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Prediction of key genes and pathways involved in trastuzumab-resistant gastric cancer
BACKGROUND: Trastuzumab has been prevailingly accepted as a beneficial treatment for gastric cancer (GC) by targeting human epidermal growth factor receptor 2 (HER2)-positive. However, the therapeutic resistance of trastuzumab remains a major obstacle, restricting the therapeutic efficacy. Therefore...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6106878/ https://www.ncbi.nlm.nih.gov/pubmed/30134903 http://dx.doi.org/10.1186/s12957-018-1475-6 |
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author | Yu, Chaoran Xue, Pei Zhang, Luyang Pan, Ruijun Cai, Zhenhao He, Zirui Sun, Jing Zheng, Minhua |
author_facet | Yu, Chaoran Xue, Pei Zhang, Luyang Pan, Ruijun Cai, Zhenhao He, Zirui Sun, Jing Zheng, Minhua |
author_sort | Yu, Chaoran |
collection | PubMed |
description | BACKGROUND: Trastuzumab has been prevailingly accepted as a beneficial treatment for gastric cancer (GC) by targeting human epidermal growth factor receptor 2 (HER2)-positive. However, the therapeutic resistance of trastuzumab remains a major obstacle, restricting the therapeutic efficacy. Therefore, identifying potential key genes and pathways is crucial to maximize the overall clinical benefits. METHODS: The gene expression profile GSE77346 was retrieved to identify the differentially expressed genes (DEGs) associated with the trastuzumab resistance in GC. Next, the DEGs were annotated by the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The DEGs-coded protein-protein interaction (PPI) networks and the prognostic values of the 20 hub genes were determined. Correlation of the hub genes were analyzed in The Cancer Genome Atlas. The prognostic values of hub genes were further validated by Kaplan-Meier (KM) plotter. RESULTS: A total of 849 DEGs were identified, with 374 in upregulation and 475 in downregulation. Epithelium development was the most significantly enriched term in biological processes while membrane-bounded vesicle was in cellular compartments and cell adhesion molecular binding was in molecular functions. Pathways in cancer and ECM-receptor interaction were the most significantly enriched for all DEGs. Among the PPI networks, 20 hub genes were defined, including CD44 molecule (CD44), HER-2, and cadherin 1 (CDH1). Six hub genes were associated with favorable OS while eight were associated with poor OS. Mechanistically, 2′-5′-oligoadenylate synthetase 1, 3 (OAS1, OAS3) and CDH1 featured high degrees and strong correlations with other hub genes. CONCLUSIONS: This bioinformatics analysis identified key genes and pathways for potential targets and survival predictors for trastuzumab treatment in GC. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12957-018-1475-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6106878 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-61068782018-08-29 Prediction of key genes and pathways involved in trastuzumab-resistant gastric cancer Yu, Chaoran Xue, Pei Zhang, Luyang Pan, Ruijun Cai, Zhenhao He, Zirui Sun, Jing Zheng, Minhua World J Surg Oncol Research BACKGROUND: Trastuzumab has been prevailingly accepted as a beneficial treatment for gastric cancer (GC) by targeting human epidermal growth factor receptor 2 (HER2)-positive. However, the therapeutic resistance of trastuzumab remains a major obstacle, restricting the therapeutic efficacy. Therefore, identifying potential key genes and pathways is crucial to maximize the overall clinical benefits. METHODS: The gene expression profile GSE77346 was retrieved to identify the differentially expressed genes (DEGs) associated with the trastuzumab resistance in GC. Next, the DEGs were annotated by the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The DEGs-coded protein-protein interaction (PPI) networks and the prognostic values of the 20 hub genes were determined. Correlation of the hub genes were analyzed in The Cancer Genome Atlas. The prognostic values of hub genes were further validated by Kaplan-Meier (KM) plotter. RESULTS: A total of 849 DEGs were identified, with 374 in upregulation and 475 in downregulation. Epithelium development was the most significantly enriched term in biological processes while membrane-bounded vesicle was in cellular compartments and cell adhesion molecular binding was in molecular functions. Pathways in cancer and ECM-receptor interaction were the most significantly enriched for all DEGs. Among the PPI networks, 20 hub genes were defined, including CD44 molecule (CD44), HER-2, and cadherin 1 (CDH1). Six hub genes were associated with favorable OS while eight were associated with poor OS. Mechanistically, 2′-5′-oligoadenylate synthetase 1, 3 (OAS1, OAS3) and CDH1 featured high degrees and strong correlations with other hub genes. CONCLUSIONS: This bioinformatics analysis identified key genes and pathways for potential targets and survival predictors for trastuzumab treatment in GC. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12957-018-1475-6) contains supplementary material, which is available to authorized users. BioMed Central 2018-08-22 /pmc/articles/PMC6106878/ /pubmed/30134903 http://dx.doi.org/10.1186/s12957-018-1475-6 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Yu, Chaoran Xue, Pei Zhang, Luyang Pan, Ruijun Cai, Zhenhao He, Zirui Sun, Jing Zheng, Minhua Prediction of key genes and pathways involved in trastuzumab-resistant gastric cancer |
title | Prediction of key genes and pathways involved in trastuzumab-resistant gastric cancer |
title_full | Prediction of key genes and pathways involved in trastuzumab-resistant gastric cancer |
title_fullStr | Prediction of key genes and pathways involved in trastuzumab-resistant gastric cancer |
title_full_unstemmed | Prediction of key genes and pathways involved in trastuzumab-resistant gastric cancer |
title_short | Prediction of key genes and pathways involved in trastuzumab-resistant gastric cancer |
title_sort | prediction of key genes and pathways involved in trastuzumab-resistant gastric cancer |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6106878/ https://www.ncbi.nlm.nih.gov/pubmed/30134903 http://dx.doi.org/10.1186/s12957-018-1475-6 |
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