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Establishing a cancer driver gene signature-based risk model for predicting the prognoses of gastric cancer patients

Despite the high prevalence of gastric cancer (GC), molecular biomarkers that can reliably detect GC are yet to be discovered. The present study aimed to establish a robust gene signature based on cancer driver genes (CDGs) that can predict GC prognosis. Transcriptional profiles and clinical data fr...

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Autores principales: Chen, Jun, Zhou, Chao, Liu, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8954960/
https://www.ncbi.nlm.nih.gov/pubmed/35288483
http://dx.doi.org/10.18632/aging.203948
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author Chen, Jun
Zhou, Chao
Liu, Ying
author_facet Chen, Jun
Zhou, Chao
Liu, Ying
author_sort Chen, Jun
collection PubMed
description Despite the high prevalence of gastric cancer (GC), molecular biomarkers that can reliably detect GC are yet to be discovered. The present study aimed to establish a robust gene signature based on cancer driver genes (CDGs) that can predict GC prognosis. Transcriptional profiles and clinical data from GC patients were analyzed using univariate Cox regression analysis and the least absolute shrinkage and selection (LASSO)-penalized Cox regression analysis to select optimal prognosis-related genes for modeling. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier analyses were done to assess the predictive power of this gene signature. A nomogram model for prediction of survival of GC patients was established using the CDG signature and clinical information, and a seven-CDG signature was identified. Risk scores were calculated using this signature, and patients were subsequently divided into high- and low-risk groups; high-risk patients in the training and validation datasets had poorer prognoses than low-risk patients. Cox regression analysis revealed that the CDG signature is an independent prognostic factor for GC. The signature and other clinical features were used to construct a nomogram for predicting overall GC patient survival. Calibration and decision curve analysis showed that the nomogram accurately predicted survival, highlighting its clinical utility. Thus, we established a novel CDG signature and nomogram for predicting GC prognosis, which may facilitate personalized treatment of GC.
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spelling pubmed-89549602022-03-28 Establishing a cancer driver gene signature-based risk model for predicting the prognoses of gastric cancer patients Chen, Jun Zhou, Chao Liu, Ying Aging (Albany NY) Research Paper Despite the high prevalence of gastric cancer (GC), molecular biomarkers that can reliably detect GC are yet to be discovered. The present study aimed to establish a robust gene signature based on cancer driver genes (CDGs) that can predict GC prognosis. Transcriptional profiles and clinical data from GC patients were analyzed using univariate Cox regression analysis and the least absolute shrinkage and selection (LASSO)-penalized Cox regression analysis to select optimal prognosis-related genes for modeling. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier analyses were done to assess the predictive power of this gene signature. A nomogram model for prediction of survival of GC patients was established using the CDG signature and clinical information, and a seven-CDG signature was identified. Risk scores were calculated using this signature, and patients were subsequently divided into high- and low-risk groups; high-risk patients in the training and validation datasets had poorer prognoses than low-risk patients. Cox regression analysis revealed that the CDG signature is an independent prognostic factor for GC. The signature and other clinical features were used to construct a nomogram for predicting overall GC patient survival. Calibration and decision curve analysis showed that the nomogram accurately predicted survival, highlighting its clinical utility. Thus, we established a novel CDG signature and nomogram for predicting GC prognosis, which may facilitate personalized treatment of GC. Impact Journals 2022-03-14 /pmc/articles/PMC8954960/ /pubmed/35288483 http://dx.doi.org/10.18632/aging.203948 Text en Copyright: © 2022 Chen et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/3.0/) (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Paper
Chen, Jun
Zhou, Chao
Liu, Ying
Establishing a cancer driver gene signature-based risk model for predicting the prognoses of gastric cancer patients
title Establishing a cancer driver gene signature-based risk model for predicting the prognoses of gastric cancer patients
title_full Establishing a cancer driver gene signature-based risk model for predicting the prognoses of gastric cancer patients
title_fullStr Establishing a cancer driver gene signature-based risk model for predicting the prognoses of gastric cancer patients
title_full_unstemmed Establishing a cancer driver gene signature-based risk model for predicting the prognoses of gastric cancer patients
title_short Establishing a cancer driver gene signature-based risk model for predicting the prognoses of gastric cancer patients
title_sort establishing a cancer driver gene signature-based risk model for predicting the prognoses of gastric cancer patients
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8954960/
https://www.ncbi.nlm.nih.gov/pubmed/35288483
http://dx.doi.org/10.18632/aging.203948
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