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A Novel RNA-Binding Protein-Based Nomogram for Predicting Survival of Patients with Gastric Cancer

BACKGROUND: We attempted to develop a prognostic model and characterize molecular subtypes for gastric cancer on the basis of ribonucleic acid (RNA)-binding proteins (RBPs). MATERIAL/METHODS: RNA sequence data of gastric cancer were obtained from The Cancer Genome Atlas. Univariate Cox regression an...

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Autores principales: Zhu, Maoshu, Cai, Jiading, Wu, Yulong, Wu, Xinhong, Feng, Lianghua, Yin, Zhijiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Scientific Literature, Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7834218/
https://www.ncbi.nlm.nih.gov/pubmed/33471782
http://dx.doi.org/10.12659/MSM.928195
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author Zhu, Maoshu
Cai, Jiading
Wu, Yulong
Wu, Xinhong
Feng, Lianghua
Yin, Zhijiang
author_facet Zhu, Maoshu
Cai, Jiading
Wu, Yulong
Wu, Xinhong
Feng, Lianghua
Yin, Zhijiang
author_sort Zhu, Maoshu
collection PubMed
description BACKGROUND: We attempted to develop a prognostic model and characterize molecular subtypes for gastric cancer on the basis of ribonucleic acid (RNA)-binding proteins (RBPs). MATERIAL/METHODS: RNA sequence data of gastric cancer were obtained from The Cancer Genome Atlas. Univariate Cox regression analysis was used to screen survival-related RBPs, followed by least absolute shrinkage and selection operator Cox modeling. Overall and stratified survival analysis was carried out between high and low risk score groups, followed by receiver operator characteristic curve construction. Univariate and multivariate survival analysis was applied to assess its independent prognostic potential. A nomogram was constructed by combining age and the risk score, which was verified by calibration curves and decision curve analyses for 1-, 3-, and 5-year survival. Molecular subtypes were identified using nonnegative matrix factorization method. Clinical features of the identified subtypes were characterized on prognosis, drug sensitivity, and immune infiltration. An external Gene Expression Omnibus dataset was used to verify the above findings. RESULTS: On the basis of 44 survival-related RBPs, a robust prognostic 15-RBP signature was constructed. Patients with high risk score had a poorer prognosis than those with low risk score. The risk score had good performance in predicting clinical outcomes for 1-, 3-, and 5-year survival. The signature was effectively independent of other clinical features. The nomogram model combining age and the 15-RBP prognostic model exhibited better practicality and reliability for prognosis. RBP expression data were utilized to define 2 distinct molecular subtypes obviously related to survival outcomes, chemotherapeutic drug sensitivity, and immune infiltration. CONCLUSIONS: Our study provides a nomogram model that consists of age and a 15-RBP signature and identifies 2 molecular subtypes for gastric cancer that possess potential value for preclinical, clinical, and translational research on gastric cancer.
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spelling pubmed-78342182021-01-26 A Novel RNA-Binding Protein-Based Nomogram for Predicting Survival of Patients with Gastric Cancer Zhu, Maoshu Cai, Jiading Wu, Yulong Wu, Xinhong Feng, Lianghua Yin, Zhijiang Med Sci Monit Database Analysis BACKGROUND: We attempted to develop a prognostic model and characterize molecular subtypes for gastric cancer on the basis of ribonucleic acid (RNA)-binding proteins (RBPs). MATERIAL/METHODS: RNA sequence data of gastric cancer were obtained from The Cancer Genome Atlas. Univariate Cox regression analysis was used to screen survival-related RBPs, followed by least absolute shrinkage and selection operator Cox modeling. Overall and stratified survival analysis was carried out between high and low risk score groups, followed by receiver operator characteristic curve construction. Univariate and multivariate survival analysis was applied to assess its independent prognostic potential. A nomogram was constructed by combining age and the risk score, which was verified by calibration curves and decision curve analyses for 1-, 3-, and 5-year survival. Molecular subtypes were identified using nonnegative matrix factorization method. Clinical features of the identified subtypes were characterized on prognosis, drug sensitivity, and immune infiltration. An external Gene Expression Omnibus dataset was used to verify the above findings. RESULTS: On the basis of 44 survival-related RBPs, a robust prognostic 15-RBP signature was constructed. Patients with high risk score had a poorer prognosis than those with low risk score. The risk score had good performance in predicting clinical outcomes for 1-, 3-, and 5-year survival. The signature was effectively independent of other clinical features. The nomogram model combining age and the 15-RBP prognostic model exhibited better practicality and reliability for prognosis. RBP expression data were utilized to define 2 distinct molecular subtypes obviously related to survival outcomes, chemotherapeutic drug sensitivity, and immune infiltration. CONCLUSIONS: Our study provides a nomogram model that consists of age and a 15-RBP signature and identifies 2 molecular subtypes for gastric cancer that possess potential value for preclinical, clinical, and translational research on gastric cancer. International Scientific Literature, Inc. 2021-01-20 /pmc/articles/PMC7834218/ /pubmed/33471782 http://dx.doi.org/10.12659/MSM.928195 Text en © Med Sci Monit, 2021 This work is licensed under Creative Common Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) )
spellingShingle Database Analysis
Zhu, Maoshu
Cai, Jiading
Wu, Yulong
Wu, Xinhong
Feng, Lianghua
Yin, Zhijiang
A Novel RNA-Binding Protein-Based Nomogram for Predicting Survival of Patients with Gastric Cancer
title A Novel RNA-Binding Protein-Based Nomogram for Predicting Survival of Patients with Gastric Cancer
title_full A Novel RNA-Binding Protein-Based Nomogram for Predicting Survival of Patients with Gastric Cancer
title_fullStr A Novel RNA-Binding Protein-Based Nomogram for Predicting Survival of Patients with Gastric Cancer
title_full_unstemmed A Novel RNA-Binding Protein-Based Nomogram for Predicting Survival of Patients with Gastric Cancer
title_short A Novel RNA-Binding Protein-Based Nomogram for Predicting Survival of Patients with Gastric Cancer
title_sort novel rna-binding protein-based nomogram for predicting survival of patients with gastric cancer
topic Database Analysis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7834218/
https://www.ncbi.nlm.nih.gov/pubmed/33471782
http://dx.doi.org/10.12659/MSM.928195
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