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Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis
Osteosarcoma (OS) is the most common primary bone malignancy, but current therapies are far from effective for all patients. A better understanding of the pathological mechanism of OS may help to achieve new treatments for this tumor. Hence, the objective of this study was to investigate ego modules...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Associação Brasileira de Divulgação Científica
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5343561/ https://www.ncbi.nlm.nih.gov/pubmed/28225867 http://dx.doi.org/10.1590/1414-431X20165793 |
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author | Chen, X.Y. Chen, Y.H. Zhang, L.J. Wang, Y. Tong, Z.C. |
author_facet | Chen, X.Y. Chen, Y.H. Zhang, L.J. Wang, Y. Tong, Z.C. |
author_sort | Chen, X.Y. |
collection | PubMed |
description | Osteosarcoma (OS) is the most common primary bone malignancy, but current therapies are far from effective for all patients. A better understanding of the pathological mechanism of OS may help to achieve new treatments for this tumor. Hence, the objective of this study was to investigate ego modules and pathways in OS utilizing EgoNet algorithm and pathway-related analysis, and reveal pathological mechanisms underlying OS. The EgoNet algorithm comprises four steps: constructing background protein-protein interaction (PPI) network (PPIN) based on gene expression data and PPI data; extracting differential expression network (DEN) from the background PPIN; identifying ego genes according to topological features of genes in reweighted DEN; and collecting ego modules using module search by ego gene expansion. Consequently, we obtained 5 ego modules (Modules 2, 3, 4, 5, and 6) in total. After applying the permutation test, all presented statistical significance between OS and normal controls. Finally, pathway enrichment analysis combined with Reactome pathway database was performed to investigate pathways, and Fisher's exact test was conducted to capture ego pathways for OS. The ego pathway for Module 2 was CLEC7A/inflammasome pathway, while for Module 3 a tetrasaccharide linker sequence was required for glycosaminoglycan (GAG) synthesis, and for Module 6 was the Rho GTPase cycle. Interestingly, genes in Modules 4 and 5 were enriched in the same pathway, the 2-LTR circle formation. In conclusion, the ego modules and pathways might be potential biomarkers for OS therapeutic index, and give great insight of the molecular mechanism underlying this tumor. |
format | Online Article Text |
id | pubmed-5343561 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Associação Brasileira de Divulgação Científica |
record_format | MEDLINE/PubMed |
spelling | pubmed-53435612017-03-16 Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis Chen, X.Y. Chen, Y.H. Zhang, L.J. Wang, Y. Tong, Z.C. Braz J Med Biol Res Biomedical Sciences Osteosarcoma (OS) is the most common primary bone malignancy, but current therapies are far from effective for all patients. A better understanding of the pathological mechanism of OS may help to achieve new treatments for this tumor. Hence, the objective of this study was to investigate ego modules and pathways in OS utilizing EgoNet algorithm and pathway-related analysis, and reveal pathological mechanisms underlying OS. The EgoNet algorithm comprises four steps: constructing background protein-protein interaction (PPI) network (PPIN) based on gene expression data and PPI data; extracting differential expression network (DEN) from the background PPIN; identifying ego genes according to topological features of genes in reweighted DEN; and collecting ego modules using module search by ego gene expansion. Consequently, we obtained 5 ego modules (Modules 2, 3, 4, 5, and 6) in total. After applying the permutation test, all presented statistical significance between OS and normal controls. Finally, pathway enrichment analysis combined with Reactome pathway database was performed to investigate pathways, and Fisher's exact test was conducted to capture ego pathways for OS. The ego pathway for Module 2 was CLEC7A/inflammasome pathway, while for Module 3 a tetrasaccharide linker sequence was required for glycosaminoglycan (GAG) synthesis, and for Module 6 was the Rho GTPase cycle. Interestingly, genes in Modules 4 and 5 were enriched in the same pathway, the 2-LTR circle formation. In conclusion, the ego modules and pathways might be potential biomarkers for OS therapeutic index, and give great insight of the molecular mechanism underlying this tumor. Associação Brasileira de Divulgação Científica 2017-02-16 /pmc/articles/PMC5343561/ /pubmed/28225867 http://dx.doi.org/10.1590/1414-431X20165793 Text en http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Biomedical Sciences Chen, X.Y. Chen, Y.H. Zhang, L.J. Wang, Y. Tong, Z.C. Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis |
title | Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis |
title_full | Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis |
title_fullStr | Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis |
title_full_unstemmed | Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis |
title_short | Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis |
title_sort | investigating ego modules and pathways in osteosarcoma by integrating the egonet algorithm and pathway analysis |
topic | Biomedical Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5343561/ https://www.ncbi.nlm.nih.gov/pubmed/28225867 http://dx.doi.org/10.1590/1414-431X20165793 |
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