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An immune-related gene signature for predicting survival and immunotherapy efficacy in hepatocellular carcinoma

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Abstract

Background

Hepatocellular carcinoma (HCC) ranks the fourth in terms of cancer-related mortality globally. Herein, in this research, we attempted to develop a novel immune-related gene signature that could predict survival and efficacy of immunotherapy for HCC patients.

Methods

The transcriptomic and clinical data of HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and GSE14520 datasets, followed by acquiring immune-related genes from the ImmPort database. Afterwards, an immune-related gene-based prognostic index (IRGPI) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. Kaplan–Meier survival curves as well as time-dependent receiver operating characteristic (ROC) curve were performed to evaluate its predictive capability. Besides, both univariate and multivariate analyses on overall survival for the IRGPI and multiple clinicopathologic factors were carried out, followed by the construction of a nomogram. Finally, we explored the possible correlation of IRGPI with immune cell infiltration or immunotherapy efficacy.

Results

Analysis of 365 HCC samples identified 11 differentially expressed immune-related genes, which were selected to establish the IRGPI. Notably, it can predict the survival of HCC patients more accurately than published biomarkers. Furthermore, IRGPI can predict the infiltration of immune cells in the tumor microenvironment of HCC, as well as the response of immunotherapy.

Conclusion

Collectively, the currently established IRGPI can accurately predict survival, reflect the immune microenvironment, and predict the efficacy of immunotherapy among HCC patients.

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Abbreviations

ALT:

Alanine transferase

AUC:

Area under curve

BP:

Biological process

CC:

Cellular component

CIs:

Confidence intervals

DAVID:

Database for Annotation, Visualization, and Integrated Discovery

DEG:

Differentially expressed gene

DEIRG:

Differentially expressed immune-related gene

FDR:

False discovery rate

GEO:

Gene Expression Omnibus

GO:

Gene Ontology

HCC:

Hepatocellular carcinoma

HRs:

Hazard ratios

ICI:

Immune checkpoint inhibitor

ImmPort:

Immunology Database and Analysis Portal

IPS:

Immunophenoscore

IRG:

Immune-related gene

IRGPI:

Immune-related gene-based prognostic index

KEGG:

Kyoto Encyclopedia of Genes and Genomes

K-M:

Kaplan–Meier

LASSO:

Least Absolute Shrinkage and Selection Operator

MAF:

Mutation Annotation Format

MF:

Molecular function

MX:

Unknown M stage

NX:

Unknown N stage

OS:

Overall survival

ROC:

Receiver operating characteristic

TCGA:

The Cancer Genome Atlas

TCIA:

The Cancer Immunome Atlas

TIME:

Tumor immune microenvironment

TMB:

Tumor mutation burden

TX:

Unknown T stage

References

  1. Bray F, Ferlay J, Soerjomataram I et al (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394–424. https://doi.org/10.3322/caac.21492

    Article  PubMed  Google Scholar 

  2. Llovet JM, Zucman-Rossi J, Pikarsky E et al (2016) Hepatocellular carcinoma. Nat Rev Dis Prim 2:16018. https://doi.org/10.1038/nrdp.2016.18

    Article  PubMed  Google Scholar 

  3. Allemani C, Matsuda T, Di Carlo V et al (2018) Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. The Lancet 391:1023–1075. https://doi.org/10.1016/S0140-6736(17)33326-3

    Article  Google Scholar 

  4. Fujiwara N, Friedman SL, Goossens N, Hoshida Y (2018) Risk factors and prevention of hepatocellular carcinoma in the era of precision medicine. J Hepatol 68:526–549. https://doi.org/10.1016/j.jhep.2017.09.016

    Article  PubMed  Google Scholar 

  5. Famularo S, Di Sandro S, Giani A et al (2018) Recurrence patterns after anatomic or parenchyma-sparing liver resection for hepatocarcinoma in a western population of cirrhotic patients. Ann Surg Oncol 25:3974–3981. https://doi.org/10.1245/s10434-018-6730-0

    Article  PubMed  Google Scholar 

  6. Llovet JM, Montal R, Sia D, Finn RS (2018) Molecular therapies and precision medicine for hepatocellular carcinoma. Nat Rev Clin Oncol 15:599–616. https://doi.org/10.1038/s41571-018-0073-4

    Article  PubMed  Google Scholar 

  7. Iñarrairaegui M, Melero I, Sangro B (2018) Immunotherapy of hepatocellular carcinoma: facts and hopes. Clin Cancer Res 24:1518–1524. https://doi.org/10.1158/1078-0432.CCR-17-0289

    Article  CAS  PubMed  Google Scholar 

  8. Heinrich B, Czauderna C, Marquardt JU (2018) Immunotherapy of hepatocellular carcinoma. Oncol Res Treat 41:292–297. https://doi.org/10.1159/000488916

    Article  CAS  PubMed  Google Scholar 

  9. Galle PR, Forner A, Llovet JM et al (2018) EASL clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol 69:182–236. https://doi.org/10.1016/j.jhep.2018.03.019

    Article  Google Scholar 

  10. Taube JM, Galon J, Sholl LM et al (2018) Implications of the tumor immune microenvironment for staging and therapeutics. Mod Pathol 31:214–234. https://doi.org/10.1038/modpathol.2017.156

    Article  CAS  PubMed  Google Scholar 

  11. Xu WH, Xu Y, Wang J et al (2019) Prognostic value and immune infiltration of novel signatures in clear cell renal cell carcinoma microenvironment. Aging 11:6999–7020. https://doi.org/10.18632/aging.102233

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Long J, Wang A, Bai Y et al (2019) Development and validation of a TP53-associated immune prognostic model for hepatocellular carcinoma. EBioMedicine 42:363–374. https://doi.org/10.1016/j.ebiom.2019.03.022

    Article  PubMed  PubMed Central  Google Scholar 

  13. Pan L, Fang J, Chen MY et al (2020) Promising key genes associated with tumor microenvironments and prognosis of hepatocellular carcinoma. World J Gastroenterol 26:789–803. https://doi.org/10.3748/wjg.v26.i8.789

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Zhang FP, Huang YP, Luo WX et al (2020) Construction of a risk score prognosis model based on hepatocellular carcinoma microenvironment. World J Gastroenterol 26:134–153. https://doi.org/10.3748/wjg.v26.i2.134

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Weinstein JN, Collisson EA, Mills GB et al (2013) The cancer genome atlas pan-cancer analysis project. Nat Genet 45:1113–1120. https://doi.org/10.1038/ng.2764

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wang Y, Gao B, Tan PY et al (2019) Genome-wide CRISPR knockout screens identify NCAPG as an essential oncogene for hepatocellular carcinoma tumor growth. FASEB J: Off Publ Fed Am Soc Exp Biol 33:8759–8770. https://doi.org/10.1096/fj.201802213RR

    Article  CAS  Google Scholar 

  17. Bhattacharya S, Andorf S, Gomes L et al (2014) ImmPort: disseminating data to the public for the future of immunology. Immunol Res 58:234–239. https://doi.org/10.1007/s12026-014-8516-1

    Article  CAS  PubMed  Google Scholar 

  18. Ritchie ME, Phipson B, Wu D et al (2015) Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47. https://doi.org/10.1093/nar/gkv007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ginestet C (2011) ggplot2: elegant graphics for data analysis. J Royal Stat Soc: Series A (Stat Soc) 174:245–246. https://doi.org/10.1111/j.1467-985x.2010.00676_9.x

    Article  Google Scholar 

  20. Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57. https://doi.org/10.1038/nprot.2008.211

    Article  CAS  Google Scholar 

  21. Ogata H, Goto S, Sato K et al (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30. https://doi.org/10.1093/nar/27.1.29

    Article  Google Scholar 

  22. Gene Ontology Consortium (2004) The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res 32:D258–D261. https://doi.org/10.1093/nar/gkh036

    Article  CAS  Google Scholar 

  23. Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33:1–22. https://doi.org/10.18637/jss.v033.i01

    Article  PubMed  PubMed Central  Google Scholar 

  24. Rhodes DR, Yu J, Shanker K et al (2004) ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia 6:1–6. https://doi.org/10.1016/s1476-5586(04)80047-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Therneau TM (2015) A package for survival analysis in S. Version 2.38. CRAN website—https://cran.r-project.org/package=survival

  26. Heagerty PJ, Lumley T, Pepe MS (2000) Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56:337–344. https://doi.org/10.1111/j.0006-341X.2000.00337.x

    Article  CAS  PubMed  Google Scholar 

  27. Harrell Jr FE (2016) rms: Regression Modeling Strategies. R package version 5.0–0. CRAN

  28. Newman AM, Liu CL, Green MR et al (2015) Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 12:453–457. https://doi.org/10.1038/nmeth.3337

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Charoentong P, Finotello F, Angelova M et al (2017) Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Reports 18:248–262. https://doi.org/10.1016/j.celrep.2016.12.019

    Article  CAS  PubMed  Google Scholar 

  30. Liu L, Bai X, Wang J et al (2019) Combination of TMB and CNA stratifies prognostic and predictive responses to immunotherapy across metastatic cancer. Clin Cancer Res 25:7413–7423. https://doi.org/10.1158/1078-0432.CCR-19-0558

    Article  CAS  PubMed  Google Scholar 

  31. Jensen MA, Ferretti V, Grossman RL, Staudt LM (2017) The NCI genomic data commons as an engine for precision medicine. Blood 130:453–459. https://doi.org/10.1182/blood-2017-03-735654

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Mayakonda A, Lin DC, Assenov Y et al (2018) Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res 28:1747–1756. https://doi.org/10.1101/gr.239244.118

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Yang S, Wu Y, Deng Y et al (2019) Identification of a prognostic immune signature for cervical cancer to predict survival and response to immune checkpoint inhibitors. OncoImmunology 8:e1659094. https://doi.org/10.1080/2162402X.2019.1659094

    Article  PubMed  PubMed Central  Google Scholar 

  34. Berraondo P, Minute L, Ajona D et al (2016) Innate immune mediators in cancer: between defense and resistance. Immunol Rev 274:290–306. https://doi.org/10.1111/imr.12464

    Article  CAS  PubMed  Google Scholar 

  35. Elola MT, Ferragut F, Méndez-Huergo SP et al (2018) Galectins: multitask signaling molecules linking fibroblast, endothelial and immune cell programs in the tumor microenvironment. Cell Immunol 333:34–45. https://doi.org/10.1016/j.cellimm.2018.03.008

    Article  CAS  PubMed  Google Scholar 

  36. Gardner A, Ruffell B (2016) Dendritic cells and cancer immunity. Trends Immunol 37:855–865. https://doi.org/10.1016/j.it.2016.09.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. 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 

  38. Banerjee K, Kumar S, Ross KA et al (2018) Emerging trends in the immunotherapy of pancreatic cancer. Cancer Lett 417:35–46. https://doi.org/10.1016/j.canlet.2017.12.012

    Article  CAS  PubMed  Google Scholar 

  39. Sanmamed MF, Chen L (2018) A paradigm shift in cancer immunotherapy: from enhancement to normalization. Cell 175:313–326. https://doi.org/10.1016/j.cell.2018.09.035

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Jiang Y, Han QJ, Zhang J (2019) Hepatocellular carcinoma: mechanisms of progression and immunotherapy. World J Gastroenterol 25:3151–3167. https://doi.org/10.3748/wjg.v25.i25.3151

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Zhao QJ, Zhang J, Xu L, Liu FF (2018) Identification of a five-long non-coding RNA signature to improve the prognosis prediction for patients with hepatocellular carcinoma. World J Gastroenterol 24:3426–3439. https://doi.org/10.3748/wjg.v24.i30.3426

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Bing Z, Tian J, Zhang J et al (2016) An integrative model of miRNA and mRNA expression signature for patients of breast invasive carcinoma with radiotherapy prognosis. Cancer Biotherapy Radiopharm 31:253–260. https://doi.org/10.1089/cbr.2016.2059

    Article  CAS  Google Scholar 

  43. Jiang X, Hao Y (2018) Analysis of expression profile data identifies key genes and pathways in hepatocellular carcinoma. Oncology Letters 15:2625–2630. https://doi.org/10.3892/ol.2017.7534

    Article  CAS  PubMed  Google Scholar 

  44. Cheng J, Xie HY, Xu X et al (2011) NDRG1 as a biomarker for metastasis, recurrence and of poor prognosis in hepatocellular carcinoma. Cancer Lett 310:35–45. https://doi.org/10.1016/j.canlet.2011.06.001

    Article  CAS  PubMed  Google Scholar 

  45. Lian YF, Huang YL, Zhang YJ et al (2019) CacYBP enhances cytoplasmic retention of p27Kip1 to promote hepatocellular carcinoma progression in the absence of RNF41 mediated degradation. Theranostics 9:8392–8408. https://doi.org/10.7150/thno.36838

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Liu M, Li Y, Chen L et al (2014) Allele-specific imbalance of oxidative stress-induced growth inhibitor 1 associates with progression of hepatocellular carcinoma. Gastroenterology 146:1084–1096. https://doi.org/10.1053/j.gastro.2013.12.041

    Article  CAS  PubMed  Google Scholar 

  47. Lv J, Zhang S, Wu H et al (2020) Deubiquitinase PSMD14 enhances hepatocellular carcinoma growth and metastasis by stabilizing GRB2. Cancer Lett 469:22–34. https://doi.org/10.1016/j.canlet.2019.10.025

    Article  CAS  PubMed  Google Scholar 

  48. Matsumoto H, Thike AA, Li H et al (2016) Increased CD4 and CD8-positive T cell infiltrate signifies good prognosis in a subset of triple-negative breast cancer. Breast Cancer Res Treat 156:237–247. https://doi.org/10.1007/s10549-016-3743-x

    Article  CAS  PubMed  Google Scholar 

  49. Yao RR, Li JH, Zhang R et al (2018) M2-polarized tumor-associated macrophages facilitated migration and epithelial-mesenchymal transition of HCC cells via the TLR4/STAT3 signaling pathway. World J Surg Oncol 16:9. https://doi.org/10.1186/s12957-018-1312-y

    Article  PubMed  PubMed Central  Google Scholar 

  50. Choi C, Yoo GS, Cho WK, Park HC (2019) Optimizing radiotherapy with immune checkpoint blockade in hepatocellular carcinoma. World J Gastroenterol 25:2416–2429. https://doi.org/10.3748/wjg.v25.i20.2416

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Longo V, Brunetti O, Gnoni A et al (2019) Emerging role of immune checkpoint inhibitors in hepatocellular carcinoma. Medicina (Lithuania) 55:698. https://doi.org/10.3390/medicina55100698

    Article  Google Scholar 

  52. Ying HQ, Deng QW, He BS et al (2014) The prognostic value of preoperative NLR, d-NLR, PLR and LMR for predicting clinical outcome in surgical colorectal cancer patients. Med Oncol 31:305. https://doi.org/10.1007/s12032-014-0305-0

    Article  CAS  PubMed  Google Scholar 

  53. Zhang G, Wu Y, Zhang J et al (2018) Nomograms for predicting long-term overall survival and disease-specific survival of patients with clear cell renal cell carcinoma. OncoTarget Ther 11:5535–5544. https://doi.org/10.2147/OTT.S171881

    Article  Google Scholar 

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Funding

This work was supported by grants from the National Natural Science Foundation of China (Grant No. 81673460) and Science and Technology Department of Sichuan Province (Grant No. 2020JDTD0022).

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Authors and Affiliations

  1. Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing, 100084, China

    Yifei Dai & Xun Lan

  2. Institute of Medicinal Plant Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100193, China

    Weijie Qiang

  3. School of Life Sciences, Tsinghua University, Beijing, 100084, China

    Kequan Lin

  4. School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China

    Yu Gui

  5. School of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China

    Dong Wang

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  1. Yifei Dai
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  2. Weijie Qiang
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  3. Kequan Lin
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  4. Yu Gui
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  5. Xun Lan
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  6. Dong Wang
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Contributions

YD, WQ and DW conceived the study; YD, WQ, KL, XL and DW designed the experiments; YD performed the experiments; YD and WQ wrote the manuscript; KL, YG, XL and DW edited the manuscript; and all authors read and gave final approval to submit the manuscript.

Corresponding authors

Correspondence to Xun Lan or Dong Wang.

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The authors declare that they have no competing interests.

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The datasets used during the current study are available from the corresponding author on reasonable request.

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Dai, Y., Qiang, W., Lin, K. et al. An immune-related gene signature for predicting survival and immunotherapy efficacy in hepatocellular carcinoma. Cancer Immunol Immunother 70, 967–979 (2021). https://doi.org/10.1007/s00262-020-02743-0

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