Identification of Pathogenic Genes and Transcription Factors in Osteosarcoma

  • Chenggang Yang
  • Di Huang
  • Cui Ma
  • Jing Ren
  • Lina Fu
  • Cheng Cheng
  • Bangling Li
  • Xiaofeng ShiEmail author
Original Article


Osteosarcoma (OS) is an aggressive malignant tumor of the bones. Our study intended to identify and analyze potential pathogenic genes and upstream regulators for OS. We performed an integrated analysis to identify candidate pathogenic genes of OS by using three Gene Expression Omnibus (GEO) databases (GSE66673, GSE49003 and GSE37552). GO and KEGG enrichment analysis were utilized to predict the functional annotation and potential pathways of differentially expressed genes (DEGs). The OS-specific transcriptional regulatory network was established to study the crucial transcriptional factors (TFs) which target the DEGs in OS. From the three GEO datasets, we identified 759 DEGs between metastasis OS samples and non-metastasis OS samples. After GO and KEGG analysis, ‘cell adhesion’ (FDR = 1.27E-08), ‘protein binding’ (FDR = 1.13E-22), ‘cytoplasm’ (FDR = 5.63E-32) and ‘osteoclast differentiation’ (FDR = 0.000992221) were significantly enriched pathways for DEGs. HSP90AA1 exhibited a highest degree (degree = 32) and was enriched in ‘pathways in cancer’ and ‘signal transduction’. BMP6, regulated by Pax-6, was enriched in the ‘TGF-beta signaling pathway’. We indicated that BMP6 may be downregulated by Pax-6 in the non-metastasis OS samples. The up-regulated HSP90AA1 and down-regulated BMP6 and ‘pathways in cancer’ and ‘signal transduction’ were deduced to be involved in the pathogenesis of OS. The identified biomarkers and biological process in OS may provide foundation for further study.


Osteosarcoma Transcription factors DEGs Integrated analysis 


Authors’ Contributions

CY and XS designed and performed the train of thought, DH and CM analyzed the resulting data, JR, LF, CC and BL contributed the analysis tools. All authors read and approved the final manuscript.

Compliance with Ethical Standards

Ethics Approval and Consent to Participate

Not applicable.

Consent to Publication

All authors consented to publication.

Competing Interests

All authors declare that they have no conflicts of interest.


  1. 1.
    Zhou W, Hao M, Du X, Chen K, Wang G, Yang J (2014) Advances in targeted therapy for osteosarcoma. Discov Med 17(96):301–307Google Scholar
  2. 2.
    Bernardini, G., Geminiani, M., Gambassi, S., Orlandini, M., Petricci, E., & Marzocchi, B., et al. (2017). Novel smoothened antagonists as anti-neoplastic agents for the treatment of osteosarcoma. J Cell PhysiolGoogle Scholar
  3. 3.
    Yan H, Zhang B, Fang C, Chen L (2018) Mir-340 alleviates chemoresistance of osteosarcoma cells by targeting zeb1. Anti-Cancer Drugs:1Google Scholar
  4. 4.
    Pang Y, Zhao J, Fowdur M, Liu Y, Wu H, He M (2018) To explore the mechanism of the grm4 gene in osteosarcoma by rna sequencing and bioinformatics approach. Med Sci Monit Basic Res 24:16–25CrossRefGoogle Scholar
  5. 5.
    Mitchell PJ, Tjian R (1989) Transcriptional regulation in mammalian cells by sequence-specific dna binding proteins. Science 245(4916):371–378CrossRefGoogle Scholar
  6. 6.
    Zhu M, Liu CC, Cheng C (2013) Reactin: regulatory activity inference of transcription factors underlying human diseases with application to breast cancer. BMC Genomics 14(1):504CrossRefGoogle Scholar
  7. 7.
    Yang L, Feng S, Yang Y (2016) Identification of transcription factors (tfs) and targets involved in the cholangiocarcinoma (cca) by integrated analysis. Cancer Gene Ther 23(12):439–445CrossRefGoogle Scholar
  8. 8.
    Diao C, Xi Y, Xiao T (2018) Identification and analysis of key genes in osteosarcoma using bioinformatics. Oncol Lett 15(3):2789–2794Google Scholar
  9. 9.
    Liu HY, Zhang CJ (2017) Identification of differentially expressed genes and their upstream regulators in colorectal cancer. Cancer Gene Ther 24:244–250CrossRefGoogle Scholar
  10. 10.
    Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate-a practical and powerful approach to multiple testing. J R Stat Soc 57(1):289–300Google Scholar
  11. 11.
    Li JJ, Wang BQ, Fei Q, Yang Y, Li D (2016) Identification of candidate genes in osteoporosis by integrated microarray analysis. Bone Joint Res 5(12):594–601CrossRefGoogle Scholar
  12. 12.
    Wang F, Wang R, Li Q, Qu X, Hao Y, Yang J, Zhao H, Wang Q, Li G, Zhang F, Zhang H, Zhou X, Peng X, Bian Y, Xiao W (2017) A transcriptome profile in hepatocellular carcinomas based on integrated analysis of microarray studies. Diagn Pathol 12(1):4CrossRefGoogle Scholar
  13. 13.
    Lee YS, Kim JK, Ryu SW, Bae SJ, Kwon K, Noh YH, Kim SY (2015) Integrative meta-analysis of multiple gene expression profiles in acquired gemcitabine-resistant cancer cell lines to identify novel therapeutic biomarkers. Asian Pac J Cancer Prev 16(7):2793–2800CrossRefGoogle Scholar
  14. 14.
    Luetke A, Meyers PA, Lewis I, Juergens H (2014) Osteosarcoma treatment - where do we stand? A state of the art review. Cancer Treat Rev 40(4):523–532CrossRefGoogle Scholar
  15. 15.
    Guan D, Tian H (2017) Integrated network analysis to explore the key genes regulated by parathyroid hormone receptor 1 in osteosarcoma. World J Sugr Oncol 15(1):177CrossRefGoogle Scholar
  16. 16.
    Taipale M, Jarosz DF, Lindquist S (2010) Hsp90 at the hub of protein homeostasis: emerging mechanistic insights. Nat Rev Mol Cell Biol 11(7):515–528CrossRefGoogle Scholar
  17. 17.
    Coskunpinar E, Akkaya N, Yildiz P, Oltulu YM, Aynaci E, Isbir T, Yaylim I (2014) The significance of hsp90aa1, hsp90ab1 and hsp90b1 gene polymorphisms in a turkish population with non-small cell lung cancer. Anticancer Res 34(2):753–757Google Scholar
  18. 18.
    Chu SH, Liu YW, Zhang L, Liu B, Li L, Shi JZ, Li L (2013) Regulation of survival and chemoresistance by hsp90aa1 in ovarian cancer skov3 cells. Mol Biol Rep 40(1):1–6CrossRefGoogle Scholar
  19. 19.
  20. 20.
    Honda Y, Knutsen R, Strong DD, Sampath TK, Baylink DJ, Mohan S (1997) Osteogenic protein-1 stimulates mrna levels of bmp-6 and decreases mrna levels of bmp-2 and -4 in human osteosarcoma cells. Calcif Tissue Int 60(3):297–301CrossRefGoogle Scholar
  21. 21.
    Shi, Y. J., & Pan, X. T. (2016). Bmp6 and bmp4 expression in patients with cancer-related anemia and its relationship with hepcidin and s-hjv. Genet Mol Res Gmr, 15(1)Google Scholar
  22. 22.
    Hu F, Zhang Y, Li M, Zhao L, Chen J, Yang S et al (2015) Bmp-6 inhibits the metastasis of mda-mb-231 breast cancer cells by regulating mmp-1 expression. Oncol Rep 35(3)Google Scholar
  23. 23.
    Liu G, Liu YJ, Lian WJ, Zhao ZW, Yi T, Zhou HY (2014) Reduced bmp6 expression by dna methylation contributes to emt and drug resistance in breast cancer cells. Oncol Rep 32(2):581–588CrossRefGoogle Scholar
  24. 24.
    Zong X, Yang H, Yu Y, Zou D, Ling Z, He X, Meng X (2011) Possible role of pax-6 in promoting breast cancer cell proliferation and tumorigenesis. BMB Rep 44(9):595–600CrossRefGoogle Scholar
  25. 25.
    Shyr CR, Tsai MY, Yeh S, Kang HY, Chang YC, Wong PL et al (2010) Tumor suppressor pax6 functions as androgen receptor co-repressor to inhibit prostate cancer growth. Prostate 70(2):190–199Google Scholar

Copyright information

© Arányi Lajos Foundation 2019

Authors and Affiliations

  1. 1.Gu’an Bojian Bio-Technology Co., LTDLangfangChina
  2. 2.Department of BigDataBeijing Medintell Bioinformatic Technology Co., LTDBeijingChina
  3. 3.School of biotechnologyJiangnan UniversityWuxiChina

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