Evidence map›Paper›PMID 38967113›Full record

ArticleCancer reports (Hoboken, N.J.)2024

Evaluation of secretome biomarkers in glioblastoma cancer stem cells: A bioinformatics analysis.

Ehsan Jangholi, Hoda Ahmari Tehran, Afsaneh Ghasemi, Mohammad Hoseinian, Sina Firoozi, Seyed Mohammad Ghodsi, Mona Tamaddon, Ahmad Bereimipour, Mahmoudreza Hadjighassem

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In one paragraph

Article in Cancer reports (Hoboken, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Downregulation ofFrontiers in oncology · 2025
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ehsan JangholiBrain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran.ORCID 0000-0002-6249-6617
Hoda Ahmari TehranDepartment of Medical Education, Qom University of Medical Sciences, Qom, Iran.
Afsaneh GhasemiDepartment of Public Health, School of Health, Fasa University of Medical Sciences, Fasa, Iran.
Mohammad HoseinianBrain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran.
Sina FirooziSchool of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Seyed Mohammad GhodsiBrain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran.
Mona TamaddonChronic Disease Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
Ahmad BereimipourDepartment of Biological Sciences and BioDiscovery Institute, University of North Texas, Denton, Texas, USA.
Mahmoudreza HadjighassemBrain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran.

Funding

Brain and Spinal Cord Injury Research Center, Neuroscience Institute, Tehran University of Medical Sciences 57665
6 · The paper itself

Abstract

backgroundGlioblastoma (GBM) is a malignant brain tumor that frequently occurs alongside other central nervous system (CNS) conditions. The secretome of GBM cells contains a diverse array of proteins released into the extracellular space, influencing the tumor microenvironment. These proteins can serve as potential biomarkers for GBM due to their involvement in key biological processes, exploring the secretome biomarkers in GBM research represents a cutting-edge strategy with significant potential for advancing diagnostic precision, treatment monitoring, and ultimately improving outcomes for patients with this challenging brain cancer.

aimThis study was aimed to investigate the roles of secretome biomarkers and their pathwayes in GBM through bioinformatics analysis. METHODS AND

resultsUsing data from the Gene Expression Omnibus and the Cancer Genome Atlas datasets-where both healthy and cancerous samples were analyzed-we used a quantitative analytical framework to identify differentially expressed genes (DEGs) and cell signaling pathways that might be related to GBM. Then, we performed gene ontology studies and hub protein identifications to estimate the roles of these DEGs after finding disease-gene connection networks and signaling pathways. Using the GEPIA Proportional Hazard Model and the Kaplan-Meier estimator, we widened our analysis to identify the important genes that may play a role in both progression and the survival of patients with GBM. In total, 890 DEGs, including 475 and 415 upregulated and downregulated were identified, respectively. Our results revealed that SQLE, DHCR7, delta-1 phospholipase C (PLCD1), and MINPP1 genes are highly expressed, and the Enolase 2 (ENO2) and hexokinase-1 (HK1) genes are low expressions.

conclusionHence, our findings suggest novel mechanisms that affect the occurrence of GBM development, growth, and/or establishment and may also serve as secretory biomarkers for GBM prognosis and possible targets for therapy. So, continued research in this field may uncover new avenues for therapeutic interventions and contribute to the ongoing efforts to combat GBM effectively.

Indexed as

Biomarkers, TumorBrain NeoplasmsComputational BiologyGene Expression Regulation, NeoplasticGlioblastomaNeoplastic Stem CellsGene Expression ProfilingGene Regulatory NetworksHumansPrognosisProtein Interaction MapsSecretomeSignal TransductionTumor MicroenvironmentBiomarkers, Tumorbioinformatics analysisbiomarkercancer stem cellsgene expression profilesglioblastoma

Identifiers

PMID38967113
PMCPMC11224916

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.