en E-learning Original Research: Basic Science in Medicine Original Research: Basic Science in Medicine <span style="font-size:13pt"><span style="text-justify:kashida"><span style="text-kashida:0%"><span style="text-autospace:none"><span new="" roman="" style="font-family:" times=""><span style="font-style:italic"><b><span style="font-size:9.0pt"><span style="font-style:normal">Background: </span></span></b><span style="font-size:9.0pt"><span style="font-style:normal">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. </span></span></span></span></span></span></span></span><br> <span style="font-size:13pt"><span style="text-justify:kashida"><span style="text-kashida:0%"><span style="text-autospace:none"><span new="" roman="" style="font-family:" times=""><span style="font-style:italic"><span style="font-size:9.0pt"><span style="font-style:normal">&nbsp;&nbsp; <b>Methods:</b> The study used ML to analyze ribonucleic acid (RNA) expression profiles from&nbsp; 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.</span></span></span></span></span></span></span></span><br> <span style="font-size:13pt"><span style="text-justify:kashida"><span style="text-kashida:0%"><span style="text-autospace:none"><span new="" roman="" style="font-family:" times=""><span style="font-style:italic"><span style="font-size:9.0pt"><span style="font-style:normal">&nbsp;&nbsp; <b>Results:</b> After normalization and filtering, we identified 3172 DEGs with a log fold change |FC| &ge; 1 and </span></span><span style="font-size:9.0pt">P</span><span style="font-size:9.0pt"><span style="font-style:normal"> < 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. </span></span></span></span></span></span></span></span><br> <span style="font-size:13pt"><span style="text-justify:kashida"><span style="text-kashida:0%"><span style="text-autospace:none"><span new="" roman="" style="font-family:" times=""><span style="font-style:italic"><span style="font-size:9.0pt"><span style="font-style:normal">&nbsp;&nbsp; <b>Conclusion:</b> Generally, our results showed that immune-related genes play a crucial role in the development, progression, and pathogenesis of gliomas. Five immune-related genes&mdash;including SLAMF7, CD209, TAC4, HLA-DRB68, and LYZ&mdash;were found to be diagnostic and prognostic biomarkers of the disease. </span></span></span></span></span></span></span></span><br> &nbsp; Glioma, Machine learning, Biomarker, Diagnosis, RNA-Seq, Immune-Genes 381 392 http://mjiri.iums.ac.ir/browse.php?a_code=A-10-8378-3&slc_lang=en&sid=1 Zahra Haghshenas Z.haghshenas.1998@gmail.com 200319475328460090685 200319475328460090685 No Proteomics Research Center, System Biology Institute, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Elham Nazari Nazari@sbmu.ac.ir 200319475328460090686 0009-0000-8452-2946 Yes Proteomics Research Center, System Biology Institute, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Ghazaleh Khalili-Tanha Ghazaleh.khalili24@gmail.com 200319475328460090687 200319475328460090687 No Department of Medical Genetics and Molecular Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran Zahra Razzaghi z.razzaghi@sbmu.ac.ir 200319475328460090688 200319475328460090688 No Laser Application in Medical Science Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran