HDAC1 Mediates Immunosuppression of the Tumor Microenvironment in Non-Small Cell Lung Cancer

Introduction

Lung cancer is the most common malignant tumor of the respiratory system and poses a major threat to human health. According to statistics, every year, more than 2 million new cases of lung cancer are diagnosed, and 1.76 million people die from it worldwide.¹ The most common subtype of lung cancer, non-small cell lung cancer (NSCLC), which comprises lung squamous carcinoma (LUSC) and lung adenocarcinoma (LUAD) subtypes, accounts for 85% of newly diagnosed cases each year.² The average 5-year survival rate of patients with lung cancer is <20% owing to the highly insidious and aggressive nature of lung cancer, which causes some patients to be diagnosed at a late stage.^3,4 Therefore, establishing effective treatments to improve the prognosis of NSCLC is required.

Immunotherapy by enhancing autoimmunity against tumor cells is a breakthrough in tumor treatment, particularly in NSCLC.^5,6 Immune checkpoint inhibitors (ICIs), mainly programmed cell death receptor 1 (PD-1), PD-ligand 1 (PD-L1), and cytotoxic T-lymphocyte-associated protein 4 receptor (CTLA4) have significantly improved the survival time and quality of life of patients with advanced NSCLC.^7,8 However, we observed unsatisfactory immunotherapy outcomes in some patients with NSCLC during long-term clinical treatment.

The tumor microenvironment (TME) is the microenvironment surrounding tumor cells. It is a complex integrated system of immune cells, extracellular matrix, bone marrow-derived inflammatory cells, surrounding blood vessels, fibroblasts and signaling molecules.^9,10 Immune cells in the TME and how they are modulated play an essential role in tumorigenesis and progression, and TME can influence the patient’s response to immune cells.^9,11,12 TME can be classified into “immune desert”, “immune inflammation”, and “immune evasion” phenotypes.¹² Patients who fit into the “immune desert” and “immune evasion” phenotypes are less responsive to immunotherapy than those who fit into the “immune inflammation”.^12,13 Therefore, the targeted TME therapy approach has provided new prospects for cancer treatment in recent years.^11,14

The protein encoded by histone deacetylase 1 (HDAC1) is a member of the HDAC family and is a component of the HDAC complex. HDAC1 catalyzes the removal of acetyl cohorts from lysine residues of histone and non-histone substrates, causing transcriptional repression.¹⁵ In the nucleus, histone acetylation and deacetylation are in dynamic equilibrium, and eukaryotic gene expression is co-regulated by histone acetyltransferase and HDAC.¹⁵ Studies have demonstrated that HDACs participate in the regulation of cell biological processes, such as cell cycle, angiogenesis, immune regulation, and DNA damage response.^15,16 Previous studies have reported that HDAC1 knockdown in NSCLC inhibits tumor cell proliferation, tumor neoangiogenesis, invasion, and migration and promotes apoptosis.¹⁷ Interestingly, it also has a role in promoting NSCLC invasion and migration in a hypoxic environment.¹⁸ Additionally, genome-wide Clustered Regularly Interspaced Short Palindromic Repeats screen conducted by Lawson et al, revealed that HDAC1 enables cancer cells to evade killing mediated by cytotoxic T lymphocytes.¹⁹

In this research, we will investigate the biological roles of HDAC1 expression in the immunological aspects of TME in NSCLC and seek new directions to improve the effectiveness of immunotherapy.

Materials and Methods

Acquisition and Analysis of HDAC1 Expression Profiles in Public Databases

The pan-cancer dataset (tumor = 10363; normal = 730) from the Cancer Genome Atlas (TCGA; https://www.cancer.gov/) public database were selected using the Sento Academic (https://www.xiantao.love/) online website to compare the unpaired differential expression of HDAC1 in tumor and normal tissues of 33 cancers. NSCLC was selected as the target of this study. Then, the transcriptomic dataset of HDAC1 in NSCLC (tumor = 1037; normal = 108) and files containing the clinicopathological parameters (including follow-up time, age, sex, total pathological stage, T stage, N stage and M stage) were retrieved from the TCGA database. Table 1 demonstrates the specific information of patients included in the TCGA dataset in this study. Analysis of paired and unpaired differential expression of HDAC1 in NSCLC tumor tissues and normal tissues in the TCGA dataset was completed using the “limma” R package.

Table 1 Clinical Information of Patients With NSCLC in TCGA and TMA Datasets

Construction of Tissue Microarray

A total of 60 pairs of NSCLC tissue and paracancerous tissue samples were used for tissue microarrays (TMA) 1 were purchased from Superbiotek Biologicals (ZL-lug1201; China). Paired difference expression analysis of HDAC1 in NSCLC tumor tissues and paraneoplastic tissues was performed in this experiment using TMA1. Tissue samples for TMA2 were collected from patients who underwent surgical resection in thoracic surgery from January to December 2005 (Zhongshan Hospital, Fudan University; China). TMA2 was composed of 140 NSCLC tissues and 10 randomly obtained normal tissues, and performed unpaired differential expression analysis of HDAC1 in NSCLC tumor tissues and normal tissues. Moreover, complete information on postoperative follow-up time (follow-up to July 2013) and clinicopathological parameters were available for the 140 patients with NSCLC. Table 1 demonstrates detailed information of patients with NSCLC samples in TMA1 and TMA2. All patients included in this study did not receive targeted therapy, radiation therapy, or chemotherapy preoperatively, and the patients were anonymized per international regulations.

Immunohistochemical Staining and Quantitative Analysis

To observe the expression level of HDAC1 protein in NSCLC tumor tissues and normal tissues by using immunohistochemical indirect staining. The specific steps were as follows:

Paraffin specimens were cut to 4 μm thickness and mounted on slides for dewaxing and rehydration operations, followed by antigen repair. On the treated slides, 10% normal goat serum was added dropwise. It was closed at room temperature (25 °C) for 60 minutes and then incubated overnight at 4 °C with the primary antibody (1:50, ab53091, Abcam, UK). The following day, the primary antibody was rinsed off, and the slides were washed twice with a phosphate-buffered salt solution for 10 minutes. Subsequently, the secondary antibody (1:250, RGAR011, Proteintech, China) was added to the slides, and the slides were incubated for 30 minutes at room temperature (25 °C), cleaned, and washed twice with phosphate buffer solution for 10 minutes each time. Furthermore, a drop of diaminobenzidine solution was added to the slides for wet box incubation and the slides were re-stained with the experimental step of hematoxylin at 37 °C for 5 minutes. Finally, the slides were dehydrated with 50%, 75%, 85%, and 95% alcohol, and sealed with 1 drop of neutral gel.

The slides were captured for examination using an optical inverted microscope, and the original section images were captured under the microscope. To quantify HDAC1 protein level expression in NSCLC, we used Image J (version: 1.8.0_172) software to convert HDAC1 protein staining intensity in immunohistochemically stained sections into average optical density (AOD) values. The equation for AOD calculation is as follows:

Finally, the expression of HDAC1 extracted by Image J in TMA1 and TMA2 was analyzed for paired and unpaired differences using GraphPad Prism software (version: 9.4.1.681).

Prognostic Analysis of HDAC1 in NSCLC

The “Auto select best cutoff” module was selected through the Kaplan Meier plotter database (https://kmplot.com/analysis/) to classify low (n = 1267) and high (n = 658) expression cohorts according to HDAC1 expression in NSCLC (Affymetrix ID: 201209_at) and to compare their overall survival (n = 1925) differences between two cohorts. Subsequently, the expression data of HDAC1 in TMA2 were integrated with the survival follow-up data. The median value of HDAC1 expression was utilized to classify the patient samples into low- and high-expression cohorts, and Kaplan Meier survival curves were plotted by GraphPad Prism software. Additionally, we used TCGA dataset to integrate HDAC1 expression levels with clinicopathological parameters (including follow-up time, age, sex, total pathological stage, T stage, N stage and M stage) to construct a nomogram survival prediction scoring system and predictive calibration curves using the “rms” R language package. Each patient’s clinicopathological stage was assigned a score, and the scores of all clinical parameters were summed to obtain the patient’s overall score. Based on the patient’s overall score, the survival rates of patients with NSCLC were predicted.²⁰

Evaluation of the Immunological Role of HDAC1 in NSCLC

To further investigate the relationship between HDAC1 and NSCLC carcinogenesis, we performed gene set enrichment analysis (GSEA) to search for signaling pathways in which HDAC1 may be involved. Furthermore, TCGA dataset was categorized into low- (n = 518) and high- (n = 519) expression cohorts on the basis of the median value (cutoff value = 38.37) of HDAC1 expression in NSCLC. We downloaded the Hallmark gene set files from the Molecular Signatures Database²¹ (http://www.gsea-msigdb.org/gsea/login.jsp), and performed enrichment analysis of HDAC1 low- and high-expression cohorts on the Hallmark gene set by Gene Set Variation Analysis (GSVA) using the “GSVA” R language package. Additionally, immune cells and functional enrichment in HDAC1 low - and high-expression cohorts were analyzed by single-sample GSEA (ssGSEA) method using the “GSVA” R language package.

The immune cell, tumor cell, and stromal cell components in the TME of NSCLC were compared in both the low- and high- expression cohorts of HDAC1 by using the “estimateScore” function. This function used the estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) algorithm. Tumor Immune System Interactions Database (TISIDB; http://cis.hku.hk/TISIDB/index.php) was utilized to explore the relationship between HDAC1 expression in NSCLC and lymphocytes.

In addition, patient tissues in TMA2 were categorized into high and low expression groups based on the AOD values of HDAC1 expression. CD8⁺ T cell (1:50, ab101500, UK) immunohistochemical staining was conducted on tissue samples from both groups to observe differences in CD8⁺ T cell infiltration between the high and low HDAC1 expression groups.

Statistical Analysis

In this study, the statistical analysis of the data was performed using GraphPad Prism software (version: 9.4.1.681) and R software (version: 4.1.1). The Log rank test method was used to make comparisons of the data between the two cohorts, and the Student’s t-test method was employed to evaluate the statistical significance of normally distributed variables for quantitative data. P value < 0.05 was considered statistically significant.

Results

HDAC1 is Overexpressed in NSCLC

Differential expression analysis in pan-cancer indicated that HDAC1 was expressed at significantly higher levels in tumor tissues than in normal tissues in various cancer types, including LUAD and LUSC (Figure 1A; P-value < 0.05). Analysis of the NSCLC dataset integrating LUAD and LUSC obtained from TCGA database revealed that HDAC1 was expressed in tumor tissues at significantly higher levels than in normal tissues in both paired difference and unpaired difference analyses (Figure 1B and C; P-value < 0.05). Additionally, our findings were further confirmed by immunohistochemical staining experiments. Paired difference analysis in TMA1 and unpaired difference analysis in TMA2 demonstrated that HDAC1 was expressed at significantly higher levels in tumor tissues than in normal tissues (Figure 1D–L; P-value < 0.05). Overall, HDAC1 is overexpressed in NSCLC.

Figure 1 Analysis of HDAC1 expression in NSCLC. (A) Differential expression analysis of HDAC1 in pan-cancer. (B) Paired and (C) unpaired differential expression of HDAC1 in NSCLC. Immunohistochemical staining experiments were performed for (D–H) paired and (I–L) unpaired differential analysis of HDAC1 differential expression in tumor tissues versus normal tissues *P-value < 0.05; **P-value < 0.01; ***P-value < 0.001; ns: not significant.

Overexpression of HDAC1 in NSCLC is Correlated with Poor Prognosis

Kaplan Meier plotter online website survival analysis suggested that the HDAC1 high-expression cohort (cutoff value = 3013) had significantly worse survival time than the low-expression cohort (Figure 2A; P-value < 0.05). Additionally, the high-expression cohort, which was classified according to the median value (cutoff value = 0.158) of HDAC1 expression in TMA2, also had significantly lower survival time compared to the low-expression cohort (Figure 2B; P-value < 0.05). Therefore, analysis of public databases combined with clinical follow-up data demonstrated that overexpression of HDAC1 in NSCLC was correlated with poor prognosis.

Figure 2 Prognostic analysis and prognosis prediction of HDAC1 in NSCLC. (A) Survival analysis of HDAC1 in NSCLC in Kaplan–Meier plotter database. (B) Combining clinical follow-up data with HDAC1 expression data in TMA for survival analysis. (C) Combining HDAC1 expression with clinicopathological parameters to construct a nomogram survival prediction system to predict 1-, 3-, and 5-year survival of patients. (D) Calibration curves to assess the predictive accuracy of the nomogram survival prediction system.

The prognostic prediction accuracy of TCGA database is higher than that of TMA2 since it contains a larger sample of patients with NSCLC. Therefore, we used TCGA dataset to integrate HDAC1 expression data with pathological parameters to construct a nomogram survival prediction scoring system to predict 1-, 3-, and 5-year survival rates of patients (Figure 2C). Furthermore, the prediction curves of 1-, 3-, and 5-year survival rates of the nomogram system fluctuated slightly above and below the calibration curve, indicating that the constructed prediction system had a high accuracy (Figure 2D).

HDAC1 Down-Regulates Immune Signaling Pathways in NSCLC

To further explore the involvement of HDAC1 in the oncogenic mechanism of NSCLC, we performed a GSEA. The findings indicated that HDAC1 down-regulates immune response-related signaling pathways in NSCLC, such as Fc epsilon RI, Janus kinase (JAK) /signal transducers and activators of transcription (STAT), peroxisome proliferator-activated receptor (PPAR), chemokine, nucleotide-binding oligomerization domain (NOD)-like receptor, Toll-like receptor, and B and T cell receptor signaling pathways (Figure 3). Therefore, we speculate that HDAC1 may mediate immunosuppression in NSCLC.

Figure 3 GSEA of the signaling pathways involved in the regulation of HDAC1 in NSCLC.

Overexpression of HDAC1 Mediates the TME Immunosuppressive State in NSCLC

We performed a multidimensional enrichment analysis to investigate whether HDAC1 mediates TME immunosuppression in NSCLC. Heatmap of enrichment analysis indicated that HDAC1 downregulated the activation of the immune regulatory signaling pathways in the high-expression cohort compared to that in the low-expression cohort. These down-regulated immune signaling pathways include interferon alpha response signaling, interferon gamma response signaling, allograft rejection signaling, interleukin (IL) 6 JAK STAT3 signaling, inflammatory response signaling, complement response signaling, IL2 STAT5 signaling, and tumor necrosis factor (TNF)-α via nuclear factor kappa B signaling (Figure 4A). The ssGSEA demonstrated that the HDAC1 high-expression cohort was significantly less enriched compared to the HDAC1 low-expression cohort in these immune signaling pathways (Figure 4B; P-value < 0.05). Subsequently, we further analyzed the effect of HDAC1 on immune cells in TME. Immune cell and functional enrichment analysis revealed that the HDAC1 high-expression cohort was significantly less enriched than the low-expression cohort (Figure 4C; P-value < 0.05). The TISIDB analysis showed that most lymphocyte infiltration was significantly negatively correlated with HDAC1 expression in LUAD and LUSC (Supplementary Figures 1 and 2). Additionally, the ESTIMATE algorithm was used to calculate the immune cell component in the overall HDAC1 high- and low-expression cohorts. The results revealed that the immune cell component in the HDAC1 high-expression cohort was significantly lower than those in the HDAC1 low-expression cohort (Figure 4D). These findings provide additional evidence that HDAC1 overexpression in NSCLC mediates the TME immunosuppressive state.

Figure 4 Analysis of the role of HDAC1 in TME of NSCLC. (A) Hallmark pathway gene set enrichment analysis in HDAC1 high- and low-expression groups. (B) Differential analysis of immunomodulatory signaling pathways enrichment in HDAC1 high- and low-expression groups. (C) Differential analysis of the enrichment of immune cells and functional gene sets in HDAC1 high- and low-expression groups. (D and E) ESTIMATE assessment of immune cell component, stromal component, and tumor component in HDAC1 high- and low-expression groups. (F) Differential analysis of tumor mutation burden in high and low HDAC1 expression groups. Differential analysis of (G) PD-L1, (H) PD-1, and (I) CTLA4 expression in HDAC1 high- and low-expression groups. *P-value < 0.05; **P-value < 0.01; ***P-value < 0.001; ns: not significant.

Overexpression of HDAC1 Mediates TME Alterations in NSCLC Consistent with the “Immune Desert” Phenotype

According to the study by Hegde et al, the TME can be classified into three phenotypes: “immune desert”, “immune inflammation”, and “immune evasion”.¹² By reviewing the PubMed literature, we conclude that the “immune desert” phenotype has the following characteristics:

1. Little immune cell infiltration in the tumor parenchyma and interstitium.^12,22–24 As previously described, we observed significantly less immune cell infiltration in the HDAC1 high-expression cohort than in the HDAC1 low-expressing cohort (Figure 4C, Supplementary Figures 1 and 2). Although various types of immune cells are infiltrated by tumors, they can be broadly divided into two categories: those that actively kill tumors, such as natural killer (NK), NKT, and CD8⁺ T cells; and those that actively suppress the immune cells responsible for killing tumors, such as regulatory T cells (Treg), type 1 helper T (Th1) and Th2 cells, and macrophages (M)1 and M2 cells.^25,26 However, our results demonstrated that the cellular levels of NK, NKT, CD8⁺ T, Treg, Th1, Th2, M1, and M2 were all significantly reduced in the HDAC1 high-expression cohort (Figure 4C, Supplementary Figures 1 and 2). Therefore, we hypothesized that HDAC1 may be involved in NSCLC immunosuppression by suppressing NK, NKT, and CD8⁺ T cells.
2. Lack of antigen presentation (low major histocompatibility complex (MHC) I expression).^12,24 As illustrated in Figure 4C, the MHC I enrichment in the HDAC1 high-expression cohort was significantly lower compared to the HDAC1 low-expression cohort.
3. Highly proliferative tumor.^12,24 Using the ESTIMATE algorithm, we calculated the tumor components in HDAC1 low- and high-expression cohorts, and the results indicated that the tumor purity components in the HDAC1 high-expression cohort were significantly more than those in the HDAC1 low-expression cohort (Figure 4E). This implies that tumor proliferation was more evident in the HDAC1 high-expression cohort.
4. Genome tends to be stable [low degree of tumor mutation burden (TMB)].^12,24 When compared to the HDAC1 low-expression cohort, the DNA repair signaling was significantly highly enriched in the HDAC1 high-expression cohort (Figure 4B). Additionally, TMB was estimated using TCGA dataset for the HDAC1 high- and low-expression cohorts and the results suggested that the median TMB values in the HDAC1 high-expression cohort were lower than those in the HDAC1 low-expression cohort (Figure 4F).
5. Poor response to single agent ICIs. Immune checkpoints are frequently employed as a crucial indicator in clinical practice to evaluate the effectiveness of immunotherapy in patients.²⁷ We compared the expression levels of PD-1, PD-L1, and CTLA4 using TCGA dataset to evaluate the impact of immunotherapy in HDAC1 high- and low-expression cohorts. The findings demonstrated that the expression levels of PD-1, PD-L1, and CTLA4 were significantly lower in the HDAC1 high-expression cohort than those in the HDAC1 low-expression cohort. This indicated that the effect of ICI monotherapy was lower in the HDAC1 high-expression cohort than in the HDAC1 low-expression cohort (Figure 4G–I; P-value < 0.05).

There are other atypical characteristics of “immune deserts”. Rapidly proliferating tumor cells alter the metabolism of TME. The immune metabolism of T cells is altered by hypoxia, lactate production, the presence of acidic TME, and increased lipogenesis, which affects T cell activation, differentiation, and proliferation.^28–32 In contrast to the HDAC1 low-expression cohort, we observed that the HDAC1 high-expression cohort was highly enriched in hypoxic and glycolytic signaling. However, the HDAC1 high-expression cohort was significantly less enriched in reactive oxygen species signaling. These findings suggest that high expression of HDAC1 mediates a hypoxic, acidic microenvironment in NSCLC. Meanwhile, in the HDAC1 high-expression cohort fatty acid metabolism and xenobiotic metabolic signaling were significantly downregulated compared to those in the HDAC1 low-expression cohort, which also suggested that high expression of HDAC1 causes increased adipogenesis in NSCLC (Figure 4A). Therefore, high expression of HDAC1 causes TME alterations in NSCLC that prevent T cell activation and proliferation.

Studies have demonstrated that some classical signaling pathways are also closely related to TME immunosuppression. Early T cell initiation and infiltration can be affected by the WNT β-catenin signaling pathway, which reduces the gradient of chemokines CXCL1, CXCL2, and CCL4 required for CD103 dendritic cells (DCs).³³ Phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling can reprogram tumor cell metabolism to suppress T cell function through regulation.³⁴ Consistently, our study showed that the HDAC1 high-expression cohort was highly enriched in WNT β-catenin and PI3K AKT signaling (Figure 4A and B).

Conclusively, we observed that HDAC1 overexpression in NSCLC mediates the “immune desert” phenotype of TME.

Immune Cell Distribution Demonstrates That HDAC1 Overexpression in NSCLC Mediates Immunosuppressive TME Formation

The core of the “immune desert”, “immune evasion”, and “immune inflammation” classification by Hegde et al, is based on the distribution of CD8⁺ T cells in the tumor stroma and parenchyma.¹² To demonstrate the aforementioned bioinformatics findings, we performed CD8⁺ T cell staining using the immunohistochemical staining technique on samples with high HDAC1 expression in NSCLC. We observed slight infiltration of CD8⁺ T cells in the tumor tissue and stroma of HDAC1-overexpressing samples, which was consistent with the “immune desert” phenotype (Figure 5A–F). Moreover, the amount of CD8+ T-cell infiltration was significantly increased in NSCLC samples with low HDAC1 expression compared to those with high HDAC1 expression (Figure 5G–L). Therefore, we concluded that HDAC1 overexpression in NSCLC mediates the formation of TME immunosuppression.

Figure 5 Distribution of CD8+ T cells in HDAC1 high-expression and low-expression tissue samples by immunohistochemical staining. Distribution of CD8+ T cells in HDAC1 high-expression samples in (A–C) LUAD and (D–F) LUSC tissue samples. Distribution of CD8+ T cells in HDAC1 low-expression samples in (G–I) LUAD and (J–L) LUSC tissue samples. Red arrow: CD8+ T.

Discussion

TME is a complex integrated system on which tumor cells depend for survival. Studies have demonstrated that TME has multiple effects on T cells. For example, TME can modulate the infiltration of T cells from the circulatory system into the tumor interior by post-translationally altering the ability of chemokines and overriding chemotactic signals that recruit T cells to the tumor.^10,35,36 Products of bone marrow monocytes such as Fas ligand, TNF-α and TNF-related apoptosis inducing ligand in TME can also induce apoptosis and thus affect T-cell survival.¹⁰ In addition, TME not only alters T cell replication by affecting CD103⁺, Baft3-dependent (dendritic cells) DCs, but also indole 2,3-dioxygenase expressed by DCs.^37,38 Myeloid-derived suppressor cells and cancer cells in TME can catabolize tryptophan and produce uranine, thereby impairing T cell proliferation in tumors.^39–41 Studies have demonstrated that oncogenes and abnormal pathway signaling are at the core of this immunosuppressive TME established in tumor patients.⁴² Therefore, it is important to find suitable genetic targets to alter immunosuppressive TME and improve patients’ long-term survival.

In this study, we further explored the impact of HDAC1 on immune regulation in TME of NSCLC based on the finding of Lawson et al that HDAC1 enables cancer cells to evade killing mediated by cytotoxic T lymphocytes.¹⁹ Pathway enrichment analysis indicated that HDAC1 downregulated immune regulatory signaling pathways in NSCLC, while immune cell infiltration analysis revealed that HDAC1 expression in NSCLC was significantly negatively correlated with immune cell infiltration. In addition, the TME characteristics of HDAC1 high and low expression cohorts were compared by bioinformatics, and the results showed that the TME in the HDAC1 high expression cohort was consistent with the “immune desert” phenotype. Finally, HDAC1 overexpression in NSCLC was demonstrated to mediate the TME immunosuppressive state by immunohistochemical staining experiments on CD8⁺ T cells. Therefore, our findings suggested that HDAC1 may serve as a core target to alter immunosuppressive TME in NSCLC, leading to a new breakthrough in NSCLC immunotherapy.

HDAC1, as a key member of epigenetic regulation, plays a crucial role in chromatin remodeling and gene expression through histone deacetylation. It is extensively involved in tumor biology, particularly in tumor immune regulation, making it a promising therapeutic target for cancer treatment.¹⁵ HDAC1 regulates dendritic cell (DC) development by modulating the expression, chromatin accessibility, and histone acetylation of transcription factors IRF4, IRF8, and SPIB. This process promotes cDC2 differentiation while impairing pDC generation and enhancing cDC1 maturation, thereby influencing anti-tumor immune responses.⁴³ Additionally, HDAC1 and HDAC2 are essential for T-cell development and genomic stability through their roles in chromatin structure modulation and TCR signaling pathways, with their dual deletion leading to developmental arrest, impaired TCR signaling, and tumorigenic transformation of immature T cells.⁴⁴ Previous studies further demonstrate HDAC1’s involvement in regulating the immune state across various tumor types. For example, HDAC1 interacts with exosomal circ_0006896 to impair CD8⁺ T-cell function by reducing LEF1 transcription and cytotoxic molecule expression, facilitating immune escape in acute myeloid leukemia.⁴⁵ Similarly, the CTBP1/HDAC1/HDAC2 complex suppresses MAT1A transcription, leading to decreased ferroptosis and reduced CD8⁺ T-cell cytotoxicity, thereby promoting immune escape in hepatocellular carcinoma.⁴⁶ Moreover, HDAC1 is implicated in immune therapy resistance. Overexpression of HDAC1/2/3 inhibits IFNγ signaling in hepatocellular carcinoma, contributing to immune checkpoint blockade resistance.⁴⁷ HDAC1 also mediates immune therapy resistance by reducing T-cell infiltration and CTL sensitivity.⁴⁸ HDAC1 upregulation suppresses cell cycle inhibitors and apoptotic regulators, fostering a stem-like, immune-resistant tumor phenotype and diminishing anti-tumor immune responses.⁴⁹ These findings underscore HDAC1 as a central regulator of immune evasion and resistance, highlighting its potential as a therapeutic target in immuno-oncology.

However, there are some shortcomings in our study. For example, the results of this study were only briefly validated at the tissue sample level and were not further investigated at the cellular level. Multiple genetic alterations may be present in tissue samples. This study selected tissue samples with high HDAC1 expression for immunohistochemical staining of CD8⁺ T cells failing to exclude other genetic interference. In addition, the specific mechanism of HDAC1-mediated TME immunosuppression in NSCLC was not investigated in this study.

Collectively, our study suggests that HDAC1 overexpression in NSCLC mediates the TME immunosuppressive state.