Open Access

Logistic regression analysis for the identification of the metastasis-associated signaling pathways of osteosarcoma

  • Authors:
    • Yang Liu
    • Wei Sun
    • Xiaojun Ma
    • Yuedong Hao
    • Gang Liu
    • Xiaohui Hu
    • Houlai Shang
    • Pengfei Wu
    • Zexue Zhao
    • Weidong Liu
  • View Affiliations

  • Published online on: January 2, 2018     https://doi.org/10.3892/ijmm.2018.3360
  • Pages: 1233-1244
  • Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

Osteosarcoma (OS) is the most common histological type of primary bone cancer. The present study was designed to identify the key genes and signaling pathways involved in the metastasis of OS. Microarray data of GSE39055 were downloaded from the Gene Expression Omnibus database, which included 19 OS biopsy specimens before metastasis (control group) and 18 OS biopsy specimens after metastasis (case group). After the differentially expressed genes (DEGs) were identified using the Linear Models for Microarray Analysis package, hierarchical clustering analysis and unsupervised clustering analysis were performed separately, using orange software and the self-organization map method. Based upon the Database for Annotation, Visualization and Integrated Discovery tool and Cytoscape software, enrichment analysis and protein-protein interaction (PPI) network analysis were conducted, respectively. After function deviation scores were calculated for the significantly enriched terms, hierarchical clustering analysis was performed using Cluster 3.0 software. Furthermore, logistic regression analysis was used to identify the terms that were significantly different. Those terms that were significantly different were validated using other independent datasets. There were 840 DEGs in the case group. There were various interactions in the PPI network [including intercellular adhesion molecule-1 (ICAM1), transforming growth factor β1 (TGFB1), TGFB1-platelet-derived growth factor subunit B (PDGFB) and PDGFB-platelet‑derived growth factor receptor-β (PDGFRB)]. Regulation of cell migration, nucleotide excision repair, the Wnt signaling pathway and cell migration were identified as the terms that were significantly different. ICAM1, PDGFB, PDGFRB and TGFB1 were identified to be enriched in cell migration and regulation of cell migration. Nucleotide excision repair and the Wnt signaling pathway were the metastasis-associated pathways of OS. In addition, ICAM1, PDGFB, PDGFRB and TGFB1, which were involved in cell migration and regulation of cell migration may affect the metastasis of OS.
View Figures
View References

Related Articles

Journal Cover

March 2018
Volume 41 Issue 3

Print ISSN: 1107-3756
Online ISSN:1791-244X

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
APA
Liu, Y., Sun, W., Ma, X., Hao, Y., Liu, G., Hu, X. ... Liu, W. (2018). Logistic regression analysis for the identification of the metastasis-associated signaling pathways of osteosarcoma. International Journal of Molecular Medicine, 41, 1233-1244. https://doi.org/10.3892/ijmm.2018.3360
MLA
Liu, Y., Sun, W., Ma, X., Hao, Y., Liu, G., Hu, X., Shang, H., Wu, P., Zhao, Z., Liu, W."Logistic regression analysis for the identification of the metastasis-associated signaling pathways of osteosarcoma". International Journal of Molecular Medicine 41.3 (2018): 1233-1244.
Chicago
Liu, Y., Sun, W., Ma, X., Hao, Y., Liu, G., Hu, X., Shang, H., Wu, P., Zhao, Z., Liu, W."Logistic regression analysis for the identification of the metastasis-associated signaling pathways of osteosarcoma". International Journal of Molecular Medicine 41, no. 3 (2018): 1233-1244. https://doi.org/10.3892/ijmm.2018.3360