A Multi-Element Expression Score Is A Prognostic Factor In Glioblastoma Multiforme
Received 21 August 2019
Accepted for publication 9 October 2019
Published 17 October 2019 Volume 2019:11 Pages 8977—8989
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Dr Eileen O'Reilly
Jun-Qi Li,1,* Qian-Ting Wang,1,2,* Ying Nie,2 Yun-Peng Xiao,3 Tao Lin,2 Ru-Jin Han,2 Zhe Li,2 Yu-Ying Fan,2 Xiao-Hui Yuan,1 Yue-Ming Wang,4 Jian Zhang,2 You-Wen He,5 Hua-Xin Liao1
1Department of Cell Biology and Institute of Biomedicine, College of Life Science and Technology, Jinan University, National Engineering Research Center of Genetic Medicine, Guangzhou 510632, People’s Republic of China; 2Guangdong 999 Brain Hospital, Guangzhou 510510, People’s Republic of China; 3Guangzhou Trinomab Biotechnology Co., Ltd, Guangzhou 510632, People’s Republic of China; 4Zhuhai Trinomab Biotechnology Co., Ltd., Zhuhai 519040, People’s Republic of China; 5Department of Immunology, Duke University Medical Center, Durham, NC 27710, USA
*These authors contributed equally to this work
Correspondence: Hua-Xin Liao
College of Life Science and Technology, 704 2nd Science and Engineering Building, Jinan University, 601 Huang Pu Avenue West, Guangzhou 510632, People’s Republic of China
Tel/Fax +86 20 8522 2062
Department of Immunology, Duke University Medical Center, Durham NC 27710, USA
Tel/Fax +1 919 613 7870
Purpose: Glioblastoma multiforme (GBM) is a highly malignant tumor of the central nervous system. Although primary GBM patients receive extensive therapies, tumors may recur within months, and there is no objective and scientific method to predict prognosis. Adoptive immunotherapy holds great promise for GBM treatment. However, the expression profiles of the tumor-associated antigens (TAAs) and tumor immune microenvironment (TME) genes used in immunotherapy of GBM patients have not been fully described. The present study aimed to develop a predictive tool to evaluate patient survival based on full analysis of the expression levels of TAAs and TME genes.
Methods: Expression profiles of a panel of 87 TAAs and 8 TME genes significantly correlated with poor prognosis were evaluated in 44 GBM patients and 10 normal brain tissues using quantitative real-time polymerase chain reaction (qRT-PCR). A linear formula (the LASSO algorithm based in the R package) weighted by regression coefficients was used to develop a multi-element expression score to predict prognosis; this formula was cross-validated by the leave-one-out method in different GBM cohorts.
Results: After analysis of gene expression, clinical features, and overall survival (OS), a total of 8 TAAs (CHI3L1, EZH2, TRIOBP, PCNA, PIK3R1, PRKDC, SART3 and EPCAM), 1 TME gene (FOXP3) and 4 clinical features (neutrophil-to-lymphocyte (NLR), number of basophils (BAS), age and treatment with standard radiotherapy and chemotherapy) were included in the formula. There were significant differences between high and low scoring groups identified using the formula in different GBM cohorts (TCGA (n=732) and GEO databases (n=84)), implying poor and good prognosis, respectively.
Conclusion: The multi-element expression score was significantly associated with OS of GBM patients. The improve understanding of TAAs and TMEs and well-defined formula could be implemented in immunotherapy for GBM to provide better care.
Keywords: glioblastoma, gene expression score, prognosis, TAAs, TME
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