Abstract
Objective
Hepatocellular carcinoma (HCC) has become the second most common tumor type that contributes to cancer‐related death worldwide. The study aimed to establish a robust immune‐related gene pair (IRGP) signature for predicting the prognosis of HCC patients.
Methods
Two RNA‐seq datasets (The Cancer Genome Atlas Program and International Cancer Genome Consortium) and one microarray dataset (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520) were included in this study. We used a series of immune‐related genes from the ImmPort database to construct gene pairs. Lasso penalized Cox proportional hazards regression was employed to develop the best prognostic signature. We assigned patients into two groups with low immune risk and high immune risk. Then, the prognostic ability of the signature was evaluated by a log‐rank test and a Cox proportional hazards regression model.
Results
After 1000 iterations, the 33‐immune gene pair model obtained the highest frequency. As a result, we chose the 33 immune gene pairs to establish the immune‐related prognostic signature. As we expected, the immune‐related signature accurately predicted the prognosis of HCC patients, and high‐risk groups showed poor prognosis in the training datasets and testing datasets as well as in the validation datasets. Furthermore, the immune‐related gene pair (IRGP) signature also showed higher predictive accuracy than three existing prognostic signatures.
Conclusion
Our prognostic signature, which reflects the link between the immune microenvironment and HCC patient outcome, is promising for prognosis prediction in HCC.
Keywords: gene pairs, HCC, prognosis, tumor immunology
We used a series of immune‐related genes to construct an immune‐related gene pair. Then the lasso‐penalized Cox proportional hazards regression was applied to develop the best prognosis signature. Finally, we validated our immune‐related gene pair signature.
1. INTRODUCTION
Hepatocellular carcinoma has been recognized as the fifth most common primary malignant tumor and the second leading cause of cancer‐related deaths globally.1 The main risk factor for tumorigenesis is chronic viral hepatitis, alcoholic liver disease, diabetes and nonalcoholic steatohepatitis (NASH).2 The outcome of HCC is poor: according to the Surveillance, Epidemiology, and End Results (SEER) database, the 5‐year survival rate of local hepatocellular carcinoma patients is 30.5%, and the rate is less than 5% for patients with distant metastasis.3 Although partial hepatectomy and liver transplantation are the main treatment methods for early‐stage patients, few patients are eligible for these treatments, and approximately 70% of patients will relapse within five years after surgery.4 Moreover, it is generally observed that HCC is not very sensitive to radiation and chemotherapy. To date, sorafenib and lenvatinib have been approved as targeted therapies for hepatocellular carcinoma by the United States Food and Drug Administration (FDA) to treat unresectable HCC; however, they have limited effectiveness.
It had been shown that several components of the immune system were key factors during tumor development and progression. Recent studies also indicated that dysregulation of the immune system including alteration in the number or function of immune cells, the release of chemokine and cytokine, and expression of inhibitory receptors or their ligands can lead to the progression of hepatocellular carcinoma.5, 6 Moreover, immune checkpoint inhibitors that specifically target PD1/PD‐L1 had indicated a manageable safety and lasting response in advanced hepatocellular carcinoma.7 So far, there is no research which has constructed a prognosis signature by using immune‐related gene.
In this study, based on immune‐related genes from the ImmPort database, we used two RNA‐seq datasets from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) and one microarray dataset (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520) to establish and validate a 33‐immune‐related gene pair signature for hepatocellular carcinoma patients. Then, we investigated the relationship between clinicalpathological factors and the prognostic signature. Finally, we compared this signature with other existing prognostic signatures to prove the predictive effectiveness and accuracy of this signature.
2. METHODS
2.1. Data source
The level‐three RNA‐seq expression data and clinical data of 377 HCC patient samples were downloaded from the TCGA data portal (https://portal.gdc.cancer.gov); patients with an overall survival time less than one month were excluded, and the dataset was randomly split into a training dataset (n = 206) and a testing dataset (n = 106). Another RNA‐seq dataset (n = 207) was downloaded from ICGC, and a microarray dataset (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520) downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo) was used as a dataset for validation of the signature. We downloaded 1534 immune‐related genes from the ImmPort database (https://immport.niaid.nih.gov). The immune‐related genes included cytokines, cytokine receptors, and genes correlated with the T‐cell receptor and B‐cell antigen receptor signaling pathways, natural killer cell cytotoxicity, and the antigen processing and presentation pathways.
2.2. Data preprocessing
When multiple probes matched the same target gene, the average expression value of the probes was used to represent the single gene expression level. When a patient had more than one sample, the average expression value of each gene was used to represent the level of gene expression in the patient.
2.3. Establishment of the prognostic signature based on immune‐related genes
A pairwise comparison was performed between the immune‐related gene expression value in each sample to obtain a score for each IRGP. If the expression level of the first IRG was higher than that of the second IRG in a specific IRGP, the score of this IRGP was 1; otherwise, the score was 0. If the score of an IRGP was 0 or 1 in more than 90% of the samples of the TCGA training dataset or the TCGA testing dataset, then we discarded the IRGP. The log‐rank test was applied to select the prognostic IRGPs (FDR < 0.01) in the training dataset, and then Lasso penalized Cox regression (iteration = 1000) was applied to generate a more stable prognostic gene model by using an R package (glmnet, version: 2.0‐16). The tuning parameter was estimated in the training dataset by performing 10‐fold cross‐validation. The most stable gene pair model was used to construct the prognostic signature, and then patients were assigned into high immune risk and low immune risk groups according to an immune risk cutoff score; the median value of the risk score was set as the cutoff value.
2.4. Validation and assessment of the IRGP signature
To validate the IRGP signature, the risk score was calculated according to the prognostic signature in every testing dataset; then, we assigned patients into low immune risk and high immune risk groups according to the median value of the risk score. The overall survival difference between the low immune risk and high immune risk groups was evaluated by the log‐rank test and Cox regression analysis. In addition, we compared the prognostic signature with three existing gene prognostic signatures by the receiver operating characteristic curve (ROC) curve and c‐index analyses in the full TCGA dataset.
2.5. Gene set enrichment analysis
To understand the underlying biological mechanisms of this immune‐related prognostic signature, we performed gene set enrichment analysis by using the MSigDB hallmark gene set (http://software.broadinstitute.org/gsea/downloads.jsp). An FDR value below 0.25 was considered statistically significant.
2.6. Statistical analysis
All statistical analyses were performed using GraphPad Prism 6 and R software (version 3.5.1, https://www.r-project.org/). The log‐rank test was used to evaluate the relationship between IRGPs and overall survival. The survival curves were generated by the R package “survminer”. The gene model was conducted with the “glmnet” package. The ROC curves were conducted by an R package called “survivalROC”. The c‐index was calculated by the R package “survcomp”.
3. RESULTS
3.1. Construction and definition of the IRGP signature
To make our investigation procedure clearer, the entire workflow is illustrated in Figure 1. As shown in Table 1, a total of 765 HCC patients were included in our study. The TCGA dataset was randomly split into a training dataset (n = 206) and a testing dataset (n = 136). A total of 822 immune‐related genes were common among all datasets, and 337 431 IRGPs were constructed. Ultimately, we kept 99 615 IRGPs after removing IRGPs with a score of 0 or 1 in more than 90% of the samples in the TCGA training or testing datasets. Using the log‐rank test, we selected 188 prognostic IRGPs that were significantly associated with patient overall survival (FDR < 0.01). Next, the prognostic IRGPs were used to construct prognostic gene models by using Lasso penalized Cox regression on the TCGA training dataset. After 1000 iterations, the 33‐gene model, which had the highest frequency of (424) compared with the other nine gene models (Table S1), was used to construct the prognostic signature (Figure 2A). The 33‐IRGP prognostic signature information is shown in Table 2. The 33 IRGPs could accurately predict patient prognosis in the training dataset (Figure S1). The area under the receiver operating characteristic curve (AUC) values of the 1‐, 3‐, and 5‐year survival rates were 0.912, 0.918, and 0.816, respectively, in the training dataset (Figure 2D), which demonstrated that the predictive ability of our IRGP prognostic signature was promising. In the training dataset, the risk score of each patient was calculated with the immune prognostic signature, and then patients were assigned into low immune risk and high immune risk groups according to the median risk score. As shown in Figure 2C, the high immune risk group had a poorer prognosis than the low immune risk group (HR: 10.89, 95%CI: 8.09‐21.07, P < .0001) in the training dataset. We also found consistent results in the subgroup analysis (Table 3, Figure 2).
Figure 1.
The workflow describes the construction and validation of our 33 IRGPs. The TCGA data were assigned into a training dataset (206) and a testing dataset (136), and the training dataset was used to construct immune‐related gene pair signatures. The testing, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520 and ICGC datasets were used to validate the 33‐immune‐related gene pair signature
Table 1.
Clinical and pathologic factors of the datasets used in this study
| TCGA (n, %) | ICGC (n, %) | http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520 (n, %) | |
|---|---|---|---|
| Age | |||
| <60 | 66 (19.3%) | 39 (19.3%) | 178 (80.5%) |
| ≥60 | 276 (80.7%) | 163 (80.7%) | 43 (19.5%) |
| Gender | |||
| Female | 109 (31.9%) | 50(24.8%) | 30(13.6%) |
| Male | 233 (68.1%) | 152 (75.2%) | 191 (86.4%) |
| Virus infection | |||
| Yes | 142(41.5%) | 173 (85.7%) | 212 (96%) |
| No | 200(58.5%) | 29 (14.3%) | 6 (2.7%) |
| NA | 3 (1.3%) | ||
| Cirrhosis | |||
| Yes | 127 (37%) | 193(95.5%) | 18(8.1%) |
| No | 72 (21%) | 9(4.5%) | 203(91.9%) |
| NA | 143 (42%) | ||
| Recurrence | |||
| Yes | 173 (50.6%) | 121 (54.8%) | |
| No | 125 (36.5%) | 100 (45.2%) | |
| NA | 44 (12.9%) | ||
| TNM stage | |||
| Stage I | 103 | 93 (42.1%) | |
| Stage II | 39 | 77 (34.8%) | |
| Stage III | 47 | 49 (22.2%) | |
| Stage IV | 2 | 2 (0.9%) | |
| NA | 0 | ||
| Survival status | |||
| Alive | 219 (64.0%) | 167 (82.7%) | 136 (61.5%) |
| Dead | 123 (36%) | 35 (17.3%) | 85 (38.5%) |
| Median follow‐up time(mo) | 20.745 (1.02‐120.7) | 27 (1‐72) | 52.3 (2‐67.4) |
Abbreviations: ICGC, ICGC LIHC dataset; NA represents information not available; TCGA, TCGA LIHC dataset.
Figure 2.
Construction and definition of IRGP signature. A, After 1000 iterations, the 33‐IRGP model achieved the highest frequency compared with the other nine IRGP models. The 33‐IRGP model was selected to construct the IRGP signature. B, The heatmap shows the score of the 33 IRGPs according to patient risk score. The patients were divided into high immune risk and low immune risk groups according to the median risk score. The red and black points represent the risk scores of high‐risk group patients and low‐risk group patients, respectively. The gray and green points represent patients who were alive or dead, respectively. C, The survival curve shows that high‐risk group patients had a poorer outcome than low‐risk group patients in the training dataset (P < .05). D, Generation of receiver operating characteristic (ROC) curves illustrated the predictive ability of the 33‐immune‐related gene pair model. The areas under the curves for 1‐, 3‐, and 5‐year survival were 0.912, 0.918, and 0.814, respectively, in the training dataset
Table 2.
Information on the 33 IRGPs
| Gene pair1 | Full name | Gene pair2 | Full name | Coefficient | |
|---|---|---|---|---|---|
| ADM2 | Adrenomedullin 2 | GHR | Growth hormone receptor | 0.094222 | |
| AMHR2 | Anti‐Mullerian hormone receptor, type II | OGN | Osteoglycin | 0.000652 | |
| ARTN | Artemin | IFI30 | Interferon, gamma‐inducible protein 30 | 0.89792 | |
| CALCR | Calcitonin receptor | KLRK1 | Killer cell lectin‐like receptor subfamily K, member 1 | 0.472908 | |
| CALCR | Calcitonin receptor | NTF3 | Neurotrophin 3 | 0.110016 | |
| CCR3 | Chemokine (C‐C motif) receptor 3 | IGF1 | Insulin‐like growth factor 1 (somatomedin C) | 0.025693 | |
| CCR3 | Chemokine (C‐C motif) receptor 3 | NCR1 | Natural cytotoxicity triggering receptor 1 | 0.070971 | |
| CD1A | CD1a molecule | FASLG | CD1a molecule | 0.231429 | |
| CD1C | CD1c molecule | SEMA3C | CD1c molecule | −0.32233 | |
| CDK4 | Cyclin‐dependent kinase 4 | PIK3R1 | Cyclin‐dependent kinase 4 | 0.489564 | |
| CHGA | Chromogranin A (parathyroid secretory protein 1) | PDCD1 | Chromogranin A (parathyroid secretory protein 1) | 0.483017 | |
| CTSE | Cathepsin E | MPL | Cathepsin E | 0.152814 | |
| CXCL1 | Chemokine (C‐X‐C motif) ligand 1 (melanoma growth stimulating activity, alpha) | RELB | Chemokine (C‐X‐C motif) ligand 1 (melanoma growth stimulating activity, alpha) | 0.082072 | |
| CXCL5 | Chemokine (C‐X‐C motif) ligand 5 | PDCD1 | Programmed cell death 1 | 0.031422 | |
| EDN1 | Endothelin 1 | SOS2 | Son of sevenless homolog 2 (Drosophila) | 0.164111 | |
| EDN1 | Endothelin 1 | TNFRSF10D | Tumor necrosis factor receptor superfamily, member 10d, decoy with truncated death domain | 0.299299 | |
| EPOR | Erythropoietin receptor | PLXNA1 | Plexin A1 | −0.11661 | |
| FYN | FYN oncogene related to SRC, FGR, YES | STC1 | Stanniocalcin 1 | −0.37861 | |
| GHR | Growth hormone receptor | PLXNA2 | Plexin A2 | −0.03843 | |
| GIP | Gastric inhibitory polypeptide | OGN | Osteoglycin | 0.227285 | |
| GMFB | Glia maturation factor, beta | PIK3R1 | Phosphoinositide‐3‐kinase, regulatory subunit 1 (alpha) | 0.003817 | |
| GPR17 | G protein‐coupled receptor 17 | IL5 | Interleukin 5 (colony‐stimulating factor, eosinophil) | −0.14056 | |
| HLA‐A | Major histocompatibility complex, class I, A | SPP1 | Secreted phosphoprotein 1 | −0.04005 | |
| HLA‐DRB1 | major histocompatibility complex, class II, DR beta 1 | SPP1 | Secreted phosphoprotein 1 | −0.14776 | |
| IL15RA | Interleukin 15 receptor, alpha | SHC2 | SHC (Src homology 2 domain containing) transforming protein 2 | 0.455341 | |
| IL18RAP | Interleukin 18 receptor accessory protein | SEMA3A | Sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3A | −0.35545 | |
| IL1RL1 | Interleukin 1 receptor‐like 1 | MTNR1A | Melatonin receptor 1A | −0.56454 | |
| IL5 | Interleukin 5 (colony‐stimulating factor, eosinophil) | OGN | Osteoglycin | 0.361037 | |
| KIR2DS4‐ | PRKCG | Protein kinase C, gamma | −0.49709 | ||
| KITLG | KIT ligand | SH3BP2 | SH3‐domain binding protein 2 | 0.035557 | |
| KITLG | KIT ligand | TGFBR3 | Transforming growth factor, beta receptor III | 0.107891 | |
| LECT2 | Leukocyte cell‐derived chemotaxin 2 | NR6A1 | Leukocyte cell‐derived chemotaxin 2 | −0.24611 | |
| LTB4R2 | Leukotriene B4 receptor 2 | SEMA3A | Leukotriene B4 receptor 2 | −0.1266 | |
Table 3.
Clinical subgroup analysis of prognosis based on our IRGP signature
| Variable | No. of patients | HR(95%CI) | Log‐rank P‐value | |
|---|---|---|---|---|
| Low risk | High risk | |||
| All | 103 | 104 | 10.89 (8.09‐21.07) | <.0001 |
| Age | ||||
| Age < 60 | 25 | 13 | 29.17 (10.05 −186.3) | <.0001 |
| Age ≥ 60 | 78 | 90 | 8.99 (8.090 −21.07) | <.0001 |
| Gender | ||||
| Female | 35 | 35 | 6.03 (4.243 −19.85) | <.0001 |
| Male | 68 | 68 | 19.95 (8.572 −28.92) | <.0001 |
| TNM | ||||
| StageI/Ⅱ | 87 | 63 | 13.88 (12.29 −48.39) | <.0001 |
| StageⅢ/Ⅳ | 15 | 40 | 5.32 (1.947 −7.748) | .0002 |
| Grade | ||||
| G1/G2 | 73 | 59 | 9.05 (8.245 −28.29) | <.0001 |
| G3/G4 | 29 | 43 | 10.35 (4.637 −23.11) | <.0001 |
| Viral infection | ||||
| No | 52 | 70 | 8.44 (5.296 ‐ 15.38) | <.0001 |
| Yes | 51 | 33 | 14.40 (7.895 −59.81) | <.0001 |
| Recurrence | 46 | 62 | 7.19 (4.375 −13.85) | <.0001 |
Abbreviations: All, TCGA LIHC dataset; CI, confidence interval; HR, hazard ratio.
3.2. Validation of the IRGP signature
In the TCGA, ICGC, and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520 datasets, the risk score of each patient was calculated with the same 33‐IRGP prognostic signature, and patients were assigned into low immune risk and high immune risk groups according to the median risk score. The high immune risk group had poorer OS in all datasets than the low immune risk group (Figure 3A‐C). The c‐index values for the training, testing, ICGC and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520 datasets were 0.78, 0.62, 0.61, and 0.59, respectively (Figure 3D). The multivariate Cox regression analysis showed that the IRGP risk score was an independent prognostic factor after adjustment for by age, sex, and TNM stage in the training (HR: 20.59, 95%CI: 8.73‐48.54, P = .000), testing (HR: 2.07, 95%CI: 1.07‐4.015, P = .031), http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520 (HR: 1.77, 95%CI: 1.09‐2.87) and ICGC datasets (HR: 2.40, 95%CI: 1.19‐4.82).
Figure 3.
Validation of the IRGP signature. As shown, patients with a high risk score have a worse overall survival rate than those in the low risk score group according to Kaplan–Meier survival analysis in the TCGA test dataset (A), http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14529 dataset (B), and ICGC dataset (C). These results show that the 33‐IRGP model has a robust predictive ability (P < .05). D: The c‐index values for the training dataset, testing dataset, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520 dataset, and ICGC dataset were 0.78, 0.62, 0.59, and 0.61, respectively
3.3. COMPARISON WITH OTHER PUBLISHED PROGNOSTIC SIGNATURES
We also compared our IRGP prognostic signature with three published gene prognostic signatures8, 9, 10 by constructing an ROC curve for 5‐year OS and determining the c‐index, and all data came from TCGA. As shown in Figure 4A‐B and Table 4, the AUC was 0.772 and the c‐index was 0.717 for our prognostic signature, and the IRGP prognostic signature possessed a higher predictive efficacy and accuracy than the existing three‐gene prognostic signature (AUC = 0.691, c‐index = 0.641), the 4‐gene prognostic signature (AUC = 0.702, c‐index = 0.674) and the autophagy‐related signature (AUC = 0.408, c‐index = 0.600) (Table 5).
Figure 4.
Determination of the receiver operating characteristic (ROC) curve (A) and c‐index (B) for different prognostic signatures. The AUC values for the IRGP model, three‐gene model, four prognostic lncRNA model, and autophagy‐related signature were 0.772, 0.691, 0.702, and 0.408, respectively. The c‐index values for the IRGP model, three‐gene model, four prognostic lncRNA model, and autophagy‐related signature were 0.772, 0.691, 0.702, and 0.408, respectively. These results indicate that our signature possesses a higher predictive efficacy and accuracy than the other models
Table 4.
Multivariate Cox analysis of clinicopathological factors and risk signatures
| Variable | HR | 95%CI | P‐value |
|---|---|---|---|
| Training dataset | |||
| Risk_score (low risk vs high risk) | 20.59 | 8.73‐48.54 | .000 |
| Age (<60 vs ≥60) | 1.17 | 0.58‐2.36 | .662 |
| Stage (I and II vs III and IV) | 1.77 | 1.06‐2.97 | .029 |
| Gender (male vs female) | 1.16 | 0.70‐1.91 | .574 |
| Testing dataset | |||
| Risk_score | 2.07 | 1.07‐4.015 | .031 |
| Age(<60 vs ≥60) | 0.68 | 0.34‐1.38 | .289 |
| Stage (I and II vs III and IV) | 2.27 | 1.21‐4.23 | .010 |
| Gender (male vs female) | 1.61 | 0.859‐3.02 | .138 |
| ICGC dataset | |||
| Risk_score | 2.40 | 1.19‐4.82 | .014 |
| Age (<60 vs ≥60) | 0.955 | 0.413‐2.21 | .913 |
| Gender (male vs female) | 0.481 | 0.24‐0.98 | .045 |
| http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520 dataset | |||
| Risk_score | 1.77 | 1.09‐2.87 | .022 |
| Age (<60 vs ≥60) | 1.05 | 0.58‐1.90 | .868 |
| Stage (I and II vs III and IV) | 2.78 | 1.71‐4.50 | .00 |
| Gender (male vs female) | 1.37 | 0.65‐2.87 | .408 |
Abbreviations: CI, confidence interval; HR, hazard ratio.
Table 5.
c‐index and AUC values between different signatures
| Signature | AUC | c‐index |
|---|---|---|
| IRGPs | 0.772 | 0.717 |
| 3 gene signature | 0.691 | 0.641 |
| 4 prognostic signature | 0.702 | 0.674 |
| Autophagy‐related signature | 0.408 | 0.600 |
Abbreviations: AUC, area under the receiver operating characteristic (ROC) curve; c‐index, concordance index.
3.4. Biological processes correlated with the IRGP signature
We assigned patients into low immune risk groups and high immune risk groups, and gene set enrichment analysis (GSEA) was performed on the training dataset. The result illustrated that a total of nine cancer hallmark gene sets were identified in the high‐risk group (Figure 5) including “MYC_TARGETS,” “GLYCOLYSIS,” and “DNA_REPAIR,” which indicated that these hallmark gene sets played a critical role in HCC progression.
Figure 5.
Gene set enrichment analysis (GSEA) between high and low immune risk groups. The results show that nine cancer hallmark gene sets are enriched in the high immune risk group in patients with HCC (P < .05, FDR < 0.25)
4. DISCUSSION
It is well‐known that the liver participates in self‐tolerance and contains the richest immune effectors in the body.11 Several components of the immune system, including immune cells, chemokines, cytokines, and inhibitory receptors and ligands, have been shown to be key factors during tumor development and progression.5, 6 The complex immune environment of the liver makes immunotherapy a promising yet complicated strategy for treatment. There has also been a rapid rise in the amount of immunotherapy clinical trials in HCC in the past 15 years.12, 13, 14 Among these trials, immune checkpoint (PD1/PDL1 and CTLA‐4) blockade therapy has received great acclaim. Nivolumab (anti‐PD1) was the first FDA‐approved immune checkpoint inhibitor for HCC. In phase I and phase II clinical trials, 20% of HCC patients treated with nivolumab had a lasting response. In addition, several clinical trials of immune checkpoint inhibitors have also shown exciting results. HCC, like many other tumors, has an immunosuppressive microenvironment that can inhibit the activation of immune effectors, making adoptive immunotherapy a promising method. Recently, adoptive immunotherapies, including CIK cells, NK cells, NKT cells and CAR T cells, accounted for approximately half of the immunotherapy clinical trials in HCC (12 trials), and several studies have reported that adoptive immunotherapy can delay recurrence and prolong survival time.15, 16, 17 Cancer vaccines are another immunotherapy that can help the immune system recognize and attack cancer cells. Unfortunately, current vaccine monotherapies do not generate significant clinical outcomes in patients with HCC.18 In summary, immunotherapy is a promising treatment approach in HCC, and the immunology of hepatocellular carcinoma needs to be further explored. So, it is necessary to construct a prognostic signal using immune‐related genes.
Traditional prognostic signatures require the preprocessing of gene expression profiles, and this is a major factor that influences other widely used models. In this study, because our IRGPs were generated by pairwise comparison and the score was calculated entirely based on gene expression in the same patient, our prognostic signature can not only overcome the batch effects of the different platforms but also does not require the scaling and normalization of data. This approach has been reported to be robust in several studies, including cancer‐related studies, and it is a major advantage in our study.19, 20
In this study, by using Lasso penalized Cox regression, we constructed a 33‐IRGP prognostic signature and validated this signature in several different datasets. The results showed that our signature could stratify patients into high immune risk and low immune risk groups. Univariate and multivariate Cox proportional hazard regression analyses showed that the score was an independent prognostic factor. In our study, unlike in traditional studies, the signature was constructed by using Lasso penalized regression, which can identify the most suitable of many variables. Moreover, our signature was validated by several datasets, including RNA‐seq and microarray datasets. Finally, compared with the other three existing prognostic signatures, our signature possesses a higher predictive efficacy and accuracy.8, 9, 10
Our 33‐IRGP signature consists of 54 immune‐related genes, and these genes are mainly involved in the functions of immune cells and antigen identification and presentation and they play an important role in the composition of the immune microenvironment. CXCL5 and CXCL1 can promote intratumoral neutrophil infiltration, and their overexpression has been correlated with poor prognosis in HCC.21, 22, 23 CDK4 is a promising anticancer target in several cancers, including hepatocellular carcinoma. Shom Goel et al recently found that CDK4/6 inhibitors could promote tumor immunogenicity and may have synergistic effects with immunotherapy.24, 25 It was reported that the downregulation of LECT2 fostered the accumulation of inflammatory monocytes, which harbor immunosuppressive properties, and promoted the progression of hepatocellular carcinoma.26 PD1 is mainly expressed on effector T cells in tumor tissues in HCC. Compared with cirrhotic tissue, tumor tissue has a higher number of PD‐1+CD8+ T cells. Moreover, patients with higher levels of tumor‐infiltrating and circulating PD‐1+CD8+ T cells tend to progress early after posthepatic resection.27 It has been reported that macrophages can be recruited into HCC tissue by SEMA3A, and overexpression of SEMA3A indicates poor prognosis in hepatocellular carcinoma.28 Artemin was shown to be related to early relapse, shortened overall survival and large tumor size.29 The involvement of all the above mentioned genes indicates that immune processes contribute to tumor development and prognosis. Other immune‐related genes in our signature can also predict the prognosis of HCC patients. In addition, expression imbalances in certain gene pairs may play a more important role than individual differentially expressed genes. GSEA indicated that “MYC_TARGETS,” “DNA_REPAIR,” and “GLYCOLYSIS” were enriched in the high‐risk group, and these results were consistent with previous reports.30, 31, 32
Nevertheless, we should acknowledge the limitations of this study. First, our research was a retrospective analysis, and a prospective cohort is needed to validate the results. Second, because the signature was constructed by using immune‐related genes, our signature does not represent diverse biological processes. Finally, the signature was constructed by using RNA‐seq and microarray expression data. Further clinical applications should be evaluated by using RT‐PCR or IHC.
In conclusion, we developed a new IRGP prognostic model in HCC.
CONFLICT OF INTEREST
The authors declare that no competing interest exists.
Supporting information
Sun X‐Y, Yu S‐Z, Zhang H‐P, Li J, Guo W‐Z, Zhang S‐J. A signature of 33 immune‐related gene pairs predicts clinical outcome in hepatocellular carcinoma. Cancer Med. 2020;9:2868–2878. 10.1002/cam4.2921
Funding information
This work was supported by grants from the National Natural Science Foundation of China (No. U1604282, 81971881), the Basic Research Project of Henan Science and Technology Department (142300410221), and the Henan Provincial Health Planning Commission and Ministry of Construction (SBGJ2018002).
DATA AVAILABILITY STATEMENT
TCGA dataset that supports the findings of this study is openly available in [University of California Santa Cruz (UCSC) Genome Browser], reference number [LIHC].
REFERENCES
- 1. Ferlay J, Soerjomataram I, Dikshit R, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136(5):E359‐E386. [DOI] [PubMed] [Google Scholar]
- 2. Parikh S, Hyman D. Hepatocellular cancer: a guide for the internist. Am J Med. 2007;120(3):194‐202. [DOI] [PubMed] [Google Scholar]
- 3. Oweira H, Petrausch U, Helbling D, et al. Prognostic value of site‐specific extra‐hepatic disease in hepatocellular carcinoma: a SEER database analysis. Expert Rev Gastroenterol Hepatol. 2017;11(7):695‐701. [DOI] [PubMed] [Google Scholar]
- 4. Galle PR, Forner A, Llovet JM, et al. EASL clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol. 2018;69(1):182‐236. [DOI] [PubMed] [Google Scholar]
- 5. Aravalli RN. Role of innate immunity in the development of hepatocellular carcinoma. World J Gastroenterol. 2013;19(43):7500‐7514. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Matsui M, Machida S, Itani‐Yohda T, Akatsuka T. Downregulation of the proteasome subunits, transporter, and antigen presentation in hepatocellular carcinoma, and their restoration by interferon‐gamma. J Gastroenterol Hepatol. 2002;17(8):897‐907. [DOI] [PubMed] [Google Scholar]
- 7. El‐Khoueiry AB, Sangro B, Yau T, et al. Nivolumab in patients with advanced hepatocellular carcinoma (CheckMate 040): an open‐label, non‐comparative, phase 1/2 dose escalation and expansion trial. Lancet (London, England). 2017;389(10088):2492‐2502. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Li B, Feng W, Luo O, et al. Development and validation of a three‐gene prognostic signature for patients with hepatocellular carcinoma. Sci Rep. 2017;7(1):5517. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Wang Z, Wu Q, Feng S, Zhao Y, Tao C. Identification of four prognostic LncRNAs for survival prediction of patients with hepatocellular carcinoma. PeerJ. 2017;5:e3575. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Lin P, He R‐Q, Dang Y‐W, et al. An autophagy‐related gene expression signature for survival prediction in multiple cohorts of hepatocellular carcinoma patients. Oncotarget. 2018;9(25):17368‐17395. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Kubes P, Jenne C. Immune responses in the liver. Annu Rev Immunol. 2018;36:247‐277. [DOI] [PubMed] [Google Scholar]
- 12. Liu D, Staveley‐O'Carroll KF, Li G. Immune‐based therapy clinical trials in hepatocellular carcinoma. J Clinical Cellular Immunol. 2015;06(06):376. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Liu D, Li G, Avella DM, et al. Sunitinib represses regulatory T cells to overcome immunotolerance in a murine model of hepatocellular cancer. Oncoimmunology. 2017;7(1):e1372079. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Greten TF, Lai CW, Li G, Staveley‐O'Carroll KF. Targeted and immune‐based therapies for hepatocellular carcinoma. Gastroenterology. 2019;156(2):510‐524. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Takayama T, Sekine T, Makuuchi M, et al. Adoptive immunotherapy to lower postsurgical recurrence rates of hepatocellular carcinoma: a randomised trial. Lancet. 2000;356(9232):802‐807. [DOI] [PubMed] [Google Scholar]
- 16. Liu J, Li H, Cao S, et al. Maintenance therapy with autologous cytokine‐induced killer cells in patients with advanced epithelial ovarian cancer after first‐line treatment. J Immunother. 2014;37(2):115‐122. [DOI] [PubMed] [Google Scholar]
- 17. Sangro B, Gomez‐Martin C, de la Mata M, et al. A clinical trial of CTLA‐4 blockade with tremelimumab in patients with hepatocellular carcinoma and chronic hepatitis C. J Hepatol. 2013;59(1):81‐88. [DOI] [PubMed] [Google Scholar]
- 18. Greten TF, Forner A, Korangy F, et al. A phase II open label trial evaluating safety and efficacy of a telomerase peptide vaccination in patients with advanced hepatocellular carcinoma. BMC Cancer. 2010;10:209. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19. Li B, Cui Y, Diehn M, Li R. Development and validation of an individualized immune prognostic signature in early‐stage nonsquamous non‐small cell lung cancer. JAMA oncology. 2017;3(11):1529‐1537. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Peng PL, Zhou XY, Yi GD, Chen PF, Wang F, Dong WG. Identification of a novel gene pairs signature in the prognosis of gastric cancer. Cancer Med. 2018;7(2):344‐350. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Li YM, Liu ZY, Wang JC, et al. Receptor‐interacting protein kinase 3 deficiency recruits myeloid‐derived suppressor cells to hepatocellular carcinoma through the chemokine (C‐X‐C Motif) ligand 1–chemokine (C‐X‐C Motif) receptor 2 axis. Hepatology. 2019;70(5):1564‐1581. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Li LI, Xu LI, Yan J, et al. CXCR2‐CXCL1 axis is correlated with neutrophil infiltration and predicts a poor prognosis in hepatocellular carcinoma. J Exp Clin Cancer Res. 2015;34:129. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Zhou S‐L, Zhou Z‐J, Hu Z‐Q, et al. Tumor‐associated neutrophils recruit macrophages and T‐regulatory cells to promote progression of hepatocellular carcinoma and resistance to sorafenib. Gastroenterology. 2016;150(7):1646‐58.e17. [DOI] [PubMed] [Google Scholar]
- 24. Zhang J, Bu X, Wang H, et al. Cyclin D‐CDK4 kinase destabilizes PD‐L1 via cullin 3‐SPOP to control cancer immune surveillance. Nature. 2018;553(7686):91‐95. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Goel S, DeCristo MJ, Watt AC, et al. CDK4/6 inhibition triggers anti‐tumour immunity. Nature. 2017;548(7668):471‐475. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. L'Hermitte A, Pham S, Cadoux M, et al. Lect2 controls inflammatory monocytes to constrain the growth and progression of hepatocellular carcinoma. Hepatology. 2019;69(1):160‐178. [DOI] [PubMed] [Google Scholar]
- 27. Kim HD, Song GW, Park S, et al. Association between expression level of PD1 by tumor‐infiltrating CD8(+) T cells and features of hepatocellular carcinoma. Gastroenterology. 2018;155(6):1936‐50.e17. [DOI] [PubMed] [Google Scholar]
- 28. Hu ZQ, Zhou SL, Zhou ZJ, et al. Overexpression of semaphorin 3A promotes tumor progression and predicts poor prognosis in hepatocellular carcinoma after curative resection. Oncotarget. 2016;7(32):51733‐51746. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29. Zhang M, Zhang W, Wu Z, et al. Artemin is hypoxia responsive and promotes oncogenicity and increased tumor initiating capacity in hepatocellular carcinoma. Oncotarget. 2016;7(3):3267‐3282. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Lin Z, Xu S‐H, Wang H‐Q, et al. Prognostic value of DNA repair based stratification of hepatocellular carcinoma. Sci Rep. 2016;6:25999. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31. Alves AP, Mamede AC, Alves MG, et al. Glycolysis inhibition as a strategy for hepatocellular carcinoma treatment? Curr Cancer Drug Targets. 2019;19(1):26‐40. [DOI] [PubMed] [Google Scholar]
- 32. Qin X‐Y, Suzuki H, Honda M, et al. Prevention of hepatocellular carcinoma by targeting MYCN‐positive liver cancer stem cells with acyclic retinoid. Proc Natl Acad Sci. 2018;115(19):4969‐4974. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
TCGA dataset that supports the findings of this study is openly available in [University of California Santa Cruz (UCSC) Genome Browser], reference number [LIHC].