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. 2020 Dec 23:8:596679.
doi: 10.3389/fcell.2020.596679. eCollection 2020.

Development and Validation of a Combined Ferroptosis and Immune Prognostic Classifier for Hepatocellular Carcinoma

Yang Liu  1 , Xi Zhang  2 , Junjun Zhang  2 , Juan Tan  1 , Jie Li  3 , Zewen Song  2
Affiliations

Development and Validation of a Combined Ferroptosis and Immune Prognostic Classifier for Hepatocellular Carcinoma

Yang Liu et al. Front Cell Dev Biol. .

Abstract

Background: Immunotherapy and sorafenib exert anti-tumor effects via ferroptosis, but reliable biomarkers for the individual treatment and prognosis prediction of hepatocellular carcinoma (HCC) based on the ferroptosis and immune status remain lacking.

Methods: Ferroptosis-related genes (FRGs) were identified by downloading data from FerrDb and by searching and reading original articles from PubMed. Immune-related genes (IRGs) were downloaded from ImmPort. Prognostic FRGs and IRGs in the GSE14520 (n = 220) and The Cancer Genome Atlas (TCGA, n = 365) datasets were identified. Least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression were used for model construction. Ferroptosis expression profiles, the infiltration of immune cells, and the somatic mutation status were analyzed and compared.

Results: Twenty-seven prognostic ferroptosis- and immune-related signatures were included to construct a comprehensive index of ferroptosis and immune status (CIFI). A subgroup of patients was identified as having a high CIFI value, which was associated with a worse prognosis. This subgroup of patients had significantly up-regulated expressions of many suppressors of ferroptosis and higher fractions of immunosuppressive cells, such as cancer-associated fibroblasts (CAFs) and myeloid-derived suppressor cells (MDSCs). Notably, somatic mutation analysis indicated that high-CIFI patients had higher levels of tumor heterogeneity and higher mutation frequencies of genes like TP53.

Conclusion: In this work, a novel prognostic classifier was developed based on ferroptosis- and IRGs in HCC, and this classifier could be used for prognostic prediction and the selection of patients for immunotherapies and targeted therapies.

Keywords: ferroptosis; hepatocellular carcinoma; immune; personalized therapy; prognosis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Identification of prognostic FRGs and IRGs in HCC. (A) A Venn diagram indicating that 1231 FRGs and IRGs were identified in the GSE14520 and TCGA cohorts. (B) A Venn diagram indicating that 85 prognostic genes were identified in the GSE14520 and TCGA cohorts. (C) A heatmap showing the expressions of the 85 prognostic genes in the tumors and normal tissues of the GSE14520 dataset. (D) A PPI network suggesting the relationship between FRGs and IRGs.
FIGURE 2
FIGURE 2
Construction of a predictive model and the CIFI of HCC. (A,B) The LASSO Cox regression model was constructed from the 85 prognostic genes, and the tuning parameter (λ) was calculated based on the partial likelihood deviance with 10-fold cross-validation. An optimal log λ value is indicated by the vertical black line in the plot. (C,D) Correlation networks (C) between the CIFI value and the 27 signature genes or (D) between the CIFI value and the ferroptosis-related genes in the GSE14520 dataset. (E,F) The distribution and optimal cutoff value of (E) the risk scores and (F) the OS status and OS in the GSE14520 dataset. (G,H) The (G) 2D and (H) 3D plots of the PCA of the GSE14520 dataset based on the expression profiles of the 27 signature genes in different risk groups.
FIGURE 3
FIGURE 3
Time-dependent ROC analysis and Kaplan–Meier analysis of the CIFI-stratified patients in the GSE14520 cohort. (A) Time-dependent ROC analysis of the CIFI regarding the OS and survival status in the GSE14520 cohort. (B,C) Kaplan–Meier plots of the (B) OS and (C) RFS in the high-CIFI and low-CIFI subgroups of the GSE14520 cohort.
FIGURE 4
FIGURE 4
Validation of the CIFI in the TCGA cohort. (A) 2D and (B) 3D plots of the PCA of the TCGA dataset. (C) Time-dependent ROC analysis of the CIFI regarding the OS and survival status in the TCGA cohort. (D–F) Kaplan–Meier plots of the (D) OS, (E) DSS, and (F) PFI in the high-CIFI and low-CIFI subgroups of the TCGA cohort.
FIGURE 5
FIGURE 5
Ferroptosis profiles in the CIFI-stratified groups. (A,B) Comparison of the expressions of the suppressors of ferroptosis between the high- and low-CIFI subgroups of the (A) GSE14520 and (B) TCGA cohorts. (C) GSEA of the CIFI-stratified groups in the GSE14520 and TCGA cohorts. **p < 0.01; ***p < 0.001; ****p < 0.0001.
FIGURE 6
FIGURE 6
Immune profiles in the CIFI-stratified groups. (A) ssGSEA and correlation analysis of the CIFI value and the immune enrichment scores of immune categories in the GSE14520 and TCGA cohorts. (B,C) ECIP and correlation analysis of the CIFI value and the fraction of immune cells in the GSE14520 and TCGA cohorts. (D) Comparison between the fractions of immune cells in the high- and low-CIFI subgroups of the TCGA cohort via the CIBERSORT method. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001.
FIGURE 7
FIGURE 7
Somatic mutation in the CIFI-stratified groups. (A,B) Oncoplots of the mutated genes in the (A) low-CIFI and (B) high-CIFI subgroups of the TCGA cohort. (C) Forest plot of the differentially mutated genes between the high- and low-CIFI groups. (D) TMB and (E) MATH scores in the high- and low-CIFI groups.
FIGURE 8
FIGURE 8
Results of the (A) univariate and (B) multivariate Cox regression analyses regarding OS in the GSE14520 cohort.
FIGURE 9
FIGURE 9
Results of the (A) univariate and (B) multivariate Cox regression analyses regarding OS in the TCGA cohort.
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