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Development and validation of a predictive model in diagnosis and prognosis of primary glioblastoma patients based on Homeobox A family

BACKGROUND: Homeobox A (HOXA) family is involved in the development of malignancies as either tumor suppressors or oncogenes. However, their roles in glioblastoma (GBM) and clinical significance have not been fully elucidated. METHODS: HOXA mutation and expressions in pan-cancers were investigated u...

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Autores principales: Zheng, Zong-Qing, Yuan, Gui-Qiang, Zhang, Guo-Guo, Nie, Qian-Qian, Wang, Zhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10290013/
https://www.ncbi.nlm.nih.gov/pubmed/37351805
http://dx.doi.org/10.1007/s12672-023-00726-y
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author Zheng, Zong-Qing
Yuan, Gui-Qiang
Zhang, Guo-Guo
Nie, Qian-Qian
Wang, Zhong
author_facet Zheng, Zong-Qing
Yuan, Gui-Qiang
Zhang, Guo-Guo
Nie, Qian-Qian
Wang, Zhong
author_sort Zheng, Zong-Qing
collection PubMed
description BACKGROUND: Homeobox A (HOXA) family is involved in the development of malignancies as either tumor suppressors or oncogenes. However, their roles in glioblastoma (GBM) and clinical significance have not been fully elucidated. METHODS: HOXA mutation and expressions in pan-cancers were investigated using GSCA and Oncomine, which in GBM were validated by cBioPortal, Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas (TCGA) datasets. Kaplan–Meier analyses were conducted to determine prognostic values of HOXAs at genetic and mRNA levels. Diagnostic roles of HOXAs in tumor classification were explored by GlioVis and R software. Independent prognostic HOXAs were identified using Cox survival analyses, the least absolute shrinkage and selection operator (LASSO) regression, quantitative real-time PCR, and immunohistochemical staining. A HOXAs-based nomogram survival prediction model was developed and evaluated using Kaplan–Meier analysis, time-dependent Area Under Curve, calibration plots, and Decision Curve Analysis in training and validation cohorts. RESULTS: HOXAs were highly mutated and overexpressed in pan-cancers, especially in CGGA and TCGA GBM datasets. Genetic alteration and mRNA expression of HOXAs were both found to be prognostic. Specific HOXAs could distinguish IDH mutation (HOXA1-7, HOXA9, HOXA13) and molecular GBM subtypes (HOXA1-2, HOXA9-11, HOXA13). HOXA1/2/3/10 were confirmed to be independent prognostic members, with high expressions validated in clinical GBM tissues. The HOXAs-based nomogram model exhibited good prediction performance and net benefits for patients in training and validation cohorts. CONCLUSION: HOXA family has diagnostic values, and the HOXAs-based nomogram model is effective in survival prediction, providing a novel approach to support the treatment of GBM patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-023-00726-y.
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spelling pubmed-102900132023-06-25 Development and validation of a predictive model in diagnosis and prognosis of primary glioblastoma patients based on Homeobox A family Zheng, Zong-Qing Yuan, Gui-Qiang Zhang, Guo-Guo Nie, Qian-Qian Wang, Zhong Discov Oncol Research BACKGROUND: Homeobox A (HOXA) family is involved in the development of malignancies as either tumor suppressors or oncogenes. However, their roles in glioblastoma (GBM) and clinical significance have not been fully elucidated. METHODS: HOXA mutation and expressions in pan-cancers were investigated using GSCA and Oncomine, which in GBM were validated by cBioPortal, Chinese Glioma Genome Atlas (CGGA), and The Cancer Genome Atlas (TCGA) datasets. Kaplan–Meier analyses were conducted to determine prognostic values of HOXAs at genetic and mRNA levels. Diagnostic roles of HOXAs in tumor classification were explored by GlioVis and R software. Independent prognostic HOXAs were identified using Cox survival analyses, the least absolute shrinkage and selection operator (LASSO) regression, quantitative real-time PCR, and immunohistochemical staining. A HOXAs-based nomogram survival prediction model was developed and evaluated using Kaplan–Meier analysis, time-dependent Area Under Curve, calibration plots, and Decision Curve Analysis in training and validation cohorts. RESULTS: HOXAs were highly mutated and overexpressed in pan-cancers, especially in CGGA and TCGA GBM datasets. Genetic alteration and mRNA expression of HOXAs were both found to be prognostic. Specific HOXAs could distinguish IDH mutation (HOXA1-7, HOXA9, HOXA13) and molecular GBM subtypes (HOXA1-2, HOXA9-11, HOXA13). HOXA1/2/3/10 were confirmed to be independent prognostic members, with high expressions validated in clinical GBM tissues. The HOXAs-based nomogram model exhibited good prediction performance and net benefits for patients in training and validation cohorts. CONCLUSION: HOXA family has diagnostic values, and the HOXAs-based nomogram model is effective in survival prediction, providing a novel approach to support the treatment of GBM patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-023-00726-y. Springer US 2023-06-23 /pmc/articles/PMC10290013/ /pubmed/37351805 http://dx.doi.org/10.1007/s12672-023-00726-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) .
spellingShingle Research
Zheng, Zong-Qing
Yuan, Gui-Qiang
Zhang, Guo-Guo
Nie, Qian-Qian
Wang, Zhong
Development and validation of a predictive model in diagnosis and prognosis of primary glioblastoma patients based on Homeobox A family
title Development and validation of a predictive model in diagnosis and prognosis of primary glioblastoma patients based on Homeobox A family
title_full Development and validation of a predictive model in diagnosis and prognosis of primary glioblastoma patients based on Homeobox A family
title_fullStr Development and validation of a predictive model in diagnosis and prognosis of primary glioblastoma patients based on Homeobox A family
title_full_unstemmed Development and validation of a predictive model in diagnosis and prognosis of primary glioblastoma patients based on Homeobox A family
title_short Development and validation of a predictive model in diagnosis and prognosis of primary glioblastoma patients based on Homeobox A family
title_sort development and validation of a predictive model in diagnosis and prognosis of primary glioblastoma patients based on homeobox a family
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10290013/
https://www.ncbi.nlm.nih.gov/pubmed/37351805
http://dx.doi.org/10.1007/s12672-023-00726-y
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