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Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model

Glioblastoma multiforme (GBM) patients show a variety of signs and symptoms that affect their quality of life (QOL) and self-dependence. Since most existing studies have examined prognostic factors based only on clinical factors, there is a need to consider the value of integrating multi-omics data...

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Autores principales: Nassani, Rayan, Bokhari, Yahya, Alrfaei, Bahauddeen M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10653472/
https://www.ncbi.nlm.nih.gov/pubmed/37972206
http://dx.doi.org/10.1371/journal.pone.0287448
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author Nassani, Rayan
Bokhari, Yahya
Alrfaei, Bahauddeen M.
author_facet Nassani, Rayan
Bokhari, Yahya
Alrfaei, Bahauddeen M.
author_sort Nassani, Rayan
collection PubMed
description Glioblastoma multiforme (GBM) patients show a variety of signs and symptoms that affect their quality of life (QOL) and self-dependence. Since most existing studies have examined prognostic factors based only on clinical factors, there is a need to consider the value of integrating multi-omics data including gene expression and proteomics with clinical data in identifying significant biomarkers for GBM prognosis. Our research aimed to isolate significant features that differentiate between short-term (≤ 6 months) and long-term (≥ 2 years) GBM survival, and between high Karnofsky performance scores (KPS ≥ 80) and low (KPS ≤ 60), using the iterative random forest (iRF) algorithm. Using the Cancer Genomic Atlas (TCGA) database, we identified 35 molecular features composed of 19 genes and 16 proteins. Our findings propose molecular signatures for predicting GBM prognosis and will improve clinical decisions, GBM management, and drug development.
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spelling pubmed-106534722023-11-16 Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model Nassani, Rayan Bokhari, Yahya Alrfaei, Bahauddeen M. PLoS One Research Article Glioblastoma multiforme (GBM) patients show a variety of signs and symptoms that affect their quality of life (QOL) and self-dependence. Since most existing studies have examined prognostic factors based only on clinical factors, there is a need to consider the value of integrating multi-omics data including gene expression and proteomics with clinical data in identifying significant biomarkers for GBM prognosis. Our research aimed to isolate significant features that differentiate between short-term (≤ 6 months) and long-term (≥ 2 years) GBM survival, and between high Karnofsky performance scores (KPS ≥ 80) and low (KPS ≤ 60), using the iterative random forest (iRF) algorithm. Using the Cancer Genomic Atlas (TCGA) database, we identified 35 molecular features composed of 19 genes and 16 proteins. Our findings propose molecular signatures for predicting GBM prognosis and will improve clinical decisions, GBM management, and drug development. Public Library of Science 2023-11-16 /pmc/articles/PMC10653472/ /pubmed/37972206 http://dx.doi.org/10.1371/journal.pone.0287448 Text en © 2023 Nassani et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Nassani, Rayan
Bokhari, Yahya
Alrfaei, Bahauddeen M.
Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model
title Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model
title_full Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model
title_fullStr Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model
title_full_unstemmed Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model
title_short Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model
title_sort molecular signature to predict quality of life and survival with glioblastoma using multiview omics model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10653472/
https://www.ncbi.nlm.nih.gov/pubmed/37972206
http://dx.doi.org/10.1371/journal.pone.0287448
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