Skip to main content
NCBI home page
Royal Society Open Science logoLink to Royal Society Open Science
. 2018 Dec 5;5(12):181006. doi: 10.1098/rsos.181006

Determining consistent prognostic biomarkers of overall survival and vascular invasion in hepatocellular carcinoma

Otília Menyhárt 1,2, Ádám Nagy 1,2, Balázs Győrffy 1,2,✉
PMCID: PMC6304123  PMID: 30662724

Abstract

Background: Potential prognostic biomarker candidates for hepatocellular carcinoma (HCC) are abundant, but their generalizability is unexplored. We cross-validated markers of overall survival (OS) and vascular invasion in independent datasets. Methods: The literature search yielded 318 genes related to survival and 52 related to vascular invasion. Validation was performed in three datasets (RNA-seq, n = 371; Affymetrix arrays, n = 91; Illumina gene chips, n = 135) by uni- and multivariate Cox regression and Mann–Whitney U-test, separately for Asian and Caucasian patients. Results: One hundred and eighty biomarkers remained significant in Asian and 128 in Caucasian subjects at p < 0.05. After multiple testing correction BIRC5 (p = 1.9 × 10−10), CDC20 (p = 2.5 × 10−9) and PLK1 (p = 3 × 10−9) endured as best performing genes in Asian patients; however, none remained significant in the Caucasian cohort. In a multivariate analysis, significance was reached by stage (p = 0.0018) and expression of CENPH (p = 0.0038) and CDK4 (p = 0.038). KIF18A was the only gene predicting vascular invasion in the Affymetrix and Illumina cohorts (p = 0.003 and p = 0.025, respectively). Conclusion: Overall, about half of biomarker candidates failed to retain prognostic value and none were better than stage predicting OS. Impact: Our results help to eliminate biomarkers with limited capability to predict OS and/or vascular invasion.

Keywords: hepatocellular carcinoma, liver cancer, survival, biomarker, vascular invasion

1. Background

In spite of tremendous efforts toward the discovery of novel prognostic or predictive biomarkers in solid tumours, less than 1% of these are estimated to enter clinical practice [1]. One critical component behind the high failure rate is poor reporting of key study elements hampering the interpretability and clinical applicability of prognostic studies [2]. Another critical requirement would be the validation of the findings by independent investigators in independent datasets, as promising biomarkers should provide reproducible results when tested in external samples.

Worldwide, liver cancer (LC) is the second leading cause of cancer-related mortality, with the vast majority of cases (83%) occurring in the less developed parts of the world causing a major health crisis in Eastern and Southeastern Asia [3]. Nonetheless, LC is also on the rise in the European Union (47 000 deaths per year) with the highest incidence in Southern Europe [4], and despite decreasing death rates for all cancers combined, LC burden is growing rapidly in the USA as well [5].

Hepatocellular carcinoma (HCC) accounts for up to 90% of primary liver malignancies with a highly unfavourable prognosis due to fast growth, early hepatic metastasis and rapid multidrug resistance. Chronic liver diseases linked to hepatitis infections, diabetes mellitus and fatty liver disease are frequently in the background of HCC [6]. Disease aetiology differs across races: in Asia, endemic for HBV and HCV, most HCCs develop as a consequence of chronic viral infection. In developed countries, HCC is linked primarily to chronic liver disease caused by cirrhosis from excessive alcohol consumption, diabetes mellitus or non-alcoholic fatty liver disease [6]. As a result of multiple causal factors, HCC is one of the most heterogeneous cancers with a highly variable clinical course. Abbreviations are listed in table 1.

Table 1.

List of Abbreviations.

AFP alpha-fetoprotein
CCKR gastrin and cholecystokinin receptors mediated signalling network
CIN chromosome instability
DAVID Database for Annotation, Visualization and Integrated Discovery
EGA European Genome-phenome Archive
FDR false discovery rate
FGF fibroblast growth factor
GEO NCBI gene expression omnibus
GnRHR gonadotropin-releasing hormone receptor
HBV Hepatitis B
HCC hepatocellular carcinoma
HCV Hepatitis C
HR hazard rate
IAP inhibitor of apoptosis protein
IGF insulin-like growth factor
IHC immunohistochemistry
LC liver cancer
miRNA micro RNA
OS overall survival
PANTHER Protein ANalysis THrough Evolutionary Relationships
PCR polymerase chain reaction
PDGF platelet-derived growth factor
PVTT portal vein tumour thrombosis
RB retinoblastoma
TCGA The Cancer Genome Atlas
TKI receptor tyrosine kinase inhibitor
VEGF vascular endothelial growth factor
Open in a new tab

Most HCC patients are diagnosed at an intermediate or advanced stage, rendering only 30% suitable for a potentially curative therapy [7]. Since 2007, patients with an advanced stage HCC (with vascular invasion and extrahepatic spread) can be treated with sorafenib, a multitarget receptor tyrosine kinase inhibitor (TKI). Sorafenib treatment is associated with increased median overall survival (OS) (7.9 versus 10.7 months) [8]. In the Asia Pacific study, 6.5 months OS was reported after sorafenib compared to 4.2 months after placebo [9]. Notably, the modest survival advantage was coupled with frequent side effects and with no increase in quality of life. Lenvatinib, an inhibitor of VEGF, FGF and PDGF receptors and also an RET and KIT inhibitor became recently approved as a first-line treatment for unresectable HCC [10]. Regorafenib (fluoro-sorafenib), another oral TKI, became approved as a second-line treatment for patients progressing on sorafenib. Regorafenib significantly improved both overall (7.8 versus 10.6 months) and progression free survival (1.5 versus 3.1 months) compared to placebo [11]. However, potent adjuvant therapies after surgery are still lacking, and only palliative care is available for patients with multiple metastases, with an estimated OS of less than three months.

HCC is characterized by extreme phenotypic and molecular heterogeneity, and molecular stratification has not yet been established [12]. An HCC can harbour 40–80 mutations, among those five to eight driver mutations per tumour [12]. Cytotoxic chemotherapies fail as ineffective or extremely toxic on already damaged cirrhotic livers. Prognosis prediction is based on clinicopathological parameters including tumour burden, proliferation markers, vascular invasion, liver function and overall health. Ongoing studies now incorporate biomarker candidates in clinical trials to test agents on patients who are most likely to benefit [13]. New single-gene prognostic marker candidates based on low-throughput technologies (e.g. polymerase chain reaction (PCR), immunohistochemistry (IHC)) appear in the literature almost on a daily basis. However, the generalizability of such markers remains questionable.

Vascular invasion of the portal or hepatic veins correlates strongly with HCC recurrence. Following liver transplantation, a 4.4-fold increased risk of HCC recurrence was present in patients with microvascular invasion [14]. At the same time, latent microscopic vascular invasion cannot be detected by pre-operative imaging [15]. Based on autopsy results, 40% of patients with tumours smaller than 5 cm already developed portal vein thrombi [16]. Therefore, identification of molecular changes that correlate with vascular invasion to ascertain the risk of HCC recurrence is a top priority to predict long-term outcome.

In the pursuit of robust prognostic genes, we conducted a meta-analysis by searching the literature for studies focusing on genes associated with OS. To cross-validate these biomarker candidates, we used a large available transcriptomic dataset representing roughly equally two ethnic groups (Asian and White/Caucasian). Prognostic potential for each gene was assessed in a univariate analysis within each ethnic cohort and the strongest markers were included in a multivariate regression. In addition, we also evaluated biomarkers associated with vascular invasion to validate their predictive potential in two independent datasets.

2. Material and methods

2.1. Identification of previously published biomarker candidates

A PubMed (http://www.pubmed.com/) search conducted in June 2017 using the keywords ‘hepatocellular’, ‘carcinoma’, ‘overall’ and ‘survival’ resulted in 22 999 hits and for terms ‘hepatocellular’, ‘carcinoma’, ‘biomarker’ and ‘mRNA’ in 1615 hits. The overlapping articles of the two sets established the database for cross-validation. All papers were included back to 1998. Only papers written in English were considered in the final biomarker selection. Only research articles were selected, excluding reviews. Biomarkers were retrieved only from studies involving patients undergoing surgical resection or liver transplantation. Only studies reporting molecular differences between tumour and healthy tissue were included in the final list, rejecting papers describing molecular differences between pre- or post-surgery serum or plasma levels.

As survival was frequently associated only with protein expression assessed by IHC or immunoblot, our analysis focusing only on studies reporting mRNA expression had to be extended to include protein-based assays as well. Studies not reporting follow-up data were excluded, same as for papers reporting non-significant associations with survival. Of note, some of the articles assessed the simultaneous co-expression of two to five genes, labelled as composed-biomarkers.

2.2. Transcriptomic dataset to validate prognostic biomarkers

The Gene Expression Omnibus (GEO), the European Genome-phenome Archive (EGA) and The Cancer Genome Atlas (TCGA) repositories were screened for datasets with available survival data and at least 30 patients. Accessible transcriptomic data of 371 patients diagnosed with HCC were obtained from the TCGA (https://cancergenome.nih.gov/), with OS available for 364 patients [17]. Most patients were diagnosed at stage I and 67% were male. Patients with Asian and White/Caucasian ethnicity made up the majority, roughly in equal proportions. There was no significant difference in survival when comparing Asian and White/Caucasian patients (p = 0.32). Detailed description of the sample population is included in table 2.

Table 2.

Characterization of patient populations obtained from three independent transcriptomic datasets used in this study to validate biomarkers of overall survival and vascular invasion.

cohort RNA-seq Illumina Affymetrix
source TCGA GSE20017 GSE9843
platform Illumina HiSeq 2000 Illumina HumanRef-8 WG-DASL v. 3.0 Affymetrix HGU133 Plus 2.0 Array
total n 371 135 91
gender
 males 250 102 54
 females 121 33 27
 NA — — 10
stage
 stage I 171 — 9
 stage II 86 — 56
 stage III 85 — 7
 stage IV 5 — 8
 NA — 135 11
race
 White/Caucasian 184 102 72
 Black or African-American 17 4 3
 Asian 158 28 4
vascular invasion — 40 45
death event 130 32 23
Open in a new tab

2.3. Biomarker candidates associated with vascular invasion

After the initial PubMed search for ‘HCC’ + ‘biomarkers’, the list of genes was further narrowed by keywords ‘vascular invasion’ and ‘PVTT’ (as for portal vein tumour thrombosis). Papers written in non-English and reviews were omitted from the final biomarker selection. The final list consists of studies reporting significant associations between mRNA/protein expression and vascular invasion/PVTT.

2.4. Transcriptomic datasets to validate predictive biomarkers

The GEO database was searched for keywords ‘hepatocellular’ + ‘carcinoma’ + ‘vascular’ + ‘invasion’ in May 2017. The search resulted in 18 hits in humans. Of these, 13 remained after filtering for expression profiling by the array. Of these, only two datasets (GSE9843 and GSE20017) had at least 50 patients. GSE9843 contained fresh frozen tissue samples from 91 patients, including 45 with and 34 without vascular invasion [7]. Of all patients, 91% were of White/Caucasian origin, 65.8% were male and 65 patients had published stage (table 2). In GSE20017, genome-wide gene expression profiling from formalin-fixed paraffin-embedded tissues of 135 patients was published. Of all patients, 75.6% were males, 102 of White/Caucasian origin and 40 patients were diagnosed with vascular invasion (table 2). The preprocessed and normalized gene expression data were used in both datasets.

2.5. Statistical analyses

A unique HUGO identifier was assigned to each biomarker candidate. Differences in OS were tested by Cox proportional hazards regression. Analyses including Kaplan–Meier survival plots, hazard rates with 95% confidence interval and log-rank p-values were calculated and plotted in R using Bioconductor packages separately for the Asian and White/Caucasian ethnic groups and also for the pooled dataset. We considered a p-value of 0.05 as significant. The p-values from univariate analysis were corrected for multiple testing by computing the false discovery rate (FDR) as described previously [18]. We made a multivariate analysis to compare significant genes to available clinical variables. The expression level of genes was compared with a Mann–Whitney U-test between patients with or without vascular invasion independently in both datasets.

3. Results

3.1. Identification of previously described HCC biomarker candidates

The PubMed search for HCC prognostic biomarkers resulted in 533 hits, of which 513 were written in English, and seven papers were reviews. Evaluation of the remaining 506 papers resulted in 355 relevant articles describing differences between mRNA and/or protein expression and survival. For some genes, multiple references were available, decreasing the number of individual biomarkers to 318, from which overexpression of 194 genes was associated with poor OS. Only 17 papers relied on samples from White/Caucasian subjects describing 21 separate biomarkers. All the other studies involved patients from Asia, mainly from China but also from Korea, Taiwan and Japan.

Molecular association with survival relied frequently on protein expression. In fact, 57% of studies compared survival between groups with low versus high protein expression as measured by IHC, 38% used mRNA expression and the remaining 5% of studies exploited previously available datasets.

Thirteen papers explored the simultaneous expression of two to five genes, thus the cross-validation was conducted on 305 unique genes and 13 composed-biomarkers. The entire gene list with the corresponding manuscript identifier PMIDs is included in electronic supplementary material, table S1.

3.2. Prognostic biomarker candidates in Asian patients

Out of the 318 candidate markers, 180 were associated with OS at p < 0.05 (electronic supplementary material, table S2). In the pursuit of the strongest biomarkers, a rigorous correction for multiple testing conducted implementing a 0.1% FDR cut-off resulted in 40 significant biomarkers, consisting of 39 unique genes and a single composed-biomarker. High expression of eight genes was associated with improved survival, and expression of 32 biomarkers was linked to poor OS (table 3). Survival plots for the two best performing genes are illustrated in figure 1a,b.

Table 3.

List of 40 significant biomarker candidates associated with overall survival at 0.1% FDR in the Asian cohort. Cox univariate regression analysis. HR, hazard rate; CI, confidence interval.

symbol gene name overall survival HR (95% CI), p worse prognosis (expression low–high) references
BIRC5 baculoviral IAP repeat containing 5 HR = 7.64 (3.66–15.94) p = 1.9 × 10−10 high [19–21]
CDC20 cell division cycle 20 HR = 5.53 (2.95–10.38) p = 2.5 × 10−9 high [22]
PLK1 Polo-like kinase 1 HR = 6.02 (3.08–11.78) p = 3 × 10−9 high [23]
ALDH2 aldehyde dehydrogenase-2 HR = 0.19 (0.1–0.35) p = 4.2 × 10−9 low [24]
CCNB1 cyclin B1 HR = 7.09 (3.29–15.29) p = 6.3 × 10−9 high [25]
FOXK2 forkhead box K2 HR = 6.83 (3.16–14.73) p = 1.5 × 10−8 high [26]
KIF18A kinesin family member 18A HR = 4.9 (2.66–9.01) p = 1.9 × 10−8 high [27]
BUB1B BUB1 mitotic checkpoint serine/threonine kinase B HR = 4.85 (2.64–8.92) p = 2.4 × 10−8 high [28]
MELK maternal embryonic leucine zipper kinase HR = 5.64 (2.84–11.22) p = 3.1 × 10−8 high [29]
KIAA1524 cancerous inhibitor of protein phosphatase 2A HR = 4.71 (2.57–8.63) p = 4.0 × 10−8 high [30]
PTTG1 pituitary tumour-transforming 1 HR = 7.67 (3.24–18.2) p = 5.2 × 10−8 high [31]
CCNF cyclin F HR = 4.72 (2.54–8.76) p = 7.1 × 10−8 high [32]
PKM2 pyruvate kinase M2 HR = 4.48 (2.45–8.18) p = 1 × 10−7 high [33,34]
DEPDC1 DEP domain containing 1 HR = 4.74 (2.47–9.1) p = 2.6 × 10−7 high [35]
STMN1 and SPP1 stathmin 1 and secreted phosphoprotein 1 (osteopontin) HR = 4.31 (2.34–7.92) p = 3.2 × 10−7 high [36]
ANGPT2 angiopoietin 2 HR = 4.25 (2.32–7.76) p = 3.3 × 10−7 high [37]
EZH2 enhancer of zeste 2 polycomb repressive complex 2 subunit HR = 5.51 (2.64–11.51) p = 3.5 × 10−7 high [38]
STMN1 stathmin 1 HR = 4.97 (2.5–9.88) p = 4.2 × 10−7 high [39]
CDC25A cell division cycle 25A HR = 4.22 (2.28–7.8) p = 6.7 × 10−7 high [40]
SLC22A1 solute carrier family 22 member 1 HR = 0.24 (0.13–0.45) p = 8.4 × 10−7 low [41]
AURKA aurora kinase A HR = 4.5 (2.33–8.66) p = 9.6 × 10−7 high [42]
SEC62 SEC62 homologue, preprotein translocation factor HR = 0.25 (0.14–0.46) p = 1.1 × 10−6 low [25]
FOXM1 forkhead box M1 HR = 4.5 (2.31–8.78) p = 1.4 × 10−6 high [43]
ADH4 alcohol dehydrogenase 4 (Class II), Pi polypeptide HR = 0.26 (0.14–0.47) p = 1.8 × 10−6 low [44]
MKI67 marker of proliferation Ki-67 HR = 3.89 (2.13–7.12) p = 2.2 × 10−6 high [22,45]
MAD2L1 MAD2 mitotic arrest deficient-like 1 (yeast) HR = 9.92 (3.06–32.14) p = 2.6 × 10−6 high [46]
CDK4 cyclin dependent kinase 4 HR = 6.8 (2.68–17.28) p = 3.1 × 10−6 high [47]
CKAP2 cytoskeleton associated protein 2 HR = 3.64 (2.01–6.61) p = 5.5 × 10−6 high [48]
IQGAP2 IQ motif containing GTPase activating protein 2 HR = 0.2 (0.09–0.44) p = 7.3 × 10−6 low [49]
SOCS2 suppressor of cytokine signalling 2 HR = 0.26 (0.13–0.49) p = 7.7 × 10−6 low [50]
E2F1 E2F transcription factor 1 HR = 8.66 (2.68–28.02) p = 1.4 × 10−5 high [39]
RRM2 ribonucleotide reductase regulatory subunit M2 HR = 3.7 (1.96–6.98) p = 1.6 × 10−5 high [22]
HMGA1 high mobility group AT-Hook 1 HR = 3.38 (1.86–6.13) p = 2.1 × 10−5 high [51]
E2F3 E2F transcription factor 3 HR = 4.1 (2.02–8.31) p = 2.3 × 10−5 high [52]
KIT KIT proto-oncogene receptor tyrosine kinase HR = 3.34 (1.84–6.05) p = 2.5 × 10−5 high [53]
CKS1B CDC28 protein kinase regulatory subunit 1B HR = 4.06 (2–8.23) p = 2.6 × 10−5 high [54]
FAM83D family with sequence similarity 83 member D HR = 3.58 (1.89–6.78) p = 2.8 × 10−5 high [55]
CENPH centromere protein H HR = 4.05 (1.99–8.21) p = 2.8 × 10−5 high [56]
PTEN phosphatase and tensin homologue HR = 0.3 (0.16–0.55) p = 3.1 × 10−5 low [57]
HLX H2.0 like homeobox HR = 0.26 (0.13–0.52) p = 3.4 × 10−5 low [58]
Open in a new tab

Figure 1.

Figure 1.

Open in a new tab

Survival for the two best performing genes in the Asian (a,b) and in the White/Caucasian cohorts (c,d). The expression of all four genes was significantly different between the Asian and White/Caucasian cohorts (bottom subfigures). Red bars: 95% confidence intervals.

To assess the robustness of our meta-analysis, we calculated the hazard rate for each of the 20 501 genes with available expression data to compute their association with OS. Importantly, of the genes identified by the literature search, the ones that remained significant after our validation were located among the strongest markers of survival (figure 2a,b).

Figure 2.

Figure 2.

Open in a new tab

Ranked hazard rates across all 20 501 genes measured by RNA-seq in the Asian (a) and in the White/Caucasian cohort (b). Genes significantly associated with overall survival are yellow-coloured. The 39 strongest single-gene biomarker candidates validated in the Asian cohort are indicated by black lines including names for selected genes—notably, all positioned among the strongest markers of overall survival. Genes significant after multivariate analysis in the Asian cohort are indicated in bold face, as well as the two biomarker candidates significant at 5% FDR in the White/Caucasian cohort.

3.3. Gene ontology analysis of prognostic biomarker candidates

The 40 potential prognostic biomarkers of the Asian cohort were subjected to gene enrichment analysis by Database for Annotation, Visualization and Integrated Discovery (DAVID) Bioinformatics Resources 6.8 to gauge the biological meaning of functionally related gene groups [59]. Gene sets related to various facets of cell cycle and cell division, such as sister chromatid cohesion, mitotic nuclear division and cyclin degradation were enriched significantly among markers of the Asian cohort, grouped by relevant function in electronic supplementary material, table S3.

3.4. Prognostic biomarker candidates in White/Caucasian patients

Out of 318 biomarkers, 128 were associated with OS at p < 0.05 in White/Caucasian subjects (electronic supplementary material, table S2). Out of 21 biomarkers that were appraised originally at White/Caucasian patients only, 10 remained significant at p < 0.05: THOC5 (p = 0.00018), PLAT (p = 0.0012), HMGA1 (p = 0.0025), PRKDC (p = 0.0096), SLC22A1 (p = 0.013), CDC20 (p = 0.016), the composed-biomarker CD274-CXCL12 (p = 0.019), CXCL12 (p = 0.027), RRM2 (p = 0.045) and ELAVL1 (p = 0.049). However, no biomarker candidates remained significant at 0.1% FDR. After a more lenient correction for multiple testing (at 5% FDR), two genes retained significance. Low expression of SOCS2 (HR = 0.37, 95% CI = 0.23–0.59, p = 1.3 × 10−5) while elevated expression of NAT10 (HR = 3.11, 95% CI = 1.76–5.52, p = 4.4 × 10−5) was coupled with the poor OS (figure 1c,d).

3.5. Overlap in significant biomarker candidates between Asian and White/Caucasian cohorts

Out of the 318 biomarker candidates, 226 were significant either in the Asian or in the White/Caucasian cohort at p < 0.05. Out of them, 98 and 46 biomarkers were unique to Asian and White/Caucasian patients, respectively, and 82 biomarker candidates were shared by both ethnic groups (electronic supplementary material, table S2). Among the shared biomarkers, 72 were originally described in Asian and 10 in White/Caucasian subjects. Although NAT10 was not among the 40 significant biomarkers associated with OS in the Asian cohort after setting the threshold at 0.1% FDR, SOCS2 was among the strongest biomarkers in both ethnic groups.

Significant biomarker candidates from univariate analysis of each ethic group were subjected to gene list analysis by the PANTHER (Protein ANalysis THrough Evolutionary Relationships, http://pantherdb.org) gene classification system [60]. Using the pathway classification function, the total number of pathway hits were 132 and 67 in the Asian and White cohorts, respectively. Pathways represented by at least three genes in the Asian and by at least two genes in the White/Caucasian cohort are illustrated in figure 3. Genes participating in FGF-, IGF-signalling, hypoxia–response, CCKR-, chemokine- and cytokine-signalling pathways were overrepresented in the Asian population compared to Caucasians, while genes participating in Wnt-signalling, Alzheimer disease-, presenilin-, PDGF-, cadherin-, JAK/STAT- and GnRHR-signalling pathways were more heavily represented in the Caucasian cohort.

Figure 3.

Figure 3.

Open in a new tab

Comparison of gene ontology pathways in Asian (a) and White/Caucasian (b) patients based on PANTHER gene classification system. All genes significant in the univariate analysis are included. The total number of pathway hits (132 and 67 in the Asian and White cohorts, respectively) represent 100%. Emphasized are those functions that are overrepresented by at least 2% in a given ethnic group compared to the other cohort.

3.6. Prognostic biomarker candidates in the pooled dataset

OS did not differ among ethnic groups (p = 0.32). After merging patient data with different ethnicity, 178 genes remained associated with OS at p < 0.05. Only 40 of these markers reached significance for OS in the analysis at 0.1% FDR, incorporating a single composed-biomarker of two genes and 39 unique genes. High expression of 17 genes correlated with a better outcome, while the high expression of 23 biomarker candidates indicated poor OS (electronic supplementary material, table S4).

3.7. Multivariate analysis

In the multivariate analysis, we included parameters which were available for the majority of patients. We had to exclude other clinically relevant features like differentiation, size, cirrhosis and alpha-fetoprotein (AFP) levels because these were only published for a few patients—a robust multivariate analysis can only take account of patients where all data are simultaneously available. In Asian samples, multivariate analysis for OS included stage, sex and expression of 39 unique single-gene biomarkers significant at 0.1% FDR. OS was strongly associated with stage (p = 0.0018). Two genes remained prognostic for OS: CENPH (p = 0.0038) and CDK4 (p = 0.038). Marginal significance was reached by CDC20 (p = 0.053). High expression of all three genes (CENPH, CDK4 and CDC20) predicted poor OS.

In the Caucasian cohort, only two genes remained significant after correction for multiple hypothesis testing (5% FDR); therefore, multivariate analysis included stage, sex and expression of these two genes, SOCS2 and NAT10. OS was associated with the expression of both SOCS2 (p = 0.046) and NAT10 (p = 0.031), but stage and sex were not prognostic.

In the pooled dataset, multivariate analysis for OS including stage, sex and expression of 39 unique, single-gene biomarkers resulted in a significant association between OS and stage (p = 2.06 × 10−5) and the expression of eight genes: SPP1 (p = 0.0002), PKM2 (p = 0.0008), EZH2 (p = 0.0017), KIF18A (p = 0.0061), DEPDC1 (p = 0.0092), CCNF (p = 0.019), SLC2A1 (p = 0.036) and WASF2 (p = 0.048). Increased expression of all eight genes was associated with poor survival outcome.

3.8. Biomarkers predictive of vascular invasion

The keywords, vascular ‘invasion’ and ‘PVTT’ narrowed the list of previously filtered 506 papers to 66 and 8 hits, respectively, scaling down our original list of biomarker candidates to 52 unique genes (electronic supplementary material, table S5).

In GSE9843, information about the presence of vascular invasion was available for 79 patients, out of which 45 patients presented with vascular invasion. Out of the 52 biomarker candidates, the expression of 9 genes was significantly different between patients with or without vascular invasion at p < 0.05 (GPC3, KIF18A, NDRG1, PYGO2, RORA, STMN1, VEGFB, VIL1 and XAF1) (table 4) and an additional 4 genes reached marginal significance (CADM2, FAM83D, NAA10 and PEBP1). The expression of GPC3, KIF18A, NDRG1, PYGO2, STMN1, VEGFB and VIL1 was increased and the expression of RORA and XAF1 was decreased in patients with vascular invasion (figure 4a).

Table 4.

Genes associated with the presence of vascular invasion based on the Affymetrix and Illumina datasets. Genes were differently expressed between patients with or without vascular invasion at p < 0.05. KIF18A is the only gene significant in both datasets.

symbol gene name Affymetrix p-value Illumina p-value
KIF18A kinesin family member 18A 0.003 0.025
GPC3 glypican-3 0.022 n.s.
NDRG1 N-Myc downstream regulated 1 0.041 n.s.
PYGO2 pygopus family PHD finger 2 0.03 n.s.
RORA RAR-related orphan receptor A 0.005 n.s.
STMN1 stathmin 1 0.022 n.s.
VEGFB vascular endothelial growth factor B 0.014 n.s.
VIL1 villin-1 0.022 n.s.
XAF1 XIAP associated factor 1 0.039 n.s.
APOA1 apolipoprotein A1 n.s. 0.0002
ENG endoglin n.s. 0.028
FABP1 fatty acid binding protein 1 n.s. 0.043
ID2 inhibitor of DNA binding 2, HLH protein n.s. 0.01
NODAL nodal growth differentiation factor n.s. 0.029
PDGFRA platelet-derived growth factor receptor alpha n.s. 0.004
STAT4 signal transducer and activator of transcription 4 n.s. 0.001
NEDD9 neural precursor cell expressed, developmentally downregulated 9 n.s. 0.025
PTP4A3 protein tyrosine phosphatase type IVA, member 3 n.s. 0.048
Open in a new tab

Figure 4.

Figure 4.

Open in a new tab

Genes differently expressed between patients with or without vascular invasion at p < 0.05 in the Affymetrix (a) and Illumina datasets (b). In Affymetrix, RORA and XAF1 were downregulated (yellow) with the remaining genes upregulated in the presence of vascular invasion (blue). In Illumina, NEDD9 and PTP4A3 (blue) along with KIF18A showed increased expression in patients with vascular invasion. KIF18A was the singular common gene upregulated in patients with vascular invasion in both datasets (c). Error bars: 95% confidence intervals.

In GSE20017, 40 out of 135 patients were diagnosed with vascular invasion. Expression of 10 genes (APOA1, ENG, FABP1, ID2, KIF18A, NEDD9, NODAL, PDGFRA, PTP4A3, STAT4) was significantly different between patients with and without vascular invasion at p < 0.05 (table 4), and expression difference of ECM1 and RPS19BP1 reached marginal significance. The expression of APOA1, ENG, FABP1, ID2, NODAL, PDGFRA and STAT4 was decreased and the expression of KIF18A, NEDD9 and PTP4A3 was increased in patients with vascular invasion (figure 4b).

KIF18A was the only common gene across the two datasets significantly different correlated to vascular invasion (table 4) and its expression was significantly higher in patients with vascular invasion in both datasets (figure 4c).

4. Discussion

The lack of targeted therapies in HCC coupled with escalating incidence called forth a paradigm shift. After a decade of phase III failures, current trials started to incorporate molecular markers, and targeted treatments are offered to preselected patients [13]. To accommodate the needs, abundant hypothesis-driven prognostic biomarkers are published in the literature, mostly assessing the transcriptome or proteome by low-throughput technologies (qPCR, IHC, immunoblot). We aimed to cross-validate these published markers to gauge their clinical generalizability. We analysed 318 markers disclosed as related to HCC prognosis in studies published back to 1998. The strongest candidates were also subjected to a multivariate regression. Vascular invasion is one of the strongest clinicopathological features predicting long-term outcome after resection or transplantation. Fifty-two genes associated with vascular invasion were validated in two independent datasets.

Our results highlight the importance of independent cross-validations, as from 318 markers only 180 and 128 genes remained prognostic in the Asian and White/Caucasian cohorts, respectively. Only 40 markers retained significance after rigorous correction for multiple comparisons in the Asian cohort, and none remained significant in the White/Caucasian cohort. This extremely high attrition rate is partially the consequence of small sample sizes in the original studies. Furthermore, in many instances, the transparent and complete reporting allowing the evaluation of the soundness of the study still did not reach the required standards set by the REMARK guidelines [2]. We have to note that survival was frequently assessed as a function of protein expression not inevitably congruent with the transcriptome.

Identifying major components of vascular invasion is vital for a successful therapeutic intervention. Out of the 52 genes described in the literature, 9 and 10 biomarkers were significantly differentially expressed between patients with or without vascular invasion in GSE9843 and GSE20017, respectively. However, of the 18 significant genes, KIF18A was the solitary common gene across both datasets. KIF18A also appeared among the 40 strongest biomarkers of survival in the Asian cohort. KIF18A mediates transport of organelles, proteins and plays a role in microtubule motor activity and mitotic chromosome alignment during cell division [61]. KIF18A has also been associated with metastasis in solid tumours (e.g. breast cancer [62]). Specific kinesin motor proteins and molecules participating in cell cycle can be potentially targeted [63].

In the presence of vascular invasion, most biomarkers were overexpressed in GSE9843, but downregulated in GSE20017. One additional strong prognostic biomarker, STMN1, was also overexpressed in patients with vascular invasion; however, only in one of the datasets. The lack of overlap highlights the heterogeneity of HCC samples across different datasets, questions the generalizability of the studied biomarkers and emphasizes the importance of external validation.

SPP1 (osteopontin) was the strongest biomarker candidate associated with survival in the pooled dataset; moreover, SPP1 expression was significantly associated with worse outcome both in the Asian and White/Caucasian datasets. SPP1 is a multitasking, highly phosphorylated extracellular matrix protein affecting ECM degradation, cell motility and adhesion, cytoskeletal rearrangement, mitosis and inflammation [64,65]. SPP1 has been previously recognized as a potential marker of early recurrence and poor prognosis and as a leading metastasis-related gene in HCC [66,67]. A meta-analysis including seven studies confirmed that plasma SPP1 elevation and AFP-based results have comparable diagnostic performance [68,69], although elevated SPP1 may be linked to other malignancies, therefore should be combined with other HCC-specific biomarkers [70]. Numerous SPP1 products are generated by genetic polymorphisms, alternative spicing and posttranslational modifications [65]. Various preclinical therapeutic approaches are being evaluated to target SPP1, although when interfering with its normal function severe adverse effects may develop [71].

In the Asian cohort, the strongest biomarkers of survival were particularly enriched by genes controlling multiple aspects of cell division, including sister chromatid cohesion and positioning (CENPH, KIF18A), mitotic checkpoint (BUB1B, MAD2L1), cell cycle transitions (CCNB1, PLK1, FOXM1, BIRC5, AURKA, CDC25A, MELK, CCNF), G1-phase progression (CDK4, E2F1) and cyclin degradation (CDC20, PTTG1). Various efforts classified HCC into non-proliferative and proliferative genotypes, with worse outcome in the latter [13]. Our results are congruent with previous findings, where the G3 tumour type overexpressing genes controlling cell cycle was associated with the most severe prognosis [72], and G3 type was confirmed as an independent predictor of recurrence [73].

BIRC5 (survivin) was the strongest predictor of poor survival based on the univariate analysis in the Asian cohort (figure 1). BIRC5 is the smallest member of the inhibitor of apoptosis protein (IAP) family, a multitasking protein that regulates mitosis, suppresses apoptosis and affects proliferation, angiogenesis and cellular stress response [74–76]. BIRC5 is almost entirely absent in adult tissue, but its upregulation is linked to worse survival in numerous solid tumours [77–79]. A systematic meta-analysis confirmed the prognostic role of BIRC5 gene in HCC across 14 different studies involving 890 patients [80]. The multiple and redundant regulation of BIRC5 poses a difficulty for inhibitor development and requires simultaneous targeting of multiple networks [81,82]. BIRC5 deficiency interferes with mitosis, increases TNFα and leads to senescence. Promising tumour regression was reported after synergistic effects of a mitotic inhibitor acting on PLK1 (BI2536) to induce mitotic arrest, and a proapoptotic agent (LCL161) to sensitize HCC cells to TNFα-triggered cell death in a murine model of HCC [83].

Two genes remained independent prognostic factors after multivariate analysis in the Asian cohort: CENPH and CDK4, both linked to poor prognosis. Centromeres and their associated kinetochores are required for proper spindle attachment, separation of sister chromatids and regulation of mitotic checkpoint activity [84]. The product of CENPH is a fundamental component of the inner kinetochore–nucleosome associated complex [85]. Misregulation of centromere and kinetochore protein levels is linked to missegregations, aneuploidy and genome rearrangement [86,87], increasing chromosomal instability (CIN) and tumourigenesis. Accumulating evidence suggests that CENPH upregulation has been linked to aneuploidy, high proliferative ability and poor prognosis in various solid tumours [88–90].

CDK4 acts on the retinoblastoma (RB) signalling pathway, promoting progression from G1 to S phase during the cell cycle. A selective CDK4/6 inhibitor, palbociclib, became recently approved as first-line treatment for oestrogen-positive, HER2-negative breast cancer patients. The majority of HCCs harbour an intact RB1 gene [91], rendering them potentially sensitive to palbociclib. In HCC, palbociclib suppressed in vitro cell proliferation, restricted in vivo tumour growth and increased survival with enhanced effects when combined with sorafenib [92].

In the White cohort only genes remained significant after p-value correction, possibly as a consequence of most articles focusing at Asian patients; in fact, only 6% of papers analysed samples from White/Caucasian subjects. Of SOCS2 and NAT10, only the latter offers a potential target for future therapies. NAT10 is involved in histone-acetylation and regulates telomerase activity through TERT-promoter [93]. It is localized mainly in the cell membrane, particularly at the invasive leading edge of tumours, increasing motility and invasive potential that correlates with nuclear β-catenin accumulation [94,95]. NAT10 also activates p53 in response to DNA damage by acetylation [96]. NAT10 is the target of a small molecule compound called ‘remodelin’ to treat laminopathies and premature ageing syndromes [97], not yet tested in the context of HCC.

Among our strongest biomarkers of survival, several genes have been repeatedly identified in the process of oncogenic transformation, including the development of LC [98]. Our gene ontology analyses revealed ethnic group-specific signalling pathway activations (figure 3). The differences are probably related to the distinct aetiology of the disease, as viral infections underlie most HCCs in Asia, while in western countries, chronic liver disease related to alcohol consumption and non-alcoholic steatohepatitis is responsible for HCC [6].

Our narrowed list of biomarkers may provide optimal targets to tackle pivotal signalling pathways of HCC including the Wnt/β-catenin, TP53, RAS, JAK–STAT, MAPK, the retinoblastoma protein pRb1, oxidative stress and chromatin remodelling pathways [13]. PKM2 is the rate-limiting catalytic activator of glycolytic metabolic pathways and also exerts a non-glycolytic function as a transcriptional activator, possibly affecting numerous signalling pathways, including Wnt/β-catenin, JAK-STAT and AKT signalling [99]. P53 inactivation is abundant in HCC even in the absence of TP53 mutations, leading to overexpression of genes normally repressed by p53 such as EZH2, a histone-methyltransferase that epigenetically regulates stem cell maintenance, and CCNB1, E2F1 and FOXM1, all present in our list of strongest prognostic markers [17]. Dosage-dependent copy number gains of E2F1 inactivate the retinoblastoma tumour suppressor pathway, driving HCC development in mice [100].

Several of our strongest prognostic biomarker candidates have also been identified with a functionally significant role in drug response. For example, the tyrosine kinase receptor KIT is a target of the kinase inhibitor sorafenib [101]. Moreover, sorafenib decreases protein levels of survivin, the product of the anti-apoptotic BIRC5 [102] and expression of numerous genes implicated in cell cycle, DNA replication and cell proliferation (e.g. CDC20, CDC25A) [103]. Our list of significant prognostic biomarkers might help to select candidates for future drug development.

Altogether, here we aimed to deliver authentication for gene- and protein expression-based HCC biomarkers by assessing the generalizability of previous findings. Integration of copy number variations, somatic mutations, methylation patterns, proteomics and mRNA expression might provide more throughout the identification of factors related to HCC [17]. Additional RNAs complementing mRNA started to emerge as potential biomarkers and therapeutic targets. A recent paper identified 223 miRNAs associated with HCC and validated their significance in OS [104].

5. Conclusion

The low reproducibility of the published prognostic biomarkers uncovered in our analysis draws attention to the need for more rigorous research practices in this heterogeneous neoplastic disease with a highly variable aetiology. In summary, we provide a unified pipeline using already available transcriptomic datasets to enable the validation and ranking of future biomarker candidates.

Supplementary Material

Supplementary Table 1
rsos181006supp1.docx (37.2KB, docx)

Supplementary Material

Supplementary Table 2
rsos181006supp2.docx (28.2KB, docx)

Supplementary Material

Supplementary Table 3
rsos181006supp3.docx (16.6KB, docx)

Supplementary Material

Supplementary Table 4
rsos181006supp4.docx (19KB, docx)

Supplementary Material

Supplementary Table 5
rsos181006supp5.docx (17.1KB, docx)

Acknowledgements

The use of the computational infrastructure of Pázmány Péter University provided within the National Bionics Program is gratefully acknowledged. We are also grateful to two anonymous reviewers for their expert and helpful comments.

Data accessibility

The datasets used and/or analysed during the current study are available at TCGA (https://cancergenome.nih.gov/) and NCBI Gene Expression Omnibus (GEO) databases (dataset GSE9843 is available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE9843 and dataset GSE20017 is available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20017).

Authors' contributions

O.M. designed the study, participated in data collection and analysis, carried out the statistical analyses and drafted the manuscript. Á.N. participated in data collection and statistical analyses, helped to interpret the data and draft the manuscript. B.G. conceived and coordinated the study, participated in data collection and statistical analyses and helped draft the manuscript. All authors gave final approval for publication.

Competing interests

The authors declare no competing interests.

Funding

This study was supported by the NVKP_16-1-2016-0037, KH-129581 and FIEK_16-1-2016-0005 grants of the National Research, Development and Innovation Office, Hungary.

References

  • 1.Kern SE. 2012. Why your new cancer biomarker may never work: recurrent patterns and remarkable diversity in biomarker failures. Cancer Res. 72, 6097– 6101. ( 10.1158/0008-5472.CAN-12-3232) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Mallett S, Timmer A, Sauerbrei W, Altman DG. 2010. Reporting of prognostic studies of tumour markers: a review of published articles in relation to REMARK guidelines. Br. J. Cancer 102, 173–180. ( 10.1038/sj.bjc.6605462) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F. 2015. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 136, E359–E386. ( 10.1002/ijc.29210) [DOI] [PubMed] [Google Scholar]
  • 4.Blachier M, Leleu H, Peck-Radosavljevic M, Valla DC, Roudot-Thoraval F. 2013. The burden of liver disease in Europe: a review of available epidemiological data. J. Hepatol. 58, 593–608. ( 10.1016/j.jhep.2012.12.005) [DOI] [PubMed] [Google Scholar]
  • 5.Ryerson AB, et al. 2016. Annual report to the nation on the status of cancer, 1975–2012, featuring the increasing incidence of liver cancer. Cancer 122, 1312–1337. ( 10.1002/cncr.29936) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Yang JD, Roberts LR. 2010. Hepatocellular carcinoma: a global view. Nat. Rev. Gastroenterol. Hepatol. 7, 448–458. ( 10.1038/nrgastro.2010.100) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Minguez B, et al. 2011. Gene-expression signature of vascular invasion in hepatocellular carcinoma. J. Hepatol. 55, 1325–1331. ( 10.1016/j.jhep.2011.02.034) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Llovet JM, et al. 2008. Sorafenib in advanced hepatocellular carcinoma. N. Engl. J. Med. 359, 378–390. ( 10.1056/NEJMoa0708857) [DOI] [PubMed] [Google Scholar]
  • 9.Cheng AL, et al. 2009. Efficacy and safety of sorafenib in patients in the Asia-Pacific region with advanced hepatocellular carcinoma: a phase III randomised, double-blind, placebo-controlled trial. Lancet Oncol. 10, 25–34. ( 10.1016/S1470-2045(08)70285-7) [DOI] [PubMed] [Google Scholar]
  • 10.Kudo M, et al. 2018. Lenvatinib versus sorafenib in first-line treatment of patients with unresectable hepatocellular carcinoma: a randomised phase 3 non-inferiority trial. Lancet 391, 1163–1173. ( 10.1016/S0140-6736(18)30207-1) [DOI] [PubMed] [Google Scholar]
  • 11.Bruix J, et al. 2017. Regorafenib for patients with hepatocellular carcinoma who progressed on sorafenib treatment (RESORCE): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet 389, 56–66. ( 10.1016/S0140-6736(16)32453-9) [DOI] [PubMed] [Google Scholar]
  • 12.Schulze K, Nault JC, Villanueva A. 2016. Genetic profiling of hepatocellular carcinoma using next-generation sequencing. J. Hepatol. 65, 1031–1042. ( 10.1016/j.jhep.2016.05.035) [DOI] [PubMed] [Google Scholar]
  • 13.Llovet JM, Villanueva A, Lachenmayer A, Finn RS. 2015. Advances in targeted therapies for hepatocellular carcinoma in the genomic era. Nat. Rev. Clin. Oncol. 12, 408–424. ( 10.1038/nrclinonc.2015.103) [DOI] [PubMed] [Google Scholar]
  • 14.Iwatsuki S, Dvorchik I, Marsh JW, Madariaga JR, Carr B, Fung JJ, Starzl TE. 2000. Liver transplantation for hepatocellular carcinoma: a proposal of a prognostic scoring system. J. Am. Coll. Surg. 191, 389–394. ( 10.1016/S1072-7515(00)00688-8) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Zavaglia C, et al. 2005. Predictors of long-term survival after liver transplantation for hepatocellular carcinoma. Am. J. Gastroenterol. 100, 2708–2716. ( 10.1111/j.1572-0241.2005.00289.x) [DOI] [PubMed] [Google Scholar]
  • 16.Yuki K, Hirohashi S, Sakamoto M, Kanai T, Shimosato Y. 1990. Growth and spread of hepatocellular carcinoma: a review of 240 consecutive autopsy cases. Cancer 66, 2174–2179. ( 10.1002/1097-0142(19901115)66:10%3C2174::AID-CNCR2820661022%3E3.0.CO;2-A) [DOI] [PubMed] [Google Scholar]
  • 17.Ally A, et al. 2017. Comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell 169, 1327–1341. ( 10.1016/j.cell.2017.05.046) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Gyorffy B, Gyorffy A, Tulassay Z. 2005. The problem of multiple testing and solutions for genome-wide studies. Orv. Hetil. 146, 559–563. [PubMed] [Google Scholar]
  • 19.Chau GY, Lee AF, Tsay SH, Ke YR, Kao HL, Wong FH, Tsou A-P, Chau Y-P. 2007. Clinicopathological significance of survivin expression in patients with hepatocellular carcinoma. Histopathology 51, 204–218. ( 10.1111/j.1365-2559.2007.02738.x) [DOI] [PubMed] [Google Scholar]
  • 20.Morinaga S, et al. 2004. Expression of survivin mRNA associates with apoptosis, proliferation and histologically aggressive features in hepatocellular carcinoma. Oncol. Rep. 12, 1189–1194. ( 10.3892/or.12.6.1189) [DOI] [PubMed] [Google Scholar]
  • 21.Ikeguchi M, Ueta T, Yamane Y, Hirooka Y, Kaibara N. 2002. Inducible nitric oxide synthase and survivin messenger RNA expression in hepatocellular carcinoma. Clin. Cancer Res. 8, 3131–3136. [PubMed] [Google Scholar]
  • 22.Rebouissou S, et al. 2017. Proliferation markers are associated with MET expression in hepatocellular carcinoma and predict tivantinib sensitivity in vitro. Clin. Cancer Res. 23, 4364–4375. ( 10.1158/1078-0432.CCR-16-3118) [DOI] [PubMed] [Google Scholar]
  • 23.He ZL, Zheng H, Lin H, Miao XY, Zhong DW. 2009. Overexpression of polo-like kinase1 predicts a poor prognosis in hepatocellular carcinoma patients. World J. Gastroenterol. 15, 4177–4182. ( 10.3748/wjg.15.4177) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Hou G, et al. 2017. Aldehyde dehydrogenase-2 (ALDH2) opposes hepatocellular carcinoma progression by regulating AMP-activated protein kinase signaling in mice. Hepatology 65, 1628–1644. ( 10.1002/hep.29006) [DOI] [PubMed] [Google Scholar]
  • 25.Weng L, Du J, Zhou Q, Cheng B, Li J, Zhang D, Ling C. 2012. Identification of cyclin B1 and Sec62 as biomarkers for recurrence in patients with HBV-related hepatocellular carcinoma after surgical resection. Mol. Cancer 11, 39 ( 10.1186/1476-4598-11-39) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Lin MF, Yang YF, Peng ZP, Zhang MF, Liang JY, Chen W, Liu X-H, Zheng Y-L. 2017. FOXK2, regulated by miR-1271–5p, promotes cell growth and indicates unfavorable prognosis in hepatocellular carcinoma. Int. J. Biochem. Cell Biol. 88, 155–161. ( 10.1016/j.biocel.2017.05.019) [DOI] [PubMed] [Google Scholar]
  • 27.Liao W, Huang G, Liao Y, Yang J, Chen Q, Xiao S, Jin J, He S, Wang C. 2014. High KIF18A expression correlates with unfavorable prognosis in primary hepatocellular carcinoma. Oncotarget 5, 10 271–10 279. ( 10.18632/oncotarget.2082) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Liu AW, Cai J, Zhao XL, Xu AM, Fu HQ, Nian H, Zhang S-H. 2009. The clinicopathological significance of BUBR1 overexpression in hepatocellular carcinoma. J. Clin. Pathol. 62, 1003–1008. ( 10.1136/jcp.2009.066944) [DOI] [PubMed] [Google Scholar]
  • 29.Hiwatashi K, et al. 2016. Expression of maternal embryonic leucine zipper kinase (MELK) correlates to malignant potentials in hepatocellular carcinoma. Anticancer Res. 36, 5183–5188. ( 10.21873/anticanres.11088) [DOI] [PubMed] [Google Scholar]
  • 30.He H, Wu G, Li W, Cao Y, Liu Y. 2012. CIP2A is highly expressed in hepatocellular carcinoma and predicts poor prognosis. Diagn. Mol. Pathol. 21, 143–149. ( 10.1097/PDM.0b013e318249fd8b) [DOI] [PubMed] [Google Scholar]
  • 31.Fujii T, et al. 2006. Overexpression of pituitary tumor transforming gene 1 in HCC is associated with angiogenesis and poor prognosis. Hepatology 43, 1267–1275. ( 10.1002/hep.21181) [DOI] [PubMed] [Google Scholar]
  • 32.Fu J, Qiu H, Cai M, Pan Y, Cao Y, Liu L, Yun J, Zhang CZ. 2013. Low cyclin F expression in hepatocellular carcinoma associates with poor differentiation and unfavorable prognosis. Cancer Sci. 104, 508–515. ( 10.1111/cas.12100) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Hu W, et al. 2015. Pyruvate kinase M2 prevents apoptosis via modulating Bim stability and associates with poor outcome in hepatocellular carcinoma. Oncotarget 6, 6570–6583. ( 10.18632/oncotarget.3262) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Liu AM, et al. 2014. miR-122 targets pyruvate kinase M2 and affects metabolism of hepatocellular carcinoma. PLoS ONE 9, e86872 ( 10.1371/journal.pone.0086872) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Yuan SG, Liao WJ, Yang JJ, Huang GJ, Huang ZQ. 2014. DEP domain containing 1 is a novel diagnostic marker and prognostic predictor for hepatocellular carcinoma. Asian Pac. J. Cancer Prev. 15, 10917–10922. ( 10.7314/APJCP.2014.15.24.10917) [DOI] [PubMed] [Google Scholar]
  • 36.Yuan RH, Jeng YM, Chen HL, Lai PL, Pan HW, Hsieh FJ, Lin C-Y, Lee P-H, Hsu H-C. 2006. Stathmin overexpression cooperates with p53 mutation and osteopontin overexpression, and is associated with tumour progression, early recurrence, and poor prognosis in hepatocellular carcinoma. J. Pathol. 209, 549–558. ( 10.1002/path.2011) [DOI] [PubMed] [Google Scholar]
  • 37.Kuboki S, et al. 2008. Angiopoietin-2 levels in the hepatic vein as a useful predictor of tumor invasiveness and prognosis in human hepatocellular carcinoma. J. Gastroenterol. Hepatol. 23, e157–e164. ( 10.1111/j.1440-1746.2007.05175.x) [DOI] [PubMed] [Google Scholar]
  • 38.Hung SY, Lin HH, Yeh KT, Chang JG. 2014. Histone-modifying genes as biomarkers in hepatocellular carcinoma. Int. J. Clin. Exp. Pathol. 7, 2496–2507. [PMC free article] [PubMed] [Google Scholar]
  • 39.Chen YL, et al. 2013. The E2F transcription factor 1 transactives stathmin 1 in hepatocellular carcinoma. Ann. Surg. Oncol. 20, 4041–4054. ( 10.1245/s10434-012-2519-8) [DOI] [PubMed] [Google Scholar]
  • 40.Xu X, et al. 2003. Overexpression of CDC25A phosphatase is associated with hypergrowth activity and poor prognosis of human hepatocellular carcinomas. Clin. Cancer Res. 9, 1764–1772. [PubMed] [Google Scholar]
  • 41.Heise M, et al. 2012. Downregulation of organic cation transporters OCT1 (SLC22A1) and OCT3 (SLC22A3) in human hepatocellular carcinoma and their prognostic significance. BMC Cancer 12, 109 ( 10.1186/1471-2407-12-109) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Jeng YM, Peng SY, Lin CY, Hsu HC. 2004. Overexpression and amplification of Aurora-A in hepatocellular carcinoma. Clin. Cancer Res. 10, 2065–2071. ( 10.1158/1078-0432.CCR-1057-03) [DOI] [PubMed] [Google Scholar]
  • 43.Sun H, et al. 2011. FOXM1 expression predicts the prognosis in hepatocellular carcinoma patients after orthotopic liver transplantation combined with the Milan criteria. Cancer Lett. 306, 214–222. ( 10.1016/j.canlet.2011.03.009) [DOI] [PubMed] [Google Scholar]
  • 44.Wei RR, Zhang MY, Rao HL, Pu HY, Zhang HZ, Wang HY. 2012. Identification of ADH4 as a novel and potential prognostic marker in hepatocellular carcinoma. Med. Oncol. 29, 2737–2743. ( 10.1007/s12032-011-0126-3) [DOI] [PubMed] [Google Scholar]
  • 45.Kitamura K, et al. 2011. Proliferative activity in hepatocellular carcinoma is closely correlated with glucose metabolism but not angiogenesis. J. Hepatol. 55, 846–857. ( 10.1016/j.jhep.2011.01.038) [DOI] [PubMed] [Google Scholar]
  • 46.Zhang SH, Xu AM, Chen XF, Li DH, Sun MP, Wang YJ. 2008. Clinicopathologic significance of mitotic arrest defective protein 2 overexpression in hepatocellular carcinoma. Hum. Pathol. 39, 1827–1834. ( 10.1016/j.humpath.2008.06.003) [DOI] [PubMed] [Google Scholar]
  • 47.Lu JW, Lin YM, Chang JG, Yeh KT, Chen RM, Tsai JJ, Su W-W, Hu R-M. 2013. Clinical implications of deregulated CDK4 and Cyclin D1 expression in patients with human hepatocellular carcinoma. Med. Oncol. 30, 379 ( 10.1007/s12032-012-0379-5) [DOI] [PubMed] [Google Scholar]
  • 48.Hayashi T, et al. 2014. Cytoskeleton-associated protein 2 is a potential predictive marker for risk of early and extensive recurrence of hepatocellular carcinoma after operative resection. Surgery 155, 114–123. ( 10.1016/j.surg.2013.06.009) [DOI] [PubMed] [Google Scholar]
  • 49.Xia FD, Wang ZL, Chen HX, Huang Y, Li JD, Wang ZM, Li X-Y. 2014. Differential expression of IQGAP1/2 in hepatocellular carcinoma and its relationship with clinical outcomes. Asian Pac. J. Cancer Prev. 15, 4951–4956. ( 10.7314/APJCP.2014.15.12.4951) [DOI] [PubMed] [Google Scholar]
  • 50.Cui M, et al. 2016. The suppressor of cytokine signaling 2 (SOCS2) inhibits tumor metastasis in hepatocellular carcinoma. Tumour Biol. 37, 13521–13531. ( 10.1007/s13277-016-5215-7) [DOI] [PubMed] [Google Scholar]
  • 51.Andreozzi M, et al. 2016. HMGA1 expression in human hepatocellular carcinoma correlates with poor prognosis and promotes tumor growth and migration in in vitro models. Neoplasia 18, 724–731. ( 10.1016/j.neo.2016.10.002) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Zeng X, Yin F, Liu X, Xu J, Xu Y, Huang J, Nan Y, Qiu X. 2014. Upregulation of E2F transcription factor 3 is associated with poor prognosis in hepatocellular carcinoma. Oncol. Rep. 31, 1139–1146. ( 10.3892/or.2014.2968) [DOI] [PubMed] [Google Scholar]
  • 53.Chung CY, Yeh KT, Hsu NC, Chang JH, Lin JT, Horng HC, Chang C-S. 2005. Expression of c-kit protooncogene in human hepatocellular carcinoma. Cancer Lett. 217, 231–236. ( 10.1016/j.canlet.2004.06.045) [DOI] [PubMed] [Google Scholar]
  • 54.Huang CW, et al. 2010. CKS1B overexpression implicates clinical aggressiveness of hepatocellular carcinomas but not p27(Kip1) protein turnover: an independent prognosticator with potential p27 (Kip1)-independent oncogenic attributes? Ann. Surg. Oncol. 17, 907–922. ( 10.1245/s10434-009-0779-8) [DOI] [PubMed] [Google Scholar]
  • 55.Liao W, et al. 2015. Upregulation of FAM83D affects the proliferation and invasion of hepatocellular carcinoma. Oncotarget 6, 24 132–24 147. ( 10.18632/oncotarget.4432) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Lu G, Shan T, He S, Ren M, Zhu M, Hu Y, Lu X, Zhang D. 2013. Overexpression of CENP-H as a novel prognostic biomarker for human hepatocellular carcinoma progression and patient survival. Oncol. Rep. 30, 2238–2244. ( 10.3892/or.2013.2675) [DOI] [PubMed] [Google Scholar]
  • 57.Su R, Nan H, Guo H, Ruan Z, Jiang L, Song Y, Nan K. 2016. Associations of components of PTEN/AKT/mTOR pathway with cancer stem cell markers and prognostic value of these biomarkers in hepatocellular carcinoma. Hepatol. Res. 46, 1380–1391. ( 10.1111/hepr.12687) [DOI] [PubMed] [Google Scholar]
  • 58.Liu T, Chen J, Xiao S, Lei X. 2016. H2.0-like homeobox 1 acts as a tumor suppressor in hepatocellular carcinoma. Tumour Biol. 37, 6419–6428. ( 10.1007/s13277-015-4490-z) [DOI] [PubMed] [Google Scholar]
  • 59.Huang DW, Sherman BT, Lempicki RA. 2009. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57. ( 10.1038/nprot.2008.211) [DOI] [PubMed] [Google Scholar]
  • 60.Mi H, et al. 2017. PANTHER version 11: expanded annotation data from gene ontology and reactome pathways, and data analysis tool enhancements. Nucleic Acids Res. 45(Database issue), D183–D189. ( 10.1093/nar/gkw1138) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Stumpff J, Von Dassow G, Wagenbach M, Asbury C, Wordeman L. 2008. The kinesin-8 motor, Kif18A, suppresses kinetochore movements to control mitotic chromosome alignment. Dev. Cell 14, 252–262. ( 10.1016/j.devcel.2007.11.014) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Kasahara M, Nagahara M, Nakagawa T, Ishikawa T, Sato T, Uetake H. 2016. Clinicopathological relevance of kinesin family member 18A expression in invasive breast cancer. Oncol. Lett. 12, 1909–1914. ( 10.3892/ol.2016.4823) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Manchado E, Guillamot M, Malumbres M. 2012. Killing cells by targeting mitosis. Cell Death Differ. 19, 369–377. ( 10.1038/cdd.2011.197) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Inoue M, Shinohara ML. 2011. Intracellular osteopontin (iOPN) and immunity. Immunol. Res. 49, 160–172. ( 10.1007/s12026-010-8179-5) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 65.Wang KX, Denhardt DT. 2008. Osteopontin: role in immune regulation and stress responses. Cytokine Growth Factor Rev. 19, 333–345. ( 10.1016/j.cytogfr.2008.08.001) [DOI] [PubMed] [Google Scholar]
  • 66.Wen Y, Jeong S, Xia Q, Kong X. 2016. Role of osteopontin in liver diseases. Int. J. Biol. Sci. 12, 1121–1128. ( 10.7150/ijbs.16445) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Qin L. 2014. Osteopontin is a promoter for hepatocellular carcinoma metastasis: a summary of 10 years of studies. Front. Med. 8, 24–32. ( 10.1007/s11684-014-0312-8) [DOI] [PubMed] [Google Scholar]
  • 68.Wan HG, Xu H, Gu YM, Wang H, Xu W, Zu MH. 2014. Comparison osteopontin vs AFP for the diagnosis of HCC: a meta-analysis. Clin. Res. Hepatol. Gastroenterol. 38, 706–714. ( 10.1016/j.clinre.2014.06.008) [DOI] [PubMed] [Google Scholar]
  • 69.Fouad SA, Mohamed NA, Fawzy MW, Moustafa DA. 2015. Plasma osteopontin level in chronic liver disease and hepatocellular carcinoma. Hepat. Mon. 15, e30753 ( 10.5812/hepatmon.30753) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Weber GF. 2011. The cancer biomarker osteopontin: combination with other markers. Cancer Genomics Proteomics 8, 263–288. [PubMed] [Google Scholar]
  • 71.Cao DX, et al. 2012. Osteopontin as potential biomarker and therapeutic target in gastric and liver cancers. World J. Gastroenterol. 18, 3923–3930. ( 10.3748/wjg.v18.i30.3923) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Boyault S, et al. 2007. Transcriptome classification of HCC is related to gene alterations and to new therapeutic targets. Hepatology 45, 42–52. ( 10.1002/hep.21467) [DOI] [PubMed] [Google Scholar]
  • 73.Villanueva A, et al. 2011. Combining clinical, pathology, and gene expression data to predict recurrence of hepatocellular carcinoma. Gastroenterology 140, 1501–1512. ( 10.1053/j.gastro.2011.02.006) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Li F, Ambrosini G, Chu EY, Plescia J, Tognin S, Marchisio PC, Altieri DC. 1998. Control of apoptosis and mitotic spindle checkpoint by survivin. Nature 396, 580–584. ( 10.1038/25141) [DOI] [PubMed] [Google Scholar]
  • 75.Mita AC, Mita MM, Nawrocki ST, Giles FJ. 2008. Survivin: key regulator of mitosis and apoptosis and novel target for cancer therapeutics. Clin. Cancer Res. 14, 5000–5005. ( 10.1158/1078-0432.CCR-08-0746) [DOI] [PubMed] [Google Scholar]
  • 76.Fortugno P, Beltrami E, Plescia J, Fontana J, Pradhan D, Marchisio PC, Sessa WC, Altieri DC. 2003. Regulation of survivin function by Hsp90. Proc. Natl Acad. Sci. USA 100, 13 791–13 796. ( 10.1073/pnas.2434345100) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Zhang LQ, Wang J, Jiang F, Xu L, Liu FY, Yin R. 2012. Prognostic value of survivin in patients with non-small cell lung carcinoma: a systematic review with meta-analysis. PLoS ONE 7, e34100 ( 10.1371/journal.pone.0034100) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78.Khan Z, Khan AA, Yadav H, Prasad GBKS, Bisen PS. 2017. Survivin, a molecular target for therapeutic interventions in squamous cell carcinoma. Cell. Mol. Biol. Lett. 22, 8 ( 10.1186/s11658-017-0038-0) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 79.Nigam J, Chandra A, Kazmi HR, Parmar D, Singh D, Gupta V. 2014. Expression of survivin mRNA in gallbladder cancer: a diagnostic and prognostic marker? Tumour Biol. 35, 9241–9246. ( 10.1007/s13277-014-2200-x) [DOI] [PubMed] [Google Scholar]
  • 80.Liu JL, Zhang XJ, Zhang Z, Zhang AH, Wang W, Dong JH. 2013. Meta-analysis: prognostic value of survivin in patients with hepatocellular carcinoma. PLoS ONE 8, e83350 ( 10.1371/journal.pone.0083350) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 81.Altieri DC. 2013. Targeting survivin in cancer. Cancer Lett. 332, 225–228. ( 10.1016/j.canlet.2012.03.005) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82.Holmes D. 2012. Cancer drug's survivin suppression called into question. Nat. Med. 18, 842–843. ( 10.1038/nm0612-842b) [DOI] [PubMed] [Google Scholar]
  • 83.Li D, et al. 2016. Hepatocellular carcinoma repression by TNFα-mediated synergistic lethal effect of mitosis defect-induced senescence and cell death sensitization. Hepatology 64, 1105–1120. ( 10.1002/hep.28637) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 84.Cleveland DW, Mao Y, Sullivan KF. 2003. Centromeres and kinetochores: from epigenetics to mitotic checkpoint signaling. Cell 112, 407–421. ( 10.1016/S0092-8674(03)00115-6) [DOI] [PubMed] [Google Scholar]
  • 85.Zhang W, Mao JH, Zhu W, Jain AK, Liu K, Brown JB, Karpen GH. 2016. Centromere and kinetochore gene misexpression predicts cancer patient survival and response to radiotherapy and chemotherapy. Nat. Commun. 7, 12 619 ( 10.1038/ncomms12619) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 86.Mishra PK, Au WC, Choy JS, Kuich PH, Baker RE, Foltz DR, Basrai MA, Sullivan BA. 2011. Misregulation of Scm3p/HJURP causes chromosome instability in Saccharomyces cerevisiae and human cells. PLoS Genet. 7, e1002303 ( 10.1371/journal.pgen.1002303) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 87.Bakhoum SF, Compton DA. 2012. Kinetochores and disease: keeping microtubule dynamics in check! Current opinion in cell biology. 24, 64–70. ( 10.1016/j.ceb.2011.11.012) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88.Liao WT, Wang X, Xu LH, Kong QL, Yu CP, Li MZ, Shi L, Zeng M-S, Song L-B. 2009. Centromere protein H is a novel prognostic marker for human nonsmall cell lung cancer progression and overall patient survival. Cancer 115, 1507–1517. ( 10.1002/cncr.24128) [DOI] [PubMed] [Google Scholar]
  • 89.Tomonaga T, Matsushita K, Ishibashi M, Nezu M, Shimada H, Ochiai T, Yoda K, Nomura F. 2005. Centromere protein H is up-regulated in primary human colorectal cancer and its overexpression induces aneuploidy. Cancer Res. 65, 4683–4689. ( 10.1158/0008-5472.CAN-04-3613) [DOI] [PubMed] [Google Scholar]
  • 90.Liao WT, et al. 2007. Centromere protein H is a novel prognostic marker for nasopharyngeal carcinoma progression and overall patient survival. Clin. Cancer Res. 13, 508–514. [DOI] [PubMed] [Google Scholar]
  • 91.Zucman-Rossi J, Villanueva A, Nault JC, Llovet JM. 2015. Genetic landscape and biomarkers of hepatocellular carcinoma. Gastroenterology 149, 1226–1239. ( 10.1053/j.gastro.2015.05.061) [DOI] [PubMed] [Google Scholar]
  • 92.Bollard J, et al. 2017. Palbociclib (PD-0332991), a selective CDK4/6 inhibitor, restricts tumour growth in preclinical models of hepatocellular carcinoma. Gut 66, 1286 ( 10.1136/gutjnl-2016-312268) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 93.Lv J, Liu H, Wang Q, Tang Z, Hou L, Zhang B. 2003. Molecular cloning of a novel human gene encoding histone acetyltransferase-like protein involved in transcriptional activation of hTERT. Biochem. Biophys. Res. Commun. 311, 506–513. ( 10.1016/j.bbrc.2003.09.235) [DOI] [PubMed] [Google Scholar]
  • 94.Zhang H, et al. 2014. GSK-3beta-regulated N-acetyltransferase 10 is involved in colorectal cancer invasion. Clin. Cancer Res. 20, 4717–4729. ( 10.1158/1078-0432.CCR-13-3477) [DOI] [PubMed] [Google Scholar]
  • 95.Ma R, Chen J, Jiang S, Lin S, Zhang X, Liang X. 2016. Up regulation of NAT10 promotes metastasis of hepatocellular carcinoma cells through epithelial-to-mesenchymal transition. Am. J. Transl. Res. 8, 4215–4223. [PMC free article] [PubMed] [Google Scholar]
  • 96.Liu X, et al. 2016. NAT10 regulates p53 activation through acetylating p53 at K120 and ubiquitinating Mdm2. EMBO Rep. 17, 349–366. ( 10.15252/embr.201540505) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 97.Larrieu D, Britton S, Demir M, Rodriguez R, Jackson SP. 2014. Chemical inhibition of NAT10 corrects defects of laminopathic cells. Science 344, 527–532. ( 10.1126/science.1252651) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 98.Andrisani OM, Studach L, Merle P. 2011. Gene signatures in hepatocellular carcinoma (HCC). Semin. Cancer Biol. 21, 4–9. ( 10.1016/j.semcancer.2010.09.002) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 99.Zhao R, Li L, Yang J. 2016. PKM2 affects the development of hepatocellular carcinoma. Int. J. Clin. Exp. Med. 9, 9744–9751. [Google Scholar]
  • 100.Kent LN, et al. 2017. Dosage-dependent copy number gains in E2f1 and E2f3 drive hepatocellular carcinoma. J. Clin. Invest. 127, 830–842. ( 10.1172/JCI87583) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 101.Tanaka S, Arii S. 2012. Molecular targeted therapies in hepatocellular carcinoma. Semin. Oncol. 39, 486–492. ( 10.1053/j.seminoncol.2012.05.005) [DOI] [PubMed] [Google Scholar]
  • 102.Fernando J, Sancho P, Fernández-Rodriguez CM, Lledó JL, Caja L, Campbell JS, Fausto N, Fabregat I. 2012. Sorafenib sensitizes hepatocellular carcinoma cells to physiological apoptotic stimuli. J. Cell. Physiol. 227, 1319–1325. ( 10.1002/jcp.22843) [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 103.Cervello M, Bachvarov D, Lampiasi N, Cusimano A, Azzolina A, McCubrey JA, Montalto G. 2012. Molecular mechanisms of sorafenib action in liver cancer cells. Cell Cycle 11, 2843–2855. ( 10.4161/cc.21193) [DOI] [PubMed] [Google Scholar]
  • 104.Nagy A, Lanczky A, Menyhart O, Gyorffy B. 2018. Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets. Sci. Rep. 8, 9227 ( 10.1038/s41598-018-27521-y) [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Supplementary Table 1
rsos181006supp1.docx (37.2KB, docx)
Supplementary Table 2
rsos181006supp2.docx (28.2KB, docx)
Supplementary Table 3
rsos181006supp3.docx (16.6KB, docx)
Supplementary Table 4
rsos181006supp4.docx (19KB, docx)
Supplementary Table 5
rsos181006supp5.docx (17.1KB, docx)

Data Availability Statement

The datasets used and/or analysed during the current study are available at TCGA (https://cancergenome.nih.gov/) and NCBI Gene Expression Omnibus (GEO) databases (dataset GSE9843 is available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE9843 and dataset GSE20017 is available at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20017).

Articles from Royal Society Open Science are provided here courtesy of The Royal Society

RESOURCES