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Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics
BACKGROUND: Oral squamous cell carcinoma (OSCC) accounts for a frequently-occurring head and neck cancer, which is characterized by high rates of morbidity and mortality. Metabolism-related genes (MRGs) show close association with OSCC development, metastasis and progression, so we constructed an MR...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9789624/ https://www.ncbi.nlm.nih.gov/pubmed/36566175 http://dx.doi.org/10.1186/s12920-022-01417-3 |
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author | Zhang, Jingfei Ma, Chenxi Qin, Han Wang, Zhi Zhu, Chao Liu, Xiujuan Hao, Xiuyan Liu, Jinghua Li, Ling Cai, Zhen |
author_facet | Zhang, Jingfei Ma, Chenxi Qin, Han Wang, Zhi Zhu, Chao Liu, Xiujuan Hao, Xiuyan Liu, Jinghua Li, Ling Cai, Zhen |
author_sort | Zhang, Jingfei |
collection | PubMed |
description | BACKGROUND: Oral squamous cell carcinoma (OSCC) accounts for a frequently-occurring head and neck cancer, which is characterized by high rates of morbidity and mortality. Metabolism-related genes (MRGs) show close association with OSCC development, metastasis and progression, so we constructed an MRGs-based OSCC prognosis model for evaluating OSCC prognostic outcome. METHODS: This work obtained gene expression profile as well as the relevant clinical information from the The Cancer Genome Atlas (TCGA) database, determined the MRGs related to OSCC by difference analysis, screened the prognosis-related MRGs by performing univariate Cox analysis, and used such identified MRGs for constructing the OSCC prognosis prediction model through Lasso-Cox regression. Besides, we validated the model with the GSE41613 dataset based on Gene Expression Omnibus (GEO) database. RESULTS: The present work screened 317 differentially expressed MRGs from the database, identified 12 OSCC prognostic MRGs through univariate Cox regression, and then established a clinical prognostic model composed of 11 MRGs by Lasso-Cox analysis. Based on the optimal risk score threshold, cases were classified as low- or high-risk group. As suggested by Kaplan–Meier (KM) analysis, survival rate was obviously different between the two groups in the TCGA training set (P < 0.001). According to subsequent univariate and multivariate Cox regression, risk score served as the factor to predict prognosis relative to additional clinical features (P < 0.001). Besides, area under ROC curve (AUC) values for patient survival at 1, 3 and 5 years were determined as 0.63, 0.70, and 0.76, separately, indicating that the prognostic model has good predictive accuracy. Then, we validated this clinical prognostic model using GSE41613. To enhance our model prediction accuracy, age, gender, risk score together with TNM stage were incorporated in a nomogram. As indicated by results of ROC curve and calibration curve analyses, the as-constructed nomogram had enhanced prediction accuracy compared with clinicopathological features alone, besides, combining clinicopathological characteristics with risk score contributed to predicting patient prognosis and guiding clinical decision-making. CONCLUSION: In this study, 11 MRGs prognostic models based on TCGA database showed superior predictive performance and had a certain clinical application prospect in guiding individualized. |
format | Online Article Text |
id | pubmed-9789624 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-97896242022-12-25 Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics Zhang, Jingfei Ma, Chenxi Qin, Han Wang, Zhi Zhu, Chao Liu, Xiujuan Hao, Xiuyan Liu, Jinghua Li, Ling Cai, Zhen BMC Med Genomics Research BACKGROUND: Oral squamous cell carcinoma (OSCC) accounts for a frequently-occurring head and neck cancer, which is characterized by high rates of morbidity and mortality. Metabolism-related genes (MRGs) show close association with OSCC development, metastasis and progression, so we constructed an MRGs-based OSCC prognosis model for evaluating OSCC prognostic outcome. METHODS: This work obtained gene expression profile as well as the relevant clinical information from the The Cancer Genome Atlas (TCGA) database, determined the MRGs related to OSCC by difference analysis, screened the prognosis-related MRGs by performing univariate Cox analysis, and used such identified MRGs for constructing the OSCC prognosis prediction model through Lasso-Cox regression. Besides, we validated the model with the GSE41613 dataset based on Gene Expression Omnibus (GEO) database. RESULTS: The present work screened 317 differentially expressed MRGs from the database, identified 12 OSCC prognostic MRGs through univariate Cox regression, and then established a clinical prognostic model composed of 11 MRGs by Lasso-Cox analysis. Based on the optimal risk score threshold, cases were classified as low- or high-risk group. As suggested by Kaplan–Meier (KM) analysis, survival rate was obviously different between the two groups in the TCGA training set (P < 0.001). According to subsequent univariate and multivariate Cox regression, risk score served as the factor to predict prognosis relative to additional clinical features (P < 0.001). Besides, area under ROC curve (AUC) values for patient survival at 1, 3 and 5 years were determined as 0.63, 0.70, and 0.76, separately, indicating that the prognostic model has good predictive accuracy. Then, we validated this clinical prognostic model using GSE41613. To enhance our model prediction accuracy, age, gender, risk score together with TNM stage were incorporated in a nomogram. As indicated by results of ROC curve and calibration curve analyses, the as-constructed nomogram had enhanced prediction accuracy compared with clinicopathological features alone, besides, combining clinicopathological characteristics with risk score contributed to predicting patient prognosis and guiding clinical decision-making. CONCLUSION: In this study, 11 MRGs prognostic models based on TCGA database showed superior predictive performance and had a certain clinical application prospect in guiding individualized. BioMed Central 2022-12-24 /pmc/articles/PMC9789624/ /pubmed/36566175 http://dx.doi.org/10.1186/s12920-022-01417-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Zhang, Jingfei Ma, Chenxi Qin, Han Wang, Zhi Zhu, Chao Liu, Xiujuan Hao, Xiuyan Liu, Jinghua Li, Ling Cai, Zhen Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics |
title | Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics |
title_full | Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics |
title_fullStr | Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics |
title_full_unstemmed | Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics |
title_short | Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics |
title_sort | construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9789624/ https://www.ncbi.nlm.nih.gov/pubmed/36566175 http://dx.doi.org/10.1186/s12920-022-01417-3 |
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