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Construction of HBV-HCC prognostic model and immune characteristics based on potential genes mining through protein interaction networks

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Abstract

Objective

To search for human protein-coding genes related to hepatocellular carcinoma (HCC) in the context of hepatitis B virus (HBV) infection, and perform prognosis risk assessment.

Methods

Genes related to HBV-HCC were selected through literature screening and protein–protein interaction (PPI) network database analysis. Prognosis potential genes (PPGs) were identified using Cox regression analysis. Patients were divided into high-risk and low-risk groups based on PPGs, and risk scores were calculated. Kaplan–Meier plots were used to analyze overall survival rates, and the results were predicted based on clinicopathological variables. Association analysis was also conducted with immune infiltration, immune therapy, and drug sensitivity. Experimental verification of the expression of PPGs was done in patient liver cancer tissue and normal liver tissue adjacent to tumors.

Results

The use of a prognosis potential genes risk assessment model can reliably predict the prognosis risk of patients, demonstrating strong predictive ability. Kaplan–Meier analysis showed that the overall survival rate of the low-risk group was significantly higher than that of the high-risk group. There were significant differences between the two subgroups in terms of immune infiltration and IC50 association analysis. Experimental verification revealed that CYP2C19, FLNC, and HNRNPC were highly expressed in liver cancer tissue, while UBE3A was expressed at a lower level.

Conclusion

PPGs can be used to predict the prognosis risk of HBV-HCC patients and play an important role in the diagnosis and treatment of liver cancer. They also reveal their potential role in the tumor immune microenvironment, clinical-pathological characteristics, and prognosis.

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Data availability

The Cancer Genome Map (TCGA) database (https://portal.gdc.cancer.gov/) and Gene expression synthesis (GSE113996 and GSE94660) database (http://www.ncbi.nlm.nih.gov/geo) contain the dataset generated and used in the current research period. The Human Protein Interaction Network Database HIPPIE (http://cbdm-01.zdv.uni-mainz.de/~mschaefer/hippie/) was used for PPI analysis, while Cancer Drug Sensitivity Genomics (GDSC, https://www.cancerrxgene.org/) was used for drug sensitivity analysis. The article/supplementary materials include the original contributions proposed in the study. For further inquiries, please contact the corresponding author directly.

References

  • Abdelgalil AA, Alkahtani HM, Al-Jenoobi FI (2019) Sorafenib. Profiles Drug Subst Excip Relat Methodol 44:239–266

    CAS  PubMed  Google Scholar 

  • Alanis-Lobato G, Andrade-Navarro MA, Schaefer MH (2017) HIPPIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks. Nucleic Acids Res 45:D408-d414

    CAS  PubMed  Google Scholar 

  • Alexopoulou A, Vasilieva L, Karayiannis P (2020) New approaches to the treatment of chronic hepatitis B. J Clin Med 9:3187

    CAS  PubMed  PubMed Central  Google Scholar 

  • Aran D, Hu Z, Butte AJ (2017) xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol 18:220

    PubMed  PubMed Central  Google Scholar 

  • Arneth B (2019) Tumor microenvironment. Medicina (kaunas) 56:15

    PubMed  Google Scholar 

  • Ashida R, Okamura Y, Ohshima K, Kakuda Y, Uesaka K et al (2018) The down-regulation of the CYP2C19 gene is associated with aggressive tumor potential and the poorer recurrence-free survival of hepatocellular carcinoma. Oncotarget 9:22058–22068

    PubMed  PubMed Central  Google Scholar 

  • Athanasios A, Charalampos V, Vasileios T, Ashraf GM (2017) Protein-protein interaction (PPI) NETWORK: RECENT ADVANCES IN DRUG DISCOVERY. Curr Drug Metab 18:5–10

    CAS  PubMed  Google Scholar 

  • Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF et al (2013) NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res 41:D991-995

    CAS  PubMed  Google Scholar 

  • Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M et al (2009) ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25:1091–1093

    CAS  PubMed  PubMed Central  Google Scholar 

  • Chan BKC (2018) Data analysis using R programming. Adv Exp Med Biol 1082:47–122

    PubMed  Google Scholar 

  • Chen Y, Tian Z (2019) HBV-induced immune imbalance in the development of HCC. Front Immunol 10:2048

    CAS  PubMed  PubMed Central  Google Scholar 

  • Cullin N, Azevedo Antunes C, Straussman R, Stein-Thoeringer CK, Elinav E (2021) Microbiome and cancer. Cancer Cell 39:1317–1341

    CAS  PubMed  Google Scholar 

  • Du Y, Han X, Ding YB, Yin JH, Cao GW (2016) Prediction and prophylaxis of hepatocellular carcinoma occurrence and postoperative recurrence in chronic hepatitis B virus-infected subjects. World J Gastroenterol 22:6565–6572

    CAS  PubMed  PubMed Central  Google Scholar 

  • El-Khoueiry AB, Hanna DL, Llovet J, Kelley RK (2021) Cabozantinib: an evolving therapy for hepatocellular carcinoma. Cancer Treat Rev 98:102221

    CAS  PubMed  Google Scholar 

  • Foerster F, Gairing SJ, Ilyas SI, Galle PR (2022) Emerging immunotherapy for HCC: A guide for hepatologists. Hepatology 75:1604–1626

    PubMed  Google Scholar 

  • Geeleher P, Cox N, Huang RS (2014) pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS ONE 9:e107468

    PubMed  PubMed Central  Google Scholar 

  • Gene Ontology Consortium (2015) Gene Ontology Consortium: going forward. Nucleic Acids Res 43:D1049-1056

    Google Scholar 

  • Guerrieri F, Belloni L, Pediconi N, Levrero M (2013) Molecular mechanisms of HBV-associated hepatocarcinogenesis. Semin Liver Dis 33:147–156

    CAS  PubMed  Google Scholar 

  • Hafezi M, Lin M, Chia A, Chua A, Ho ZZ et al (2021) Immunosuppressive drug-resistant armored T-cell receptor T cells for immune therapy of HCC in liver transplant patients. Hepatology 74:200–213

    CAS  PubMed  Google Scholar 

  • Harjunpää H, Guillerey C (2020) TIGIT as an emerging immune checkpoint. Clin Exp Immunol 200:108–119

    PubMed  Google Scholar 

  • Hassoba H, Mahmoud M, Fahmy H, Leheta O, Sayed A et al (2003) TT virus infection among Egyptian patients with hepatocellular carcinoma. Egypt J Immunol 10:9–16

    PubMed  Google Scholar 

  • Iasonos A, Schrag D, Raj GV, Panageas KS (2008) How to build and interpret a nomogram for cancer prognosis. J Clin Oncol 26:1364–1370

    PubMed  Google Scholar 

  • Iglesia MD, Parker JS, Hoadley KA, Serody JS, Perou CM et al (2016) Genomic analysis of immune cell infiltrates across 11 tumor types. J Natl Cancer Inst 108:djw144

    PubMed  PubMed Central  Google Scholar 

  • Jia L, Gao Y, He Y, Hooper JD, Yang P (2020) HBV induced hepatocellular carcinoma and related potential immunotherapy. Pharmacol Res 159:104992

    CAS  PubMed  Google Scholar 

  • Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30

    CAS  PubMed  PubMed Central  Google Scholar 

  • Kelly RJ, Rixe O (2010) Axitinib (AG-013736). Recent results. Cancer Res 184:33–44

    CAS  Google Scholar 

  • Kim Y, Park JH, Cho YR (2022) Network-based approaches for disease-gene association prediction using protein-protein interaction networks. Int J Mol Sci 23:7411

    CAS  PubMed  PubMed Central  Google Scholar 

  • Kley RA, Leber Y, Schrank B, Zhuge H, Orfanos Z et al (2021) FLNC-associated myofibrillar myopathy: new clinical, functional, and proteomic data. Neurol Genet 7:e590

    PubMed  PubMed Central  Google Scholar 

  • Lee JC, Mehdizadeh S, Smith J, Young A, Mufazalov IA et al (2020) Regulatory T cell control of systemic immunity and immunotherapy response in liver metastasis. Sci Immunol 5:eaba0759. https://doi.org/10.1126/sciimmunol.aba0759

  • Li M, Li D, Tang Y, Wu F, Wang J (2017) CytoCluster: a cytoscape plugin for cluster analysis and visualization of biological networks. Int J Mol Sci 18:1880

    PubMed  PubMed Central  Google Scholar 

  • Li T, Fu J, Zeng Z, Cohen D, Li J et al (2020) TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res 48:W509-w514

    CAS  PubMed  PubMed Central  Google Scholar 

  • Li J, Tao H, Wang W, Li J, Zhang E (2022) The detection and verification of two heterogeneous subgroups and a risk model based on ferroptosis-related genes in hepatocellular carcinoma. J Oncol 2022:1182383

    PubMed  PubMed Central  Google Scholar 

  • Liang X, Liangliang X, Peng W, Tao Y, Jinfu Z et al (2021) Combined prognostic nutritional index and albumin-bilirubin grade to predict the postoperative prognosis of HBV-associated hepatocellular carcinoma patients. Sci Rep 11:14624

    CAS  PubMed  PubMed Central  Google Scholar 

  • Liu X, Li M, Wang X, Dang Z, Jiang Y et al (2019) PD-1(+) TIGIT(+) CD8(+) T cells are associated with pathogenesis and progression of patients with hepatitis B virus-related hepatocellular carcinoma. Cancer Immunol Immunother 68:2041–2054

    CAS  PubMed  Google Scholar 

  • Liu D, Luo X, Xie M, Zhang T, Chen X et al (2022) HNRNPC downregulation inhibits IL-6/STAT3-mediated HCC metastasis by decreasing HIF1A expression. Cancer Sci 113:3347–3361

    CAS  PubMed  PubMed Central  Google Scholar 

  • Maere S, Heymans K, Kuiper M (2005) BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21:3448–3449

    CAS  PubMed  Google Scholar 

  • Martin MD, Badovinac VP (2018) Defining memory CD8 T cell. Front Immunol 9:2692

    PubMed  PubMed Central  Google Scholar 

  • Meng F, Zhao J, Tan AT, Hu W, Wang SY et al (2021) Immunotherapy of HBV-related advanced hepatocellular carcinoma with short-term HBV-specific TCR expressed T cells: results of dose escalation, phase I trial. Hepatol Int 15:1402–1412

    PubMed  Google Scholar 

  • Moudi B, Mahmoudzadeh-Sagheb H, Heidari Z (2020) Hepatocyte paraffin 1 and arginase-1 are effective panel of markers in HBV-related HCC diagnosis in fine-needle aspiration specimens. BMC Res Notes 13:388

    CAS  PubMed  PubMed Central  Google Scholar 

  • Nun-Anan P, Chonprasertsuk S, Siramolpiwat S, Tangaroonsanti A, Bhanthumkomol P et al (2015) CYP2C19 genotype could be a predictive factor for aggressive manifestations of hepatocellular carcinoma related with chronic hepatitis B infection in Thailand. Asian Pac J Cancer Prev 16:3253–3256

    PubMed  Google Scholar 

  • Overgaard NH, Jung JW, Steptoe RJ, Wells JW (2015) CD4+/CD8+ double-positive T cells: more than just a developmental stage? J Leukoc Biol 97:31–38

    PubMed  Google Scholar 

  • Parikh ND (2017) PRO: HEPATOCELLULAR CARCINOMA SURVEILLANCE: A USEFUL TOOL AGAINST THE RISING TIDE OF HCC. Am J Gastroenterol 112:1632–1633

    PubMed  Google Scholar 

  • Qi Y, Xu F, Chen L, Li Y, Xu Z et al (2016) Quantitative proteomics reveals FLNC as a potential progression marker for the development of hepatocellular carcinoma. Oncotarget 7:68242–68252

    PubMed  PubMed Central  Google Scholar 

  • Qi X, Yang M, Ma L, Sauer M, Avella D et al (2020) Synergizing sunitinib and radiofrequency ablation to treat hepatocellular cancer by triggering the antitumor immune response. J Immunother Cancer 8:e001038

    PubMed  PubMed Central  Google Scholar 

  • Ravi R, Noonan KA, Pham V, Bedi R, Zhavoronkov A et al (2018) Bifunctional immune checkpoint-targeted antibody-ligand traps that simultaneously disable TGFβ enhance the efficacy of cancer immunotherapy. Nat Commun 9:741

    PubMed  Google Scholar 

  • Riley RS, June CH, Langer R, Mitchell MJ (2019) Delivery technologies for cancer immunotherapy. Nat Rev Drug Discov 18:175–196

    CAS  PubMed  PubMed Central  Google Scholar 

  • Sartorius K, An P, Winkler C, Chuturgoon A, Li X et al (2021) The epigenetic modulation of cancer and immune pathways in hepatitis b virus-associated hepatocellular carcinoma: the influence of HBx and miRNA dysregulation. Front Immunol 12:661204

    CAS  PubMed  PubMed Central  Google Scholar 

  • Sebaugh JL (2011) Guidelines for accurate EC50/IC50 estimation. Pharm Stat 10:128–134

    CAS  PubMed  Google Scholar 

  • Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT et al (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504

    CAS  PubMed  PubMed Central  Google Scholar 

  • Siegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics, 2022. CA Cancer J Clin 72:7–33

    PubMed  Google Scholar 

  • Speiser DE, Chijioke O, Schaeuble K, Münz C (2023) CD4(+) T cells in cancer. Nat Cancer 4:317–329

    CAS  PubMed  Google Scholar 

  • Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R et al (2021) The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 49:D605-d612

    CAS  PubMed  Google Scholar 

  • Tan T, Han G, Cheng Z, Jiang J, Zhang L et al (2022) Genetic polymorphisms in CYP2C19 cause changes in plasma levels and adverse reactions to anlotinib in chinese patients with lung cancer. Front Pharmacol 13:918219

    CAS  PubMed  PubMed Central  Google Scholar 

  • Tanaka A, Sakaguchi S (2019) Targeting Treg cells in cancer immunotherapy. Eur J Immunol 49:1140–1146

    CAS  PubMed  Google Scholar 

  • Tomczak K, Czerwińska P, Wiznerowicz M (2015) The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (pozn) 19:A68-77

    PubMed  Google Scholar 

  • Trépo C, Chan HL, Lok A (2014) Hepatitis B virus infection. Lancet 384:2053–2063

    PubMed  Google Scholar 

  • Van Calster B, Wynants L, Verbeek JFM, Verbakel JY, Christodoulou E et al (2018) Reporting and interpreting decision curve analysis: a guide for investigators. Eur Urol 74:796–804

    PubMed  PubMed Central  Google Scholar 

  • Wang W, Chen Y, Wu L, Zhang Y, Yoo S et al (2022) HBV genome-enriched single cell sequencing revealed heterogeneity in HBV-driven hepatocellular carcinoma (HCC). BMC Med Genom 15:134

    CAS  Google Scholar 

  • Xie DY, Ren ZG, Zhou J, Fan J, Gao Q (2020) 2019 Chinese clinical guidelines for the management of hepatocellular carcinoma: updates and insights. Hepatobil Surg Nutr 9:452–463

    Google Scholar 

  • Yang JD, Hainaut P, Gores GJ, Amadou A, Plymoth A et al (2019) A global view of hepatocellular carcinoma: trends, risk, prevention and management. Nat Rev Gastroenterol Hepatol 16:589–604

    PubMed  PubMed Central  Google Scholar 

  • Yao RQ, Ren C, Zheng LY, Xia ZF, Yao YM (2022) Advances in immune monitoring approaches for sepsis-induced immunosuppression. Front Immunol 13:891024

    CAS  PubMed  PubMed Central  Google Scholar 

  • Yeh CN, Wu RC, Cheng CT, Tsai CY, Chang YR et al (2018) HO-1 is a favorable prognostic factor for HBV-HCC patients who underwent hepatectomy. Cancer Manag Res 10:6049–6059

    CAS  PubMed  PubMed Central  Google Scholar 

  • Yu G, Wang LG, Han Y, He QY (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16:284–287

    CAS  PubMed  PubMed Central  Google Scholar 

  • Zainal-Abidin RA, Afiqah-Aleng N, Abdullah-Zawawi MR, Harun S, Mohamed-Hussein ZA (2022) Protein-Protein interaction (PPI) network of zebrafish oestrogen receptors: a bioinformatics workflow. Life (basel) 12:650

    CAS  PubMed  Google Scholar 

  • Zhang G, Sun J (2022) Endoplasmic reticulum stress-related signature for predicting prognosis and immune features in hepatocellular carcinoma. J Immunol Res 2022:1366508

    PubMed  PubMed Central  Google Scholar 

  • Zhao L, Jin Y, Yang C, Li C (2020) HBV-specific CD8 T cells present higher TNF-α expression but lower cytotoxicity in hepatocellular carcinoma. Clin Exp Immunol 201:289–296

    CAS  PubMed  PubMed Central  Google Scholar 

  • Zheng Z, Zhang B, Yu H, Li S, Song N et al (2021) UBE3A activates the NOTCH pathway and promotes esophageal cancer progression by degradation of ZNF185. Int J Biol Sci 17:3024–3035

    CAS  PubMed  PubMed Central  Google Scholar 

  • Zhu GQ, Yang Y, Chen EB, Wang B, Xiao K et al (2019) Development and validation of a new tumor-based gene signature predicting prognosis of HBV/HCV-included resected hepatocellular carcinoma patients. J Transl Med 17:203

    PubMed  PubMed Central  Google Scholar 

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Funding

This study is supported by the General Program of the National Natural Science Foundation of China (No. 81772178, led by Duan Changzhu).

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Authors and Affiliations

  1. Department of Cell Biology and Medical Genetics, Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, 400016, China

    Qingxiu Li, Kejia Wu, Yiqi Zhang, Yuxin Liu, Yalan Wang & Changzhu Duan

  2. Department of Hepatobillary Surgery, The First Affiliated Hospital, Chongqing Medical University, Chongqing, 400016, China

    Yong Chen

  3. Chongqing Medical and Pharmaceutical College, No. 82, University Town Middle Road, Shapingba District, Chongqing, 400016, China

    Shuangling Sun

Authors
  1. Qingxiu Li
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  2. Kejia Wu
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  3. Yiqi Zhang
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  4. Yuxin Liu
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  5. Yalan Wang
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  6. Yong Chen
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Contributions

QL conducted data analysis and drafted the manuscript, while KW, YW, and YC provided valuable contributions to ensure the smooth progress of the experiment. SS, YL, and YZ assisted with data analysis and engaged in productive discussions. DC is credited with the conceptualization of the research. All authors have reviewed the manuscript.

Corresponding author

Correspondence to Changzhu Duan.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This study was approved by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (No. 2019005). The patient/participant provided written informed consent to participate in this study.

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Supplementary Information

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432_2023_4989_MOESM1_ESM.jpg

Supplementary Figure 1. Functional analysis of HBV-HCC related genes. A. The top 12 ranked items obtained after GO analysis. The circles' size indicates the quantity of genes contained in the item., and the color from blue to red indicates the increasing strength of the correlation with the item.B. The top 15 pathways obtained after KEGG pathway analysis

432_2023_4989_MOESM2_ESM.jpg

Supplementary Figure 2.The interaction between core modules and the distribution of central genes. The red pentagon represents the central gene position of 13 modules, while the green circle represents the gene position within each module

432_2023_4989_MOESM3_ESM.jpg

Supplementary Figure 3.Functional analysis of core modules. A. GO analysis shows that the size of hexagon represents the number of genes included, the color depth is related to the enrichment degree (P value size), and the darker the color is, the higher the enrichment degree is. B. KEGG pathway analysis. The size of the circle indicates the number of genes involved, and different colors indicate different functions of the pathway. Several colors in a circle indicate that this path appears in several functions

432_2023_4989_MOESM4_ESM.tiff

Supplementary Figure 4.Correlation of immune cell infiltration levels with risk scores and four selected. Figure A compares the immune scores of high-risk and low-risk groups. Figures B and C evaluate the expression of 35 immune cells between the high-risk and low-risk groups using the XCELL formula, where Figure B shows phagocytes and other initial immune cells, and Figure C shows lymphocytes. Figures D-G compare the infiltration levels of nine immune cells based on the expression levels of CYP2C19 (D), FLNC (E), HNRNPC (F), and UBE3A (G). *p < 0.05, **p < 0.01, ***p < 0.001

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Li, Q., Wu, K., Zhang, Y. et al. Construction of HBV-HCC prognostic model and immune characteristics based on potential genes mining through protein interaction networks. J Cancer Res Clin Oncol 149, 11263–11278 (2023). https://doi.org/10.1007/s00432-023-04989-4

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