Single-cell Histone deacetylation factor regulator patterns guide intercellular communication of tumor microenvironment that contribute to colorectal cancer progression and immunotherapy

Single-cell Histone deacetylation factor regulator patterns guide intercellular communication of tumor microenvironment that contribute to colorectal cancer progression and immunotherapy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Single-cell Histone deacetylation factor regulator patterns guide intercellular communication of tumor microenvironment that contribute to colorectal cancer progression and immunotherapy Zihan Zhao, Yarui Wu, Xuhua Geng, Congrui Yuan, Guibin Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3562456/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract In this study, single-cell RNA-seq data were collected to analyze the characteristics of Histone deacetylation factor (HDF). The tumor microenvironment (TME) cell clusters related to prognosis and immune response were identified by using CRC tissue transcriptome and immunotherapy cohorts from public repository. We explored the expression characteristics of HDF in stromal cells, macrophages, T lymphocytes and B lymphocytes of the CRC single-cell dataset TME and further identified 4 to 6 cell subclusters using the expression profiles of HDF-associated genes, respectively. The regulatory role of HDF-associated genes on the CRC tumor microenvironment was explored by using single-cell trajectory analysis, and the cellular subtypes identified by biologically characterized genes were compared with those identified by HDF-associated genes. The interaction of HDF-associated gene-mediated microenvironmental cell subtypes and tumor epithelial cells were explored by using intercellular communication analysis, revealing the molecular regulatory mechanism of tumor epithelial cell heterogeneity. Based on the expression of feature genes mediated by HDF-related genes in the microenvironment T-cell subtypes, enrichment scoring was performed on the feature gene expression in the CRC tumor tissue transcriptome dataset. It was found that the feature gene scoring of microenvironment T-cell subtypes (HDF-TME score) has a certain predictive ability for the prognosis and immunotherapy benefits of CRC tumor patients, providing data support for precise immunotherapy in CRC tumors. Histone deacetylation factor Histone deacetylases tumor microenvironment colorectal cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Colorectal cancer (CRC) is a malignant tumor of gastrointestinal tract which has become one of the most common tumors in the world and it accounts for about 10%fo all annually diagnosed cancers 1 . According to the data provided in the article, there are estimated annual 19.3 million diagnosed new-onset colorectal carcinoma worldwide and about 10 million died of it 2 . A recent review found that the global incidence rate of CRC is not uniform in terms of region, especially in the regions that have undergone developmental transition or with low socioeconomical index, which brings huge economic and medical burdens 3 . The role of genetic and epigenetic factors in tumorigenesis and progression is documented in so many researches. Histone deacetylases (HDACs), histone methyltransferases (HMTs), and DNA methyltransferases (DNMTs) are the main proteins involved in the regulation of chromatin conformation 4 . Especially, HDACs and induce gene repression leading to cancer. Therefore, Histone deacetylation factors (HDF) plays a critical role in regulating the initiation and progression of the tumor. Previous studies have not confirmed the effect of HDF on the development of CRC. In this research, we investigated the influence of HDF on the main TME cells based on single-cell RNA-seq data of 65362 cells derived from 33 colorectal cancer (CRC) samples. By the analysis of single-cell trajectory and enrichment score, we revealed the predictive power of HDF in the prognosis and immunotherapy benefit of CRC tumor patients. Materials and methods Study design and data collection Single-cell mRNA sequence (scRNA-seq) data were obtained from 23 CRC patients with 33 samples in the SMC dataset. Among them, 23 tumor tissues, and 10 normal adjacent tissues were collected to analyze the overall distribution of HDF expression by using colorectal cancer single cell data after initial data integration. Full data of CRC were downloaded from GSE132465 in the Gene Expression Omnibus (GEO) database ( www.ncbi.nlm.nih.gov/geo ). In addition, 11 public datasets of bulk mRNA sequence or microarray data for 2653 CRC patients were also obtained from The Cancer Genome Atlas (TCGA) and GEO databases. HDF genes were obtained from GOBP_HISTONE_DEACETYLATION (June 2022) Visualization of TME cell types and subtypes in CRC` Analysis and visualization of single-cell transcriptome data was handled by the Seurat package in R software. Based on the Find Variable Features function in the Seurat package, the top 2000 genes were selected as the top variable features and used to normalize the analysis and integration of scRNA-seq data for each cell. The ScaleData and RunPCA functions were further executed to obtain the number of principal components (PC), and the results were subsequently visualized using the dimensionality reduction method of t-SNE (t-distributed stochastic neighbor embedding). Finally, the Idents and DimPlot functions were used to annotate and visualize the cells of the major TME cell types or subtypes. Pseudotime trajectory analysis of HDF regulators for TME cells HDAC is a class of proteases that plays an important role in structural modification of chromosomes and regulation of gene expression. To explore the relationship between cell pseudotime trajectories and HDF, we eemployed the Monocle2 R package to process single-cell RNA data from all cell types in CRC. Highly variable genes were set according to the following filtering criteria: mean expression ≥ 0.1 and dispersion_empirical ≥ 1* dispersion_fit. The DDRTree method was used for dimensionality reduction. Then we used the ‘plot_pseudotime_heatmap’ function to visualize heatmaps showing the dynamic expression of HDF in the pseudotime trajectories of different TME cell types in CRC. Non-negative matrix factorization of HDF in TME cells To better observe the effects of HDAC-mediated cellular subtypes on TME cell types, we used a non-negative matrix factorization (NMF) algorithm for cellular subtype identification. Based on the expression profiles of 103 HDF, specific cellular taxa in TMA cells were further classified into 4–6 cellular subpopulations using the NMF R package (version 0.20.6). Identification of the marker genes of HDF-related cell subtypes in TME cells We used the FindAllMarkers function to list the marker genes for each NMF cluster for each cell type in CRC, where the threshold parameters min.pct and logfc.log were set to 0.15 and the corrected P-value was set to less than 0.05. The Dotplot function was used to show the genes that were highly expressed in each NMF cell subpopulation. Functional Enrichment Analysis for HDF-related subtypes in TME cells Functional enrichment analysis of highly expressed genes in NMF clusters was performed based on Gene Ontology (GO) and Genes and Genomes (KEGG) databases using the ClusterProfile R package. Functional enrichment of transcription factors for highly expressed genes in NMF clusters was performed based on the Metascape database. Analysis of intercellular communication of HDF-related subtypes in TME cells CellChat is an R package for analyzing intercellular communication based on the human ligand-receptor interaction database. First, based on CellChatDB.human, we used CellChat to evaluate the major signal input and output cell clusters in all NMF TME. Subsequently, the netVisual_circle function was used to show the strength or weakness of cell–cell communication networks from the target cell cluster to different cell clusters in all NMF clusters, which contained different cell clusters in the NMF. Survival analysis of HDF-related signatures in TME cells The FindAllmarker function based on the Seurat R package was used to generate gene signatures associated with histone deacetylated cell subtypes, and the scores of gene signatures of major cell types in CRC were calculated using GSVA (Gene set variation analysis). Cox risk proportion analysis was used to assess the predictive power of the genes characterizing the major cell subtypes for the prognosis of CRC patients. Assessment of the benefit of immunotherapy for HDF-related signaling genes in TME cells Screening for appropriate populations is a prerequisite and key to the benefit of immunotherapy. In the era of precision medicine, the application of immunotherapy-related biomarkers, especially the large Panel, has received increasing attention. Therefore, data from study cohorts based on immunotherapy was used to assess the effectiveness of immune risk model scores in predicting immunotherapy. Statistical analysis and hypothesis testing All statistical comparisons involved in this study, as well as hypothesis testing for the significance of differences between groups, were based on the statistical analysis methods in R 3.6. Results Molecular characterization of HDF in CRC The colorectal cancer (CRC) single cell transcriptome dataset was the data base for exploring the cell types and interactions between cells in the TME, and it could be found that the main cell types in the TME were Epithelial cells, Stromal cells, Myeloid cells, B lymphocyte cells, T lymphocyte cells and Mast cells. CRC tumor cells were mainly derived from the malignant transformation of epithelial cells (Fig. 1 A). Subsequently, assessment of the interactions among these cells using the CellChat tool revealed strong interactions between Epithelial cells and Stromal cells. Moreover, the interactions between these microenvironment cells were widespread (Fig. 1 B). HDAC was a kind of proteases that played an important role in structural modification of chromosomes and regulation of gene expression. The mRNA expression of genes involved in the HDAC pathway was found to differ significantly between cell types from CRC single cell transcriptome data. High mRNA expression of many related genes was showed in the cell subtypes of Epithelial cells (Fig. 1 C), which was consistent with the previously reported overexpression of HDAC related genes in cancer 4 . Based on HDAC related genes, cell subtypes were further identified using NMF for the major cell types in the TME, including four cell subtypes for stromal cells, five cell subtypes for macrophage cells, four cell subtypes for B cells, and five cell subtypes for T cells. It could be found that inter-tumor heterogeneity among different patients was reflected both in the proportional differences of major cell types and cell subtypes (Fig. 1 D). Characterization of HDF-mediated stromal cell subtypes in CRC Pseudotime trajectories analysis is an important indicator of the distance of cell differentiation using the single cell transcriptome, and the cells of TME transfers by corresponding state transitions during tumor progression. In order to investigate the role of HDAC in the transition of stromal cell state, we used the Monocle2 R package for pseudotime trajectories analysis of stromal cells, and then analyzed the relationship between HDAC-related genes and pseudotime trajectories, it could be found that the high expression of some genes significantly appeared at the head or tail of the pseudotime trajectories. The main highly expressed genes that appeared in the tail of the pseudotime trajectories were BRMS1, CHD3, HDAC2, MORF4L1, PRKD1, HOPX, MAPK8, DR1, MORF4L2 and PHB. On the contrary, the main highly expressed genes that appeared in the head of the pseudotime trajectories were HDAC10, PRMT6, C6orf89, RCOR2, SIRT3, MTA3, TRERF1, LRRK2, HDAC7 and PRDM5 (Fig. 2 A). A comparison of HDAC-mediated interactions between subtypes of stromal cell and epithelial cells showed no significant differences between each of the four fibroblast subtypes and epithelial cells (Fig. 2 B). The proportional distribution of the four fibroblast subtypes in tumor tissues compared to normal tissues was also not significantly different (Fig. 2 C). However, Fib_C2 and Fib_C4 subtypes of fibroblast had a slightly up-regulated trend in the proportional distribution. Based on the KEGG database, functional enrichment analysis of genes specifically highly expressed in the four fibroblast subtypes revealed that the Fib_C2 subtype was enriched for TNF signaling pathway, IL-17 signaling pathway and Osteoclast differentiation. Fib_C4 subtype was enriched for Autophagy animal. Fib_C1 subtype was enriched for Protein digestion and absorption, ECM-receptor interaction, and Proteoglycans in cancer. Fib_C3 subtype was enriched for Focal adhesion, Hypertrophic cardiomyopathy and Dilated cardiomyopathy (Fig. 2 D). Further correlation analysis of HDAC-mediated stromal cell subtypes revealed that the marker proteins significantly associated with Fib_C2 subtype and Fib_C4 subtype were mainly ADAMDEC1 and PLVAP. Moreover, the marker proteins significantly associated with Fib_C1 subtype and Fib_C1 subtype were mainly CREM (Fig. 2 E). The genes were highly expressed in Fib_C2 subtype were mainly IRF1, CCL2, SOCS3, JUNB and FOSB (Fig. 2 F) Characterization of HDF-mediated myeloid cell subtypes in CRC To investigate the function of HDAC in myeloid cell state transition, the CellChat tool was used to compare the interaction between five HDAC-mediated macrophage subtypes and epithelial cells, and it could be found that the Mac_C3 subtype of macrophages with SFPQ as a highly expressed gene had a strong interaction with epithelial cells (Fig. 3 A), and macrophage Mac_C3 subtype had a strong correlation with macrophage M1 type (anti-cancer and pro-inflammatory type) (Fig. 3 B), which suggested that HDAC had the potential ability to regulate macrophage functional polarization in the microenvironment of CRC tumors. Mac_C1 subtype with MORF4L1 as a highly expressed gene, macrophage Mac_C2 subtype with VEGFA as a highly expressed gene, and macrophage Mac_C4 subtype with PHB as a highly expressed gene also had some interactions with epithelial cells (Fig. 3 A), and macrophage Mac_C1 subtype and Mac_C4 subtype had a strong correlation with macrophage M2 type (pro-oncogenic and anti-inflammatory type) (Fig. 3 B), which further suggested the important role of HDAC in the regulation of macrophage functional polarization in the microenvironment of CRC tumors. The functional polarization of macrophages had a very important role in CRC tumor progression, and it could be found that the distribution of M1-type and M2-type features of macrophages in TME was widespread and did not have strong cluster distribution characteristics (Fig. 3 C), which suggested that the functional polarization of macrophages in TME was dynamic The macrophage subtypes with GZMB as the highly expressed gene, the macrophage subtype with IL32 as the highly expressed gene and the macrophage subtype with S100A8 as the highly expressed gene showed strong cluster distribution characteristics in the tSNE descending plot (Fig. 3 C), which may be related to their corresponding fixed biological functions. Based on the KEGG database, functional enrichment analysis of genes specifically highly expressed in five adult macrophage subtypes revealed that the pathways enriched in the Mac_C3 subtype of macrophages were mainly SPLICEOSOME, while the pathways enriched in the Mac_C1 and Mac_C4 subtypes of macrophages were mainly PYRUVATE METABOLISM, LIMONENE AND PINENE DEGRADATION, GLUTATHIONE METABOLISM, PROTEASOME, PROPANOATE METABOLISM, and FRUCTOSE AND MANNOSE METABOLISM (Fig. 3 D). Characterization of HDF-mediated B-cell subtypes in CRC In order to investigate the role of HDAC in the transition of B cells state, we used the Monocle2 R package for pseudotime trajectories analysis of B cells, and then analyzed the relationship between HDAC-related genes and pseudotime trajectories, it could be found that the high expression of some genes significantly appeared at the tail of the pseudotime trajectories. The main highly expressed genes that appear in the tail of the pseudotime trajectories were SDR16C5, PER1, CAMK2D, LRRK2, NCAPG2, SUV39H1, BCL6, BRMS1L, UCN, CHD3, MTA2 (Fig. 4 A). Comparison of the interactions between the five HDAC-mediated B-cell subtypes and epithelial cells using the CellChat tool revealed that the interactions were all relatively weak (Fig. 4 B). Subsequent comparison of the correlation between the five HDAC-mediated B-cell subtypes and protein marker-labeled B-cell subtypes revealed that the five B-cell subtypes showed a more consistent correlation, all showing strong correlation with IgG and IgA proteins, and weak correlation with CD19 and CD20 proteins (Fig. 4 C). Intercellular interactions were further explored using the NichNet tool, which considereed not only ligand-receptor interactions but also target genes activated after ligand-receptor interactions, and it could be found that the more active ligands in B cells were MANF, LGALS3, ARF1, RPS19, TIMP1, CD99 and GSTP1, while the more active target genes in epithelial cells were CCND1, BID, CCL2, EDN1, TIMP1, NQO1and JUNB. The activation of target genes had some ligand genes, suggesting that the B cell-epithelial cell interaction process had a more complex multiple network relationship (Fig. 4 D). Assessment of the differentiation potential of B cells using the CytoTRACE tool revealed that the differentiation potential of B cells was characterized by a more significant cluster distribution, but there was a poor agreement between the cluster characteristics of B cells differentiation potential and HDAC-mediated clusters (Fig. 4 E). Based on the KEGG database, functional enrichment analysis of genes specifically highly expressed in five adult B-cell subtypes revealed that the main enriched pathways in B-cell subtypes Bcell_C1 and Bcell_C4 were Ribosome, Coronavirus disease-COVID-19, Allograft rejection, Leishmaniasis, Asthma, Intestinal immune network for IgA production, Viral myocarditis, while B-cell subtypes Bcell_C3 and Bcell_C5 were mainly enriched for Thyroid hormone synthesis, Protein export, Protein processing in endoplasmic reticulum (Fig. 4 F). Characterization of HDF-mediated T-cell subtypes in CRC Five subtypes of T cells were annotated: KLRB1-positive T cells, CD8-positive T cells, regulatory T cells, cytotoxic T cells, and KRT18-positive T cells (Fig. 5 A). To investigate the role of HDAC in T cell state transition, we compared the correlation between HDAC-mediated T-cell subtypes and marker protein-annotated T-cell subtypes, and found that HDAC-mediated T-cell subtype Tcell_C3 was mainly distributed in CD8-positive T cells and cytotoxic T cells, while other T-cell subtypes did not have significant distribution differences (Fig. 5 B), suggesting that HDAC might play a regulatory role in the killing process of T cells against tumor cells. Further comparison of the interactions between T-cell subtypes and epithelial cells mediated by the combination of 25 HDAC-related genes and marker proteins using the CellChat tool revealed both CD8-positive T cells and cytotoxic T cells had strong interactions with epithelial cells (Fig. 5 C), and there was no significant difference in the interactions between the five HDAC-mediated T-cell subtypes and epithelial cells in the cluster of CD8-positive T cells, with the same trend in cytotoxic T cells. These results further emphasized the important role of HDAC in the process of killing function of T cells. Subsequently, five HDAC-mediated T-cell subtypes were compared for immune response-related scores and gene expression differences, and it could be found that the HDAC-mediated T-cell subtype Tcell_C3 had a strong signal on the Cytotoxic score, Exhaustion score, and Teffect score. High expression of genes in Co -inhibitors included HAVCR2, TGFB1, TIGIT, CD247, CD160, CD244, CD96, LAG3, Co-stimuiations included CD226, TMIGD2, ENTPD1, TNFRSF9, and genes highly expressed in T-function Genes were CD8B, GZMB, PRF1, TBX21. (Fig. 5 D). Finally, we compared the correlation between HDAC-mediated B-cell subtypes and marker protein-annotated B-cell subtypes, it could be found that NRT18-positive B-cell subtypes mainly contain HDAC-mediated B-cell subtype Bcell_C3 (Fig. 5 E). The prognosis assessment of HDF-mediated microenvironmental cell subtype features in CRC To further assess the clinical prognostic significance of genes characterizing the subtypes of major cell types in the HDAC-mediated CRC tumor microenvironment, six GEO datasets (GSE72970, GSE39582, GSE39084, GSE17538, GSE17536, and GSE103479) with overall survival (OS) information were collected, and using univariate Cox analysis to assess their relationship on OS, it could be found that most of the genetic characteristics had some prognostic significance (Fig. 6ABCD). Eight GEO datasets (GSE72970, GSE39582, GSE38832, GSE33113, GSE17538, GSE17536, GSE14333, GSE103479) with progression-free survival (PFS) information were collected, and similar results to OS assessment could be found (Fig. 6EFGH). The predictive power assessment of immunotherapy benefit for HDF-mediated micro-environmental cell subtype characterization in CRC To further assessed the clinical immunotherapeutic significance of HDAC-mediated subtype-specific genes of major cell types in TME, the GSE78220 and IMvigor210 immunotherapy datasets were collected and evaluated accordingly. In the GSE78220 dataset, the risk score allowed to divide the sample into two groups, High_RiskScore and Low_RiskScore (Fig. 7 A). There was a significant difference in the survival curves between the high and low risk groups (Fig. 7 B), and the proportion of immunotherapy responsed between the high and low risk groups trend (Fig. 7 C), suggesting that patients in the low-risk score group were more likely to benefit from immunotherapy. A consistent trend (Fig. 7DEF) can be found in the IMvigor210 dataset. Discussion HDACs are specialized enzymes that regulate chromatin remodeling 4 . To date, several studies have revealed the relevance of HDF to the alteration and pathogenesis of various cancer 5–8 . However, only a few have investigated the potential tumorigenic role of HDF. Overexpression of HDAC leads to enhanced deacetylation, which increases the gravitational force between DNA and histones by restoring the positive histone charge, thus making the relaxed nucleosomes very tight and detrimental to the expression of specific genes, including some tumor suppressor genes, so it is necessary to explore in depth the roles of HDAC-related genes in TME. In our study, we have for the first time comprehensively explored the modifying factors of HDF on the major cell types in the TME of CRC and further identify the diversity of cellular interactions in the TME of CRC. By analyzing and visualizing single-cell transcriptome data, we found that the main cell types in TME were epithelial cells, stromal cells, myeloid cells, B cells, T cells and mast cells, where CRC tumor cells were mainly derived from malignant changes in epithelial cells. At the same time, we found that the interactions between cells in the above microenvironment were widespread. we found that the TME cells, including stromal cells, myeloid cells, T cells and B cells all manifested the diverse HDF regulatory patterns and the extensive communication with tumor epithelial cells based on the single-cell analysis. Cancer-associated fibroblasts (CAFs) have been a topic of interest in the field of cancer research. These fibroblasts are a type of cell found in TME that play a crucial role in tumor growth, invasion, and metastasis. Recent advancements in the understanding of CAFs have shed light on their complex interactions with cancer cells. It has been discovered that CAFs can promote tumor progression by secreting various growth factors, cytokines, and extracellular matrix components. These factors create a favorable environment for cancer cells to proliferate and invade surrounding tissues 9 . In our study, we found that HDAC-related genes could be found at the head and tail of the Pseudotime trajectories analysis, respectively, and the HDAC-mediated stromal cell subtypes had a slightly up-regulated tendency in the proportional distribution between the Fib_C2 and Fib_C4. Further pathway analysis also revealed the participation of CAFs in TNF signaling pathway, IL − 17 signaling pathway, Osteoclast differentiation and Autophagy animal. The correlation analysis of HDAC-mediated stromal cell subtypes and stromal cell subtypes identified by marker proteins in the literature revealed that the marker proteins significantly associated with Fib_C2 subtype and Fib_C4 subtype were mainly ADAMDEC1 and PLVAP. Therefore, we speculated that HDAC modification CAFs may form the interaction with tumor cells to promote the progress and metastasis of tumor. To date, increasing studies have elaborated the relationship between HDAC and immune cells. Andreas von Knethen 10 reported that HDACs and HDACi were the starting point for altering Treg function. In the study of Xu-Wen Guan and colleagues 11 , they used the HADC inhibitor to treat the tumor growth by upregulating CD20 and its efficacy had been proven. In our research, we found Mac_C3 subtypes with SFPQ as a highly expressed gene had strong interactions with epithelial cells, which performed strong correlation with macrophage M1 type (anti-cancer pro-inflammatory). On the contrary, Mac_C1 subtypes with MORF4L1 as a highly expressed gene and Mac_C4 subtypes with PHB as a highly expressed gene had strong correlation with macrophage M2 type (pro-oncogenic and anti-inflammatory type). The above suggested an important role for HDAC in the regulation of macrophage functional polarization in the microenvironment of CRC tumors. Moreover, except for macrophages, we found that HDAC-mediated T cells and B cells also showed extensive interaction with tumor cells. Through analysis of B cells, we found that the interaction between B cells and epithelial cells involved a complex but relatively weak network relationship. Furthermore, there was poor consistency between the subpopulation characteristics of B cell differentiation potential and the subpopulations mediated by HDAC. Through analysis of T cells, we observed that the HDAC-mediated T-cell subtype, Tcell_C3, was predominantly present in CD8-positive T cells and cytotoxic T cells, while other T-cell subtypes did not exhibit significant disparities in distribution. These findings implied that HDAC might exert a modulatory role in T cell-mediated cytotoxicity against tumor cells. Therefore, we speculated that HDAC modification immune cells may play a significant role in immune escape and the tumor-promoting effect. Due to the intrinsic role of HDAC for TME, we further evaluated its prognosis as well as therapeutic expectations and showed that it was predictive of prognosis and was more likely to benefit from immunotherapy for patients in the low-risk score group. Conclusion We identified HDF associated cell subtypes of TME cells by using the single-cell sequencing analysis method and revealed the HDAC mediated intercellular communication of TME in the regulation of tumor growth and tumor immunomodulatory processes Declarations Ethics approval and consent to participate: Not available Consent for publication: Not applicable Availability of data and materials: All data are available in a public, open access repository. The datasets used and/or analyzed during the current study are available in the Gene Expression1 Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) and The Cancer Genome Atlas (TCGA) network (https://cancergenome.nih.gov/). R and other custom scripts for analyzing data are available upon reasonable request. Competing interests: The authors have no conflicts of interest to declare Funding: Not applicable Acknowledgements Not applicable References Dekker E, Tanis PJ, Vleugels JLA, Kasi PM, Wallace MB. Colorectal cancer. Lancet (London, England). 2019;394(10207):1467-1480. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians. 2021;71(3):209-249. Baidoun F, Elshiwy K, Elkeraie Y, et al. Colorectal Cancer Epidemiology: Recent Trends and Impact on Outcomes. Current drug targets. 2021;22(9):998-1009. Ramaiah MJ, Tangutur AD, Manyam RR. Epigenetic modulation and understanding of HDAC inhibitors in cancer therapy. Life sciences. 2021;277:119504. Hu XT, Xing W, Zhao RS, et al. HDAC2 inhibits EMT-mediated cancer metastasis by downregulating the long noncoding RNA H19 in colorectal cancer. Journal of experimental & clinical cancer research : CR. 2020;39(1):270. Li T, Zhang C, Hassan S, et al. Histone deacetylase 6 in cancer. Journal of hematology & oncology. 2018;11(1):111. Glozak MA, Seto E. Histone deacetylases and cancer. Oncogene. 2007;26(37):5420-5432. Swierczynski S, Klieser E, Illig R, Alinger-Scharinger B, Kiesslich T, Neureiter D. Histone deacetylation meets miRNA: epigenetics and post-transcriptional regulation in cancer and chronic diseases. Expert opinion on biological therapy. 2015;15(5):651-664. Biffi G, Tuveson DA. Diversity and Biology of Cancer-Associated Fibroblasts. Physiological reviews. 2021;101(1):147-176. von Knethen A, Heinicke U, Weigert A, Zacharowski K, Brüne B. Histone Deacetylation Inhibitors as Modulators of Regulatory T Cells. International journal of molecular sciences. 2020;21(7). Guan XW, Wang HQ, Ban WW, et al. Novel HDAC inhibitor Chidamide synergizes with Rituximab to inhibit diffuse large B-cell lymphoma tumour growth by upregulating CD20. Cell death & disease. 2020;11(1):20. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Jan, 2024 Reviews received at journal 25 Dec, 2023 Reviewers agreed at journal 25 Dec, 2023 Reviewers agreed at journal 12 Nov, 2023 Reviewers invited by journal 11 Nov, 2023 Editor assigned by journal 06 Nov, 2023 Submission checks completed at journal 06 Nov, 2023 First submitted to journal 05 Nov, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3562456","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":246573304,"identity":"e5cd1aa8-ee68-457f-9002-a8f95d244edc","order_by":0,"name":"Zihan Zhao","email":"","orcid":"","institution":"Aerospace center hosiptal","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zihan","middleName":"","lastName":"Zhao","suffix":""},{"id":246573308,"identity":"02f70eff-8901-40de-a25c-d011e09c03ab","order_by":1,"name":"Yarui Wu","email":"","orcid":"","institution":"Aerospace center hosiptal","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yarui","middleName":"","lastName":"Wu","suffix":""},{"id":246573310,"identity":"819b9695-69cb-4dde-886d-be8fb30add7b","order_by":2,"name":"Xuhua Geng","email":"","orcid":"","institution":"Aerospace center hosiptal","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xuhua","middleName":"","lastName":"Geng","suffix":""},{"id":246573312,"identity":"bb8a2ae4-bb24-4aa4-9e02-7a1ebaff294c","order_by":3,"name":"Congrui Yuan","email":"","orcid":"","institution":"Aerospace center hosiptal","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Congrui","middleName":"","lastName":"Yuan","suffix":""},{"id":246573313,"identity":"8c0655aa-f8aa-4ed2-a54e-db198b5f870a","order_by":4,"name":"Guibin Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3RMQrCMBSA4SeBujyoY0PEMzwJFIeCV6kImTp07Kg4uHgARfAskYIuPUDc1F6gBxA04ubQdBTMvyXkI7wEwOf7yXrLa0pPDPsLu8gBIjdhJTU5G/KNBtDUiQSKbxuWkEk7EjKZlEgB8l19Eg0lI75gt7tpJ+MaCTEUSkWalBQQSJm1kPhzS4R8n8WWlLMDYCAcJBZIhHSpOhNlx6cUyeCH7F1kWtX2kUkj36j5pLKz8JVjFr6e2a986GnYL4+mKJJRdF7d6jYCg/R7h7Udfxdq1wmfz+f7+14spkqzUlDQngAAAABJRU5ErkJggg==","orcid":"","institution":"Aerospace center hosiptal","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Guibin","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2023-11-05 12:44:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3562456/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3562456/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":46173190,"identity":"7d8c28d4-0274-4b36-acde-86d0bc608206","added_by":"auto","created_at":"2023-11-09 17:39:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1568055,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of HDAC gene expression distribution in CRC single cell data\u003c/p\u003e\n\u003cp\u003eA Cell type annotation by using the Seurat t-distributed stochastic neighbor embedding (t-SNE) plot from the SMC dataset (GSE132465). B Cell–Cell communications between main six cell types by Cell chat analysis. C Heatmap of GOBP_HISTONE_DEACETYLATION pathway gene expression among subgroups. D NMF cluster by using the HDAC related gene expression respectively for the main four types of TME cells in the scRNA data\u003c/p\u003e","description":"","filename":"OnlineFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3562456/v1/800a237e0382c8b1909edd33.png"},{"id":46173188,"identity":"b8986d39-53d8-4a2b-a218-f3a0184897f9","added_by":"auto","created_at":"2023-11-09 17:39:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":922921,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of gene-mediated fibroblast subtypes and interactions with epithelial cells in CRC single-cell data sets\u003c/p\u003e\n\u003cp\u003eA Heatmap of GOBP_HISTONE_DEACETYLATION pathway gene expression with pseudotime. B Endothelial cell-fibroblast interactions by ligand number reticulation. C Comparison of the proportion of fibroblast NMF clusters composition in tumor samples and normal samples. D KEGG enrichment results for the top 100 differential genes for NMF clusters. E Correlation of fibroblast NMF clusters with known clusters. F Expression of genes related to the KEGG enrichment pathway.\u003c/p\u003e","description":"","filename":"OnlineFig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3562456/v1/dc3410adc2d430261abaa70d.png"},{"id":46173186,"identity":"ac63d5fb-a9da-4240-b2fc-9e7f8b526fa8","added_by":"auto","created_at":"2023-11-09 17:39:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1403277,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of HDAC gene-mediated myeloid immune cell subtypes and interactions with epithelial cells in a colorectal cancer single cell data set\u003c/p\u003e\n\u003cp\u003eA Analysis of macrophage NMF clusters interactions with endothelial cells. B Correlation analysis of macrophage NMF clusters with Seurat annotated clusters. C Seurat annotation-related marker TSNE diagram. D NMF clusters KEGG pathway GSVA score.\u003c/p\u003e","description":"","filename":"OnlineFig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3562456/v1/f4f65a90ab23e012331367dd.png"},{"id":46173805,"identity":"e0d88579-a12e-457c-9154-b35a71a3967b","added_by":"auto","created_at":"2023-11-09 17:47:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1512614,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of HDAC gene-mediated B-cell subtypes and interactions with epithelial cells in the CRC single-cell data set.\u003c/p\u003e\n\u003cp\u003eA Heatmap of the correlation between HDAC genes and B-cell trajectory analysis. B Analysis of the interaction between B-cell NMF clusters and epithelial cells. C Correlation analysis of B-cell NMF clusters with marker protein annotated clusters. D NichNet assessment of target gene activation after B cell-epithelial cell interaction. E Th differentiation potential assessment of B cells by CytoTRACE. F NMF clusters KEGG pathway GSVA score in B cells\u003c/p\u003e","description":"","filename":"OnlineFig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3562456/v1/aab69980b637cc38a1252b79.png"},{"id":46173193,"identity":"a3a8eddd-7122-40fb-bb27-33734ebb9db9","added_by":"auto","created_at":"2023-11-09 17:39:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1322801,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of HDAC gene-mediated T-cell subtypes and interactions with epithelial cells in the CRC single-cell data set.\u003c/p\u003e\n\u003cp\u003eA TSNE plots of T cells Seurat annotated clusters. B Percentage and absolute number of NMF clusters in each Seurat annotated cluster of T cells. C Cellular interactions of the NMF clusters of each T-cell Seurat clusters. D some marker lists for GSVA visualization by author. E Percentage and absolute number of NMF clusters in each Seurat annotated cluster of B cells.\u003c/p\u003e","description":"","filename":"OnlineFig5.png","url":"https://assets-eu.researchsquare.com/files/rs-3562456/v1/e5a81104772abbb56c8404fa.png"},{"id":46173191,"identity":"3b2b572b-a2a2-4b72-9918-a22373530459","added_by":"auto","created_at":"2023-11-09 17:39:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":380278,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between HDAC-mediated TME cell subtype score (HDF-TME Score) on OS and PFS.\u003c/p\u003e\n\u003cp\u003eA, B, C, D: Bubble plot of GSVA scores of HDAC subtypes in stromal cells, macrophages, B cells, and T cells, respectively, with overall survival (OS) of CRC tumor patients.\u003c/p\u003e\n\u003cp\u003eE, F, G, H: Bubble plot of GSVA scores of HDAC subtypes in stromal cells, macrophages, B cells, and T cells, respectively, with progression free survival (PFS) of CRC tumor patients.\u003c/p\u003e","description":"","filename":"OnlineFig6.png","url":"https://assets-eu.researchsquare.com/files/rs-3562456/v1/19da0cf30e1e28c8a0d43daf.png"},{"id":46173192,"identity":"3997da52-d811-423e-8749-fc7d54024e31","added_by":"auto","created_at":"2023-11-09 17:39:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":712177,"visible":true,"origin":"","legend":"\u003cp\u003eThe role of HDAC-mediated tumor microenvironment cell subtype score (HDF-TME Score) in the prediction of immunotherapeutic benefit fits.\u003c/p\u003e\n\u003cp\u003eA Optimal Density Segmentation Curve for HDF-TME Score in GSE78220 Dataset. B Relationship between HDF-TME Score and OS in GSE78220 dataset. C Relationship between HDF-TME Score and immunotherapy response in the GSE78220 dataset. D Optimal Density Segmentation Curve for HDF-TME Score in IMvigor210 Dataset. E Relationship between HDF-TME Score and OS in IMvigor210 dataset. F Association of HDF-TME Score with immunotherapy response in the IMvigor210 dataset\u003c/p\u003e","description":"","filename":"OnlineFig7.png","url":"https://assets-eu.researchsquare.com/files/rs-3562456/v1/ac2a9d7fded6368b540862d1.png"},{"id":46174670,"identity":"c955b76e-4620-4663-a384-ce6198e48e13","added_by":"auto","created_at":"2023-11-09 17:55:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2827656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3562456/v1/063667e9-4961-4009-ba30-820b69a9dd56.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single-cell Histone deacetylation factor regulator patterns guide intercellular communication of tumor microenvironment that contribute to colorectal cancer progression and immunotherapy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eColorectal cancer (CRC) is a malignant tumor of gastrointestinal tract which has become one of the most common tumors in the world and it accounts for about 10%fo all annually diagnosed cancers\u003csup\u003e1\u003c/sup\u003e. According to the data provided in the article, there are estimated annual 19.3\u0026nbsp;million diagnosed new-onset colorectal carcinoma worldwide and about 10\u0026nbsp;million died of it\u003csup\u003e2\u003c/sup\u003e. A recent review found that the global incidence rate of CRC is not uniform in terms of region, especially in the regions that have undergone developmental transition or with low socioeconomical index, which brings huge economic and medical burdens\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe role of genetic and epigenetic factors in tumorigenesis and progression is documented in so many researches. Histone deacetylases (HDACs), histone methyltransferases (HMTs), and DNA methyltransferases (DNMTs) are the main proteins involved in the regulation of chromatin conformation\u003csup\u003e4\u003c/sup\u003e. Especially, HDACs and induce gene repression leading to cancer. Therefore, Histone deacetylation factors (HDF) plays a critical role in regulating the initiation and progression of the tumor. Previous studies have not confirmed the effect of HDF on the development of CRC. In this research, we investigated the influence of HDF on the main TME cells based on single-cell RNA-seq data of 65362 cells derived from 33 colorectal cancer (CRC) samples. By the analysis of single-cell trajectory and enrichment score, we revealed the predictive power of HDF in the prognosis and immunotherapy benefit of CRC tumor patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and data collection\u003c/h2\u003e \u003cp\u003eSingle-cell mRNA sequence (scRNA-seq) data were obtained from 23 CRC patients with 33 samples in the SMC dataset. Among them, 23 tumor tissues, and 10 normal adjacent tissues were collected to analyze the overall distribution of HDF expression by using colorectal cancer single cell data after initial data integration. Full data of CRC were downloaded from GSE132465 in the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.ncbi.nlm.nih.gov/geo\" target=\"_blank\"\u003ewww.ncbi.nlm.nih.gov/geo\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In addition, 11 public datasets of bulk mRNA sequence or microarray data for 2653 CRC patients were also obtained from The Cancer Genome Atlas (TCGA) and GEO databases. HDF genes were obtained from GOBP_HISTONE_DEACETYLATION (June 2022)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eVisualization of TME cell types and subtypes in CRC`\u003c/h2\u003e \u003cp\u003eAnalysis and visualization of single-cell transcriptome data was handled by the Seurat package in R software. Based on the Find Variable Features function in the Seurat package, the top 2000 genes were selected as the top variable features and used to normalize the analysis and integration of scRNA-seq data for each cell. The ScaleData and RunPCA functions were further executed to obtain the number of principal components (PC), and the results were subsequently visualized using the dimensionality reduction method of t-SNE (t-distributed stochastic neighbor embedding). Finally, the Idents and DimPlot functions were used to annotate and visualize the cells of the major TME cell types or subtypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePseudotime trajectory analysis of HDF regulators for TME cells\u003c/h2\u003e \u003cp\u003eHDAC is a class of proteases that plays an important role in structural modification of chromosomes and regulation of gene expression. To explore the relationship between cell pseudotime trajectories and HDF, we eemployed the Monocle2 R package to process single-cell RNA data from all cell types in CRC. Highly variable genes were set according to the following filtering criteria: mean expression\u0026thinsp;\u0026ge;\u0026thinsp;0.1 and dispersion_empirical\u0026thinsp;\u0026ge;\u0026thinsp;1* dispersion_fit. The DDRTree method was used for dimensionality reduction. Then we used the \u0026lsquo;plot_pseudotime_heatmap\u0026rsquo; function to visualize heatmaps showing the dynamic expression of HDF in the pseudotime trajectories of different TME cell types in CRC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eNon-negative matrix factorization of HDF in TME cells\u003c/h2\u003e \u003cp\u003eTo better observe the effects of HDAC-mediated cellular subtypes on TME cell types, we used a non-negative matrix factorization (NMF) algorithm for cellular subtype identification. Based on the expression profiles of 103 HDF, specific cellular taxa in TMA cells were further classified into 4\u0026ndash;6 cellular subpopulations using the NMF R package (version 0.20.6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of the marker genes of HDF-related cell subtypes in TME cells\u003c/h2\u003e \u003cp\u003eWe used the FindAllMarkers function to list the marker genes for each NMF cluster for each cell type in CRC, where the threshold parameters min.pct and logfc.log were set to 0.15 and the corrected P-value was set to less than 0.05. The Dotplot function was used to show the genes that were highly expressed in each NMF cell subpopulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis for HDF-related subtypes in TME cells\u003c/h2\u003e \u003cp\u003eFunctional enrichment analysis of highly expressed genes in NMF clusters was performed based on Gene Ontology (GO) and Genes and Genomes (KEGG) databases using the ClusterProfile R package. Functional enrichment of transcription factors for highly expressed genes in NMF clusters was performed based on the Metascape database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of intercellular communication of HDF-related subtypes in TME cells\u003c/h2\u003e \u003cp\u003eCellChat is an R package for analyzing intercellular communication based on the human ligand-receptor interaction database. First, based on CellChatDB.human, we used CellChat to evaluate the major signal input and output cell clusters in all NMF TME. Subsequently, the netVisual_circle function was used to show the strength or weakness of cell\u0026ndash;cell communication networks from the target cell cluster to different cell clusters in all NMF clusters, which contained different cell clusters in the NMF.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis of HDF-related signatures in TME cells\u003c/h2\u003e \u003cp\u003eThe FindAllmarker function based on the Seurat R package was used to generate gene signatures associated with histone deacetylated cell subtypes, and the scores of gene signatures of major cell types in CRC were calculated using GSVA (Gene set variation analysis). Cox risk proportion analysis was used to assess the predictive power of the genes characterizing the major cell subtypes for the prognosis of CRC patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of the benefit of immunotherapy for HDF-related signaling genes in TME cells\u003c/h2\u003e \u003cp\u003eScreening for appropriate populations is a prerequisite and key to the benefit of immunotherapy. In the era of precision medicine, the application of immunotherapy-related biomarkers, especially the large Panel, has received increasing attention. Therefore, data from study cohorts based on immunotherapy was used to assess the effectiveness of immune risk model scores in predicting immunotherapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis and hypothesis testing\u003c/h2\u003e \u003cp\u003eAll statistical comparisons involved in this study, as well as hypothesis testing for the significance of differences between groups, were based on the statistical analysis methods in R 3.6.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eMolecular characterization of HDF in CRC\u003c/h2\u003e\n \u003cp\u003eThe colorectal cancer (CRC) single cell transcriptome dataset was the data base for exploring the cell types and interactions between cells in the TME, and it could be found that the main cell types in the TME were Epithelial cells, Stromal cells, Myeloid cells, B lymphocyte cells, T lymphocyte cells and Mast cells. CRC tumor cells were mainly derived from the malignant transformation of epithelial cells (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Subsequently, assessment of the interactions among these cells using the CellChat tool revealed strong interactions between Epithelial cells and Stromal cells. Moreover, the interactions between these microenvironment cells were widespread (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). HDAC was a kind of proteases that played an important role in structural modification of chromosomes and regulation of gene expression. The mRNA expression of genes involved in the HDAC pathway was found to differ significantly between cell types from CRC single cell transcriptome data. High mRNA expression of many related genes was showed in the cell subtypes of Epithelial cells (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eC), which was consistent with the previously reported overexpression of HDAC related genes in cancer\u003csup\u003e4\u003c/sup\u003e. Based on HDAC related genes, cell subtypes were further identified using NMF for the major cell types in the TME, including four cell subtypes for stromal cells, five cell subtypes for macrophage cells, four cell subtypes for B cells, and five cell subtypes for T cells. It could be found that inter-tumor heterogeneity among different patients was reflected both in the proportional differences of major cell types and cell subtypes (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacterization of HDF-mediated stromal cell subtypes in CRC\u003c/h2\u003e\n \u003cp\u003ePseudotime trajectories analysis is an important indicator of the distance of cell differentiation using the single cell transcriptome, and the cells of TME transfers by corresponding state transitions during tumor progression. In order to investigate the role of HDAC in the transition of stromal cell state, we used the Monocle2 R package for pseudotime trajectories analysis of stromal cells, and then analyzed the relationship between HDAC-related genes and pseudotime trajectories, it could be found that the high expression of some genes significantly appeared at the head or tail of the pseudotime trajectories. The main highly expressed genes that appeared in the tail of the pseudotime trajectories were BRMS1, CHD3, HDAC2, MORF4L1, PRKD1, HOPX, MAPK8, DR1, MORF4L2 and PHB. On the contrary, the main highly expressed genes that appeared in the head of the pseudotime trajectories were HDAC10, PRMT6, C6orf89, RCOR2, SIRT3, MTA3, TRERF1, LRRK2, HDAC7 and PRDM5 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). A comparison of HDAC-mediated interactions between subtypes of stromal cell and epithelial cells showed no significant differences between each of the four fibroblast subtypes and epithelial cells (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). The proportional distribution of the four fibroblast subtypes in tumor tissues compared to normal tissues was also not significantly different (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). However, Fib_C2 and Fib_C4 subtypes of fibroblast had a slightly up-regulated trend in the proportional distribution. Based on the KEGG database, functional enrichment analysis of genes specifically highly expressed in the four fibroblast subtypes revealed that the Fib_C2 subtype was enriched for TNF signaling pathway, IL-17 signaling pathway and Osteoclast differentiation. Fib_C4 subtype was enriched for Autophagy animal. Fib_C1 subtype was enriched for Protein digestion and absorption, ECM-receptor interaction, and Proteoglycans in cancer. Fib_C3 subtype was enriched for Focal adhesion, Hypertrophic cardiomyopathy and Dilated cardiomyopathy (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). Further correlation analysis of HDAC-mediated stromal cell subtypes revealed that the marker proteins significantly associated with Fib_C2 subtype and Fib_C4 subtype were mainly ADAMDEC1 and PLVAP. Moreover, the marker proteins significantly associated with Fib_C1 subtype and Fib_C1 subtype were mainly CREM (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE). The genes were highly expressed in Fib_C2 subtype were mainly IRF1, CCL2, SOCS3, JUNB and FOSB (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF)\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacterization of HDF-mediated myeloid cell subtypes in CRC\u003c/h2\u003e\n \u003cp\u003eTo investigate the function of HDAC in myeloid cell state transition, the CellChat tool was used to compare the interaction between five HDAC-mediated macrophage subtypes and epithelial cells, and it could be found that the Mac_C3 subtype of macrophages with SFPQ as a highly expressed gene had a strong interaction with epithelial cells (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA), and macrophage Mac_C3 subtype had a strong correlation with macrophage M1 type (anti-cancer and pro-inflammatory type) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB), which suggested that HDAC had the potential ability to regulate macrophage functional polarization in the microenvironment of CRC tumors. Mac_C1 subtype with MORF4L1 as a highly expressed gene, macrophage Mac_C2 subtype with VEGFA as a highly expressed gene, and macrophage Mac_C4 subtype with PHB as a highly expressed gene also had some interactions with epithelial cells (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA), and macrophage Mac_C1 subtype and Mac_C4 subtype had a strong correlation with macrophage M2 type (pro-oncogenic and anti-inflammatory type) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB), which further suggested the important role of HDAC in the regulation of macrophage functional polarization in the microenvironment of CRC tumors. The functional polarization of macrophages had a very important role in CRC tumor progression, and it could be found that the distribution of M1-type and M2-type features of macrophages in TME was widespread and did not have strong cluster distribution characteristics (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC), which suggested that the functional polarization of macrophages in TME was dynamic The macrophage subtypes with GZMB as the highly expressed gene, the macrophage subtype with IL32 as the highly expressed gene and the macrophage subtype with S100A8 as the highly expressed gene showed strong cluster distribution characteristics in the tSNE descending plot (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC), which may be related to their corresponding fixed biological functions. Based on the KEGG database, functional enrichment analysis of genes specifically highly expressed in five adult macrophage subtypes revealed that the pathways enriched in the Mac_C3 subtype of macrophages were mainly SPLICEOSOME, while the pathways enriched in the Mac_C1 and Mac_C4 subtypes of macrophages were mainly PYRUVATE METABOLISM, LIMONENE AND PINENE DEGRADATION, GLUTATHIONE METABOLISM, PROTEASOME, PROPANOATE METABOLISM, and FRUCTOSE AND MANNOSE METABOLISM (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacterization of HDF-mediated B-cell subtypes in CRC\u003c/h2\u003e\n \u003cp\u003eIn order to investigate the role of HDAC in the transition of B cells state, we used the Monocle2 R package for pseudotime trajectories analysis of B cells, and then analyzed the relationship between HDAC-related genes and pseudotime trajectories, it could be found that the high expression of some genes significantly appeared at the tail of the pseudotime trajectories. The main highly expressed genes that appear in the tail of the pseudotime trajectories were SDR16C5, PER1, CAMK2D, LRRK2, NCAPG2, SUV39H1, BCL6, BRMS1L, UCN, CHD3, MTA2 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). Comparison of the interactions between the five HDAC-mediated B-cell subtypes and epithelial cells using the CellChat tool revealed that the interactions were all relatively weak (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). Subsequent comparison of the correlation between the five HDAC-mediated B-cell subtypes and protein marker-labeled B-cell subtypes revealed that the five B-cell subtypes showed a more consistent correlation, all showing strong correlation with IgG and IgA proteins, and weak correlation with CD19 and CD20 proteins (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC). Intercellular interactions were further explored using the NichNet tool, which considereed not only ligand-receptor interactions but also target genes activated after ligand-receptor interactions, and it could be found that the more active ligands in B cells were MANF, LGALS3, ARF1, RPS19, TIMP1, CD99 and GSTP1, while the more active target genes in epithelial cells were CCND1, BID, CCL2, EDN1, TIMP1, NQO1and JUNB. The activation of target genes had some ligand genes, suggesting that the B cell-epithelial cell interaction process had a more complex multiple network relationship (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD). Assessment of the differentiation potential of B cells using the CytoTRACE tool revealed that the differentiation potential of B cells was characterized by a more significant cluster distribution, but there was a poor agreement between the cluster characteristics of B cells differentiation potential and HDAC-mediated clusters (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE). Based on the KEGG database, functional enrichment analysis of genes specifically highly expressed in five adult B-cell subtypes revealed that the main enriched pathways in B-cell subtypes Bcell_C1 and Bcell_C4 were Ribosome, Coronavirus disease-COVID-19, Allograft rejection, Leishmaniasis, Asthma, Intestinal immune network for IgA production, Viral myocarditis, while B-cell subtypes Bcell_C3 and Bcell_C5 were mainly enriched for Thyroid hormone synthesis, Protein export, Protein processing in endoplasmic reticulum (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacterization of HDF-mediated T-cell subtypes in CRC\u003c/h2\u003e\n \u003cp\u003eFive subtypes of T cells were annotated: KLRB1-positive T cells, CD8-positive T cells, regulatory T cells, cytotoxic T cells, and KRT18-positive T cells (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). To investigate the role of HDAC in T cell state transition, we compared the correlation between HDAC-mediated T-cell subtypes and marker protein-annotated T-cell subtypes, and found that HDAC-mediated T-cell subtype Tcell_C3 was mainly distributed in CD8-positive T cells and cytotoxic T cells, while other T-cell subtypes did not have significant distribution differences (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB), suggesting that HDAC might play a regulatory role in the killing process of T cells against tumor cells. Further comparison of the interactions between T-cell subtypes and epithelial cells mediated by the combination of 25 HDAC-related genes and marker proteins using the CellChat tool revealed both CD8-positive T cells and cytotoxic T cells had strong interactions with epithelial cells (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC), and there was no significant difference in the interactions between the five HDAC-mediated T-cell subtypes and epithelial cells in the cluster of CD8-positive T cells, with the same trend in cytotoxic T cells. These results further emphasized the important role of HDAC in the process of killing function of T cells. Subsequently, five HDAC-mediated T-cell subtypes were compared for immune response-related scores and gene expression differences, and it could be found that the HDAC-mediated T-cell subtype Tcell_C3 had a strong signal on the Cytotoxic score, Exhaustion score, and Teffect score. High expression of genes in Co -inhibitors included HAVCR2, TGFB1, TIGIT, CD247, CD160, CD244, CD96, LAG3, Co-stimuiations included CD226, TMIGD2, ENTPD1, TNFRSF9, and genes highly expressed in T-function Genes were CD8B, GZMB, PRF1, TBX21. (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eD). Finally, we compared the correlation between HDAC-mediated B-cell subtypes and marker protein-annotated B-cell subtypes, it could be found that NRT18-positive B-cell subtypes mainly contain HDAC-mediated B-cell subtype Bcell_C3 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eE).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eThe prognosis assessment of HDF-mediated microenvironmental cell subtype features in CRC\u003c/h2\u003e\n \u003cp\u003eTo further assess the clinical prognostic significance of genes characterizing the subtypes of major cell types in the HDAC-mediated CRC tumor microenvironment, six GEO datasets (GSE72970, GSE39582, GSE39084, GSE17538, GSE17536, and GSE103479) with overall survival (OS) information were collected, and using univariate Cox analysis to assess their relationship on OS, it could be found that most of the genetic characteristics had some prognostic significance (Fig. 6ABCD). Eight GEO datasets (GSE72970, GSE39582, GSE38832, GSE33113, GSE17538, GSE17536, GSE14333, GSE103479) with progression-free survival (PFS) information were collected, and similar results to OS assessment could be found (Fig. 6EFGH).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eThe predictive power assessment of immunotherapy benefit for HDF-mediated micro-environmental cell subtype characterization in CRC\u003c/h2\u003e\n \u003cp\u003eTo further assessed the clinical immunotherapeutic significance of HDAC-mediated subtype-specific genes of major cell types in TME, the GSE78220 and IMvigor210 immunotherapy datasets were collected and evaluated accordingly. In the GSE78220 dataset, the risk score allowed to divide the sample into two groups, High_RiskScore and Low_RiskScore (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA). There was a significant difference in the survival curves between the high and low risk groups (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB), and the proportion of immunotherapy responsed between the high and low risk groups trend (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC), suggesting that patients in the low-risk score group were more likely to benefit from immunotherapy. A consistent trend (Fig. 7DEF) can be found in the IMvigor210 dataset.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHDACs are specialized enzymes that regulate chromatin remodeling\u003csup\u003e4\u003c/sup\u003e. To date, several studies have revealed the relevance of HDF to the alteration and pathogenesis of various cancer\u003csup\u003e5\u0026ndash;8\u003c/sup\u003e. However, only a few have investigated the potential tumorigenic role of HDF. Overexpression of HDAC leads to enhanced deacetylation, which increases the gravitational force between DNA and histones by restoring the positive histone charge, thus making the relaxed nucleosomes very tight and detrimental to the expression of specific genes, including some tumor suppressor genes, so it is necessary to explore in depth the roles of HDAC-related genes in TME. In our study, we have for the first time comprehensively explored the modifying factors of HDF on the major cell types in the TME of CRC and further identify the diversity of cellular interactions in the TME of CRC.\u003c/p\u003e \u003cp\u003eBy analyzing and visualizing single-cell transcriptome data, we found that the main cell types in TME were epithelial cells, stromal cells, myeloid cells, B cells, T cells and mast cells, where CRC tumor cells were mainly derived from malignant changes in epithelial cells. At the same time, we found that the interactions between cells in the above microenvironment were widespread. we found that the TME cells, including stromal cells, myeloid cells, T cells and B cells all manifested the diverse HDF regulatory patterns and the extensive communication with tumor epithelial cells based on the single-cell analysis.\u003c/p\u003e \u003cp\u003eCancer-associated fibroblasts (CAFs) have been a topic of interest in the field of cancer research. These fibroblasts are a type of cell found in TME that play a crucial role in tumor growth, invasion, and metastasis. Recent advancements in the understanding of CAFs have shed light on their complex interactions with cancer cells. It has been discovered that CAFs can promote tumor progression by secreting various growth factors, cytokines, and extracellular matrix components. These factors create a favorable environment for cancer cells to proliferate and invade surrounding tissues\u003csup\u003e9\u003c/sup\u003e. In our study, we found that HDAC-related genes could be found at the head and tail of the Pseudotime trajectories analysis, respectively, and the HDAC-mediated stromal cell subtypes had a slightly up-regulated tendency in the proportional distribution between the Fib_C2 and Fib_C4. Further pathway analysis also revealed the participation of CAFs in TNF signaling pathway, IL\u0026thinsp;\u0026minus;\u0026thinsp;17 signaling pathway, Osteoclast differentiation and Autophagy animal. The correlation analysis of HDAC-mediated stromal cell subtypes and stromal cell subtypes identified by marker proteins in the literature revealed that the marker proteins significantly associated with Fib_C2 subtype and Fib_C4 subtype were mainly ADAMDEC1 and PLVAP. Therefore, we speculated that HDAC modification CAFs may form the interaction with tumor cells to promote the progress and metastasis of tumor.\u003c/p\u003e \u003cp\u003eTo date, increasing studies have elaborated the relationship between HDAC and immune cells. Andreas von Knethen\u003csup\u003e10\u003c/sup\u003e reported that HDACs and HDACi were the starting point for altering Treg function. In the study of Xu-Wen Guan and colleagues\u003csup\u003e11\u003c/sup\u003e, they used the HADC inhibitor to treat the tumor growth by upregulating CD20 and its efficacy had been proven. In our research, we found Mac_C3 subtypes with SFPQ as a highly expressed gene had strong interactions with epithelial cells, which performed strong correlation with macrophage M1 type (anti-cancer pro-inflammatory). On the contrary, Mac_C1 subtypes with MORF4L1 as a highly expressed gene and Mac_C4 subtypes with PHB as a highly expressed gene had strong correlation with macrophage M2 type (pro-oncogenic and anti-inflammatory type). The above suggested an important role for HDAC in the regulation of macrophage functional polarization in the microenvironment of CRC tumors. Moreover, except for macrophages, we found that HDAC-mediated T cells and B cells also showed extensive interaction with tumor cells. Through analysis of B cells, we found that the interaction between B cells and epithelial cells involved a complex but relatively weak network relationship. Furthermore, there was poor consistency between the subpopulation characteristics of B cell differentiation potential and the subpopulations mediated by HDAC. Through analysis of T cells, we observed that the HDAC-mediated T-cell subtype, Tcell_C3, was predominantly present in CD8-positive T cells and cytotoxic T cells, while other T-cell subtypes did not exhibit significant disparities in distribution. These findings implied that HDAC might exert a modulatory role in T cell-mediated cytotoxicity against tumor cells. Therefore, we speculated that HDAC modification immune cells may play a significant role in immune escape and the tumor-promoting effect.\u003c/p\u003e \u003cp\u003eDue to the intrinsic role of HDAC for TME, we further evaluated its prognosis as well as therapeutic expectations and showed that it was predictive of prognosis and was more likely to benefit from immunotherapy for patients in the low-risk score group.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe identified HDF associated cell subtypes of TME cells by using the single-cell sequencing analysis method and revealed the HDAC mediated intercellular communication of TME in the regulation of tumor growth and tumor immunomodulatory processes\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eNot available\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eAll data are available in a public, open access repository. The datasets used and/or analyzed during the current study are available in the Gene Expression1 Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) and The Cancer Genome Atlas (TCGA) network (https://cancergenome.nih.gov/). R and other custom scripts for analyzing data are available upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors have no conflicts of interest to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDekker E, Tanis PJ, Vleugels JLA, Kasi PM, Wallace MB. Colorectal cancer. \u003cem\u003eLancet (London, England).\u0026nbsp;\u003c/em\u003e2019;394(10207):1467-1480.\u003c/li\u003e\n \u003cli\u003eSung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA: a cancer journal for clinicians.\u0026nbsp;\u003c/em\u003e2021;71(3):209-249.\u003c/li\u003e\n \u003cli\u003eBaidoun F, Elshiwy K, Elkeraie Y, et al. Colorectal Cancer Epidemiology: Recent Trends and Impact on Outcomes. \u003cem\u003eCurrent drug targets.\u0026nbsp;\u003c/em\u003e2021;22(9):998-1009.\u003c/li\u003e\n \u003cli\u003eRamaiah MJ, Tangutur AD, Manyam RR. Epigenetic modulation and understanding of HDAC inhibitors in cancer therapy. \u003cem\u003eLife sciences.\u0026nbsp;\u003c/em\u003e2021;277:119504.\u003c/li\u003e\n \u003cli\u003eHu XT, Xing W, Zhao RS, et al. HDAC2 inhibits EMT-mediated cancer metastasis by downregulating the long noncoding RNA H19 in colorectal cancer. \u003cem\u003eJournal of experimental \u0026amp; clinical cancer research : CR.\u0026nbsp;\u003c/em\u003e2020;39(1):270.\u003c/li\u003e\n \u003cli\u003eLi T, Zhang C, Hassan S, et al. Histone deacetylase 6 in cancer. \u003cem\u003eJournal of hematology \u0026amp; oncology.\u0026nbsp;\u003c/em\u003e2018;11(1):111.\u003c/li\u003e\n \u003cli\u003eGlozak MA, Seto E. Histone deacetylases and cancer. \u003cem\u003eOncogene.\u0026nbsp;\u003c/em\u003e2007;26(37):5420-5432.\u003c/li\u003e\n \u003cli\u003eSwierczynski S, Klieser E, Illig R, Alinger-Scharinger B, Kiesslich T, Neureiter D. Histone deacetylation meets miRNA: epigenetics and post-transcriptional regulation in cancer and chronic diseases. \u003cem\u003eExpert opinion on biological therapy.\u0026nbsp;\u003c/em\u003e2015;15(5):651-664.\u003c/li\u003e\n \u003cli\u003eBiffi G, Tuveson DA. Diversity and Biology of Cancer-Associated Fibroblasts. \u003cem\u003ePhysiological reviews.\u0026nbsp;\u003c/em\u003e2021;101(1):147-176.\u003c/li\u003e\n \u003cli\u003evon Knethen A, Heinicke U, Weigert A, Zacharowski K, Br\u0026uuml;ne B. Histone Deacetylation Inhibitors as Modulators of Regulatory T Cells. \u003cem\u003eInternational journal of molecular sciences.\u0026nbsp;\u003c/em\u003e2020;21(7).\u003c/li\u003e\n \u003cli\u003eGuan XW, Wang HQ, Ban WW, et al. Novel HDAC inhibitor Chidamide synergizes with Rituximab to inhibit diffuse large B-cell lymphoma tumour growth by upregulating CD20. \u003cem\u003eCell death \u0026amp; disease.\u0026nbsp;\u003c/em\u003e2020;11(1):20.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biochemical-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bigi","sideBox":"Learn more about [Biochemical Genetics](http://link.springer.com/journal/10528)","snPcode":"10528","submissionUrl":"https://submission.nature.com/new-submission/10528/3","title":"Biochemical Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Histone deacetylation factor, Histone deacetylases, tumor microenvironment, colorectal cancer","lastPublishedDoi":"10.21203/rs.3.rs-3562456/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3562456/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this study, single-cell RNA-seq data were collected to analyze the characteristics of Histone deacetylation factor (HDF). The tumor microenvironment (TME) cell clusters related to prognosis and immune response were identified by using CRC tissue transcriptome and immunotherapy cohorts from public repository. We explored the expression characteristics of HDF in stromal cells, macrophages, T lymphocytes and B lymphocytes of the CRC single-cell dataset TME and further identified 4 to 6 cell subclusters using the expression profiles of HDF-associated genes, respectively. The regulatory role of HDF-associated genes on the CRC tumor microenvironment was explored by using single-cell trajectory analysis, and the cellular subtypes identified by biologically characterized genes were compared with those identified by HDF-associated genes. The interaction of HDF-associated gene-mediated microenvironmental cell subtypes and tumor epithelial cells were explored by using intercellular communication analysis, revealing the molecular regulatory mechanism of tumor epithelial cell heterogeneity.\u003c/p\u003e \u003cp\u003eBased on the expression of feature genes mediated by HDF-related genes in the microenvironment T-cell subtypes, enrichment scoring was performed on the feature gene expression in the CRC tumor tissue transcriptome dataset. It was found that the feature gene scoring of microenvironment T-cell subtypes (HDF-TME score) has a certain predictive ability for the prognosis and immunotherapy benefits of CRC tumor patients, providing data support for precise immunotherapy in CRC tumors.\u003c/p\u003e","manuscriptTitle":"Single-cell Histone deacetylation factor regulator patterns guide intercellular communication of tumor microenvironment that contribute to colorectal cancer progression and immunotherapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-09 17:39:05","doi":"10.21203/rs.3.rs-3562456/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-01-03T10:03:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-12-25T11:38:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49407e5b-c019-4691-b6cd-cde8b6343ec2","date":"2023-12-25T10:15:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5b9f0ad1-592f-4e00-9077-7ae6101b8a16","date":"2023-11-12T17:59:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-11-11T07:04:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-11-06T16:40:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-11-06T16:40:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biochemical Genetics","date":"2023-11-05T12:42:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biochemical-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bigi","sideBox":"Learn more about [Biochemical Genetics](http://link.springer.com/journal/10528)","snPcode":"10528","submissionUrl":"https://submission.nature.com/new-submission/10528/3","title":"Biochemical Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1d847598-ae9e-4439-bdeb-30041531001b","owner":[],"postedDate":"November 9th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-01-31T15:31:25+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-09 17:39:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3562456","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3562456","identity":"rs-3562456","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}