Establishment of a prediction model of ferroptosis-related genes for overall survival risk in hepatocellular carcinoma and bioinformatic analysis of the novel gene FLT3

doi:10.21203/rs.3.rs-2744966/v1

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Research Article

Establishment of a prediction model of ferroptosis-related genes for overall survival risk in hepatocellular carcinoma and bioinformatic analysis of the novel gene FLT3

https://doi.org/10.21203/rs.3.rs-2744966/v1

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Objective: Hepatocellular carcinoma (HCC) is a malignant tumor caused by malignant transformation of cells in the liver. It is the most common type of chronic liver cancer in adults. Ferroptosis has been found to play a key role in many tumor suppressions, but its prognostic value and key factors in hepatocellular carcinoma need to be further explored.

Methods: We downloaded the RNAseq data and corresponding clinical information of HCC patients from TCGA database and ICGC database. The differential expression analysis and survival analysis of ferroptosis-related genes in tumor and normal tissues were carried out by R software. Univariate and multivariable cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression algorithm were used to build a overall survival (OS) risk prediction model for HCC in TCGA cohort, which was verified in ICGC cohort. The “immunedeconv” and "GSVA" packages were used to explore the important roles of single genes in HCC.

Results: We successfully built a prediction model of 12 ferroptosis-related genes for overall survival risk in HCC, and confirmed the validity of the prediction model in the ICGC cohort. In addition, FLT3 gene was significantly associated with multiple pathways such as HCC tumor inflammation, Apoptosis, inflammatory response, and PI3K-AKT-mTOR.

Conclusions: Ferroptosis plays a key role in the prognosis and treatment of primary HCC, and the prediction model of 12 ferroptosis-related genes for OS risk in HCC has significant effects. The ferroptosis-related gene FLT3, as a key immune-related factor, may become a new prognostic biomarker and a potential target for the treatment of HCC.

Hepatocellular carcinoma

Prognosis and treatment

Ferroptosis

FLT3

Bioinformation analysis

Hepatocellular carcinoma is a malignancy of the liver that occurs in liver cells or intrahepatic bile ducts ^[1]. According to the data released by GLOBOCAN 2020, HCC is the sixth most common tumor worldwide and the third highest mortality rate ^[2]. In clinical practice, the treatment of HCC is mainly based on early diagnosis, surgical resection, postoperative radiotherapy and chemotherapy, which is the key to improving the long-term treatment effect of HCC patients and is also the most effective method for the clinical treatment of HCC. The etiology of HCC is not fully understood, and heterogeneity has been shown to vary greatly at the inter-patient, inter-tumor, and intra-tumor levels ^[3–7], making it challenging to predict the prognosis of HCC patients. Considering the limited treatment strategies and difficult prognosis of HCC, it is necessary to develop a new prognosis model of HCC^[8].

Ferroptosis is an iron dependent form of cell death, which is different from apoptosis, necrosis and autophagy ^[9–11]. Ferroptosis is characterized by the accumulation of lipid peroxide in cells and the imbalance of redox. Compared with other cell death modes, it shows specific biological and morphological characteristics ^[12]. Research findings in recent years, the induction of ferroptosis can cause cancer cell apoptosis, which is expected to become a new alternative to cancer treatment ^[13], especially for malignant tumors with poor effect of traditional treatment schemes ^[14–15]. At present, the most concerned genes in ferroptosis research include GPX4, ACSL4, STAT3, MTOR, KEAP1, etc. which focus on lung cancer, colorectal cancer, gastric cancer, breast cancer, etc. ^[16–20], but there is less research in HCC. A few studies on ferroptosis in HCC are only related to the inducer of ferroptosis, the regulatory process and the physical and chemical methods to induce ferroptosis. There is a lack of in-depth research on the differential expression, positive and negative correlation, and mechanism of action of ferroptosis related genes in tumor tissues and normal tissues.

It is very important to determine effective biomarkers for prognosis of HCC ^[21]. Previous studies have explored some diagnostic and prognostic markers of HCC, such as G6PD, GPX4, NQO1 ^[22–25]. These genes can regulate drug-induced iron degeneration in HCC. However, these markers are not comprehensive, especially lack of in-depth study of individual genes.

In order to explore the differential characteristics and prognostic value of ferroptosis related genes in HCC, we downloaded the mRNA expression profile and corresponding clinical data of HCC patients from the public database, and carried out the following research: 1) The prognosis model of OS in HCC was constructed by screening ferroptosis related genes with significant expression and significant difference in prognosis in TCGA cohort. 2) The prognosis model was validated in ICGC queue. 3) The prognostic model and 12 genes were analyzed by Cox with single and multiple factors. 4) The specific gene FLT3 was analyzed by single gene analysis. Through the above research, we confirmed the significant difference of ferroptosis related genes in HCC tissues and normal tissues. A prognostic prediction model of OS in HCC was constructed, and 12 genes with independent prognostic value were analyzed. We investigated the prognostic value of FLT3 as a specific gene in HCC and its impact and function in HCC patients.

2.1 Data Sources

The RNA-sequencing (RNA-sep) expression (level 3) profiles and corresponding clinical information of 371 cases for HCC were obtained from the TCGA dataset (https://portal.gdc.com). The RNA-sep expression (level 3) profiles and corresponding clinical information of 240 HCC cases and 202 normal samples were obtained from the ICGC database (https:// dcc.icgc.org / releases / current / projects). Ferroptosis-related genes were downloaded from the FerrDB database (http://www.zhounan.org/ferrdb).

2.2 Bioinformatic analysis

The “ggplot2” package in the R software (v4.0.3) was used to study differentially expressed genes (DEGs) in HCC and normal samples. Survival analysis using “survival” and “survminer” R package. Univariate and multivariate cox regression analysis were performed to identify the proper terms to build the nomogram. “forestplot” R package was used to show the p-value, hazard ratio (HR) and 95% confidence interval (CI) of each variable through. After multi-factor cox regression analysis, we used step function to perform the iteration and the optimal model was selected as the final model. “glmnet” package was used for the LASSO regression algorithm which 10-fold cross-validation was used. In signal gene analysis, “immuneeconv” package was used to assess the results of immune score evaluation. “GSVA” package was used to evaluate the correlations between signal gene and 20 pathways, and scores were analyzed by Spearman correlation.

2.3 Statistical analysis

The data used in this study are all standardized TPM data, and the data distribution is close to the normal distribution. Kruskal-Wallis one-way ANOVA is a method for comparing differences between three groups and above. For Kaplan-Meier curves, p value and HR with 95% CI were generated by log-rank tests and univariate Cox proportional hazards regression. All p value < 0.05 was considered statistically significant.

3.1 Research Process

The research process is shown in the flow chart (Fig. 1). The study included 371 HCC patients from the TCGA-LIHC cohort and 231 HCC patients from the ICGC (LIRI-JP) cohort.

3.2 Identification of ferroptosis- related DEGs in TCGA cohort

The “ggplot2” package in the R software was used to study DEGs in HCC and normal samples. Among the 138 ferroptosis-related genes, 113 genes were significantly differentially expressed between tumor tissues and normal tissues (Fig. 2, P < 0.05). There were 79 genes significantly high expression, 18 genes significantly low expression and 16 genes with no significant difference.

3.3 Identification of ferroptosis-related DEGs associated with prognostic in the TCGA cohort

We analyzed the differential expression of 113 ferroptosis-related genes in HCC tissues under different prognostic outcomes. The results showed that the expression differences of 44 ferroptosis-related genes (44/113, 38.9%) in HCC tissues were significantly as sociated with OS. When the prognosis of HCC was poor, 42 genes showed high expression (Fig. 3a, b, c, d) and 2 genes showed low expression (Fig. 3e, f).

3.4 Construction and validation of prognostic model in TCGA cohort

To further investigate the role of ferroptosis-related genes in HCC, we established a prognostic model using 44 ferroptosis-related genes associated with OS in HCC by LASSO Cox regression analysis method. The introduction of the variable λ value determines which variables can optimize the model, and cross-validation can be used to find the optimal λ value (Fig. S1). We identified 12 genes for constructing the prognostic model of OS risk score which was calculated as follows:

risk scores = (0.0911) * G6PD+(0.0124) * KEAP1 + (0.0833) * ATG3 + (0.0469) * MAPK1 + (0.0487) * ATG13 + (0.2367) * MYB + (-0.3434) * FLT3 + (0.0174) * SLC1A5 + (0.0782) * SLC7A11 + (0.0359) * LONP1 + (0.1901) * HILPDA + (0.2949) * PRDX6

Patients were divided into high-risk groups (n = 185) and low-risk groups (n = 185) according to the median cut-off value (Fig. 4a). The Kaplan-Meier curve indicated that the OS of patients in the high-risk group was significantly lower than that in the low-risk group (Fig. 4b, P < 0.001). The survival rate of patients in the high-risk group was lower than that in the low-risk group (Fig. 4c). The predictive performance of the OS risk score was assessed by time-dependent ROC curves. The area under the curve (AUC) reaching 0.829 at 1 year, 0.768 at 3 years, and 0.768 at 5 years (Fig. 4d).

Then we used the ICGC database to verify the prognostic model of 12 genes. The results showed that patients in the high-risk group had a higher risk of survival than those in the low-risk group (Fig. 5a, 5b), and the survival time was shortened (Fig. 5c, P < 0.001). In addition, the AUC of the 12-gene signature was 0.775 at 1 year, 0.748 at 2 year, 0.763 at 3 year, and 0.743 at 4 year (Fig. 5d).

3.5 Independent prognostic analysis of the risk score and 12 genes

We then used univariate and multivariate Cox regression analysis to verify whether the risk score and 12 genes were independent prognostic predictors. Univariate Cox regression analysis showed that the risk score (HR = 3.750, 95% CI = 2.783 − 5.052 ,P < 0.0001) and 12 genes (P < 0.05) were significantly positively correlated with OS in HCC patients (Fig. 6a). We used multivariate Cox regression analysis after adjusting for other confounding factors. FLT3 (HR = 0.541, 95% CI = 0.302–0.970, P = 0.039), MYB (HR = 1.671, 95% CI = 1.075–2.599, P = 0.023) ), PRDX6 (HR = 1.887, 95% CI = 1.338–2.662, P = 0.0003) were still shown to be independent predictors of protective factor for OS in HCC patients (Fig. 6b).

3.6 Functional enrichment analysis of ferroptosis-related gene FLT3

When investigating the role of ferroptosis-related genes in HCC, we found that FLT3 gene was significantly under expressed in HCC tissues (Fig. 7a). Patients with high expression of FLT3 had good prognosis (P < 0.05) (Fig. 7b). The coefficients regression analysis showed that the role of FLT3 was prominent (Fig. S2). We conducted in-depth research on the FLT3 gene. The pan-cancer correlation analysis of FLT3 was performed by the “forestplot” package. FLT3 gene was significantly associated with prognosis of Breast invasive carcinoma (BRCA, HR = 0.67559, CI95% [0.49144, 0.92875], P = 0.0157), Head and Neck squamous cell carcinoma (HNSC, HR = 0.54985, CI 95%[0.41833, 0.72272], P < 0.0001), Kidney renal clear cell carcinoma (KIRC), Acute Myeloid Leukemia (LAML, HR = 1.68519, CI 95% [1.10071, 2.58002], P = 0.0163), Lung adenocarcinoma (LUAD, HR = 0.64355, CI 95% [0.47824, 0.866], P = 0.0036), Sarcoma (SARC, HR = 0.54636, CI 95% [0.36392, 0.82028], P = 0.0036) and other ten types of cancer (P < 0.05), as shown in Fig. 7c.

There was a significant difference in the abundance of immune cells between HCC tissues with high expression of FLT3 gene and HCC tissues with low expression of FLT3 gene by TIMER Scors analysis. In HCC tissues with high expression of FLT3 gene, the abundance of immune cells such as B cell, T cell CD4+, T cell CD8+, Neutrophil, Macrophag, Myeloid dendritic cell was higher than that in HCC tissues with low expression of FLT3 (Fig. 8).

To further explore the biological role of FLT3 gene in HCC tissues, we performed pathway enrichment analysis on FLT3 gene (Fig. 9). The results indicated that the FLT3 gene was associated with multiple pathways in HCC tissues. FLT3 gene was significantly associated with Tumor Inflammation Signature ( loge(S) = 15.02, P = 1.11e − 38, ρSpearman = 0.61, CI95% [0.54, 0.67], npairs = 371), Collagen formation (loge(S) = 15.48, P = 2.92e − 14, ρSpearman = 0.38, CI95% [0.29, 0.47], npairs = 371), EMT markers (loge(S) = 15.59, P = 2.16e − 09, ρSpearman = 0.30, CI95% [0.21, 0.40], npairs = 371), Apoptosis (loge(S) = 15.02, P = 7.84e − 39, ρSpearman = 0.61, CI95% [0.54, 0.67], npairs = 371), Degradation of ECM (loge(S) = 15.44, P = 3.7e − 16, ρSpearman = 0.41, CI95% [0.31, 0.49], npairs = 371]), ECM − relatted genes(loge(S) = 15.31, P = 1.66e − 22, ρSpearman = 0.48, CI95% [0.39, 0.55], npairs = 371) and other pathways. Especially, FLT3 showed a high positive correlation with Tumor Inflammation Signature and a high negative correlation with Tumor Proliferation Signature.

By systematically studying the expression of 44 ferroptosis related genes in (HCC) tumor tissues and their relationship with OS, we constructed a new prognostic model of 12 ferroptosis related genes for the first time, and verified it in an external cohort. Functional analysis showed that the immune pathways related to the 12-gene signature were enriched. Single gene analysis showed that FLT3 might be a potential target and biomarker for the treatment of HCC.

The prognostic model constructed in this study consists of 12 ferroptosis-related genes (ATG13, ATG3, FLT3, G6PD HILPDA, KEAP1, LONP1, MAPK1, MYB, PRDX6, SLC1A5, SLC7A11). Among them, the G6PD gene, which is significantly differentially expressed in HCC tissue, can inhibit erastin-induced ferroptosis when it is inhibited in non-small cell lung cancer cells ^[26–27], and has a certain protective effect on red blood cells, preventing them from being threatened by oxidative substances ^[28–29]. Inactivation of the SLC7A11 gene induces potent ferroptosis in many cancer cells ^[30–31]. Conversely, SLC7A11 overexpression in cancer cells promotes GSH biosynthesis and ferroptosis resistance ^[32]. Studies demonstrate that all other SLC7A11 inhibitors except HG106 induce ferroptosis ^[33]. Elimination and overexpression of the SLC1A5 variant gene can alter the growth of cancer cells and tumors and promote tumorigenesis ^[34–35].

Accumulating evidence indicates that FLT3 is associated with the prognosis of various cancers, providing a potential target for the treatment of malignant tumors. In acute myeloid leukemia (AML), FLT3 is one of the most frequently mutated genes, and mutations in this gene are associated with lower overall survival ^[36]. FLT3 is also associated with a variety of immune cells in breast cancer, namely CD4⁺ T cells, CD8⁺ T cells, myeloid dendritic cells, and neutrophils, and is positively correlated with the abundance of immunosuppressants, especially CTLA4 ^[37]. At the same time, FLT3 is related to various pathways such as tumor infection signaling, angiogenesis, and DNA repair in cancers such as colorectal cancer, pancreatic cancer, and bone cancer ^[38–39].

Based on the above research results, we tested the pan-cancer correlation of FLT3 and found that FLT3 showed significant correlation with BRCA, HNSC, LAML, LUAD, SARC and other cancers. In order to further explore the specificity of FLT3 in hepatocellular carcinoma, we conducted in-depth analysis of FLT3 gene. It was found that patients with low expression of FLT3 gene in HCC have poor prognosis, and patients with high expression of FLT3 gene have a good prognosis. immune cells (such as CD8⁺T cells) in HCC tissues with high FLT3 gene expression were higher than those in HCC tissues with low FLT3 gene expression ^[40–42]. Contrary to HCC, some studies have shown that high expression of FLT3 in blood cancer was associated with poor prognosis ^[43–48]. The mechanism and potential value of FLT3 on tumor development need further study.

Our research also has several limitations. First, our prognosis model was built and verified by retrospective data from public databases, and more prospective real world data are needed to verify its clinical effectiveness. Second, the correlation of risk scores and immune activity in this study has not been resolved experimentally and needs to be confirmed by further studies.

In conclusion, we constructed a prediction model of 12 ferroptosis-related genes for overall survival risk in HCC and validated its effectiveness. At the same time ,we analyzed the dual value of FLT3 gene in the prognosis and treatment of HCC. The ferroptosis-related gene FLT3, as a key immune-related factor, may be a potential target for the treatment of HCC and a novel prognostic biomarker. This study provides new insights for the follow-up in-depth scientific research of ferroptosis genes in the prognosis prediction of HCC.

Acknowledgements

Health Innovation Special Project of Hebei Provincial Key R&D Program (223777101D)

Authors’ contributions

DC, RF: Methodology, Data curation, Writing and Validation; ZS, ML, ZL: Methodology and Validation; WG: Reviewing, Editing and Funding acquisition; JF: Conceptualizaion, Supervision and Funding acquisition

Disclosure of conflict of interest

None.

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No competing interests reported.

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Establishment of a prediction model of ferroptosis-related genes for overall survival risk in hepatocellular carcinoma and bioinformatic analysis of the novel gene FLT3

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Version 1

Abstract

Figures

1 Introduction

2 Materials And Methods

2.1 Data Sources

2.2 Bioinformatic analysis

2.3 Statistical analysis

3 Results

3.1 Research Process

3.2 Identification of ferroptosis- related DEGs in TCGA cohort

3.3 Identification of ferroptosis-related DEGs associated with prognostic in the TCGA cohort

3.4 Construction and validation of prognostic model in TCGA cohort

3.5 Independent prognostic analysis of the risk score and 12 genes

3.6 Functional enrichment analysis of ferroptosis-related gene FLT3

4 Discussion

Declarations

References

Additional Declarations

Supplementary Files

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