Next Article in Journal
A Bloody Conspiracy— Blood Vessels and Immune Cells in the Tumor Microenvironment
Previous Article in Journal
The Immune Contexture of Liposarcoma and Its Clinical Implications
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cancers
Volume 14
Issue 19
10.3390/cancers14194580
cancers-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Molecular Classification of Hepatocellular Carcinoma Using Wnt–Hippo Signaling Pathway-Related Genes

by
Ya-Sian Chang
1,2,3,4,
Yu-Pao Chou
1,2,
Chin-Chun Chung
1,2,
Ya-Ting Lee
1,2,
Ju-Chen Yen
1,2,
Long-Bin Jeng
5,*,† and
Jan-Gowth Chang
1,2,3,4,6,*,†
1
Center for Precision Medicine, China Medical University Hospital, Taichung 404332, Taiwan
2
Epigenome Research Center, China Medical University Hospital, Taichung 404332, Taiwan
3
Department of Laboratory Medicine, China Medical University Hospital, Taichung 404332, Taiwan
4
School of Medicine, China Medical University, Taichung 404333, Taiwan
5
Organ Transplantation Center, China Medical University Hospital, Taichung 404332, Taiwan
6
Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cancers 2022, 14(19), 4580; https://doi.org/10.3390/cancers14194580
Submission received: 24 August 2022 / Revised: 14 September 2022 / Accepted: 19 September 2022 / Published: 21 September 2022
Browse Figures
Versions Notes

Abstract

:

Simple Summary

The characters of Taiwanese hepatocellular carcinoma (HCC) are different from other parts of the world. We characterized the molecular features of HCC using a newly developed classification system based on the expression of the Wnt–Hippo signaling pathway-related genes. We analyzed the data in terms of prognostic value, transcriptome features, immune infiltration, and clinical characteristics, and compared the resulting subclasses with previously published classifications. A new molecular classification method based on a 272 gene panel of Wnt–Hippo pathways that may provide a new target for the treatment.

Abstract

In Taiwan, a combination of hepatitis B and C infection, economic boom-related food and alcohol overconsumption, and Chinese medicine prescriptions has led to a high rate of hepatocellular carcinoma (HCC). However, the causative factors and underlying tumor biology for this unique HCC environment have not been identified. Wnt and Hippo signaling pathways play an important regulatory role in HCC development, and their functions are generally considered as positive and negative regulators of cell proliferation, respectively. In this study, we characterized the molecular features of HCC using a newly developed classification system based on the expression of the Wnt–Hippo signaling pathway-related genes. RNA sequencing (RNA-Seq) was performed on liver tumor tissues from 100 patients with liver cancer. RNA-Seq data for 272 previously characterized Wnt–Hippo signaling pathway-related genes were used for hierarchical clustering. We analyzed the data in terms of prognostic value, transcriptome features, immune infiltration, and clinical characteristics, and compared the resulting subclasses with previously published classifications. Four subclasses of HCC (HCCW1–4) were identified. Subclass HCCW1 displayed the highest PCDHB4 expression. Subclass HCCW2 displayed lower Edmondson–Steiner grades (I and II) and CTNNB1 mutation frequencies. Subclass HCCW3 was associated with a good prognosis, the highest PCDHGB7 expression, high CD8+ naïve T cells abundance, and relatively low TP53 mutation rates. Subclass HCCW4 was associated with a poor prognosis, the highest PCDHB2 and PCDHB6 expression, a relatively high abundance of Th1 cells, NKT and class-switched memory B cells, relatively low enrichment of cDC, iDC, and CD4+ memory T cells, and high Edmondson–Steiner grades (III and IV). We also identified Wnt–Hippo signaling pathway-related genes that may influence immune cell infiltration. We developed a panel of 272 Wnt–Hippo signaling pathway-related genes to classify HCC into four groups based on Taiwanese HCC and The Cancer Genome Atlas Liver Hepatocellular Carcinoma datasets. This novel molecular classification system may aid the treatment of HCC.

1. Introduction

Liver cancer is the fifth most common cancer and the second common cause of cancer-related death worldwide, and hepatocellular carcinoma (HCC) is its predominant form [1,2]. In addition to genetic factors, HCC development is associated with chronic viral infection, alcohol abuse, diabetes mellitus (DM), obesity, metabolic diseases, hemochromatosis, and autoimmune hepatitis [3,4]. The incidence of noninfectious HCC in developed countries is increasing due to increased rates of obesity, DM, and metabolic diseases [5,6]. These conditions induce liver injury and progressive inflammation, such that liver cells show a cycle of necrosis, regeneration, somatic mutations, and chromosomal instability [7,8,9]. Recent studies have revealed many genomic alterations of HCCs, and numerous frequently altered genes including TP53, CTNNB1, and TERT [10,11,12,13]. Surgery resection is the major treatment for HCC; however, many HCCs found at the unresected stage require chemotherapy or targeted therapy. Despite many potential therapeutic targets, few drugs have shown clinically promising effects, such as the multikinase inhibitors sorafenib and regorafenib, and immune checkpoint inhibitors; however, most of these drugs increased survival by only a few months, indicating the need for new drugs to treat HCC [4,6].
Nationwide programs have been initiated in Taiwan to reduce hepatitis B virus (HBV)- and hepatitis C virus (HCV)-related HCC, beginning in 1984; aggressive countermeasures against HBV and HCV infections have also been implemented by the government. These measures have resulted in a reduction in the number of HBV carriers and a decrease in the rate of HBV-related HCC among younger individuals [14,15,16]. There is no vaccine available for hepatitis C even nowadays. Chinese herbs are also prescribed for the treatment of diseases, including chronic hepatitis, under the national health system; these herbs may contain compounds that modulate aristolochic acid or liver toxins [17]. Moreover, an economic boom was associated with higher rates of DM, metabolic disorders, and obesity [14,18,19]. HCC in Taiwan is unique due to the higher prevalence of chronic hepatitis B than in the Western world. Nonalcoholic fatty liver disease, alcoholic liver disease, and chronic hepatitis C are more common in Western countries.
Aberrant Wnt signaling promotes the development and/or progression of HCC. Mutations of CTNNB1, a gene that codes for β-catenin in the Wnt signaling pathway, are common in HCC [20]. Aberrant activation of Hippo signaling pathway components has also been observed in HCC, especially the Yes-associated protein and transcriptional co-activator with PDZ-binding motif transcription factors [21]. Inactivation of the Hippo pathway is significantly associated with poor prognosis in human HCC [22]. Molecular classification of HCC plays an important role in the selection of therapeutic strategies and evaluation of therapeutic responses and prognoses [10,11,12,13,23,24,25,26]. For example, a recently developed metabolic network classified HCC patients into three subgroups (iHCC1–iHCC3), where the iHCC2 phenotype was associated with aberrant Wnt signaling: 75% of iHCC2 tumors found to carry CTNNB1 mutations and displayed upregulated expression of β-catenin target genes [24]. Another study identified three HCC subtypes (S1–S3), where S1 tumors exhibited Wnt pathway activation through two mechanisms: CTNNB1 mutation and TGF-β activation [25].
In this study, we applied bioinformatic tools for RNA sequencing (RNA-Seq) analysis of 100 Taiwanese HCCs, and then used several approaches to develop a survival-related molecular classification method. This is the first study to analyze the Wnt–Hippo pathways for the molecular classification of HCCs in Taiwan. Our findings improve our understanding of HCC etiology and provide a basis for the identification of new treatment targets.

2. Materials and Methods

2.1. Liver Samples

HCC was determined by pathological diagnosis. Tumor samples were collected and frozen at −80 °C after surgical resection at the Tissue Bank of the China Medical University Hospital (CMUH). The CMUH Tissue Bank was established in 2005 and accredited by Taiwan Government on 25 October 2012 and has collected more than 20,000 cancer tissues from patients with more than 20 cancer types. This study was approved by the Ethics Committee of the CMUH (CMUH 109-REC3-055), and written informed consent was obtained from all participants according to the standard procedure of the CMUH Tissue Bank.

2.2. RNA Extraction and RNA-Seq

RNA was extracted from tissue samples using the NucleoSpin RNA Kit (Macherey–Nagel GmBH, Duren, Germany) following the manufacturer’s instructions. The quality, quantity, and integrity of the RNA were evaluated using a NanoDrop1000 spectrophotometer and Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA).
RNA-Seq was performed as described previously [27]. Briefly, samples with an RNA integrity number > 6.0 were used for RNA-Seq. An mRNA-focused, barcoded library was generated using the TruSeq strand mRNA Library Preparation Kit (Illumina, San Diego, CA, USA). The libraries were sequenced on the Nova Seq 6000 instrument (Illumina), using 2 × 151-bp paired-end sequencing flow cells following the manufacturer’s instructions.

2.3. RNA-Seq Data Analysis

The RNA-Seq data were analyzed as described previously [28]. Briefly, data quality control at the Q20 level was performed using the Trimmomatic tool [29], read alignment to the GRCh38 human genome was conducted using the HISAT2 alignment program [30], expression was quantified with reference to GENCODE v35 (excluding mitochondrial genes), and transcripts were normalized to transcripts per million (TPM) using the StringTie assembler [31].

2.4. Molecular Classification of HCC

Molecular classification of HCC was performed using transcriptomics-based analysis integrating Wnt [32] and Hippo [33] signaling pathways-related gene panels, coupled with survival analysis, and then validated using data from The Cancer Genome Atlas (TCGA). Subjects were also classified according to molecular signatures reported in other HCC studies [24,25]. For the molecular classification, hierarchical clustering was performed using Spearman rank correlation for sample distance calculation and the “average” linkage method using the Morpheus web tool (https://software.broadinstitute.org/morpheus/, accessed on 13 April 2022). Important nodes and subnetworks were predicted and explored using CytoHubba [34], a Cytoscape plugin [35].

2.5. Cell Enrichment Analysis

The xCell [36] was used to examine the enrichment of various immune cells in the tumors and to calculate an immune score from the TPM expression matrix.

2.6. Statistical Analyses

The Kaplan–Meier method was used to construct overall survival curves, and the p-values were obtained using a log-rank test. The Kruskal–Wallis test and Mann–Whitney U test were used to compare groups in terms of non-normally distributed variables. Contingency table variables were analyzed using Fisher’s exact test. All statistical analyses were performed in the Python programming environment. A two-tailed p < 0.05 was considered statistically significant.

3. Results

3.1. Molecular Subtypes of Taiwanese HCC Based on the Expression Profiles of 254 Wnt Pathway Genes

Hierarchical clustering analysis identified four subclasses (A–D) of Taiwanese HCC patients (Figure 1A). Kaplan–Meier plots of the results of individual and grouped pairwise analysis showed significant differences in survival between subclasses B and D (p = 0.019) and C and D (p = 0.035), but there was no significant difference in a comparison of all four groups (p = 0.077) (Figure 1B–H). Moreover, we investigated the expression of CTNNB1 up/downstream genes for CTNNB1 overexpression or down expression. FZD1, FZD3, FZD4, FZD5, FZD6, FZD7, GSK3B, TCF7L2, TLE4, LRP5, TLE2, TLE5, and TLE6 were found to be differentially expressed in CTNNB1 overexpression or down expression (Supplementary Figure S1). We found positive correlation for the expression of CTNNB1 with the expression of FZD1, FZD3, FZD4, FZD5, FZD6, FZD7, GSK3B, TCF7L2, and TLE4. However, the expression of CTNNB1 was inversely correlated with the expression of LRP5, TLE2, TLE5, and TLE6.

3.2. Molecular Subtypes of Taiwanese HCC Based on the Expression Profiles of 18 Hippo Pathway Genes

Hierarchical clustering analysis identified two subclasses (A and B) of Taiwanese HCC patients (Figure 2A). A Kaplan–Meier plot of overall survival showed no significant difference between the two subclasses (p = 0.751) (Figure 2B). In addition, we examined the expression of YAP1 up/downstream genes for YAP1 overexpression or down expression. We found TAOK1, TAOK2, TAOK3, SAV1, LATS1, LATS2, STK4, MOB1A, MOB1B, NF2, TAZ, TEAD1, TEAD3, TEAD4, and FRMD6 were differentially expressed in YAP1 overexpression and down expression (Supplementary Figure S2) and positive correlation for the expression of YAP1 with the expression of TAOK1, TAOK3, SAV1, LATS1, LATS2, STK4, MOB1A, MOB1B, TEAD1, and FRMD6. However, the expression of YAP1 was inversely correlated with the expression of TAOK2, NF2, TAZ, TEAD3, and TEAD4.

3.3. Molecular Subtypes of Taiwanese HCC Based on the Expression Profiles of 272 Wnt–Hippo Pathways Genes

A panel of Wnt pathway-related genes showed that some were corrected with survival; therefore, we added several other functional gene panels to improve the power of the analysis. Finally, among 18 Hippo pathway- and 254 Wnt pathway-related genes, we identified four molecular subclasses (HCCW1-4) (Figure 3A) that were correlated with patient’s survival (p = 0.040) (Figure 3B). The average survival time was significantly shorter for HCCW4 (2575 days; 95% confidence interval (CI): 1953–3197, n = 18) than for HCCW2 (3075 days; 95% CI: 2649–3502, n = 30), HCCW1 (3446 days; 95% CI: 2792–4101, n = 18), and HCCW3 (3556 days; 95% CI: 3143–3968, n = 34). HCCW3 was associated with a statistically significantly better prognosis than HCCW4 based on a two-sided log-rank test (p = 0.005) (Figure 3C).
To validate the findings, we applied a similar molecular classification approach using HCC (LIHC) data from TCGA and obtained similar results (p = 0.008; Figure 4A,B). HCCW4 showed significantly lower survival than HCCW2 (p = 0.003) (Figure 4C) and HCCW3 (p = 0.007) (Figure 4D), with a more distinct grouping than that seen for Taiwanese HCC patients, perhaps due to a larger sample size. The Bidkhori classification method [24] applied to our cases based on a panel of 51 metabolic genes revealed no correlation with survival (p = 0.943) (Figure 3D), whereas the results of the Hoshida classification [25] showed a correlation with survival (p = 0.039) (Figure 3E).

3.4. Transcriptomes of the HCC Subclasses

We further analyzed the expression of genes that may influence the molecular classification of Taiwanese HCC patients, the results showed differential expression of many genes among the four groups; however, we detected no correlations with survival (Supplementary Table S1). We further analyzed survival-related expression genes and found that PCDHB6, PCDHGB7, PCDHB2, and PCDHB4 were correlated with the molecular classification and patient survival (Figure 5A–D and Supplementary Table S1). Patients with high PCDHB6, PCDHB2, and PCDHB4 mRNA expression levels had lower overall survival. By contrast, high PCDHGB7 mRNA expression was associated with longer survival. We used similar approaches to analyze the TCGA data, and the results showed that 56 genes were correlated with both the molecular classification and survival (Supplementary Table S1).
Among the significantly differentially expressed genes related to the Wnt–Hippo pathway, 110 and 14 were up- and downregulated, respectively, in HCCW4 compared with those in HCCW1–3 (Supplementary Table S2). The most important nodes and subnetworks of protein–protein interactions among the 110 upregulated genes in HCCW4 were predicted and explored using CytoHubba; the 10 most significant node genes were WNT8B, SMARCA4, ARID1A, SMARCB1, SMARCC2, SMARCD1, SMARCD2, SMARCD3, FZD2, and WNT9A (Figure 6).
We also identified 26 and 11 differentially expressed genes that were up- and downregulated, respectively, in HCCW3 compared with those in HCCW1, HCCW2, and HCCW4 (Supplementary Table S3).

3.5. Correlation of HCC Subclasses with Immune Infiltration

We investigated the associations between the subclasses and the expression levels of 34 immune cells. Significant differences were observed between HCCW4 and the other three subclasses, with higher abundance seen for three immune cell populations (Th1, natural killer T (NKT), and class-switched memory B-cells) in HCCW4 than compared with HCCW1-3 (Figure 7A), and lower enrichment for three immune cell populations (cDC, iDC, and CD4+ memory T-cells) (Figure 7B). In the nonhematopoietic group, hepatocytes were significantly less enriched in HCCW4 (Figure 7C). In addition, CD8+ naïve T-cells were more abundant in HCCW3 (Figure 7D).

3.6. Correlation of the HCC Subclasses with Clinical Characteristics

Next, we explored the tumor-related clinicopathological variables associated with our classification results. Fisher’s exact test revealed significant correlations between clinicopathological features and HCC subclasses in our cohort. A lack of advanced Edmondson–Steiner grade (p = 0.0007) and lower CTNNB1 mutation frequency (p = 0.0586) were associated with the HCCW2 subclass, and Edmondson–Steiner grade III/IV (p = 0.0203) was associated with HCCW4 subclass. HCCW3 was negatively correlated with the TP53 mutation rate (p = 0.0049) (Table 1).
We also compared our classification results with those of the Bidkhor (iHCC1-3) and Hoshida (S1–S3) classification systems. HCCW3 was marginally significantly associated with the Hoshida S3 class (p = 0.0526), whereas HCCW4 was significantly associated with the Bidkhor iHCC3 class (p < 0.0001) and Hoshida S1 and S2 classes (p = 0.0039) (Table 1).

4. Discussion

Comprehensive integrated analysis of HCC is a powerful tool for understanding molecular events relevant to HCC [10,11,12,13,24,25]. In this study, we used a functional gene panel approach to explore correlations of molecular events with molecular changes and survival and devised a new molecular panel including 254 and 18 Wnt and Hippo signaling pathway-related genes, respectively, to classify Taiwanese HCC patients into four molecular subgroups (HCCW1-4). Then, the prognostic value, transcriptome features, immune infiltration, and clinical characteristics of the subgroups were explored.
The expression of PCDHB4 was highest for HCCW1. HCCW2 was associated with relatively low Edmondson–Steiner grade (I–II) and a relatively low CTNNB1 mutation frequency rate. HCCW3 was associated with a good prognosis, the highest expression of PCDHGB7, relatively high CD8+ naïve T-cell abundance, and relatively lower TP53 mutation rates. HCCW4 was associated with a poor prognosis, the highest PCDHB2 and PCDHB6 expression levels, relatively high abundance of four immune cell populations (Th1, NKT, and class-switched memory B-cells), relatively low enrichment of seven immune cell populations (cDC, iDC, and CD4+ memory T-cells), and relatively high Edmondson–Steiner grade (III–IV). Our method was also applied to TCGA HCC data; the results showed that the subgroup with the poorest survival (HCCW4) was well correlated with Hoshida classification groups S1 and S2, and Bidkhori metabolic classification group iHCC3 [24,25].
PCDHs are a group of cell adhesion molecules in the cadherin superfamily that can be divided into clustered (cPCDHs) and nonclustered molecules (ncPCDHs). PCDHs can interact with intracellular molecules including components of the WAVE complex, Wnt pathway, and apoptotic cascades. PCDHs are expressed prominently in the central nervous system, where they play important neurobiological roles. They are also involved in cancer development; loss or dysregulation of PCDHs is associated with multiple types of cancer [37]. Most PCDH genes are considered to be tumor suppressors, and epigenetic mechanisms govern their expression through their unique genomic organization that allows for long-range epigenetic silencing [37,38]. Several studies have shown that PCDH genes are also involved in liver cancer development, including PCDH10, PCDH17, PCDH19, PCDH20, PCDHGC4, and PCDHGC5 [39,40,41,42,43]. Alterations of these PCDHs can occur through several oncogenic pathways; for example, PCDH20 functions as a tumor-suppressor gene by antagonizing the Wnt/β-catenin signaling pathway in HCC [43]. In this study, PCDHB2, PCDHB4, and PCDHB6 were overexpressed in the HCCW4 subgroup, in association with worse survival; this finding contradicts a tumor suppressor role. Our PCDHB6 results were confirmed by the TCGA-LIHC data. Aberrant expression of these genes may have resulted from context-dependent differences among PCDHs. We also found that PCDHGB7 upregulation was correlated with better HCC survival, which suggests a tumor suppressor role for PCDHGB7 in HCC. Moreover, we examined the PCDHB2, PCDHB4, PCDHB6, and PCDHGB7 mRNA and protein expression level by TCGA and Human Protein Atlas [44] databases, respectively. Among them, the RNA expression of PCDHB4 and PCDHGB7 were correlated with the protein level in LIHC. However, PCDHB2 and PCDHB6 expression were detected only at the mRNA level. PCDHB2 and PCDHB6 protein levels were not observed in >50% of liver cancer specimens by immunohistochemistry.
Wnt signaling pathway components play important regulatory roles in immune suppression and immune cell exclusion within the tumor microenvironment [45,46], and include macrophages and DC, T, NK, and myeloid-derived suppressor cells [47]. In a mouse model of HCC, β-catenin pathway activation promotes immune escape and resistance to anti-PD-1 treatment through Wnt/β-catenin signaling; this resulted in defective recruitment of DCs and impaired T cell activity, in turn leading to an impaired anti-tumor immune response [48]. The Wnt and Hippo pathways play important role in HCC development [20,49,50,51]; our results confirm these findings and suggest that more aggressive and specific treatment options are needed for the poor survival group.
In HCC, TP53 loss of function is associated with high levels of centrosome amplification, aneuploidy cell proliferation, and chromosome instability [10,52], as well as poor prognosis [53]. In the current study, the HCCW3 subclass had lower TP53 mutation frequencies; patients in this group had a better prognosis than those in the other groups. Studies assessing the prognostic impact of TP53 mutations on the outcomes of gastroesophageal adenocarcinomas have demonstrated conflicting results [54,55,56]. From these results, we suggest that TP53 mutations may not completely correlate with poor survival, and other genetic alterations may also play a role for the survival of patient as our two patients with TP53 mutations in the HCCW3 group with a good prognosis.
In this study, we obtained a list of Wnt–Hippo pathways-related genes which may influence the infiltrations of immune cells between the good and poor prognosis groups. We also found higher proportion of Th1, NKT, and class-switched memory B-cells in the poor prognosis group. NKT cells contribute to fibrosis progression in nonalcoholic fatty liver disease [57]. A total of 110 upregulated Wnt–Hippo signaling pathway-related genes were identified in our poor prognosis group. Among these genes, the 10 most significant node genes were WNT8B, SMARCA4, ARID1A, SMARCB1, SMARCC2, SMARCD1, SMARCD2, SMARCD3, FZD2, and WNT9A. These genes may play important regulatory roles in immune cell migration, differentiation, and activation in HCC. Furthermore, we also conducted RNA-Seq in 12 normal samples. The expression of 88 genes were significantly higher in HCC tissues than that in adjacent nontumor tissues (82 genes in Wnt signaling pathway and 6 genes in Hippo signaling pathway), while the expression of 40 genes were significantly lower in HCC tissues than that in adjacent nontumor tissues (37 genes in Wnt signaling pathway and 3 genes in Hippo signaling pathway) (Supplementary Table S4). These alterations may be used as markers for the diagnosis of HCC and need more cases to confirm it.

5. Conclusions

In summary, comprehensive and integrated approaches, such as that developed in this study, will lead to a better understanding of the molecular mechanisms of HCC, and could in turn lead to the discovery of new therapeutic strategies. The results of this study highlight the uniqueness of Taiwanese HCC patients; this uniqueness arises from complex interactions among genetic, lifestyle, and environmental conditions that have arisen in the past 40 years.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers14194580/s1, Table S1: Details descriptions of the 272 genes correlated with molecular classification and patient’s survival among 100 Taiwanese HCCs and TCGA-LIHC. Significant genes are those with p-value < 0.05; Table S2: Differential expression of Wnt–Hippo pathway-related genes between the HCCW4 (poorest prognosis) and HCCW1-3 subgroups. Significant genes are those with p-value < 0.05; Table S3: Differential expression of Wnt–Hippo pathway-related genes between the HCCW3 (best prognosis) and HCCW1, 2, 4 subgroups. Significant genes are those with p-value < 0.05; Table S4: The expression difference of Wnt–Hippo signaling pathway-related genes between HCC and normal tissues; Figure S1: Expression of Wnt signaling pathway-related genes in CTNNB1 overexpression and down-expression. *: p < 0.05, **: p < 0.005, ***: p < 0.0005; Figure S2: Expression of Hippo signaling pathway-related genes in YAP1 overexpression and down-expression. *: p < 0.05, **: p < 0.005, ***: p < 0.0005.

Author Contributions

Conceptualization: Y.-S.C., L.-B.J. and J.-G.C.; methodology, Y.-S.C., Y.-P.C., C.-C.C., Y.-T.L. and J.-C.Y.; software, Y.-P.C.; formal analysis, Y.-S.C.; investigation, Y.-S.C.; resources, L.-B.J. and J.-G.C.; data curation, Y.-P.C., C.-C.C., Y.-T.L. and J.-C.Y.; writing—original draft, Y.-S.C.; writing—review and editing, L.-B.J. and J.-G.C.; visualization, Y.-P.C.; supervision, J.-G.C.; project administration, J.-G.C.; funding acquisition, J.-G.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Ministry of Science and Technology of Taiwan (grants no. MOST-109-2320-B-039-052 and no. MOST-110-2321-B-039-002) and China Medical University Hospital (grant no. DMR-111-131).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the China Medical University Hospital (CMUH 109-REC3-055).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The RNA-Seq data from this study was submitted to the NCBI Sequence Read Archive (SRA) under BioProject accession nos. PRJNA866195.

Acknowledgments

We would like to thank Yu-Hsuan Juan for graphical and tabular assistance.

Conflicts of Interest

The authors declare no potential conflict of interest.

References

  1. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef] [PubMed]
  2. Siegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer Statistics, 2021. CA Cancer J. Clin. 2021, 71, 7–33. [Google Scholar] [CrossRef] [PubMed]
  3. McGlynn, K.A.; Petrick, J.L.; El-Serag, H.B. Epidemiology of Hepatocellular Carcinoma. Hepatology 2021, 73 (Suppl. 1), 4–13. [Google Scholar] [CrossRef] [PubMed]
  4. Llovet, J.M.; Kelley, R.K.; Villanueva, A.; Singal, A.G.; Pikarsky, E.; Roayaie, S.; Lencioni, R.; Koike, K.; Zucman-Rossi, J.; Finn, R.S. Hepatocellular carcinoma. Nat. Rev. Dis. Primers 2021, 7, 6. [Google Scholar] [CrossRef] [PubMed]
  5. Yu, M.W.; Lin, C.L.; Liu, C.J.; Yang, S.H.; Tseng, Y.L.; Wu, C.F. Influence of Metabolic Risk Factors on Risk of Hepatocellular Carcinoma and Liver-Related Death in Men with Chronic Hepatitis B: A Large Cohort Study. Gastroenterology 2017, 153, 1006–1017.e1005. [Google Scholar] [CrossRef] [PubMed]
  6. Anstee, Q.M.; Reeves, H.L.; Kotsiliti, E.; Govaere, O.; Heikenwalder, M. From NASH to HCC: Current concepts and future challenges. Nat. Rev. Gastroenterol. Hepatol. 2019, 16, 411–428. [Google Scholar] [CrossRef] [PubMed]
  7. Muller, M.; Bird, T.G.; Nault, J.C. The landscape of gene mutations in cirrhosis and hepatocellular carcinoma. J. Hepatol. 2020, 72, 990–1002. [Google Scholar] [CrossRef] [PubMed]
  8. Friedman, S.L.; Neuschwander-Tetri, B.A.; Rinella, M.; Sanyal, A.J. Mechanisms of NAFLD development and therapeutic strategies. Nat. Med. 2018, 24, 908–922. [Google Scholar] [CrossRef]
  9. Letouze, E.; Shinde, J.; Renault, V.; Couchy, G.; Blanc, J.F.; Tubacher, E.; Bayard, Q.; Bacq, D.; Meyer, V.; Semhoun, J.; et al. Mutational signatures reveal the dynamic interplay of risk factors and cellular processes during liver tumorigenesis. Nat. Commun. 2017, 8, 1315. [Google Scholar] [CrossRef]
  10. Cancer Genome Atlas Research Network. Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma. Cell 2017, 169, 1327–1341.e1323. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Candia, J.; Bayarsaikhan, E.; Tandon, M.; Budhu, A.; Forgues, M.; Tovuu, L.O.; Tudev, U.; Lack, J.; Chao, A.; Chinburen, J.; et al. The genomic landscape of Mongolian hepatocellular carcinoma. Nat. Commun. 2020, 11, 4383. [Google Scholar] [CrossRef]
  12. Xue, R.; Chen, L.; Zhang, C.; Fujita, M.; Li, R.; Yan, S.M.; Ong, C.K.; Liao, X.; Gao, Q.; Sasagawa, S.; et al. Genomic and Transcriptomic Profiling of Combined Hepatocellular and Intrahepatic Cholangiocarcinoma Reveals Distinct Molecular Subtypes. Cancer Cell 2019, 35, 932–947.e938. [Google Scholar] [CrossRef]
  13. Chaisaingmongkol, J.; Budhu, A.; Dang, H.; Rabibhadana, S.; Pupacdi, B.; Kwon, S.M.; Forgues, M.; Pomyen, Y.; Bhudhisawasdi, V.; Lertprasertsuke, N.; et al. Common Molecular Subtypes Among Asian Hepatocellular Carcinoma and Cholangiocarcinoma. Cancer Cell 2017, 32, 57–70.e53. [Google Scholar] [CrossRef] [PubMed]
  14. Lin, C.W.; Lin, C.C.; Mo, L.R.; Chang, C.Y.; Perng, D.S.; Hsu, C.C.; Lo, G.H.; Chen, Y.S.; Yen, Y.C.; Hu, J.T.; et al. Heavy alcohol consumption increases the incidence of hepatocellular carcinoma in hepatitis B virus-related cirrhosis. J. Hepatol. 2013, 58, 730–735. [Google Scholar] [CrossRef] [PubMed]
  15. Chang, M.H.; You, S.L.; Chen, C.J.; Liu, C.J.; Lai, M.W.; Wu, T.C.; Wu, S.F.; Lee, C.M.; Yang, S.S.; Chu, H.C.; et al. Long-term Effects of Hepatitis B Immunization of Infants in Preventing Liver Cancer. Gastroenterology 2016, 151, 472–480.e471. [Google Scholar] [CrossRef] [PubMed]
  16. Liao, S.H.; Chen, C.L.; Hsu, C.Y.; Chien, K.L.; Kao, J.H.; Chen, P.J.; Chen, T.H.; Chen, C.H. Long-term effectiveness of population-wide multifaceted interventions for hepatocellular carcinoma in Taiwan. J. Hepatol. 2021, 75, 132–141. [Google Scholar] [CrossRef] [PubMed]
  17. Chen, C.J.; Yang, Y.H.; Lin, M.H.; Lee, C.P.; Tsan, Y.T.; Lai, M.N.; Yang, H.Y.; Ho, W.C.; Chen, P.C.; Health Data Analysis in Taiwan Research Group. Herbal medicine containing aristolochic acid and the risk of hepatocellular carcinoma in patients with hepatitis B virus infection. Int. J. Cancer 2018, 143, 1578–1587. [Google Scholar] [CrossRef] [PubMed]
  18. Seyda Seydel, G.; Kucukoglu, O.; Altinbasv, A.; Demir, O.O.; Yilmaz, S.; Akkiz, H.; Otan, E.; Sowa, J.P.; Canbay, A. Economic growth leads to increase of obesity and associated hepatocellular carcinoma in developing countries. Ann. Hepatol. 2016, 15, 662–672. [Google Scholar] [CrossRef] [PubMed]
  19. Huang, S.F.; Chang, I.C.; Hong, C.C.; Yen, T.C.; Chen, C.L.; Wu, C.C.; Tsai, C.C.; Ho, M.C.; Lee, W.C.; Yu, H.C.; et al. Metabolic risk factors are associated with non-hepatitis B non-hepatitis C hepatocellular carcinoma in Taiwan, an endemic area of chronic hepatitis B. Hepatol. Commun. 2018, 2, 747–759. [Google Scholar] [CrossRef] [PubMed]
  20. He, S.; Tang, S. WNT/beta-catenin signaling in the development of liver cancers. Biomed. Pharmacother. 2020, 132, 110851. [Google Scholar] [CrossRef]
  21. Liu, Y.; Wang, X.; Yang, Y. Hepatic Hippo signaling inhibits development of hepatocellular carcinoma. Clin. Mol. Hepatol. 2020, 26, 742–750. [Google Scholar] [CrossRef] [PubMed]
  22. Sohn, B.H.; Shim, J.J.; Kim, S.B.; Jang, K.Y.; Kim, S.M.; Kim, J.H.; Hwang, J.E.; Jang, H.J.; Lee, H.S.; Kim, S.C.; et al. Inactivation of Hippo Pathway Is Significantly Associated with Poor Prognosis in Hepatocellular Carcinoma. Clin. Cancer Res. 2016, 22, 1256–1264. [Google Scholar] [CrossRef] [PubMed]
  23. Moeini, A.; Torrecilla, S.; Tovar, V.; Montironi, C.; Andreu-Oller, C.; Peix, J.; Higuera, M.; Pfister, D.; Ramadori, P.; Pinyol, R.; et al. An Immune Gene Expression Signature Associated with Development of Human Hepatocellular Carcinoma Identifies Mice That Respond to Chemopreventive Agents. Gastroenterology 2019, 157, 1383–1397.e1311. [Google Scholar] [CrossRef] [PubMed]
  24. Bidkhori, G.; Benfeitas, R.; Klevstig, M.; Zhang, C.; Nielsen, J.; Uhlen, M.; Boren, J.; Mardinoglu, A. Metabolic network-based stratification of hepatocellular carcinoma reveals three distinct tumor subtypes. Proc. Natl. Acad. Sci. USA 2018, 115, E11874–E11883. [Google Scholar] [CrossRef]
  25. Hoshida, Y.; Nijman, S.M.; Kobayashi, M.; Chan, J.A.; Brunet, J.P.; Chiang, D.Y.; Villanueva, A.; Newell, P.; Ikeda, K.; Hashimoto, M.; et al. Integrative transcriptome analysis reveals common molecular subclasses of human hepatocellular carcinoma. Cancer Res. 2009, 69, 7385–7392. [Google Scholar] [CrossRef]
  26. Llovet, J.M.; Montal, R.; Sia, D.; Finn, R.S. Molecular therapies and precision medicine for hepatocellular carcinoma. Nat. Rev. Clin. Oncol. 2018, 15, 599–616. [Google Scholar] [CrossRef]
  27. Chang, Y.S.; Tu, S.J.; Yen, J.C.; Lee, Y.T.; Fang, H.Y.; Chang, J.G. The Fusion Gene Landscape in Taiwanese Patients with Non-Small Cell Lung Cancer. Cancers 2021, 13, 1343. [Google Scholar] [CrossRef]
  28. Chang, Y.S.; Tu, S.J.; Chiang, H.S.; Yen, J.C.; Lee, Y.T.; Fang, H.Y.; Chang, J.G. Genome-Wide Analysis of Prognostic Alternative Splicing Signature and Splicing Factors in Lung Adenocarcinoma. Genes 2020, 11, 1300. [Google Scholar] [CrossRef] [PubMed]
  29. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef]
  30. Kim, D.; Paggi, J.M.; Park, C.; Bennett, C.; Salzberg, S.L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 2019, 37, 907–915. [Google Scholar] [CrossRef]
  31. Kovaka, S.; Zimin, A.V.; Pertea, G.M.; Razaghi, R.; Salzberg, S.L.; Pertea, M. Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome Biol. 2019, 20, 278. [Google Scholar] [CrossRef] [PubMed]
  32. Rouillard, A.D.; Gundersen, G.W.; Fernandez, N.F.; Wang, Z.; Monteiro, C.D.; McDermott, M.G.; Ma’ayan, A. The harmonizome: A collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database 2016, 2016, baw100. [Google Scholar] [CrossRef] [PubMed]
  33. Gu, C.; Chen, J.; Dang, X.; Chen, C.; Huang, Z.; Shen, W.; Shi, X.; Dai, C.; Chen, C. Hippo Pathway Core Genes Based Prognostic Signature and Immune Infiltration Patterns in Lung Squamous Cell Carcinoma. Front. Oncol. 2021, 11, 680918. [Google Scholar] [CrossRef]
  34. Chin, C.H.; Chen, S.H.; Wu, H.H.; Ho, C.W.; Ko, M.T.; Lin, C.Y. cytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 2014, 8 (Suppl. 4), S11. [Google Scholar] [CrossRef] [PubMed]
  35. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef] [PubMed]
  36. Aran, D.; Hu, Z.; Butte, A.J. xCell: Digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017, 18, 220. [Google Scholar] [CrossRef] [PubMed]
  37. Pancho, A.; Aerts, T.; Mitsogiannis, M.D.; Seuntjens, E. Protocadherins at the Crossroad of Signaling Pathways. Front. Mol. Neurosci. 2020, 13, 117. [Google Scholar] [CrossRef]
  38. Walle, P.; Mannisto, V.; de Mello, V.D.; Vaittinen, M.; Perfilyev, A.; Hanhineva, K.; Ling, C.; Pihlajamaki, J. Liver DNA methylation of FADS2 associates with FADS2 genotype. Clin. Epigenet. 2019, 11, 10. [Google Scholar] [CrossRef]
  39. Ying, J.; Li, H.; Seng, T.J.; Langford, C.; Srivastava, G.; Tsao, S.W.; Putti, T.; Murray, P.; Chan, A.T.; Tao, Q. Functional epigenetics identifies a protocadherin PCDH10 as a candidate tumor suppressor for nasopharyngeal, esophageal and multiple other carcinomas with frequent methylation. Oncogene 2006, 25, 1070–1080. [Google Scholar] [CrossRef]
  40. Fang, S.; Huang, S.F.; Cao, J.; Wen, Y.A.; Zhang, L.P.; Ren, G.S. Silencing of PCDH10 in hepatocellular carcinoma via de novo DNA methylation independent of HBV infection or HBX expression. Clin. Exp. Med. 2013, 13, 127–134. [Google Scholar] [CrossRef]
  41. Zhang, T.; Guan, G.; Chen, T.; Jin, J.; Zhang, L.; Yao, M.; Qi, X.; Zou, J.; Chen, J.; Lu, F.; et al. Methylation of PCDH19 predicts poor prognosis of hepatocellular carcinoma. Asia Pac. J. Clin. Oncol. 2018, 14, e352–e358. [Google Scholar] [CrossRef] [PubMed]
  42. Dang, Z.; Shangguan, J.; Zhang, C.; Hu, P.; Ren, Y.; Lv, Z.; Xiang, H.; Wang, X. Loss of protocadherin-17 (PCDH-17) promotes metastasis and invasion through hyperactivation of EGFR/MEK/ERK signaling pathway in hepatocellular carcinoma. Tumor Biol. 2016, 37, 2527–2535. [Google Scholar] [CrossRef] [PubMed]
  43. Lv, J.; Zhu, P.; Yang, Z.; Li, M.; Zhang, X.; Cheng, J.; Chen, X.; Lu, F. PCDH20 functions as a tumour-suppressor gene through antagonizing the Wnt/beta-catenin signalling pathway in hepatocellular carcinoma. J. Viral Hepat. 2015, 22, 201–211. [Google Scholar] [CrossRef] [PubMed]
  44. Uhlen, M.; Zhang, C.; Lee, S.; Sjostedt, E.; Fagerberg, L.; Bidkhori, G.; Benfeitas, R.; Arif, M.; Liu, Z.; Edfors, F.; et al. A pathology atlas of the human cancer transcriptome. Science 2017, 357, eaan2507. [Google Scholar] [CrossRef]
  45. Goldsberry, W.N.; Londono, A.; Randall, T.D.; Norian, L.A.; Arend, R.C. A Review of the Role of Wnt in Cancer Immunomodulation. Cancers 2019, 11, 771. [Google Scholar] [CrossRef]
  46. Patel, S.; Alam, A.; Pant, R.; Chattopadhyay, S. Wnt Signaling and Its Significance within the Tumor Microenvironment: Novel Therapeutic Insights. Front. Immunol. 2019, 10, 2872. [Google Scholar] [CrossRef]
  47. Suryawanshi, A.; Hussein, M.S.; Prasad, P.D.; Manicassamy, S. Wnt Signaling Cascade in Dendritic Cells and Regulation of Anti-tumor Immunity. Front. Immunol. 2020, 11, 122. [Google Scholar] [CrossRef]
  48. Ruiz de Galarreta, M.; Bresnahan, E.; Molina-Sanchez, P.; Lindblad, K.E.; Maier, B.; Sia, D.; Puigvehi, M.; Miguela, V.; Casanova-Acebes, M.; Dhainaut, M.; et al. beta-Catenin Activation Promotes Immune Escape and Resistance to Anti-PD-1 Therapy in Hepatocellular Carcinoma. Cancer Discov. 2019, 9, 1124–1141. [Google Scholar] [CrossRef]
  49. Perugorria, M.J.; Olaizola, P.; Labiano, I.; Esparza-Baquer, A.; Marzioni, M.; Marin, J.J.G.; Bujanda, L.; Banales, J.M. Wnt-beta-catenin signalling in liver development, health and disease. Nat. Rev. Gastroenterol. Hepatol. 2019, 16, 121–136. [Google Scholar] [CrossRef]
  50. Kim, W.; Khan, S.K.; Gvozdenovic-Jeremic, J.; Kim, Y.; Dahlman, J.; Kim, H.; Park, O.; Ishitani, T.; Jho, E.H.; Gao, B.; et al. Hippo signaling interactions with Wnt/beta-catenin and Notch signaling repress liver tumorigenesis. J. Clin. Investig. 2017, 127, 137–152. [Google Scholar] [CrossRef] [Green Version]
  51. Manmadhan, S.; Ehmer, U. Hippo Signaling in the Liver—A Long and Ever-Expanding Story. Front. Cell Dev. Biol. 2019, 7, 33. [Google Scholar] [CrossRef] [PubMed]
  52. Rao, C.V.; Asch, A.S.; Yamada, H.Y. Frequently mutated genes/pathways and genomic instability as prevention targets in liver cancer. Carcinogenesis 2017, 38, 2–11. [Google Scholar] [CrossRef] [PubMed]
  53. Liu, J.; Ma, Q.; Zhang, M.; Wang, X.; Zhang, D.; Li, W.; Wang, F.; Wu, E. Alterations of TP53 are associated with a poor outcome for patients with hepatocellular carcinoma: Evidence from a systematic review and meta-analysis. Eur. J. Cancer 2012, 48, 2328–2338. [Google Scholar] [CrossRef] [PubMed]
  54. Lim, B.H.; Soong, R.; Grieu, F.; Robbins, P.D.; House, A.K.; Iacopetta, B.J. p53 accumulation and mutation are prognostic indicators of poor survival in human gastric carcinoma. Int. J. Cancer 1996, 69, 200–204. [Google Scholar] [CrossRef]
  55. Blanchet, A.; Bourgmayer, A.; Kurtz, J.E.; Mellitzer, G.; Gaiddon, C. Isoforms of the p53 Family and Gastric Cancer: A Menage a Trois for an Unfinished Affair. Cancers 2021, 13, 916. [Google Scholar] [CrossRef]
  56. Deng, W.; Hao, Q.; Vadgama, J.; Wu, Y. Wild-Type TP53 Predicts Poor Prognosis in Patients with Gastric Cancer. J. Cancer Sci. Clin Ther 2021, 5, 134–153. [Google Scholar] [CrossRef]
  57. Syn, W.K.; Oo, Y.H.; Pereira, T.A.; Karaca, G.F.; Jung, Y.; Omenetti, A.; Witek, R.P.; Choi, S.S.; Guy, C.D.; Fearing, C.M.; et al. Accumulation of natural killer T cells in progressive nonalcoholic fatty liver disease. Hepatology 2010, 51, 1998–2007. [Google Scholar] [CrossRef] [Green Version]
Cancers 14 04580 g001 550
Figure 1. Molecular classification of Taiwanese hepatocellular carcinoma (HCC) using a panel of 254 Wnt pathway genes. (A) Heatmap showing normalized expression levels of 254 signature genes (rows) across subjects (columns) divided into four molecular subclasses (A–D) using an unsupervised approach. Each signature gene was significantly up- or downregulated in one subclass relative to the other subclasses, as indicated by color changes, where blue and red indicate low and high expression, respectively. Signature genes and subjects were hierarchically clustered within each subclass. Significant differences in demographic characteristics, clinical annotations, molecular subclasses, and hepatitis B and C virus infection status across subjects are shown at the top of the figure. (B–H) Overall survival of subjects in each molecular subclass.
Figure 1. Molecular classification of Taiwanese hepatocellular carcinoma (HCC) using a panel of 254 Wnt pathway genes. (A) Heatmap showing normalized expression levels of 254 signature genes (rows) across subjects (columns) divided into four molecular subclasses (A–D) using an unsupervised approach. Each signature gene was significantly up- or downregulated in one subclass relative to the other subclasses, as indicated by color changes, where blue and red indicate low and high expression, respectively. Signature genes and subjects were hierarchically clustered within each subclass. Significant differences in demographic characteristics, clinical annotations, molecular subclasses, and hepatitis B and C virus infection status across subjects are shown at the top of the figure. (B–H) Overall survival of subjects in each molecular subclass.
Cancers 14 04580 g001
Cancers 14 04580 g002 550
Figure 2. Molecular classification of Taiwanese HCC patients using a panel of 18 Hippo pathway genes. (A) Heatmap showing normalized expression levels of the 18 signature genes (rows) across subjects (columns) divided into two molecular subclasses (A,B) using an unsupervised approach. Each signature gene was significantly up- or downregulated compared with the other subclasses, as indicated by color changes, where blue and red indicates low and high expression, respectively. Signature genes and subjects were hierarchically clustered within each class. Significant differences in demographic characteristics, clinical annotations, molecular subclasses, and hepatitis B and C virus infection status across subjects are shown at the top of the figure. (B) Overall survival of subjects in each molecular subclass.
Figure 2. Molecular classification of Taiwanese HCC patients using a panel of 18 Hippo pathway genes. (A) Heatmap showing normalized expression levels of the 18 signature genes (rows) across subjects (columns) divided into two molecular subclasses (A,B) using an unsupervised approach. Each signature gene was significantly up- or downregulated compared with the other subclasses, as indicated by color changes, where blue and red indicates low and high expression, respectively. Signature genes and subjects were hierarchically clustered within each class. Significant differences in demographic characteristics, clinical annotations, molecular subclasses, and hepatitis B and C virus infection status across subjects are shown at the top of the figure. (B) Overall survival of subjects in each molecular subclass.
Cancers 14 04580 g002
Cancers 14 04580 g003 550
Figure 3. Molecular classification of Taiwanese HCC patients using a panel of 272 Wnt–Hippo pathway genes. (A) Heatmap showing normalized expression levels of the 272 signature genes (rows) across subjects (columns) divided into four molecular subclasses (HCCW1–4), where blue and red indicate low and high expression, respectively. Significant differences in demographic characteristics, clinical annotations, molecular subclasses, and hepatitis B and C virus infection status across subjects are shown at the top of the figure. (B) Overall survival for subjects in each molecular subclass. (C) HCCW3 showed significantly better prognosis than HCCW4 based on two-sided log-rank tests. (D) The Bidkhori classification results were not significantly correlated with survival, whereas (E) the Hoshida classification results did show a significant correlation.
Figure 3. Molecular classification of Taiwanese HCC patients using a panel of 272 Wnt–Hippo pathway genes. (A) Heatmap showing normalized expression levels of the 272 signature genes (rows) across subjects (columns) divided into four molecular subclasses (HCCW1–4), where blue and red indicate low and high expression, respectively. Significant differences in demographic characteristics, clinical annotations, molecular subclasses, and hepatitis B and C virus infection status across subjects are shown at the top of the figure. (B) Overall survival for subjects in each molecular subclass. (C) HCCW3 showed significantly better prognosis than HCCW4 based on two-sided log-rank tests. (D) The Bidkhori classification results were not significantly correlated with survival, whereas (E) the Hoshida classification results did show a significant correlation.
Cancers 14 04580 g003
Cancers 14 04580 g004 550
Figure 4. Molecular classification of HCC data from The Cancer Genome Atlas (TCGA) using a panel of 272 Wnt–Hippo pathway genes. (A) Heatmap showing normalized expression levels of 272 signature genes (rows) across subjects (columns) divided into four molecular subclasses (HCCW1–4), where blue and red indicate low and high expression, respectively. Significant differences in demographic characteristics, clinical annotations, molecular subclasses, and hepatitis B and C virus infection status across subjects are shown at the top of the figure. (B) Overall survival for subjects in each molecular subclass. (C) HCCW2 showed significantly better prognosis than HCCW4 based on two-sided log-rank tests. (D) HCCW3 showed significantly better prognosis than HCCW4 based on two-sided log-rank tests.
Figure 4. Molecular classification of HCC data from The Cancer Genome Atlas (TCGA) using a panel of 272 Wnt–Hippo pathway genes. (A) Heatmap showing normalized expression levels of 272 signature genes (rows) across subjects (columns) divided into four molecular subclasses (HCCW1–4), where blue and red indicate low and high expression, respectively. Significant differences in demographic characteristics, clinical annotations, molecular subclasses, and hepatitis B and C virus infection status across subjects are shown at the top of the figure. (B) Overall survival for subjects in each molecular subclass. (C) HCCW2 showed significantly better prognosis than HCCW4 based on two-sided log-rank tests. (D) HCCW3 showed significantly better prognosis than HCCW4 based on two-sided log-rank tests.
Cancers 14 04580 g004
Cancers 14 04580 g005 550
Figure 5. Correlations between molecular classifications and patient survival. Boxplot showing the expression level of (A) PCDHB2, (B) PCDHB4, (C) PCDHB6, and (D) PCDHGB7 in four HCC subclasses. The statistical difference was compared through the Kruskal–Wallis test and Mann–Whitney U test, and the p-values were shown above each boxplot. Survival analysis of patients with HCC according to above-mentioned genes expression. Patients with high PCDHB2, PCDHB4, and PCDHB6 expression had significantly shorter overall survival compared with patients with low expression.
Figure 5. Correlations between molecular classifications and patient survival. Boxplot showing the expression level of (A) PCDHB2, (B) PCDHB4, (C) PCDHB6, and (D) PCDHGB7 in four HCC subclasses. The statistical difference was compared through the Kruskal–Wallis test and Mann–Whitney U test, and the p-values were shown above each boxplot. Survival analysis of patients with HCC according to above-mentioned genes expression. Patients with high PCDHB2, PCDHB4, and PCDHB6 expression had significantly shorter overall survival compared with patients with low expression.
Cancers 14 04580 g005
Cancers 14 04580 g006 550
Figure 6. Ten upregulated hub genes in the HCCW4 subclass. The shade of the color indicates the degree of importance of the genes. The sequence of colors is red–orange–yellow from most importance to less importance.
Figure 6. Ten upregulated hub genes in the HCCW4 subclass. The shade of the color indicates the degree of importance of the genes. The sequence of colors is red–orange–yellow from most importance to less importance.
Cancers 14 04580 g006
Cancers 14 04580 g007 550
Figure 7. Immune characteristics of HCC subclasses. (A,B) Boxplots of immune cells differing between the subgroup with the worst prognosis (HCCW4) and the other subgroups. (C) Boxplot of hepatocytes differing between the subgroup with the worst prognosis (HCCW4) and the other subgroups. (D) Boxplots of immune cells differing between the subgroup with the best prognosis (HCCW3) and the other subgroups. The statistical difference was compared through the Mann–Whitney U test, and the p-values were shown above each boxplot.
Figure 7. Immune characteristics of HCC subclasses. (A,B) Boxplots of immune cells differing between the subgroup with the worst prognosis (HCCW4) and the other subgroups. (C) Boxplot of hepatocytes differing between the subgroup with the worst prognosis (HCCW4) and the other subgroups. (D) Boxplots of immune cells differing between the subgroup with the best prognosis (HCCW3) and the other subgroups. The statistical difference was compared through the Mann–Whitney U test, and the p-values were shown above each boxplot.
Cancers 14 04580 g007
Table
Table 1. Association between molecular subgroups HCCW1–4 and clinical variables.
Table 1. Association between molecular subgroups HCCW1–4 and clinical variables.
Variable HCCW1HCCW2HCCW3HCCW4p-Value
Edmondson–Steiner gradeI, II13282180.0007
III, IV5213100.0203
Pathologic stageI, II16273115>0.05
III, IV2333
Macrovascular invasionNo16222412>0.05
Yes2893
CirrhosisNo3022>0.05
Yes15303216
Fatty liverNo3023>0.05
Yes15303215
HBVNo7181310>0.05
Yes1112218
HCVNo12172110>0.05
Yes613138
AFPNormal5873>0.05
Abnormal13222715
CTNNB1Wild-type162926150.0586
Mutation2183
TERTWild-type1111209>0.05
Mutation719149
TP53Wild-type122132130.0049
Mutation6925
HoshidaS318112550.0526
S1, S20199130.0039
BidkhoriHCC1, 230303312<0.0001
iHCC30016
p-value by Fisher’s exact test.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Chang, Y.-S.; Chou, Y.-P.; Chung, C.-C.; Lee, Y.-T.; Yen, J.-C.; Jeng, L.-B.; Chang, J.-G. Molecular Classification of Hepatocellular Carcinoma Using Wnt–Hippo Signaling Pathway-Related Genes. Cancers 2022, 14, 4580. https://doi.org/10.3390/cancers14194580

AMA Style

Chang Y-S, Chou Y-P, Chung C-C, Lee Y-T, Yen J-C, Jeng L-B, Chang J-G. Molecular Classification of Hepatocellular Carcinoma Using Wnt–Hippo Signaling Pathway-Related Genes. Cancers. 2022; 14(19):4580. https://doi.org/10.3390/cancers14194580

Chicago/Turabian Style

Chang, Ya-Sian, Yu-Pao Chou, Chin-Chun Chung, Ya-Ting Lee, Ju-Chen Yen, Long-Bin Jeng, and Jan-Gowth Chang. 2022. "Molecular Classification of Hepatocellular Carcinoma Using Wnt–Hippo Signaling Pathway-Related Genes" Cancers 14, no. 19: 4580. https://doi.org/10.3390/cancers14194580

APA Style

Chang, Y. -S., Chou, Y. -P., Chung, C. -C., Lee, Y. -T., Yen, J. -C., Jeng, L. -B., & Chang, J. -G. (2022). Molecular Classification of Hepatocellular Carcinoma Using Wnt–Hippo Signaling Pathway-Related Genes. Cancers, 14(19), 4580. https://doi.org/10.3390/cancers14194580

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop