A genomic-clinical nomogram predicting recurrence-free survival for patients diagnosed with hepatocellular carcinoma

Introduction

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and a contributor to the third population of cancer-related deaths, ranking sixth among the most frequent malignancies worldwide (Forner, Reig & Bruix, 2018). Liver resection is the most commonly used therapeutic strategy for HCC, and accounts for an overall 5-year survival rate of ∼70% (Akoad & Pomfret, 2015; Orcutt & Anaya, 2018). However, for these patients, survival is impacted by postoperative tumor recurrence (Tabrizian et al., 2015).

Previous studies have suggested that more than half of HCC patients would suffer from a disease relapse after a hepatectomy (Akoad & Pomfret, 2015). Disease recurrence was divided into two types depending on their different etiologies: those who relapsed within 2 years after surgery (early recurrence) and those whose relapse occurred more than 2 years after the operation (late recurrence) (Sherman, 2008). Many clinical characteristics, such as the AFP level, tumor size, vascular invasion (including microscopic and macroscopic), and HBsAg level proved to be risk factors for recurrence and several predictive models were established to predict the postoperative recurrence of HCC (Lee, Tan & Chung, 2014; Qiu et al., 2017; Chan et al., 2018; He et al., 2018). However, these studies and models only focused on the relationships between clinical traits and HCC recurrence with little focus on the crucial role of molecular data.

Advances in biomedical research and high-throughput technologies have greatly contributed to the identification of molecular biomarkers related to cancer development, recurrence, and prognosis in the past few decades (Teufel, 2015). A large number of mRNAs, microRNAs and other kinds of biomarkers have been identified and regarded as potential targets for cancer diagnosis and treatment (Lee, Tan & Chung, 2014). Biomarkers could reflect the molecular mechanisms of tumor recurrence and, as a result, the identification of reliable biomarkers could improve the accuracy in predicting cancer recurrence, thereby contributing to cancer treatment (Gu et al., 2018; Long et al., 2018). Consequently, gene signatures for recurrence and prognosis prediction are receiving more attention.

In this study we explored differently expressed genes (DEGs) between the HCC and non-tumor samples using data obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. A total of three recurrence-related genes (RRGs) were then identified and a gene-based risk model was established from the data of the TCGA dataset. Furthermore, a genomic-clinical nomogram containing the gene-based risk model and clinical characteristics was constructed to predict the 1-, 2-, and 3-year recurrence-free survival (RFS) for HCC patients. We also assessed the performance of the nomogram using the Harrell’s concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curves, and decision curves analysis (DCA).

Materials and Methods

Results

Exploration of DEGs

Figure 1 reveals the workflow of this study. First, four GEO datasets, GSE25097, GSE36376, GSE14520 and GSE76427 containing 1,812 samples (1,037 HCC samples and 775 non-tumor samples) were obtained to screen DEGs and a total of 1,454, 413, 1,088 and 398 genes were found dysregulated in the tumor tissues, respectively (Figs. S1A–S1E). Meanwhile, 9,314 dysregulated genes were identified in the TCGA dataset (Fig. S1F). After an overlapping analysis, 123 genes containing 12 up-regulated genes and 111 down-regulated genes were regarded as DEGs aberrantly expressed in HCC samples compared to non-tumor samples and were selected for further analysis (Figs. 2A and 2B).

Identification of recurrence-related genes

After a rigorous screening, 252 cases from the TCGA dataset were selected for the identification of RRGs from the discovery set. Using univariate analysis, we found that 79 DEGs were significantly related to HCC recurrence with a p-value < 0.05. LASSO logistic regression analysis was used to find candidates associated with recurrence and the optimal tuning parameters related to the minimum generalization error were determined from 10-fold cross-validation (Fig. 3). Consequently, eight DEGs (PZP, C1RL, PRC1, PTTG1, UBE2C, AFM, SPP2, HGFAC) were screened. Finally, we performed stepwise multivariate regression analysis and found that three genes, PZP, SPP2 and PRC1, were biomarkers independently associated with HCC recurrence and were included in the construction of a gene-based risk model. Using X-tile software, the optimal cut-off values of the three RRGs’ expression levels were found in the discovery and validation sets, and the HCC patients were divided into two groups (low-risk group and high-risk group). The KM analysis showed that the three RRGs were significantly related to HCC recurrence among all of the three datasets (Fig. 4).

According to the relative coefficient in the regression model, a risk score could be calculated to evaluate the risk of recurrence for each patient based on the expression levels of RRGs: risk score = (−0.0793 × PZP expression level) + (0.2295 × PRC1 expression level) + (−0.0662 × SPP2 expression level). We then used X-tile software to select the optimal cut-off value of the risk score for recurrence, and the patients were divided into a low-risk group or a high-risk group in the discovery and validation sets, respectively (Fig. S3). The RFS in the low-risk group was significantly longer than that of the high-risk group among all of the three datasets (Figs. 5A–5C). The C-index of the risk model in the TCGA, GSE14520, and GSE76427 datasets was 0.663 (95% CI [0.615–0.711]), 0.571 (95% CI [0.519–0.623]) and 0.599 (95% CI [0.492–0.706]), respectively. The ROC curves also suggested that the risk model had a good predictive efficiency among all of the three datasets (Figs. 5D–5F). Finally, the calibration curves suggested that the risk model had a good predictive efficiency compared to the observed outcomes for 1-, 2- and 3-year postoperative recurrence (Figs. 5G–5I).

Identification of genomic and clinical factors associated with recurrence

The RNA-seq and clinical characteristics of the TCGA dataset were used for exploring genomic and clinical factors related to recurrence. After removing patients who were diagnosed with fibrolamellar carcinoma and combined hepatocellular-cholangiocarcinoma, and who did not have intact clinical information (including age, height, weight, race, hepatitis B virus status, alcohol consumption, tumor grade, AJCC stage, APF level, platelet level, albumin level, Eastern Cooperative Oncology Group performance status (ECOG-PS) and sex), a total of 162 HCC patients with both intact genomic and clinical information were included for further analysis. Univariate and multivariate cox regression analyses were used to find factors associated with HCC recurrence. As shown in Fig. 6, gene-based risk model, race, AJCC staging system and ECOG-PS were found to be potential factors related to HCC recurrence in univariate analysis. Furthermore, the multivariate analysis indicated that the gene-based risk model, AJCC staging system and ECOG-PS were factors associated with HCC recurrence (Fig. 6).

Construction and validation of genomic-clinical nomogram

Based on the results of the multivariate analysis, a genomic-clinical nomogram including the gene-based risk model, AJCC staging system, and ECOG-PS was established to predict the 1-, 2-, and 3-year RFS for HCC patients (Fig. 7A). The calibration curves of the nomogram for predicting the 1-, 2-, and 3-year probability of recurrence indicated that it performed well (Fig. 7B). The C-index of the nomogram was 0.678 (95% CI [0.618–0.738]).

The DCA results showed that the nomogram was more clinically useful than using the gene-based risk model, AJCC staging system, or ECOG-PS alone (Figs. 7C–7E). Afterward, we performed ROC analysis to assess the discrimination ability of the genomic-clinical nomogram and the results suggested that the AUC of the nomogram in predicting the 1-, 2-, and 3-year probability of recurrence was greater than that of using the gene-based risk model or AJCC staging system alone (Figs. 7F–7H). Finally, according to the total points of each patient in the nomogram, the patients were divided into two groups (low-risk group and high-risk group) using X-tile software, and KM analysis demonstrated that the discrimination ability of the nomogram was satisfactory (p < 0.0001) (Fig. 8).

Discussion

As the sixth most frequently occurring malignancy and the third leading cause of cancer-related deaths, HCC remains a significant medical problem in the world (Forner, Reig & Bruix, 2018). Although advances in therapeutic strategies such as liver resection, transplantation, radiofrequency ablation and transcatheter arterial chemoembolization have improved the chances of survival for certain patients, the prognosis of these patients remains unsatisfactory (Tanwar et al., 2009; Zamora-Valdes, Taner & Nagorney, 2017; Bailey & Sydnor, 2019; Sun et al., 2019). Recurrence is a significant challenge for HCC patients after liver resection and usually leading to a poor prognosis (Chan et al., 2018). Therefore, it is vitally important to find reliable biomarkers in the recurrent progression of the disease and to explore the mechanisms and risk factors closely associated with HCC recurrence.

In this study, we identified 123 DEGs in HCC samples compared to non-tumor samples using data obtained from GEO and TCGA databases. Univariate, LASSO, and multivariate regression analyses were used to find DEGs related to recurrence. Consequently, three RRGs were identified. A gene-based risk model was then constructed using the relative coefficients in the multivariate model and the expression levels of the three RRGs. The risk model had a good predictive efficiency for HCC recurrence. Furthermore, based on the clinical information obtained from the TCGA database, a genomic-clinical nomogram, including the gene-based risk model, AJCC staging system, and ECOG-PS was established, the C-index of the nomogram was 0.678, the calibration curves fitted well and the ROC analysis and DCA also showed the nomogram performed well in discerning those patients with a high probability of recurrence.

Three RRGs, PZP, SPP2 and PRC1, were identified as factors associated with HCC recurrence. PRC1, also known as protein regulator of cytokinesis, is a microtubule binding protein that plays crucial roles in mitosis (Subramanian et al., 2010). Due to its essential function in the cell cycle, dysregulation of PRC1 could be regarded as an important factor for carcinogenesis. The development of various cancers, such as breast cancer (Shimo et al., 2007), lung adenocarcinoma (Zhan et al., 2017) and pancreatic cancer (Nakamura et al., 2004), was associated with the overexpression of PRC1. Meanwhile, the dysregulation of PRC1 was demonstrated to be related to the early recurrence and chemoresistance of HCC (Chen et al., 2016; Wang et al., 2017). PRC1 could contribute to the early recurrence of HCC via the regulation of the Wnt/β-catenin signaling pathway, which has been proven to be a crucial pathway in hepatocarcinogenesis and a potential therapeutic target (Pez et al., 2013). Furthermore, Liu et al. (2018) suggested that knockdown PRC1 using siRNA could block cytokines and limit the proliferation of HCC, which could bring new insights into HCC treatment. Although previous studies have demonstrated that the other two RRGs, PZP and SPP2, were related to the prognosis of HCC, there was no study discussing their potential functions in HCC recurrence (Yang et al., 2018; Zheng et al., 2018). Robust experimental results also suggested that SPP2 and PZP were associated with the stability of spliceosome and chromatin (Warkocki et al., 2015; Klein et al., 2016), and their dysregulation might affect the progression of cell proliferation and apoptosis, which could lead to carcinogenesis and disease progression. In addition, in validation of RRGs’ performance, the overexpression of PZP was found to be negatively associated with recurrence in the TCGA dataset, while positively related to recurrence in the GSE14520 and GSE76427 datasets. Further exploration with larger HCC and non-tumor samples should be performed to validate this issue. In summary, the mechanisms of the three RRGs in HCC development and recurrence have not been clear until now and they should be more closely followed as these could be prospective therapeutic targets for identifying the potential for HCC recurrence.

The genomic-clinical nomogram contained two clinical traits, the AJCC staging system and the ECOG-PS, aside from the gene-based risk model. The AJCC staging system is a conventional staging system used for evaluating the characteristics of cancers, selecting optimal therapeutic strategies and predicting prognosis (Chun, Pawlik & Vauthey, 2018). In this study, patients with AJCC stage II and III had a higher risk for HCC recurrence. According to the 8th AJCC staging system the tumors classified into stage II and III were those with vascular invasion, bigger tumor size, multiple tumor numbers, or the invasion into adjacent organs compared with the stage-I tumors. Tumors with these characteristics were more likely to have a greater invasive ability and a later tumor stage and, as a result, these patients were easier to determine as having HCC recurrence and were given higher points in the nomogram. ECOG-PS was used to assess the patients’ functional capability of self-care (Sorensen et al., 1993). Previous studies demonstrated that ECOG-PS was closely related to the survival of cancer patients. In the BCLC staging system, ECOG-PS was listed as an independent factor for selecting therapeutic strategies and predicting prognosis (Forner, Reig & Bruix, 2018). Recently, a Chinese study discovered that patients with higher ECOG-PS were more likely to have poor molecular phenotypes in colorectal cancer, which could lead to a higher probability of cancer recurrence (Chen et al., 2016). It is clear that more focus should be placed on the functions of ECOG-PS in the recurrence of HCC, which could provide new insights into cancer treatment.

Functional enrichment analysis showed that the DEGs were mainly enriched in extracellular parts, such as the extracellular exosome, extracellular region, and extracellular space. Furthermore, metabolism-related pathways and immune-related pathways were significantly enriched by DEGs. It is well known that the liver is associated with various metabolic processes, which could help to maintain the microenvironmental stability of various tissues (Rui, 2014). As a result, the aberrant metabolism could provide for the unique needs of the tumor cells, contain macromolecular biosynthesis, increase energy production, and maintain the redox balance, which could provide a selective advantage for the proliferation, growth and survival of tumor cells (De Matteis et al., 2018). Recently, the advances of metabolomics have brought new insights into the mechanisms of HCC development, recurrence and prognosis, and the related findings could be therapeutic targets for HCC treatment (Shang, Qu & Wang, 2016). One of the most widely-known special mechanisms of carcinogenesis is immune invasion (Aerts et al., 2016). However, until the discovery of immune checkpoints, including programed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte protein 4 (CTLA-4), there had not been any breakthroughs in immunotherapy of HCC. The immune checkpoint inhibitors have been proven to possess significant clinical functions and are promising strategies for the systemic treatment of HCC (Inarrairaegui, Melero & Sangro, 2018). The metabolism-related pathways and immune-related pathways played crucial roles in the recurrence of HCC and could certainly be potential therapeutic targets in the future.

Several other studies also reported the establishment of models to predict RFS for HCC after liver resection (Cui et al., 2017; Gu et al., 2019). For instance, using the gene expression file GSE76427, Gu et al. (2019) discovered that six long non-coding RNA (MSC-AS1, POLR2J4, EIF3J-AS1, SERHL, RMST and PVT1) were associated with a poor prognosis of HCC and constructed a nomogram including the six-lncRNA signature, TNM stage and ECOG to predict RFS for patients with small HCC. The C-index of their nomogram was 0.684 (95%CI [0.635–0.733]), which was comparable with our nomogram (C-index 0.678; 95% CI [0.618–0.738]). Furthermore, besides the gene-based signature, TNM stage and ECOG were also integrated into their nomogram, which was robust evidence for our nomogram (including the AJCC staging system and ECOG). However, Gu’s study only analyzed one gene expression file with a relatively small number of samples, which could make the results inaccurate to some extent. Our study analyzed a total of 1,645 samples from four GEO datasets, containing 870 HCC samples and 775 normal samples. Consequently, the RRGs found in our study were more reliable. Combined with rigorous validations, it was clear that our nomogram was could performed well in the prediction of RFS for HCC patients after liver resection.

However, limitations existed in the current study. First of all, the expression of RRGs were only explored and validated among online databases; no experiments were conducted to confirm them. Consequently, experiments about the dysregulation of the RRGs and their mechanisms in HCC recurrence should be explored in the future. Secondly, we failed to make a comparison of the genomic-clinical nomogram to other widely accepted staging systems, such as the BCLC staging system. Lastly, we did not perform an external validation for the genomic-clinical nomogram, and further validations with additional data should be performed to validate the performance of our nomogram.

Conclusions

Three new biomarkers were found that were related to postoperative recurrence for HCC patients after hepatectomy and a reliable genomic-clinical nomogram was established to predict the 1-, 2-, and 3-year RFS. The C-index, ROC analysis and DCA showed that the nomogram performed well. These findings could bring new insights into molecular mechanisms for HCC recurrence, contribute to HCC treatment, and help surgeons to predict the likelihood of recurrence and the prognosis for HCC patients postoperatively.

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