A Multi-Element Expression Score Is A Prognostic Factor In Glioblastoma Multiforme.
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ABSTRACT:
SUBMITTER: Li JQ
PROVIDER: S-EPMC6805247 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
Publications
A Multi-Element Expression Score Is A Prognostic Factor In Glioblastoma Multiforme.
Li Jun-Qi JQ Wang Qian-Ting QT Nie Ying Y Xiao Yun-Peng YP Lin Tao T Han Ru-Jin RJ Li Zhe Z Fan Yu-Ying YY Yuan Xiao-Hui XH Wang Yue-Ming YM Zhang Jian J He You-Wen YW Liao Hua-Xin HX
Cancer management and research 20191017
<h4>Purpose</h4>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 f ...[more]
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Project description:Galectin-1, a radioresistance marker, was found in our previous study to be a prognostic factor for cervical cancer. The aim of current study is to determine the prognostic significance of the galectin-1 expression level in patients with glioblastoma multiforme (GBM) undergoing adjuvant radiotherapy (RT).We included 45 patients with GBM who were treated with maximal safe surgical resection or biopsy alone followed by adjuvant RT of EQD2 (equivalent dose in 2-Gy fractions)?>?or?=?60 Gy for homogeneous treatment. Paraffin-embedded tissues acquired from the Department of Pathology were analyzed using immunohistochemical staining for galectin-1 expression. The primary endpoint was overall survival (OS).Patients with weak expression had a better median survival (27.9 months) than did those with strong expression (10.7 months; p?=?0.009). We compared characteristics between weak and strong galectin-1 expression, and only the expression level of galectin-3 showed a correlation. The group with weak galectin-1 expression displayed a 3-year OS of 27.3% and a 3-year cancer-specific survival (CSS) of 27.3%; these values were only 5.9% and 7.6%, respectively, in the group with strong galectin-1 expression (p?=?0.009 and 0.020, respectively). Cox regression was used to confirm that the expression level of galectin-1 (weak vs. strong) is a significant factor of OS (p?=?0.020) and CSS (p?=?0.022). Other parameters, such as the expression level of galectin-3, Eastern Cooperative Oncology Group (ECOG) performance, gender, surgical method, age???50 years, tumor size, or radiation field were not significant factors.The expression level of galectin-1 affects survival in patients with GBM treated with adjuvant RT. Future studies are required to analyze the effect of other factors, such as O(6)-methylguanine-DNA methyltransferase (MGMT)-promoter methylation status, in patients with weak and strong galectin-1 expression.
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A multidimensional scaling method (BRB Arrays Tools version 3.0) was also utilized by using Euclidean distance criteria [27]. Supervised learning was used to identify genes with statistically significant changes in expression among different classes by using the Significant Analysis of Microarrays (SAM) algorithm [28]. All data were permuted over 100 cycles by using the two-class (unpaired) and multi-class response format. Significant genes were selected based on the lowest false discovery ratio (between 0.6 and 0.9). In addition, nonparametric tests such as Wilcoxon rank sum test and Kruskal-Wallis test to compare more than two unpaired group were also used (SPSS 18, SPSS Inc). 3. Functional annotation of microarray data Probe sets showing significant expression change were functionally annotated and grouped according to biological function criteria using GeneOntology biological process descriptions. 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Amplifications were performed using the iCycler apparatus (Bio-Rad Laboratories, California, USA). Analyses of data were done using the iCycler iQ Optical System Software, version 3.0a (Bio-Rad Laboratories). Primers to GAPDH were used as intersample normalizing controls. Variations in expression of VAV1 mRNA were represented as the mean value of the fold change respect the VAV1 expression levels detected in sample #19209 with both pairs of oligonucleotide primers. 5. Immunohistochemical analyses The VAV1 antibody was generated in rabbits using a synthetic peptide and purified by affinity chromatography in Bustelo’s laboratory. This antibody recognizes VAV1 proteins from humans and mice but it does not recognize other VAV family members (unpublished data). For immunostaining, tissue sections were washed thrice with Xylene and once with 100% ethanol, rehydrated by sequential changes in 80%, 70%, and 50% ethanol and a final incubation in phosphate-buffered saline (PBS). 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The genomic DNA was extracted as indicated by Isola et al [31] with modifications to small DNA amounts. Those included the resuspension of the microdissected sections in extraction buffer followed by a digestion with proteinase K (0.6 mg/ml). All samples were resuspended in 10 ul of 10 mM Tris-HCl (pH 7.4) and 0.1 mM EDTA. DOP-PCR amplification was performed in two steps. For the first, low-stringency step, 1 ul of sample was added to 4 ul of buffer A (2.5 ul of 600 uM dNTPs (Roche, Pleasanton, CA), 0.5 ul of 10 uM DOP primer 5’-CCGACTCGAGNNNNNNNATGTGG-3’, where N= A, C, G, or T) [32] and 1 ul of 5x Sequenase Reaction Buffer (Amersham, Cleveland, OH). Reactions were performed using 5 cycles of 30ºC for 5 min, 37ºC for 2 min, and 96ºC for 2 min, adding 0.65 units of Sequenase in each 30ºC step. The first phase product was then subjected to the second step usin
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Project description:BackgroundThe Cancer Genome Atlas (TCGA) has divided patients with glioblastoma multiforme (GBM) into four subtypes based on mRNA expression microarray. The mesenchymal subtype, with a larger proportion, is considered a more lethal one. Clinical outcome prediction is required to better guide more personalized treatment for these patients.AimsThe objective of this study was to identify a mRNA expression signature to improve outcome prediction for patients with mesenchymal GBM.ResultsFor signature identification and validation, we downloaded mRNA expression microarray data from TCGA as training set and data from Rembrandt and GSE16011 as validation set. Cox regression and risk-score analysis were used to develop the 4 signatures, which were function and prognosis associated as revealed by Gene Ontology (GO) analysis and Gene Set Variation Analysis (GSVA). Patients who had high-risk scores according to the signatures had poor overall survival compared with patients who had low-risk scores.ConclusionsThe signatures were identified as risk predictors that patients who had a high-risk score tended to have unfavorable outcome, demonstrating their potential for personalizing cancer management.
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