Skip to main content

Advertisement

Log in

Identification of Three Key Genes Associated with Hepatocellular Carcinoma Progression Based on Co-expression Analysis

  • Original Paper
  • Published:
Cell Biochemistry and Biophysics Aims and scope Submit manuscript

Abstract

Hepatocellular carcinoma (HCC) is the fifth most common cancer and one of the leading causes of cancer-related death in the world. Due to the recurrence of HCC, its survival rate is still low. Therefore, it is vital to seek prognostic biomarkers for HCC. In this study, differential analysis was conducted on gene expression data in The Cancer Genome Atlas -LIHC, and 4482 differentially expressed genes in tumor tissue were selected. Then, weighted gene co-expression network analysis was used to analyze the co-expression of the gained differential genes. By module–trait correlation analysis, the turquoise gene module that was significantly related to tumor grade, pathologic_T stage, and clinical stage was identified. Thereafter, enrichment analysis of genes in this module uncovered that the genes were mainly enriched in the signaling pathways involved in spliceosome and cell cycle. After that, through correlation analysis, 18 hub genes highly correlated with tumor grade, clinical stage, pathologic_T stage, and the turquoise module were selected. Meanwhile, protein–protein interaction (PPI) network was constructed by using genes in the module. Finally, three key genes, heterogeneous nuclear ribonucleoprotein L, serrate RNA effector molecule, and cyclin B2, were identified by intersecting the top 30 genes with the highest connectivity in PPI network and the previously obtained 18 hub genes in the turquoise module. Further survival analysis revealed that high expression of the three key genes predicted poor prognosis of HCC. These results indicated the direction for further research on clinical diagnosis and prognostic biomarkers of HCC.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data availability

The data used to support the findings of this study are included within the article. The data and materials in the current study are available from the corresponding author on reasonable request.

References

  1. Torre, L. A., et al. (2015). Global cancer statistics, 2012. CA: A Cancer Journal for Clinicians, 65, 87–108. https://doi.org/10.3322/caac.21262.

    Article  Google Scholar 

  2. Coskun, M. (2017). Hepatocellular carcinoma in the cirrhotic liver: Evaluation using computed tomography and magnetic resonance imaging. Experimental and Clinical Transplantation, 15, 36–44. https://doi.org/10.6002/ect.TOND16.L10.

    Article  PubMed  Google Scholar 

  3. Lang, H., et al. (2007). Survival and recurrence rates after resection for hepatocellular carcinoma in noncirrhotic livers. Journal of the American College of Surgeons, 205, 27–36. https://doi.org/10.1016/j.jamcollsurg.2007.03.002.

    Article  PubMed  Google Scholar 

  4. Jiao, Y., Fu, Z., Li, Y., Meng, L., & Liu, Y. (2018). High EIF2B5 mRNA expression and its prognostic significance in liver cancer: A study based on the TCGA and GEO database. Cancer Management and Research, 10, 6003–6014. https://doi.org/10.2147/CMAR.S185459.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Byeon, H., et al. (2018). Long-term prognostic impact of osteopontin and Dickkopf-related protein 1 in patients with hepatocellular carcinoma after hepatectomy. Pathology – Research and Practice, 214, 814–820. https://doi.org/10.1016/j.prp.2018.05.002.

    Article  CAS  Google Scholar 

  6. Shen, Y., et al. (2017). Screening effective differential expression genes for hepatic carcinoma with metastasis in the peripheral blood mononuclear cells by RNA-seq. Oncotarget, 8, 27976–27989. https://doi.org/10.18632/oncotarget.15855.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Cheng, J., et al. (2018). Integrative analysis of DNA methylation and gene expression reveals hepatocellular carcinoma-specific diagnostic biomarkers. Genome Medicine, 10, 42. https://doi.org/10.1186/s13073-018-0548-z.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kalinich, M., et al. (2017). An RNA-based signature enables high specificity detection of circulating tumor cells in hepatocellular carcinoma. Proceedings of the National Academy of Sciences of the United States of America, 114, 1123–1128. https://doi.org/10.1073/pnas.1617032114.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Sia, D., et al. (2017). Identification of an immune-specific class of hepatocellular carcinoma, based on molecular features. Gastroenterology, 153, 812–826. https://doi.org/10.1053/j.gastro.2017.06.007.

    Article  CAS  PubMed  Google Scholar 

  10. Tavazoie, S., Hughes, J. D., Campbell, M. J., Cho, R. J., & Church, G. M. (1999). Systematic determination of genetic network architecture. Nature Genetics, 22, 281–285. https://doi.org/10.1038/10343.

    Article  CAS  PubMed  Google Scholar 

  11. Langfelder, P., & Horvath, S. (2008). WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics, 9, 559. https://doi.org/10.1186/1471-2105-9-559.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Chen, L., et al. (2018). Identification of biomarkers associated with pathological stage and prognosis of clear cell renal cell carcinoma by co-expression network analysis. Frontiers in Physiology, 9, 399. https://doi.org/10.3389/fphys.2018.00399.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Bruix, J., Gores, G. J., & Mazzaferro, V. (2014). Hepatocellular carcinoma: Clinical frontiers and perspectives. Gut, 63, 844–855. https://doi.org/10.1136/gutjnl-2013-306627.

    Article  CAS  PubMed  Google Scholar 

  14. Zheng, J., et al. (2017). Actual 10-year survivors after resection of hepatocellular carcinoma. Annals of Surgical Oncology, 24, 1358–1366. https://doi.org/10.1245/s10434-016-5713-2.

    Article  PubMed  Google Scholar 

  15. Wang, D., Liu, J., Liu, S., & Li, W. (2020). Identification of crucial genes associated with immune cell infiltration in hepatocellular carcinoma by weighted gene co-expression network analysis. Frontiers in Genetics, 11, 342. https://doi.org/10.3389/fgene.2020.00342.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zhang, C., et al. (2017). The identification of key genes and pathways in hepatocellular carcinoma by bioinformatics analysis of high-throughput data. Medical Oncology, 34, 101. https://doi.org/10.1007/s12032-017-0963-9.

    Article  CAS  PubMed  Google Scholar 

  17. Zeng, Z., Cao, Z., & Tang, Y. (2020). Identification of diagnostic and prognostic biomarkers, and candidate targeted agents for hepatitis B virus-associated early stage hepatocellular carcinoma based on RNA-sequencing data. Oncology Letters, 20, 231. https://doi.org/10.3892/ol.2020.12094.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Venkata Subbaiah, K. C., Wu, J., Potdar, A., & Yao, P. (2019). hnRNP L-mediated RNA switches function as a hypoxia-induced translational regulon. Biochemical and Biophysical Research Communications, 516, 753–759. https://doi.org/10.1016/j.bbrc.2019.06.106.

    Article  CAS  PubMed  Google Scholar 

  19. Lv, D., et al. (2017). HnRNP-L mediates bladder cancer progression by inhibiting apoptotic signaling and enhancing MAPK signaling pathways. Oncotarget, 8, 13586–13599. https://doi.org/10.18632/oncotarget.14600.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Zhou, X., et al. (2017). HnRNP-L promotes prostate cancer progression by enhancing cell cycling and inhibiting apoptosis. Oncotarget, 8, 19342–19353. https://doi.org/10.18632/oncotarget.14258.

    Article  PubMed  Google Scholar 

  21. Klingenberg, M., et al. (2018). The long noncoding RNA cancer susceptibility 9 and RNA binding protein heterogeneous nuclear ribonucleoprotein L form a complex and coregulate genes linked to AKT signaling. Hepatology, 68, 1817–1832. https://doi.org/10.1002/hep.30102.

    Article  CAS  PubMed  Google Scholar 

  22. Wilson, M. D., et al. (2008). ARS2 is a conserved eukaryotic gene essential for early mammalian development. Molecular and Cellular Biology, 28, 1503–1514. https://doi.org/10.1128/MCB.01565-07.

    Article  CAS  PubMed  Google Scholar 

  23. Gruber, J. J., et al. (2009). Ars2 links the nuclear cap-binding complex to RNA interference and cell proliferation. Cell, 138, 328–339. https://doi.org/10.1016/j.cell.2009.04.046.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Yin, J., et al. (2020). ARS2/MAGL signaling in glioblastoma stem cells promotes self-renewal and M2-like polarization of tumor-associated macrophages. Nature Communications, 11, 2978. https://doi.org/10.1038/s41467-020-16789-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Chen, Y., et al. (2018). Ars2 promotes cell proliferation and tumorigenicity in glioblastoma through regulating miR-6798-3p. Scientific Reports, 8, 15602. https://doi.org/10.1038/s41598-018-33905-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. He, Q., Huang, Y., Cai, L., Zhang, S., & Zhang, C. (2014). Expression and prognostic value of Ars2 in hepatocellular carcinoma. The International Journal of Clinical Oncology, 19, 880–888. https://doi.org/10.1007/s10147-013-0642-6.

    Article  CAS  PubMed  Google Scholar 

  27. Huang, Y., Sramkoski, R. M., & Jacobberger, J. W. (2013). The kinetics of G2 and M transitions regulated by B cyclins. PLoS One, 8, e80861. https://doi.org/10.1371/journal.pone.0080861.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Lei, C. Y., Wang, W., Zhu, Y. T., Fang, W. Y., & Tan, W. L. (2016). The decrease of cyclin B2 expression inhibits invasion and metastasis of bladder cancer. Urologic Oncology, 34, 237.e231–210. https://doi.org/10.1016/j.urolonc.2015.11.011.

    Article  CAS  Google Scholar 

  29. Li, R., et al. (2019). Cyclin B2 overexpression in human hepatocellular carcinoma is associated with poor prognosis. Archives of Medical Research, 50, 10–17. https://doi.org/10.1016/j.arcmed.2019.03.003.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to data analysis, drafting and revising the article, gave final approval of the version to be published, and agreed to be accountable for all aspects of the work.

Corresponding author

Correspondence to Fangfang Zhang.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Consent for publication

All authors consent to submit the manuscript for publication.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, J., Zhang, F. Identification of Three Key Genes Associated with Hepatocellular Carcinoma Progression Based on Co-expression Analysis. Cell Biochem Biophys 80, 301–309 (2022). https://doi.org/10.1007/s12013-021-01028-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12013-021-01028-2

Keywords

Navigation