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BMC Cancer
Published in: BMC Cancer 1/2023

Open Access 01-12-2023 | Hepatocellular Carcinoma | Research

Identification of a Novel CD8+ T cell exhaustion-related gene signature for predicting survival in hepatocellular carcinoma

Authors: Kejun Liu, Junhao Liu, Xusheng Zhang, Di Liu, Weijie Yao, Yang Bu, Bendong Chen

Published in: BMC Cancer | Issue 1/2023

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Abstract

Background

Hepatocellular carcinoma (HCC) is a major health concern, necessitating a deeper understanding of its prognosis and underlying mechanisms. This study aimed to investigate the mechanism and prognostic value of CD8+ T Cell exhaustion (CD8+ TEX)-related genes in HCC and construct a survival prognosis prediction model for patients with HCC.

Methods

CD8+ TEX-related genes associated with HCC prognosis were analysed and identified, and a prognostic prediction model was constructed using the ‘least absolute shrinkage and selection operator’ Cox regression model. Immunohistochemistry was used to verify the expression of the model genes in HCC tissues. A nomogram was constructed based on risk scores and clinical features, and its predictive efficacy was verified. The expression of STAM, ANXA5, and MAD2L2 in HCC cell lines was detected by western blotting; subsequently, these genes were knocked down in HCC cell lines by small interfering RNA, and their effects on the proliferation and migration of HCC cell lines were detected by colony formation assay, cck8, wound healing, and transwell assays.

Results

Six genes related to CD8+ TEX were included in the risk-prediction model. The prognosis of patients with HCC in the low-risk group was significantly better than that of those in the high-risk group. Cox regression analysis revealed that the risk score was an independent risk factor for the prognosis of patients with HCC. The differentially expressed genes in patients with high-risk HCC were mainly enriched in the nucleotide-binding oligomerization domain-containing protein-like receptor, hypoxia-inducible factor-1, and tumour programmed cell death protein (PD)-1/PD-L1 immune checkpoint pathways. The CD8+ TEX-related genes STAM, ANXA5, and MAD2L2 were knocked down in HCC cell lines to significantly inhibit cell proliferation and migration. The prediction results of the nomogram based on the risk score showed a good fit and application value.

Conclusion

The prediction model based on CD8+ TEX-related genes can predict the prognosis of HCC and provide a theoretical basis for the early identification of patients with poor HCC prognosis.
Appendix
Available only for authorised users
Literature
1.
Chidambaranathan-Reghupaty S, Fisher PB, Sarkar D. Hepatocellular carcinoma (HCC): epidemiology, etiology and molecular classification. Adv Cancer Res. 2021;149:1–61.CrossRefPubMed
2.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021; 71(3):209–249.
3.
Chow A, Perica K, Klebanoff CA, Wolchok JD. Clinical implications of T cell exhaustion for cancer immunotherapy. Nat Rev Clin Oncol. 2022;19(12):775–90.CrossRefPubMed
4.
Schietinger A, Philip M, Krisnawan VE, Chiu EY, Delrow JJ, Basom RS, Lauer P, Brockstedt DG, Knoblaugh SE, Hämmerling GJ, et al. Tumor-specific T cell dysfunction is a dynamic antigen-driven differentiation program initiated early during tumorigenesis. Immunity. 2016;45(2):389–401.CrossRefPubMedPubMedCentral
5.
Beltra J, Manne S, Abdel-Hakeem MS, Kurachi M, Giles JR, Chen Z, Casella V, Ngiow SF, Khan O, Huang YJ, et al. Developmental relationships of four exhausted CD8+ T cell subsets reveals underlying transcriptional and epigenetic landscape control mechanisms. Immunity. 2020;52(5):825–41.CrossRefPubMedPubMedCentral
6.
McLane LM, Abdel-Hakeem MS, Wherry EJ. CD8 T cell exhaustion during chronic viral infection and cancer. Annu Rev Immunol. 2019;37:457–95.CrossRefPubMed
7.
Franco F, Jaccard A, Romero P, Yu YR, Ho PC. Metabolic and epigenetic regulation of T-cell exhaustion. Nat Metab. 2020;2(10):1001–12.CrossRefPubMed
8.
Liu Y, Zhou N, Zhou L, Wang J, Zhou Y, Zhang T, Fang Y, Deng J, Gao Y, Liang X, et al. IL-2 regulates tumor-reactive CD8(+) T cell exhaustion by activating the aryl hydrocarbon receptor. Nat Immunol. 2021;22(3):358–69.CrossRefPubMed
9.
10.
Wang X, Lu X, Sun B. The pros and cons of dying tumour cells in adaptive immune responses. Nat Rev Immunol. 2017;17(9):591.CrossRefPubMed
11.
Flecken T, Schmidt N, Hild S, Gostick E, Drognitz O, Zeiser R, Schemmer P, Bruns H, Eiermann T, Price DA, et al. Immunodominance and functional alterations of tumor-associated antigen-specific CD8+T-cell responses in hepatocellular carcinoma. Hepatology. 2014;59(4):1415–26.CrossRefPubMed
12.
Hashimoto M, Araki K, Cardenas MA, Li P, Jadhav RR, Kissick HT, Hudson WH, McGuire DJ, Obeng RC, Wieland A, et al. PD-1 combination therapy with IL-2 modifies CD8(+) T cell exhaustion program. Nature. 2022;610(7930):173–81.CrossRefPubMedPubMedCentral
13.
Zhang Z, Chen L, Chen H, Zhao J, Li K, Sun J, Zhou M. Pan-cancer landscape of T-cell exhaustion heterogeneity within the tumor microenvironment revealed a progressive roadmap of hierarchical dysfunction associated with prognosis and therapeutic efficacy. EBioMedicine. 2022;83: 104207.CrossRefPubMedPubMedCentral
14.
Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, Doncheva NT, Legeay M, Fang T, Bork P, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1):D605–12.CrossRefPubMed
15.
Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4.CrossRefPubMed
16.
Wang H, Lengerich BJ, Aragam B, Xing EP. Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data. Bioinformatics. 2019;35(7):1181–7.CrossRefPubMed
17.
Ma X, Cheng J, Zhao P, Li L, Tao K, Chen H. DNA methylation profiling to predict recurrence risk in stage Iota lung adenocarcinoma: development and validation of a nomogram to clinical management. J Cell Mol Med. 2020;24(13):7576–89.CrossRefPubMedPubMedCentral
18.
Sherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC, Imamichi T, Chang W. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022;50(W1):W216–21.CrossRefPubMed
19.
20.
21.
Kanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023;51(D1):D587–92.CrossRefPubMed
22.
23.
24.
Huitzil-Melendez FD, Capanu M, O’Reilly EM, Duffy A, Gansukh B, Saltz LL, Abou-Alfa GK. Advanced hepatocellular carcinoma: which staging systems best predict prognosis? J Clin Oncol. 2010;28(17):2889–95.CrossRefPubMedPubMedCentral
25.
Foerster F, Hess M, Gerhold-Ay A, Marquardt JU, Becker D, Galle PR, Schuppan D, Binder H, Bockamp E. The immune contexture of hepatocellular carcinoma predicts clinical outcome. Sci Rep. 2018;8(1):5351.CrossRefPubMedPubMedCentral
26.
Yao W, He J, Yang Y, Wang J, Qian Y, Yang T, Ji L. The Prognostic value of tumor-infiltrating lymphocytes in hepatocellular carcinoma: a systematic review and meta-analysis. Sci Rep. 2017;7(1):7525.CrossRefPubMedPubMedCentral
27.
Gabrielson A, Wu Y, Wang H, Jiang J, Kallakury B, Gatalica Z, Reddy S, Kleiner D, Fishbein T, Johnson L, et al. Intratumoral CD3 and CD8 T-cell densities associated with relapse-free survival in HCC. Cancer Immunol Res. 2016;4(5):419–30.CrossRefPubMedPubMedCentral
28.
Mizukoshi E, Nakamoto Y, Arai K, Yamashita T, Sakai A, Sakai Y, Kagaya T, Yamashita T, Honda M, Kaneko S. Comparative analysis of various tumor-associated antigen-specific t-cell responses in patients with hepatocellular carcinoma. Hepatology. 2011;53(4):1206–16.CrossRefPubMed
29.
Gehring AJ, Ho ZZ, Tan AT, Aung MO, Lee KH, Tan KC, Lim SG, Bertoletti A. Profile of tumor antigen-specific CD8 T cells in patients with Hepatitis B virus-related hepatocellular carcinoma. Gastroenterology. 2009;137(2):682–90.CrossRefPubMed
30.
Zheng C, Zheng L, Yoo J, Guo H, Zhang Y, Guo X, Kang B, Hu R, Huang JY, Zhang Q, et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell. 2017;169(7):1342–56.CrossRefPubMed
31.
Hara K, Taharazako S, Ikeda M, Fujita H, Mikami Y, Kikuchi S, Hishiki A, Yokoyama H, Ishikawa Y, Kanno S, et al. Dynamic feature of mitotic arrest deficient 2–like protein 2 (MAD2L2) and structural basis for its interaction with chromosome alignment–maintaining phosphoprotein (CAMP). J Biol Chem. 2017;292(43):17658–67.CrossRefPubMed
32.
Dejarnac O, Hafirassou ML, Chazal M, Versapuech M, Gaillard J, Perera-Lecoin M, Umana-Diaz C, Bonnet-Madin L, Carnec X, Tinevez J, et al. TIM-1 ubiquitination mediates dengue virus entry. Cell Rep. 2018;23(6):1779–93.CrossRefPubMed
33.
Guo X, Song J, Zhao J, Wang B, Yang Z, Sun P, Hu M. Impact of ANXA5 polymorphisms on glioma risk and patient prognosis. J Neurooncol. 2019;142(1):11–26.CrossRefPubMed
34.
Peng B, Liu S, Guo C, Sun X, Sun MZ. ANXA5 level is linked to in vitro and in vivo tumor malignancy and lymphatic metastasis of murine hepatocarcinoma cell. Future Oncol. 2016;12(1):31–42.CrossRefPubMed
35.
Kuang X, Zhu J, Peng Z, Wang J, Chen Z. Transducin (Beta)-Like 1 X-linked receptor 1 correlates with clinical prognosis and epithelial-mesenchymal transition in hepatocellular carcinoma. Digest Dis Sci. 2016;61(2):489–500.CrossRefPubMed
36.
Lai W, Yue Y, Zeng G. MicroRNA-34c-5p reduces malignant properties of lung cancer cells through regulation of TBL1XR1/Wnt/β-catenin signaling. Curr Mol Med. 2024;24(1):114–22.
37.
Lu J, Bang H, Kim SM, Cho SJ, Ashktorab H, Smoot DT, Zheng CH, Ryeom SW, Yoon SS, Yoon C, et al. Lymphatic metastasis-related TBL1XR1 enhances stemness and metastasis in gastric cancer stem-like cells by activating ERK1/2-SOX2 signaling. Oncogene. 2021;40(5):922–36.CrossRefPubMed
38.
Yamaguchi T, Kurisaki A, Yamakawa N, Minakuchi K, Sugino H. FKBP12 functions as an adaptor of the Smad7–Smurf1 complex on activin type I receptor. J Mol Endocrinol. 2006;36(3):569–79.CrossRefPubMed
39.
Kamada R, Kudoh F, Ito S, Tani I, Janairo J, Omichinski JG, Sakaguchi K. Metal-dependent Ser/Thr protein phosphatase PPM family: evolution, structures, diseases and inhibitors. Pharmacol Therapeut. 2020;215.CrossRef
40.
Feldman RI, Wu JM, Polokoff MA, Kochanny MJ, Dinter H, Zhu D, Biroc SL, Alicke B, Bryant J, Yuan S, et al. Novel small molecule inhibitors of 3-phosphoinositide-dependent kinase-1. J Biol Chem. 2005;280(20):19867–74.CrossRefPubMed
41.
Wang B, Song K, Chen L, Su H, Gao L, Liu J, Huang A. Targeted inhibition of ACK1 can inhibit the proliferation of hepatocellular carcinoma cells through the PTEN/AKT/mTOR pathway. Cell Biochem Funct. 2020;38(5):642–50.CrossRefPubMed
42.
Zheng P, Zhou C, Lu L, Liu B, Ding Y. Elesclomol: a copper ionophore targeting mitochondrial metabolism for cancer therapy. J Exp Clin Canc Res. 2022;41(1):271.CrossRef
43.
Fuentes-Baile M, Ventero MP, Encinar JA, García-Morales P, Poveda-Deltell M, Pérez-Valenciano E, Barberá VM, Gallego-Plazas J, Rodríguez-Lescure Á, Martín-Nieto J, et al. Differential effects of IGF-1R small molecule tyrosine kinase inhibitors BMS-754807 and OSI-906 on human cancer cell lines. Cancers. 2020;12(12):3717.CrossRefPubMedPubMedCentral
Metadata
Title
Identification of a Novel CD8+ T cell exhaustion-related gene signature for predicting survival in hepatocellular carcinoma
Authors
Kejun Liu
Junhao Liu
Xusheng Zhang
Di Liu
Weijie Yao
Yang Bu
Bendong Chen
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Cancer / Issue 1/2023
Electronic ISSN: 1471-2407
DOI
https://doi.org/10.1186/s12885-023-11648-x

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