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Construction of a prognostic risk assessment model for lung adenocarcinoma based on Integrin β family‐related genes
BACKGROUND: Integrin β (ITGB) superfamily plays an essential role in the intercellular connection and signal transmission. It was exhibited that overexpressing of ITGB family members promotes the malignant progression of lung adenocarcinoma (LUAD), but the relationship between ITGB superfamily and t...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9169214/ https://www.ncbi.nlm.nih.gov/pubmed/35403268 http://dx.doi.org/10.1002/jcla.24419 |
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author | Wu, Yuanlin Fu, Linhai Wang, Bin Li, Zhupeng Wei, Desheng Wang, Haiyong Zhang, Chu Ma, Zhifeng Zhu, Ting Yu, Guangmao |
author_facet | Wu, Yuanlin Fu, Linhai Wang, Bin Li, Zhupeng Wei, Desheng Wang, Haiyong Zhang, Chu Ma, Zhifeng Zhu, Ting Yu, Guangmao |
author_sort | Wu, Yuanlin |
collection | PubMed |
description | BACKGROUND: Integrin β (ITGB) superfamily plays an essential role in the intercellular connection and signal transmission. It was exhibited that overexpressing of ITGB family members promotes the malignant progression of lung adenocarcinoma (LUAD), but the relationship between ITGB superfamily and the LUAD prognosis remains unclear. METHODS: In this study, the samples were assigned to different subgroups utilizing non‐negative matrix factorization clustering according to the expression of ITGB family members in LUAD. Kaplan–Meier (K‐M) survival analysis revealed the significant differences in the prognosis between different ITGB subgroups. Subsequently, we screened differentially expressed genes among different subgroups and conducted univariate Cox analysis, random forest feature selection, and multivariate Cox analysis. 9‐feature genes (FAM83A, AKAP12, PKP2, CYP17A1, GJB3, TMPRSS11F, KRT81, MARCH4, and STC1) in the ITGB superfamily were selected to establish a prognostic assessment model for LAUD. RESULTS: In accordance with the median risk score, LUAD samples were divided into high‐ and low‐risk groups. The receiver operating characteristic (ROC) curve of LUAD patients’ survival was predicted via K‐M survival curve and principal component analysis dimensionality reduction. This model was found to have a favorable performance in LUAD prognostic assessment. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of differentially expressed genes between groups and Gene Set Enrichment Analysis (GSEA) of intergroup samples confirmed that the high‐ and low‐risk groups had evident differences mainly in the function of extracellular matrix (ECM) interaction. Risk score and univariate and multivariate Cox regression analyses of clinical factors showed that the prognostic model could be applied as an independent prognostic factor for LUAD. Then, we draw the nomogram of 1‐, 3‐, and 5‐year survival of LUAD patients predicted with the risk score and clinical factors. Calibration curve and clinical decision curve proved the favorable predictive ability of nomogram. CONCLUSION: We constructed a LUAD prognostic risk model based on the ITGB superfamily, which can provide guidance for clinicians on their prognostic judgment. |
format | Online Article Text |
id | pubmed-9169214 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91692142022-06-07 Construction of a prognostic risk assessment model for lung adenocarcinoma based on Integrin β family‐related genes Wu, Yuanlin Fu, Linhai Wang, Bin Li, Zhupeng Wei, Desheng Wang, Haiyong Zhang, Chu Ma, Zhifeng Zhu, Ting Yu, Guangmao J Clin Lab Anal Research Articles BACKGROUND: Integrin β (ITGB) superfamily plays an essential role in the intercellular connection and signal transmission. It was exhibited that overexpressing of ITGB family members promotes the malignant progression of lung adenocarcinoma (LUAD), but the relationship between ITGB superfamily and the LUAD prognosis remains unclear. METHODS: In this study, the samples were assigned to different subgroups utilizing non‐negative matrix factorization clustering according to the expression of ITGB family members in LUAD. Kaplan–Meier (K‐M) survival analysis revealed the significant differences in the prognosis between different ITGB subgroups. Subsequently, we screened differentially expressed genes among different subgroups and conducted univariate Cox analysis, random forest feature selection, and multivariate Cox analysis. 9‐feature genes (FAM83A, AKAP12, PKP2, CYP17A1, GJB3, TMPRSS11F, KRT81, MARCH4, and STC1) in the ITGB superfamily were selected to establish a prognostic assessment model for LAUD. RESULTS: In accordance with the median risk score, LUAD samples were divided into high‐ and low‐risk groups. The receiver operating characteristic (ROC) curve of LUAD patients’ survival was predicted via K‐M survival curve and principal component analysis dimensionality reduction. This model was found to have a favorable performance in LUAD prognostic assessment. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of differentially expressed genes between groups and Gene Set Enrichment Analysis (GSEA) of intergroup samples confirmed that the high‐ and low‐risk groups had evident differences mainly in the function of extracellular matrix (ECM) interaction. Risk score and univariate and multivariate Cox regression analyses of clinical factors showed that the prognostic model could be applied as an independent prognostic factor for LUAD. Then, we draw the nomogram of 1‐, 3‐, and 5‐year survival of LUAD patients predicted with the risk score and clinical factors. Calibration curve and clinical decision curve proved the favorable predictive ability of nomogram. CONCLUSION: We constructed a LUAD prognostic risk model based on the ITGB superfamily, which can provide guidance for clinicians on their prognostic judgment. John Wiley and Sons Inc. 2022-04-11 /pmc/articles/PMC9169214/ /pubmed/35403268 http://dx.doi.org/10.1002/jcla.24419 Text en © 2022 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Wu, Yuanlin Fu, Linhai Wang, Bin Li, Zhupeng Wei, Desheng Wang, Haiyong Zhang, Chu Ma, Zhifeng Zhu, Ting Yu, Guangmao Construction of a prognostic risk assessment model for lung adenocarcinoma based on Integrin β family‐related genes |
title | Construction of a prognostic risk assessment model for lung adenocarcinoma based on Integrin β family‐related genes |
title_full | Construction of a prognostic risk assessment model for lung adenocarcinoma based on Integrin β family‐related genes |
title_fullStr | Construction of a prognostic risk assessment model for lung adenocarcinoma based on Integrin β family‐related genes |
title_full_unstemmed | Construction of a prognostic risk assessment model for lung adenocarcinoma based on Integrin β family‐related genes |
title_short | Construction of a prognostic risk assessment model for lung adenocarcinoma based on Integrin β family‐related genes |
title_sort | construction of a prognostic risk assessment model for lung adenocarcinoma based on integrin β family‐related genes |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9169214/ https://www.ncbi.nlm.nih.gov/pubmed/35403268 http://dx.doi.org/10.1002/jcla.24419 |
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