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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Associação Brasileira de Divulgação Científica
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| Series: | Brazilian Journal of Medical and Biological Research |
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| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2017000200607&lng=en&tlng=en |
| _version_ | 1811307362573615104 |
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| author | X.Y. Chen Y.H. Chen L.J. Zhang Y. Wang Z.C. Tong |
| author_facet | X.Y. Chen Y.H. Chen L.J. Zhang Y. Wang Z.C. Tong |
| author_sort | X.Y. Chen |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-13T09:03:04Z |
| format | Article |
| id | doaj.art-346c9a1e512d419694710a2e9b671a1a |
| institution | Directory Open Access Journal |
| issn | 1414-431X |
| language | English |
| last_indexed | 2024-04-13T09:03:04Z |
| publisher | Associação Brasileira de Divulgação Científica |
| record_format | Article |
| series | Brazilian Journal of Medical and Biological Research |
| spelling | doaj.art-346c9a1e512d419694710a2e9b671a1a2022-12-22T02:53:04ZengAssociação Brasileira de Divulgação CientíficaBrazilian Journal of Medical and Biological Research1414-431X50210.1590/1414-431x20165793S0100-879X2017000200607Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysisX.Y. ChenY.H. ChenL.J. ZhangY. WangZ.C. TongOsteosarcoma (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.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2017000200607&lng=en&tlng=enOsteosarcomaEgoGenesModulesPathways |
| spellingShingle | X.Y. Chen Y.H. Chen L.J. Zhang Y. Wang Z.C. Tong Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis Brazilian Journal of Medical and Biological Research Osteosarcoma Ego Genes Modules Pathways |
| 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 | Osteosarcoma Ego Genes Modules Pathways |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2017000200607&lng=en&tlng=en |
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