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Shugosin 2 is a biomarker for pathological grading and survival prediction in patients with gliomas.

ABSTRACT: Glioblastomas are the most common type of adult primary brain neoplasms. Clinically, it is helpful to identify biomarkers to predict the survival of patients with gliomas due to its poor outcome. Shugoshin 2 (SGO2) is critical in cell division and cell cycle progression in eukaryotes. However, the association of SGO2 with pathological grading and survival in patients with gliomas remains unclear. We analyzed the association between SGO2 expression and clinical outcomes from Gene Expression Omnibus (GEO) dataset profiles, The Cancer Genome Atlas (TCGA), and Chinese Glioma Genome Atlas (CGGA). SGO2 mRNA and protein expression in normal brain tissue and glioma cell lines were investigated via quantitative RT-PCR, Western blot, and IHC staining. The roles of SGO2 in proliferation, migration, and apoptosis of GBM cells were studied with wound-healing assay, BrdU assay, cell cycle analysis, and JC-1 assay. The protein-protein interaction (PPI) was analyzed via Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). SGO2 mRNA expression predicted higher grade gliomas than non-tumor brain tissues. Kaplan-Meier survival analysis showed that patients with high-grade gliomas with a higher SGO2 expression had worse survival outcomes. SGO2 mRNA and protein expression were upper regulated in gliomas than in normal brain tissue. Inhibition of SGO2 suppressed cell proliferation and migration. Also, PPI result showed SGO2 to be a potential hub protein, which was related to the expression of AURKB and FOXM1. SGO2 expression positively correlates with WHO pathological grading and patient survival, suggesting that SGO2 is a biomarker that is predictive of disease progression in patients with gliomas.

SUBMITTER: Kao Y

PROVIDER: S-EPMC8448842 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Shugosin 2 is a biomarker for pathological grading and survival prediction in patients with gliomas.

Kao Ying Y Tsai Wen-Chiuan WC Chen Ssu-Han SH Hsu Shao-Yuan SY Huang Li-Chun LC Chang Chih-Ju CJ Huang Shih-Ming SM Hueng Dueng-Yuan DY

Scientific reports 20210917 1

Glioblastomas are the most common type of adult primary brain neoplasms. Clinically, it is helpful to identify biomarkers to predict the survival of patients with gliomas due to its poor outcome. Shugoshin 2 (SGO2) is critical in cell division and cell cycle progression in eukaryotes. However, the association of SGO2 with pathological grading and survival in patients with gliomas remains unclear. We analyzed the association between SGO2 expression and clinical outcomes from Gene Expression Omnib ...[more]

PMID: 34535705

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Project description:PurposeTo develop a predictive grading model based on diffusion kurtosis imaging (DKI) metrics in children affected by gliomas, and to investigate the clinical impact of the predictive model by correlating with overall survival and progression-free survival.Materials and methods59 patients with a histological diagnosis of glioma were retrospectively studied (33 M, 26 F, median age 7.2 years). Patients were studied on a 3T scanner with a standardized MR protocol, including conventional and DKI sequences. Mean kurtosis (MK), axial kurtosis (AK), radial kurtosis (RK), fractional anisotropy (FA), and apparent diffusion coefficient (ADC) maps were obtained. Whole tumour volumes (VOIs) were segmented semi-automatically. Mean DKI values were calculated for each metric. The quantitative values from DKI-derived metrics were used to develop a predictive grading model to develop a probability prediction of a high-grade glioma (pHGG). Three models were tested: DTI-based, DKI-based, and combined (DTI and DKI). The grading accuracy of the resulting probabilities was tested with a receiver operating characteristics (ROC) analysis for each model. In order to account for dataset imbalances between pLGG and pHGG, we applied a random synthetic minority oversampling technique (SMOTE) analysis. Lastly, the most accurate model predictions were correlated with progression-free survival (PFS) and overall survival (OS) using the Kaplan-Meier method.ResultsThe cohort included 46 patients with pLGG and 13 patients with pHGG. The developed model predictions yielded an AUC of 0.859 (95%CI: 0.752-0.966) for the DTI model, of 0.939 (95%CI: 0.879-1) for the DKI model, and of 0.946 (95%CI: 0.890-1) for the combined model, including input from both DTI and DKI metrics, which resulted in the most accurate model. Sample estimation with the random SMOTE analysis yielded an AUC of 0.98 on the testing set. Model predictions from the combined model were significantly correlated with PFS (25.2 months for pHGG vs. 40.0 months for pLGG, p < 0.001) and OS (28.9 months for pHGG vs. 44.9 months for pLGG, p < 0.001).Conclusionsa DKI-based predictive model was highly accurate for pediatric glioma grading. The combined model, derived from both DTI and DKI metrics, proved that DKI-based model predictions of tumour grade were significantly correlated with progression-free survival and overall survival.

Project description:BackgroundDiffuse gliomas, grades II and III, hereafter called lower-grade gliomas (LGG), have variable, difficult to predict clinical courses, resulting in multiple studies to identify prognostic biomarkers. The purpose of this study was to assess expression or methylation of the homeobox family gene SHOX2 as independent markers for LGG survival.MethodsWe downloaded publically available glioma datasets for gene expression and methylation. The Cancer Genome Atlas (TCGA) (LGG, n=516) was used as a training set, and three other expression datasets (n=308) and three other methylation datasets (n=320), were used for validation. We performed Kaplan-Meier survival curves and univariate and multivariate Cox regression model analyses.FindingsSHOX2 expression and gene body methylation varied among LGG patients and highly significantly predicted poor overall survival. While they were tightly correlated, SHOX2 expression appeared more potent as a prognostic marker and was used for most further studies. The SHOX2 prognostic roles were maintained after analyses by histology subtypes or tumor grade. We found that the combination of SHOX2 expression and IDH genotype status identified a subset of LGG patients with IDH wild-type (IDHwt) and low SHOX2 expression with considerably favorable survival. We further investigated the combination of SHOX2 with other known clinically relevant markers of LGG (TERT expression, 1p/19q chromosome co-deletion, MGMT methylation, ATRX mutation and NES expression). When combined with SHOX2 expression, we identified subsets of LGG patients with significantly favorable survival outcomes, especially in the subgroup with worse prognosis for each individual marker. Finally, multivariate analysis demonstrated that SHOX2 was a potent independent survival marker.InterpretationWe have identified that SHOX2 expression or methylation are potent independent prognostic indicators for predicting LGG patient survival, and have potential to identify an important subset of LGG patients with IDHwt status with significantly better overall survival. The combination of IDH or other relevant markers with SHOX2 identified LGG subsets with significantly different survival outcomes, and further understanding of these subsets may benefit therapeutic target identification and therapy selections for glioma patients.

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Shugosin 2 is a biomarker for pathological grading and survival prediction in patients with gliomas.

Publications

Shugosin 2 is a biomarker for pathological grading and survival prediction in patients with gliomas.

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