Skip to main content

Advertisement

SpringerLink
Log in

Predicting the prognosis of hepatocellular carcinoma based on the interaction between pyroptosis, apoptosis, and necroptosis

  • Original Article
  • Published:
Clinical and Experimental Medicine Aims and scope Submit manuscript

Abstract

Multiple programmed cell death pathways (pyroptosis, apoptosis, and necroptosis) are closely related to the progression of hepatocellular carcinoma (HCC). Furthermore, molecular interactions among pyroptotic, apoptotic, and necroptotic components may be new targets for cancer therapy. However, the signature of the genes involved in the interaction between pyroptosis, apoptosis, and necroptosis (PANRGs), and their prognostic value, is still unclear in HCC. In this study, we used HCC clinical and expression data from TCGA and GEO to explore the relationship between PANRGs and HCC. First, we determined the copy number variation incidence of 41 PANRGs genes and explored the prognostic correlation of these genes in HCC. Based on PANRGs, two molecular subgroups of HCC associated with prognosis were identified. We also found significant differences in the overall survival time and the immune infiltration of HCC patients between the two subgroups. Finally, based on the nine PANRGs (CDC25B, EZH2, HMOX1, PLK1, SQSTM1, WEE1, TREM2, MYCN, and FLT3), we constructed a prognostic model using LASSO-Cox regression analysis. The prognostic model could predict OS of HCC patients in TCGA and GEO cohorts with high accuracy. Significant correlations were found between prognosis-related PANRGs and the tumor immune microenvironment (TIME), tumor mutational burden (TMB), and drug sensitivity. In conclusion, we explored the role of PANRGs in HCC and the association of these genes with TIME, TMB, and drug sensitivity.

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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

The RNAseq data and simple nucleotide of hepatocellular carcinoma patients that support the findings of this study are available in TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/) databases. The copy number variation data of HCC patients are available in UCSC Xena (https://xena.ucsc.edu/).

References

  1. Anwanwan D, Singh SK, Singh S, Saikam V, Singh R. Challenges in liver cancer and possible treatment approaches. Biochim Biophys Acta Rev Cancer. 1873;1:188314. https://doi.org/10.1016/j.bbcan.2019.188314.

    Article  CAS  Google Scholar 

  2. Piñero F, Dirchwolf M, Pessôa MG. Biomarkers in hepatocellular carcinoma: diagnosis, prognosis and treatment response assessment. Cells. 2020;9(6):1370. https://doi.org/10.3390/cells9061370.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Craig AJ, von Felden J, Garcia-Lezana T, Sarcognato S, Villanueva A. Tumour evolution in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol. 2020;17(3):139–52. https://doi.org/10.1038/s41575-019-0229-4 (Epub 2019 Dec 2 PMID: 31792430).

    Article  PubMed  Google Scholar 

  4. Woo Y, Lee HJ, Jung YM, Jung YJ. Regulated necrotic cell death in alternative tumor therapeutic strategies. Cells. 2020;9(12):2709. https://doi.org/10.3390/cells9122709.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Luedde T, Kaplowitz N, Schwabe RF. Cell death and cell death responses in liver disease: mechanisms and clinical relevance. Gastroenterology. 2014;147(4):765-783.e4. https://doi.org/10.1053/j.gastro.2014.07.018.

    Article  CAS  PubMed  Google Scholar 

  6. Yan Z, Da Q, Li Z, Lin Q, Yi J, Su Y, Yu G, Ren Q, Liu X, Lin Z, Qu J, Yin W, Liu J. Inhibition of NEK7 suppressed hepatocellular carcinoma progression by mediating cancer cell pyroptosis. Front Oncol. 2022;12: 812655. https://doi.org/10.3389/fonc.2022.812655.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. García-Pras E, Fernández-Iglesias A, Gracia-Sancho J, Pérez-Del-Pulgar S. Cell death in hepatocellular carcinoma: pathogenesis and therapeutic opportunities. Cancers (Basel). 2021;14(1):48. https://doi.org/10.3390/cancers14010048.

    Article  CAS  PubMed  Google Scholar 

  8. Heslop KA, Rovini A, Hunt EG, Fang D, Morris ME, Christie CF, Gooz MB, DeHart DN, Dang Y, Lemasters JJ, Maldonado EN. JNK activation and translocation to mitochondria mediates mitochondrial dysfunction and cell death induced by VDAC opening and sorafenib in hepatocarcinoma cells. Biochem Pharmacol. 2020;171:113728. https://doi.org/10.1016/j.bcp.2019.113728.

    Article  CAS  PubMed  Google Scholar 

  9. Wang Y, Kanneganti TD. From pyroptosis, apoptosis and necroptosis to PANoptosis: a mechanistic compendium of programmed cell death pathways. Comput Struct Biotechnol J. 2021;19:4641–57. https://doi.org/10.1016/j.csbj.2021.07.038.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Samir P, Malireddi RKS, Kanneganti TD. The PANoptosome: a deadly protein complex driving pyroptosis, apoptosis, and necroptosis (PANoptosis). Front Cell Infect Microbiol. 2020;3(10):238. https://doi.org/10.3389/fcimb.2020.00238.

    Article  CAS  Google Scholar 

  11. Identification of pyroptosis-related subtypes, the development of a prognosis model, and characterization of tumor microenvironment infiltration in colorectal cancer.

  12. Predicting the Prognosis of Esophageal Adenocarcinoma by a Pyroptosis-Related Gene Signature.

  13. A Novel Pyroptosis-related Prognostic Model for Hepatocellular Carcinoma.

  14. A prognostic model for hepatocellular carcinoma based on apoptosis-related genes.

  15. Necroptosis-Related lncRNAs: Predicting Prognosis and the Distinction between the Cold and Hot Tumors in Gastric Cancer.

  16. Identification and Validation a Necroptosis‑related Prognostic Signature and Associated Regulatory Axis in Stomach Adenocarcinoma.

  17. Simon N, Friedman J, Hastie T, Tibshirani R. Regularization paths for Cox’s proportional hazards model via coordinate descent. J Stat Softw. 2011;39(5):1–13. https://doi.org/10.18637/jss.v039.i05.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Zhao Z, Liu H, Zhou X, Fang D, Ou X, Ye J, Peng J, Xu J. Necroptosis-related lncRNAs: predicting prognosis and the distinction between the cold and hot tumors in gastric cancer. J Oncol. 2021;2021:6718443. https://doi.org/10.1155/2021/6718443.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Koren E, Fuchs Y. Modes of regulated cell death in cancer. Cancer Discov. 2021;11(2):245–65. https://doi.org/10.1158/2159-8290.CD-20-0789 (Epub 2021 Jan 18 PMID: 33462123).

    Article  CAS  PubMed  Google Scholar 

  20. Moon H, Ro SW. MAPK/ERK signaling pathway in hepatocellular carcinoma. Cancers (Basel). 2021;13(12):3026. https://doi.org/10.3390/cancers13123026.

    Article  CAS  PubMed  Google Scholar 

  21. Chen Y, Huang Z, Chen X, Ye H. Activation of the toll-like receptor 2 signaling pathway inhibits the proliferation of HCC cells in vitro. Oncol Rep. 2019;42(6):2267–78. https://doi.org/10.3892/or.2019.7340.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Wang J, Zhou Y, Li D, Sun X, Deng Y, Zhao Q. TSPAN31 is a critical regulator on transduction of survival and apoptotic signals in hepatocellular carcinoma cells. FEBS Lett. 2017;591(18):2905–18. https://doi.org/10.1002/1873-3468.12737 (Epub 2017 Sep 7 PMID: 28670683).

    Article  CAS  PubMed  Google Scholar 

  23. Xing X, Chen J, Chen M. Expression of CDC25 phosphatases in human gastric cancer. Dig Dis Sci. 2008;53(4):949–53. https://doi.org/10.1007/s10620-007-9964-4 (Epub 2007 Oct 13 PMID: 17934831).

    Article  CAS  PubMed  Google Scholar 

  24. Kristjánsdóttir K, Rudolph J. Cdc25 phosphatases and cancer. Chem Biol. 2004;11(8):1043–51. https://doi.org/10.1016/j.chembiol.2004.07.007 (PMID: 15324805).

    Article  CAS  PubMed  Google Scholar 

  25. Yan X, Chua MS, He J, So SK. Small interfering RNA targeting CDC25B inhibits liver tumor growth in vitro and in vivo. Mol Cancer. 2008;12(7):19. https://doi.org/10.1186/1476-4598-7-19.

    Article  CAS  Google Scholar 

  26. Lou X, Zhu H, Ning L, Li C, Li S, Du H, Zhou X, Xu G. EZH2 regulates intestinal inflammation and necroptosis through the JNK signaling pathway in intestinal epithelial cells. Dig Dis Sci. 2019;64(12):3518–27. https://doi.org/10.1007/s10620-019-05705-4 (Epub 2019 Jul 4 PMID: 31273598).

    Article  CAS  PubMed  Google Scholar 

  27. Yang F, Lv LZ, Cai QC, Jiang Y. Potential roles of EZH2, Bmi-1 and miR-203 in cell proliferation and invasion in hepatocellular carcinoma cell line Hep3B. World J Gastroenterol. 2015;21(47):13268–76. https://doi.org/10.3748/wjg.v21.i47.13268.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Jiang C, He ZL, Hu XH, Ma PY. MiRNA-15a-3p inhibits the metastasis of hepatocellular carcinoma by interacting with HMOX1. Eur Rev Med Pharmacol Sci. 2020;24(24):12694–700. https://doi.org/10.26355/eurrev_202012_24167 (PMID: 33378016).

    Article  CAS  PubMed  Google Scholar 

  29. Wang D, Chang R, Wang G, Hu B, Qiang Y, Chen Z. Polo-like kinase 1-targeting chitosan nanoparticles suppress the progression of hepatocellular carcinoma. Anticancer Agents Med Chem. 2017;17(7):948–54. https://doi.org/10.2174/1871520616666160926111911.

    Article  CAS  PubMed  Google Scholar 

  30. Deeraksa A, Pan J, Sha Y, Liu XD, Eissa NT, Lin SH, Yu-Lee LY. Plk1 is upregulated in androgen-insensitive prostate cancer cells and its inhibition leads to necroptosis. Oncogene. 2013;32(24):2973–83. https://doi.org/10.1038/onc.2012.309.

    Article  CAS  PubMed  Google Scholar 

  31. Zhang H, Zhang Y, Zhu X, Chen C, Zhang C, Xia Y, Zhao Y, Andrisani O, Kong L. DEAD box protein 5 inhibits liver tumorigenesis by stimulating autophagy via interaction with p62/SQSTM1. Hepatology. 2019;69(3):1046–63. https://doi.org/10.1002/hep.30300.

    Article  CAS  PubMed  Google Scholar 

  32. Jiang SP, Li ZR. MiR-503-5p regulates cell epithelial-to-mesenchymal transition, metastasis and prognosis of hepatocellular carcinoma through inhibiting WEE1. Eur Rev Med Pharmacol Sci. 2019;23(5):2028–37. https://doi.org/10.26355/eurrev_201903_17242 (PMID: 30915746).

    Article  PubMed  Google Scholar 

  33. Tang W, Lv B, Yang B, Chen Y, Yuan F, Ma L, Chen S, Zhang S, Xia J. TREM2 acts as a tumor suppressor in hepatocellular carcinoma by targeting the PI3K/Akt/β-catenin pathway. Oncogenesis. 2019;8(2):9. https://doi.org/10.1038/s41389-018-0115-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Qu W, Wang Y, Wu Y, Liu Y, Chen K, Liu X, Zou Z, Huang X, Wu M. Triggering receptors expressed on myeloid cells 2 promotes corneal resistance against pseudomonas aeruginosa by inhibiting caspase-1-dependent pyroptosis. Front Immunol. 2018;25(9):1121. https://doi.org/10.3389/fimmu.2018.01121.

    Article  CAS  Google Scholar 

  35. Watanabe S, Suzuki T, Hara F, Yasui T, Uga N, Naoe A, Polyphyllin D. a steroidal saponin in Paris polyphylla, induces apoptosis and necroptosis cell death of neuroblastoma cells. Pediatr Surg Int. 2017;33(6):713–9. https://doi.org/10.1007/s00383-017-4069-4 (Epub 2017 Mar 4 PMID: 28260192).

    Article  PubMed  Google Scholar 

  36. Yasukawa K, Liew LC, Hagiwara K, Hironaka-Mitsuhashi A, Qin XY, Furutani Y, Tanaka Y, Nakagama H, Kojima S, Kato T, Ochiya T, Gailhouste L. MicroRNA-493-5p-mediated repression of the MYCN oncogene inhibits hepatic cancer cell growth and invasion. Cancer Sci. 2020;111(3):869–80. https://doi.org/10.1111/cas.14292.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Sun W, Li SC, Xu L, Zhong W, Wang ZG, Pan CZ, Li J, Jin GZ, Ta N, Dong W, Liu D, Liu H, Wang HY, Ding J. High FLT3 levels may predict sorafenib benefit in hepatocellular carcinoma. Clin Cancer Res. 2020;26(16):4302–12. https://doi.org/10.1158/1078-0432.CCR-19-1858 (Epub 2020 Apr 24 PMID: 32332018).

    Article  CAS  PubMed  Google Scholar 

  38. Samstein RM, Lee CH, Shoushtari AN, Hellmann MD, Shen R, Janjigian YY, Barron DA, Zehir A, Jordan EJ, Omuro A, Kaley TJ, Kendall SM, Motzer RJ, Hakimi AA, Voss MH, Russo P, Rosenberg J, Iyer G, Bochner BH, Bajorin DF, Al-Ahmadie HA, Chaft JE, Rudin CM, Riely GJ, Baxi S, Ho AL, Wong RJ, Pfister DG, Wolchok JD, Barker CA, Gutin PH, Brennan CW, Tabar V, Mellinghoff IK, DeAngelis LM, Ariyan CE, Lee N, Tap WD, Gounder MM, D’Angelo SP, Saltz L, Stadler ZK, Scher HI, Baselga J, Razavi P, Klebanoff CA, Yaeger R, Segal NH, Ku GY, DeMatteo RP, Ladanyi M, Rizvi NA, Berger MF, Riaz N, Solit DB, Chan TA, Morris LGT. Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nat Genet. 2019;51(2):202–6. https://doi.org/10.1038/s41588-018-0312-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

This study was funded by the National Key R&D Program of China (2018YFC2000205), Strategic Priority Research Program of Chinese Academy of Sciences (XDB38050200, XDA26040304), National Natural Science Foundation of China (No.61803257) and the Natural Science Foundation of Shanghai (No.18ZR1417200). We acknowledge the TCGA, GEO and STRING databases for free use.

Funding

This work was supported by the National Key R&D Program of China (2018YFC2000205), Strategic Priority Research Program of Chinese Academy of Sciences (XDB38050200, XDA26040304), National Natural Science Foundation of China (No. 61803257) and Natural Science Foundation of Shanghai (No. 18ZR1417200).

Author information

Authors and Affiliations

  1. Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China

    Fang Qian & Kai Wei

  2. College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave, Shanghai, 201306, People’s Republic of China

    Wei Kong & Shuaiqun Wang

Authors
  1. Fang Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuaiqun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kai Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conception and design of the research: FQ and WK. Acquisition, analysis, and interpretation of data: FQ, WK, SW and KW. Statistical analysis: FQ and KW. Molecular biological analysis: FQ. Drafting the manuscript: FQ. Manuscript revision for important intellectual content: WK. All authors have read and approved the manuscript.

Corresponding author

Correspondence to Fang Qian.

Ethics declarations

Conflict of interest

The authors confirm that there are no conflicts of interest.

Consent for publication

All authors consent to the publication of this study.

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

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qian, F., Kong, W., Wang, S. et al. Predicting the prognosis of hepatocellular carcinoma based on the interaction between pyroptosis, apoptosis, and necroptosis. Clin Exp Med 23, 2087–2104 (2023). https://doi.org/10.1007/s10238-022-00910-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10238-022-00910-4

Keywords

Search

Navigation