ArticleJournal of translational medicine2023
Proteomic analysis predicts anti-angiogenic resistance in recurred glioblastoma.
Article in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 12 citations in OpenAlex.
- Sublethal heat stress synergizes with the tumor microenvironment to drive recurrence of hepatocellular carcinoma after thermal ablation: mechanisms, molecular predictors, and targeted interventions.Cell biology and toxicology · 2026Review
- Response to Antiangiogenic Therapy Is Associated with AIMP Protein Family Expression in Glioblastoma and Lower-Grade Gliomas.Cancer research communications · 2025Article
- Review
- Deciphering Radiotherapy Resistance: A Proteomic Perspective.Proteomes · 2025Review
- Serum RNA Profile Reflects Fluid Status and Atrophic Retinal Changes in Neovascular Age-Related Macular Degeneration.International journal of molecular sciences · 2025Article
- Immunophenotyping identifies key immune biomarkers for coronary artery disease through machine learning.PloS one · 2025Article
- Article
- Proteometabolomics of initial and recurrent glioblastoma highlights an increased immune cell signature with altered lipid metabolism.Neuro-oncology · 2024Article
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Authors and funding
14 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecurrence is common in glioblastoma multiforme (GBM) because of the infiltrative, residual cells in the tumor margin. Standard therapy for GBM consists of surgical resection followed by chemotherapy and radiotherapy, but the median survival of GBM patients remains poor (~ 1.5 years). For recurrent GBM, anti-angiogenic treatment is one of the common treatment approaches. However, current anti-angiogenic treatment modalities are not satisfactory because of the resistance to anti-angiogenic agents in some patients. Therefore, we sought to identify novel prognostic biomarkers that can predict the therapeutic response to anti-angiogenic agents in patients with recurrent glioblastoma.
methodsWe selected patients with recurrent GBM who were treated with anti-angiogenic agents and classified them into responders and non-responders to anti-angiogenic therapy. Then, we performed proteomic analysis using liquid-chromatography mass spectrometry (LC-MS) with formalin-fixed paraffin-embedded (FFPE) tissues obtained from surgical specimens. We conducted a gene-ontology (GO) analysis based on protein abundance in the responder and non-responder groups. Based on the LC-MS and GO analysis results, we identified potential predictive biomarkers for anti-angiogenic therapy and validated them in recurrent glioblastoma patients.
resultsIn the mass spectrometry-based approach, 4957 unique proteins were quantified with high confidence across clinical parameters. Unsupervised clustering analysis highlighted distinct proteomic patterns (n = 269 proteins) between responders and non-responders. The GO term enrichment analysis revealed a cluster of genes related to immune cell-related pathways (e.g., TMEM173, FADD, CD99) in the responder group, whereas the non-responder group had a high expression of genes related to nuclear replisome (POLD) and damaged DNA binding (ERCC2). Immunohistochemistry of these biomarkers showed that the expression levels of TMEM173 and FADD were significantly associated with the overall survival and progression-free survival of patients with recurrent GBM.
conclusionsThe candidate biomarkers identified in our protein analysis may be useful for predicting the clinical response to anti-angiogenic agents in patients with recurred GBM.
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