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Hepatoma Res 2018;4:21. 10.20517/2394-5079.2018.44 © The Author(s) 2018.

Pathway analysis provides insight into the genetic susceptibility to hepatocellular carcinoma and insight into immuno-therapy treatment response

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1Computational Sciences and Informatics, Complex Adaptive Systems, Arizona State University, Tempe, AZ 85287, USA.

2Center for Evolution and Medicine, Arizona State University, Tempe, AZ 85287, USA.

3School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA.

Correspondence Address: Dr. Kenneth Howard Buetow, Computational Sciences and Informatics, Complex Adaptive Systems, Arizona State University, Tempe, AZ 85287, USA. E-mail:

This article belongs to the Special Issue Molecular Mechanism of Hepatocellular Carcinoma
© The Author(s) 2018. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License (, which permits unrestricted use, sharing, adaptation, distribution and reproduction in any medium or format, for any purpose, even commercially, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.


Clear evidence exists for genetic susceptibility to hepatocellular carcinoma (HCC). Genome-wide association studies have identified multiple candidate susceptibility loci. These loci suggest that genetic variation in the immune system may underpin HCC susceptibility. Genes for the antigen processing and presentation pathway have been observed to be significantly enriched across studies and the pathway is identified directly through genome-wide studies of variation using pathway methods. Detailed analysis of the pathway indicates both variation in the antigen presenting loci and in the antigen processing are different in cases in controls. Pathway analysis at the transcriptional level also shows difference between normal liver and liver in individuals with HCC. Assessing differences in the pathway may prove important in improving immune therapy for HCC and in identifying responders for immune checkpoint therapy.


Hepatocellular carcinoma, genetic susceptibility, genome-wide association study, pathway analysis, antigen presentation and processing, immune checkpoint therapy


Hepatocellular carcinoma (HCC), the most common form of primary liver cancer, is ranked 5th in global incidence and 2nd in mortality[1]. With the exception of East Asia, the incidence of HCC is increasing in almost all regions of the world and has doubled in the USA since the early 1980s[2]. This increase is attributable to increases in obesity and type II diabetes[3,4]. Liver cancer’s 5-year survival is the second worst among all cancers (18.1%)[5].

In this manuscript, the role of genetic susceptibility to HCC is examined. Novel tools that evaluate genetic data using collections of genes and their interactions within biologic networks are used to identify key biologic processes driving susceptibility. The relationship of germline and somatic variation is explored. The importance of these findings is assessed in the context of current therapeutic interventions for HCC.

Somatic genetic etiology of HCC

Like other solid tumors, at a somatic level, HCC appears to arise via alterations in numerous genes that modify multiple biologic processes. An early whole-genome sequencing effort identified an average of 9718 nucleotide alternations, 271 insertion/deletions, and 41 structural variations per tumor, with substantial variability from tumor to tumor[6]. Within coding sequences, it has been reported that there are an average of 21 synonymous and 64 non-synonymous mutations per tumor[7]. Tumors of larger size are observed to have greater numbers of point mutations, which are speculated to contribute to heterogeneity within the tumors. The Cancer Genome Atlas (TCGA) Research network’s evaluation of HCC[8] finds alterations over-represented in the RAS pathway, WNT pathway, cell cycle regulation pathways and chromatin modification pathways with high mutation rates in TP53 (31%), CTNNB1 (27%), AXIN1 (8%), ARID1A (7%), ARID2 (5%), RB1 (4%), PIK3CA (4%), CDKN2A (2%), KRAS (1%), NRAS (1%), high deletion frequencies of RB1 (19%), CDKN2A (13%), PTEN (7%) and amplification of CCND1 (6%). The most commonly mutated locus was TERT with promoter mutations found in 44% of tumors[8]. The TCGA data unexpectedly also showed high mutation rates in ALB (13%) and APOB (10%).

Genetic susceptibility to HCC

In contrast to other common tumors, genetic susceptibility to HCC remains poorly characterized. Studies have identified evidence for familiality of HCC, over and above familial exposures such as HBV infection[9-14]. For example, after accounting for HBV infection, individuals with a family history of HCC have a rate ratio of 2.4[10]. To date, these studies have examined only hepatitis virus associated HCC and have yet to explore the role of obesity and diabetes related susceptibility.

A limited number of studies have been conducted to identify the loci underpinning this familiality. Original studies focused on candidate genes whose observed single nucleotide polymorphisms (SNPs) could plausibly modify known environmental risk factors for HCC including aflatoxin, alcohol, or tobacco. A meta-analysis of these studies found associations with 5 genes HFE, IL-1B, MnSOD, MDM, and 2UGT1A7[15].

HCC has had a small number of genome wide association studies (GWAS) conducted with modest success in identifying risk loci. The NHGRI-EBI Catalog lists a total of 11 studies that have identified 22 loci[16]. These studies examine East Asian populations and have included HCC associated with hepatitis B virus (HBV), hepatitis C virus (HCV), and non-alcoholic steatohepatitis (NASH) etiologies. The studies have identified SNPs in the genomic proximity (intronic, upstream and/or downstream) of twenty protein coding loci.

Clues to the biologic basis of HCC susceptibility across GWAS studies can be identified by looking for non-random enrichment. Using the resources of the Gene Ontology consortium (GO) (, the twenty protein coding loci were examined for biologic process enrichment in Homo sapiens. This enrichment analysis uses the tools of Panther ( Four high level GO processes were observed to be significantly enriched “T cell receptor signaling pathway” (P = 0.0366), “interferon-gamma-mediated signaling pathway” (P = 0.0026), “T cell costimulation” (P = 0.0020), and “antigen processing and presentation of exogenous peptide antigen via MHC class II” (P = 0.0001).

We have previously looked for inherited susceptibility using genome-wide genotyping and a novel analytic approach that uses biologic networks - Pathways of Distinction Analysis (PoDA)[17]. In PoDA, the network is the unit of analysis and accounts for interactions among features within the network. In this analysis “antigen processing and presentation” was identified as having significant differences in variability in a population of Korean HBV associate HCC cases and controls. Consistent with the results of the enrichment analysis, re-analysis of this dataset with an extended set of 1200 pathways again identified “antigen processing and presentation”, but also “interferon gamma signaling”, “TCR signaling”, and “T cell receptor signaling pathway” [Table 1] suggesting that immune response may be a key driver of HCC susceptibility.

Table 1

Updated significant networks identified through pathway of distinction analysis

PoDA pathway name Source DS OR No. of genes No. of SNPs
Axon guidance KEGG 1.888 3.1699 245 13,044
GPCR downstream signaling REACTOME 1.706 2.4122 695 16,949
Focal adhesion KEGG 0.802 2.3329 197 7999
Pathways in cancer KEGG 0.570 2.2487 284 10,406
MAPK signaling pathway KEGG 0.620 2.1152 245 7368
PI3K-Akt signaling pathway KEGG -0.339 2.0837 314 10,409
Calcium signaling pathway KEGG -1.030 1.8479 163 8684
Regulation of actin cytoskeleton KEGG -1.004 1.8207 195 5681
Glycerolipid metabolism KEGG 2.003 1.7607 55 1590
Mechanism of gene regulation by peroxisome proliferators via ppara BIOCARTA 2.371 1.7272 49 1076
Interleukin-3, 5 and GM-CSF signaling REACTOME 2.969 1.7235 41 1188
Glycerophospholipid biosynthesis REACTOME 2.201 1.7208 70 1714
T cell receptor signaling pathway BIOCARTA 2.493 1.6792 55 1500
Dopaminergic synapse KEGG -1.348 1.6651 116 5396
Stabilization and expansion of the E-cadherin adherens junction NCI/NATURE 2.142 1.6630 40 1449
Eicosanoid metabolism BIOCARTA 3.026 1.6620 16 800
Netrin-mediated signaling events NCI/NATURE 1.965 1.6620 28 2400
Pre-NOTCH expression and processing REACTOME 3.240 1.6343 45 1451
Purine metabolism KEGG -1.190 1.6284 150 4726
Toxoplasmosis KEGG 2.470 1.5901 110 2088
Angiopoietin receptor Tie2-mediated signaling NCI/NATURE 2.163 1.5806 47 1331
Circadian entrainment KEGG -1.498 1.5738 88 5919
Systemic lupus erythematosus KEGG 3.873 1.5688 82 1185
Bioactive peptide induced signaling pathway BIOCARTA 2.276 1.5677 42 1260
Role of mef2d in t-cell apoptosis BIOCARTA 2.138 1.5522 30 946
Herpes simplex infection KEGG 2.816 1.5388 170 1994
Glycosphingolipid biosynthesis - lacto and neolacto series KEGG 2.756 1.5285 23 535
Multi-step regulation of transcription by pitx2 BIOCARTA 2.935 1.5253 22 526
Retrograde endocannabinoid signaling KEGG -1.990 1.5208 94 4960
TCR signaling REACTOME 3.001 1.4913 51 1226
TPO signaling pathway BIOCARTA 2.556 1.4896 23 635
Growth hormone signaling pathway BIOCARTA 2.144 1.4813 28 768
Rheumatoid arthritis KEGG 2.895 1.4801 84 978
Huntington’s disease KEGG 2.156 1.4658 152 1647
Inactivation of gsk3 by akt causes accumulation of b-catenin in alveolar macrophages BIOCARTA 2.467 1.4628 32 709
Chaperones modulate interferon signaling pathway BIOCARTA 2.486 1.4615 18 313
Phospholipase c signaling pathway BIOCARTA 2.886 1.4577 10 849
GnRH signaling pathway KEGG -1.259 1.4488 84 3622
Oocyte meiosis KEGG -1.285 1.4371 102 2727
Biosynthesis of unsaturated fatty acids KEGG 1.927 1.4342 19 495
GMCSF-mediated signaling events NCI/NATURE 1.843 1.4339 30 841
p75 NTR receptor-mediated signalling REACTOME -1.195 1.4335 76 2466
E-cadherin signaling in keratinocytes NCI/NATURE 2.123 1.4326 21 477
Signaling events mediated by HDAC Class III NCI/NATURE 1.927 1.4323 26 565
Keratan sulfate/keratin metabolism REACTOME 1.962 1.4251 28 447
Morphine addiction KEGG -3.158 1.4243 86 4524
IL3-mediated signaling events NCI/NATURE 2.199 1.4233 22 399
Intestinal immune network for IgA production KEGG 3.740 1.4224 45 506
lectin induced complement pathway BIOCARTA 2.541 1.4167 11 359
Leishmaniasis KEGG 2.734 1.4130 68 927
Alternative complement pathway BIOCARTA 2.196 1.4075 11 236
Autoimmune thyroid disease KEGG 2.835 1.4056 39 513
Graft-versus-host disease KEGG 3.079 1.4025 33 240
Activation of pkc through g-protein coupled receptors BIOCARTA 1.733 1.3968 11 892
Allograft rejection KEGG 3.327 1.3952 30 253
Costimulation by the CD28 family REACTOME 2.648 1.3931 62 1270
Eicosanoid ligand-binding receptors REACTOME 2.798 1.3899 11 174
Staphylococcus aureus infection KEGG 3.410 1.3766 52 504
Serotonergic synapse KEGG -1.854 1.3733 73 3128
N-glycan antennae elongation in the medial/trans-Golgi REACTOME 2.122 1.3729 14 396
Integrins in angiogenesis NCI/NATURE -1.929 1.3622 74 2110
Tandem pore domain potassium channels REACTOME 2.299 1.3589 4 206
Fatty acid elongation in mitochondria REACTOME 2.226 1.3579 12 170
IL5-mediated signaling events NCI/NATURE 2.080 1.3568 12 304
Antigen processing and presentation KEGG 3.506 1.3397 65 400
Asthma KEGG 3.713 1.3246 31 200
Neurotransmitter release cycle REACTOME 1.846 1.3233 9 326
Classical complement pathway BIOCARTA 2.682 1.3164 12 239
Antigen processing and presentation BIOCARTA 2.938 1.2857 9 52
Interferon gamma signaling REACTOME 3.080 1.0558 61 2598
Antigen processing-cross presentation REACTOME 2.187 1.0371 59 1962

The role of antigen processing and presentation in HCC

To assess what might be the key factors within “antigen processing and presentation”, we performed analysis utilizing a modified version of PoDA using the Korean HCC dataset. In this analysis, all 400 of the SNPs genotyped in the data set for the 65 genes in the pathway were contrasted in the cases and controls. After assessing significance of the odds ratio for the entire set of SNPs, each individual SNP was removed one at a time from the dataset and the significance was re-assessed. The SNP which least affected the significance of the odds ratio was then removed and the process was repeated. SNPs were progressively removed in this “stepdown” procedure until the significance of the odds ratio was no longer improved. Interestingly, it was observed that initial removal of SNPs substantially improved significance of the difference between cases and controls. When stepdown was completed, a total of 49 SNPs in 26 genes were observed [Table 2].

Table 2

Significant genes and SNPs within the KEGG antigen processing and presentation pathway

Gene symbol Gene name SNP (rs id)
CANX Calnexin rs7734102
CD4 CD4 molecule rs1075835
CD74 CD74 molecule, major histocompatibility complex, class II invariant chain rs2748249
CIITA Class II, major histocompatibility complex, transactivator rs6498122
CIITA Class II, major histocompatibility complex, transactivator rs7203275
CIITA Class II, major histocompatibility complex, transactivator rs11074934
CIITA Class II, major histocompatibility complex, transactivator rs6498119
CTSS Cathepsin S rs11204722
HLA-A Major histocompatibility complex, class I, A  rs12202296
HLA-DMA Major histocompatibility complex, class II, DM alpha rs11539216
HLA-DMA Major histocompatibility complex, class II, DM alpha rs17617515
HLA-DMB Major histocompatibility complex, class II, DM beta  rs3132132
HLA-DMB Major histocompatibility complex, class II, DM beta  rs714289
HLA-DOA Major histocompatibility complex, class II, DO alpha  rs3129304
HLA-DOA Major histocompatibility complex, class II, DO alpha  rs3129303
HLA-DOA Major histocompatibility complex, class II, DO alpha  rs3130602
HLA-DOA Major histocompatibility complex, class II, DO alpha  rs3129302
HLA-DPB1 Major histocompatibility complex, class II, DP beta 1 rs9277378
HLA-DQA2 Major histocompatibility complex, class II, DQ alpha 2 rs9275356
HLA-DQA2 Major histocompatibility complex, class II, DQ alpha 2 rs9276427
HLA-DQA2 Major histocompatibility complex, class II, DQ alpha 2 rs9469266
HLA-DRA Major histocompatibility complex, class II, DR alpha  rs7194
HLA-G Major histocompatibility complex, class I, G  rs2517898
HSP90AB1 Heat shock protein 90kDa alpha (cytosolic), class B member 1 rs504697
HSPA2 Heat shock 70kDa protein 2 rs4313734
HSPA4 Heat shock 70kDa protein 4 rs7702889
HSPA5 Heat shock 70kDa protein 5 rs12009
HSPA8 Heat shock 70kDa protein 8 rs4936770
KIR2DL3 Killer cell immunoglobulin-like receptor, two domains, long cytoplasmic tail, 3 rs9797797
KIR2DL3 Killer cell immunoglobulin-like receptor, two domains, long cytoplasmic tail, 3 rs13344915
KIR2DL4 Killer cell immunoglobulin-like receptor, two domains, long cytoplasmic tail, 4 rs10500318
KIR2DL4 Killer cell immunoglobulin-like receptor, two domains, long cytoplasmic tail, 4 rs3865509
KIR2DS4 Killer cell immunoglobulin-like receptor, two domains, short cytoplasmic tail, 4 rs11673276
KLRD1 Killer cell lectin-like receptor subfamily D, member 1  rs17206564
LGMN Legumain rs8177528
LGMN Legumain rs2250672
LGMN Legumain rs716097
LGMN Legumain rs12885208
LGMN Legumain rs9791
LOC100509457 HLA class II histocompatibility antigen, DQ alpha 1 chain-like rs2647015
LOC100509457 HLA class II histocompatibility antigen, DQ alpha 1 chain-like rs2859090
LOC100509457 HLA class II histocompatibility antigen, DQ alpha 1 chain-like rs9272219
RFXAP Regulatory factor X-associated protein rs6563500
TAP1 Transporter 1, ATP-binding cassette, sub-family B (MDR/TAP) rs4148882
TAP2 Transporter 2, ATP-binding cassette, sub-family B (MDR/TAP) rs3819720
TAP2 Transporter 2, ATP-binding cassette, sub-family B (MDR/TAP) rs2228396
TAP2 Transporter 2, ATP-binding cassette, sub-family B (MDR/TAP) rs241428
TAP2 Transporter 2, ATP-binding cassette, sub-family B (MDR/TAP) rs9784758
TAP2 Transporter 2, ATP-binding cassette, sub-family B (MDR/TAP) rs241431

While the genes identified included key genes seen in the GWAS catalog, specifically members of HLA class II, other genes associated with antigen processing were also observed [Figure 1]. The design of Genome-wide association studies does not permit the specific etiologic effects of the variation. By design, the variation used in the studies is not chosen for function, but instead the ability to test differences between populations. The high linkage disequilibrium observed between variations in humans further complicates the capacity to interpret the molecular mechanisms of action.

Pathway analysis provides insight into the genetic susceptibility to hepatocellular carcinoma and insight into immuno-therapy treatment response

Figure 1. Gene-based SNPs associated with HCC in the antigen processing and presentation pathway. The genes and their relationships obtained from KEGG’s antigen processing and presentation pathway. Purple boxes with white letters indicate genes SNP variations associated with HCC from the PoDA stepdown analysis. Removal of these loci reduced the overall threshold of significance below that observed for the entire pathway. Genes in open boxes (with orange letters) indicate gens which could be removed without altering significance of the pathway’s association. HCC: hepatocellular carcinoma

Nevertheless, this study identifies variation of genes of potential significance in etiology. Of particular interest are the proteasome (HSPA2, HSPA4, HSPA5 HSP90AB1), endoplasmic reticulum TAP1, TAP2, CANX), and exosome (LGMN) genes associated with the processing of antigens so that they may be presented by HLA loci. The pathway also identifies genes on the surface of immune cells - NK cells (KIR2DL3, KIR2DL4, and KIR2DL5) and CD4 T cells (CD4) that may compromise immune surveillance and regulation.

It is possible to examine the intra-pathway associations of the variants. Using the analytic tool PLINK[18], one can estimate the association (r2) between loci in cases and controls [Table 3]. As expected by the PoDA analysis, variants within the pathways are associated with one another. Both variants within loci and between loci are observed to be associated. Interestingly, the magnitude of associations differs between cases and controls. This confirms that the pathway utilizes information (interactions between loci) that would not be observed in simple single locus GWAS assessments.

Table 3

Association of case and control SNP variation with r2 greater than 0.1 within the KEGG antigen processing and presentation pathway

SNP_A SNP_B Case r2 Control r2
SNP_A-4289896 - KIR2DL3 SNP_A-8561730 - KIR2DL3 0.88 0.95
SNP_A-8566010 - HLA-DQA1L SNP_A-2200530 - TAP2 0.38 0.20
SNP_A-8515749 - HLA-G SNP_A-8649593 - HLA-A 0.16 0.37
SNP_A-2214036 - HLA-DQA1L SNP_A-4206711 - HLADQA1 0.16 0.14
SNP_A-8524421 - KIR2DL4 SNP_A-8613821 - KIR2DS4 0.14 < 0.1
SNP_A-1985650 - HLA-DOA SNP_A-8430032 - KIR2DL3 0.12 < 0.1
SNP_A-2214036 - HLA-DQA1L SNP_A-2200530 - TAP2 0.11 < 0.1
SNP_A-8451478 - TAP2 SNP_A-8415280 - TAP2 0.10 < 0.1
SNP_A-2305613 - CSTB SNP_A-1944939 - CSTB < 0.1 1.00
SNP_A-8566010 - HLA-DQA1L SNP_A-1985650 - HLA-DOA < 0.1 0.28
SNP_A-4223083 - HLA-DQA1L SNP_A-8415280 - CIITA < 0.1 0.18
SNP_A-4206711 - HLA-DQA1 SNP_A-8451478 - TAP2 < 0.1 0.16
SNP_A-4277940 - HLA-DQA1L SNP_A-1985650 - HLA-DOA < 0.1 0.14

“Antigen processing and presentation” transcriptional activity

It is possible to assess whether the germline variation in “antigen processing and presentation” translates into functionally significant difference in normal liver when contrasted to tumor adjacent liver and HCC. This can be done by looking at the transcriptome of these tissues using publicly accessible data from the Gene Tissue Expression project (GTEx)[19-21] and the TCGA[8]. Data from both sources were processed with a common analytic pipeline that included realignment of sequencing reads to Hg38[22,23], uniform count scoring[24] and adjustment for over-dispersion[25,26].

The scored transcript data was then evaluated using the novel pathway analysis tool PathOlogist[27-29]. PathOlogist utilizes the logical information contained within networks to compute network scores. By utilizing the structure of a network, in this approach the conditional state of genes determines expectations for the state of other members of the network. Two different scores are provided. The first assesses whether the activity state of the network differs. In the second, an assessment of the logical state of the network is measured as consistency. Consistency determines whether the transcription patterns follow the expected logic of the network.

Examination of the transcriptional state of “antigen processing and presentation” provides additional insight into the susceptibility findings. First, “antigen processing and presentation” activity is observed to be significantly higher in normal liver (GTEx) compared to TCGA tumor-adjacent (adjusted P < 0.0001) and tumor (adjusted P < 0.0001) while no difference is observed between tumor adjacent and tumor (adjusted P = 0.87). This suggests that individuals with HCC have a different “antigen processing and presentation” profile in both their non-tumor and tumor than normal liver.

No significant difference is observed between the consistency scores of normal liver (GTEx) and TCGA tumor-adjacent (adjusted P = 0.64) and tumor adjacent and tumor (adjusted P = 0.89b) for “antigen processing and presentation”. However, significant difference is observed between normal liver and tumor (adjusted P < 0.0001). This suggests that “antigen processing and presentation” may be a target of mutagenesis in HCC.

Immune checkpoint therapy and “antigen processing and presentation”

“Antigen processing and presentation” may be an important mediator of treatment response for HCC. Immune checkpoint therapy is dramatically altering the cancer therapeutic landscape[30]. Checkpoint therapy targets inhibitory signals to the immune system such as CTLA-4 and PD-1/PD-L1. These treatments show promising, durable response results in previously treatment resistant cancers such as melanoma[31] and non-small cell lung cancer[32]. The US FDA has approved checkpoint therapy for second line treatment of HCC. Numerous studies are in progress to assess the efficacy as 1st line treatment (

Unfortunately only a minority of individuals respond to the treatments[33]. It is unknown what mediates response. Indicators of response include DNA mismatch repair capabilities[34] and tumor mutational burden[35]. But these have poor predictive capabilities.

For checkpoint therapy to work, an intact immune response is required. As implied from the indicators of response, the immune system must have the capacity to recognize tumor antigens as foreign. This recognition is mediated through antigen processing and presentation. Inherited variability may indicate individuals in which this capacity is compromised. Moreover, variation in these processes may indicate individual response to immune directed therapeutic interventions.

In conclusion, the results of the germline variation studies suggest that immune mediating processes are polymorphic in the population and systematically different in HCC. Individuals with HCC have significantly lower activity for these processes and HCC shows alterations in the “logic” of the processing and presentation pathways. As such, it may be possible to predict response to checkpoint therapy through the evaluation of the inherited genetic state of “antigen processing and presentation”. Understanding these differences may provide opportunities designing new immune checkpoint modulators and provide a rational basis for combinatorial therapy.



The Korean HCC case-control study was collected by Dr. Myung Lyu (NCI/NIH/DHSS) and Dr. Young-Hwa Chung (Asan Medical Center, Seoul, South Korea).

Authors’ contributions

Data analysis: Lu YK, Brill JM, Aghili A

Design of the work, data analysis, manuscript drafting and revising, and final approval of the version to be published: Buetow KH

Availability of data and materials

Not applicable.

Financial support and sponsorship


Conflicts of interest

Buetow KH is an advisor for the Bristol Myers Squibb IO-ICON project.

Ethical approval and consent to participate

Not applicable.

Consent for publication

Not applicable.


© The Author(s) 2018.


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Cite This Article

OAE Style

Lu YK, Brill JM, Aghili A, Buetow KH. Pathway analysis provides insight into the genetic susceptibility to hepatocellular carcinoma and insight into immuno-therapy treatment response. Hepatoma Res 2018;4:21.

AMA Style

Lu YK, Brill JM, Aghili A, Buetow KH. Pathway analysis provides insight into the genetic susceptibility to hepatocellular carcinoma and insight into immuno-therapy treatment response. Hepatoma Research. 2018; 4: 21.

Chicago/Turabian Style

Lu, Yih-Kuang, Jacob Morris Brill, Ardesher Aghili, Kenneth Howard Buetow. 2018. "Pathway analysis provides insight into the genetic susceptibility to hepatocellular carcinoma and insight into immuno-therapy treatment response" Hepatoma Research. 4: 21.

ACS Style

Lu, Y.K.; Brill JM.; Aghili A.; Buetow KH. Pathway analysis provides insight into the genetic susceptibility to hepatocellular carcinoma and insight into immuno-therapy treatment response. Hepatoma. Res. 2018, 4, 21.



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