Evidence map›Paper›PMID 40740562›Full record

ArticleMedical journal of the Islamic Republic of Iran2025

Identification of Prognostic and Diagnostic Biomarkers for Glioma Utilizing Immune System Gene Profiling.

Zahra Haghshenas, Elham Nazari, Ghazaleh Khalili-Tanha, Zahra Razzaghi

Abstract read
In one paragraph

Article in Medical journal of the Islamic Republic of Iran, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Zahra HaghshenasProteomics Research Center, System Biology Institute, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Elham NazariProteomics Research Center, System Biology Institute, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.ORCID https://orcid.org/0009-0000-8452-2946
Ghazaleh Khalili-TanhaDepartment of Medical Genetics and Molecular Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Zahra RazzaghiLaser Application in Medical Science Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 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.

Indexed as

BiomarkerDiagnosisGliomaImmune-GenesMachine learningRNA-Seq

Identifiers

PMID40740562
PMCPMC12309318

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