Identification of CD44 as a Reliable Biomarker for Glioblastoma Invasion: Based on Magnetic Resonance Imaging and Spectroscopic Analysis of 5-Aminolevulinic Acid Fluorescence.
Akihiro InoueTakanori OhnishiMasahiro NishikawaHideaki WatanabeKosuke KusakabeMashio TaniwakiHajime YanoYoshihiro OhtsukaShirabe MatsumotoSatoshi SuehiroDaisuke YamashitaSeiji ShigekawaHisaaki TakahashiRiko KitazawaJunya TanakaTakeharu KuniedaPublished in: Biomedicines (2023)
Recurrent glioblastoma multiforme (GBM) is largely attributed to peritumoral infiltration of tumor cells. As higher CD44 expression in the tumor periphery correlates with higher risk of GBM invasion, the present study analyzed the relationship between CD44 expression and magnetic resonance imaging (MRI)-based invasiveness of GBM on a large scale. We also quantitatively evaluated GBM invasion using 5-aminolevulinic acid (5-ALA) spectroscopy to investigate the relationship between CD44 expression and tumor invasiveness as evaluated by intraoperative 5-ALA intensity. Based on MRI, GBM was classified as high-invasive type in 28 patients and low-invasive type in 22 patients. High-invasive type expressed CD44 at a significantly higher level than low-invasive type and was associated with worse survival. To quantitatively analyze GBM invasiveness, the relationship between tumor density in the peritumoral area and the spectroscopic intensity of 5-ALA was investigated. Spectroscopy showed that the 5-ALA intensity of infiltrating tumor cells correlated with tumor density as represented by the Ki-67 staining index. No significant correlation between CD44 and degree of 5-ALA-based invasiveness of GBM was found, but invasiveness of GBM as evaluated by 5-ALA matched the classification from MRI in all except one case, indicating that CD44 expression at the GBM periphery could provide a reliable biomarker for invasiveness in GBM.
Keyphrases
- magnetic resonance imaging
- poor prognosis
- contrast enhanced
- end stage renal disease
- nk cells
- ejection fraction
- newly diagnosed
- chronic kidney disease
- computed tomography
- single molecule
- machine learning
- peritoneal dialysis
- high intensity
- high resolution
- squamous cell carcinoma
- deep learning
- binding protein
- long non coding rna
- cell migration
- magnetic resonance
- rectal cancer
- patient reported outcomes
- neoadjuvant chemotherapy
- locally advanced
- energy transfer