Ferroptosis regulator genes are a favorable biomarker for hepatocellular carcinoma

Background In this study, we comprehensively analyzed the relationship between ferroptosis regulator genes (FRGs) and prognosis of hepatocellular carcinoma (HCC), determined the prognostics value of FRGs, established a prediction model, and explored the relationship with immunotherapy for HCC. Methods The mRNA transcriptional levels and clinical information of HCC were obtained from The Cancer Genome Atlas (TCGA) database. The 24 FRGs were combined with the differential expression genes (DEGs) of HCC for further analysis. The prognostics values of differential FRGs via the construction of model and validation by the Cox regression analysis. Result There were three genes (CARS1, FANCD2, and SLC7A11) were identied as independent risk factors for HCC, and a predictive model was constructed based on CARS1, FANCD2, and SLC7A11. The model showed that the low-risk group HCC patients with a more prolonged overall survival (OS) than the high-risk group (P=0.001). The high-risk group with higher expression of FRGs than the low-risk group. Finally, the relations between FGEs and immune inltration showed that CARS1, FANCD2, and SLC7A11 had a positive relationship with macrophage inltration. From these, three genes might be the potential therapeutic targets. Our study indicated that CARS1, FANCD2, and SLC7A11 might have potential value for therapeutic strategies as practical and reliable prognostic tools for HCC.


Introduction
HCC is one of the leading digestive cancer, and ranking fth in cancer-related death, Chinese patients composed more than half of these deaths. In China, since the high incidence and mortality, HCC has become a big challenge for public healthy [1][2][3] . With the advanced diagnostics and therapeutics methods, the lifespan of HCC has been lengthened via induvial and comprehensive therapy such as surgical resection, liver transplantation, radiofrequency ablation, and chemotherapy according to [4,5] . However, the rates of recurrence, metastasis, and mortality remain high. Therefore, it urgently needs to understand further the underlying mechanisms of tumorigenesis, progression, recurrence, and therapeutics resistance for HCC.
Induction of tumor cell death is one of the clinical cancer treatment strategies. Apoptosis and necrosis are the two most common forms of cell death. Dr. Stokes reported in 2008 that iron death, a cell death pattern characterized by the iron-dependent accumulation of reactive lipid oxygen [6] . Because of its morphological, molecular biological, and metabolic characteristics, it is different from other types of cell death and has become one of the research hotspots in cancer. Numbers of studies revealed that iron death could be found in multiple cancer types. Several studies have shown that iron-death was the regulator of tumor cell growth in ovarian cancer, head and neck cancer, liver cancer, cervical cancer, pancreatic cancer, and kidney cancer [7][8][9][10][11] . Iron death may serve as the anti-cancer effect, and there were several studies indicated that accelerated the iron death to promote the death of cancer cells [12,13] . As the largest digestive gland, the liver is closely related to iron metabolism. Current studies showed two forms (iron metabolism and lipid metabolism) of iron death for HCC tumor cells [14][15][16] . Sorafenib is one of the commonly used targets therapy drugs for HCC patients. One of the anti-cancer mechanisms of sorafenib is increasing lipid oxidation levels, resulting in cell death closely related to iron death [17] .
Furthermore, sorafenib combined with other drugs can enhance iron death for HCC tumor cells [16] .
In the present study, we aimed to investigate the relationship between FRGs and the prognosis of HCC and immune cell in ltration. There are two phrases in the present study that including the discovery phase and the veri cation phase. In the discovery phase, FRGs that are important to the prognosis of HCC were screened out through bioinformatics analysis. In the subsequent validation phase, the relationship between the screened genes and prognosis was evaluated in The Cancer Genome Atlas (TCGA) cohort and the tissue microarray-based cohort. A clinical model was established for internal data validation.
Finally, we further evaluated the relationship between selected FRGs and immune in ltration also explored their predictive value for HCC.

Data source
We obtained RNA-seq transcriptomic data and relevant clinical data of patients with primary HCC from TCGA (https ://cancergenome.nih.gov/). All RNA-SEQ gene expression data has been standardized using Perl. A total of 370 HCC cases and 50 normal control samples were included for subsequent analysis. ACyl-coa Synthetase long-chain family member 4 (ACSL4) [18,19] . We used the "EdgeR" software package to identify FRGs differentially expressed between HCC samples and standard control samples. Univariate Cox regression analysis was used to analyze the prognostic value of differentially expressed genes for HCC, and genes with a P value less than 0.05 were selected.

Risk Characteristics and Prognosis
To further evaluate the relationship between the above-screened gene expression and HCC patients' prognosis, after determining the independent FRGs and their coe cients, we used a free-based risk score prediction model to divide HCC patients into high-risk and low-risk groups. Receiver operating characteristic (ROC) curves were used to detect the predictive e ciency of the survival model. Signi cant factors (including age, sex, histological grade, and clinical stage) were screened for HCC. Simultaneously, to further understand the genetic relationship with the immune, using the Timer database (https://cistrome.shinyapps.io/timer/) is analyzed, and explores the value of his and the prognosis of patients with HCC.

Statistical analysis
For continuous variables, differences between the groups were analyzed using ANOVA, a t-test, or a Wilcoxon rank-sum test following the data's concrete types. A chi-squared test was used to differentiate the rates of different groups. The ROC curves and the AUC measured the diagnostic accuracy of the genes. The Kaplan-Meier method was conducted to calculate the overall survival curves. A log-rank test was used to determine differences in survival rates. These analyses were performed by utilizing MedCalc software (15.2.2; Mariakerke, Belgium). Statistical signi cance was considered at P < 0.05.

IHC staining
The tumor tissue and adjacent non-tumor tissue were xed in 10% formalin for 1 week, then embedded in para n, and then the tissue specimens were sectioned, depara n treated, and the antigen was repaired by EDTA Antigen Retrieval Solution( Solution). The following antibodies were used for immunostaining of CARS (proteintech, 15296-1-AP), SLC7A11 (proteintech, 26864-1-AP), and FANCD2 proteintech, 24006-1-AP).

Construction and validation of prognostic features
The three genes in the above FRGs (CARS1, FANCD2, and SLC7A11) were used as predictive genes, univariate Cox analysis was conducted, and the coe cients were extracted from them. The risk score formula for OS prediction was as follows: Risk score= (CARS1* 0.049) + (FANCD2* 0.266) + (SLC7A11* 0.073) (Figure3, A), suggesting that FANCD2 has the most signi cant in uence on the prognosis of patients, followed by SLC7A11 and CARS1, which are independent risk factors affecting the prognosis of HCC patients. We used time-dependent ROC analysis to assess the prognostic value of these three risk genes, and the results showed that the area under the ROC curve (AUC) of 1-year, 3-year, and 5-year OS was 0.713, 0.637, and 0.627, respectively (Figure3, B). Patients were divided into high-risk and low-risk groups based on the median risk, and the distribution of risk scores and the survival time and status of patients were shown in Figure 4. Survival analysis showed that patients in the high-risk group had a worse OS than those in the low-risk group (Figure3 C).
The three genes included above were included as independent OS risk factors in the nomogram, which had a total number of 160. The number of points for each factor was determined by drawing a vertical line upward. The sum of the points corresponding to the three factors was the patient's total number of points. The patients' survival probability was 1 year, 3 years, and 5 years when projected to the corresponding points of the total number of points. It can be seen that the more total points, the worse the prognosis. The calibration curves for 1-year, 3-year, and 5-year survival in the validation cohort showed good tting. (Figure 5).
These three genes were included as independent OS risk factors in NOMogram, a total of 160 genes. The number of points for each aspect is determined by drawing a vertical line upwards. The sum of the points corresponding to the three factors is the total number of patients. When projected to the corresponding points of the total number of points, patients' survival probability is 1 year, 3 years, and 5 years. As can be seen, the higher the total score, the worse the prognosis. The calibration curves for 1-year, 3-year, and 5year survival in the validation cohort showed good tting. (Figure 5).
We performed immunohistochemical (IHC) to test the expression of CARS1, FANCD2 and SLC7A11 proteins in hepatocellular carcinoma tissues and their counterparts. We found that the expression of FANCD2 and SLC7A11 protein in HCC tissue was higher than that in normal tissue, while the expression of CARS1 protein in HCC tissue was not signi cantly different from that in normal tissue (Figure 9).

Discussion
The liver is an essential hub for sugar, lipid, and amino acids' metabolism, the body's three major nutrients. Studies have shown that HCC presents various characteristic metabolic changes [20] , thus providing energy and bio-macromolecular synthesis raw materials for rapidly growing and proliferating tumor cells. The discovery of iron death has attracted extensive attention from researchers. During iron death in tumor cells, intracellular iron, lipid, and amino acid metabolism play a signi cant regulatory role [21,22] . There is currently a lack of systematic research on the iron death of HCC and its regulatory genes.
Studies have identi ed 24 FRGs through high-throughput sequencing [18] . We attempted to study the value of these genes with HCC prognosis. Therefore, TCGA data were used to compare 24 FRGs in HCC and control samples, and 21 genes were found to have differences in expression. Further analysis was conducted on the relationship with OS, and nally, three genes were found to be prognostic factors for HCC patients. Besides, HCC patients were divided into two groups according to their risk level, and there were signi cant differences in OS and some clinical features.
In this study, we aimed to obtain HCC related RNA sequences through TCGA data and perform correlation analysis with FRGs to determine THE FRGs related to HCC prognosis. We found that the prognostic model constructed by three genes (CARS1, FANCD2, and SLC7A11) has an excellent predictive function, which can independently predict HCC patients' prognosis. Moreover, these three genes' expression characteristics are not affected by underlying diseases, suggesting that the prognostic model constructed has good population adaptability. Besides, for HCC patients who have completed genetic testing, clinicians can use this model to make better clinical decisions and enhance follow-up or early intervention for patients with high-risk recurrence factors. CARS1 alias CARS, encoding type 1 ammonia acyl tRNA synthetase, in ammonia acyl tRNA synthetase, the gene is located in chromosome 11 p15. 5 on an essential area of the tumor suppressor gene imprinting genes domain near one of several genes, such as Krebs people nd expression of HBV speci c CARS CD8 (+) T cells can recognize different HBV subtypes, and immune function in HBV transgenic mice transplanted and ampli cation, can effectively control the replication of HBV quickly, only transient hepatic damage [23] . Besides, Cho et al. found that the special region of antigen-presenting cells was associated with CARS secreted by cancer cells to activate the immune response, thus stimulating a strong humoral and cellular immune response in the body [24] suggesting CARS may be related to immunity. FANCD2 gene is closely associated with DNA damage response. Studies have shown that interference with FANCD2 will affect DNA interstrand cross-linking agents' sensitivity, which is correlated with HCC resistance to chemotherapy [25,26] . Komatsu et al. found that FANCD2 plays an important role in DNA replication of HCC cells and inhibiting liver cancer [27] . It was also found that FANCD2 expression was up-regulated in HCC tumor tissues, and patients with high expression had a poor prognosis and were associated with larger tumor size and invasive phenotype.
FANCD2 gene knockout can inhibit the proliferation and invasion of liver cancer cells [27] . SLC7A11, also known as xCT, encodes a highly speci c all-source of cysteine and glutamate. Studies have shown that blocking SLC7A11 can inhibit HCC cells' growth through the ROS/ autophagy pathway [28] . SLC7A11 can induce iron death when lipid peroxides accumulate excessively [29,30] . Yue's study found that SLC7A11 expression was associated with the prognosis of liver cancer [31] .
Immunotherapy is a tumor therapy that utilizes the human body's immune system to generate an antitumor response, providing a new therapeutic approach for the treatment of HCC [32][33][34] , especially the application of immune checkpoint inhibition, with better objective remission rate and longer progressionfree survival time for HCC patients [35] . Our study found that FRGs in 3 were associated with high expression of B cells, macrophages, and dendritic cells in high-risk HCC patients and were associated with prognosis, re ecting that iron death may be involved in the regulation of the immune microenvironment. These differences may promote the growth and development of HCC, leading to poor prognosis. In addition, the use of immune checkpoint inhibitors in high-risk HCC patients may be of more signi cant bene t. Although we found that 3 FRGs are closely related to THE prognosis of HCC, the speci c mechanism of action is poorly understood. Therefore, it is necessary further to determine the prognostic signi cance of these genes in HCC.
In conclusion, we determined that 3 FRGs were associated with the prognosis of HCC and had good predictive value. The model based on these three genes showed good predictive performance. The further immune analysis showed that these three genes may be related to the immunity of HCC and may be potential therapeutic targets.

Declarations
Ethical Approval and Consent to participate All studies were approved by the Ethical Review Committee of the First A liated Hospital of Guangxi Medical University. All participants signed an informed consent form.

Consent for publication
Agree to conditions of submission, BioMed Central's copyright and license agreement and articleprocessing charge (APC).

Availability of supporting data
The data and materials used to support the ndings of this study are available from the corresponding author upon request.

Competing interests
The authors declare no potential con icts of interest.

Authors' Contributions
Tao Peng designed the study. Yongfei He analysis of data, interpreted the results and wrote the article. Tianyi Liang, Shuqi Zhao, Zhongliu Wei and Yongguang Wei collected the data and specimens. Xin Zhou conducted immunohistochemical analysis.Chuangye Han contributed to guidance writing. Tao Peng edited and funding the article. All authors discussed the results, and approved the nal version of the manuscript.  Figure 1 heat map of FRGs expression in HCC tumor tissues and normal tissues (up-regulated in red and downregulated in green). *P < 0.05, **P < 0.01 and ***P < 0.001)   In uence of FRGs on HCC prognosis. A. Single-factor Cox analysis of the three FRGs; B. ROC curves of 3 FRGs for predicting prognosis of HCC; C. Kaplan-Meier overall survival curve of patients in the TCGA data set was divided into a high-risk group and low-risk group according to the risk score. In uence of risk score on the prognosis of HCC patients. A. The heat map shows the expression of three FRGs (up-regulated in red and down-regulated in green); B. Patients were divided into a high-risk group and low-risk group based on the median risk; C. Distribution of risk scores and survival status of patients.     The Expression of 3 FRGs in hepatocellular carcinoma (IHC).