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Analysis of Collagen type X alpha 1 (COL10A1) expression and prognostic significance in gastric cancer based on bioinformatics
Collagen type X alpha 1 (COL10A1) is a member of the collagen family and the main matrix component. However, COL10A1 expression and prognosis relationship remains unclear in gastric cancer (GC). Through the analysis of database of Oncomine, the Cancer Genome Atlas (TCGA) as well as the Gene Expressi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8291830/ https://www.ncbi.nlm.nih.gov/pubmed/33371777 http://dx.doi.org/10.1080/21655979.2020.1864912 |
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author | Chen, Shuai Wei, Yi Liu, Hanyang Gong, Yu Zhou, Yan Yang, Haojun Tang, Liming |
author_facet | Chen, Shuai Wei, Yi Liu, Hanyang Gong, Yu Zhou, Yan Yang, Haojun Tang, Liming |
author_sort | Chen, Shuai |
collection | PubMed |
description | Collagen type X alpha 1 (COL10A1) is a member of the collagen family and the main matrix component. However, COL10A1 expression and prognosis relationship remains unclear in gastric cancer (GC). Through the analysis of database of Oncomine, the Cancer Genome Atlas (TCGA) as well as the Gene Expression Omnibus (GEO), in contrast to the tissue of normal gastric, COL10A1 in gastric cancer, had been upregulated. The high expression of COL10A1 was obviously related to T stage (P = 0.025) and lymph node metastasis (P = 0.025). It has been illustrated by the analysis of logistic regression that COL10A1’s heightened expression in gastric cancer had been essentially linked with pathological stage, tumor differentiation, and T classification. The Kaplan–Meier curve in the Kaplan-Meier plotter database (P = 0.0371) and GSE84437 (P = 0.002) indicate that patients with high COL10A1 expression possess poor prognosis, specifically GC patients with lymph node metastasis have it. TCGA’s Multivariate analysis (P = 0.025) and GSE84437 dataset (P = 0.034) show that high expression COL10A1 is a key independent predictor of poor overall survival. Searching KEGG pathway enrichment by GSEA, the results suggested that 29 pathways were enriched. qRT-PCR technique was used for verification of the COL10A1’s high expression in gastric cancer in contrast to the normal gastric tissues. In conclusion, COL10A1 is of great importance in predicting the survival rate of GC patients. |
format | Online Article Text |
id | pubmed-8291830 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-82918302021-09-01 Analysis of Collagen type X alpha 1 (COL10A1) expression and prognostic significance in gastric cancer based on bioinformatics Chen, Shuai Wei, Yi Liu, Hanyang Gong, Yu Zhou, Yan Yang, Haojun Tang, Liming Bioengineered Research Paper Collagen type X alpha 1 (COL10A1) is a member of the collagen family and the main matrix component. However, COL10A1 expression and prognosis relationship remains unclear in gastric cancer (GC). Through the analysis of database of Oncomine, the Cancer Genome Atlas (TCGA) as well as the Gene Expression Omnibus (GEO), in contrast to the tissue of normal gastric, COL10A1 in gastric cancer, had been upregulated. The high expression of COL10A1 was obviously related to T stage (P = 0.025) and lymph node metastasis (P = 0.025). It has been illustrated by the analysis of logistic regression that COL10A1’s heightened expression in gastric cancer had been essentially linked with pathological stage, tumor differentiation, and T classification. The Kaplan–Meier curve in the Kaplan-Meier plotter database (P = 0.0371) and GSE84437 (P = 0.002) indicate that patients with high COL10A1 expression possess poor prognosis, specifically GC patients with lymph node metastasis have it. TCGA’s Multivariate analysis (P = 0.025) and GSE84437 dataset (P = 0.034) show that high expression COL10A1 is a key independent predictor of poor overall survival. Searching KEGG pathway enrichment by GSEA, the results suggested that 29 pathways were enriched. qRT-PCR technique was used for verification of the COL10A1’s high expression in gastric cancer in contrast to the normal gastric tissues. In conclusion, COL10A1 is of great importance in predicting the survival rate of GC patients. Taylor & Francis 2020-12-29 /pmc/articles/PMC8291830/ /pubmed/33371777 http://dx.doi.org/10.1080/21655979.2020.1864912 Text en © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Paper Chen, Shuai Wei, Yi Liu, Hanyang Gong, Yu Zhou, Yan Yang, Haojun Tang, Liming Analysis of Collagen type X alpha 1 (COL10A1) expression and prognostic significance in gastric cancer based on bioinformatics |
title | Analysis of Collagen type X alpha 1 (COL10A1) expression and prognostic significance in gastric cancer based on bioinformatics |
title_full | Analysis of Collagen type X alpha 1 (COL10A1) expression and prognostic significance in gastric cancer based on bioinformatics |
title_fullStr | Analysis of Collagen type X alpha 1 (COL10A1) expression and prognostic significance in gastric cancer based on bioinformatics |
title_full_unstemmed | Analysis of Collagen type X alpha 1 (COL10A1) expression and prognostic significance in gastric cancer based on bioinformatics |
title_short | Analysis of Collagen type X alpha 1 (COL10A1) expression and prognostic significance in gastric cancer based on bioinformatics |
title_sort | analysis of collagen type x alpha 1 (col10a1) expression and prognostic significance in gastric cancer based on bioinformatics |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8291830/ https://www.ncbi.nlm.nih.gov/pubmed/33371777 http://dx.doi.org/10.1080/21655979.2020.1864912 |
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