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Updated: Sep 13, 2025
Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
Published on: January 9, 2019
Identification of Prognostic and Diagnostic Biomarkers for Glioma Utilizing Immune System Gene Profiling
Zahra Haghshenas1, Elham Nazari1, Ghazaleh Khalili-Tanha2
1Proteomics Research Center, System Biology Institute, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Background:
Approximately 80% of all malignant brain tumors and the most common cause of death that occur as a result of primary brain tumors belong to glioma. Hence, identifying effective biomarkers for early diagnosis and prognosis can have a significant impact on patient treatment. Recent years have witnessed a significant increase in the use of machine learning (ML) to analyze RNAseq data to identify new cancer biomarkers. In this study, diagnostic and prognostic biomarkers for Glioma were identified through the collection of patient data from the TCGA database and analysis using ML algorithms and bioinformatics.
Methods:
The study used ML to analyze ribonucleic acid (RNA) expression profiles from Glioma patients (GBMLGG) to identify differentially expressed genes (DEGs). In general, the sample of 1012 patients and 35 controls, which included 613 men and 434 women, was used in this study. Biomarkers of prognosis have been identified using the Kaplan-Meier analysis of survival curves. The coexpression of DEGs, protein-protein interactions (PPIs), and the correlation between DEGs and clinical data were also examined. The receiver operating characteristic (ROC) curve analysis was used to determine diagnostic markers.
Results:
After normalization and filtering, we identified 3172 DEGs with a log fold change |FC| ≥ 1 and P < 0.0.05. According to a survival analysis, 15 upregulated genes (GRAPL, LOC339240, LOC723809, NODAL, SILV, SPINK8, TAC4, ANG, CD209, F2RL2, LYZ, SLAMF7, psiTPTE22, SFRP4 and DKFZP) and 9 downregulated genes (PCDHGC5, CES8, CHD5, DNAJC6, DNM1, KIRREL3, NCOA7, RASAL1, SNCA) were associated with overall survival (OS). In addition, the ML algorithm identified 20 genes, among which PSD, TUBA4A, and PCDHGC5 were identified as candidates with high correlation coefficients.
Conclusion:
Generally, our results showed that immune-related genes play a crucial role in the development, progression, and pathogenesis of gliomas. Five immune-related genes-including SLAMF7, CD209, TAC4, HLA-DRB68, and LYZ-were found to be diagnostic and prognostic biomarkers of the disease.
