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Pan-Cancer Multi-Omics Analysis Uncovers CHD4 Driving Tumor Progression via Epigenetic Regulation of Genomic Stability and the Immune MicroenvironmentGuangxu Fu1,2,3,4,5, Yong Tao1,2,3, Keyi Feng1,2,3, Yuxing Chen1,2,3,, Wen Zhang1,2,3, Zhen Zhang4,5, Guoda Hu4,5,* Yunsheng Ou1,2,3,*
1Department of Orthopedic Surgery, The First Affiliated Hospital of Chongqing Medical University, Yuzhong, Chongqing, 400016, China
2Chongqing Municipal Health Commission Key Laboratory of Musculoskeletal Regeneration and Translational Medicine, Yuzhong, Chongqing, 400016, China
3Orthopaedic Research Laboratory of Chongqing Medical University, Yuzhong, Chongqing, 400016, China
4Department of Orthopedic Surgery, The People's Hospital of Lichuan City, Enshi Tujia and Miao Autonomous Prefecture, Hubei Province 445400, China
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5Hubei Provincial Key Laboratory of Occurrence and Intervention of Rheumatic Diseases, Enshi Tujia and Miao Autonomous Prefecture, Hubei, 445000, China
*Corresponding Author: Guoda Hu. Email: huguoda0011@163.com; Yunsheng Ou. Email: ouyunsheng2001@163.com
Abstract
Background
Chromodomain Helicase DNA-Binding Protein 4 (CHD4), the core ATPase subunit of the NuRD complex, plays a critical role in epigenetic regulation. However, its systematic function across pan-cancer contexts and the synergistic regulatory mechanisms governing genomic stability and the immune microenvironment remain poorly characterized.
Methods
This study analyzed the expression patterns, clinical prognostic value, and genomic alteration profiles of CHD4 across pan-cancer based on multi-omics data. Associations with genomic instability, the tumor immune microenvironment, and therapeutic responsiveness were investigated. Functional enrichment, drug sensitivity screening, and in vitro experiments were conducted to validate its molecular mechanisms.
Results
CHD4 was significantly upregulated in multiple cancer types, and its elevated expression was strongly associated with poor patient prognosis. Integrated analysis revealed that CHD4 expression correlated strongly with markers of genomic instability, including homologous recombination deficiency (HRD) and loss of heterozygosity (LOH), and was concurrently associated with an immunosuppressive microenvironment, characterized by decreased CD8 + T cell infiltration and increased expression of immune checkpoint molecules. Mechanistically, CHD4 expression was closely associated with components of the NuRD complex, including HDAC1 and HDAC2, and facilitated histone deacetylation, which altered chromatin accessibility and thereby promoted immune evasion and genomic instability. Furthermore, drug sensitivity analyses revealed that tumors with high CHD4 expression exhibited significant sensitivity to histone deacetylase (HDAC) inhibitors, such as vorinostat and panobinostat.
Conclusion
This study reveals that CHD4 functions as a master epigenetic orchestrator that promotes tumor progression by concurrently inducing genomic instability and fostering an immunosuppressive microenvironment. Furthermore, the predictive value of CHD4 for sensitivity to HDAC inhibitors offers a novel strategy for epigenetics-guided precision therapy.
Keywords:
CHD4
pan-cancer analysis
genomic instability
tumor immune microenvironment
HDAC inhibitor
epigenetic therapy
precision medicine
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Introduction
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Cancer remains a major global public health threat and the second leading cause of mortality worldwide. According to 2022 global cancer statistics, malignant neoplasms account for approximately 10 million annual deaths, with the disease burden continuing to rise [1,2]. Despite considerable advances in cancer treatment—particularly the introduction of immune checkpoint inhibitors and molecularly targeted therapies, which have markedly improved survival for patients with various cancers—tumor heterogeneity and therapy resistance remain significant obstacles to improving clinical efficacy [3–5]. Tumorigenesis is a complex, multi-step process driven by genomic mutations, epigenetic modifications, and remodeling of the tumor microenvironment. Collectively, these alterations enable tumor cells to acquire sustained proliferative capacity, undergo metabolic reprogramming, and evade immune surveillance [6,7]. The role of epigenetic regulation in cancer progression has attracted considerable interest. Chromatin remodeling complexes, as pivotal mediators of epigenetic regulation, modulate gene expression programs by altering chromatin structure and accessibility, thereby influencing tumor cell immunogenicity and heterogeneity [8,9].CHD4, the core ATPase subunit of the nucleosome remodeling and deacetylation (NuRD) complex, is an evolutionarily conserved chromatin remodeler. Through ATP hydrolysis-driven nucleosome remodeling and coordination with histone deacetylase activity, CHD4 functions as a key regulator of fundamental biological processes, including transcriptional regulation, DNA damage repair, and cell fate determination [10,11]. Empirical studies indicate that CHD4 serves as an early responder in the DNA damage response (DDR), rapidly localizing to double-strand breaks to promote the assembly of repair protein complexes [12,13]. The oncogenic role of CHD4 is highly context-dependent. For instance, it promotes chemoresistance in gastric cancer via activation of the MEK/ERK pathway [14], while in triple-negative breast cancer (BRCA), it facilitates tumor progression by modulating integrin signaling [15]. In some settings, CHD4 exerts tumor-promoting effects by enhancing proliferation, invasion, and metastasis; in others, it helps maintain genomic integrity, suggesting a dual, context-specific functionality [16–19]. Despite these insights, a comprehensive understanding of CHD4 is still lacking. A systematic analysis of its expression patterns, genomic alterations, and clinical prognostic significance across diverse cancers is unavailable. Moreover, the epigenetic mechanisms by which CHD4 modulates the tumor TME remain poorly characterized, and its potential as a predictive biomarker for epigenetic therapies (e.g., HDAC inhibitors) has not been systematically validated.
To address these gaps, we employed integrated multi-omics approaches to characterize CHD4 expression profiles, evaluate its clinical relevance, and investigate its regulatory roles in genomic instability and immune modulation across cancer types. We also explored therapeutic strategies for CHD4-high tumors, particularly their response to epigenetic drugs. Our findings provide novel insights into the oncogenic functions of CHD4 and establish a foundation for CHD4-targeted precision therapy. An outline of our methodological approach is provided in Fig. 1.
Fig. 1
Study flowchart
Materials and Methods
Data Sources and Processing
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Transcriptomic data and corresponding clinical information were obtained from the UCSC Xena platform (https://xenabrowser.net/), which provides integrated access to The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and Genotype-Tissue Expression (GTEx) projects. This collective dataset forms a pan-cancer cohort (PANCAN, N = 19,131 samples, G = 60,499 genes) [20]. Expression values were normalized using a log2(TPM + 0.001) transformation. Cancer types with fewer than three samples were excluded, resulting in a final cohort of 34 cancer types for downstream analysis. CHD4 expression profiles in cancer cell lines were sourced from the Cancer Cell Line Encyclopedia (CCLE) database [21]. Genomic alteration data, including single nucleotide variants (SNVs) and methylation profiles, were retrieved from cBioPortal (http://www.cbioportal.org/) [22]. Protein expression validation was performed using datasets from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), accessed via the UALCAN portal (http://ualcan.path.uab.edu/) [23]. Additionally, immunohistochemistry (IHC) images from the Human Protein Atlas (HPA) (https://www.proteinatlas.org/) were employed to visually assess CHD4 expression patterns in tumor versus normal tissues at the histomorphological level[24]. The cancer types analyzed in this study are listed in Table S1 (Supplementary material 2).Evaluation of CHD4 expression and clinical relevance
CHD4 mRNA levels were compared between tumor and normal tissues across the 34 cancer types in the pan-cancer dataset. A paired-sample analysis was performed for 15 tumor types with sufficient matched samples. Differential expression analysis was conducted using the limma R package, and results were visualized with ggplot2. A similar analysis of CHD4 expression patterns was carried out across 32 tumor types using CCLE data. Differences in CHD4 protein expression between tumor and normal tissues were validated using CPTAC data. IHC staining images from the HPA database were employed to visually compare CHD4 protein expression in tumor versus normal tissues for six cancer types. Using TCGA clinical data, we visualized expression differences using boxplots stratified by TNM stage, pathological stage, and tumor grade.
The R package pROC was used to generate receiver operating characteristic (ROC) curves, with an area under the curve (AUC) greater than 0.7 considered indicative of satisfactory diagnostic performance. Univariate Cox regression analysis was performed using the survival and forestplot R packages to evaluate the prognostic significance of CHD4 expression for overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). Patients were stratified into high and low CHD4 expression groups based on the optimal cut-off value determined by the surv_cutpoint function from the survminer package. Differences in survival outcomes between these groups were visualized using Kaplan-Meier curves and assessed with the log-rank test.
Analysis of genomic alterations and genomic instability
The frequency of CHD4 genomic alterations, including mutations, amplifications, and deep deletions, was examined across various cancer types using the "Cancer Types Summary" module in cBioPortal. Pan-cancer single nucleotide variant (SNV) data were processed and visualized using the maftools R package to depict the mutational landscape across protein domains and the overall pan-cancer mutation spectrum. Kaplan-Meier survival analyses, performed using the Copy Number module of the Tumor Immune Dysfunction and Exclusion (TIDE) tool (http://tide.dfci.harvard.edu/) [25], were used to assess the prognostic significance of CHD4 copy number variations (CNVs). We also investigated the correlation between CHD4 CNVs and the activity of several canonical oncogenic signaling pathways, including the TP53, RTK/RAS, PI3K, and cell cycle pathways.
A comprehensive set of genomic metrics was assessed, including tumor mutational burden (TMB), microsatellite instability (MSI), mutant-allele tumor heterogeneity (MATH) score, neoantigen load [26], tumor purity, ploidy, homologous recombination deficiency (HRD) score, loss of heterozygosity (LOH), aneuploidy score, and the ratio of non-synonymous to synonymous mutations. These metrics were calculated using the maftools package, and their interrelationships were analyzed. To investigate the association between CHD4 expression, immune response, and genomic characteristics, patients were categorized into four distinct groups based on CHD4 expression levels, following the immune and genomic classification methodology proposed by Thorsson et al. [27]. The mean value of each genomic score was calculated for each group, and the results were visualized as a heatmap.
Analysis of DNA repair, tumor stemness, and epigenetic regulation
The study visualized correlations between CHD4 expression and five DNA mismatch repair (DMMR) genes [28], eleven histone modification-associated proteins, and four DNA methyltransferases (DNMTs) [29]. The relationship between CHD4 and a homologous recombination repair (HRR) signature was analyzed using GEPIA2, based on a gene set from the ARIEL3 clinical trial [30]. Additionally, correlations between CHD4 expression and tumor stemness scores—including RNA-based scores (RNAss, EREG.EXPss) and DNA methylation-based scores (DNAss, EREG-METHss, DMPss, ENHss)—were assessed. Associations with patient survival outcomes and patterns of cytotoxic T lymphocyte (CTL) infiltration were examined using the survival R package and the methylation module of the TIDE platform, respectively. Bubble plots illustrated differences and correlations in CHD4 methylation levels across various genetic loci. A heatmap was generated to represent correlations between CHD4 and the expression of 44 genes involved in N1-methyladenosine (m1A), 5-methylcytosine (m5C), and N6-methyladenosine (m6A) RNA modifications [31].
Analysis of alternative splicing events
Clinically significant alternative splicing (AS) events of CHD4 were identified using the ClinicalAS module of the OncoSplicing platform (http://www.oncosplicing.com/) [32]. Splicing percentages (PSI) in tumor and normal tissues from the TCGA-GTEx pan-cancer dataset were visualized using PanPlot. The prognostic significance of notable AS events was assessed via Kaplan-Meier survival analysis in the pan-cancer cohort.
Functional enrichment and interaction analysis
Experimentally supported protein-protein interactions (PPIs) involving CHD4 were retrieved from the STRING database [33]. Somatic alteration profiles across key oncogenic pathways were examined using UALCAN, while associations between CHD4 expression and pathway activity features were evaluated with GEPIA2.0. Using GEPIA2.0 [34], we identified the top 100 genes exhibiting the most significant pan-cancer co-expression with CHD4. Functional enrichment analysis of these genes for Gene Ontology (GO) terms was conducted using the R packages clusterProfiler and org.Hs.eg.db. To explore biological pathways linked to CHD4 activity, samples were classified into high- and low-expression groups (top and bottom 30%) and subjected to Gene Set Enrichment Analysis (GSEA) [35] using Hallmark and KEGG gene sets. To assess CHD4's functional role in cancer phenotypes, we incorporated 14 functional state gene sets from the CancerSEA database [36], encompassing pathways such as apoptosis, cell cycle regulation, differentiation, DNA damage response, epithelial-mesenchymal transition (EMT), hypoxia, inflammation, invasion, metabolism, metastasis, proliferation, quiescence, stemness, and angiogenesis. The z-score algorithm developed by Lee et al. [37] was implemented via the R package GSVA to compute the combined z-score [38] for each functional state. The resulting scores were normalized using the scale function to generate pathway activity scores. The relationship between CHD4 expression and each functional activity score was then determined and visualized using scatter plots.
Exploration of CHD4 in the pan-cancer immune microenvironment
To examine the correlation between CHD4 expression and immunological characteristics, researchers acquired a dataset of 68 established immune-related gene expression profiles from the UCSC Xena database. The ESTIMATE package in R was utilized to compute stromal, immune, and ESTIMATE scores for 33 cancer types. The correlation between CHD4 expression and the infiltration levels of various tumor microenvironment components was analyzed. This analysis encompassed 19 immune cell types (including B cells, T cell subsets such as CD4+, CD8+, and regulatory T cells), natural killer (NK) cells, dendritic cells, and macrophages, alongside two stromal cell types (endothelial cells and fibroblasts). It was conducted utilizing various deconvolution algorithms (TIMER, CIBERSORT, xCell, EPIC, MCP-counter, and quanTIseq) integrated within the R package IOBR [39]. Additionally, we examined the relationships between CHD4 mRNA expression and immune checkpoint molecules [27], along with five types of immunomodulatory genes (chemokines, receptors, MHC molecules, immunoinhibitors, and immunostimulators). The expression profile of CHD4 across immunological subtypes (C1-C6) was analyzed using the TISIDB database (http://cis.hku.hk/TISIDB/)[40]. The TISMO platform (http://tismo.cistrome.org/) was used to model the impact of cytokine therapy (e.g., IFN-γ, TNF-α) and immune checkpoint inhibitor therapy (anti-CTLA-4/anti-PD-1) on CHD4 expression [41]. Spatially resolved transcriptome data from SpatialDB (https://www.spatialomics.org/SpatialDB/) were employed to investigate the spatial co-localization of CHD4 with the epithelial marker CDH1 and the proliferative T-cell marker CCND1 in BRCA [42]. Finally, a single-cell analysis of CHD4 expression across diverse malignancies was conducted using the Tumor Immune Single-Cell Hub (TISCH) (http://tisch.comp-genomics.org/)[43].
Evaluation of treatment response and drug sensitivity
The relationship between CHD4 expression and the efficacy of chemotherapy, targeted therapy, and endocrine therapy in patients with BRCA, GBM, COAD, and OV was assessed using the ROC Plotter online tool (https://rocplot.org/) [44]. The Connectivity Map (CMAP) (https://clue.io/) was used to identify the 30 most promising small-molecule compounds capable of reversing the CHD4-high expression profile (connectivity score > 90) [45], and their mechanisms of action (MoA) were evaluated. The correlation between CHD4 expression and the half-maximal growth inhibitory concentration (GI50) of the top candidate compounds identified by CMAP was analyzed in cell lines using the COMPARE tool from the National Cancer Institute (NCI) Developmental Therapeutics Program (DTP). Furthermore, the correlation between sensitivity to these candidate compounds, particularly HDAC inhibitors, and CHD4 expression levels was meticulously confirmed across five independent pharmacogenomic databases: CTRP, GDSC1, GDSC2, PRISM, and RNAactDrug.
Experimental methods
The experimental methods are detailed in Supplementary material 3.
Statistical analysis
All analysis were performed using R software (version 4.2.1). Comparisons of continuous variables between two groups were performed using the Student’s t-test for normally distributed data or the Mann-Whitney U test for non-normally distributed data. For comparisons among multiple groups, the Kruskal-Wallis test was applied, followed by Dunn's post-hoc test when a significant difference was identified. Survival analysis was carried out using the Kaplan-Meier method, and the log-rank test was applied to compare differences between groups. Univariate prognostic analysis was performed using Cox proportional hazards regression. Correlations between variables were assessed using either Pearson or Spearman correlation coefficients, depending on the distribution of the data. A significance threshold of P < 0.05 was applied for all tests. For analyses involving multiple comparisons, the false discovery rate (FDR) was controlled using the Benjamini-Hochberg method.
Results
CHD4 is upregulated across pan-cancer and associates with poor prognosis
Transcriptomic analysis of 34 cancer types from TCGA and GTEx revealed elevated CHD4 mRNA levels in tumors—including BRCA, COAD, and LUAD—compared to normal tissues (Fig. 2A). This finding was corroborated in a paired-sample analysis of 15 cancer types, where tumor tissues consistently showed higher CHD4 expression than matched normal samples (Fig. S1A). CHD4 mRNA was also widely expressed across 32 cancer cell lines (CCLE), with particularly high levels in leukemias, lymphomas, and several solid tumors (Fig. 2B). Protein-level upregulation was confirmed using CPTAC data, which revealed substantial CHD4 enrichment in 10 cancer types, including BRCA, COAD, and GBM (Fig. 2C). Immunohistochemistry (HPA) further validated elevated CHD4 protein expression in tumor tissues from BRCA, COAD, and LIHC (Fig. 2D). Clinically, CHD4 expression positively correlated with advanced TNM stage, pathological stage, and tumor grade (Fig. 2E), implicating it in disease progression.
CHD4 expression demonstrated significant diagnostic value, with AUC values exceeding 0.7 in discriminating tumor from normal tissue in multiple cancers, including BLCA, BRCA, and HNSC (Fig. S1B). Prognostically, high CHD4 expression was associated with poorer overall survival in LIHC, KICH, and UVM but conferred a protective effect in GBMLGG, THYM, and PAAD (Fig. 2F). Kaplan–Meier analysis further illustrated significant survival stratification between CHD4-high and -low groups in specific cancers (Fig. S2A). Collectively, these results establish CHD4 as a frequently dysregulated and clinically relevant pan-cancer biomarker.
Fig. 2
Pan-cancer analysis of CHD4 expression and prognostic relevance. (A) Differential CHD4 mRNA expression between tumor and normal tissues from TCGA and GTEx datasets. (B) CHD4 mRNA expression in cancer cell lines from the CCLE datasets. (C) CHD4 protein expression in pan-cancer tissues from the UALCAN-CPTAC dataset. (D) Immunohistochemistry images of CHD4 protein expression in normal and tumor tissues from the HPA database. (E) CHD4 expression across different TNM stages, histological grades, and pathological stages in pan-cancer. (F) Forest plots of hazard ratios for the association between CHD4 expression and OS, DSS, DFI, and PFI in TCGA cancers. * p < 0.05, ** p < 0.01, *** p < 0.001; ns: not significant
CHD4 genomic alterations associate with genomic instability
We characterized the genomic alteration landscape of CHD4 across cancers. CHD4 amplifications were frequent in UCS, OV, TGCT, and LGG, while SNVs predominated in UCEC, UCS, SKCM, and STAD; deep deletions were rare (Fig. 3A, B). Elevated CHD4 copy number was associated with improved survival in AML, LIHC, and STAD but predicted poorer outcomes in BRCA-LumA, BRCA-TN, Lymphoma-DLBC, and COADREAD (Fig. 3C). Co-alteration analysis frequently implicated TP53 in CHD4-altered tumors (Fig. S3A), and CHD4 expression correlated with mutational status in key oncogenic pathways—including TP53, RTK/RAS, PI3K, and Cell Cycle—suggesting cooperative roles in tumorigenesis (Fig. 3D).
CHD4 expression exhibited cancer-type-specific associations with TMB and MSI, showing a positive correlation in LIHC but a negative correlation in STES, THYM, and MESO (Fig. 3E,F). CHD4 exhibited consistent pan-cancer positive correlations with HRD and LOH (Fig. 3G,H), thereby underscoring its pivotal role in DNA damage repair. CHD4 expression was also positively associated with tumor heterogeneity (MATH score) in 9 of 13 cancer types (Fig. S3B) and with increased tumor purity in 18 cancers (Fig. S3C). In contrast, correlations with ploidy and aneuploidy were heterogeneous across malignancies (Fig. 3I,J). Tissue-specific relationships were observed between CHD4 expression and neoantigen load (Fig. S3D), with a stronger correlation to synonymous versus non-synonymous mutation rates (Fig. S3E,F). Integrated heatmap analysis further connected CHD4 expression to co-occurring immune and genomic instability phenotypes (Fig. S3G). Collectively, these results establish CHD4 as a key modulator of genomic instability in human cancers.
Fig. 3
CHD4 expression is associated with genomic instability. (A) Genetic alteration features of CHD4 in TCGA pan-cancer cohorts via cBioPortal. (B) Landscape of CHD4 SNVs across cancer types. (C) Kaplan-Meier curves from the TIDE platform showing the predictive significance of CHD4 CNVs in six cancer types. (D) Co-mutation analysis between CHD4 and key oncogenic signaling pathways. (E-G) Lollipop plots of Spearman correlation between CHD4 expression and TMB (E), MSI (F), and LOH (G). (H-J) Radar charts showing Spearman correlation between CHD4 expression and HRD (H), tumor ploidy (I), and aneuploidy score (J). The colored curve indicates the correlation coefficient; the blue value indicates the range. * p < 0.05, ** p < 0.01, *** p < 0.001
CHD4 Links DNA repair, stemness maintenance, and epigenetic regulation
Given the strong association between genomic instability and CHD4, we examined whether CHD4 affects tumor progression by regulating epigenetic states, DNA repair mechanisms, and tumor stemness. CHD4 expression positively correlated with DNA mismatch repair (DMMR) genes across multiple cancer types, including THYM, KIHC, and BRCA (Fig. 4A). Furthermore, CHD4 showed a significant positive association with homologous recombination repair (HRR) activity in 12 cancer types, including KIHC and BRCA (Fig. 4B). Tumor stemness indices exhibited tissue-specific correlations with CHD4: DNA methylation-based scores (DNAss, EREG-METHss, ENHss, DMPss) correlated positively in HNSC and LUSC, whereas RNA-based stemness scores (RNAss, EREG.EXPss) showed negative correlations in KIRP and THCA (Fig. S4A–F), suggesting context-dependent regulation of cancer stemness.
To explore underlying mechanisms, we analyzed CHD4’s role in epigenetic networks. CHD4 expression strongly and positively correlated with core NuRD components HDAC1 and HDAC2, and more moderately with HDAC3 and SIRT1 across cancers (Fig. 4C, D). Conversely, negative correlations were observed with histone acetyltransferases EP300 and CREBBP, supporting a role for CHD4 in maintaining a hypoacetylated, repressive chromatin state. At the DNA methylation level, CHD4 correlated positively with DNMTs in LUSC, READ, and UCEC, but inversely in ACC and KICH (Fig. 4E). CHD4 expression also negatively correlated with its promoter methylation (Fig. S4G), which predicted poor survival in COADREAD and PAAD (Fig. S5A) and was linked to cytotoxic T lymphocyte infiltration and clinical risk (Fig. 4F).
Differential methylation analysis revealed tumor-specific hypermethylation at DHS sites, CpG shores, and shelves in seven cancer types (Fig. 4G). Methylation levels in these regions negatively correlated with CHD4 expression (Fig. 4H), suggesting transcriptional repression via promoter and CpG-rich region hypermethylation. Finally, CHD4 was broadly associated with 44 RNA modification regulators involved in m1A, m5C, and m6A pathways (Fig. S5B), implicating it in a previously unexplored layer of post-transcriptional epigenetic regulation.
Fig. 4
CHD4 is associated with DNA repair, stemness and epigenetic modifications. (A) Heatmap of Spearman correlations between CHD4 and five MMR genes across pan-cancer. (B) Scatter plot of the correlation between CHD4 expression and HRR score. (C) Heatmap of correlations between CHD4 and 11 histone modification-associated proteins. (D) Scatter plots of correlations between CHD4 and key epigenetic regulators (HDAC1, HDAC2, EP300) in three representative cancer types. (E) Heatmap of associations between CHD4 and four DNMTs. (F) Scatter plots of correlations between CHD4 promoter methylation levels and CTL marker expression. (G) Bubble plot of differential CHD4 methylation levels at specific genetic loci. (H) Bubble plot of correlations between CHD4 expression and its methylation levels across various genetic loci
Alternative splicing of CHD4 is associated with patient survival
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AS is a crucial mechanism of post-transcriptional regulation. Comprehensive analysis identified 25 clinically relevant alternative splicing (AS) events in CHD4 (Supplementary material 2, Table S2). We focused subsequent characterization on the CHD4_alt_3prime_57972 event (TCGA SpIAdderSeq; Fig. 5A,B), which displayed substantial pan-cancer variability. PSI values were significantly elevated in KIRP and PCPG compared to normal tissues but reduced in BRCA, CHOL, and KICH. Elevated PSI of this splicing event predicted poorer overall survival in patients with ESCA, KICH, KIRC, and SARC (Fig. 5C). Parallel analysis of the CHD4_AA_19897 event (TCGA SpliceSeq; Fig. S6A-C) further supported the prognostic relevance of CHD4 splicing patterns. These findings bring attention to the post-transcriptional regulation of CHD4 and nominate specific splice isoforms as independent prognostic biomarkers..Fig. 5
CHD4 alternative splicing correlated to patient prognosis. (A) Reads-in, reads-out, and PSI values of the alternative splicing event CHD4_alt_3prime_57972 across pan-cancer, adjacent, and normal tissues. (B) Differences in PSI values between tumor-adjacent and tumor-normal comparisons, and their association with OS and PFI. The red dashed line indicates FDR = 0.05. Point size corresponds to tumor PSI values. (C) Kaplan-Meier curves from OncoSplicing showing the prognostic significance of the CHD4_alt_3prime_57972 event
Functional enrichment analysis implicates CHD4 in DNA damage response, and oncogenic pathway
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Functional profiling placed CHD4 within key oncogenic networks. Through protein-protein interaction analysis, we identified ten CHD4-binding partners that were experimentally validated (Fig. 6A). A pathway-based evaluation indicated that CHD4 expression was elevated in BRCA patients exhibiting somatic alterations in chromatin modifiers, as well as in the Hippo, Nrf2, and SWI/SNF pathways; however, it showed decreased expression in PRAD (Fig. 6B). Additionally, CHD4 expression consistently correlated with the activation of these pathways across various cancers (Fig. 6C; Supplementary material 2, Table S3), implying its potential role as an upstream regulator.A
Co-expression analysis further delineated CHD4-associated transcriptional programs. The top 100 CHD4-co-expressed genes included SF3B2, FUS, UBTF, DHX9, and DDX39B as the most frequently correlated across malignancies (Fig. 6D). GO enrichment analysis indicated strong involvement in homologous recombination, double-strand break repair, chromatin assembly, and cell cycle checkpoint regulation (Fig. 6E; Supplementary material 2, Table S4), reinforcing CHD4’s central role in genomic integrity.GSEA demonstrated coordinated activation of E2F targets, G2M checkpoint, glycolysis, oxidative phosphorylation, and epithelial-mesenchymal transition in CHD4-high samples (Fig. S7A). Single-cell functional analysis confirmed positive correlations between CHD4 expression and pro-tumorigenic states, including cell cycle progression, DNA damage response, proliferation, and stemness (Fig. 6F). Together, these data establish CHD4 as a multimodal regulator integrating proliferative, metabolic, and invasive programs during malignant progression.
Fig. 6
Functional enrichment and co-expression analysis of CHD4. (A) Protein-protein interaction (PPI) network of CHD4 from the STRING database. (B) CHD4 expression in six cancer types stratified by somatic alteration status in indicated pathways. (C) Correlations between CHD4 expression and oncogenic pathway signatures. (D) Heatmap of pan-cancer correlations between CHD4 and its top five co-expressed genes. (E) GO enrichment analysis of the top 100 CHD4 co-expressed genes. (F) Scatter plots of Pearson correlation between CHD4 expression and functional state activity scores for 14 cancer-related processes
CHD4 is a key regulator of the tumor immune microenvironment
CHD4 expression correlated positively with TGF-β signaling and myeloid activation markers (e.g., TREM1) but negatively with antitumor immunity features, including CD8 + T cell infiltration, T cell receptor signaling, interferon response programs, and MHC-I expression (Fig. S8A). CHD4 was also associated with upregulation of immune checkpoint molecules, including PD-L1, CTLA-4, LAG-3, and TIGIT (Fig. S8B).
ESTIMATE-based deconvolution revealed cancer-type-specific correlations between CHD4 and stromal, immune, and ESTIMATE scores, with consistently negative associations in SARC and ACC and positive associations in LGG (Fig. 7A). Cellular infiltration analysis linked high CHD4 to reduced antitumor effector cells (M1 macrophages, T gamma delta cells) and enrichment of immunosuppressive populations, including cancer-associated fibroblasts, endothelial cells, M0 macrophages, neutrophils, mast cells, hematopoietic stem cells, and Tregs (Fig. 7B). Positive correlations with CD4 + and CD8 + T cells likely reflect an exhausted T cell state, consistent with broad immune suppression. CHD4 expression correlated with immunomodulatory genes in a cancer-dependent manner—positively with 89 factors in THCA but negatively in THYM (Fig. S9A). It was highest in IFN-γ–dominant immune subtypes (C1, C2) and lowest in the C6 subtype (Fig. 7C), indicating subtype-specific regulation.
Spatial transcriptomics confirmed CHD4 co-localization with epithelial marker CDH1 and proliferation marker CCND1 in BRCA (Fig. 7D). Single-cell RNA-seq (TISCH) revealed CHD4 expression in malignant, epithelial, endothelial, and fibroblast populations (Fig. 7E). CHD4 was upregulated following cytokine stimulation (IFN-γ, TNF-α) and immune checkpoint blockade (anti-CTLA-4/PD-1) (Fig. S9B, C), supporting its role as a dynamic biomarker of immunotherapy response.
Fig. 7
CHD4 orchestrates a suppressive tumor immune microenvironment across multi-omic dimensions. (A) Heatmap of Spearman correlation between CHD4 expression and tumor microenvironment scores (Stromal, Immune, ESTIMATE) and 21 immune cell infiltration subsets. (B) Scatter plots of the three cancer types with the most significant correlations between CHD4 expression and Stromal, Immune, or ESTIMATE scores. (C) CHD4 expression across six immune subtypes in UCEC, HNSC, LUAD, STAD, LUSC, and LIHC. (D) Spatial transcriptomic sections of CHD4 with epithelial marker CDH1 and proliferation marker CCND1, showing spatial co-expression patterns. (E) Single-cell resolution analysis of CHD4 expression across cell types in the PAAD_GSE111672 dataset
CHD4 expression predicts therapy response and confers specific sensitivity to HDAC inhibition
We subsequently assessed the clinical translational potential of CHD4 as a prognostic indicator of treatment response. ROC plotter analysis indicated that increased CHD4 expression forecasted treatment efficacy for capecitabine and fluorouracil-based therapies in COAD, although it correlated with non-responsiveness in OV (Fig. 8A). The cMAP screening identified 30 potential drugs targeting CHD4-associated vulnerabilities (Fig. 8B), with considerable enrichment for HDAC and topoisomerase inhibitors in the mechanism-of-action analysis (Fig. 8C). The COMPARE-based evaluation of GI50 values indicated that elevated CHD4 expression was associated with diminished drug sensitivity (elevated GI50) in leukemia, colon cancer, and melanoma, while reduced CHD4 levels predicted higher sensitivity in NSCLC, ovarian, prostate, and breast cancers (Fig. 8D).
Integrated pharmacogenomic analysis across five independent databases consistently linked elevated CHD4 expression to increased sensitivity to HDAC inhibitors, showing significant negative correlations between CHD4 levels and IC50/AUC values for vorinostat, panobinostat, and entinostat (Fig. 8E-I). The XSum algorithm nominated entinostat (MS-275) as a top candidate for reversing CHD4-driven transcriptional signatures (Fig. 8J). Strikingly, expanded analysis revealed that high CHD4 expression also correlated with broad sensitization to conventional chemotherapeutic agents across four additional databases (Fig. 8K), suggesting CHD4 as a biomarker of generalized chemosensitivity rather than therapy resistance.
These findings establish CHD4 as a novel regulator of therapeutic response and define CHD4-high tumors as therapeutically vulnerable to both HDAC inhibition and conventional chemotherapy.
Fig. 8
CHD4 predicts therapy response and potential targeted drugs. (A) CHD4 expression in responders vs. non-responders and ROC curve for predicting response. (B) Heatmap of the top 30 candidate drugs for CHD4-high tumors from cMAP. (C) MoA analysis for the top 30 compounds in (B). (D) Correlation between mocetinostat GI50 and CHD4 expression in NCI60 cell lines. (E-I) Lollipop plots of Spearman correlation between CHD4 expression and drug sensitivity in five pharmacogenomic databases: (E) CTRP, (F) PRISM, (G) GDSC1, (H) PRISM, (I) RNAactDrug (computational score). (J) Identification of small-molecule compounds targeting CHD4 using the xSum algorithm. (K) Bubble plot of Spearman correlation between CHD4 expression and chemotherapy drug sensitivity across four integrated database
CHD4 promotes proliferation, migration, and invasion in osteosarcoma cells
CHD4 was significantly upregulated in osteosarcoma cell lines at both mRNA and protein levels compared to normal osteoblasts. The highest expression was observed in 143B and Saos-2 cells (Fig. 9A, B). We therefore established stable CHD4-knockdown models using lentiviral RNA interference, confirming optimal silencing efficiency with shCHD4-2 for subsequent experiments (Fig. 9C,D). CHD4 depletion markedly reduced expression of metastasis-associated proteins MMP2 and MMP9 (Fig. 9E). Functionally, CHD4 knockdown significantly suppressed cellular proliferation (Fig. 9F), impaired clonogenic survival (Fig. 9G), inhibited invasion in Transwell assays (Fig. 9H), and attenuated migration in wound healing assays (Fig. 9I). Collectively, these results establish CHD4 as a critical oncogenic driver in osteosarcoma, essential for sustaining proliferative, migratory, and invasive capacities.
.
A
Fig. 9
CHD4 is upregulated in osteosarcoma and essential for malignant phenotypes in vitro. (A,B) CHD4 mRNA (A) and protein (B) expression in normal osteoblastic (hFOB1.19) and osteosarcoma cell lines. (C,D) Validation of CHD4 knockdown by qPCR (C) and Western blot (D) in 143B and Saos-2 cells expressing control (sh-NC) or CHD4-targeting shRNAs. (E) Western blot analysis of MMP2 and MMP9 expression following CHD4 knockdown. (F,G) The impact of CHD4 knockdown on the proliferative capacity of osteosarcoma cells was assessed by CCK-8 assay (F) and colony formation assay (G). (H) Cellular invasion capacity determined by Transwell assay (Scale bar: 100 µm). (I) Cell migration evaluated by wound healing assay (Scale bar: 100 µm). *P < 0.05, **P < 0.01, ***P < 0.001
Discussion
In this study, we define an oncogenic role for the chromatin remodeler CHD4 through integrated pan-cancer multi-omics analysis and functional validation. We demonstrate that CHD4 is frequently upregulated across cancers and that its elevated expression correlates with adverse prognosis, markers of genomic instability, and an immunosuppressive TME. Crucially, we identify CHD4 as a novel predictive biomarker for sensitivity to HDAC inhibitors. Collectively, our findings position CHD4 as a master epigenetic coordinator that concurrently promotes genomic instability and immune evasion, thereby driving tumor progression.
Our pan-cancer analysis corroborates CHD4's oncogenic role, demonstrating its frequent upregulation at mRNA and protein levels, which correlates with advanced tumor stage and grade. This aligns with prior studies in gastric, ovarian, and breast cancers [14,17,18,46]. CHD4's prognostic impact, however, is context-dependent, acting as a risk factor in LIHC, KICH, and BRCA, but a protective factor in GBMLGG, THYM, and PAAD. This duality is exemplified in the literature: CHD4 maintains genomic stability in Ewing sarcoma [10]yet promotes chemoresistance in AML [19]. Our in vitro findings add to this narrative by confirming that CHD4 drives proliferation, migration, and invasion in osteosarcoma.
We propose that CHD4 exerts its diverse effects by fundamentally modulating the epigenetic landscape. Its strong association with core NuRD components (HDAC1, HDAC2, and HDAC3) and inverse correlation with histone acetyltransferases, such as EP300 and CREBBP, suggest that CHD4 serves as a central mediator of NuRD activity. This role facilitates histone deacetylation and transcriptional repression [11]. This chromatin remodeling capacity underlies its context-dependent outcomes. Thus, CHD4 acts as an epigenetic fulcrum: in normal or stressed cells, it pivots towards genome protection, while in established tumors, it tilts the balance towards malignant progression by co-opting the same machinery. In contexts demanding genomic integrity, CHD4 promotes efficient DNA damage repair, as evidenced by its robust pan-cancer correlation with HRD and LOH, aligning with its established DDR functions [12,13,19]. Conversely, in tumorigenic settings, the same machinery is co-opted to drive malignancy. This oncogenic program includes maintaining stemness, promoting EMT, and—as a key finding of our study—orchestrating an immunosuppressive microenvironment. Our model thus reconciles seemingly discordant literature: CHD4's role in oxidative damage repair [16] and LINE-1 suppression [46] reflects its genome-protective function, while its promotion of metastasis and chemoresistance [17] highlights its oncogenic capacity, potentially via aberrant hypermethylation and silencing of tumor suppressors.
Beyond its well-established role in maintaining genomic stability, our study points out the vital function of CHD4 in shaping an immunosuppressive tumor microenvironment. Elevated CHD4 was associated with an immunologically “cold” phenotype, characterized by reduced CD8 + T cell infiltration, downregulated MHC-I, and upregulated inhibitory checkpoints (PD-L1, CTLA-4, LAG-3, TIGIT). These findings are consistent with reports that CHD4 depletion enhances anti-tumor immunity [16,47], and clinical evidence linking it to immunomodulation [48]. Spatial transcriptomic and single-cell analyses suggest CHD4 coordinates these effects across multiple cell types—including epithelial cells, CAFs, and myeloid cells—implying both cell-autonomous and non-autonomous immunosuppressive mechanisms.
Interestingly, we observed correlations between CHD4 and regulators of RNA modifications (m⁶A, m5C, m1A), suggesting its potential involvement in a previously unexplored layer of post-transcriptional immunoregulation. We propose that CHD4 may influence immune checkpoint expression either by transcriptionally regulating RNA-modifying enzymes [49,50], or by leveraging the established role of m6A in controlling the stability of immune-related mRNAs (e.g., PD-L1, CXCL9) [51,52]. This novel association reveals a previously unexplored layer of CHD4-mediated immune regulation and complements its established transcriptional mechanisms, further underscoring its therapeutic relevance.
Building upon these mechanistic insights, the epigenetic state driven by CHD4 presents direct therapeutic implications. Our integrated pharmacogenomic analyses reveal a distinctive dual-sensitization phenotype: CHD4-high tumors exhibit enhanced sensitivity to both HDAC inhibitors and a broad spectrum of conventional chemotherapeutic agents. Our finding challenges the conventional view of CHD4 as solely a mediator of therapy resistance. Mechanistically, the vulnerability to HDAC inhibitors likely stems from the role of CHD4 in establishing a state of global histone hypoacetylation via HDAC1/2 [53,54]. This creates a dependency on epigenetic homeostasis—a form of non-oncogene addiction—thereby sensitizing cells to HDAC inhibition [55]. Pharmacological targeting of HDAC1/2 alters the CHD4-NuRD complex's exact equilibrium, potentially causing a synthetic lethal effect in CHD4-high malignancies via chromatin disorder and DNA damage accumulation [56,57]. The extensive chemosensitivity shows that CHD4 orchestrates a state of epigenomic instability. While boosting development and immune evasion, this fragile condition may also impair DNA repair fidelity and disrupt cell cycle checkpoints, making tumors more susceptible to genotoxic chemicals. This situation establishes a distinct therapeutic vulnerability, indicating that CHD4-high cancers are particularly receptive to combination treatments that combine HDAC inhibitors with conventional chemotherapy. This finding suggests a novel therapeutic strategy: leveraging CHD4 expression as a biomarker to identify patients who may derive exceptional benefit from the combination of epigenetic priming with HDAC inhibitors and conventional DNA-damaging chemotherapy.
In summary, our study identifies CHD4 as a key epigenetic driver of tumor progression that also promotes genomic instability and creates an immunosuppressive microenvironment. This reprogramming leads to a vulnerability that can be therapeutically targeted. Furthermore, recognizing CHD4 as a predictive biomarker for responses to HDAC inhibitors and chemotherapy marks a significant advancement in the field of epigenetics-guided precision medicine. The principal conclusions of this study, derived from public database analyses, require validation in prospective clinical cohorts. Furthermore, definitive causal relationships require direct functional experiments, which are currently hampered by the lack of highly selective CHD4 inhibitors. Therefore, future efforts should prioritize developing potent CHD4-targeting compounds and evaluating their efficacy, both alone and in rational combinations with HDAC inhibitors or immunotherapies, to accelerate the clinical translation of these findings.
Supplementary information
Supplementary material 1: Fig S1. Pan-cancer assessment of CHD4’s differential expression and diagnostic performance. Fig S2. Prognostic value of CHD4 expression in pan-cancer analysis. Fig S3. The genomic landscape and immunogenomic associations of CHD4 across cancers. Fig S4. CHD4 correlates with cancer stemness and promoter methylation in pan-cancer analysis. Fig S5. Prognostic and epitranscriptomic associations of CHD4 methylation. Fig S6. Prognostic significance of CHD4 alternative splicing. Fig S7. Gene Set Enrichment Analysis (GSEA) of CHD4-associated phenotypes. Fig S8. CHD4 expression links to immune landscape and checkpoint molecules. Fig S9. Comprehensive characterization of CHD4 in immune regulation and response to immunotherapy. Supplementary material 2: Table S1. List of abbreviations. Table S2. CHD4 clinical-relevant AS events on OncoSplicing. Table S3. Genes involved in oncogenic pathways. Table S4. GO pathways enriched by the top 100 CHD4 co-expressed genes identified on GEPIA2.0. Supplementary material 3: Experimental Methods.
Abbreviations
See Supplementary Table S1 in Supplementary Material 2 for a complete list of abbreviations used.
Acknowledgements
The authors express gratitude to the public databases, websites, and software utilized in the paper.
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Funding
This work was supported by the National Natural Science Foundation of China (82172682 and 82373221), Natural Science Foundation of Chongqing (CSTB2023NSCQ-MSX0472), and Science and Technology Program Project of Enshi Tujia and Miao Autonomous Prefecture, Hubei Province (ESQH20240043 and ESQH20240044).
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Author Contribution
Conceptualization: OY, HG; Methodology: FG, OY, TY, OY, HG; Validation: FG, OY, TY, FK, TF, ZW; Investigation: FG, HG, CY; Data Curation: FK, TF, YS; Visualization: FG, OY, TY, ZZ; Writing-Original Draft Preparation: FG, OY, TY; Writing-Review & Editing: FG, OY, TY, FK, TF, HG, CY; Project Administration: OY, HG; Funding Acquisition: OY, HG; Supervision: OY, HG. All authors contributed to the article and approved the submitted version. All authors have read and approved the submitted manuscript.
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Data Availability
All data generated or analysed during this study are included in this article and its supplementary information files.
Declarations
Ethics Approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing of Interests
The authors declares no conflicts of interest to report regarding the present study.
Electronic Supplementary Material
Below is the link to the electronic supplementary material
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