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Journal of Cancer logoLink to Journal of Cancer
. 2019 Aug 28;10(21):5173–5190. doi: 10.7150/jca.29655

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

Xiang-Kun Wang 1, Xi-Wen Liao 1, Cheng-Kun Yang 1, Ting-Dong Yu 1, Zheng-Qian Liu 1, Yi-Zhen Gong 2, Ke-Tuan Huang 1, Xian-Min Zeng 1, Chuang-Ye Han 1, Guang-Zhi Zhu 1, Wei Qin 1, Tao Peng 1,✉
PMCID: PMC6775598  PMID: 31602270

Abstract

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.

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 1).

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 1A, C, E, H-K, M-Q). However, other genes did not show significance (all P > 0.05, Figure 1B, D, F, G, L, R-S). 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 1). A bodymap of HLA family members in human organs was shown in Supplementary Figure 2. Transcriptional analysis indicated that all the members consistently showed higher transcripts per millions in tumour tissues compared with normal tissues (Figure 2).

Figure 1.

Figure 1

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Relative mRNA expressions of HLA family in tumor and non-tumor tissues. A-S: HLA-A, B, C, DMA, DMB, DOA, DOB, DPA1, DPB1, DQA1, DQB1, DQB2, DRA, DRB4, DRB6,E, F, G respectively. Note: *: P ≤ 0.05; **: P ≤ 0.01; ***: P ≤ 0.001; ****: P ≤ 0.0001; #: P>0.05.

Figure 2.

Figure 2

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Transcriptional levels of HLA family in tumor and normal tissues. A-S: HLA-A, B, C, DMA, DMB, DOA, DOB, DPA1, DPB1, DQA1, DQB1, DQB2, DRA, DRB4, DRB6,E, F, G respectively.

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 3A, C, E-K, M-Q). HLA-C showed the highest area under curve (AUC, 0.784).

Figure 3.

Figure 3

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Diagnostic receiver operating characteristic curves of HLA family. A-S HLA-A, B, C, DMA, DMB, DOA, DOB, DPA1, DPB1, DQA1, DQB1, DQB2, DRA, DRB4, DRB6,E, F, G respectively.

In the univariate OS analysis, only HLA-DPB1 and HLA-F showed significance (crude P = 0.039 and 0.043, respectively; Table 1, Figure 4H, R). In the multivariate OS analysis, HLA-DQA1 and HLA-F showed significance (adjusted P = 0.012 and 0.014, Table 1). In the univariate RFS analysis, only HLA-C showed significance (crude P = 0.022; Table 2, Figure 5C). 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 2).

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

Figure 4.

Figure 4

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Overall survival analysis plot of HLA family. A-S: HLA-A, B, C, DMA, DMB, DOA, DOB, DPA1, DPB1, DQA1, DQB1, DQB2, DRA, DRB4, DRB6, E, F, G respectively.

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

Figure 5.

Figure 5

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Recurrence-free survival analysis plot of HLA family. A-S: HLA-A, B, C, DMA, DMB, DOA, DOB, DPA1, DPB1, DQA1, DQB1, DQB2, DRA, DRB4, DRB6, E, F, G respectively.

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 3). 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 3). 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).

Table 3.

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

Category Group Score Patients MST Crude HR
95%CI		 Crude P value Adjusted HR
95%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
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(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.)

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 4). 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 4 and 5.

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

Expression model and nomogram construction

Expression models were constructed for OS and RFS prognosis in Figure 6 and 7, respectively. Expressions, survival statuses and heatmaps were shown in Figure 6A and 7A. Prognostic receiver operating characteristic (ROC) curves were shown in Figure 6B 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.

Figure 6.

Figure 6

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Expression model constructed using HLA-F gene. A: Expression model including expression, survival status and heatmap; B: Time dependent receiver operating characteristic curves at 1, 3- and 5- year respectively.

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 8).

Figure 8.

Figure 8

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Nomograms constructed using overall survival and recurrence-free survival-related clinical factors and genes. A: Nomogram of overall survival-related genes and clinical factors; B: Nomogram of recurrence-free survival-related genes and clinical factors.

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 9A, D, M-N). 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 10A, D, N-O). Detailed GSEA results were shown in Figure 9 and 10 and Supplementary Figure 3-5.

Figure 9.

Figure 9

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Gene set enrichment analysis results of HLA-F gene. A-L: Results of biological processes; M-P: Results of KEGG pathways.

Figure 10.

Figure 10

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Gene set enrichment analysis results of HLA-A gene. A-L: Results of biological processes; M-P: Results of KEGG pathways.

Interaction and co-expression networks and enrichment analysis

Comparison between low and high levels of expression were shown in Figure 11A and B. Significant P values were exhibited in all HLA family members (all P ≤ 0.05). Matrices showed Pearson correlations among HLA members (Figure 11C). Co-expression interaction and PPI networks showed relationships among HLA members (Figure 11D and E).

Figure 11.

Figure 11

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Scatter plots, matrix and interaction networks analysis. A-B: Scatter plots of HLA complex family expressions; C: Co-expression network of HLA complex gene family; D: Protein-protein interaction network of HLA complex; E: Matrix of Pearson correlation of HLA complex family.

The top 10 GO terms and KEGG pathways were exhibited in Figure 12. Detailed GO terms and KEGG pathways were shown in Supplementary Table 1. Visualised interactions of GO terms constructed using BiNGO were shown in Supplementary Figure 6.

Figure 12.

Figure 12

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Enriched top 10 GO terms and metabolic pathways of HLA complex. A: enriched GO terms, including biological process, cellular component and molecular function; B: enriched KEGG pathways

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 13C, H, P-R). However, other genes did not show significance (all P > 0.05, Figure 13).

Figure 13.

Figure 13

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Kaplan-Meier plots of overall survival of HLA complex. A-R: HLA-A, B, C, DMA, DMB, DOA, DOB, DPA1, DPB1, DQA1, DQB1, DQB2, DRA, DRB6, E, F, G respectively.

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 Material

Supplementary figures and table.

Click here for additional data file. (17MB, pdf)

Figure 7.

Figure 7

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Expression model constructed using HLA-C gene. A: Expression model including expression, survival status and heatmap; B: Time dependent receiver operating characteristic curves at 1, 3- and 5- year respectively.

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)		 Adjusted
P value		 Low High Adjusted HR
(95%CI)		 Adjusted
P 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
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Note: Ѱ: P values were adjusted for gender, cirrhosis and BCLC stage.

Acknowledgments

The authors would like to acknowledge those researchers for their contribution on open access of GTEx portal, GEPIA, Kaplan-Meier Plotter, STRING and The Human Protein Atlas website. In addition, the authors would like to acknowledge invaluable help from peer reviewers.

Data availability

The datasets analyzed during the current study were available from the corresponding author on reasonable request.

Funding

This work was supported in part by the National Natural Science Foundation of China (No.: 81560535, 81802874, 81072321, 30760243, 30460143 and 30560133), Natural Science Foundation of Guangxi Province of China (Grant No.2017JJB140189y, 2018GXNSFAA050119), 2009 Program for New Century Excellent Talents in University (NCET), Guangxi Natural Sciences Foundation (No.: GuiKeGong 1104003A-7), and Guangxi Health Ministry Medicine Grant (Key-Scientific Research-Grant Z201018). The present study is also partly supported by Scientific Research Fund of the Health and Family Planning Commission of Guangxi Zhuang Autonomous Region (Z2016318), Key laboratory of High-Incidence-Tumor Prevention & Treatment (Guangxi Medical University), Ministry of Education (GKE2018-01, GKE2019-11), The Basic Ability Improvement Project for Middle-aged and Young Teachers in Colleges and Universities in Guangxi (2018KY0110), and 2018 Innovation Project of Guangxi Graduate Education (YCBZ2018036). As well as, the present study is also partly supported by Research Institute of Innovative Think-tank in Guangxi Medical University (The gene-environment interaction in hepatocarcinogenesis in Guangxi HCCs and its translational applications in the HCC prevention). We would also acknowledge the supported by the Key laboratory of High-Incidence-Tumor Prevention & Treatment (Guangxi Medical University), Ministry of Education.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary figures and table.

Click here for additional data file. (17MB, pdf)

Data Availability Statement

The datasets analyzed during the current study were available from the corresponding author on reasonable request.

Articles from Journal of Cancer are provided here courtesy of Ivyspring International Publisher

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