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A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma

Abstract

Background

Hepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice.

Methods

Using The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model’s effectiveness.

Results

We identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. The validation in both the TCGA cohort and GEO cohort demonstrated that the 7-gene prognostic model can predict the RFS of HCC patients. Meanwhile, the results of the multivariate Cox regression analysis showed that the 7-gene risk score model could function as an independent prognostic factor. In addition, according to the time-dependent ROC curve, the 7-gene risk score model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. Furthermore, these seven genes were found to be related to the occurrence and development of liver cancer by exploring three other databases.

Conclusion

Our study identified a seven-gene signature for HCC RFS prediction that can be used as a novel and convenient prognostic tool. These seven genes might be potential target genes for metabolic therapy and the treatment of HCC.

Peer Review reports

Background

In 2018, liver cancer remained among the top six prevalent carcinomas. There were 841,080 new patients, and 781,631 patients died of liver cancer according to the Global Cancer Statistics [1, 2]. Hepatocellular carcinoma (HCC) is the most frequent liver cancer, accounting for approximately 90% of primary liver cancers [3]. Currently, Hepatectomy and Radiofrequency ablation are the main two ways to treat HCC [4, 5]. Despite the continuous development of medical technology, the outcome of many patients who receive treatment and the prognosis of liver cancer remain poor with a 2-year recurrence rate of 76.9% [6,7,8]. And many studies have shown that HCC is the most difficult to cure cancer, and because of this, HCC has been described as a “chemoresistant” tumor [9]. Because of this, the prognosis of HCC is poor. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan [10]. Thus, it is necessary to accurately forecast HCC patients’ prognosis to improve the prognosis of HCC. Most previous studies constructed prognostic models using the Tumor-Node-Metastasis (TNM) staging system to assess the prognosis of HCC patients [11]. However, the TNM staging system does not predict the prognosis of HCC. Therefore, it is important to develop a reliable tool for clinicians to predict the prognosis of patients with HCC.

Given the remarkable advances in high-throughput technologies, the development of The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) and the intergovernmental Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/gds) database provides an abundance of high-quality information regarding HCC [12]. Hence, it is urgent to develop methods to identify reliable therapeutic gene targets that could enable earlier prognostic evaluation and better therapeutic strategies [13]. Therefore, we considered whether we could build a gene-based risk score model [14]. Our goal was to generate simple and effective prognostic tools based on several genes and other factors that may affect RFS [13, 15]. Using the TCGA dataset, we selected 7 genes by robust likelihood-based survival modeling and built a risk score system [16, 17]. We used an independent dataset (GSE76427) to validate the effectiveness of the risk score system and demonstrate that its clinical value in predicting RFS in HCC patients is better than that of the TNM staging system.

Methods

Data collection and survival analyses

First, we downloaded gene expression profiles and clinical information from The Cancer Genome Atlas-liver hepatocellular carcinoma (TCGA-LIHC) dataset, which included 334 HCC samples [18]. We used GSE76427, which contained the gene expression and clinical information of 115 HCC samples, as the validation group. The samples in TCGA-LIHC and GSE76427 that met the following inclusion criteria were included in this study: all samples had mRNA sequencing data and clinical information related to RFS [19].

Identification of genes associated with RFS

The raw count data were normalized with a log(a + 1) transformation. Then, using the “survfit” function in the “survival” package, we plotted Kaplan-Meier curves for the high and low expression groups of each gene. A log rank test with a p-value less than 0.05 was considered statistically significant [20].

Enrichment analysis of GO functions and KEGG pathways

For the selected genes, we used WebGestalt (http://bioinfo.vanderbilt.edu/webgestalt) based on Gene Ontology (GO) functions and the Kyoto Encyclopedia of Genes and Genomes (KEGG) to understand the biological significance of the identified genes [21].

Identification of the best genes for modeling

A robust likelihood-based survival approach was used to identify the best genes for modeling after determining the genes associated with RFS [22]. We used the “rbsurv” package in R to complete this modeling process.

Construction and validation of the risk score system

A multivariate Cox regression analysis and “rbsurv” analysis were performed to identify the genes related to RFS and construct the prognostic gene signature. The “survivalROC” package in R was used to investigate the time-dependent prognostic value. The optimal cut-off values based on ROC curves were obtained to classify the patients into low-risk groups and high-risk groups. A calibration curve and the concordance index (C-index) were used to evaluate the risk score system.

External validation of the risk score system

We calculated the risk score in the GSE76427 dataset. Then, the AUCs of the 12-month, 15-month, and 18-month RFS and Kaplan-Meier curves were used to verify the risk score system. A calibration curve was used to validate the risk score system. In addition, the prognosis-related genes included in the risk score system were verified at the protein level by using The Human Protein Atlas database. The CBioPortal for cancer genomics was used to study genetic alterations in the risk score system [23].

Statistical analysis

The statistical tests were performed using R software and SPSS. Univariate and multivariate Cox regression analyses were performed using a forward stepwise procedure. A p-value less than 0.05 was considered statistically significant [23].

Results

Acquisition of the gene expression and clinical data

We downloaded the TCGA-LIHC dataset from The Cancer Genome Atlas (http://portal.gdc.cancer.gov/). The TCGA-LIHC dataset included 334 samples, 308 patients received hepatectomy, and the remaining 26 patients received radiofrequency ablation, and all samples included data regarding the RFS time and censoring status. The GSE76427 dataset was downloaded from the Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/gov/). The GSE76427 dataset included 115 samples from HCC patients, but 7 patients had missing information regarding the RFS time and censoring status. Thus, 108 samples were included in this study, all 115 patients received hepatectomy. The median RFS times in the TCGA and GSE76427 series were 390 and 252 days, respectively, and the two datasets contained clinical information, such as gender, age, and the TNM stage.

Genes associated with RFS

We used the “survfit” function in the “survival” package and found 1331 genes associated with RFS. Then, to explore the genetic biological implications, we analyzed the 1331 genes through Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. As shown in Fig. 1, in the KEGG analysis, we found that these genes are enriched in signaling pathways, such as the cell cycle, homologous recombination, DNA replication, the Fanconi anemia pathway, complement and coagulation cascades, and the T cell receptor signaling pathway.

Fig. 1
figure1

GO functional and KEGG pathway analyses. a Summary of the differentially expressed genes and GO pathway enrichment. Red, blue, and green bars represent the biological process, cellular component, and molecular function categories, respectively. The height of the bar represents the number of differentially expressed genes observed in each category. b The top 10 pathways of genes associated with RFS

Construction of the prognostic model in TCGA-LIHC

Then, “rbsurv” was used to identify seven genes to construct the risk score system. The seven genes included in the system were TTK protein kinase (TTK), chromosome 16 open reading frame 54 (C16orf54), phosphoribosyl pyrophosphate amido transferase (PPAT), CD3e molecule associated protein (CD3EAP), solute carrier organic anion transporter family member 2A1 (SLCO2A1), acetyl-CoA acetyltransferase 1 (ACAT1), and growth-arrest specific 2 like 3 (GAS2L3) (Table 1).

Table 1 The best genes predicting recurrence-free survival of hepatocellular carcinoma patients

The risk score was calculated with the following formula: risk score = (− 0.038)*expression of TTK+(− 0.357)*expression of C16orf54 + 0.634*expression of PPAT+ 0.221*expression of CD3EAP+(− 0.076)*expression of SLCO2A1 + (− 0.184)*expression of ACAT1 + 0.277*expression of GAS2L3.

In total, 334 patients were divided into two groups (134 high-risk patients and 200 low-risk patients) using a cut-off of 4.9798 for the risk score. Furthermore, the survival curve revealed that the RFS in the high-risk group was significantly poorer than that in the low-risk group (p < 0.0001; Fig. 2).

Fig. 2
figure2

Analysis of the seven-gene signature of HCC in TCGA dataset. a Risk score of each patient; b The RFS time and RFS status of the HCC patients; c the expression levels of TTK, C16orf105, PPAT, CD3EAP, SLCO2A1, ACAT1 and GAS2L3 in the signature; Kaplan-Meier analysis of the TCGA dataset; d The Kaplan-Meier curve for the risk score model in TCGA dataset

Validation of the prognostic model in GSE76427

We validated the risk score system in the GSE76427 cohort. In total, 108 patients were divided into two groups (45 high-risk patients and 63 low-risk patients) using a cut-off of 3.4144 for the risk score. Furthermore, the survival curve revealed that the RFS in the high-risk group was significantly poorer than that in the low-risk group (p = 0.011; Fig. 3). In summary, these results indicate that the prognostic model has moderate sensitivity and specificity.

Fig. 3
figure3

Analysis of the seven-gene signature of HCC in GEO dataset. a risk score of each patient; b The RFS time and RFS status of the HCC patients; c The expression levels of TTK, C16orf105, PPAT, CD3EAP, SLCO2A1, ACAT1 and GAS2L3 in the signature; Kaplan-Meier analysis of the GSE76427 dataset; d The Kaplan-Meier curve for the risk score model in GEO dataset

Association between the prognostic model and the clinical characteristics of the patients

While assessing the correlation between the seven-gene signature and the clinical characteristics of the HCC patients, we found that a high risk score was significantly correlated with the TNM stage (p < 0.001), grade (p = 0.001), and AFP (p = 0.014), but was not significantly associated with the gender, age, BMI, or Child-Pugh score of the patients with HCC (Table 2). In GSE76427, the results showed that the 7-gene signature was not significantly associated with gender, age, BCLC (Barcelona Clinic Liver Cancer) or the TNM stage (Table 3).

Table 2 Characteristics of HCC patients in TCGA-LIHC dataset
Table 3 Characteristics of HCC patients in GSE 76427 dataset

Independent prognostic role of the prognostic gene signature

Moreover, the results of the multivariate Cox regression analysis showed that the TNM stage (HR = 1.680, p < 0.001) and our prognostic model (HR = 3.607, p < 0.001) were both independent factors of RFS among the 334 TCGA-LIHC patients. However, among the 108 patients in the GSE76427 cohort, the TNM stage was not an independent prognostic factor for RFS [24]. The prognostic model (HR = 2.407, p = 0.014) was also an independent factor for RFS (Fig. 4). In addition, we performed univariate and multivariate Cox regression with other well-known pathological factors such as vascular invasion and hepatic virus infection status in TCGA-LIHC hepatectomized patients. The results prove that our prognostic model is an independent prognostic factor as well (Table 4).

Fig. 4
figure4

Multivariate Cox regression analysis. a Multivariate Cox regression analysis of the TCGA dataset. b Multivariate Cox regression analysis of the GSE76427 dataset

Table 4 Univariate and multivariate Cox regression in TCGA-LIHC hepatectomized patients

Comparison of the TNM stage model and BCLC model

To compare the accuracy of the prognostic model and the TNM model, we calculated the AUCs of the 12-month, 15-month, and 18-month RFS. In the TCGA-LIHC dataset, the prognostic model’s AUCs of the 12-month, 15-month, and 18-month RFS were 0.7768, 0.7934, and 0.7529, and the TNM model’s AUCs of the 12-month, 15-month, and 18-month RFS were 0.6884, 0.7026, and 0.6721, respectively (Fig. 5). In the GSE76427 dataset, the prognostic model’s AUCs of the 12-month, 15-month, and 18-month RFS were 0.6159, 0.6118, and 0.6217, and the TNM model’s AUCs of the 12-month, 15-month, and 18-month RFS were 0.6122, 0.6009, and 0.5762, respectively. In addition, the BCLC model’s AUCs of the 12-month, 15-month, and 18-month RFS were 0.5669, 0.5627, and 0.5684, respectively (Table 5). Overall, our prognostic model showed a benefit in predicting the RFS, which might help doctors with targeted treatment (Fig. 6).

Fig. 5
figure5

Validation of the risk score predicting RFS for HCC patients in TCGA-LIHC dataset. a The prognostic model’s AUCs of the 12-, 15-, and 18-month RFS in the TCGA-LIHC dataset. b The TNM stage model’s AUCs of the 12-, 15-, and 18-month RFS in the TCGA-LIHC dataset

Table 5 Comparison of the prognostic model with the TNM and BCLC model
Fig. 6
figure6

Validation of the risk score predicting RFS for HCC patients in GSE76427 dataset. a The prognostic model’s AUCs of the 12-, 15-, and 18- month RFS in the GSE76427 dataset. b The TNM stage model’s AUCs of the 12-, 15-, and 18-month RFS in the GSE76427 dataset. c The BCLC model’s AUCs of the 12-, 15-, and 18-month RFS in the GSE76427 dataset

Development of the calibration curve

We calculated the C-index and drew calibration curves for the 12-, 15- and 18-month survival predictions to evaluate the calibration in the TCGA-LIHC dataset and the GSE76427 dataset. The C-index of the TCGA-LIHC dataset and GSE76427 dataset was 0.717 and 0.647, respectively, as shown in Figs. 7 and 8.

Fig. 7
figure7

Calibration curve for the 12-month, 15-month, and 18-month periods in the TCGA-LIHC dataset. a The prognostic model was used to generate a calibration curve for the 12-month RFS prediction. b The prognostic model was used to generate a calibration curve for the 15-month RFS prediction. c The prognostic model was used to generate a calibration curve for the 18-month RFS prediction

Fig. 8
figure8

Calibration curve for the 12-month, 15-month, and 18-month periods in the GSE76427 dataset. a The prognostic model was used to generate a calibration curve for the 12-month RFS prediction. b The prognostic model was used to generate a calibration curve for the 15-month RFS prediction. c The prognostic model was used to generate a calibration curve for the 18-month RFS prediction

External validation in an online database

The representative protein expression levels of SLCO2A1, PPAT, GAS2L3, CD3EAP, and ACAT1 were explored in the Human Protein Profiles. Then, we explored the TTK, C16orf54, PPAT, CD3EAP, SLCO2A1, ACAT1, and GAS2L3 genes in the CBioPortal for cancer genomics. TTK exhibited the most frequent genetic alterations (3%), and deep deletion was the most frequent alteration. The second most altered gene was CD3EAP (1.3%), and the most frequent alterations were amplification mutations (Fig. 9). The expression levels of the seven genes in different cancers are shown in Fig. 10. In summary, the aberrant expression of these seven genes may explain some of the abnormal expression of these genes.

Fig. 9
figure9

External validation in online databases. a Representative protein expression levels of the seven genes in HCC and normal liver tissue. b Genetic alterations of the seven genes

Fig. 10
figure10

Expression levels of the seven genes in different cancers

Discussion

In this study, we developed a risk score based on seven genes that has the ability to predict the probability of RFS in HCC patients and is more accurate than clinical indicators. Using this model, we can identify patients with HCC who have a higher risk of recurrence, indicating that these patients need more attention. In the TCGA-LIHC dataset, in total, 1331 genes were found to be associated with RFS in HCC patients. In the KEGG analysis, we found that the 1331 genes were enriched in signaling pathways, such as the cell cycle, homologous recombination, DNA replication, the Fanconi anemia pathway, complement and coagulation cascades, and the T cell receptor signaling pathway. This finding suggests that the 7-gene signature might affect the RFS of HCC patients through these pathways. Then, we selected the best 7 genes to develop the risk score model as follows: TTK, C16orf105, PPAT, CD3EAP, SLCO2A1, ACAT1, and GAS2L3. Additionally, our study showed that the TNM staging system is not an accurate indicator for the prediction of RFS in HCC patients, which is consistent with the results of other studies. According to the prognostic model, we divided the patients into low- and high-risk groups, which exhibited significant differences in RFS. This result indicated that the prognostic model could be used as a conventional tool for the prediction of the RFS of HCC patients.

The prognostic model was validated using another independent dataset, i.e., GSE76427. The area under the curve revealed the ability of the prognostic model to differentiate the patients’ prognoses; the survival curve represents the survival of the high-risk group, which had a worse prognosis compared with that of the low-risk group. These findings demonstrate that the prognostic model has the ability to forecast RFS in HCC patients.

Most of the seven genes in our prognostic model have been reported to be involved in cancer. The TTK protein levels differ in human liver cancer between liver cancer cells and adjacent noncancerous liver cells [25]. This study also tested the utility of TTK-targeted inhibition and demonstrated its therapeutic potential in an experimental model of liver cancer in vivo. Furthermore, our study demonstrated its effectiveness and incorporated it into the prognostic model. PPAT, which a member of the purine/pyrimidine phosphoribosyl transferase family, regulates pyruvate kinase activity and cell proliferation and invasion and is a biomarker of lung adenocarcinoma. Acetyl-CoA acetyltransferase (ACAT) was recently reported to be elevated in human cancer cell lines [16]. ACAT1 exhibits acetyltransferase activity and can acetylate pyruvate dehydrogenase (PDH), which affects tumor growth [26].

In other scholars’ prognostic analysis of HCC, CD3EAP is also a predictor, suggesting that CD3EAP is an important predictor of HCC prognosis, but the function of CD3EAP is not completely clear [27]. The function of GAS2L3 is still unknown, and GAS2L3 may be involved in mediating the absorption and clearance of prostaglandins, but its function in liver cancer has not been reported [19]. Moreover, SLCO2A1 and C16orf105 have not been reported in previous HCC studies, indicating that these genes may be potential factors in the treatment of HCC. Understanding the function of these genes may promote the development of HCC treatment.

However, despite the potential substantial clinical significance of our results, this study still has some limitations. One limitation is that although the calibration curve performance and AUC value were excellent in the validation group, multicenter clinical application is needed to further evaluate the external utility of the prognostic model [28]. Second, only 1331 genes were defined as genes associated with RFS and evaluated for the prognostic model construction. Some important genes could have been excluded before building the prognostic model [29]. In addition, knowledge regarding signaling pathways is urgently needed to reveal the functions of these genes in HCC. Finally, other well-known pathological factors, such as vascular invasion and hepatic virus infection status, should be key topics of our further studies. After collecting clinical tumor tissues with pathological information, we will find a way to combine our risk score with these clinical characteristics. Meanwhile, we have realized that many studies showed that different surgical methods had an impact on the prognosis of HCC patients. We will pay attention to distinguishing surgical methods when collecting clinical cases and compare the difference in the predictive effect of risk score on RFS in patients receiving different surgical methods in our future study.

Conclusions

In conclusion, we developed and validated a prognostic model for the prediction of the RFS probability of HCC patients. The simple prognostic model has the ability to predict RFS and could be a useful tool for doctors conducting an evaluation of HCC and selecting treatment plans for HCC patients.

Availability of data and materials

The gene expression profiles and clinical information datasets downloaded from The Cancer Genome Atlas (TCGA-LIHC)(https://portal.gdc.cancer.gov) and the Gene Expression Omnibus (GEO)(https://www.ncbi.nlm.nih.gov), accession numbers: GSE76427. Genetic alterations was retrieved from the cBioPortal website (http://www.cbioportal.org/).

Abbreviations

HCC:

Hepatocellular carcinoma

RFS:

Recurrence-free survival

TCGA:

The Cancer Genome Atlas

GEO:

The intergovernmental Gene Expression Omnibus

ROC:

Receiver Operating Characteristic curve

TNM:

Tumor Node Metastasis

BCLC:

Barcelona Clinic Liver Cancer

TCGA-LIHC:

The Cancer Genome Atlas-liver hepatocellular carcinoma

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

C-index:

Concordance index

AUC:

Area Under Curve

BMI:

Body mass index

AFP:

alpha fetoprotein

HR:

Hazard Ratio

NA:

Not available

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Acknowledgements

The authors would like to thank all patients and staff who have participated in and contributed to the TCGA-LIHC registry.

Funding

This research was partially supported by a grant from the National Natural Science Foundation of China (91180525 to QL). The funder is also the corresponding author, participated in the design of this research, and edited the manuscript.

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Contributions

WW, LW, YY, XX, YL and QL conceived and designed the study. WW, YL and QL analyzed the data. XX, YY and YL performed the literature search. WW, LW, and YY wrote the paper, LW, XX and YL created the Figs. QL reviewed and edited the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Quqin Lu.

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Wang, W., Wang, L., Xie, X. et al. A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma. BMC Cancer 21, 6 (2021). https://doi.org/10.1186/s12885-020-07692-6

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Keywords

  • TCGA
  • Hepatocellular carcinoma
  • Recurrence-free survival
  • Risk score
  • Prognostic model