Skip to main content
SpringerLink
Log in

Immune Analysis and Small Molecule Drug Prediction of Hepatocellular Carcinoma Based on Single Sample Gene Set Enrichment Analysis

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

Abstract

Though patients with hepatocellular carcinoma (HCC) benefit from the treatment of immune checkpoint inhibitor (ICB), it is still of vital significance to develop more effective drugs and predict patients’ response to ICB therapy. Herein, we utilized single sample gene set enrichment analysis (ssGSEA) to score the downloaded tumor samples from TCGA-LIHC based on 29 immune gene sets, thus reflecting the immunologic competence of samples. Then samples were classified into high, moderate, and low immunity groups. Additionally, we utilized survival analysis and ESTIMATE score to verify the reliability of the immunity grouping. We then performed differential expression analysis on the samples in these two groups and obtained 716 differentially expressed genes (DEGs). Next, the DEGs mentioned above were subjected to GO and KEGG analyses. The outcomes demonstrated that these DEGs were mostly correlated with the immune-related biological functions. To further verify biological processes in which DEGs might be involved, we constructed a protein–protein interaction network. Afterward, we used MCODE plugin to conduct subnetwork analysis. Thereafter, KEGG enrichment analysis was performed on two genes with the highest score in the subnetwork. The results exhibited that these genes were gathered in pathways such as Th1 and Th2 cell differentiation and NF-κB. Finally, we utilized Connectivity Map to find possible drugs for the treatment of HCC and obtained complex methyl-angolensate. The above results may contribute to distinguishing HCC patients who are eligible for immunotherapy and providing the foundations for the development of therapeutic drugs for HCC.

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

Access this article

Log in via an institution

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

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. Siegel, R. L., Miller, K. D. & Jemal, A. (2020). Cancer statistics. CA: A Cancer Journal For Clinicians, 70, 7–30. https://doi.org/10.3322/caac.21590.

    Article  Google Scholar 

  2. Cong, Y. et al. (2018) Dual drug backboned shattering polymeric theranostic nanomedicine for synergistic eradication of patient-derived lung cancer. Advanced Materials. 30, https://doi.org/10.1002/adma.201706220.

  3. Hou, J., Zhang, H., Sun, B., & Karin, M. (2020). The immunobiology of hepatocellular carcinoma in humans and mice: basic concepts and therapeutic implications. Journal of Hepatology, 72, 167–182. https://doi.org/10.1016/j.jhep.2019.08.014.

    Article  CAS  PubMed  Google Scholar 

  4. Binnewies, M., et al. (2018). Understanding the tumor immune microenvironment (TIME) for effective therapy. Nature Medicine, 24, 541–550. https://doi.org/10.1038/s41591-018-0014-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Smyth, M. J., Ngiow, S. F., Ribas, A., & Teng, M. W. (2016). Combination cancer immunotherapies tailored to the tumour microenvironment. Nature Reviews Clinical Oncology, 13, 143–158. https://doi.org/10.1038/nrclinonc.2015.209.

    Article  CAS  PubMed  Google Scholar 

  6. Racanelli, V., & Rehermann, B. (2006). The liver as an immunological organ. Hepatology, 43, S54–62. https://doi.org/10.1002/hep.21060.

    Article  CAS  PubMed  Google Scholar 

  7. Prieto, J., Melero, I., & Sangro, B. (2015). Immunological landscape and immunotherapy of hepatocellular carcinoma. Nature Reviews Gastroenterology Hepatology, 12, 681–700. https://doi.org/10.1038/nrgastro.2015.173.

    Article  CAS  PubMed  Google Scholar 

  8. Cariani, E., & Missale, G. (2019). Immune landscape of hepatocellular carcinoma microenvironment: implications for prognosis and therapeutic applications. Liver International, 39, 1608–1621. https://doi.org/10.1111/liv.14192.

    Article  PubMed  Google Scholar 

  9. Rui, X., Shao, S., Wang, L., & Leng, J. (2019). Identification of recurrence marker associated with immune infiltration in prostate cancer with radical resection and build prognostic nomogram. BMC Cancer, 19, 1179 https://doi.org/10.1186/s12885-019-6391-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Bao, X., et al. (2019). Immune landscape of invasive ductal carcinoma tumor microenvironment identifies a prognostic and immunotherapeutically relevant gene signature. Frontiers in Oncology, 9, 903 https://doi.org/10.3389/fonc.2019.00903.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Subramanian, A., et al. (2017). A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell, 171, 1437–1452 e1417. https://doi.org/10.1016/j.cell.2017.10.049.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hanzelmann, S., Castelo, R., & Guinney, J. (2013). GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics, 14, 7. https://doi.org/10.1186/1471-2105-14-7.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Zhou, C., et al. (2020). High RPS3A expression correlates with low tumor immune cell infiltration and unfavorable prognosis in hepatocellular carcinoma patients. American Journal of Cancer Research, 10, 2768–2784.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Ringelhan, M., Pfister, D., O’Connor, T., Pikarsky, E., & Heikenwalder, M. (2018). The immunology of hepatocellular carcinoma. Nature Immunology, 19, 222–232. https://doi.org/10.1038/s41590-018-0044-z.

    Article  CAS  PubMed  Google Scholar 

  15. Hu, Y., Sun, H., Zhang, H., & Wang, X. (2020). An Immunogram for an individualized assessment of the antitumor immune response in patients with hepatocellular carcinoma. Frontiers in Oncology, 10, 1189 https://doi.org/10.3389/fonc.2020.01189.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Mlecnik, B., et al. (2016). Integrative analyses of colorectal cancer show immunoscore is a stronger predictor of patient survival than microsatellite instability. Immunity, 44, 698–711. https://doi.org/10.1016/j.immuni.2016.02.025.

    Article  CAS  PubMed  Google Scholar 

  17. Sun, C., et al. (2015). The predictive value of centre tumour CD8(+) T cells in patients with hepatocellular carcinoma: comparison with Immunoscore. Oncotarget, 6, 35602–35615. https://doi.org/10.18632/oncotarget.5801.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Yao, Q., et al. (2017). Prognostic value of immunoscore to identify mortality outcomes in adults with HBV-related primary hepatocellular carcinoma. Medicine (Baltimore), 96, e6735 https://doi.org/10.1097/MD.0000000000006735.

    Article  CAS  Google Scholar 

  19. Ng, H. H. M. et al. Immunohistochemical scoring of CD38 in the tumor microenvironment predicts responsiveness to anti-PD-1/PD-L1 immunotherapy in hepatocellular carcinoma. J Immunother Cancer 8, https://doi.org/10.1136/jitc-2020-000987 (2020).

  20. Owen, K. L., Brockwell, N. K. & Parker, B. S. (2019) JAK-STAT signaling: a double-edged sword of immune regulation and cancer progression. Cancers (Basel) 11, https://doi.org/10.3390/cancers11122002.

  21. Lu, X., et al. (2018). The anti-inflammatory NHE-06 restores antitumor immunity by targeting NF-kappaB/IL-6/STAT3 signaling in hepatocellular carcinoma. Biomedicine & Pharmacotherapy, 102, 420–427. https://doi.org/10.1016/j.biopha.2018.03.099.

    Article  CAS  Google Scholar 

  22. Chiruvella, K. K., Panjamurthy, K., Choudhary, B., Joy, O., & Raghavan, S. C. (2010). Methyl angolensate from callus of Indian redwood induces cytotoxicity in human breast cancer cells. International Journal of Biomedical Science, 6, 182–194.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Chiruvella, K. K., & Raghavan, S. C. (2011). A natural compound, methyl angolensate, induces mitochondrial pathway of apoptosis in Daudi cells. Investigational New Drugs, 29, 583–592. https://doi.org/10.1007/s10637-010-9393-7.

    Article  CAS  PubMed  Google Scholar 

Download references

Funding

This study received support from Fuzhou Key Subject Project (No. 201912002), the Key Project of the Guidance of Fujian Province (No. 2019Y0068) and the Surface Project of Natural Science Foundation of Fujian Province (No. 2020J011144). The funders have no role in any process of this study.

Author information

Authors and Affiliations

  1. Department of Hepatobiliary Surgery, The 900th Hospital of the Joint Logistic Support Force of People’s Liberation Army, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, Fujian Province, 350025, P. R. China

    Xinghua Huang

  2. Department of Hepatobiliary Surgery, The 900th Hospital of the Joint Logistic Support Force of People’s Liberation Army, Fuzhou, Fujian Province, 350025, P. R. China

    Huanzhang Hu, Jianyong Liu, Xiaojin Zhang, Yi Jiang, Lizhi Lv & Suming Du

Authors
  1. Xinghua Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huanzhang Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianyong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaojin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yi Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lizhi Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Suming Du
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

XH: conceptualization, methodology, software. HH: data curation, writing- original draft preparation. JS: visualization, investigation. XZ: supervision. YJ: software, validation. LL: writing- reviewing and editing. SD: writing - review & editing, project administration. All the authors have approved the final version of the manuscript.

Corresponding author

Correspondence to Suming Du.

Ethics declarations

Conflict of Interest

The authors declare no competing interests.

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

Huang, X., Hu, H., Liu, J. et al. Immune Analysis and Small Molecule Drug Prediction of Hepatocellular Carcinoma Based on Single Sample Gene Set Enrichment Analysis. Cell Biochem Biophys 80, 427–434 (2022). https://doi.org/10.1007/s12013-022-01070-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12013-022-01070-8

Keywords

Search

Navigation