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

Cancer Immunology, Immunotherapy volume 70, pages 967–979 (2021)Cite this article

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

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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).

Author information

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

Authors
  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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Competing interests

The authors declare that they have no competing interests.

Availability of data and material

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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  • DOI: https://doi.org/10.1007/s00262-020-02743-0

Keywords

  • Hepatocellular carcinoma
  • Immune-related gene
  • Prognostic index
  • Immune microenvironment
  • Cancer immunotherapy