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. 2025 Dec 12;39(24):e71343. doi: 10.1096/fj.202501932R

MMP14‐Dependent Activation of TGF‐β Signaling Enhances Malignancies via Promoting Necroptosis in Glioblastoma

Haoyu Zhou 1,2, Wei Wu 1,2, Yiyang Cao 1,2, Beichen Zhang 1,2, Mingjing Zhou 1,2, Yichang Wang 1, Maode Wang 1,2,✉, Jia Wang 1,2,✉
PMCID: PMC12700191  PMID: 41384860

ABSTRACT

Glioblastoma (GBM) exhibits profound genetic heterogeneity and poor prognosis, and a wide range of biological processes are proved to be enrolled in its tumorigenesis and progression. Necroptosis, which is identified as a regulated cell death process, has been widely confirmed to be essential in shaping malignant behaviors among multiple tumors; nevertheless, the functions of necroptosis in GBM still remain elusive. Herein, matrix metalloproteinase‐14 (MMP14) was identified as a necroptosis‐related hub gene in GBM by using weighted gene co‐expression network analysis (WGCNA) of bulk transcriptomic data. Moreover, single‐cell analysis and spatial transcriptomics mapped a cell subpopulation in which MMP14 and necroptosis are closely correlated. Additionally, MMP14 emerged as a poor prognostic marker in GBM. Functionally, knockdown of MMP14 suppressed GBM malignant behavior, including proliferation, immigration, invasion, and tumorigenesis, with an increased susceptibility to necroptosis. As an underlying mechanism, TGF‐β signaling was critical for MMP14‐mediated necroptosis activation, with SMAD Family Member 2 (SMAD2) directly binding to the Receptor‐Interacting Protein 1 (RIP1) promoter. Altogether, MMP14 promotes a range of malignant behaviors and orchestrates a TGF‐β‐dependent necroptosis heterogeneity landscape in GBM; therefore, targeting MMP14‐TGF‐β signaling could be a novel strategy to counteract therapeutic resistance in GBM.

Keywords: glioblastoma, matrix metalloproteinase 14, necroptosis, smad proteins, transforming growth factor beta

MMP14 amplifies TGF‐β signaling to create a necroptosis‐heterogeneous landscape that fuels GBM progression. Targeting this axis may disrupt the adaptive mechanisms underlying treatment failure, offering a path to precision therapy for this intractable malignancy.

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1. Introduction

Glioblastoma (GBM) is the most aggressive and lethal primary brain tumor in adults, characterized by a high level of intratumoral heterogeneity and therapeutic resistance [1, 2, 3]. Despite the advancements in treatment modalities such as surgery, radiotherapy, and chemotherapy during the past decades, the prognosis for GBM patients still remains poor, with a median survival of only 15 months after diagnosis [4]. Therefore, this predicament leads to further investigation of the underlying mechanisms of tumorigenesis and therapy resistance in GBM [5, 6].

Necroptosis, which is identified as a programmed cell death, has been widely confirmed to be essential in shaping its malignant behaviors among multiple tumors [7, 8, 9, 10, 11]. Unlike apoptosis, necroptosis is a caspase‐independent form of programmed necrosis mediated by death signaling pathways that cause cell swelling and membrane rupture [12, 13]. Necroptosis is mediated by specific signaling pathways, primarily involving receptor‐interacting protein kinase 3 (RIPK3) and mixed lineage kinase domain‐like protein (MLKL), and serves as a backup mechanism when apoptosis is inhibited [14, 15, 16]. Accumulating evidence underscores the regulatory role of necroptosis in cancer progression and therapeutic resistance. In stage I non‐small cell lung cancer (NSCLC) of squamous cell carcinoma subtype, reduced expression of RIPK3 serves as a significant prognostic indicator for patient survival [17]. Mechanistically, hepatocyte growth factor (HGF) gene activation in colorectal carcinoma has been shown to confer resistance to necroptotic cell death through downstream survival signaling modulation [18]. Epigenetic dysregulation further shapes necroptotic susceptibility, with RIP3 promoter hypermethylation emerging as a key mechanism of cancer cell adaptation [19]. In GBM, isocitrate dehydrogenase (IDH) mutations drive accumulation of the oncometabolite 2‐hydroxyglutarate (2‐HG), which facilitates DNA methyltransferase 1 (DNMT1)‐mediated RIP3 promoter methylation and consequent necroptosis evasion [20]. Therapeutically, pharmacological induction of necroptosis demonstrates efficacy in reversing therapeutic resistance in both prostate and bladder malignancies [21, 22]. Current therapeutic innovations, including ultrasound‐mediated necroptosis activation and small‐molecule agonists, effectively bypass apoptosis resistance pathways and restore temozolomide (TMZ) sensitivity in refractory tumors [23, 24, 25]. However, the functional landscape of necroptosis in GBM, including its key regulators, spatial heterogeneity, and crosstalk with oncogenic signaling, remains poorly defined, limiting its therapeutic exploitation.

Emerging evidence indicates that matrix metalloproteinases (MMPs), particularly matrix metalloproteinase‐14 (MMP14), may play pivotal roles in various tumor‐associated processes, including cell invasion and angiogenesis [26, 27, 28, 29], and also induce diverse and complex changes including proteolysis of extracellular matrix (ECM) in the tumor microenvironment [30, 31, 32]. Notably, clinical analyses reveal pronounced MMP14 overexpression in human gliomas, with maximal expression observed in GBM specimens across both in vivo and in vitro models, correlating positively with histological grade and inversely with patient survival [33, 34]. Mechanistically, MMP14 drives GBM malignancy through dual tumor cell‐autonomous and microenvironment‐modulating effects. Its enzymatic activity directly facilitates the proMMP‐2 activation cascade, subsequently amplifying MMP‐2 and MMP‐9 proteolytic networks to promote ECM degradation and peritumoral niche restructuring [35, 36, 37]. Furthermore, MMP14 mediates tumor cell migration along white matter tracts [38], while simultaneously enhancing stem‐like properties via DLL4‐Notch3 signaling axis activation, thereby conferring TMZ [39]. Preclinical validation demonstrates that MMP14 knockdown significantly attenuates angiogenic potential and extends survival in orthotopic glioma models [40]. Despite these mechanistic insights, the functional interplay between MMP14 and tumor cell death, especially necroptosis, in GBM remains an unexplored frontier.

In this study, MMP14 was characterized as a central hub gene exhibiting dual associations with inhibited necroptotic activity and adverse clinical outcomes in GBM. Comprehensive survival analyses revealed a significant positive correlation between MMP14 expression and reduced overall survival, establishing its potential as a novel dual‐function biomarker for both necroptosis monitoring and prognostic stratification in GBM. Intriguingly, multi‐omics integration demonstrated coordinated dysregulation of MMP14 and necroptosis‐related genes at single‐cell resolution, with spatial transcriptomics further identifying colocalization patterns in specific niches. Functionally, attenuated GBM malignant behavior and increased necroptosis correlated with exogenous inhibition of MMP14, indicating a meaningful role played by this gene in GBM. As an underlying mechanism, TGF‐β signaling was critical for MMP14‐mediated necroptosis activation, with SMAD2 directly binding to the Receptor‐Interacting Protein 1 (RIP1) promoter. Taken together, our findings suggest that MMP14 serves as a novel prognostic biomarker in GBM; moreover, it indicates that MMP14/TGFβ‐independent necroptosis could be a potential therapeutic target for GBM.

2. Material and Methods

Through WGCNA of bulk transcriptomic data and single‐cell sequencing and spatial transcriptome sequencing, MMP14 was identified as a necroptosis‐related hub gene in GBM. Validation of clinical relevance utilized TCGA and CGGA datasets, clinical GBM specimens, and in vitro models. Functional assays (knockdown/overexpression) assessed MMP14's impact on malignant behaviors. Necroptosis levels were quantified by flow cytometry (7‐ADD+/Annexin V+ cells) and phospho‐MLKL/RIPK3 Western blotting. Mechanistic studies linked MMP14 to TGF‐β pathway activation, validated by Western blotting and ChIP‐qPCR.

Detailed information can be found in Doc S1.

3. Results

3.1. MMP14 Is One of the Most Highly Correlated Genes With Necroptosis in GBM

To investigate the gene expression characteristics of glioblastoma (GBM), differential gene expression analysis was performed by using data from The Cancer Genome Atlas (TCGA) GBM database. A total of 18 933 differentially expressed genes (DEGs) were identified, including 1118 significantly down‐regulated and 805 significantly up‐regulated genes. The distribution of DEGs was visualized using a volcano plot (Figure 1a) and a heatmap (Figure 1b), which demonstrated distinct expression patterns between tumor and non‐tumor tissues. To further elucidate the relationship between these DEGs and necroptosis in GBM, we incorporated the necroptosis gene set [41] as a phenotypic reference and performed Weighted Gene Co‐expression Network Analysis (WGCNA). The co‐expression network was constructed based on the optimal soft thresholding power (Figure S1a,b). Following module identification, a gene dendrogram was generated (Figure 1c), revealing that genes within the black module exhibited a high degree of clustering. Subsequently, we calculated the correlation and significance of all genes and constructed a correlation heatmap (Figure S1c). Notably, the black module, comprising 94 genes, demonstrated the strongest correlation with the necroptosis gene set (Figure 1d and Figure S1d). Based on this strong association, we identified the black module as the key module for subsequent analysis.

FIGURE 1.

FIGURE 1

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MMP14 is one of the most highly correlated genes with necroptosis in GBM. (a) Volcano plot of differentially expressed genes about GBM, using TCGA database, based on log2FC > 1.5 and an adjusted p‐value < 0.05. (b) Heatmap of differentially expressed genes about GBM, using TCGA database. (c) Gene dendrogram and modules of differentially expressed genes, genes that were closer together (clustered into the same branch) were assigned to the same module. (d) Pearson correlation analysis of all modules and necroptosis score. (e) Consensus map of NMF clustering about black module, using the TCGA database. (f) The Kaplan–Meier analysis of overall survival in patients with GBM based on two clusters, using the TCGA database (p = 0.026, with log‐rank test). (g) Principal component analysis based on two clusters, using the TCGA database. (h) Volcano plot of differentially expressed genes about two clusters, based on log2FC > 1.5 and an adjusted p‐value < 0.05. (i) A correlation was found between the necroptosis score and the differentially expressed genes (*p < 0.05, **p < 0.01 and ***p < 0.001, with independent t‐test).

To further investigate the role of the genes represented by the black module in GBM necroptosis, we performed non‐negative matrix factorization (NMF) to analyze the expression of these genes in GBM patients from the TCGA database (Figure 1e). The results revealed that the patient samples could be divided into two distinct clusters with markedly different expression profiles. Additionally, survival analysis demonstrated a significant difference in prognosis between these two clusters, with Cluster 1 exhibiting notably shorter survival times compared to Cluster 2 (Figure 1f). To validate these findings, principal component analysis (PCA) was conducted, further confirming the division of GBM samples into two distinct clusters (Figure 1g). To identify genes most closely associated with necroptosis in the black module, we examined the differentially expressed genes between the two clusters. As shown in Figure 1h, a total of 38 significantly differentially expressed genes were identified, with 9 genes down‐regulated and 29 up‐regulated. A heatmap illustrating these differences is presented in Figure S1e. By analyzing the correlation between necroptosis scores and these 38 differential genes, MMP14 emerged as one of the most strongly correlated genes with necroptosis in GBM (Figure 1i). However, due to the large heterogeneity of GBM, the correlation between necroptosis and MMP14 needs to be further explored.

3.2. MMP14 and Necroptosis are Spatially Closely Related in GBM

To clarify the correlation between MMP14 and necroptosis at the single‐cell level and spatial level, we analyzed the expression patterns of MMP14 and necroptosis using the Ivy Glioblastoma Atlas Project database (https://glioblastoma.alleninstitute.org/). The results suggested that both MMP14 and necroptosis were highly expressed in the Cellular Tumor (CT) region (Figure 2a,b). Closer inspection of signature enrichment patterns revealed a close correlation between the MMP14 and necroptosis scores (Figure 2c). Further examination using single‐cell RNA sequencing (scRNA‐seq) data from the GEO database (GSE235676) revealed highly coincident expression of MMP14 and necroptosis scores (Figure 2d). Clustering of cells into 17 distinct groups via UMAP analysis identified Radial glial cells as the primary cell type with high MMP14 expression and necroptosis scores (Figure 2e). To quantify intratumoral heterogeneity in glioblastoma (GBM) ecosystems, Shannon entropy was systematically calculated across cellular states. Elevated entropy indices demonstrated interpatient conservation of recurrent cellular programs, while diminished entropy values revealed case‐restricted transcriptional archetypes dominated by single‐sample derivation. This entropy spectrum mechanistically links tumor‐wide biological coherence with patient‐specific transcriptional divergence. Given the low entropy observed in Radial glial cells, along with their varying content in patients with newly diagnosed and recurrent gliomas, these findings suggest strong heterogeneity in Radial glial populations (Figure 2f,g).

FIGURE 2.

FIGURE 2

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MMP14 and necroptosis were analyzed at the single‐cell and spatial levels. (A, B) Expression of MMP14 and necroptosis scores in different regions of GBM, using Ivy GAP database (**p < 0.01 and ****p < 0.0001, with independent t‐test); CT: Cellular Tumor, LE: Leading Edge, IT: Infiltrating Tumor. (c) Correlation analysis between MMP14 expression and necroptosis score (p < 0.0001, with Pearson correlation coefficient). (d) Distribution of MMP14 expression and necroptosis score across single‐cell dataset. (e) GBM cells from GEO database were clustered into 17 distinct groups, using t‐SNE dimensionality reduction (colors indicate groups). (f) Shannon entropy of different cell groups (p < 0.0001, with Kruskal‐Wallis test). (g) Radial glial content of different patients including primary and recurrent (colors indicate cell types). (h) Spatial transcriptome sequencing data divided into 15 niches (colors indicate niches). (i) Distribution of MMP14 expression and necroptosis score across single‐cell spatial transcriptome sequencing data. (j) Heatmap showing large‐scale CNV profile of different necroptosis scores and the content of Radial glial. Red and blue colors represent high and low CNV level, respectively. (k) Major estimated the expression of MMP14, necroptosis score and Radial glial distribution of each bead/spot inferred by RCTD methods.

We then identified 15 spatial niches in GBM spatial datasets (Figure 2h). Gene expression analysis indicated that MMP14 and necroptosis scores were spatially co‐localized in niche 8, predominantly derived from patient ‘Pt_16’ (Figure 2i). A subsequent analysis focused on patient ‘Pt_16’ was performed using Robust Cell Type Decomposition (RCTD) to match the single‐cell data with spatial transcriptome sequencing data. Somatic large‐scale chromosomal copy number variation (CNV) was inferred, and CNV scores were calculated to identify malignant cells. The results revealed a consistent distribution of necroptosis scores, MMP14, and Radial glial cells. Regions with high levels of Radial glial cells and necroptosis scores showed chromosomal alterations such as 1q gain and 10p loss, which are characteristic of GBM [42, 43, 44] (Figure 2j,k). These results suggest that necroptosis and MMP14 are significantly co‐localized in a subgroup of malignant cells. However, the specific role of MMP14 in necroptosis in GBM cells needs to be further explored.

3.3. MMP14 is Significantly Enriched and is Correlated With Poor Prognosis in GBM

Given that MMP14 was identified as playing a significant role in necroptosis in GBM and has been reported to be up‐regulated in gliomas and associated with malignant behaviors [32, 39, 45], we further investigated its expression in gliomas. Expression data was derived from the TCGA and CGGA databases to examine MMP14 expression in gliomas, and the results showed a significantly elevated expression of MMP14 with the progression of WHO grade (Figure 3a,b). To validate these findings, MMP14 expression was assessed in clinical samples and cell lines via western blot analysis. The results revealed that MMP14 expression was markedly up‐regulated in tumor tissues and GBM cell lines compared to non‐tumor tissues and SVGp12(Human astrocytes) (Figure 3c,d). The results of qRT‐PCR showed that the expression level of MMP14 was significantly increased in GBM cell lines (Figure 3e). Consistently, immunohistochemical (IHC) staining of clinical glioma samples further confirmed that MMP14 expression was increased with advancing WHO grade (Figure 3f). In addition, Kaplan–Meier survival analysis conducted using data from the database as well as our collected dataset revealed that patients with high MMP14 expression in GBM and all grades of glioma exhibited significantly shorter overall survival times (Figure 3g–k). These findings suggest that high MMP14 expression is closely associated with poor prognosis in glioma patients, although its precise cellular role remains to be further elucidated.

FIGURE 3.

FIGURE 3

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Highly expressed MMP14 in GBM. (a) Analysis of MMP14 expression in different WHO grades of glioma, using TCGA database (***p < 0.001, with Tukey's Honest Significant Difference). (b) Analysis of MMP14 expression in different WHO grades of glioma, using CGGA database (**p < 0.01 and ***p < 0.001, with Tukey's Honest Significant Difference). (c) Protein expression of MMP14 in GBM samples, compared with adjacent non‐tumor tissue, GAPDH served as an internal control. T, tumor tissue; N, adjacent non‐tumor tissue (**p < 0.01, ****p < 0.0001, with independent t‐test). (d) Protein expression of MMP14 in GBM cell lines, compared with Normal Human Astrocytes (SVGp12), GAPDH served as an internal control (**p < 0.01, with one‐way ANOVA followed by Dunnett's post‐test). (e) qRT‐PCR analysis of MMP14 in cell lines, GAPDH served as an internal control (*p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001 with independent t‐test). (f) Immunohistochemical staining of MMP14 in different grades glioma samples (*p < 0.05, ****p < 0.0001, with independent t‐test). (g–k) The Kaplan–Meier analysis of overall survival in patients conducted using data from the TCGA and CGGA database as well as dataset from The First Affiliated Hospital of Xi'an Jiaotong University in GBM and all grades of glioma based on MMP14 expression.

3.4. Overexpression of MMP14 Promotes Malignant Behaviors in GBM

MMP14 exhibits oncogenic features in many cancers [46, 47, 48, 49], to gain further insight into the role of MMP14 in GBM, we exogenously overexpressed MMP14 in U251 and A172 cell lines. Western blotting and qRT‐PCR confirmed the efficiency of overexpression (Figure 4a,b). Wound healing formation assays and transwell assays demonstrated that MMP14 overexpression enhanced the migratory and invasive capabilities of GBM cells in vitro (Figure 5c,d). Moreover, overexpression of MMP14 also promoted cell proliferation and colony formation compared to the control groups (Figure 5e,f). To simulate the in vivo tumor environment, U251 cells overexpressing MMP14 were used to establish an intracranial tumor model. Mice in the MMP14 overexpression group exhibited significantly shorter survival times and larger tumor volumes compared to the control group (Figure 5g,h). Altogether, these findings indicate that MMP14 regulates multiple malignancies of GBM, including increased proliferation, migration, invasion, and colony formation both in vitro and in vivo.

FIGURE 4.

FIGURE 4

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Overexpression of MMP14 promotes malignant behaviors in GBM. (a, b) Western blotting and qRT‐PCR analysis of MMP14 in U251 and A172 cell lines transduced with MMP14 overexpression (MMP14) or empty vector (EV) lentivirus, GAPDH served as an internal control (****p < 0.0001, with independent t‐test). (c) Invasion ability of U251 and A172 cell lines pre‐treated with MMP14 or EV lentivirus, respectively (*p < 0.05 and **p < 0.01, with independent t‐test). (d) The migration ability of U251 and A172 cell lines pretreated with MMP14 or EV lentivirus, respectively (*p < 0.05 and **p < 0.01, with independent t‐test). (e) The colony formation of U251 and A172 cell lines pre‐treated with MMP14 or EV lentivirus, respectively (**p < 0.01 and ***p < 0.001, with independent t‐test). (f) In vitro cell growth assay for MMP14 overexpression (MMP14) and empty vector (EV) in U251 and A172 cell lines, respectively (*p < 0.05 and **p < 0.01, with independent t‐test). (g‐h) Representative images of intracranial xenograft and Kaplan–Meier analysis for mice using U251 cells pre‐treated with MMP14 or EV (**p < 0.01 with log‐rank test).

FIGURE 5.

FIGURE 5

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Knock‐down of MMP14 affects the malignant behaviors and necroptosis of GBM cells. (a) Western blotting of MMP14 protein in U87 and U373 cells transfected with lentivirus shNT, shMMP14#1, and shMMP14#2, GAPDH served as an internal control. (b) qRT‐PCR analysis for measuring MMP14 mRNA in U87 and U373 cells transfected with lentivirus shNT, shMMP14#1 and shMMP14#2 (****p < 0.0001, with independent t‐test). (c) The migration ability of U87 and U373 cells pretreated with shNT or shMMP14 lentivirus, respectively (*p < 0.05 and **p < 0.01, with independent t‐test). (d) In vitro EDU staining for MMP14 knock‐down in U87 and U373 cells (*p < 0.05 and **p < 0.01, with independent t‐test). (e) The colony formation of U87 and U373 cells pre‐treated with shNT or shMMP14 lentivirus, respectively (*p < 0.05, **p < 0.01 and ***p < 0.001, with independent t‐test). (f) Transwell assays were performed to study the role of MMP14 on U87 and U373 cell invasion by knocking down MMP14 (*p < 0.05 and **p < 0.01, with independent t‐test). (G‐H) Representative images of intracranial tumors and Kaplan–Meier analysis for in vivo intracranial xenografted mice using U373 cells pre‐treated with shNT and shMMP14 lentivirus (*p < 0.05 with log‐rank test). (i, j) Flow cytometry analysis using Annexin V‐PE and 7‐ADD for apoptosis analyses in U87 and U373 cell lines pretreated with shNT and shMMP14 lentivirus and the indicated agents, respectively (**p < 0.01, ***p < 0.001 and ****p < 0.0001, with independent t‐test). (k, l) Western blotting of necroptosis markers, GAPDH served as an internal control. (m) representative images of immunofluorescence staining of P‐MLKL (S358) (red) and RIP1 (green).

3.5. Suppression of MMP14 Enhanced Necroptosis thus Attenuated Malignant Characters in GBM

To explore the functional role of MMP14 in GBM malignancy, we silenced MMP14 expression in U87 and U373 cell lines using lentivirus‐mediated RNA interference. The efficiency of transfection was assessed by western blotting and qRT‐PCR (Figure 5a,b). Wound healing and transwell assays were performed to investigate the effect of MMP14 silencing on tumor‐related behaviors. Results indicated that the migratory and invasive capabilities of GBM cells were significantly reduced upon MMP14 knockdown (Figure 5c,f). In vitro cell proliferation and colony formation assays revealed that MMP14 silencing affected the proliferation rate of U87 and U373 cells (Figure 5d,e). To assess the in vivo impact, an intracranial xenograft mouse model was established. Kaplan–Meier survival analysis indicated that MMP14 knockdown significantly inhibited tumor growth and improved mouse survival (Figure 5g,h).

As mentioned above, a close relationship between MMP14 and necroptosis in GBM was found by bioinformatics analysis. To validate these results, flow cytometry was used to assess apoptosis in GBM cell lines transfected with lentivirus. In MMP14‐knockdown U87 and A373 cells, apoptosis rates were significantly increased, which could be inhibited by Necrostatin‐1 (Nec‐1), a necroptosis inhibitor (Figure 5i,j). Western blot analysis further confirmed that MMP14 overexpression led to significant increases in the phosphorylation of receptor‐interacting protein 3 (P‐RIP3) and mixed lineage kinase domain‐like protein (P‐MLKL), key mediators of necroptosis, which could be rescued by Necrostatin‐1 (Figure 5k,l). Immunofluorescence showed that the formation of necrosome could be rescued using the same method in MMP14 knockdown GBM cell lines (Figure 5m). These findings suggest that MMP14 knockdown promotes necroptosis, thus attenuating malignant characters in GBM, whereas the underlying mechanism needs to be further explored.

3.6. MMP14 Regulated Necroptosis in GBM via TGF‐β Signaling Activation

To further explore the downstream pathways involved, Gene Set Enrichment Analysis (GSEA) identified the transforming growth factor‐β (TGF‐β) signaling pathway as significantly enriched in the differential gene expression profiles of the black module clusters (Figure 6a). Additionally, single‐sample Gene Set Enrichment Analysis (ssGSEA) indicated that the TGF‐β pathway was associated with both necroptosis scores and MMP14 in GBM (Figure 6b). To confirm these results, we performed lentiviral knock‐down of MMP14 with or without SRI‐011381, a TGF‐β pathway agonist, in U87 and U373 cells. Western blot analysis confirmed that MMP14 knock‐down led to decreased phosphorylation of SMAD Family Member 2 (SMAD2), a key downstream effector of TGF‐β signaling (Figure 6c). In vivo experiments yielded results similar to those obtained in vitro (Figure 6d). Flow cytometry analysis showed that SRI‐011381 partially rescued necroptosis induced by MMP14 knock‐down (Figure 6h). Furthermore, this pharmacological activation of the TGF‐β pathway significantly rescued the inhibitory effects on the malignant behaviors of GBM cells (Figure 6e–g). It is well known that cell death is regulated by multiple factors. Recent studies have reported that the expression of Receptor‐Interacting Protein 1 (RIP1) has a great contribution to necroptosis [50]. To this end, we hypothesized that SMAD2 might bind to the promoter of RIP1 and regulate its transcriptional activity of RIP1 (Figure 6c,d). We used the JASPAR database (https://jaspar.elixir.no/) for prediction analysis and found that SMAD2 bound to the RIP1 promoter region (Figure 6i and Table S5). Moreover, the ChIP‐qPCR results showed that SMAD2 directly binds to the promoter of RIP1 (Figure 6j). Collectively, these results suggest that MMP14 reduces necroptosis in GBM by up‐regulating RIP1 expression through TGF‐β signaling.

FIGURE 6.

FIGURE 6

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Necroptosis is associated with the expression of MMP14 in the TGF‐β pathway. (a) The GSEA analysis revealed significantly different pathways in 2 clusters of the black module. (b) A correlation was found between the single‐sample GSEA score of necroptosis and the expression of MMP14 in the TGF‐β pathway (*p < 0.05, **p < 0.01 and ***p < 0.001, with independent t‐test). (c) Western blotting of the key proteins of TGF‐β pathway and necroptosis in U87 and U373 cells transfected with lentivirus shNT, shMMP14, and SRI‐011381, GAPDH served as an internal control. (d) Representative images of immunofluorescence staining about key proteins of TGF‐β pathway and necroptosis in vivo. (e–g) The proliferation, migration and invasion ability of U87 and U373 cells pretreated with shMMP14 lentivirus and SRI‐011381, respectively (*p < 0.05, **p < 0.01 and ***p < 0.001, with independent t‐test). (h) Flow cytometry analysis using Annexin V‐PE and 7‐ADD for apoptosis analyses in U87 and U373 cell lines pre‐treated with shNT and shMMP14 lentivirus and the indicated agents, respectively (**p < 0.01 and ****p < 0.0001, with independent t‐test). (i) The predicted sequence of SMAD2 binds to the RIP1 promoter. (j) Combination of the RIP1 promoter with SMAD2 as determined by ChIP‐qPCR (***p < 0.001 and ****p < 0.0001, with independent t‐test).

4. Discussion

Glioblastoma (GBM) exhibits remarkable heterogeneity and therapeutic resistance, necessitating a deeper understanding of the molecular mechanisms driving its progression [51, 52]. This study establishes MMP14 as a critical orchestrator of necroptosis heterogeneity and malignant progression in GBM. By integrating multi‐omics data and functional validation, we demonstrate that MMP14 not only serves as a prognostic biomarker but also drives TGF‐β/SMAD2‐dependent transcriptional reprogramming to promote necroptosis resilience and tumor aggression. These findings bridge the gap between necroptosis regulation and oncogenic signaling in GBM, offering actionable insights for therapeutic development.

MMP14, a membrane‐bound matrix metalloproteinase, has been implicated in cancer cell migration, invasion, and extracellular matrix remodeling [53, 54]. In GBM, MMP14 is widely recognized for its role in a variety of malignant behaviors, such as invasion, migration, angiogenesis, and resistance to both radiotherapy and chemotherapy [38, 55, 56, 57, 58], and our experiments yielded similar results. Functional assays demonstrated that MMP14 overexpression enhances GBM cell proliferation and invasion, while MMP14 knock‐down had the opposite effect. In addition, this study uncovers its novel function in necroptosis modulation. MMP14 was identified as a pivotal necrosis‐related gene through weighted gene co‐expression network analysis (WGCNA), data from The Cancer Genome Atlas (TCGA), and corroborated by Western blotting and flow cytometry assays, which expanded its canonical role in extracellular matrix (ECM) remodeling and invasion [53]. Notably, our single‐cell and spatial transcriptomic analyses revealed a distinct MMP14high subpopulation co‐localized with necroptosis markers, suggesting that MMP14‐driven heterogeneity contributes to therapeutic resistance in specific cellular compartments.

The dual role of necroptosis in cancer remains controversial. In cancer cells, the expression of major necroptosis pathway regulators is frequently down‐regulated, which has been found to be associated with adverse outcomes [19, 59, 60]. Necroptosis activation can also trigger anti‐tumor immunity [61]. But, under certain conditions, it contributes to tumor adaptation, angiogenesis, proliferation, metastasis [10, 62, 63]. In GBM, our data suggest that MMP14 knockdown sensitizes GBM cells to necroptosis while reducing invasion. These results resonate with recent studies highlighting necroptosis as a mediator of therapy resistance in solid tumors [64, 65, 66, 67]. While our orthotopic models recapitulated human MMP14 expression patterns, the immune‐deficient microenvironment precludes analysis of necroptosis‐immune interactions. Given that necroptosis can trigger anti‐tumor immunity via DAMP [68], future studies using syngeneic models are warranted.

Mechanistic studies revealed that MMP14 regulates necroptosis through activation of the TGF‐β signaling pathway. MMP14 is renowned for its collagenolytic activity [69]; however, it also proteolytically processes a diverse array of biologically active signaling molecules [70]. Pro‐TGFβ has been identified as a target of MMP14‐mediated cleavage in multiple contexts [70], specifically through the cleavage of latent‐transforming growth factor beta‐binding protein 1 (LTBP1) [71], leading to the activation of TGFβ‐mediated SMAD2/3 signaling. Moreover, while MMP14 is known to promote GBM and other cancers progression via proteolytic activation of TGF‐β and ECM degradation [72, 73], our work mechanistically links its non‐proteolytic signaling function to necrosome activation. The TGFβ signaling pathway exerted its effects through phosphorylating SMAD2/3 and then translocating them into the cell nucleus [74]. The SMAD2‐dependent transcriptional regulation of RIP1—validated by ChIP‐qPCR—reveals a direct molecular bridge between MMP14 and necroptotic machinery, which may complement previous studies on the relationship between them [75]. While TGF‐β is recognized for its immunosuppressive and mesenchymal‐promoting functions [74, 76, 77], our findings further expand its functional landscape by demonstrating that TGF‐β activation contributes to necroptosis suppression in GBM. While the interaction between SMAD2 and the RIP1 promoter has been identified, the precise binding sites of SMAD2 on the RIP1 promoter warrant further investigation. Future studies, such as the crosstalk between MMP14 and other necroptosis regulators (e.g., RIPK3/MLKL phosphorylation states), warrant further exploration.

Given the central role of MMP14 in necroptosis regulation and tumor progression, targeting the MMP14‐TGF‐β axis may represent a novel therapeutic strategy in GBM. For instance, inhibiting MMP14 alongside necroptosis inducers or TGF‐β blockers may overcome resistance mechanisms in MMP14high GBM subclones. Such strategies could be particularly relevant in recurrent GBM, where radial glial cells, closely related to MMP14 and necroptosis, are prevalent.

In conclusion, we propose a paradigm wherein MMP14 amplifies TGF‐β signaling to create a necroptosis‐heterogeneous landscape that fuels GBM progression. Targeting this axis may disrupt the adaptive mechanisms underlying treatment failure, offering a path to precision therapy for this intractable malignancy.

Author Contributions

H.Z.: data curation, formal analysis, investigation, writing – original draft. W.W.: data curation, funding acquisition, visualization, and writing – review and editing. Y.C.: data curation, formal analysis, investigation. B.Z.: data curation, formal analysis, writing – review and editing. M.Z.: data curation, formal analysis, investigation, writing – review and editing. Y.W.: data curation, investigation, supervision, and writing – review and editing. M.W.: supervision, writing – review and editing. J.W.: funding acquisition, project administration, supervision, writing – review and editing.

Funding

The research leading to these results received funding from [National Natural Science Foundation of China] under Grant Agreement No [82173285], [Innovation Capacity Support Plan of Shaanxi Province] under Grant Agreement No [2024ZC‐KJXX‐091], [Natural Science Basic Research Program of Shaanxi Province] under Grant Agreement No [S2024‐JC‐QN‐1021], [The First Affiliated Hospital of Xi’an jiaotong University Scientific Research Foundation] under Grant Agreement No [PT001789].

Ethics Statement

The research involving the utilization of the biological specimens received ethical approval from The Scientific Ethics Committee of The First Affiliated Hospital of Xi'an Jiaotong University (approval NO. 2016‐18). Informed consent was obtained through the signing of consent forms for the utilization of clinical samples; all experimental protocols used in the present study were in accordance with the guidelines of the Declaration of Helsinki. All animal procedures were conducted in accordance with NIH guidelines and approved by the Institutional Animal Care and Use Committee (Approval No. 2021‐695).

Consent

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Figure S1: Critical module analysis of WGCNA. (a) The correlation coefficients of log(k) and log(p(k)) corresponding to different soft thresholds β. (b) The average value of gene adjacency coefficient in gene networks corresponding to different soft threshold β, which reflects the average connection level of the network. (c) Module heatmap of all genes in GBM. (d) Eigengene adjacency heatmap of all modules and necroptosis score. (e) Heatmap of differentially expressed genes about two clusters.

FSB2-39-e71343-s002.tif (1.2MB, tif)

Data S1: fsb271343‐sup‐0001‐Text1.docx.

FSB2-39-e71343-s003.docx (53.5KB, docx)

Table S1: Antibodies for Western blot.

Table S2: Short hairpin RNA (shRNA) target sequences.

Table S3: Primer sequences used for real‐time PCR.

Table S4: Primers used for ChIP‐ qPCR.

Table S5: The result of JASPAR database.

Table S6: Antibodies for immunofluorescence staining.

FSB2-39-e71343-s001.pdf (144.7KB, pdf)

Zhou H., Wu W., Cao Y., et al., “MMP14‐Dependent Activation of TGF‐β Signaling Enhances Malignancies via Promoting Necroptosis in Glioblastoma,” The FASEB Journal 39, no. 24 (2025): e71343, 10.1096/fj.202501932R.

Haoyu Zhou and Wei Wu should be considered joint first authors.

Contributor Information

Maode Wang, Email: maodewang@163.com.

Jia Wang, Email: jiawang_xjtu@163.com.

Data Availability Statement

The data that support the findings of this study are openly available in [Chinese Glioma Genome Atlas; The Cancer Genome Atlas Program; Ivy Glioblastoma Atlas Project; Gene Expression Omnibus database with accession number GSE235676 and GSE235672] at http://www.cgga.org.cn/; https://www.cancer.gov/ccg/research/genome‐sequencing/tcga; https://glioblastoma.alleninstitute.org/ and https://www.ncbi.nlm.nih.gov/geo/.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1: Critical module analysis of WGCNA. (a) The correlation coefficients of log(k) and log(p(k)) corresponding to different soft thresholds β. (b) The average value of gene adjacency coefficient in gene networks corresponding to different soft threshold β, which reflects the average connection level of the network. (c) Module heatmap of all genes in GBM. (d) Eigengene adjacency heatmap of all modules and necroptosis score. (e) Heatmap of differentially expressed genes about two clusters.

FSB2-39-e71343-s002.tif (1.2MB, tif)

Data S1: fsb271343‐sup‐0001‐Text1.docx.

FSB2-39-e71343-s003.docx (53.5KB, docx)

Table S1: Antibodies for Western blot.

Table S2: Short hairpin RNA (shRNA) target sequences.

Table S3: Primer sequences used for real‐time PCR.

Table S4: Primers used for ChIP‐ qPCR.

Table S5: The result of JASPAR database.

Table S6: Antibodies for immunofluorescence staining.

FSB2-39-e71343-s001.pdf (144.7KB, pdf)

Data Availability Statement

The data that support the findings of this study are openly available in [Chinese Glioma Genome Atlas; The Cancer Genome Atlas Program; Ivy Glioblastoma Atlas Project; Gene Expression Omnibus database with accession number GSE235676 and GSE235672] at http://www.cgga.org.cn/; https://www.cancer.gov/ccg/research/genome‐sequencing/tcga; https://glioblastoma.alleninstitute.org/ and https://www.ncbi.nlm.nih.gov/geo/.

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