Application of flow cytometry for evaluating clinical prognosis and histopathological grade of human glioma.
OBJECTIVE
Flow cytometry was applied to predict the biological parameters of tumor behavior based on the DNA content distribution of tumors. We used flow cytometry to determine the number of cell cycles for the characterization of intracranial gliomas and its possible prognostic role.


METHODS
Flow cytometric analysis of the DNA content was performed for 37 fresh operative glioma specimens. The expression of Ki-67 in glioma specimens was detected using immunohistochemistry staining. The check points of G2/M-phase fractions, cyclin B, and pCdk1 (Y15) were analyzed using Western immunoblotting.


RESULTS
Compared to low-grade (grade I/II) gliomas, significant differences in the Ki-67, cyclin B, G2/M-phase, and S+G2/M-phase expressions were found in high-grade (grade III/IV) gliomas. Furthermore, receiver operating characteristic (ROC) analysis indicated optimal cutoff points for the G2/M-phase and S+G2/M-phase fractions of 13.47 and 17.26%, respectively, which can be used to differentiate cases with low- and high-grade gliomas. Additionally, both G2/M-phase and S+G2/M-phase fractions had significant association with the expression of Ki-67 in the gliomas. The gliomas were classified by the DNA content. We found that patients with high-grade glioma had worse survival rate than patients with low-grade glioma. Meanwhile, ROC curve analysis gave cutoffs for G2/M-phase of 9.4% and for S+G2/M-phase fractions of 15.04% as best predicting survival. The patients with glioma had poor survival when the levels of G2/M-phase and S+G2/M-phase were more than 9.4 and 15.04%, respectively. In contrast, no significant association between the DNA content of glioma patients and their age, tumor recurrence, and tumor size was found.


CONCLUSIONS
Our results indicate that flow cytometry analysis for G2/M-phase and S+G2/M-phase fractions can be used for tumor grading for rapidly differentiating low- from high-grade gliomas.
a Neurophysiology Laboratory, Department of Surgery , National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University , Tainan , Taiwan.
Yu-Wen
YW
Lin
a Neurophysiology Laboratory, Department of Surgery , National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University , Tainan , Taiwan.
Shih-Huang
SH
Tai
a Neurophysiology Laboratory, Department of Surgery , National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University , Tainan , Taiwan.
Che-Chao
CC
Chang
a Neurophysiology Laboratory, Department of Surgery , National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University , Tainan , Taiwan.
Liang-Chun
LC
Chao
b Department of Statistics , College of Management, National Cheng Kung University , Tainan , Taiwan.
Miin-Jye
MJ
Wen
a Neurophysiology Laboratory, Department of Surgery , National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University , Tainan , Taiwan.
Yu-Hsuan
YH
Huang
c School of Pharmacy , National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University , Tainan , Taiwan.
Hsin-Yi
HY
Hung
a Neurophysiology Laboratory, Department of Surgery , National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University , Tainan , Taiwan.
E-Jian
EJ
Lee
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000704:Analysis of Variance; D001932:Brain Neoplasms / Q000378:metabolism / Q000473:pathology*; D002453:Cell Cycle / Q000502:physiology; D019926:Cyclin B / Q000378:metabolism; D004247:DNA / Q000378:metabolism; D005260:Female; D005434:Flow Cytometry / Q000379:methods*; D005910:Glioma / Q000150:complications / Q000473:pathology*; D006801:Humans; D019394:Ki-67 Antigen / Q000378:metabolism; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading / Q000379:methods*; D012372:ROC Curve; D012189:Retrospective Studies
D019926:Cyclin B; D019394:Ki-67 Antigen; D004247:DNA
DNA content; Flow cytometry; Glioma; Ki-67