FGF2 as a potential prognostic biomarker for proneural glioma patients. (March 2015)

• Record Type: Journal Article
• Authors: Sooman, Linda; Freyhult, Eva; Jaiswal, Archita; Navani, Sanjay; Edqvist, Per-Henrik; Pontén, Fredrik; Tchougounova, Elena; Smits, Anja; Elsir, Tamador; Gullbo, Joachim; Lennartsson, Johan; Bergqvist, Michael; Ekman, Simon
• Is Part Of: Acta oncologica. Volume 54:Number 3(2015)
• Page Start: 385
• Page End: 394
• Publication Date: 2015-03
• Subjects: Oncology -- Periodicals; Cancer -- Treatment -- Periodicals; 616.992
• DOI: 10.3109/0284186X.2014.951492
• Languages: English
• ISSNs: 0284-186X

Abstract

Abstract: Background. The survival of high-grade glioma patients is poor and the treatment of these patients can cause severe side effects. This fosters the necessity to identify prognostic biomarkers, in order to optimize treatment and diminish unnecessary suffering of patients. The aim of this study was to identify prognostic biomarkers for high-grade glioma patients. Methods. Eleven proteins were selected for analysis due to their suggested importance for survival of patients with other types of cancers and due to a high variation in protein levels between glioma patients (according to the Human Protein Atlas, www.proteinatlas.org ). Protein expression patterns of these 11 proteins were analyzed by immunohistochemistry in tumor samples from 97 high-grade glioma patients. The prognostic values of the proteins were analyzed with univariate and multivariate Cox regression analyses for the high-grade glioma patients, including subgroup analyses of histological subtypes and immunohistochemically defined molecular subtypes. Results. The proteins with the most significant (univariate and multivariate p < 0.05) correlations were analyzed further with cross-validated Kaplan-Meier analyses for the possibility of predicting survival based on the protein expression pattern of the corresponding candidate. Random Forest classification with variable subset selection was used to analyze if a protein signature consisting of any combination of the 11 proteins could predict survival for the high-grade glioma patients and the subgroup with glioblastoma patients. The proteins which correlated most significantly (univariate and multivariate p < 0.05) to survival in the Cox regression analyses were Myc for all high-grade gliomas and FGF2, CA9 and CD44 for the subgroup of proneural gliomas, with FGF2 having a strong negative predictive value for survival. No prognostic signature of the proteins could be found. Conclusion. FGF2 is a potential prognostic biomarker for proneural glioma patients, and warrants further investigation.