Bio-analytical Identification of Key Genes that Could Contribute to the Progression and Metastasis of Osteosarcoma

Fei Wang; Guoqing Qin; Junzhi Liu; Xiunan Wang; Baoguo Ye

doi:10.2174/1574893615999200801014939

ISSN: 1574-8936
E-ISSN: 2212-392X

Bio-analytical Identification of Key Genes that Could Contribute to the Progression and Metastasis of Osteosarcoma
By Fei Wang, Guoqing Qin, Junzhi Liu, Xiunan Wang and Baoguo Ye
Source: Current Bioinformatics, Volume 16, Issue 2, Feb 2021, p. 216 - 224
DOI: https://doi.org/10.2174/1574893615999200801014939
- Available online: 01 Feb 2021

Abstract

Background: Osteosarcoma (OS) is one of the most common primary malignant bone tumors in children and adolescents. OS metastasis has been a challenge in the treatment of OS. The present study screened progression related genes in OS by analyzing a public dataset GSE42352, and identified 691 up-regulated and 945 down-regulated genes in advanced stage OS compared to early-stage OS samples. Methods: Protein-protein interaction (PPI) networks were further employed to reveal the interaction among these genes. Bioinformatics analysis showed that progression related differently expressed genes (DEGs) were significantly associated with the regulation of cell proliferation and metabolisms. Results: This study revealed that progression related DEGs were dysregulated in metastatic OS compared to non-metastatic OS samples. Further analysis showed CSF1R, CASP1, CD163, AP1B1, LAPTM5, PEX19, SLA, STAB1, YWHAH, PLCB2, and GPR84 were associated with the metastasis-free survival time in patients with OS. Conclusion: These findings provided novel information for us to understand the mechanisms underlying the progression and metastasis of OS.

Article metrics loading...

/content/journals/cbio/10.2174/1574893615999200801014939

2021-02-01

2025-06-17

From This Site

/content/journals/cbio/10.2174/1574893615999200801014939

dcterms_title,dcterms_subject,pub_keyword

-contentType:Contributor -contentType:Concept -contentType:Institution

10

5

Full text loading...

/content/journals/cbio/10.2174/1574893615999200801014939

Article Type: Research Article

Keyword(s): bioinformatics analysis; genes; metastasis; Osteosarcoma; protein; signaling pathway

Most Cited Most Cited RSS feed

- A Review of Ensemble Methods in Bioinformatics
  
  Authors: Pengyi Yang, Yee Hwa Yang, Bing B. Zhou and Albert Y. Zomaya
- Bioinformatics Tools for Mass Spectroscopy-Based Metabolomic Data Processing and Analysis
  
  Authors: Masahiro Sugimoto, Masato Kawakami, Martin Robert, Tomoyoshi Soga and Masaru Tomita
- Distance-based Support Vector Machine to Predict DNA N6- methyladenine Modification
  
  Authors: Haoyu Zhang, Quan Zou, Ying Ju, Chenggang Song and Dong Chen
- A Review on the Recent Developments of Sequence-based Protein Feature Extraction Methods
  
  Authors: Jun Zhang and Bin Liu
- Molecular Genetic Markers: Discovery, Applications, Data Storage and Visualisation
  
  Authors: Chris Duran, Nikki Appleby, David Edwards and Jacqueline Batley
- A Brief Survey of Machine Learning Methods in Protein Sub-Golgi Localization
  
  Authors: Wuritu Yang, Xiao-Juan Zhu, Jian Huang, Hui Ding and Hao Lin
- Cancer Diagnosis Through IsomiR Expression with Machine Learning Method
  
  Authors: Zhijun Liao, Dapeng Li, Xinrui Wang, Lisheng Li and Quan Zou
- The Advances and Challenges of Deep Learning Application in Biological Big Data Processing
  
  Authors: Li Peng, Manman Peng, Bo Liao, Guohua Huang, Weibiao Li and Dingfeng Xie
- Gene Expression Profile Classification: A Review
  
  Authors: Musa H. Asyali, Dilek Colak, Omer Demirkaya and Mehmet S. Inan
- Relevance of Molecular Docking Studies in Drug Designing
  
  Authors: Ritu Jakhar, Mehak Dangi, Alka Khichi and Anil K. Chhillar
More Less

Bio-analytical Identification of Key Genes that Could Contribute to the Progression and Metastasis of Osteosarcoma

Abstract

Most Read This Month

Most Cited Most Cited RSS feed