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Proteins inform survival-based differences in patients with glioblastoma.
Stetson, L C; Ostrom, Quinn T; Schlatzer, Daniela; Liao, Peter; Devine, Karen; Waite, Kristin; Couce, Marta E; Harris, Peggy L R; Kerstetter-Fogle, Amber; Berens, Michael E; Sloan, Andrew E; Islam, Mohammad M; Rajaratnam, Vilashini; Mirza, Shama P; Chance, Mark R; Barnholtz-Sloan, Jill S.
Afiliação
  • Stetson LC; Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
  • Ostrom QT; Department of Medicine and Division of Hematology-Oncology, Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA.
  • Schlatzer D; Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, Texas, USA.
  • Liao P; Center for Proteomics and Bioinformatics and Department of Nutrition, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
  • Devine K; Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
  • Waite K; Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
  • Couce ME; Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
  • Harris PLR; Department of Population and Quantitative Health Sciences and Cleveland Center for Health Outcomes Research (CCHOR), Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
  • Kerstetter-Fogle A; Department of Pathology, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
  • Berens ME; Brain Tumor and Neuro-Oncology Center & Center of Excellence, Translational Neuro-Oncology, Department of Neurosurgery, Seidman Cancer Center, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
  • Sloan AE; Brain Tumor and Neuro-Oncology Center & Center of Excellence, Translational Neuro-Oncology, Department of Neurosurgery, Seidman Cancer Center, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
  • Islam MM; Translational Genomics Research Institute (TGen), Phoenix, Arizona, USA.
  • Rajaratnam V; Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA.
  • Mirza SP; Brain Tumor and Neuro-Oncology Center & Center of Excellence, Translational Neuro-Oncology, Department of Neurosurgery, Seidman Cancer Center, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA.
  • Chance MR; Department of Chemistry and Biochemistry, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA.
  • Barnholtz-Sloan JS; Department of Chemistry and Biochemistry, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA.
Neurooncol Adv ; 2(1): vdaa039, 2020.
Article em En | MEDLINE | ID: mdl-32642694
ABSTRACT

BACKGROUND:

Improving the care of patients with glioblastoma (GB) requires accurate and reliable predictors of patient prognosis. Unfortunately, while protein markers are an effective readout of cellular function, proteomics has been underutilized in GB prognostic marker discovery.

METHODS:

For this study, GB patients were prospectively recruited and proteomics discovery using liquid chromatography-mass spectrometry analysis (LC-MS/MS) was performed for 27 patients including 13 short-term survivors (STS) (≤10 months) and 14 long-term survivors (LTS) (≥18 months).

RESULTS:

Proteomics discovery identified 11 941 peptides in 2495 unique proteins, with 469 proteins exhibiting significant dysregulation when comparing STS to LTS. We verified the differential abundance of 67 out of these 469 proteins in a small previously published independent dataset. Proteins involved in axon guidance were upregulated in STS compared to LTS, while those involved in p53 signaling were upregulated in LTS. We also assessed the correlation between LS MS/MS data with RNAseq data from the same discovery patients and found a low correlation between protein abundance and mRNA expression. Finally, using LC-MS/MS on a set of 18 samples from 6 patients, we quantified the intratumoral heterogeneity of more than 2256 proteins in the multisample dataset.

CONCLUSIONS:

These proteomic datasets and noted protein variations present a beneficial resource for better predicting patient outcome and investigating potential therapeutic targets.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Revista: Neurooncol Adv Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Guideline / Prognostic_studies Idioma: En Revista: Neurooncol Adv Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Estados Unidos