HMGB1 affects the progression of neurofibromatosis type 1-associated malignant peripheral nerve sheath tumors through E2F2

Abstract

Background

Malignant peripheral nerve sheath tumors (MPNSTs) represent the most severe complication of neurofibromatosis type 1 (NF1), with limited therapeutic options and poor prognosis. High-mobility group box 1 (HMGB1) is a chromatin-associated protein implicated in various cancers, yet its functional role and mechanistic involvement in NF1-MPNST progression remain poorly understood.

Methods

We integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq analyses of patient-derived NF1-MPNST and plexiform neurofibroma (PNF) tissues. Functional validation was performed using NF1 cell lines with HMGB1 knockdown and overexpression. Mechanistic insights were explored via CUT&Tag, ChIP‒qPCR, qPCR, and Western blot. In vivo tumor growth was assessed using a xenograft mouse model.

Results

HMGB1 was significantly upregulated in malignant CNV-high subpopulations of MPNSTs and correlated with poor patient survival. Functional assays demonstrated that HMGB1 knockdown suppressed tumor proliferation, migration, and invasion, and induced G1 arrest, while its overexpression promoted these phenotypes. Mechanistically, HMGB1 directly bound to the E2F2 promoter and activated its transcription, thereby driving the G1/S transition. In vivo, HMGB1 overexpression accelerated tumor growth, whereas knockdown suppressed it, consistent with modulated E2F2 and Ki-67 expression.

Conclusions

Our study identifies HMGB1 as a key oncogenic driver in NF1-MPNST progression, functioning through direct transcriptional activation of E2F2 to promote cell cycle progression and tumor malignancy. These findings position HMGB1 as both a prognostic biomarker and a promising therapeutic target for NF1-associated MPNSTs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-025-04165-3.

Introduction

Neurofibromatosis type 1 (NF1) is an autosomal dominant disorder affecting approximately 1 in 2,500 births [1]. This is characterized by dermatological manifestations, such as café-au-lait macules, axillary freckling, and cutaneous neurofibromas, potential multisystem involvement and an elevated risk of malignancy [2]. A major clinical concern in NF1 is the development of plexiform neurofibromas (PNFs), which occur in 30–50% of patients. Although histologically benign, PNFs can lead to substantial morbidity, including pain, organ compression or displacement, and functional impairment [3–5]. Most critically, PNFs exhibit the potential for malignant transformation into highly aggressive malignant peripheral nerve sheath tumors (MPNSTs), which represent the most life-threatening complication and the primary cause of premature mortality in NF1 patients [6]. MPNSTs remain difficult to diagnose and treat, with surgical resection as the mainstay of management [7]. There are no effective treatments for MPNSTs, underscoring the urgent need to elucidate the molecular drivers underlying NF1 progression and malignant transformation.

High-mobility group box 1 (HMGB1) is a widely expressed chromatin-binding factor that regulates key cellular processes, including transcription, nucleosome sliding, telomere maintenance, and DNA damage repair [8]. Like histone H1, nuclear HMGB1 associates with chromatin and modulates transcriptional activity by perturbing nucleosome stability [9]. Research has also demonstrated that HMGB1 functions as a transcription factor to promote immune responses by increasing IFN-γ expression in CD8⁺ T cells through the direct regulation of Eomes, a key transcription factor for IFN-γ [10]. However, HMGB1 functions as a double-edged sword, exerting conflicting effects on both the development and therapeutic response of cancers [11]. Elevated expression of HMGB1 has been associated with poor prognosis in patients with various types of cancer, including gastric cancer, neuroblastoma, and fibrosarcoma [12–14]. Conversely, heightened HMGB1 levels are associated with improved survival outcomes in glioblastoma, colorectal carcinoma, esophageal squamous cell carcinoma and lung cancer cohorts [15–17]. Furthermore, the precise functional significance of HMGB1 in the advancement of MPNSTs is still poorly understood.

To address this gap, we investigated the role of HMGB1 in driving the progression of MPNSTs by an integrated approach combining single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq. Our findings demonstrate that HMGB1 overexpression drives malignant progression and predicts poor survival in MPNST patients through the enhancement of tumor cell proliferation, migration, and invasion. Mechanistically, cleavage under targets and tagmentation (CUT&Tag) and chromatin immunoprecipitation (ChIP)–quantitative polymerase chain reaction (qPCR) revealed the possible transcription factor functions of HMGB1, and E2F2 is one of the HMGB1 binding sites. Meanwhile, E2F2 activates the expression of genes required for the G1-to-S phase transition[18].This study established the HMGB1–E2F2 pathway as a key molecular driver and potential therapeutic target in NF1-MPNSTs.

Methods

Single-cell RNA sequencing (scRNA-seq) data analysis

scRNA-seq of tumors from three patients with MPNSTs was performed. The raw 10 × Chromium data were subjected to GRCh38-aligned read quantification using CellRanger (v4.0.10), followed by sparse matrix conversion using Seurat (v4.3.0). We filtered cells expressing fewer than 500 genes or containing > 10% mitochondrial reads. Doublets cells were identified and removed with DoubletFinder (v2.0.3) using sample-optimized parameters. Following SCTransform normalization, Harmony (v1.2.0) was applied for batch effect correction. UMAP visualization incorporated the top 40 Harmony dimensions, while principal components for downstream analysis were determined based on elbow plots and variance-distribution heatmaps. Clustering at a resolution of 0.8 employed the FindNeighbors/FindClusters functions in Seurat. Differential gene expression analysis was performed via the FindAllMarkers method.

CNV estimation in neoplastic cells

Initial copy number variations (CNVs) in neoplastic cells were inferred using the inferCNV package in R (v1.14.0), with macrophages employed as the reference. CNVs across all cell types were calculated on the basis of the expression levels derived from single-cell RNA sequencing. The analysis was performed with the parameters –cutoff 0.1 and –noise_filter 0.2 to establish relative chromosomal expression intensities, which were visualized as heatmaps. Additionally, CNV accumulation scores were computed to assess the extent of genomic alterations within neoplastic clusters.

Bulk RNA-seq and differential gene analysis

Bulk RNA sequencing was performed on the basis of matched MPNST and PNF tumor samples obtained from three patients. The raw sequencing data were processed by removing adapters and trimming low-quality bases using TrimGalore (v0.6.10). The filtered reads were then aligned to the GRCh38 reference genome using HISAT2 (v2.2.1). The resulting SAM files were sorted and converted into BAM files using SAMtools (v1.17). Gene expression was quantified with featureCounts (v2.0.3) to generate read counts. Differential gene expression analysis between PNF and MPNST samples was conducted using both DESeq2 (v1.38.2) and limma (v3.54.0), with stringent thresholds applied to identify differentially expressed genes (DEGs). Significantly DEGs were subsequently subjected to Gene Ontology (GO) enrichment analysis using clusterProfiler.

Cell culture and reagents

The human MPNST cell line sNF96.2 and the plexiform neurofibroma cell line ipNF05.5 were purchased from the American Type Culture Collection (ATCC, China) and were cultured in DMEM (Gibco, USA) supplemented with 10% fetal bovine serum (FBS; Gibco, USA) at 37 °C in a humidified incubator containing 5% CO2. The knockdown TCGGGAGGAGCATAAGAAGAA) and overexpression (NCBI, NM-002128.7) lentiviruses were provided by Shanghai Genechem Co., Ltd. The treated cells were screened with puromycin (Solarbio, China) after virus infection.

Cleavage under targets and tagmentation (CUT&Tag)

Cut&Tag was performed on the ipNF05.5 and sNF96.2 cell lines according to the standard protocol. Briefly, cells were permeabilized and incubated with an anti-HMGB1 primary antibody, followed by conjugation with protein A-Tn5 transposase. The tagmented DNA fragments were used to construct sequencing libraries with dual indexing. The libraries were subsequently sequenced on an Illumina NovaSeq 6000 instrument to generate 150-bp paired-end reads. Raw FASTQ files were subjected to quality control using FastQC (v0.12.1) and adapter trimming with TrimGalore (v0.6.10). Clean reads were aligned to the GRCh38 reference genome using Bowtie2 (v2.5.1) with the default parameters. Duplicate reads were removed via SAMtools (v1.17). Peak calling was conducted with MACS2 (v2.2.7.1) using the parameters –nomodel, –shift −100, and –extsize 200. Downstream analyses included peak annotation with ChIPseeker (v1.34.1), de novo motif analysis using HOMER (v4.11), and visualization with the Integrative Genomics Viewer (IGV, v2.16.2).

Quantitative RT‒PCR

Total RNA was extracted from ipNF05.5 and sNF96.2 cells with TRIzol reagent (Thermo Fisher Scientific, USA) according to the manufacturer’s protocol. The purity and concentration of the RNA were measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA). Subsequently, 1.0 μg of total RNA was reverse transcribed with M-MLV Reverse Transcriptase (Thermo Fisher Scientific, USA) to generate high-quality cDNA. Quantitative PCR (qPCR) was performed with a 10 μL reaction system containing AceQ qPCR SYBR Green Master Mix (Thermo Fisher Scientific, USA). The amplification conditions included initial denaturation (95 °C, 30 s) and 40 cycles of denaturation (95 °C, 10 s), annealing (60 °C, 30 s), and extension (72 °C, 30 s). Relative gene expression levels were calculated via the 2 − ΔΔCt method, with normalization to the expression of the endogenous control GAPDH.

ChIP–qPCR

Adherent cells were grown to 80–90% confluence, and 250 µl of 37% solution A was added to a dish containing 9 ml of medium. Aldehyde was added to a final concentration of 1%, and the mixture was swirled and incubated at room temperature for 10 min and swirled again halfway through the incubation period. Then, 925 µl of 10X glycine was added to a final concentration of 0.125 M; after mixing, the cells were incubated at room temperature for 5 min, and the adherent cells were directly discarded. The medium was removed, 1 ml of precooled PBS (supplemented with 0.5 mM PMSF) was added to the culture dish, the bottom of the dish was scraped, and the cell suspension was collected in a 2 ml centrifuge tube. Next, added 1 ml of precooled PBS (supplemented with 0.5 mM PMSF) was directly added to the cell suspension, followed by centrifugation at 4 °C for 5 min, after which the supernatant was discarded to obtain the cell pellet. An appropriate amount of ChIP-Lysis-Buffer was added, the mixture was incubated at 4 °C for 30 min, after which 800 µl of ChIP buffer was added. After mixing well, the sample was ultrasonicated on ice, and 50 µl of the subsequent input sample was separated for DNA purification and recovery via 1.5% agarose-gel electrophoresis. Next, a magnetic bead protein A/G original tube was inverted 10 times and after the magnetic beads and liquid were mixed well, 30 µl of the mixture was removed. The protein A/G magnetic beads and liquid were mixed and divided into IgG and IP samples. Then, 500 µl of ChIP buffer was added to resuspend the magnetic beads, followed by incubation on a slow mixer at 4 °C for 3 h or overnight. After rinsing to obtain the precipitate, 200 µl of elution buffer was added, the samples were incubated in a 65 °C water bath for 30 min. To both the IgG and IP groups, 2 μl of RNase A (100 mg/ml) was added, followed by incubation at 37 °C in a water bath for 30 min. The samples in both groups then received 8 μl of 5 M NaCl and 3 μl of proteinase K (20 mg/ml) and were incubated at 65 °C in water bath overnight after thorough mixing. Finally, DNA extraction was performed according to the instructions for the DNA extraction kit (Beyotime, China), and PCR was performed as described above.

Western blotting (WB)

Protein was extracted from each cell group with ice-cold RIPA lysis buffer. The protein concentration was quantified with a BCA protein assay kit (Biosharp, China). Thirty microgram aliquots were subsequently separated on 10% SDS‒PAGE gels (Beyotime, China), followed by electrotransfer to polyvinylidene fluoride (PVDF) membranes (Merck, Germany). The membranes were blocked for 2 h with 5% nonfat milk and then incubated overnight at 4 °C with primary antibodies. Following washing, an HRP-conjugated goat anti-rabbit secondary antibody was applied. The protein bands were visualized using Immobilon Western Chemiluminescent HRP Substrate (ECL-Plus™; Millipore, Germany) and detected with an X-ray imaging system (Kodak, USA). GAPDH served as the loading control. All the assays were conducted on identical membranes, each with three independent replicates.

EdU assays

EdU assays were performed to assess cell proliferation. Cells were seeded in culture plates and incubated with 10 μM EdU (Click-iT™ EdU reagent; Thermo Fisher) for 2 h. After being washed with PBS, the cells were fixed with 4% paraformaldehyde (15 min) and permeabilized with 0.5% Triton X-100 (20 min). The click reaction mixture (containing Alexa Fluor™ 488 azide, CuSO₄, and buffer) was added for 30 min of incubation in the dark. The nuclei were counterstained with Hoechst 33,342 (5 μg/mL, 10 min). After the final washes with PBS, images were acquired using a fluorescence microscope. EdU-positive (proliferating) cells were quantified by calculating the ratio of the fluorescence intensity of Alexa Fluor 488⁺/Hoechst⁺ cells.

Cell invasion and migration assays

Transwell assays were performed using 8-μm pore polycarbonate membrane inserts (Corning, USA). For invasion assays, membranes were precoated with Matrigel (1:8 dilution; Corning, USA). Serum-starved cells (5 × 10⁴ in 200 μL of serum-free medium) were seeded into the upper chamber. The lower chamber contained 600 μL of medium supplemented with 10% FBS as a chemoattractant. After 24 h of incubation at 37 °C, nonmigrated cells on the upper surface were removed with cotton swabs. The migrated/invading cells on the lower membrane were fixed with 100% methanol (15 min) and stained with 0.1% crystal violet (20 min). Cells in five random fields per insert were counted under a bright-field microscope (200 ×).

Cells were starved in serum-free DMEM for 12 h prior to the migration assay. After trypsinization and centrifugation, they were resuspended in serum-free medium at an appropriate density and seeded into the upper chamber of a Transwell insert. The lower chamber was filled with medium containing 10% fetal bovine serum as a chemoattractant. The plate was then incubated at 37 °C in a humidified atmosphere with 5% CO₂ for 24 h. Following incubation, non‑migrated cells on the upper surface of the membrane were carefully removed with a cotton swab. Cells that had migrated to the lower surface were fixed with 4% paraformaldehyde for 15 min and stained with 0.1% crystal violet for 15 min. After air‑drying, at least five randomly selected fields per insert were photographed under a 200 × inverted light microscope for subsequent statistical analysis.

Cell cycle assay

We used a cell cycle and apoptosis analysis kit (BL114A; Biosharp, China) to analyze the cell cycle distribution. Cells were harvested, washed twice with cold PBS, and then fixed with 70% ethanol at 4 °C overnight. Afterward, each sample was stained with 25 µl of PI and 10 µl of RNase A solution for 30 min at 37 °C in the dark. Finally, the cell cycle distributions of these samples were detected by flow cytometry (BD C6 Plus, Beckman Coulter, USA). FACS data were analyzed with FlowJo software.

Mouse xenograft model

Healthy female BALB/c nude mice (4 weeks old; n = 18; Shanghai SLAC Laboratory Animal Co.) were randomized into shCtrl and shHMGB1 groups. Xenograft models were established via subcutaneous injection of 5 × 10⁶ shCtrl or SGC-7901-shHMGB1 cells. Growth parameters, including body weight and tumor dimensions (length (L) and width (W)) were monitored and recorded weekly, and tumor volume calculated as V = (L × W²)/2. Following euthanasia by 2% pentobarbital sodium administration, tumors were excised for analysis and mass measurement. All procedures received ethical approval from Tiantan Hospital, Capital Medical University.

Immunostaining

Xenograft tumors were harvested from the mouse models, fixed in formalin, and embedded in paraffin. Sections (2 μm) were subjected to xylene deparaffinization followed by 10 min of hydration in 100% ethanol. Antigen retrieval was performed by incubating the slides in 0.1% Tween-20/PBS-H₂O₂ at room temperature for 10 min. The slides were subsequently probed overnight at 4 °C with primary antibodies against Ki-67 (1:200; Abcam #ab16667), HMGB1 (1:200; ABclonal, China #A19529), and E2F2 (1:300; ABclonal, China #A3297) and then incubated for 2 h at room temperature with an HRP-conjugated goat anti-rabbit IgG secondary antibody (1:400). The immunostained specimens were finally visualized via bright-field microscopy.

Statistical analysis

All experiments were conducted in triplicate, and the results are presented as the mean ± SD. Statistical analyses were performed using Student’s t test for two-group comparisons and one-way ANOVA for multigroup assessments in GraphPad Prism. Significance thresholds were defined as follows: *P < 0.05, **P < 0.01, ***P < 0.001, and **P < 0.0001.

Results

HMGB1 expression is increased in the most malignant CNV-high clusters of MPNSTs

In this study, 10 × single-cell sequencing was performed of tumor samples from 3 patients with MPNSTs. Following doublet removal and rigorous quality control, 946,343 high-quality cells were obtained. UMAP-based dimensionality reduction and clustering analysis revealed seven nonmalignant cell subpopulations, namely, fibroblasts, T cells, macrophages, endothelial cells, B cells, mast cells, and vascular smooth muscle cells (VSMCs), and four malignant subpopulations, namely, neoplastic cluster 1, neoplastic cluster 2, neoplastic cluster 3 and neoplastic cluster 4 (Fig. 1a, b and Supplementary Table 1). HMGB1 was ubiquitously expressed across all the cell types (Fig. 1c). The cancer cells exhibited patient-specific expression patterns, indicating that substantial heterogeneity was likely driven by copy number variations (CNVs). To investigate this, we applied inferCNV to infer the copy number changes across neoplastic clusters. When macrophages were used as a reference, the CNV scores were significantly greater in neoplastic clusters 3 and 4 than in clusters 1 and 2 (P < 0.05), suggesting that the malignant potential of these clusters was elevated (Fig. 1d and e). Consistent with these findings, the single-cell sequencing results confirmed that compared with other cell types, HMGB1 was expressed at higher levels in neoplastic cell clusters 3 and 4 (Fig. 1f).

Fig. 1.

Single-cell transcriptomic analysis reveals HMGB1 overexpression in CNV-high malignant clusters of MPNSTs. a, UMAP projection of 946,343 high-quality single cells from 3 MPNST patients, color-coded to identify 11 major cell types. b, Dot plot of representative signatures in different clusters. c, UMAP visualization showing the ubiquitous expression of HMGB1 across all cell types. d, InferCNV plot inferring CNV patterns across the malignant clusters, using macrophages as a reference. Neoplastic clusters 3 and 4 exhibit widespread CNV alterations. e, Quantitative analysis of CNV scores across the four neoplastic clusters. Data are presented as mean ± SEM. f, Dot plot comparing HMGB1 expression levels across the four malignant neoplastic clusters, confirming its highest expression in clusters 3 and 4

HMGB1 overexpression drives malignant progression and predicts poor survival in MPNST patients

Parallel bulk-tissue sequencing of matched MPNST and PNF samples (n = 3 pairs) revealed 1,890 upregulated genes and 963 downregulated genes in MPNSTs (|log2FC|> 2, P < 0.05), with HMGB1 being significantly upregulated (Fig. 2a and Supplementary Table 2). Gene Ontology (GO) analysis of the upregulated genes revealed enrichment in nuclear division, the meiotic cell cycle process, and regulation of nuclear division (Fig. 2b). Survival analysis confirmed that elevated HMGB1 expression correlated with shorter overall survival in MPNST patients (log-rank P < 0.01) (Fig. 2c). Immunohistochemical staining confirmed the increase in HMGB1 protein expression in MPNST tissues compared with that in PNF tissues (Fig. 2d). Collectively, these findings suggest that HMGB1 overexpression may contribute to the malignant progression from PNF to MPNST.

Fig. 2.

HMGB1 is upregulated in MPNST and predicts poor patient survival. a, Volcano plot displaying differentially expressed genes (DEGs) between matched MPNST and plexiform neurofibroma (PNF) tissues (n = 3 pairs). b, Gene Ontology (GO) biological process terms enriched for the upregulated genes in MPNSTs. The most significantly enriched terms are related to cell cycle and nuclear division. c, Kaplan–Meier survival curve comparing overall survival of MPNST patients with high versus low HMGB1 expression. Elevated HMGB1 expression is significantly associated with shorter survival (log-rank test, P < 0.01). d, Representative immunohistochemical (IHC) staining images of HMGB1 protein expression in PNF and MPNST tissues

HMGB1 modulates NF1 cell migration, invasion, and proliferation

Building upon these findings, we validated the complex role of HMGB1 in human NF1-derived cell lines (ipNF05.5 and sNF96.2) through lentiviral delivery of HMGB1-knockout (KO), HMGB1-overexpressing (OE), negative control (NC) and empty vector (EV). Compared with NC treatment, HMGB1-OE lentivirus treatment significantly increased HMGB1 expression levels in the ipNF05.5 and sNF96.2 cell lines (Fig. 3a and b). EdU staining demonstrated that compared with NC, HMGB1 knockdown significantly inhibited cell proliferation, whereas HMGB1 overexpression increased the proliferation of both ipNF05.5 and sNF96.2 cells (Fig. 3c). Transwell migration and invasion assays revealed that HMGB1 overexpression in ipNF05.5 and sNF96.2 cells increased cell migration and invasion compared with that in normal cells. However, knockdown of HMGB1 had the opposite effects on ipNF05.5 and sNF96.2 cells (Fig. 3d, P < 0.05). Collectively, these data establish HMGB1 as a master regulator of NF1 progression through the coordinated modulation of proliferation, migration and invasion.

Fig. 3.

HMGB1 regulates proliferation, migration, and invasion of NF1-derived cells in vitro. a, b, Western blot analysis (a) and qPCR (b) confirming the efficiency of HMGB1 knockout (KO) and overexpression (OE) via lentiviral transduction in ipNF05.5 and sNF96.2 human NF1-derived cell lines. Negative control (NC) and empty vector (EV) were used as controls. GAPDH was used as a loading control. Data are presented as mean ± SEM from three independent experiments. *P < 0.05, **P < 0.01, ***P < 0.001. c, Representative images and quantitative analysis of EdU incorporation assays evaluating cell proliferation. HMGB1-KO inhibited, while HMGB1-OE promoted, the proliferation of ipNF05.5 and sNF96.2 cells. Data are presented as mean ± SEM (n = 3). *P < 0.05, **P < 0.01. d, Representative images and quantitative analysisof Transwell migration and Matrigel invasion assays. HMGB1-OE enhanced, whereas HMGB1-KO impaired, the migratory and invasive capabilities of both cell lines. Data are presented as mean ± SEM (n = 3). *P < 0.05, **P < 0.01

HMGB1 overexpression promotes cell cycle progression from the G1 phase to the S and G2 phases by increasing E2F2 expression

Cell cycle analysis by flow cytometry confirmed that compared with control ipNF05.5 and sNF96.2 cells, HMGB1 overexpression reduced the proportion of cells in the G1 phase, whereas HMGB1 knockdown increased the population of cells in the G1 phase (Fig. 4a). Moreover, we performed RNA-seq analysis on HMGB1-KO and NC ipNF05.5 and sNF96.2 cells. HMGB1 knockout resulted in 1024 upregulated and 1189 downregulated genes in ipNF05.5 cells, whereas in sNF96.2 cells, knockout resulted in 1588 upregulated and 1304 downregulated genes. Moreover, knockdown of HMGB1 in both cell lines led to a decrease in E2F2 expression (Fig. 4b). Accumulating evidence has indicated that the aberrant expression or activation of E2F2 is a common phenomenon in various malignancies and that E2F2 plays a crucial role in driving tumor development and progression, particularly during the transition from the G1 phase to the S phase [19, 20]. Gene Ontology analysis revealed that the upregulated genes were enriched in immune and migratory processes, such as the response to viruses and epithelial cell migration. This analysis also confirmed that the downregulated genes were involved in cell cycle-related processes (Fig. 4c). Single-cell sequencing revealed that E2F2 expression was highest in neoplastic cluster 4 (Fig. 4d and 4e), and the expression of HMGB1 was positively correlated with the activity of the E2F target pathway (Fig. 4f). Considering that HMGB1 may play a major role in the cell cycle of MPNST cells, we investigated the association between HMGB1 and E2F2 by CUT&Tag and ChIP‒qPCR. Visualization with Integrative Genomics Viewer (IGV) revealed strong signal enrichment of HMGB1 within 1 kb of the TSS of the E2F2 gene by CUT&Tag (Fig. 4g). ChIP‒qPCR analyses confirmed that in the peak regions proximal to E2F2 genes, the HMGB1-binding signal was stronger than that in the negative control loci (Fig. 4h). These results identified E2F2 as a potential target gene of the HMGB1 protein. WB and qPCR also confirmed that HMGB1 overexpression upregulated E2F2 expression, whereas HMGB1 knockdown downregulated it (Fig. 4i and j). Collectively, these findings demonstrate that HMGB1 regulates the cell cycle in MPNST cells by controlling E2F2 expression, identifying it as a direct transcription factor of E2F2.

Fig. 4.

HMGB1 promotes G1/S phase transition by directly transcriptionally regulating E2F2. a, Cell cycle distribution analyzed by flow cytometry in ipNF05.5 and sNF96.2 cells after HMGB1 knockout (KO) and overexpression (OE). Histograms show the percentage of cells in G1, S, and G2/M phases. Data are presented as mean ± SEM (n = 3). b, Volcano plots of RNA-seq data from HMGB1-KO versus NC cells in both ipNF05.5 and sNF96.2 lines. Significantly downregulated E2F2 is highlighted. c, Gene Ontology (GO) analysis of biological processes enriched among the genes downregulated by HMGB1-KO in both cell lines, showing significant enrichment for cell cycle-related terms. d, e, UMAP visualization (d) and Violin plot (e) from single-cell RNA-seq data showing E2F2 expression across the four malignant clusters, with the highest level in neoplastic cluster 4. f, Positive correlation between HMGB1 expression and the activity of the E2F target pathway. The correlation was assessed by Pearson’s correlation coefficient. g, Representative IGV snapshot of CUT&Tag sequencing tracks showing HMGB1 binding enrichment near the transcription start site (TSS) of the E2F2 gene. h, ChIP-qPCR analysis quantifying HMGB1 binding at the E2F2 promoter peak region identified in (g). A known negative genomic region was used as a control. Data are presented as mean ± SEM (n = 3). **P < 0.01; unpaired t-test. i, j, Western blot (i) and qPCR (j) analyses confirming that HMGB1-OE upregulates, while HMGB1-KO downregulates, E2F2 expression at both the protein and mRNA levels in ipNF05.5 and sNF96.2 cells. GAPDH was used as a loading control for WB and as an internal control for qPCR. Data are presented as mean ± SEM (n = 3). *P < 0.05, **P < 0.01

HMGB1 affects NF1 tumor growth in a xenograft mouse model

Using an ipNF05.5 xenograft mouse model, we further confirmed the effect of the HMGB1/E2F2 axis on NF1 tumors. Compared with control mice, mice injected subcutaneously with HMGB1-OE ipNF05.5 cells developed significantly larger tumors (P < 0.05), whereas HMGB1-KO mice formed significantly smaller tumors (P < 0.01), as determined by tumor weight and volume assessments (Fig. 5a and b). On day 35 postinjection, the tumor volume was significantly greater in the overexpression group and lower in the knockdown group than in the control group (Fig. 5c). In vivo Western blot analysis further confirmed that HMGB1 expression modulates E2F2 levels. Compared with the NC group, the group in which HMGB1 was overexpressed had increased E2F2 expression, while the group in which HMGB1 was knocked down had reduced E2F2 expression (Fig. 5d). IHC analysis also revealed correspondingly higher E2F2 and Ki67 expression in HMGB1-overexpressing tumors and lower expression in HMGB1-knockdown tumors, confirming a positive correlation between HMGB1 expression and tumor growth (Fig. 5e).

Fig. 5.

HMGB1 promotes NF1 tumor growth in vivo through the E2F2 pathway. a, Representative images of tumors resected from nude mice at the endpoint of the study (day 35 post-injection). Mice were subcutaneously injected with ipNF05.5 cells stably expressing negative control (NC), HMGB1-OE, or HMGB1-KO constructs. b, Quantitative analysis of tumor weight from the xenograft model. Data are presented as mean ± SEM. *P < 0.05, **P < 0.01 vs. NC group; one-way ANOVA. c, Tumor growth curves measured by volume over 35 days. Data are presented as mean ± SEM. *P < 0.05, **P < 0.01 for HMGB1-OE or HMGB1-KO group vs. NC group at day 35; two-way ANOVA with repeated measures. d, In vivo Western blot analysis of HMGB1 and E2F2 protein levels in representative tumor lysates from each group, confirming that HMGB1 modulates E2F2 expression in the xenograft tumors. GAPDH was used as a loading control. e, Representative immunohistochemical (IHC) staining images and quantitative analysis of E2F2 and Ki67 expression in xenograft tumor tissues. Scale bar, 50 μm. HMGB1-OE tumors showed enhanced E2F2 and Ki67 expression, while HMGB1-KO tumors showed reduced expression. Data are presented as mean ± SEM (n = 5–8). *P < 0.05, **P < 0.01; one-way ANOVA

Discussion

The therapeutic options for MPNSTs are limited. Therefore, it is necessary to identify effective therapeutic targets to both halt tumor growth and predict malignant transformation. HMGB1 is a multifunctional protein with important nuclear and extracellular functions [21] and pivotal for tumor progression because it promotes inflammation, cell migration, and survival [22]. In this study, we revealed that HMGB1 regulates the cell cycle by controlling the expression of E2F2 in MPNSTs. Single-cell sequencing revealed robust upregulation of HMGB1 specifically within a distinct population of neoplastic cells characterized by high CNV scores in MPNSTs. Compared with patients with PNF, patients with MPNST exhibited significantly elevated levels of HMGB1. There was a strong inverse correlation between HMGB1 expression and survival time in the MPNST cohort, suggesting the role of HMGB1 as a negative prognostic marker. In this study, HMGB1 promoted the proliferation, migration, and invasion of MPNST cells both in vitro and in vivo. Furthermore, HMGB1 overexpression induced cell cycle progression from the G1 phase to the S and G2 phases in ipNF05.5 and sNF96.2 cells by upregulating the expression of E2F2. Targeting HMGB1 may provide a new therapeutic strategy for MPNSTs.

Extensive research into tumor progression has revealed that HMGB1 exerts paradoxical functions in modulating cell fate through its regulation of diverse signaling pathways [23]. In breast cancer cells, the upregulation of HMGB1 expression acts as a tumor suppressor and inhibits tumor progression through an RB-dependent pathway [24]. However, our scRNA-seq study confirmed high HMGB1 expression in MPNST neoplastic clusters with higher CNV scores and its association with reduced patient survival. CNVs are a type of structural variation defined as the loss or gain of genomic DNA segments between 50 bp and 1 Mb in size [25]. The mean CNV value serves as a criterion for determining tumor cell malignancy [26, 27]; a higher CNV score generally indicates a higher degree of malignancy. The neoplastic clusters with the highest CNV scores exhibited the highest HMGB1 expression in our experiments. Moreover, bulk RNA sequencing and immunostaining confirmed that compared with that in PNF cells, the expression of HMGB1 is elevated in MPNST cells, suggesting that HMGB1 is one of the factors that promotes MPNST progression. Numerous studies have reported that elevated levels of HMGB1 in tumor tissues are associated with poor prognosis [28]. In osteosarcoma tissues, increased HMGB1 levels are associated with increased TNM stage, lymph node metastasis, and distant metastasis [29]. The overexpression of HMGB1 is correlated with glioma malignancy and influences key pathways such as RAGE/MEK/ERK and RAGE/Rac1[30]. In addition, we revealed that HMGB1 knockdown inhibited MPNST cell migration, invasion, and proliferation. These findings reveal that Compared with PNF, HMGB1 is highly expressed in MPNST, and its high expression promotes tumor cell migration and invasion, as well as cell cycle progression.

Furthermore, to elucidate the regulatory mechanism through which HMGB1 promotes the malignancy of MPNST cells, we compared gene expression between the HMGB1-KO and NC ipNF05.5 and sNF96.2 cells. Gene Ontology analysis revealed that upon HMGB1 knockdown, the upregulated genes were enriched in immune-related pathways, including the response to viruses and the defense response to viruses, whereas the downregulated genes were associated with the cell cycle and Wnt signaling pathway. Notably, in both the ipNF05.5 and sNF96.2 cell lines, HMGB1 knockdown reduced the expression of E2F2, a critical cell cycle regulator. E2F2 is recognized as a pivotal oncogenic factor that contributes to tumorigenesis and metastasis. Extensive research has demonstrated the role of E2F2 in promoting critical oncogenic processes such as tumor cell proliferation, angiogenesis, and invasion [20]. Subsequent CUT&Tag profiling and ChIP‒qPCR validation demonstrated that HMGB1 directly binds to the E2F2 promoter region, establishing its role as a transcriptional regulator of E2F2. This regulatory relationship was also demonstrated by Western blot and qPCR analyses, which revealed that HMGB1 overexpression upregulated E2F2 expression, whereas HMGB1 knockdown had the opposite effect on the ipNF05.5 and sNF96.2 cell lines. E2F family motifs are enriched in human umbilical vein endothelial cells and IMR90 cells, and the expression of these cell cycle-regulating TFs (e.g., E2F2 and E2F6) is strongly downregulated during HMGB1-mediated senescence [31]. In our study, HMGB1 knockdown resulted in G1 arrest concomitant with a significant decrease in the percentage of ipNF05.5 and sNF96.2 cells in the G1 phase after HMGB1 overexpression. E2F2 promotes the progression of the cancer cell cycle from the G1 phase to the S phase, thus increasing their proliferation [32]. Some studies have demonstrated that E2F2 knockdown significantly reduces the proliferation of glioma cells and glioma stem-like cells (GSCs) [33]. These results suggest a potential mechanism by which HMGB1 promotes cancer activity in MPNSTs: HMGB1↑ → E2F2↑ → decrease in the G1 phase ratio → cell proliferation.

Conclusion

This study revealed that HMGB1 is upregulated in NF1-associated MPNST tissues and that its high expression is positively correlated with poor prognosis. Further investigation demonstrated that HMGB1-OE promotes the progression from the G1 phase to the G2 phase and the proliferation, migration, and invasion of NF1 cells by regulating E2F2 expression. Targeting HMGB1 could suppress E2F2 transcription and disrupt the cell cycle in MPNSTs, offering a valuable complement to current therapeutic strategies.

Abbreviations

ANOVA

Analysis of variance

ATCC

American type culture collection

Bp

Base pair

CAFs

Cancer-associated fibroblasts

ChIP

Chromatin immunoprecipitation

CNV

Copy number variation

CUT&Tag

Cleavage under targets and tagmentation

Ct

Cycle threshold

DEGs

Differentially expressed genes

DMEM

Dulbecco’s modified eagle medium

EdU

5-Ethynyl-2′-deoxyuridine

FACS

Fluorescence-activated cell sorting

FBS

Fetal bovine serum

GC

Gastric cancer

GO

Gene ontology

GSCs

Glioma stem-like cells

HMGB1

High-mobility group box 1

HRP

Horseradish peroxidase

IHC

Immunohistochemistry

IGV

Integrative genomics viewer

KO

Knockout

MPNSTs

Malignant peripheral nerve sheath tumors

NC

Negative control

NF1

Neurofibromatosis type 1

OE

Overexpression

PBS

Phosphate-buffered saline

PI

Propidium iodide

PNFs

Plexiform neurofibromas

PVDF

Polyvinylidene fluoride

qPCR

Quantitative polymerase chain reaction

RNA-seq

RNA sequencing

scRNA-seq

Single-cell RNA sequencing

SD

Standard deviation

TME

Tumor microenvironment

TSS

Transcription start site

UMAP

Uniform manifold approximation and projection

VSMCs

Vascular smooth muscle cells

WB

Western blotting

Author contributions

S SQ: Formal analysis, Visualization, Writing-Original Draft, Supervision, Project administration. H CH: Visualization, Methodology. Y LB: Methodology, Formal Analysis. L DZ: Conceptualization, Resources, Funding. G C: Validation, Visualization. S L: Resources, Supervision.

Funding

This study was supported by the National Natural Science Foundation of China, No. 82171364 (to SL).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Beijing Tiantan Hospital in accordance with the principles of the Declaration of Helsinki, and written informed consent was obtained from all participants prior to surgery.

Consent for publication

All authors have reviewed and approved the final version of the manuscript for publication.

Competing interests

The authors declare no competing interests.