsequently incubated at 37 °C for 2 h. After incubation, the cells were fixed and permeabilized. The cells were subsequently incubated with the Click-iT reaction cocktail (Invitrogen) according to the manufacturer’s guidelines. Nuclei were counterstained with Hoechst. Proliferation rates were assessed by counting the number of EDU-positive cells observed under a fluorescence microscope and analyzing at least five random fields per sample. The number of positive stained cells was then calculated and compared across the different experimental conditions. The staining intensity was quantified via ImageJ 2.9.0.
Wound healing assay
The cells were grown to confluence in a 6-well plate, and a sterile 200 μL pipette tip was used to create a wound. The detached cells were removed with PBS. Images of the wound area were captured at various times via an inverted microscope. The degree of wound closure was determined by measuring the residual wound width.
Statistical analysis
Data analysis was conducted via R version 4.3.1 and GraphPad Prism 9. Statistical analyses and the number of samples (n) were described in detail for each figure panel. Prognostic differences were evaluated via the log-rank test. Hazard ratios were computed with a univariate Cox regression model. Survival data are represented by Kaplan–Meier curves, and tests for different groups were conducted with log-rank test (two-sided) statistics Spearman correlation coefficients were calculated to assess the relationships between HNF1B and gene sets related to the inflammatory response, autophagy, apoptosis, and DNA repair. Group differences were assessed via a two-tailed unpaired Student’s t test. The results are presented as the means ± SDs, with P values less than 0.05 regarded as statistically significant.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We are very grateful to TCGA database and GEO datasets for providing valuable data resources.
Author contributions
Author Contributions: Conceptualization, F.G., W.G; methodology, F.G, H.Y and W.G; software, Z.Z and H.H; validation, Z.Z. and F.G.; formal analysis, H.H.; writing—original draft preparation, F.G.; writing—review and editing, F.G. and Y.G.; visualization, W.G.; supervision, F.S, Y.G, J.H.; project administration, F.S, Y.G, J.H.; funding acquisition, F.S, Y.G, J.H. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the National Key R&D Program of China (2023YFC3503200, 2023YFC3503205; 2021YFC2501900), the National Natural Science Foundation of China (82188102; 32100574, 82372812; 82122053), the Beijing Municipal Science & Technology Commission (Z191100006619115), CAMS Initiative for Innovative Medicine (2021-I2M-1–012, 2021-I2M-1–015; 2021-I2M-1–067), Aiyou Foundation (KY201701), R&D Program of Beijing Municipal Education Commission (KJZD20191002302), Key-Area Research and Development Program of Guangdong Province (2021B0101420005), Shenzhen Science and Technology Program (RCJC20221008092811025, ZDSYS20220606101604009), Shenzhen High-level Hospital Construction Fund, and Sanming Project of Medicine in Shenzhen (SZSM202211011).
Data availability
Only publicly available data were used in this study. The datasets used are available from the corresponding author on reasonable request.
Declarations
Conflict of interest
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Fushan Gao and Wenlin Gong have contributed equally to this work and are co-first authors.
Contributor Information
Fei Shao, Email: shaofei@cicams.ac.cn.
Yibo Gao, Email: gaoyibo@cicams.ac.cn.
Jie He, Email: hejie@cicams.ac.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Only publicly available data were used in this study. The datasets used are available from the corresponding author on reasonable request.