Diagnostic and prognostic biomarkers of Human Leukocyte Antigen complex for hepatitis B virus-related hepatocellular carcinoma.

Background: Hepatitis B virus infection had been identified its relationship with liver diseases, including liver tumors. We aimed to explore diagnostic and prognostic values between the Human Leukocyte Antigen (HLA) complex and hepatocellular carcinoma (HCC).
Methods: We used the GSE14520 dataset to explore diagnostic and prognostic significance between HLA complex and HCC. A nomogram was constructed to predict survival probability of HCC prognosis. Gene set enrichment analysis was explored using gene ontologies and metabolic pathways. Validation of prognostic values of the HLA complex was performed in the Kaplan-Meier Plotter website.
Results: We found that HLA-C showed the diagnostic value (P <0.0001, area under curve: 0.784, sensitivity: 93.14%, specificity: 62.26%). In addition, HLA-DQA1 and HLA-F showed prognostic values for overall survival, and HLA-A, HLA-C, HLA-DPA1 and HLA-DQA1 showed prognostic values for recurrence-free survival (all P ≤ 0.05, elevated 0.927, 0.992, 1.023, 0.918, 0.937 multiples compared to non-tumor tissues, respectively). Gene set enrichment analysis found that they were involved in antigen processing and toll like receptor signalling pathway, etc. The nomogram was evaluated for survival probability of HCC prognosis. Validation analysis indicated that HLA-C, HLA-DPA1, HLA-E, HLA-F and HLA-G were associated with HCC prognosis of overall survival (all P ≤ 0.05, elevated 0.988 and 0.997 multiples compared to non-tumor tissues, respectively).
Conclusion: HLA-C might be a diagnostic and prognostic biomarker for HCC. HLA-DPA1 and HLA-F might be prognostic biomarkers for HCC. © The author(s).

Introduction

In less developed countries, liver cancer was the second leading cause of cancer-related deaths worldwide in the male population 1. It was estimated that roughly 782,500 cases of new liver cancer and 745,500 deaths occurred in 2012, with China accounting for 50% of all the new cases and deaths 1. Hepatocellular carcinoma (HCC) accounted for 70% to 90% of primary liver cancers worldwide 2. Aetiologically, many factors, such as dietary aflatoxin exposure, alcohol consumption 3, hepatitis B virus (HBV) infection, hepatitis C virus infection, diabetes mellitus, obesity 4 and cirrhosis 5, had been reported to be associated with the development of HCC. Meanwhile, many treatments had been applied, such as radical hepatectomy, liver transplantation, percutaneous ethanol injection, radiofrequency ablation and transarterial chemoembolisation 5. Even with these advances, the prognosis of HCC patients remained poor, with the 5-year survival rate less than 15% 6, 7. Currently, the diagnosis of HCC had relied on α-fetoprotein (AFP) levels. However, the sensitivity and specificity of AFP levels were not sufficient for HCC diagnosis, as patients with cirrhosis and chronic hepatitis could show elevated AFP levels 8. Therefore, it was of significance to identify new biomarkers for the early diagnosis of HCC.

Human Leukocyte Antigen (HLA) complex, ~4Mb and on chromosome 6 p21 in humans, is composed of HLA-A, HLA-B, HLA-C, HLA-DMA, HLA-DMB, HLA-DOA, HLA-DOB, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DQB2, HLA-DRA, HLA-DRB1, HLA-DRB4, HLA-DRB6, HLA-E, HLA-F and HLA-G. HLA plays a key role in antigen presentation to T cells and the basic formation of host defence mechanisms against pathogens 9, 10. HLA, encoding major histocompatibility complex (MHC), is also important in vaccine development and has a determining role in transplantation outcomes 11. Members of this complex have been investigated for disease initiation and progression. A good clinical outcome is associated with high-solution HLA-matching in haematopoietic stem cell transplantation 12, 13. HAL-B Bw4-80lle, combined with the KIR3DS1 gene, can significantly affect outcomes of chronic hepatitis B patients who were treated with alpha interferon 14. It had been documented that HLA-G, a nonclassical HLA class I molecule, positivity was related to the disease in breast cancer, renal cell carcinoma, lung cancer and malignant melanoma, also indicating differential expressions in lobular and ductal subtypes 15. HLA-G played a pivotal function in maternal-foetal tolerance during pregnancy 16, and aberrant expression of HLA-G was observed in multiple malignant cell types, which might be related to the procedure of escape host immunosurveillance 17. HLA-DRB1*01 had been observed to be associated with hepatic hypersensitivity reactions 18. Given previous studies on members of the HLA complex with tumours, we, therefore, conducted an investigation that aimed to find relationship a between the HLA complex and HCC.

Materials and Methods

Data collection

Profiling data of the GSE14520 dataset was obtained from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse14520, accessed May 5, 2018) website. This dataset contains two platforms: GPL571 (Affymetrix Human Genome U133A 2.0 Array) and GPL3921 (Affymetrix HT Human Genome U133A Array) 19, 20. Only GPL3921 data were used in our study to avoid batch effect. Patients with HBV infection were included in the present study.

Expression collection of HLA family genes

Transcription, interactive bodymap and gene expression levels were collected from Gene Expression Profiling Interactive Analysis (GEPIA, http://gepia.cancer-pku.cn/index.html, accessed May 6, 2018) 21. Protein expressions of HLA family genes were collected from The Human Protein Atlas (https://www.proteinatlas.org/, accessed accessed May 6, 2018) website 22.

Gene set enrichment analysis

Gene set enrichment analysis (GSEA) was performed to obtain biological processes and metabolic pathways of HLA family genes at the transcriptional level. Datasets of c2.cp.kegg.v6.1.symbols.gmt, c5.bp.b6.1.symbols.gmt, c5.cc.v6.1.symbols.gmt and c5.mf.v6.1.symbols.gmt were utilised to analyse significant gene ontology (GO), including biological process (BP), cellular component (CC), molecular function (MF) and metabolic pathway 23, 24.

Association and interaction analysis

Pearson correlation analysis among HLA family genes was performed using R version 3.5.0 (https://www.r-project.org/). A co-expression interactive network of gene-gene was constructed using the geneMANIA plugin of Cytoscape software version 3.6.0 25, 26. A protein-protein interaction (PPI) network was constructed using STRING (https://string-db.org/cgi/input.pl, accessed May 8, 2018) website 27. Visualised enrichment analysis of GO was conducted using the BiNGO plugin of Cytoscape software version 3.6.0 28.

Diagnostic and survival analysis

Overall survival (OS) and recurrence-free survival (RFS) were calculated using Kaplan-Meier and Cox proportional hazards regression models. Gene expressions were categorised into low expression and high expression groups at a cut-off of median expression level. Statistically significant factors were adjusted for survival analysis. OS and RFS-related genes were further analysed for joint-analysis. Validation of prognostic values of HLA family genes were further conducted in the Kaplan-Meier Plotter (http://kmplot.com/analysis/, accessed May 12, 2018) website 29.

Expression model and nomogram construction

To further explore prognosis-related genes in both univariate and multivariate analyses for HCC survival, we further constructed expression models for OS and RFS prediction. Nomograms were constructed using clinical factors and gene expressions. Different factors and gene expressions showed different points. Taken together, total points can predict HCC patient probability of survival at 1 year, 3 years and 5 years.

Stratified and joint-effect analysis

Prognosis (OS and RFS)-related genes were further stratified for analysis in clinical factors. Genes related to OS and RFS in the multivariate analysis were further stratified for analysis with demographic and clinical factors. In addition, prognosis (OS and RFS)-related genes were joined for combination analysis. Expressions that indicated a good prognosis were conferred a score of 1, whereas bad prognoses were conferred a score of 0.

Statistical analysis

Survival analyses were performed using SPSS software version 16.0 (IBM, Chicago, IL). Median survival time (MST) and log-rank P were calculated by Kaplan-Meier method, as well as 95% confidence interval (CI) and hazard ratio (HR) were calculated by univariate and multivariate Cox proportional hazards regression models. Box plots and survival plots were obtained using GraphPad software version 7.0. P value ≤ 0.05 was statistically significant.

Results

Demographic and clinical characteristics of HCC patients

A total of 212 HBV-related HCC patients were included in the present study. Tumour size, cirrhosis, AFP and BCLC stage showed significance in OS (P = 0.002, 0.041, 0.049 and < 0.0001, respectively). Gender, cirrhosis and BCLC stage showed significance in RFS (P = 0.002, 0.036, and < 0.0001, respectively). Other factors did not show significance (all P > 0.05, Supplementary Table ).

Expression and transcription analysis

mRNA expression of HLA family members showed that HLA-A, HLA-C, HLA-DMA, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRA, HLA-DRB1, HLA-DRB4, HLA-DRB6 and HLA-E showed significance in the comparison between tumour and normal tissues (all P ≤ 0.05, Figure ). However, other genes did not show significance (all P > 0.05, Figure ). Protein expression showed that all of the HLA family members had low levels of expression in the liver, except HLA-A, HLA-C, HLA-DQB2, HLA-DRB4, HLA-DRB6 and HLA-F did show in the Human Protein Atlas website (Supplementary Figure ). A bodymap of HLA family members in human organs was shown in Supplementary Figure . Transcriptional analysis indicated that all the members consistently showed higher transcripts per millions in tumour tissues compared with normal tissues (Figure ).

Diagnostic and prognostic analysis

In the diagnostic analysis of the HLA family, HLA-A, HLA-C, HLA-DMB, HLA-DOA, HLA-DOB, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRA, HLA-DRB1, HLA-DRB4, HLA-DRB6 and HLA-E showed significance for HCC diagnosis (all P ≤ 0.05, Figure ). HLA-C showed the highest area under curve (AUC, 0.784).

In the univariate OS analysis, only HLA-DPB1 and HLA-F showed significance (crude P = 0.039 and 0.043, respectively; Table , Figure ). In the multivariate OS analysis, HLA-DQA1 and HLA-F showed significance (adjusted P = 0.012 and 0.014, Table ). In the univariate RFS analysis, only HLA-C showed significance (crude P = 0.022; Table , Figure ). In the multivariate RFS analysis, HLA-A, HLA-C, HLA-DPA1 and HLA-DQA1 showed significance (adjusted P = 0.019, 0.031, 0.040 and 0.030, respectively; Table ).

Joint-effect analysis and stratified analysis

Expressions that indicated a good prognosis were conferred a score of 1, whereas bad prognosis was conferred a score of 0. In the joint-effect analysis of OS, the 2 scores group exhibited significant P value compared with the 0 score group (adjusted P = 0.001, Table ). In the joint-effect analysis of RFS, the 2 scores group exhibited significant P value compared with the 0 score group (adjusted P = 0.001, Table ). Groups of 2 scores, 3 scores and 4 scores showed significance compared with the 0 score group (adjusted P = 0.005, 0.012 and < 0.001, respectively).

In the stratification of HLA-DQA1 for OS analysis, high expression showed significance in males, age ≤ 60 years, active viral replication-chronic carriers of HBV, cirrhosis, single nodular, low AFP levels (≤ 300 ng/ml) and A stage of the BCLC system compared with low expression (Table ). In the stratification of HLA-A for RFS analysis, high expression showed significance in males, age > 60 years, chronic HBV carriers, tumour size > 5 cm, cirrhosis, low AFP levels (≤ 300 ng/ml) and C stage of the BCLC system compared with low expression. Detailed results of the stratified analysis were shown in Table and 5.

Expression models were constructed for OS and RFS prognosis in Figure and 7, respectively. Expressions, survival statuses and heatmaps were shown in Figure and 7A. Prognostic receiver operating characteristic (ROC) curves were shown in Figure and 7B (P = 0.041 and 0.021, respectively). AUCs at 1 year, 3 years and 5 years were 0.862, 0.942 and 0.993, respectively, in the OS risk score model and 0.511, 0.533 and 0.568, respectively, in the RFS risk score model.

In addition, clinical factors and prognosis-related genes were further constructed in nomograms. High expression levels always led to low points. The same points indicated a highest probability of survival at 1 year and a lowest probability of survival at 5 years. Survival probability at 3 years was seated in the middle (Figure ).

GSEA analysis

GSEA results of the OS-related gene HLA-F indicated that GO and pathways were involved in positive regulation of the immune response, leukocyte cell-cell adhesion, chemokine signalling pathway and focal adhesion (Figure ). GSEA results of the RFS-related gene HLA-A indicated that GO and pathways were involved in antigen processing and presentation of peptide antigens via MHC class I, cell defence response, autoimmune thyroid disease and toll like receptor signalling pathway (Figure ). Detailed GSEA results were shown in Figure and 10 and Supplementary Figure .

Interaction and co-expression networks and enrichment analysis

Comparison between low and high levels of expression were shown in Figure and B. Significant P values were exhibited in all HLA family members (all P ≤ 0.05). Matrices showed Pearson correlations among HLA members (Figure ). Co-expression interaction and PPI networks showed relationships among HLA members (Figure ).

The top 10 GO terms and KEGG pathways were exhibited in Figure . Detailed GO terms and KEGG pathways were shown in Supplementary Table . Visualised interactions of GO terms constructed using BiNGO were shown in Supplementary Figure

Validation of prognostic values of HLA family members

Prognostic values of HLA family members were further validated in the whole population. HLA-C, HLA-DPA1, HLA-E, HLA-F and HLA-G showed significance in OS (all P ≤ 0.05, Figure ). However, other genes did not show significance (all P > 0.05, Figure ).

Discussion

In the present study, we conducted an investigation on the relationships between the HLA complex and HBV-related HCC patients. We found that members of the HLA complex, HLA-A, HLA-C, HLA-DMB, HLA-DOA, HLA-DOB, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRA, HLA-DRB1, HLA-DRB4, HLA-DRB6 and HLA-E, showed significant diagnostic values for HCC. Among them, HLA-C showed the highest diagnostic value. In addition, HLA-DQA1 and HLA-F showed prognostic values for OS, and HLA-A, HLA-C, HLA-DPA1 and HLA-DQA1 showed prognostic values for RFS. Then, joint-effect and stratified analyses were explored the prognostic values of all the prognosis-related genes. GSEA found that they were involved in positive regulation of the immune response, antigen processing and presentation of peptide antigens via MHC class I, chemokine signalling pathway, focal adhesion and toll like receptor signalling pathway. Risk score models and nomograms were constructed to evaluate HCC prognosis. Further validation of prognosis-related genes in the Kaplan-Meier Plotter website indicated that HLA-C, HLA-DPA1, HLA-E, HLA-F and HLA-G were associated with HCC prognosis in OS. Therefore, we concluded that HLA-C, HLA-DPA1 and HLA-F gene expression were associated with HCC prognosis, and HLA-A and HLA-DQA1 gene expression were associated with prognosis of HBV-related HCC. HLA-C had diagnostic value for HCC, and HLA-A, HLA-C, HLA-DMB, HLA-DOA, HLA-DOB, HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRA, HLA-DRB1, HLA-DRB4, HLA-DRB6 and HLA-E had potentially diagnostic values for HCC.

The MHC complex, also known as the HLA in humans, consists of more than 200 genes on chromosome 6 and can be categorised into three groups: class I, class II and class III 30. Class I, which is characterised by CD8+ T cells, is composed of three genes: HLA-A, HLA-B and HLA-C 30. Class II, which is characterised by CD4+ T cells, is composed of six main genes: HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRA and HLA-DRB1 30. HLA genes were documented as numerous and highly polymorphic in order to bind many kinds of peptides originating from self or foreign antigens 30. More than 1500 alleles of the HLA-B gene had been identified 31. Variants of the HLA complex played a pivotal role in determining the susceptibility to autoimmune diseases and infections 32. Meanwhile, they were crucial in the field of transplantation surgery, where HLA-matching compatibility was a precondition in the donors and recipients 32. An association between the presence of HLA-B*57:01 alleles and abacavir hypersensitivity was observed in Australian and British cohorts 33, 34. Genome-wide association studies showed that the HAL-A *31:01 allele had a strong association with carbamazepine-induced hypersensitivity in Northern Europeans, Japanese and Koreans (OR = 25.93, 10.8 and 7.3, respectively) 35-38.

Alterations of amino acids bringing in structural and functional dissimilarities between HLA-DPB1 alleles revealed a strong median impact of alloreactive responses to these molecules 39. It had been observed that a high frequency of the HLA-DRB1*15:01-DRB5*01:01-DQB1*06:02 haplotype in patients with amoxicillin clavulanate-induced drug-induced liver injury compared with normal healthy controls (57.1% (case) versus 11.7% (controls), P < 10-6). A study focusing on HLA-DQB1 and HLD-DRB1 in the Tunisian population revealed the involvement of rs6457617 locus as a risk variant for susceptibility/severity to rheumatoid arthritis and highlighted gene-gene interaction between the two genes 40.

Recently, many researches had been performed to investigate the relationships among initiation and progression of tumours and the HLA family. Single nucleotide polymorphisms of HLA-A and amino acid variants were associated with nasopharyngeal carcinoma in Malaysian Chinese 41. HLA-B-associated transcript 3 polymorphisms were suggested as risk factors for lung cancer in a meta-analysis 42. An association was observed between an increase in HLA-C1/KIR2DL2 and HLA-C1/KIRDL3 pairs and invasive cervical cancer patients at high-risk from human papillomavirus infection 43. Hu et al. reported, for the first time, that genetic variants in the HLA-DP/DQ loci might be marker polymorphisms for both HBV infection and risk of developing HCC 44. HLA-DPB1 polymorphisms increased the risk for cervical squamous cell carcinoma in Taiwanese women 45.

HLA-DQA1 gene copy number polymorphism was associated with gastric cancer susceptibility in the Chinese population 46. Rs17879599 in the second exon of the HLA-DRB1 gene had been suggested as an independent leading contributor to HCC risk in Han Chinese 47. Expression of HLA-E and HLA-G were found differently upregulated in HCC tissues 48. However, our present study found that HLA-E and HLA-G were found differently upregulated in non-HCC tissues, and a statistically significant difference was shown only in HLA-E.

To date, the HLA family had been widely explored with regard to their prognostic values in multiple tumours. However, associations between the HLA family and HCC had not been fully explored until now. We, for the first time, explored diagnostic and prognostic values among the HLA complex and HCC. HBV infection had widely been recognized as a risk factor for HCC. Two HLA-DRB1-DQB1 haplotypes, such as DRB1*15:02-DQB1*06:01 and DRB1*13:02-DQB1*06:04, and three DPB1 alleles, such as DPB1*02:01, *04:02, and *05:01, were found associations with chronic HBV infection in Japanese population 49.

Dianwu Liu et al. suggested that thirty four variants of eight HLA genes, including HLA-B, HLA-C, HLA-DPA1, HLA-DQA1, HLA-DQB1, HLA-DQB2, HLA-DRB1, and HLA-DRB5, were strongly associated with HBV-related HCC 50. Weiping Zhou et al. suggested that new associations at rs9272105 (HLA-DQA1/DRB1) on 6p21.32 (odds ratio=1.30, P=1.13E-19) 51. This finding is consistent with our present study of association between HLA-DQA1 and HCC.

Hyon-Suk Kim et al. found that serum level of soluble HLA-G (sHLA-G) was correlated with the progression of HBV infection 52. In addition, AUC of sHLA-G for distinguishing HCC from liver cirrhosis was higher than that of AFP and would be a diagnostic biomarker for HCC 52. Moreover, they indicated that sHLA-G should not to be considered as severity of HBV infections and HCC but rather reflects phases of diseases including HBV-related HCC and concluded that increased sHLA-G expression could be one of the immune escape mechanisms of both HBV infection and HCC 52. Nonetheless, our present study did not find clinical significance of HLA-G for HCC. This contradiction might be attributed to the difference of study population and ethnicity.

There were some limitations in the present study that need to be recognised. First, larger population cohorts were warranted to further validate our findings. Second, multivariate analyses were needed to generalise our results, a HBV-related HCC cohort. Third, functional trials were needed in future studies to explore the properties of prognosis-related genes in tumour proliferation, metastasis and angiogenesis.

Conclusions

In the present study, we conducted investigations for associations between the HLA complex and HCC. We found that some genes had diagnostic values for HCC. Among them, HLA-C was the most diagnostic biomarker for HCC. In addition, HLA-DQA1 and HLA-F had prognostic values for OS, whereas HLA-A, HLA-C, HLA-DPA1 and HLA-DQA1 had prognostic values for RFS. GSEA found they were involved in positive regulation of the immune response, antigen processing, chemokine signalling pathway and toll like receptor signalling pathway. Risk score models and nomograms were used to evaluate values of prognosis-related genes for HCC. Further validation in the Kaplan-Meier Plotter website indicated that HLA-C, HLA-DPA1, HLA-E, HLA-F and HLA-G were associated with HCC prognosis in OS. Therefore, we concluded that HLA-C, HLA-DPA1 and HLA-F expression were associated with HCC prognosis, and HLA-A and HLA-DQA1 gene expression were associated with prognosis of HBV-related HCC. HLA-C might be a diagnostic biomarker for HCC.

Supplementary figures and table.

Click here for additional data file.

Table 1

Overall survival analysis of HLA family genes

Variables		Overall survival
	Patients (n=212)	No. of event	MST (month)	HR (95%CI)	Crude P value	HR (95%CI)	Adjusted P valueɸ
HLA-A
Low expression	106	43	NA	Ref.		Ref.
High expression	106	39	NA	0.824 (0.534-1.271)	0.382	0.730 (0.468-1.139)	0.166
HLA-B
Low expression	106	43	NA	Ref.		Ref.
High expression	106	39	NA	0.921 (0.597-1.421)	0.711	1.016 (0.657-1.573)	0.942
HLA-C
Low expression	106	45	NA	Ref.		Ref.
High expression	106	37	NA	0.770 (0.498-1.190)	0.239	0.736 (0.474-1.141)	0.170
HLA-DMA
Low expression	106	44	NA	Ref.		Ref.
High expression	106	38	NA	0.809 (0.524-1.249)	0.339	0.785 (0.508-1.215)	0.278
HLA-DMB
Low expression	106	38	NA	Ref.		Ref.
High expression	106	44	NA	1.204 (0.780-1.859)	0.401	1.173 (0.745-1.845)	0.491
HLA-DOA
Low expression	106	43	NA	Ref.		Ref.
High expression	106	39	NA	0.923 (0.598-1.423)	0.716	0.997 (0.636-1.562)	0.988
HLA-DOB
Low expression	106	37	NA	Ref.		Ref.
High expression	106	45	NA	1.295 (0.838-2.001)	0.245	1.304 (0.837-2.030)	0.241
HLA-DPA1
Low expression	106	38	NA	Ref.		Ref.
High expression	106	44	NA	1.110 (0.719-1.713)	0.638	1.198 (0.766-1.874)	0.429
HLA-DPB1
Low expression	106	35	NA	Ref.		Ref.
High expression	106	47	60.50	1.587 (1.024-2.460)	0.039	1.566 (0.998-2.458)	0.051
HLA-DQA1
Low expression	106	44	NA	Ref.		Ref.
High expression	106	38	NA	0.846 (0.548-1.306)	0.449	0.563 (0.359-0.883)	0.012
HLA-DQB1
Low expression	106	38	NA	Ref.		Ref.
High expression	106	44	NA	1.222 (0.792-1.888)	0.365	1.393 (0.896-2.166)	0.141
HLA-DQB2
Low expression	106	40	NA	Ref.		Ref.
High expression	106	42	NA	1.012 (0.656-1.560)	0.957	0.968 (0.624-1.501)	0.884
HLA-DRA
Low expression	106	42	NA	Ref.		Ref.
High expression	106	40	NA	0.920 (0.596-1.418)	0.705	0.816 (0.523-1.272)	0.369
HLA-DRB1
Low expression	106	42	NA	Ref.		Ref.
High expression	106	40	NA	0.927 (0.601-1.430)	0.732	0.742 (0.468-1.177)	0.205
HLA-DRB4
Low expression	106	44	NA	Ref.		Ref.
High expression	106	38	NA	0.905 (0.586-1.398)	0.654	0.869 (0.562-1.345)	0.529
HLA-DRB6
Low expression	106	46	NA	Ref.		Ref.
High expression	106	36	NA	0.738 (0.477-1.142)	0.173	0.893 (0.569-1.402)	0.624
HLA-E
Low expression	106	42	NA	Ref.		Ref.
High expression	106	40	NA	0.909 (0.589-1.402)	0.666	1.102 (0.691-1.755)	0.684
HLA-F
Low expression	106	46	NA	Ref.		Ref.
High expression	106	36	NA	0.636 (0.411-0.985)	0.043	0.576 (0.371-0.896)	0.014
HLA-G
Low expression	106	40	NA	Ref.		Ref.
High expression	106	42	NA	1.128 (0.731-1.739)	0.586	1.289 (0.830-2.003)	0.258

Note: ɸ: P values were adjusted for tumor size, cirrhosis, AFP and BCLC stage.

Table 2

Recurrence-free survival analysis of HLA family genes

Variables		Recurrence-free survival
	Patients (n=212)	No. of event	MST (month)	HR (95%CI)	Crude P value	HR (95%CI)	Adjusted P valueѰ
HLA-A
Low expression	106	61	35.20	Ref.		Ref.
High expression	106	55	51.60	0.780 (0.542-1.123)	0.182	0.638 (0.439-0.930)	0.019
HLA-B
Low expression	106	58	43.20	Ref.		Ref.
High expression	106	58	45.90	0.989 (0.687-1.424)	0.954	1.105 (0.764-1.598)	0.597
HLA-C
Low expression	106	67	30.7	Ref.		Ref.
High expression	106	49	57.9	0.649 (0.448-0.938)	0.022	0.664 (0.458-0.963)	0.031
HLA-DMA
Low expression	106	61	40.4	Ref.		Ref.
High expression	106	55	49.1	0.894 (0.621-1.287)	0.545	0.923 (0.640-1.332)	0.669
HLA-DMB
Low expression	106	52	53.0	Ref.		Ref.
High expression	106	64	29.9	1.387 (0.962-2.001)	0.080	1.378 (0.943-2.015)	0.098
HLA-DOA
Low expression	106	60	36.0	Ref.		Ref.
High expression	106	56	51.1	0.906 (0.629-1.305)	0.596	0.894 (0.617-1.294)	0.553
HLA-DOB
Low expression	106	56	51.1	Ref.		Ref.
High expression	106	60	37.9	1.179 (0.819-1.697)	0.376	1.116 (0.772-1.615)	0.559
HLA-DPA1
Low expression	106	63	36.6	Ref.		Ref.
High expression	106	53	53.3	0.795 (0.552-1.146)	0.219	0.673 (0.462-0.982)	0.040
HLA-DPB1
Low expression	106	57	49.1	Ref.		Ref.
High expression	106	59	30.9	1.236 (0.858-1.779)	0.255	1.257 (0.860-1.837)	0.237
HLA-DQA1
Low expression	106	61	40.4	Ref.		Ref.
High expression	106	55	53.3	0.887 (0.616-1.277)	0.518	0.658 (0.451-0.960)	0.030
HLA-DQB1
Low expression	106	61	46.3	Ref.		Ref.
High expression	106	55	40.4	0.948 (0.658-1.365)	0.772	1.009 (0.699-1.458)	0.960
HLA-DQB2
Low expression	106	55	46.3	Ref.		Ref.
High expression	106	61	37.9	1.164 (0.809-1.676)	0.413	1.202 (0.833-1.735)	0.324
HLA-DRA
Low expression	106	60	37.9	Ref.		Ref.
High expression	106	56	51.1	0.851 (0.591-1.226)	0.386	0.786 (0.541-1.141)	0.206
HLA-DRB1
Low expression	106	57	46.3	Ref.		Ref.
High expression	106	59	41.6	1.000 (0.695-1.440)	0.998	0.937 (0.635-1.380)	0.740
HLA-DRB4
Low expression	106	58	32.7	Ref.		Ref.
High expression	106	58	49.1	0.992 (0.689-1.429)	0.967	0.952 (0.659-1.374)	0.793
HLA-DRB6
Low expression	106	59	43.2	Ref.		Ref.
High expression	106	57	46.1	0.943 (0.655-1.357)	0.753	1.006 (0.696-1.453)	0.975
HLA-E
Low expression	106	57	41.6	Ref.		Ref.
High expression	106	59	46.1	0.999 (0.694-1.439)	0.997	1.087 (0.745-1.585)	0.667
HLA-F
Low expression	106	62	28.7	Ref.		Ref.
High expression	106	54	53.0	0.736 (0.511-1.061)	0.100	0.706 (0.488-1.021)	0.064
HLA-G
Low expression	106	60	37.9	Ref.		Ref.
High expression	106	56	46.3	0.933 (0.648-1.343)	0.708	1.042 (0.717-1.514)	0.831

Note: Ѱ: P values were adjusted for gender, cirrhosis and BCLC stage.

Table 3

Joint-effect analysis of prognostic-related genes for overall survival and recurrence-free survival

Category	Group	Score	Patients	MST	Crude HR95%CI	Crude P value	Adjusted HR95%CI	Adjusted P valueɸ	Adjusted P valueѰ
OS	I	0	51	57.9		0.112		0.002
	II	1 score	110	NA	0.873 (0.525-1.453)	0.602	0.856 (0.509-1.439)	0.558
	III	2 scores	51	NA	0.503 (0.257-0.984)	0.045	0.311 (0.156-0.620)	0.001
RFS	A	0	12	35.2		0.022			<0.001
	B	1 score	59	21.5	1.275 (0.597-2.721)	0.530	0.565 (0.243-1.317)		0.186
	C	2 scores	74	54.8	0.689 (0.320-1.485)	0.342	0.297 (0.126-0.699)		0.005
	D	3 scores	51	53.3	0.783 (0.355-1.724)	0.543	0.331 (0.140-0.783)		0.012
	E	4 scores	16	NA	0.394 (0.129-1.204)	0.102	0.107 (0.032-0.359)		<0.001

(Note: ɸ: P values in overall survival were adjusted for tumor size, cirrhosis, AFP and BCLC stage. P values in recurrence-free survival were adjusted for gender, cirrhosis and BCLC stage. Scores in overall survival: 0: Low HLA-DQA1 + Low HLA-F expression; 1 score: Low HLA-DQA1 + High HLA-F expression, High HLA-DQA1 + Low HLA-F expression; 2 scores: High HLA-DQA1 + High HLA-F expression. Scores in disease-free survival: 0: Low HLA-A + Low HLA-C + Low HLA-DPA1 + Low HLA-DQA1 expression; 1 score: High HLA-A + Low HLA-C + Low HLA-DPA1 + Low HLA-DQA1 expression, Low HLA-A + High HLA-C + Low HLA-DPA1 + Low HLA-DQA1 expression, Low HLA-A + Low HLA-C + High HLA-DPA1 + Low HLA-DQA1 expression, Low HLA-A + Low HLA-C + Low HLA-DPA1 + High HLA-DQA1 expression; 2 scores: High HLA-A + High HLA-C + Low HLA-DPA1 + Low HLA-DQA1 expression, High HLA-A + Low HLA-C + High HLA-DPA1 + Low HLA-DQA1 expression, High HLA-A + Low HLA-C + Low HLA-DPA1 + High HLA-DQA1 expression, Low HLA-A + High HLA-C + High HLA-DPA1 + Low HLA-DQA1 expression, Low HLA-A + High HLA-C + Low HLA-DPA1 + High HLA-DQA1 expression, Low HLA-A + Low HLA-C + High HLA-DPA1 + High HLA-DQA1 expression; 3 scores: High HLA-A + High HLA-C + High HLA-DPA1 + Low HLA-DQA1 expression, High HLA-A + High HLA-C + Low HLA-DPA1 + High HLA-DQA1 expression, High HLA-A + Low HLA-C + High HLA-DPA1 + High HLA-DQA1 expression, Low HLA-A + High HLA-C + High HLA-DPA1 + High HLA-DQA1 expression; 4 scores: High HLA-A + High HLA-C + High HLA-DPA1 + High HLA-DQA1 expression.)

Table 4

Stratified analysis of prognostic-related genes for overall survival

Variables	HLA-DQA1				HLA-F
	Low	High	Adjusted HR (95%CI)	Adjusted P valueɸ	Low	High	Adjusted HR (95%CI)	Adjusted P valueɸ
Gender
Male	88	95	0.507 (0.315-0.814)	0.005	91	92	0.612 (0.386-0.971)	0.037
Female	18	11	11.866 (0.919-153.153)	0.058	15	14	0.139 (0.014-1.409)	0.095
Age
≤60 years	89	86	0.535 (0.326-0.878)	0.013	90	85	0.579 (0.357-0.937)	0.026
>60 years	17	20	0.939 (0.295-2.987)	0.915	16	21	0.485 (0.129-1.832)	0.286
HBV status
AVR-CC	30	26	0.360 (0.130-0.993)	0.049	26	30	0.615 (0.254-1.488)	0.281
CC	76	80	0.687 (0.399-1.182)	0.175	80	76	0.530 (0.306-0.917)	0.023
Tumor size
≤5 cm	72	65	0.591 (0.311-1.123)	0.108	62	75	0.652 (0.360-1.179)	0.157
>5 cm	34	40	0.550 (0.279-1.086)	0.085	43	31	0.509 (0.251-1.032)	0.061
Cirrhosis
Yes	91	104	0.567 (0.361-0.890)	0.014	97	98	0.595 (0.381-0.929)	0.022
No	15	2	0.287 (2.599E-19-3.176E17)	0.953	9	8	0.002 (8.086E-22-4.683E15)	0.773
Multinodular
Yes	18	27	0.719 (0.290-1.780)	0.476	21	24	0.307 (0.122-0.772)	0.012
No	88	79	0.503 (0.293-0.864)	0.013	85	82	0.631 (0.373-1.069)	0.087
AFP
≤300 ng/ml	59	56	0.432 (0.217-0.861)	0.017	54	61	0.857 (0.450-1.631)	0.639
>300 ng/ml	44	50	0.716 (0.390-1.315)	0.282	50	44	0.433 (0.227-0.824)	0.011
BCLC stage
0	13	7	3.055 (0.190-49.157)	0.431	8	12	0.404 (0.024-6.735)	0.528
A	75	68	0.437 (0.240-0.796)	0.007	74	69	0.616 (0.343-1.108)	0.106
B	8	14	0.462 (0.110-1.933)	0.290	11	11	0.109 (0.021-0.566)	0.008
C	10	17	1.381 (0.489-3.902)	0.542	13	14	0.800 (0.310-2.061)	0.644

Note: ɸ: P values were adjusted for tumor size, cirrhosis, AFP and BCLC stage.

Table 5

Stratified analysis of prognostic-related genes for recurrence-free survival

Variables	HLA-A				HLA-C
	Low	High	Adjusted HR (95%CI)	Adjusted P value	Low	High	Adjusted HR (95%CI)	Adjusted P value
Gender
Male	89	94	0.567 (0.383-0.839)	0.005	96	87	0.660 (0.447-0.974)	0.036
Female	17	12	1.491 (0.321-6.921)	0.610	10	19	0.541 (0.116-2.515)	0.433
Age
≤60 years	86	89	0.710 (0.470-1.074)	0.105	91	84	0.675 (0.448-1.017)	0.060
>60 years	20	17	0.377 (0.148-0.958)	0.040	15	22	0.674 (0.270-1.680)	0.397
HBV status
AVR-CC	29	27	0.817 (0.408-1.634)	0.567	23	33	0.469 (0.234-0.940)	0.033
CC	77	79	0.610 (0.386-0.964)	0.034	83	73	0.664 (0.422-1.044)	0.664
Tumor size
≤5 cm	70	67	0.844 (0.529-1.348)	0.478	67	70	0.554 (0.345-0.889)	0.014
>5 cm	35	39	0.391 (0.208-0.735)	0.004	38	36	0.791 (0.418-1.495)	0.470
Cirrhosis
Yes	97	98	0.643 (0.439-0.943)	0.024	96	99	0.694 (0.475-1.014)	0.059
No	9	8	1.356 (0.222-8.267)	0.741	10	7	0.576 (0.061-5.460)	0.631
Multinodular
Yes	20	25	0.559 (0.232-1.347)	0.195	24	21	0.567 (0.236-1.363)	0.205
No	86	81	0.679 (0.445-1.036)	0.072	82	85	0.582 (0.371-0.914)	0.019
AFP
≤300 ng/ml	51	64	0.580 (0.346-0.973)	0.039	57	58	0.673 (0.405-1.119)	0.127
>300 ng/ml	54	40	0.709 (0.400-1.258)	0.240	48	46	0.629 (0.361-1.097)	0.103
BCLC stage
0	9	11	2.624E5 (8.254E-158-8.340E167)	0.948	9	11	0.402 (0.042-3.877)	0.431
A	73	70	0.662 (0.416-1.051)	0.080	72	71	0.663 (0.416-1.057)	0.084
B	11	11	0.853 (0.259-2.811)	0.794	11	11	0.599 (0.198-1.813)	0.364
C	13	14	0.261 (0.096-0.709)	0.008	14	13	0.848 (0.346-2.079)	0.719
Variables	HLA-DPA1				HLA-DQA1
	Low	High	Adjusted HR(95%CI)	AdjustedP value	Low	High	Adjusted HR(95%CI)	AdjustedP value
Gender
Male	91	92	0.724 (0.489-1.072)	0.107	88	95	0.603 (0.407-0.893)	0.012
Female	15	14	0.249 (0.050-1.238)	0.089	18	11	1.978 (0.379-10.320)	0.418
Age
≤60 years	86	89	0.723 (0.479-1.090)	0.122	89	86	0.652 (0.430-0.989)	0.044
>60 years	20	17	0.445 (0.166-1.193)	0.108	17	20	0.702 (0.281-1.753)	0.448
HBV status
AVR-CC	28	28	0.377 (0.182-0.779)	0.008	30	26	0.382 (0.174-0.840)	0.017
CC	78	78	0.794 (0.503-1.255)	0.323	76	80	0.739 (0.471-1.159)	0.188
Tumor size
≤5 cm	75	62	0.658 (0.401-1.080)	0.098	72	65	0.607 (0.371-0.996)	0.048
>5 cm	31	43	0.788 (0.423-1.468)	0.453	34	40	0.489 (0.358-1.325)	0.264
Cirrhosis
Yes	95	100	0.694 (0.472-1.020)	0.063	91	104	0.666 (0.455-0.973)	0.036
No	11	6	0.563 (0.062-5.083)	0.609	15	2	NA	NA
Multinodular
Yes	19	26	1.318 (0.578-3.005)	0.511	18	27	1.057 (0.466-2.401)	0.894
No	87	80	0.562 (0.362-0.873)	0.010	88	79	0.518 (0.331-0.812)	0.004
AFP
≤300 ng/ml	55	60	0.744 (0.444-1.248)	0.263	59	56	0.585 (0.343-0.999)	0.050
>300 ng/ml	48	46	0.540 (0.304-0.961)	0.036	44	50	0.692 (0.401-1.192)	0.184
BCLC stage
0	13	7	0.453 (0.050-4.104)	0.481	13	7	1.165 (0.207-6.559)	0.862
A	73	70	0.649 (0.407-1.033)	0.069	75	68	0.050 (0.309-0.809)	0.005
B	12	10	2.093 (0.674-6.498)	0.201	8	14	1.165 (0.356-3.812)	0.801
C	8	19	0.717 (0.284-1.812)	0.482	10	17	1.056 (0.420-2.655)	0.908

Note: Ѱ: P values were adjusted for gender, cirrhosis and BCLC stage.