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Radiogenomics Profiling for Glioblastoma-related Immune Cells Reveals CD49d Expression Correlation with MRI parameters and Prognosis
MRI 영상 매개변수 및 환자 예후와 상관관계를 갖는 종양면역세포 분석: 교모세포종에서의 Radiogenomics 연구

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Authors
전혜진
Advisor
최승홍
Major
의과대학 협동과정 종양생물학전공
Issue Date
2018
Publisher
서울대학교 대학원
Description
학위논문 (석사)-- 서울대학교 대학원 : 의과대학 협동과정 종양생물학전공, 2018. 8. 최승홍.
Abstract
Purpose: Although there have been a plethora of radiogenomics studies related to glioblastoma multiforme (GBM), most of them only used genomic information from tumor cells. In this study, we used radiogenomics profiling to identify MRI-associated immune cell markers in GBM, which was also correlated with prognosis. Methods: Expression levels of immune cell markers were correlated with quantitative MRI parameters in a total of 60 GBM patients. Fourteen immune cell markers (i.e., CD11b, CD68, CSF1R, CD163, CD33, CD123, CD83, CD63, CD49d and CD117 for myeloid cells, and CD4, CD3e, CD25 and CD8 for lymphoid cells) were selected for RNA-level analysis using quantitative RT-PCR. For MRI analysis, quantitative MRI parameters from FLAIR, contrast-enhanced (CE) T1WI, dynamic susceptibility contrast perfusion MRI and diffusion-weighted images were used. In addition, PFS associated with interesting mRNA data was performed by Kaplan-Meier survival analysis.

Results: CD163, which marks tumor associated microglia/macrophages (TAMs), showed the highest expression level in GBM patients. CD68 (TAMs), CSF1R (TAMs), CD33 (myeloid-derived suppressor cell) and CD4 (helper T cell, regulatory T cell) levels were highly positively correlated with nCBV values, while CD3e (helper T cell, cytotoxic T cell) and CD49d showed a significantly negative correlation with apparent diffusion coefficient (ADC) values. Moreover, regardless of any other molecular characteristics, CD49d was revealed as one independent factor for PFS of GBM patients by Cox proportional-hazards regression analysis (P = 0.0002).

Conclusions: CD49d expression level CD49d correlated with ADC can be considered as a candidate biomarker to predict progression of GBM patients.
Language
English
URI
http://hdl.handle.net/10371/144069
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College of Medicine/School of Medicine (의과대학/대학원)Program in Cancer Biology (협동과정-종양생물학전공)Theses (Master's Degree_협동과정-종양생물학전공)
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