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Systematic Analysis of 4-gene Prognostic Signature in Patients with Diffuse Gliomas Based on Gene Expression Profiles

Overview
Journal J Cancer
Specialty Oncology
Date 2021 Jun 7
PMID 34093830
Citations 1
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Abstract

Diffuse gliomas are a group of diseases that contain different degrees of malignancy and complex heterogeneity. Previous studies proposed biomarkers for certain grades of gliomas, but few of them have conducted a systematic analysis of different grades to search for molecular markers. WGCNA was used to find significant genes associated with malignant progression of diffuse glioma in TCGA glioma sequencing expression data and the GEO expression profile-merge meta dataset. Lasso regression was used for potential model building and the best model was selected by CPE, IDI, and C_index. Risk score model was used to evaluate the gene signature prognostic power. Multi-omics data, including CNV, methylation, clinical traits, and mutation, were used for model evaluation. We found out 67 genes significantly associated with malignant progression of diffuse glioma by WGCNA. Next, we established a new 4 gene molecular marker (KDELR2, EMP3, TIMP1, and TAGLN2). Multivariate cox analysis identified the risk score of the 4 genes as an independent predictor of prognosis in patients with diffuse gliomas, and its predictive power was independent of the histopathological grades of glioma. Further, we had confirmed in five independent test datasets and the risk score remained good predictive power. The combination of the prognosis model with specific molecular characteristics possessed a better predictive power. Furthermore, we divided the low-risk group into three subtypes: LowRisk_IDH1, LowRisk_IDH1/ATRX, and LowRisk_IDH1/ATRX by combining IDH1 mutation with ATRX mutation, which possessed obvious survival difference. In further analysis, we found that the 4 gene prognosis model possessed multi-omics features. We established a malignant-related 4-gene molecular marker by glioma expression profile data from multiple microarrays and sequencing data. The four markers had good predictive power on the overall survival of glioma patients and were associated with gliomas' clinical and genetic backgrounds, including clinical features, gene mutation, methylation, CNV, signal pathways.

Citing Articles

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Pan T, Wang S, Wang Z J Cancer. 2023; 14(10):1809-1836.

PMID: 37476180 PMC: 10355213. DOI: 10.7150/jca.84454.

References
1.
Jooma R, Waqas M, Khan I . Diffuse Low-Grade Glioma - Changing Concepts in Diagnosis and Management: A Review. Asian J Neurosurg. 2019; 14(2):356-363. PMC: 6516028. DOI: 10.4103/ajns.AJNS_24_18. View

2.
Ruggiero C, Fragassi G, Grossi M, Picciani B, Di Martino R, Capitani M . A Golgi-based KDELR-dependent signalling pathway controls extracellular matrix degradation. Oncotarget. 2015; 6(5):3375-93. PMC: 4413660. DOI: 10.18632/oncotarget.3270. View

3.
Wang Q, Hu B, Hu X, Kim H, Squatrito M, Scarpace L . Tumor Evolution of Glioma-Intrinsic Gene Expression Subtypes Associates with Immunological Changes in the Microenvironment. Cancer Cell. 2017; 32(1):42-56.e6. PMC: 5599156. DOI: 10.1016/j.ccell.2017.06.003. View

4.
Zhao Z, Zhang K, Wang Z, Wang K, Liu X, Wu F . A comprehensive review of available omics data resources and molecular profiling for precision glioma studies. Biomed Rep. 2018; 10(1):3-9. PMC: 6299212. DOI: 10.3892/br.2018.1168. View

5.
Tang J, Kong D, Cui Q, Wang K, Zhang D, Gong Y . Prognostic Genes of Breast Cancer Identified by Gene Co-expression Network Analysis. Front Oncol. 2018; 8:374. PMC: 6141856. DOI: 10.3389/fonc.2018.00374. View