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Development and validation of an individual alternative splicing prognostic signature in gastric cancer
Gastric cancer (GC) is a heterogeneous disease with different clinical manifestations and prognoses. Alternative splicing (AS) is a determinant of gene expression and contributes to protein diversity from a rather limited gene transcript in metazoans. AS events are associated with different aspects...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7950272/ https://www.ncbi.nlm.nih.gov/pubmed/33612482 http://dx.doi.org/10.18632/aging.202507 |
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author | Lou, Shenghan Zhang, Jian Zhai, Zhao Yin, Xin Wang, Yimin Fang, Tianyi Xue, Yingwei |
author_facet | Lou, Shenghan Zhang, Jian Zhai, Zhao Yin, Xin Wang, Yimin Fang, Tianyi Xue, Yingwei |
author_sort | Lou, Shenghan |
collection | PubMed |
description | Gastric cancer (GC) is a heterogeneous disease with different clinical manifestations and prognoses. Alternative splicing (AS) is a determinant of gene expression and contributes to protein diversity from a rather limited gene transcript in metazoans. AS events are associated with different aspects of cancer biology, including cell proliferation, apoptosis, invasion, etc. Here, we present a comprehensive analysis of the prognostic AS profile in GC. GC-specific AS (GCAS) events were analyzed, and overall survival-associated GCAS (OS-GCAS) events were verified among the genome-wide AS events identified in The Cancer Genome Atlas (TCGA) database. In total, 1,287 GCAS events of 837 genes and 173 OS-GCAS events of 130 genes were identified. The parental genes of OS-GCAS events were significantly enriched in the development of GC. Protein-protein interaction (PPI) and OS-GCAS-associated splicing factor (SF) interaction networks were constructed. Multivariate Cox regression analysis with least absolute shrinkage and selection operator (LASSO) penalty was performed to establish a prognostic risk formula, representing 23 OS-GCAS events. The low-risk group had better OS than the high-risk group and lower immune and stromal scores. Cox proportional hazard regression was applied to generate an AS-clinical integrated prognostic model with a considerable area under the curve (AUC) value in both the training and validation datasets. Our study provides a profile of OS-GCAS events and an AS-clinical nomogram to predict the prognosis of GC. |
format | Online Article Text |
id | pubmed-7950272 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Impact Journals |
record_format | MEDLINE/PubMed |
spelling | pubmed-79502722021-03-23 Development and validation of an individual alternative splicing prognostic signature in gastric cancer Lou, Shenghan Zhang, Jian Zhai, Zhao Yin, Xin Wang, Yimin Fang, Tianyi Xue, Yingwei Aging (Albany NY) Research Paper Gastric cancer (GC) is a heterogeneous disease with different clinical manifestations and prognoses. Alternative splicing (AS) is a determinant of gene expression and contributes to protein diversity from a rather limited gene transcript in metazoans. AS events are associated with different aspects of cancer biology, including cell proliferation, apoptosis, invasion, etc. Here, we present a comprehensive analysis of the prognostic AS profile in GC. GC-specific AS (GCAS) events were analyzed, and overall survival-associated GCAS (OS-GCAS) events were verified among the genome-wide AS events identified in The Cancer Genome Atlas (TCGA) database. In total, 1,287 GCAS events of 837 genes and 173 OS-GCAS events of 130 genes were identified. The parental genes of OS-GCAS events were significantly enriched in the development of GC. Protein-protein interaction (PPI) and OS-GCAS-associated splicing factor (SF) interaction networks were constructed. Multivariate Cox regression analysis with least absolute shrinkage and selection operator (LASSO) penalty was performed to establish a prognostic risk formula, representing 23 OS-GCAS events. The low-risk group had better OS than the high-risk group and lower immune and stromal scores. Cox proportional hazard regression was applied to generate an AS-clinical integrated prognostic model with a considerable area under the curve (AUC) value in both the training and validation datasets. Our study provides a profile of OS-GCAS events and an AS-clinical nomogram to predict the prognosis of GC. Impact Journals 2021-02-17 /pmc/articles/PMC7950272/ /pubmed/33612482 http://dx.doi.org/10.18632/aging.202507 Text en Copyright: © 2021 Lou 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 Lou, Shenghan Zhang, Jian Zhai, Zhao Yin, Xin Wang, Yimin Fang, Tianyi Xue, Yingwei Development and validation of an individual alternative splicing prognostic signature in gastric cancer |
title | Development and validation of an individual alternative splicing prognostic signature in gastric cancer |
title_full | Development and validation of an individual alternative splicing prognostic signature in gastric cancer |
title_fullStr | Development and validation of an individual alternative splicing prognostic signature in gastric cancer |
title_full_unstemmed | Development and validation of an individual alternative splicing prognostic signature in gastric cancer |
title_short | Development and validation of an individual alternative splicing prognostic signature in gastric cancer |
title_sort | development and validation of an individual alternative splicing prognostic signature in gastric cancer |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7950272/ https://www.ncbi.nlm.nih.gov/pubmed/33612482 http://dx.doi.org/10.18632/aging.202507 |
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