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. 2022 Jan 5:9:807862.
doi: 10.3389/fcell.2021.807862. eCollection 2021.

Integrated Analysis of Ferroptosis-Related Biomarker Signatures to Improve the Diagnosis and Prognosis Prediction of Ovarian Cancer

Huan Wang  1   2   3 , Qi Cheng  1   3 , Kaikai Chang  1   3 , Lingjie Bao  1   3 , Xiaofang Yi  1   3
Affiliations

Integrated Analysis of Ferroptosis-Related Biomarker Signatures to Improve the Diagnosis and Prognosis Prediction of Ovarian Cancer

Huan Wang et al. Front Cell Dev Biol. .

Abstract

Ovarian cancer remains the most lethal gynecological malignancy. Ferroptosis, a specialized form of iron-dependent, nonapoptotic cell death, plays a crucial role in various cancers. However, the contribution of ferroptosis to ovarian cancer is poorly understood. Here, we characterized the diagnostic, prognostic, and therapeutic value of ferroptosis-related genes in ovarian cancer by analyzing transcriptomic data from The Cancer Genome Atlas and Gene Expression Omnibus databases. A reliable 10-gene ferroptosis signature (HIC1, ACSF2, MUC1, etc.) for the diagnosis of ovarian cancer was identified. Notably, we constructed and validated a novel prognostic signature including three FRGs: HIC1, LPCAT3, and DUOX1. We also further developed a risk score model based on these three genes which divided ovarian cancer patients into two risk groups. Functional analysis revealed that immune response and immune-related pathways were enriched in the high-risk group. Meanwhile, the tumor microenvironment was distinct between the two groups, with more M2 Macrophage infiltration and higher expression of key immune checkpoint molecules in the high-risk group than in the other group. Low-risk patients exhibited more favorable immunotherapy and chemotherapy responses. We conclude that crosstalk between ferroptosis and immunity may contribute to the worse prognosis of patients in the high-risk group. In particular, HIC1 showed both diagnostic and prognostic value in ovarian cancer. In vitro experiments demonstrated that inhibition of HIC1 improved drug sensitivity of chemotherapy and immunotherapy agents by inducing ferroptosis. Our findings provide new insights into the potential role of FRGs in the early detection, prognostic prediction, and individualized treatment decision-making for ovarian cancer patients.

Keywords: chemotherapy; diagnosis; ferroptosis-related genes; immunotherapy; ovarian cancer; 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 DE-FRGs and construction of diagnostic signature with FRGs in GEO cohort. (A) Venn diagram for identifying DEGs between ovarian carcinoma samples and normal ovaries that belonged to FRGs. (B) Heatmap of DE-FRGs in ovarian carcinoma samples compared with normal ovaries. (C) Coefficients of the key prognostic FRGs in the LASSO model, each curve represents a gene. (D) 10‐fold cross‐validation for tuning parameter selection in the LASSO model. (E) ROC curve analysis of the 10-FRG diagnosis signature in GSE66957 dataset. DEGs, differentially expressed genes; DE-FRGs, Differentially Expressed-ferroptosis-related genes; FRGs, ferroptosis-related genes; LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic.
FIGURE 2
FIGURE 2
Construction of prognostic gene signature with FRGs in TCGA cohort. (A, B) Forest plots showing the results of the univariate and multivariate Cox regression analysis between gene expression and OS in the training set. Distribution of risk score for each patient and survival status of OC patients in the training set (C) and validation set (D). (E) Kaplan-Meier curves for the OS of patients in the high-risk group and low-risk group in the training set (left) and validation set (right). (F) The ROC analysis of training set (left) and validation set (right) for survival prediction by the three-gene signature. (G) Heatmap of the gene-expression profiles of the FRGs signature in the training set (left) and validation set (right). OS, overall survival; OC, ovarian cancer.
FIGURE 3
FIGURE 3
Independent prognostic value of the risk score based on the 3-gene signature. Forest plot of univariate (A) and multivariate (B) Cox proportional hazards regression analysis. (C) A nomogram based on risk score and clinical indicators for predicting 1-, 3-, and 5-year OS of ovarian cancer patients in TCGA cohort. (D) Calibration plot of nomogram for predicting probabilities of 1-year, 3-year, and 5-year OS of patients. The dotted line indicates actual survival. (E–G) Decision curve analysis shows the expected net benefits at 1- (E), 3- (F), and 5-year (G) based on the nomogram prediction at different threshold probabilities in the TCGA dataset. None: assume an event will occur in no patients (horizontal solid line); All: assume an event will occur in all patients (green dash line). OS, overall survival.
FIGURE 4
FIGURE 4
GSEA analysis in TCGA dataset. (A) The enriched gene sets in GO-BP category by the DEGs between the two risk groups. (B) The enriched gene sets in GO-CC category by the DEGs between the two risk groups. (C) Enriched gene sets in GO-MF category by the DEGs between the two risk groups. (D) Enrichment plot of the DEGs between the high- and low-risk groups using GSEA-KEGG. Each line representing one particular gene set with unique color, and up-regulated genes located in the left approaching the origin of the coordinates, by contrast the down-regulated lay on the right of x-axis. Only gene sets with adjusted p < 0.05 were considered significant. And only several leading gene sets were displayed in the plot. GSEA, gene set enrichment analysis; GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; DEGs, differentially expressed genes; KEGG, Kyoto Encyclopedia of Genes and Genomes.
FIGURE 5
FIGURE 5
Correlation between the 3-gene signature risk score and immune status. (A–C) The differences of Stromal scores, Immune scores and ESTIMATE scores in two risk groups. (D) Barplot showed the composition of 22 immune cells in each patient from the high-risk and low-risk groups analyzed by CIBERSORT. (E) The Violin plot showed the ratio differentiation of 22 immune cells between OC samples with high or low risk scores, and Wilcoxon rank sum was used for the significance test. (F) Heatmap showing the correlation between 22 kinds of immune cells and the DE-FRGs, the asterisk in each tiny box indicating the p value of correlation between two kinds of parameters. The shade of each tiny color box represented corresponding correlation value between two parameters, and Spearman coefficient was used for significance test. ESTIMATE, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data; DE-FRGs, Differentially Expressed-ferroptosis-related genes.
FIGURE 6
FIGURE 6
Treatment response prediction of chemotherapy and immunotherapy in the high-risk and low-risk ovarian cancer patients. (A) Comparison of the expressions of the immune checkpoint molecules between the low- and high-risk groups. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001. (B) Circle plot illustrating the association between risk score and main immune checkpoint molecules. (C) Immunotherapy response prediction of ovarian cancer patients in the low- and high-risk groups based on TIDE analysis. (D) Immunotherapeutic responses to anti-CTLA-4 and anti-PD-1 treatments in high- and low-risk patients. (E) IC50 values of 8 typical or potential therapeutic agents for ovarian cancer in the Genomics of Drug Sensitivity in Cancer database for low- and high-risk groups.
FIGURE 7
FIGURE 7
Inhibition of HIC1 improved drug sensitivity of chemotherapy and anti-PD1 therapy via inducing ferroptosis in ovarian cancer cells. (A) HIC1 expression in five ovarian cancer cell lines (ES-2, OVCAR5, HEY, A2780, and SKOV3) and the human normal ovarian epithelial cell line IOSE80 was detected by Western Blotting. A2780 cells were transfected with negative control siRNA or HIC1 siRNA and then GSH content, GSSG content, the ratio of GSH/GSSG (B–D) and MDA content (E) were assessed. HEY cells were transfected with empty vector or HIC1 expression plasmid, then GSH content, GSSG content, the ratio of GSH/GSSG (F–H) and MDA content (I) were detected. (J) A2780 cells were treated 2 μM Fer-1 or 20 μM DFO for 24 h after transfected with negative control siRNA or HIC1 siRNAs, then cell viability was evaluated by CCK8 assays. Cell viability was evaluated by CCK8 assays in A2780 cells with or without HIC1 knockdown and HEY cells with or without HIC1 overexpression after treatment with different concentration of cisplatin (K), paclitaxel (L), or BMS-1 (M) for 24 h, respectively.
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