Evidence map›Paper›PMID 39733410›Full record

ArticleAsian Pacific journal of cancer prevention : APJCP2024

The G Protein-Coupled Receptor-Related Gene Signatures for Diagnosis and Prognosis in Glioblastoma: A Deep Learning Model Using RNA-Seq Data.

Ghazaleh Khalili-Tanha, Nima Khalili-Tanha, Masoumeh Farahani, Mostafa Rezaei-Tavirani, Elham Nazari

Abstract read
In one paragraph

Article in Asian Pacific journal of cancer prevention : APJCP, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
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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2 · The registry

The trial behind it

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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

1 citing paper in PubMed.

  1. 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

5 authors.

Ghazaleh Khalili-TanhaDepartment of Medical Genetics and Molecular Medicine, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Nima Khalili-TanhaDepartment of Small Animal Clinical Sciences, Western College of Veterinary Medicine, University of Saskatchewan, 52Campus Drive, Saskatoon, SK S7N 5B4, Canada.
Masoumeh FarahaniProteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.ORCID 0000-0001-7412-3474
Mostafa Rezaei-TaviraniProteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Elham NazariProteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGlioblastoma (GBM) is the most aggressive cancer in the central nervous system in glial cells. Finding novel biomarkers in GBM offers numerous advantages that can contribute to early detection, personalized treatment, improved patient outcomes, and advancements in cancer research and drug development. Integrating machine learning with RNAseq data in medicine holds significant potential for identifying novel biomarkers in various diseases, including cancer.

methodsGene expression raw data was used to detect differentially expressed genes (DEGs) within a cohort of 532 GBM patients. The molecular pathway analysis, disease ontology, and protein-protein interactions of DEGs were assessed. Machine learning methods were performed to identify candidate genes. Survival curves were estimated using the Kaplan-Meier method and Cox proportional hazard to find prognostic biomarkers.

resultsThe molecular pathway analysis revealed that key dysregulated genes are in GPCRs, class A rhodopsin-like, MAPK signaling pathway, and calcium regulation in cardiac cells. Additionally, survival analysis showed that ten downregulated genes, including CPLX3, GPR162, LCNL1, SLC5A5, GPR61, GPR68, IL1RL2, HCRTR1, AIPL1, and SYTL1, and also ten upregulated genes, including C1orf92, CATSPER1, CCDC19, EPS8L1, FAIM3, FAM70B, FCN3, GPR157, IGFBP1, and MYBPH decreased the overall survival in GBM patients. Furthermore, the machine learning detected twenty genes, among which LRRTM2 and OPRL1 were candidates with high correlation coefficients.

conclusionOur data suggest that genes belonging to G Protein-Coupled Receptors play a critical role in various aspects of glioblastoma progression and pathogenesis. Four members of GPCRs, including GPR162, GPR61, GPR68, and GPR157, can be considered prognostic biomarkers. Additionally, the combination of A2BP1 and GPR157 was reported as a diagnostic marker.

Indexed as

Biomarkers, TumorBrain NeoplasmsDeep LearningGlioblastomaReceptors, G-Protein-CoupledFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMiddle AgedPrognosisRNA-SeqSurvival RateTranscriptomeBiomarkers, TumorReceptors, G-Protein-CoupledBiomarkersDeep LearningGlioblastomaMachine LearningRNA-Seq

Identifiers

PMID39733410
PMCPMC12008356

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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 OpenQuestion graph.