ArticleScientific reports2024
Matrix metalloproteinase 9 expression and glioblastoma survival prediction using machine learning on digital pathological images.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- CDK2/9 blockade remodels chromatin and drives phenotypic plasticity in glioblastoma organoids.Cell communication and signaling : CCS · 2026Article
- Circulating PDIA4, MMP-2, MMP-9, and Claudin-2 in Vitamin D-Deficient Patients with Brain Tumors: An Exploratory Clinical Pilot Study.Journal of clinical medicine · 2026Article
- Identification of drug candidates against glioblastoma with machine learning and high-throughput screening of heterogeneous cellular models.Digital discovery · 2026Article
- Review
- Postoperative early recurrence prediction of pancreatic cancer based on CT and pathomics.BMC gastroenterology · 2025Article
- MiR-7 inhibits progression of glioblastoma by impairing autophagy resolution, energy metabolism and ECM remodeling.Journal of experimental & clinical cancer research : CR · 2025Article
- Predicting the efficacy of bevacizumab on peritumoral edema based on imaging features and machine learning.Scientific reports · 2025Article
- Molecular mechanisms and therapeutic targets in glioblastoma multiforme: network and single-cell analyses.Scientific reports · 2025Article
- Understanding Neovascularization in Glioblastoma: Insights from the Current Literature.International journal of molecular sciences · 2025Review
- Effect of TIMPs and their minimally engineered variants in blocking invasion and migration of brain cancer cells.Oncotarget · 2025Article
- PTPRN promotes glioma cell proliferation, migration, and invasion by regulating the EMT pathway.International journal of clinical and experimental pathology · 2025Article
- Trigeminal nerve-driven neurogenic inflammation linking migraine to glioblastoma invasion: a literature review.Frontiers in immunology · 2025Review
- High intra-tumoral and serum matrix metalloproteinase 9 levels are associated with reduced survival of patients with glioblastoma and brain metastases.Frontiers in oncology · 2025Article
- Expression of Lumican and Osteopontin in Perivascular Areas of the Glioblastoma Peritumoral Niche and Its Value for Prognosis.International journal of molecular sciences · 2024Article
- Clinicopathological Parameters and Immunohistochemical Profiles in Correlation with MRI Characteristics in Glioblastomas.International journal of molecular sciences · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This study aimed to apply pathomics to predict Matrix metalloproteinase 9 (MMP9) expression in glioblastoma (GBM) and investigate the underlying molecular mechanisms associated with pathomics. Here, we included 127 GBM patients, 78 of whom were randomly allocated to the training and test cohorts for pathomics modeling. The prognostic significance of MMP9 was assessed using Kaplan-Meier and Cox regression analyses. PyRadiomics was used to extract the features of H&E-stained whole slide images. Feature selection was performed using the maximum relevance and minimum redundancy (mRMR) and recursive feature elimination (RFE) algorithms. Prediction models were created using support vector machines (SVM) and logistic regression (LR). The performance was assessed using ROC analysis, calibration curve assessment, and decision curve analysis. MMP9 expression was elevated in patients with GBM. This was an independent prognostic factor for GBM. Six features were selected for the pathomics model. The area under the curves (AUCs) of the training and test subsets were 0.828 and 0.808, respectively, for the SVM model and 0.778 and 0.754, respectively, for the LR model. The C-index and calibration plots exhibited effective estimation abilities. The pathomics score calculated using the SVM model was highly correlated with overall survival time. These findings indicate that MMP9 plays a crucial role in GBM development and prognosis. Our pathomics model demonstrated high efficacy for predicting MMP9 expression levels and prognosis of patients with GBM.
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Registered trials
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