Construction of a Risk Score Prognosis Model for Hepatocellular Carcinoma based on Ferroptosis and Pyroptosis Related Genes

doi:10.21203/rs.3.rs-484395/v1

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Construction of a Risk Score Prognosis Model for Hepatocellular Carcinoma based on Ferroptosis and Pyroptosis Related Genes

https://doi.org/10.21203/rs.3.rs-484395/v1

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Background: This study aimed to build a prognostic score model based on ferroptosis- and pyroptosis-related genes to predict overall survival (OS) and optimize the treatment of the hepatocellular carcinoma(HCC) patient.

Methods: The gene expression data and clinical information of HCC patients were acquired from The Cancer Genome Atlas (TCGA). Using data mining approach, this predictive signature was constructed and validated. The performance of predictive signature was assessed by the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC).

Results: A total of 4 genes (ABCC1, G6PD, GSDME, HMOX1) were utilized to construct a prognosis model. The prognosis model exhibited good performance based on the AUC (AUC: 0.826 at 1 years, 0.796 at 3 years, 0.867 at 5 years). The calibration plots showed excellent calibration. Functional analysis revealed that significant enrichment of immune related processes.

Conclusion: A prognostic risk model was developed from ferroptosis- and pyroptosis-related genes for HCC. The model could help clinicians more accurately and convenient predict the OS of individual HCC patients.

Cancer Biology

hepatocellular carcinoma

pyroptosis

overall survival

ferroptosis

signature

Hepatocellular carcinoma(HCC) is the most common form of liver cancer, accounting for around 90% of the incidence of all liver cancers^[1-2]. China accounts for about half of the total number of new HCC cases and deaths worldwide each year^[3]. HCC is associated with high mortality. Prognosis median survival in patients with advanced HCC has been reported to be approximately 6–20 months after diagnosis^[4]. Currently, successful available treatments for HCC remain scarce, the main treatment of HCC is surgical resection^[5]. The best treatment for patients with advanced HCC remains unclear. The early phase of HCC is generally asymptomatic. HCC is often diagnosed at an advanced stage, when it is often untreatable. The multifactorial etiology of HCC makes prognostic prediction challenging, there is a clear need for the development of novel prognostic models. Ferroptosis,the process of programmed cell death caused by the accumulation of iron-dependent lipid hydroperoxides,is a novel phenomenon that differs from apoptosis,necrosis,and other traditional types of cell death^[6]. Pyroptosis is a form of programmed cell death that is highly proinflammatory. Activated caspases cleavage pore-making effect protein Gasdermin D(GSDMD) causing rupture of the plasma membrane, leading to pro-inflammatory cytokine interleukin(IL)-1β and IL-18^[7]. The current considerable research have confirmed ferroptosis and pyroptosis are potentially effective therapy to cure many malignancies^[8-11]. However, whether these genes are correlated with HCC patient prognosis remains largely unknown. In the present study, mRNA expression profiles and corresponding follow-up clinical information were collected from the TCGA database and GEO database . Then, we constructed a prognostic multigene signature with ferroptosis and pyroptosis -related differentially expressed genes (DEGs). In addition,we performed functional enrichment analysis to explore the underlying mechanisms associated with the identified genes.

Datasets

Downloaded data RNA-sequencing (RNA-seq) expression data of HCC and corresponding clinical data were downloaded from TCGA . The TCGA cohort was used as a training cohort. Meanwhile, Samples which include gene expression data and corresponding clinical data were collected from 36 human HCC patients from The First Affiliated Hospital of Zhengzhou University. The training cohort included 50 normal human liver samples and 374 HCC samples. Baseline demographic profiles were collected including TNM stage, gender, age, and follow-up(Table 1,Table 2). Then, a total of 60 ferroptosis-related genes and 33 pyroptosis-related genes from previous systematic reviews were extracted and are provided in Supplementary Table S1 and S2^[12-15]. TCGA and GEO belong to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles.

Table 1

Clinical characteristics of the HCC patients used in the derivation cohort

Characteristic	levels	Overall
n		374
T stage, n (%)	T1	183 (49.3%)
	T2	95 (25.6%)
	T3	80 (21.6%)
	T4	13 (3.5%)
N stage, n (%)	N0	254 (98.4%)
	N1	4 (1.6%)
M stage, n (%)	M0	268 (98.5%)
	M1	4 (1.5%)
Pathologic stage, n (%)	Stage I	173 (49.4%)
	Stage II	87 (24.9%)
	Stage III	85 (24.3%)
	Stage IV	5 (1.4%)
Gender, n (%)	Female	121 (32.4%)
	Male	253 (67.6%)
Race, n (%)	Asian	160 (44.2%)
	Black or African American	17 (4.7%)
	White	185 (51.1%)
Age, n (%)	<=60	177 (47.5%)
	>60	196 (52.5%)
OS event, n (%)	Alive	244 (65.2%)
	Dead	130 (34.8%)
Histologic grade, n (%)	G1	55 (14.9%)
	G2	178 (48.2%)
	G3	124 (33.6%)
	G4	12 (3.3%)
Age, median (IQR)		61 (52, 69)

Table 2

Clinical characteristics of the HCC patients used in the validation cohort

Characteristic	levels	Overall
n		36
T stage, n (%)	T1	19 (52.8%)
	T2	12 (33.3%)
	T3	5 (13.9%)
	T4	0 (0%)
N stage, n (%)	N0	26 (83.9%)
	N1	5 (16.1%)
M stage, n (%)	M0	28 (84.8%)
	M1	5 (15.2%)
Pathologic stage, n (%)	Stage I	19 (52.8%)
	Stage II	9 (25%)
	Stage III	1 (2.8%)
	Stage IV	7 (19.4%)
Gender, n (%)	Female	20 (55.6%)
	Male	16 (44.4%)
Age, n (%)	<=65	17 (47.2%)
	>65	19 (52.8%)
OS event, n (%)	Alive	18 (50%)
	Dead	18 (50%)
Histologic grade, n (%)	G1	1 (2.8%)
	G2	15 (41.7%)
	G3	18 (50%)
	G4	2 (5.6%)
Age, median (IQR)		66.5 (56.5, 72)

Identification of differentially expressed genes

The Bioconductor package edgeR was uesed to normalized read count values of gene expression. Differentially expressed genes (DEGs) analysis performed by limma R package. The criteria of DEGs were as follows: FDR < 0.05 and |log2FC| ≥ 1. Among all the DEGs, a total of 60 ferroptosis or pyroptosis-related genes.

Development and validation of the ferroptosis and pyroptosis-related genes prognostic model

Cox regression analysis was performed in order to further assess the prognostic values of the DEGs. In order to ensure accurate results, 0.05 was setted as the cut-off P-value, and 19 survival-related genes were identified for further analysis. In order to avoid overfitting problems, Lasso-penalized Cox regression analysis was performed to develop the best prognostic signature. The penalty parameter(λ) adjustment was performed by tenfold cross-validation based on minimum criteria. Finally, for every patient, the individual risk score was constructed as a linear combination of the expression level of each gene multiplied by the regression coefficient derived from the multivariate Cox regression model(beta). The prognostic signature as risk score = (beta1 * expression level of gene1) + (beta2 * expression level of gene2) + (beta3 * expression level of gene3) + (betan * expression level of genen). HCC patients were divided into two groups(high-risk and low-risk groups) by a median of risk score. Then, prcomp function in R from the stats package was used to carry out principal component analysys (PCA). PCA was visualized using the ggplot2 R package. In Kaplan–Meier survival analysis, the cut-off value was determined using the function surv_cutpoint of the survival package in R. In addition, a time-dependent receiver operating characteristic (ROC) curve analysis by employing an R package "survivalROC" was plotted to evaluate the predictive accuracy of the gene signature for time-dependent cancer death.

Quantitative Real-Time PCR and Western blot

To validate our prognostic model, tumor tissue and normal tissue collected from 36 HCC patients in The First Affiliated Hospital of Zhengzhou University. Using the same risk score formula, the risk score of each patient in the validation cohort was calculated, and patients were classified into the high- or low-risk groups according to the cutoff point of the median risk score. Meanwhile, for protective factors, we performed real time quantitative PCR (qRT-PCR) and Western blotting. Ethics approval was obtained from the local Ethics Committee.

qRT-PCR

Total RNA was extracted from the target tissue samples and ground thoroughly in a mortar with liquid nitrogen. We added 1 ml of Trizol reagent (Life technology, Grand Island, NY, United States) directly to the lysis for 15 min at room temperature on a shaker. To assess the expression level of mRNA, Using parts of total RNA , the RevertAid First Strand cDNA Synthesis Kit (thermo scientific, Lithuania) was applied for the synthesis of first-strand cDNA. Quantitative PCR was performed by Roche LightCycler® 480 Real-Time PCR System with SYBR® Green qPCR mix 2.0 kit for measurement. The primers applied in this study are obtained from TsingKe biologicaltechnology(Nanjing,China), The relative mRNA levels were calculated by the 2-ΔΔCt method.

Western blot

The evaluation of protein expression was performed by Western blot analysis. The amounts of protein were denatured for 5 min at 95°C in sample buffer and separated by SDS–PAGE. Western blot analysis was used with the help of antibodies against mouse monoclonal antibody-anti-human ABCC1, G6PD, GSDME, HMOX1 and mouse monoclonal antibody-anti-human β-actin (Santa Cruz Biotechnology, followed by incubation with a horseradish peroxidase (HRP)-coupled mouse secondary antibody (1:10,000). Blots were re-probed with a β-actin antibody (BD Bioscience, United States). Signals were quantified with an image analyzer (UVtec, United Kingdom). The data are showed as the percentages of the normalized control signal.

Functional enrichment analysis

Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses of DEGs were performed by using the “clusterProfiler” R package, The infiltrating score of 16 immune cells and the activity of 13 immune-related pathways were calculated using the single-sample gene set enrichment analysis (ssGSEA) method in the Gene Set Variation Analysis (GSVA) package of R software. Meanwhile,we analyzed the correlation of 3 pyroptosis-related DEGs expression with infiltrating immune cells with the help of ssGSEA. SIGLEC15, IDO1, CD274, HAVCR2, PDCD1, CTLA4, LAG3, and PDCD1LG2 were selected to be immune-checkpoint–relevant transcripts and the expression values of these eight genes were extracted. All the above analysis methods and R package were implemented by R foundation for statistical computing (2020) version 4.0.3 and software packages ggplot2 and pheatmap. The Connectivity Map (CMap) was used to explore a novel and effective therapy drug for HCC.

Identification of prognostic ferroptosis and pyroptosis-related DEGs and Construction of a prognostic model

A total of 50 normal tissue samples and 374 tumor tissue samples of HCC were screened from the TCGA database. Table1 shows the clinicopathological data of the patients with HCC included in the study. A total of 60 differential genes were identified between tumor and the adjacent nontumor tissues. The DEGs expression in tumor and the adjacent nontumor tissues was represented in the heatmap(Fig.1a, blue: low expression level;red:high expression level). Also, The relationship between these genes is presented in Fig1b,which suggesting the tight relationship between these genes.

The prognostic values of DEGs were evaluated by univariate Cox analysis, and 19 genes that were significantly related to the OS of patients were identified. There are many ways to minimize overfitting, recently, Lasso penalized Cox regression has been employed frequently and it could minimize overfitting. To build a prognostic model for HCC, we used training dataset and applied the LASSO Cox regression analysis to identify stable markers from 19-survival-related genes mentioned above. To find an optimal λ, 10-fold cross validation with minimum criteria was employed, so the final model gave minimum cross validation error. Then, 4 DEGs(ABCC1, G6PD, GSDME, HMOX1)were identified(Fig.2a-2b). Using the following formula, the risk score was calculated 0.045673* expression level of ABCC1+ 0.005602* expression level of G6PD+ 0.143950* expression level of GSDME+ 0.005637* expression level of HMOX1. A median cut-off value was applied to stratify patients into a high-risk group (n=183) and a low-risk group (n =182)(Fig.3a). The PCA showed that patients in different risk groups were distributed in two directions by the constructed model cohort (Fig.3b). In the high risk score group, the risk of mortality obviously increased with the risk score increased (Fig.3c). Kaplan-Meier analysis showed that the probability of survival was significantly higher in low-risk patients than in high-risk patients(Fig.3d,p<0.05). Also, the AUC of the time-dependent ROC curve was plotted to assess the prognostic effect. the AUC of the prognostic risk assessment model was 0.826 at 1 years, 0.796 at 3 years, 0.867 at 5 years (Fig. 3e). The calibration plots showed that calibration appeared good between the predicted and observed risks(Fig. 3f).

Validation of the 4-gene prognostic model

In order to evaluate the robustness of the prognostic model constructed using data from the training cohort, its performance was also assessed using validation cohort. The risk score for each patient in validation cohort was calculated with the same formula as training group. Similar, patients in the high-risk group were significantly worse off the overall survival time compared to the low-risk group(Fig.4a). Besides, the AUC of the 4-gene signature was 0.860 at 1 years, 0.724 at 3 years, 0.769 at 5 years(Fig.4b).

Expression level of DEGs in HCC tissues

Quantitative real-time PCR (qRT-PCR) and western blotting was used to detect DEGs expression. The results showed that ABCC1, G6PD, GSDME expression in HCC was significantly higher than that in adjacent tissues. On the other hand, gene expression of HMOX1 showing significantly lower expression in tumors compared to adjacent tissues (Fig.5a). Correspondingly, the expression of ABCC1, G6PD, GSDME protein is upregulated in HCC tissue as compared to normal tissue in comparison to that in normal tissue with the help of Human Protein Atlas.

Independent prognostic value of the risk model

To investigate the prognostic value of different clinical features in HCC, univariate Cox regression analysis was performed. According to the results of univariate Cox regression analysis, the risk model showed their prognostic value in predicting OS(HR= 1.9757, 95% CI: 1.3507−2.8901, Fig.6a). The multivariable Cox regression analysis suggested risk score had prognostic value for HCC patients. Even after adjusting for confounding factors, the risk score is still a prognostic factor(HR= 1.8146, 95% CI: 1.2267−2.6841, Fig.6b) for patients with HCC. Fig.6c shows heatmaps of the pyroptosis-related DEGs modules on the clinical data and found a different distribution of patients' age and survival status between low- and high-risk subgroups.

DEGs-based tumor classification.

In addition, We explore the connections between the expression of DEGs and HCC subtypes, consensus clustering analysis was used to classify HCC patients into subtypes. By increasing the clustering variable (k) from 2 to 10, we found that the highest intra-group correlation and low inter-group correlation was observed when k=2 (Fig. 7a). The results showed that the HCC patients could be well divided into two clusters based on the DEGs.The gene expression profile were matched to the clinical data and was presented in a heatmap (Fig. 7b), We also compared the OS in 2 clusters.There were significant differences in 2 clusters (Fig. 7c).

Functional analyses and infiltrating immune cells

Meanwhile, GO function and KEGG pathway enrichment analysis were used to investigate the intrinsic pathway of DEGs. GO function analysis was performed using three ontologies: biological process (BP), cellular component (CC), and molecular function (MF). Based on GO analysis, the DEGs were most enriched in BP, and specifically in immune related processes(such as T cell activation, regulation of T cell activation, leukocyte cell−cell adhesion, regulation of mononuclear cell proliferation, regulation of leukocyte proliferation, regulation of leukocyte cell−cell adhesion, Fig.8a). In addition, KEGG pathway analyses indicated that these DEGs were highly enriched in Cytokine−cytokine receptor interaction, Neuroactive ligand−receptor interaction, Chemokine signaling pathway, Th1 and Th2 cell differentiation, Th17 cell differentiation. To further explore relationship between immune cell infiltration and risk scores in the prognostic risk mode, ssGSEA score was used to quantify the level of enrichment of immune cell functions or pathways in cancer samples(Fig.9a-b). One interesting finding is the many types of immune cells were significantly different between the two groups(such as macrophages, activated dendritic cell, Treg). Another intriguing fact was the immune cell functions were also significantly different between the two groups(such as APC_co_inhibition, CCR, Check−point, HLA, MHC_class_I, Type_II_IFN_Reponse). Complementarily, we found the expression of immune cheeckpoint have a dramatically different in different tumor grade (Fig.9c). After uploading the upregulated genes in the HCC samples and querying the Cmap database for connectivity scores, the top 3 negatively correlated compounds were filtered out: cicloheximide(-0.538), 4,5-dianilinophthalimide(-0.504), natamycin(-0.484).

Worldwide, liver cancer is the fourth most common cause of cancer-related death, with the sixth highest incidence rate^[16]. HCC accounts for 70%–85% of primary liver cancer cases and is an important medical problem ^[17-18]. If caught in time, HCC can be cured by surgery or liver transplantation, but patients in the early stages of HCC are generally asymptomatic, tumors grow rapidly during this period ^[19]. Many HCC patients are diagnosed at intermediate or late stages, only 20-30% of HCC are detected at an early stage ^[20-21]. As treatment modalities are limited and the high mortality rates of HCC^[22], the prognostic evaluation of HCC patients is very important. Ferroptosis is defined as a novel cell death pattern caused by iron peroxide-dependent lipid peroxide formation^[23]. Iron death is the damage mechanism of some nervous system diseases. Iron ions accumulate in the process of cancer development, and leading to ferroptosis is a new target of cancer treatment. Pyroptosis is a newly identified type of programmed cell death that is different from apoptosis and necrosis and can be initiated in response to various intracellular or extracellular stimuli. Pyroptosis is mainly mediated by Caspase-1/3/4/5/11 and Gasdermin family, and characterized by cell swelling,membrane lysis and release of proinflammatory factors.

In this study, we constructed a prognostic score model of a four-gene signature(ABCC1, G6PD, GSDME, HMOX1) for predicting the prognosis of HCC. with the help of Lasso-penalized Cox regression, the model showed good results by its ability to predict OS for HCC(AUC: 0.826 at 1 years, 0.796 at 3 years, 0.867 at 5 years in training group). Combining our own collected and sequenced samples, it attest to the fact that the four-gene signature is useful tools(AUC: 0.860 at 1 years, 0.724 at 3 years, 0.769 at 5 years in validation group). The calibration plots showed excellent agreement.

ABCC1 is a member of the ABC transporter protein family subfamily. Overexpression of ABCC1 enhances the drug resistance response to tumor chemotherapy^[24]. Interference with G6PD decreases the activity of G6PD, thus reducing the consumption of glucose by cells and decreasing the glycolytic capacity, thereby inhibiting the growth and proliferation of tumor cells and their ability to invade^[25]. GSDME is probably not directly involved in tumor development, but GSDME which as potential predictive biomarker in cancer is an important ally in chemotherapy treatment^[26]. HMOX1 is directly regulated by TGF-beta1 signaling pathway. Previous studies have shown that HMOX1is directly involved in the invasion of GBM cells and poor prognosis.

During the past few years, the role of the ferroptosis and pyroptosis in tumor susceptibility has become an intense area of research, the potential roles of ferroptosis and pyroptosis in the tumor immune remain elusive^[27-28]. Based on the significant results of the GO enrichment analysis, there were immune-related biological processes and pathways were enriched which suggests a surprisingly close association with tumor immunity. From this, it can be inferred that ferroptosis and pyroptosis with tumor immune have a close relationship. Ferroptosis and pyroptosis may have affected progress of the tumor through immune mechanisms. Macrophages, activated dendritic cell, Treg were significantly different between the two groups. Previous studies have demonstrated that parainflammation, a low-grade form of inflammation, is widely prevalent in human cancer. Therefore, widespread parainflammation can explain the enrichment of many immune-related biological processes in HCC patients. The tumor microenvironment (TME) consists of various cell types which can influences tumor progression^[29]. After the early stages, the immune tumor microenvironment shifts to support cancer cells and promote tumor development while suppressing immune cell-mediated cytotoxicity^[29]. For example, tumour associated macrophages promote tumor angiogenesis, nourish tumor cells, and even cause cancer cell metastasis^[30]. Immune cheeckpoint are regulatory molecules that play an inhibitory role in the immune system and are essential for maintaining self-tolerance, preventing autoimmune responses, and minimizing tissue damage by controlling the duration and intensity of the immune response^[31]. Expression of immune cheeckpoint on immune cells will inhibit the function of immune cells and prevent the body from producing an effective anti-tumor immune response, and the tumor will form an immune escape. On the basis of our results, there was significant difference in immune cheeckpoint between patients with different HCC stages. Previous studies have confirmed Treg was important immune suppressor cells that play a suppressive function in the immune system. Tumor cells can use immune checkpoints to escape immune cell attacks.

In summary, we developed a novel prognostic model based on four ferroptosis and pyroptosis related genes and used it to predict the prognosis risk of in HCC patients. This model proved to be significantly associated with the OS in both the derivation and validation cohorts, which might provide insight into the prediction of HCC prognosis.

TCGA: The Cancer Genome Atlas; DEGs: Differentially expressed genes; OS: Overall survival; ROC: Receiver operating characteristic; FDR: False discovery rate; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; ssGSEA: Single-sample gene set enrichment analysis; PCA: Principal component analysis; AUC: Area under the curve; HR: Hazard ratio; CI: Confidence interval; APC: Antigen presenting cell; aDC: Activated dendritic cell; iDC: Immature dendritic cell; pDC: Plasmacytoid dendritic cell; Tcm:T central memory;Tfh: T follicular helper cell; TIL: Tumor Infiltrating Lymphocyte; Tgd: T gamma delta; HLA: Human leukocyte antigen; CCR: Cytokine-cytokine receptor.

Ethics approval and consent to participate:

All procedures performed in the studies involving human participants were in accordance with the Scientific Research Projects Approval Determination of Independent Ethics Committee of The First Affiliated Hospital of Zhengzhou University, with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Patient consent:

Obtained.

Consent for publication:

All authors consent to publication of this paper.

Availability of data and material:

The data was obtained from TCGA(https://portal.gdc.cancer. gov/repository). The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles.

Competing interests:

This article has no conflict of interests

Funding:

This study received Fundamental research program funding of Ninth People's Hospital affiliated to Shanghai Jiao Tong university School of Medicine (No. JYZZ076), Clinical Research Program of Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine (No. JYLJ201801, JYLJ201911), the China Postdoctoral Science Foundation (No. 2017M611585) and the National Natural Science Foundation of China (No. 81871458).

Contributors:

Xi-tao Yang, Xue-yuan Li designed experiments;Lu-yu Zhang, Xin-yu Li , carried out experiments, and wrote the manuscript, Xi-tao Yang, Xue-yuan Li performed manuscript review.

Acknowledgements:

no other acknowledgements.

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Construction of a Risk Score Prognosis Model for Hepatocellular Carcinoma based on Ferroptosis and Pyroptosis Related Genes

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Abstract

Figures

Background

Materials And Methods

Datasets

Identification of differentially expressed genes

Development and validation of the ferroptosis and pyroptosis-related genes prognostic model

Quantitative Real-Time PCR and Western blot

qRT-PCR

Western blot

Functional enrichment analysis

Results

Identification of prognostic ferroptosis and pyroptosis-related DEGs and Construction of a prognostic model

Validation of the 4-gene prognostic model

Expression level of DEGs in HCC tissues

Independent prognostic value of the risk model

DEGs-based tumor classification.

Functional analyses and infiltrating immune cells

Discussion

Conclusion

Abbreviations

Declarations

Ethics approval and consent to participate:

Patient consent:

Consent for publication:

Availability of data and material:

Competing interests:

Funding:

Contributors:

Acknowledgements:

References

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