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. 2020 Feb 11;18(1):67.
doi: 10.1186/s12967-020-02255-6.

Development and validation of a novel immune-related prognostic model in hepatocellular carcinoma

Zheng Wang  1 , Jie Zhu  1 , Yongjuan Liu  2 , Changhong Liu  3 , Wenqi Wang  3 , Fengzhe Chen  4 , Lixian Ma  5
Affiliations

Development and validation of a novel immune-related prognostic model in hepatocellular carcinoma

Zheng Wang et al. J Transl Med. .

Abstract

Background: Growing evidence has suggested that immune-related genes play crucial roles in the development and progression of hepatocellular carcinoma (HCC). Nevertheless, the utility of immune-related genes for evaluating the prognosis of HCC patients are still lacking. The study aimed to explore gene signatures and prognostic values of immune-related genes in HCC.

Methods: We comprehensively integrated gene expression data acquired from 374 HCC and 50 normal tissues in The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) analysis and univariate Cox regression analysis were performed to identify DEGs that related to overall survival. An immune prognostic model was constructed using the Lasso and multivariate Cox regression analyses. Furthermore, Cox regression analysis was applied to identify independent prognostic factors in HCC. The correlation analysis between immune-related signature and immune cells infiltration were also investigated. Finally, the signature was validated in an external independent dataset.

Results: A total of 329 differentially expressed immune-related genes were detected. 64 immune-related genes were identified to be markedly related to overall survival in HCC patients using univariate Cox regression analysis. Then we established a TF-mediated network for exploring the regulatory mechanisms of these genes. Lasso and multivariate Cox regression analyses were applied to construct the immune-based prognostic model, which consisted of nine immune-related genes. Further analysis indicated that this immune-related prognostic model could be an independent prognostic indicator after adjusting to other clinical factors. The relationships between the risk score model and immune cell infiltration suggested that the nine-gene signature could reflect the status of tumor immune microenvironment. The prognostic value of this nine-gene prognostic model was further successfully validated in an independent database.

Conclusions: Together, our study screened potential prognostic immune-related genes and established a novel immune-based prognostic model of HCC, which not only provides new potential prognostic biomarkers and therapeutic targets, but also deepens our understanding of tumor immune microenvironment status and lays a theoretical foundation for immunotherapy.

Keywords: Bioinformatics; Hepatocellular carcinoma; Immune related gene; Prognosis; Prognostic signature.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Differentially expressed immune-related genes and transcription factors (TFs) in hepatocellular carcinoma (HCC). a Heatmap of significantly differentially expressed immune-related genes in HCC. The color from green to red represents the progression from low expression to high expression. b Volcano plot of differentially expressed immune-related genes. The red dots in the plot represents upregulated genes and green dots represents downregulated genes with statistical significance. Black dots represent no differentially expressed genes. c Heatmap of significantly differentially expressed TFs in HCC. Red represents higher expression while green represents lower expression. d Volcano plot of differentially expressed TFs in HCC. Colored dots represent differentially expressed TFs and black dots represent no differentially expressed TFs
Fig. 2
Fig. 2
Functional enrichment analysis of differentially expressed immune-related genes. a Gene ontology analysis; From top to bottom, the figure represents biological process, cellular component and molecular function, respectively. b The top 30 most significant Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways
Fig. 3
Fig. 3
Functional enrichment analysis of prognostic immune‐related genes. a Gene ontology analysis; The outer circle is a bar plot where the height of the bar indicates the significance of GO terms. The inner ring shows a scatter plot of the expression (logFC) of differentially expressed immune-related genes in each enriched gene ontology term. b Top 8 enriched KEGG pathways for the prognostic immune-related genes
Fig. 4
Fig. 4
The main regulatory network constructed based on prognosis-related transcription factors and prognostic immune‐related genes. The red circular represent differentially expressed prognostic immune‐related genes and the green diamond represent prognosis-related transcription factors, respectively
Fig. 5
Fig. 5
Construction of an immune-related prognostic signature for hepatocellular carcinoma. a The risk score distribution of HCC patients in the The Cancer Genome Atlas (TCGA) database. b Survival status and duration of patients. c Heatmap of the nine immune‐related genes expression in HCC patients. d Survival curves for the low risk and high risk groups. e Receiver operating characteristic curve (ROC) analysis predicted overall survival using the risk score
Fig. 6
Fig. 6
Relationships between the risk score model and infiltration abundances of six types of immune cells
Fig. 7
Fig. 7
Genetic alterations and biological function of nine prognostic immune‐related genes. a The genetic alteration of nine genes in HCC patients using the cBioPortal database. b The network contained 59 nodes, including nine query genes and the 50 most frequently altered neighbor genes (only five out of nine were correlated with the 50 genes). The relationship between key prognostic immune‐related and cancer drugs was illustrated
Fig. 8
Fig. 8
ROC and Kaplan–Meier analysis of the nine-gene signature in International Cancer Genome Consortium (ICGC) datase. a The Kaplan–Meier curve of the overall survival between the high risk and low risk groups stratified by the median risk score in ICGC. b ROC analysis of the predictive efficiency of the nine-gene prognostic model on overall survival based on risk score
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