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Discover Oncology
Published in: Discover Oncology 1/2024

Open Access 01-12-2024 | Hepatocellular Carcinoma | Research

A programmed cell death-related gene signature to predict prognosis and therapeutic responses in liver hepatocellular carcinoma

Authors: Xinyu Gu, Jie Pan, Yanle Li, Liushun Feng

Published in: Discover Oncology | Issue 1/2024

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Abstract

Background

Programmed cell death (PCD) functions critically in cancers and PCD-related genes are associated with tumor microenvironment (TME), prognosis and therapeutic responses of cancer patients. This study stratified hepatocellular carcinoma (HCC) patients and develop a prognostic model for predicting prognosis and therapeutic responses.

Methods

Consensus clustering analysis was performed to subtype HCC patients in The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) among the subtypes were filtered and subjected to the least absolute shrinkage and selection operator (LASSO) regression analysis and univariate Cox regression analysis to filter prognostic genes. A PCD-related prognostic gene signature in TCGA was constructed and validated in ICGC-LIRI-JP and GSE14520 datasets. TME was analyzed using CIBERSORT, MCP-counter, TIMER and EPIC algorithms. Drug sensitivity was predicted by oncoPredict package. Spearman analysis was used to detect correlation.

Results

Four molecular subtypes were categorized based on PCD-related genes. Subtype C1 showed the poorest prognosis, the most infiltration of Fibroblasts, dentritic cell (DC) and cancer-associated fibroblasts (CAFs), and the highest TIDE score. C4 had a better prognosis survival outcome, and lowest immune cell infiltration. The survival outcomes of C2 and C3 were intermediate. Next, a total of 69 co-DEGs were screened among the four subtypes and subsequently we identified five prognostic genes (MCM2, SPP1, S100A9, MSC and EPO) for developing the prognostic model. High-risk patients not only had unfavorable prognosis, higher clinical stage and grade, and more inflammatory pathway enrichment, but also possessed higher possibility of immune escape and were more sensitive to Cisplatin and 5. Fluorouracil. The robustness of the prognostic model was validated in external datasets.

Conclusion

This study provides new insights into clinical subtyping and the PCD-related prognostic signature may serve as a useful tool to predict prognosis and guide treatments for patients with HCC.
Appendix
Available only for authorised users
Literature
1.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer J Clin. 2021;71(3):209–49.
2.
Chidambaranathan-Reghupaty S, Fisher PB, Sarkar D. Hepatocellular carcinoma (HCC): epidemiology, etiology and molecular classification. Adv Cancer Res. 2021;149:1–61.PubMedCrossRef
3.
Yang S, Zhou Y, Zhang X, Wang L, Fu J, Zhao X, et al. The prognostic value of an autophagy-related lncRNA signature in hepatocellular carcinoma. BMC Bioinform. 2021;22(1):217.CrossRef
4.
Shu X, Wang Q, Wu Q. The Eph/Ephrin system in hepatocellular carcinoma: functional roles and potential therapeutic targets. Oncologie. 2022;24(3):427–39.CrossRef
5.
Song Y, Zhou B, Du X, Wang Y, Zhang J, Ai Y, et al. Folic acid (FA)-conjugated mesoporous silica nanoparticles combined with MRP-1 siRNA improves the suppressive effects of myricetin on non-small cell lung cancer (NSCLC). Biomed Pharmacother. 2020;125:109561.PubMedCrossRef
6.
Xie D-Y, Ren Z-G, Zhou J, Fan J, Gao Q. 2019 Chinese clinical guidelines for the management of hepatocellular carcinoma: updates and insights. Hepatobiliary Surg Nutr. 2020;9(4):452.PubMedPubMedCentralCrossRef
7.
Strasser A, Vaux DL. Cell death in the origin and treatment of cancer. Mol Cell. 2020;78(6):1045–54.PubMedCrossRef
8.
9.
Djulbegovic MB, Uversky VN. Ferroptosis–an iron-and disorder-dependent programmed cell death. Int J Biol Macromol. 2019;135:1052–69.PubMedCrossRef
10.
Wang Y, Kanneganti T-D. From pyroptosis, apoptosis and necroptosis to PANoptosis: a mechanistic compendium of programmed cell death pathways. Comput Struct Biotechnol J. 2021;19:4641–57.PubMedPubMedCentralCrossRef
11.
12.
Mishra AP, Salehi B, Sharifi-Rad M, Pezzani R, Kobarfard F, Sharifi-Rad J, et al. Programmed cell death, from a cancer perspective: an overview. Mol Diagn Ther. 2018;22:281–95.PubMedCrossRef
13.
14.
15.
Zhang Y, He R, Lei X, Mao L, Jiang P, Ni C, et al. A novel pyroptosis-related signature for predicting prognosis and indicating immune microenvironment features in osteosarcoma. Front Genet. 2021;12:780780.PubMedPubMedCentralCrossRef
16.
17.
18.
Zou Y, Xie J, Zheng S, Liu W, Tang Y, Tian W, et al. Leveraging diverse cell-death patterns to predict the prognosis and drug sensitivity of triple-negative breast cancer patients after surgery. Int J Surg. 2022;107:106936.PubMedCrossRef
19.
20.
Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics. 2010;26(12):1572–3.PubMedPubMedCentralCrossRef
21.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucl Acids Res. 2015;43(7):e47.PubMedPubMedCentralCrossRef
22.
Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database hallmark gene set collection. Cell Syst. 2015;1(6):417–25.PubMedPubMedCentralCrossRef
23.
Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics A J Integr Biol. 2012;16(5):284–7.CrossRef
24.
Engebretsen S, Bohlin J. Statistical predictions with glmnet. Clin Epigenet. 2019;11(1):123.CrossRef
25.
Blanche P. TimeROC: Time-dependent ROC curve and AUC for censored survival data. R package version. 2015;2.
26.
Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA. Profiling tumor infiltrating immune cells with CIBERSORT. In: von Stechow Louise, editor. Cancer systems biology. New York: Springer; 2018. p. 243–59.CrossRef
27.
Becht E, Giraldo NA, Lacroix L, Buttard B, Elarouci N, Petitprez F, et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 2016;17(1):218.PubMedPubMedCentralCrossRef
28.
Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77(21):e108–10.PubMedPubMedCentralCrossRef
29.
Yeo JG, Wasser M, Kumar P, Pan L, Poh SL, Ally F, et al. The extended polydimensional immunome characterization (EPIC) web-based reference and discovery tool for cytometry data. Nat Biotechnol. 2020;38(6):679–84.PubMedCrossRef
30.
Hu F-F, Liu C-J, Liu L-L, Zhang Q, Guo A-Y. Expression profile of immune checkpoint genes and their roles in predicting immunotherapy response. Brief Bioinform. 2021;22(3):176.CrossRef
31.
Geeleher P, Cox N, Huang RS. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS ONE. 2014;9(9):e107468.ADSPubMedPubMedCentralCrossRef
32.
Tomiyama T, Itoh S, Iseda N, Toshida K, Morinaga A, Yugawa K, et al. Myeloid-derived suppressor cell infiltration is associated with a poor prognosis in patients with hepatocellular carcinoma. Oncol Lett. 2022;23(3):1–9.CrossRef
33.
Hu C-E, Gan J, Zhang R-D, Cheng Y-R, Huang G-J. Up-regulated myeloid-derived suppressor cell contributes to hepatocellular carcinoma development by impairing dendritic cell function. Scand J Gastroenterol. 2011;46(2):156–64.PubMedCrossRef
34.
He Q, Liu M, Huang W, Chen X, Zhang B, Zhang T, et al. IL-1β-Induced elevation of solute carrier family 7 member 11 promotes hepatocellular carcinoma metastasis through Up-regulating programmed death ligand 1 and colony-stimulating factor 1. Hepatology. 2021;74(6):3174–93.PubMedCrossRef
35.
Nishida N, Kudo M. Immune checkpoint blockade for the treatment of human hepatocellular carcinoma. Hepatol Res. 2018;48(8):622–34.PubMedCrossRef
36.
37.
38.
Chen X, Cubillos-Ruiz JR. Endoplasmic reticulum stress signals in the tumour and its microenvironment. Nat Rev Cancer. 2021;21(2):71–88.PubMedCrossRef
39.
Colombo M, Mirandola L, Chiriva-Internati M, Basile A, Locati M, Lesma E, et al. Cancer cells exploit notch signaling to redefine a supportive cytokine milieu. Front Immunol. 2018;9:1823.PubMedPubMedCentralCrossRef
40.
Jung HI, Jeong D, Ji S, Ahn TS, Bae SH, Chin S, et al. Overexpression of PD-L1 and PD-L2 is associated with poor prognosis in patients with hepatocellular carcinoma. Cancer Res Treat. 2017;49(1):246–54.PubMedCrossRef
41.
Hu J, Cao J, Topatana W, Juengpanich S, Li S, Zhang B, et al. Targeting mutant p53 for cancer therapy: direct and indirect strategies. J Hematol Oncol. 2021;14(1):157.PubMedPubMedCentralCrossRef
42.
Biterge Sut B. Computational analysis of TP53 vs. CTNNB1 mutations in hepatocellular carcinoma suggests distinct cancer subtypes with differential gene expression profiles and chromatin states. Comput Biol Chem. 2020;89:107404.CrossRef
43.
Calderaro J, Couchy G, Imbeaud S, Amaddeo G, Letouze E, Blanc JF, et al. Histological subtypes of hepatocellular carcinoma are related to gene mutations and molecular tumour classification. J Hepatol. 2017;67(4):727–38.PubMedCrossRef
44.
Schulze K, Imbeaud S, Letouze E, Alexandrov LB, Calderaro J, Rebouissou S, et al. Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets. Nat Genet. 2015;47(5):505–11.PubMedPubMedCentralCrossRef
45.
46.
Zhou X, Luo J, Xie H, Wei Z, Li T, Liu J, et al. MCM2 promotes the stemness and sorafenib resistance of hepatocellular carcinoma cells via hippo signaling. Cell Death Discov. 2022;8(1):418.PubMedPubMedCentralCrossRef
47.
Tang H, Chen J, Han X, Feng Y, Wang F. Upregulation of SPP1 is a marker for poor lung cancer prognosis and contributes to cancer progression and cisplatin resistance. Front Cell Dev Biol. 2021;9:646390.PubMedPubMedCentralCrossRef
48.
Liu H, Wei S, Zhang L, Yuan C, Duan Y, Wang Q. Secreted phosphoprotein 1 promotes the development of small cell lung cancer cells by inhibiting autophagy and apoptosis. Pathol Oncol Res. 2019;25:1487–95.PubMedCrossRef
49.
50.
Shabani F, Farasat A, Mahdavi M, Gheibi N. Calprotectin (S100A8/S100A9): a key protein between inflammation and cancer. Inflamm Res. 2018;67:801–12.PubMedCrossRef
51.
Guo C, Tang Y, Yang Z, Li G, Zhang Y. Hallmark-guided subtypes of hepatocellular carcinoma for the identification of immune-related gene classifiers in the prediction of prognosis, treatment efficacy, and drug candidates. Front Immunol. 2022;13:958161.PubMedPubMedCentralCrossRef
52.
Chiu Y-K, Lin I-Y, Su S-T, Wang K-H, Yang S-Y, Tsai D-Y, et al. Transcription factor ABF-1 suppresses plasma cell differentiation but facilitates memory B cell formation. J Immunol. 2014;193(5):2207–17.PubMedCrossRef
53.
Golding B, Luu A, Jones R, Viloria-Petit AM. The function and therapeutic targeting of anaplastic lymphoma kinase (ALK) in non-small cell lung cancer (NSCLC). Mol Cancer. 2018;17(1):52.PubMedPubMedCentralCrossRef
54.
Lim C-J, Hong J-Y, Ko Y-S, Chung M-W, Jun C-H, Choi S-K, et al. High-dose versus low-dose 5-fluorouracil and cisplatin based hepatic arterial infusion chemotherapy for advanced hepatocellular carcinoma. J Liver Cancer. 2019;19(1):38–45.CrossRef
55.
Mei N, Zhao N, Tian T, Jiao M, Li C. Biological features, gene expression profile, and mechanisms of drug resistance of two-and three-dimensional hepatocellular carcinoma cell cultures. Pharmacol Res Perspect. 2021;9(1):e00715.PubMedPubMedCentralCrossRef
56.
Shen B, Zhang G, Liu Y, Wang J, Jiang J. Identification and analysis of immune-related gene signature in hepatocellular carcinoma. Genes. 2022;13(10):1834.PubMedPubMedCentralCrossRef
57.
Hua X, Zhang H, Jia J, Chen S, Sun Y, Zhu X. Roles of S100 family members in drug resistance in tumors: status and prospects. Biomed Pharmacother. 2020;127:110156.PubMedCrossRef
Metadata
Title
A programmed cell death-related gene signature to predict prognosis and therapeutic responses in liver hepatocellular carcinoma
Authors
Xinyu Gu
Jie Pan
Yanle Li
Liushun Feng
Publication date
01-12-2024
Publisher
Springer US
Published in
Discover Oncology / Issue 1/2024
Print ISSN: 1868-8497
Electronic ISSN: 2730-6011
DOI
https://doi.org/10.1007/s12672-024-00924-2

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