Machine learning and multi-omics data reveal driver gene-based molecular subtypes in hepatocellular carcinoma for precision treatment _ PLOS Computational Biology.pdf

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Abstract
The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC
into molecular subtypes with similar features is crucial for personalized anti-tumor therapies. Although driver genes play pivotal
roles in cancer progression, their potential in HCC subtyping has been largely overlooked. This study aims to utilize driver genes to
construct HCC subtype models and unravel their molecular mechanisms. Utilizing a novel computational framework, we expanded
the initially identified 96 driver genes to 1192 based on mutational aspects and an additional 233 considering driver dysregulation.
These genes were subsequently employed as stratification markers for further analyses. A novel multi-omics subtype classification
algorithm was developed, leveraging mutation and expression data of the identified stratification genes. This algorithm successfully
categorized HCC into two distinct subtypes, CLASS A and CLASS B, demonstrating significant differences in survival outcomes.
Integrating multi-omics and single-cell data unveiled substantial distinctions between these subtypes regarding transcriptomics,
mutations, copy number variations, and epigenomics. Moreover, our prognostic model exhibited excellent predictive performance in
training and external validation cohorts. Finally, a 10-gene classification model for these subtypes identified TTK as a promising
therapeutic target with robust classification capabilities. This comprehensive study provides a novel perspective on HCC
stratification, offering crucial insights for a deeper understanding of its pathogenesis and the development of promising treatment
strategies.
Author summary
Dividing highly heterogeneous HCC into molecular subtypes with similar characteristics is crucial for personalized anti-tumor
therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely
overlooked. In this work, we developed a multi-omics network-based stratification algorithm that utilizes patient mutation data and
requires smaller computational resources for subtype assignment. Through this algorithm, we categorized HCC into two subtypes,
CLASS A and CLASS B. Using multi-omics and single-cell data, we identified differences between these subtypes in gene
expression, methylation, immune infiltration, and other aspects. Beyond subtype characterization, our study established a robust
clinical prediction model (https://mike-wang-bjut.shinyapps.io/DynNomapp_HCC_Sutypes/) incorporating subtype information and
typical clinical features, enabling precise survival predictions. Finally, we developed a high-performing machine learning classifier
for our subtype. Analyzing this classification model and reviewing previous experimental papers, we identified TTK as a potential
diagnostic marker and therapeutic target specific to our subtypes. In conclusion, our research offers a novel perspective on HCC
stratification, which is crucial for a deeper understanding of its pathogenesis and developing promising treatment strategies.
Citation: Wang M, Yan X, Dong Y, Li X, Gao B (2024) Machine learning and multi-omics data reveal driver gene-based
molecular subtypes in hepatocellular carcinoma for precision treatment. PLoS Comput Biol 20(5): e1012113.
https://doi.org/10.1371/journal.pcbi.1012113
Editor: Stacey D. Finley, University of Southern California, UNITED STATES
Received: January 2, 2024; Accepted: April 24, 2024; Published: May 10, 2024
Copyright: © 2024 Wang et al. This is an open access article distributed under the terms of the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author
and source are credited.
Data Availability: The data used in this study are all from public databases TCGA
(https://www.cancer.gov/ccg/research/genome-sequencing/tcga) and ICGC (https://dcc.icgc.org/). The Fudan cohort and
single-cell dataset are from the GEO database with accession numbers GSE14520 and GSE149614, respectively. The
LIMORE dataset was obtained from its original paper (PMID: 31378681). To facilitate clinical translation, we developed an
interactive HCC prognosis model: https://mike-wang-bjut.shinyapps.io/DynNomapp_HCC_Sutypes/. The subtype classifiers
SVM_10 and SVM_TTK, along with the relevant code, are stored at https://github.com/Mike-
W29/SVM_model_for_HCC_subtype.
Funding: This study was supported by the National Key Research and Development Program of China (2022YFC2704804 to
BG), and the National Natural Science Foundation of China (61931013 to BG). The funders had no role in study design, data
collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors declare that they have no competing interests.
Introduction
Hepatocellular carcinoma (HCC) is recognized as the most prevalent primary liver malignancy, ranking as the third leading cause of
cancer-related deaths globally and experiencing a notable increase in incidence [1,2]. The molecular and pathological
heterogeneity of HCC presents a formidable obstacle to developing personalized therapeutic approaches [3]. Therefore, using key
features to classify different HCC patients into relatively homogeneous subtypes is clinically essential.
Machine learning and multi-omics data reveal driver gene-
based molecular subtypes in hepatocellular carcinoma for
precision treatment
Version 2
Published: May 10, 2024
https://doi.org/10.1371/journal.pcbi.1012113
Meng Wang, Xinyue Yan, Yanan Dong, Xiaoqin Li
, Bin Gao
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Recent advances in high-throughput sequencing technologies have facilitated the comprehensive profiling of patient dysfunctions
across multiple biological systems. Through various omics techniques, potential oncogenic factors can be discerned [4]. Utilizing
big data in molecular subtyping of HCC has become increasingly feasible [5]. For instance, Zhang et al. employed mass cytometry
data to categorize HCC into three subtypes, each exhibiting diverse immune activities [6]. Poirion et al. developed the DeepProg
deep learning method, which integrates RNA, DNA methylation, and miRNA data to classify HCC into two subtypes with distinct
survival differences and biological profiles [7].
In tumors, driver genes are often causally related to tumor progression. Compiling a complete list of driver genes is crucial for
oncology diagnosis and drug development [8]. We have identified recurrently altered driver genes in HCC, and some of these
genes have been suggested to be associated with specific molecular subtypes [9]. However, no systematic subtyping studies of
HCC using driver genes exist to our knowledge.
Gene families, representing a cluster of genes with shared ancestry and similar biochemical functions, present an opportunity to
identify rare carcinogenic mutations [10–12]. In this study, we utilized gene families to expand the list of driver genes, culminating in
creating two distinct molecular subtypes for HCC. By integrating multi-omics data and single-cell information, we explored the
unique characteristics defining these subtypes, spanning transcriptomics, genomics, epigenomics, immune infiltration, and tumor
stem cell activity. Additionally, we developed an interactive prognostic model website for the two subtypes, empowering users to
effortlessly generate personalized survival predictions based on critical patient information, including age, stage, virus infection
status, and subtype classification. Finally, we established a 10-gene classification model for these subtypes and singled out TTK as
a promising therapeutic target with strong classification capabilities. In summary, our study provides a fresh perspective on the
construction of HCC subtypes and offers promising avenues for future therapeutic strategies.
Result
Obtaining stratification genes from driver genes and their family members
To ensure a comprehensive and high-confidence selection of driver genes, we gathered HCC driver gene lists from three distinct
studies. Bailey et al. [13] employed diverse driver gene discovery algorithms and conducted meticulous manual curation to
construct their driver gene list. Martínez-Jiménez et al. [14] extended the scope by analyzing a larger sample size and adopting a
more comprehensive approach to exploring driver genes. Meanwhile, Fujimoto et al. [15] concentrated on HCC, providing valuable
insights into the specific driver mechanisms of HCC. By amalgamating the findings of these three studies, we curated a list of 96
HCC driver genes for our subsequent analyses. Furthermore, we enriched this selection by including their corresponding family
members, which were sourced from InterPro (https://www.ebi.ac.uk/interpro/), UniProtKB (https://www.uniprot.org/), as well as
several other references [16–21]. (S1 Table).
To identify protein domains with significant mutation burden, we first annotated the domains for each gene using the PfamScan
(https://www.ebi.ac.uk/Tools/pfa/pfamscan/) and excluded those with an e-value greater than 1e-5. Additionally, considering the
potential role of the Degron region in transcription factors for cancer growth [22], we included Degron as a protein domain based on
Degpred predictions [23]. Through permutation testing, we identified 75 protein domains with significant mutation burden (Fig 1A).
Focusing on protein domains with higher entropy values, which may indicate novel oncogenic alterations, we identified 1192
stratification genes mutated in domains with entropy values greater than 0.5 (S2 and S3 Tables).
Fig 1. illustrates the process of obtaining stratification genes.
(A) The workflow for protein domains with significant mutation burden (B) illustrates the process of defining DDGs.
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The progression of HCC involves genetic mutations, epigenetic variations, and dysregulated gene expression [24–26]. Many well-
known HCC driver gene alterations are associated with an epigenetic variation or copy number variation (CNV). To complete the
stratification genes, we conducted differential analysis between normal and tumor tissues, identifying 244 differentially expressed
genes within gene families. Then, we integrated other omics data to define 223 driver-dysregulated genes (DDGs) (Fig 1B and S3
Table).
Driver gene-related HCC Subtypes
We stratified HCC into distinct subtypes using mutation data from stratification genes and DDGs expression data. Firstly, we
smoothed the patient mutation matrix using the Network-based stratification (NBS) algorithm [27]. Next, we integrated the
smoothed mutation data with DDGs expression data using the Similarity network fusion (SNF) algorithm to construct a similarity
matrix among samples [28]. The amalgamation of SNF with a consensus cluster facilitated patient subtype assignment, yielding
more robust and desirable clustering outcomes (Fig 2A, Table 1). The combination of smoothed mutation data and DDG expression
data proved effective in capturing information and enhancing the clustering analysis.
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Fig 2. Identification of driver gene-related subtypes.
(A) Flowchart depicting the process of the subtype classification algorithm; (B) Silhouette coefficients for different values of k;
(C) Silhouette plot specifically for k = 2; (D) Heatmap of the consensus matrix defining the two subtypes; (E) Five-year
survival curves for the two subtypes, with CLASS A represented in red and CLASS B represented in blue.
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Table 1. Comparison of different clustering methods.
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Choosing the suitable gene interaction network is crucial for the NBS algorithm’s smoothing effect. We compared two commonly
used networks, String (Requested score = medium confidence, FDR stringency = medium) and HumanNetv3 (top 10% confidence)
[29]. We found that HumanNetv3 performed better in stratification based on silhouette coefficients and p-values (Table 1).
Additionally, we examined the stratification outcomes by utilizing only driver genes versus incorporating gene family members for
HCC. The results demonstrated that the latter approach exhibited superior performance in both clustering stability and survival
differences, as reflected in silhouette coefficients and p-values (Table 1).
To determine the optimal cluster count, we computed silhouette coefficients for various cluster numbers (k), revealing that k = 2 was
the most suitable parameter for delineating HCC subtypes (Fig 2B and 2C). We designated these subtypes as CLASS A and
CLASS B (Fig 2D), where patients with CLASS B had worse prognostic outcomes (median OS time 17 months vs 20.55 months)
(Fig 2E). Subsequent validation of our method on the TCGA_LIHC cohort further confirmed significant survival differences between
the two subtypes (median OS time 15.97 months vs 22.42 months) (S1 Fig). This demonstrates that integrating smoothed mutation
data with gene expression data effectively classifies HCC subtypes.
Our identified HCC subtypes significantly correlated with independently studied subtypes [30–33] (chi-square test, see S4 and S5
Tables, S2 Fig). We constructed Cox regression models based on these subtypes. We assessed predictive performance using the
C-index and Integrated Brier Score (IBS) to evaluate the association of different subtypes with clinical prognosis. The results
demonstrate that our constructed subtypes excel in C-index, reaching 0.611 (95% CI = 0.587–0.635), significantly higher than most
other models (S3 Fig). Simultaneously, the IBS is 0.183, lower than other models (Hoshida: 0.195, Bidkhori: 0.205, Benfeitas:
0.194, TCGA: 0.180). This indicates that our subtypes exhibit higher consistency and accuracy in predicting patient survival than
others.
Biological properties of different subtypes
To explore distinct biological properties, we performed gene set variation analysis (GSVA) on KEGG, Reactome, Hallmark, and
oncogenic signature gene sets for the two subtypes. The results revealed high consistency in enrichment patterns across these
gene sets for both subtypes (Fig 3A–3C and S6 Table). CLASS A showed higher GSVA scores in metabolic pathways such as
oxidative phosphorylation, organic acid metabolism, fatty acid metabolism, and glycolysis. In contrast, CLASS B displayed a
heightened proliferative profile, enriched in pathways associated with mitosis, cell cycle, DNA replication, and cell cycle checkpoint.
Additionally, CLASS B had upregulated activity in pathways related to histone methylation, DNA methylation, and inflammatory
signaling (S6 Table).
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Fig 3. Different biological properties of the two subtypes.
(A-C) Differential analysis of KEGG, Hallmark and oncogenic signature pathways between these two subtypes. (D) The
immune cell abundance in these two subtypes using TIMER, with statistical significance assessed by the Mann-Whitney U
test. (E) Box plots depicting the expression levels of six immune checkpoint genes in these two subtypes, with red boxes
indicating significantly differentially expressed genes (p.adjust < 0.05, |log2FC| > 1). (F) Box plot showing the mRNA
stemness scores (mRNAsi) of the two subtypes.
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Next, we analyzed immune cell composition using TIMER [34] and xCell algorithms [35], revealing a higher abundance of immune
cells, including T cells, B cells, and macrophages, in CLASS B than CLASS A. CLASS B had a higher stromal score. In contrast,
CLASS A had a higher microenvironmental score (Figs 3D, S4A and S4B).
We also examined the expression of six common immune checkpoints [36,37], finding higher expression levels in CLASS B than in
CLASS A (Fig 3E). Specifically, PD-1 (PDCD1), CTLA-4 (CTLA4), TIM-3 (HAVCR2), and TIGIT were differentially expressed in
CLASS B, suggesting potential responsiveness to immune checkpoint inhibitors.
Finally, we used Malta et al.’s machine learning [38] algorithm to examine the stemness features of two subtypes. The results
showed that CLASS B exhibited stronger stemness features, indicating a higher potential for invasion and metastasis (Fig 3F). This
alteration may be associated with the high expression of KRT19, a marker for biliary/hepatic progenitor cells, in CLASS B (S5 Fig)
[39].
Multi-omics properties of subtypes
Despite no significant difference in mutation count and TMB values between the subtypes (Figs 4A and S6), CLASS B showed
higher chromosomal instability (Fig 4B). Specifically, CLASS B displayed deletions in 4p, 4q, 13q, 16p, 16q, and 17p, while CLASS
A predominantly manifested amplifications in 5q (Fig 4C).
Fig 4. Distinct multi-omics features of the two subtypes.
(A) Box plot showing the number of nonsynonymous mutations in each subtype. (B) Box plot displaying the CIN ratio in each
subtype. (C) Heatmap illustrating CNVs of the 22 autosomes in both subtypes, with red and blue indicating copy number
amplifications and deletions, respectively. (D) Oncoplot presents the top 20 significantly mutated genes in the subtypes based
on the p-values. (E) Identifying of subtype-specific methylation probes in the two groups using the R package ChAMP, with a
threshold of p.adjust < 0.05 and |Δβ| > 0.2. (F) Expression profiles of DNA methyltransferase family members DNMT1,
DNMT3A, and DNMT3B in these two subtypes.
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Using the chi-square test, we identified significantly mutated genes in these subtypes (Fig 4D). TP53 and CTNNB1, well-known
HCC driver genes, had different distributions between subtypes. Consistent with previous research, TP53 was mainly found in
CLASS B, associated with a poorer prognosis, while CTNNB1 was mainly associated with CLASS A, linked to a better prognosis
[40,41]. Additionally, RB1 mutations were predominantly enriched in CLASS B, possibly contributing to its high proliferative profile
(S6 Table).
By analyzing TCGA data, we found significant hypermethylation patterns in CLASS B (Fig 4E). This might link to the high
expression of DNA methyltransferase family members (DNMT1, DNMT3A, and DNMT3B) in this subtype (Fig 4F).
Machine learning-based diagnostic models for HCC subtypes and identify potential therapeutic targets
In China, liver lesion biopsy is essential for HCC treatment and prognosis determination. To translate research findings into clinical
applications, we used machine learning to identify diagnostic markers for HCC subtypes and built corresponding diagnostic models
(Fig 5A). Specifically, we first selected differentially expressed genes in the subtypes (p.adjust < 0.001,|log2FC| > 1). Next, we
filtered out genes with overlapping expression patterns between the subtypes and utilized elastic net regularization to identify
informative genes for classification. From the elastic net results, we identified 10 significant genes for classification. Using a support
vector machine (SVM), we created the SVM_10 classification model with 70% of the data for training and 30% for validation.
SVM_10 achieved 87% accuracy on the validation set, with high AUC values confirming its reliability and effectiveness (Fig 5B–5D
and Table 2).
Fig 5. Machine learning classifier for HCC subtypes.
(A) Workflow of the subtype SVM classifier. The process of selecting subtype-specific genes followed the differential analysis
procedure described in the Methods, with genes retained based on criteria of p.adjust < 0.001 and |log2FC| > 1. The mean
interval was defined as [μ−σ, μ+σ], where μ represents the average expression of the gene in the patient cohort, and σ
represents the standard deviation of gene expression in the patient cohort. (B) Predictive results of the SVM_10 model on the
validation set. (C) ROC curve of the SVM_10 model on the training set. (D) ROC curve of the SVM_10 model on the
validation set. (E) Expression distribution of the 10 classification genes across these two subtypes. (F) Heatmap of the
expression levels of the 10 classification genes in different subtypes and normal samples.
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Table 2. Classification results of subtype classifiers in the validation set.
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Several studies provided evidence linking the dysregulated expression of these 10 classifier genes to adverse outcomes in HCC,
including poor prognosis, increased proliferation, metastasis, and recurrence [43–50]. Notably, these genes showed significant
expression differences between different subtypes and normal samples (Fig 5E and 5F), indicating their crucial involvement in HCC
progression and potential as therapeutic targets. Among these genes, TTK has related drug information in the DrugBank database
(https://go.drugbank.com/), indicating its potential as a therapeutic marker for HCC subtypes.
To assess the independent classification performance of TTK, we developed an additional SVM classifier, SVM_TTK, using TTK
expression values. SVM_TTK demonstrated good classification performance, with 84% accuracy on the validation set (S7A Fig and
Table 2). It also showed high AUC values on the validation and training sets (0.91 and 0.88, respectively, S7B and S7C Fig).
SVM_TTK effectively classified patients from the Fudan cohort (n = 225, GEO accession number: GSE14520) [42] into two
subtypes with significant survival differences (S8A Fig). These subtypes showed distinct TTK gene expression patterns (Mann-
Whitney U test, S8B Fig), highlighting TTK as a potential diagnostic marker for HCC.
Clinical and prognostic characteristics of HCC subtypes with interactive survival prediction Tool
The two subtypes showed significant differences in clinical characteristics such as gender, age, and tumor stage (chi-square test,
S4 Table). Patients with CLASS A had a better prognosis and mainly exhibited lower alpha-fetoprotein (AFP) expression levels in
early-stage samples (Mann-Whitney U test). On the other hand, CLASS B had a higher proportion of mid to late-stage samples,
higher AFP expression levels, and a higher frequency of viral infection.
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Univariate and multivariate Cox regression analyses confirmed that the subtypes were independent prognostic factors for HCC
patients (Fig 6A and 6B). Considering the lack of relatively comprehensive clinical information in the other two cohorts, we
developed a multivariable prognostic model using the TCGA cohort. This model integrates age, viral infection, tumor staging, and
the probability of CLASS B output by a machine learning diagnostic tool (Fig 6C). The C-index for this model is 0.698 (95% CI =
0.671 ~ 0.723), and the IBS is 0.166. Illustrated through a calibration plot, we demonstrate the model’s outstanding accuracy and
predictive performance concerning 1, 3, and 5-year survival rates (Fig 6D and 6E).
Fig 6. Construction and Evaluation of Clinical Prognostic Model.
(A) Univariate Cox analysis of clinical features and subtypes in TCGA cohort. (B) Multivariate Cox analysis of clinical features
and subtypes in TCGA cohort. (C) Nomogram model predicting HCC patients’ prognosis. (D) Calibration plot showing 1-, 3-,
and 5-year survival probabilities for the nomogram model. (E) ROC curve evaluating predictive performance of the nomogram
model in TCGA cohort.
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Furthermore, decision curves indicated the superiority of the nomogram model over independent prognostic models using other
predictors (S9 Fig). Additionally, we validated the model using the GSE14520 cohort [42] and achieved comparable AUC values
(S8C Fig). To facilitate clinical application, we developed a user-friendly website allowing input of relevant information to
automatically generate survival plots and probabilities (https://mike-wang-bjut.shinyapps.io/DynNomapp_HCC_Sutypes/).
Analyzing subtype differences at the single-cell level
To understand the differences between these two subtypes at a higher resolution, we downloaded scRNA-seq data from 10 primary
HCC patients from GSE149614 [51] and performed cellular-level QC. Pseudo-bulk data was created by summing gene expression
across sample cells. After standardization, the data was classified into two subtypes using the SVM_10 model (Fig 7A).
Fig 7. Single-cell analysis of HCC subtypes.
(A) SVM_10 assigned subtypes to 10 primary HCC samples from GSE149614. (B) UMAP plot showing 21 cell clusters. (C)
Cell type annotation in different subtypes using marker genes.
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Using the UMAP method, we identified 21 cell clusters, and each cluster was annotated through the gene enrichment analysis
method (Fig 7B). The single-cell-level results strongly agreed with our bulk-level findings, indicating that CLASS B had enriched
immune cells and cancer stem cells compared to CLASS A (Fig 7C). Moreover, T cells from CLASS B exhibited higher expression
levels of four immune checkpoint genes, consistent with their differential expression at the bulk level (S10 Fig). These findings
further prove that the CLASS B subtype may have a higher stemness phenotype and could be more responsive to targeted immune
checkpoint therapies in HCC.
Drug sensitivity differences across subtypes
Finally, to investigate the differences in drug sensitivity across subtypes, we applied the SVM_10 model to the LIMORE dataset,
containing the mutation, RNA, and drug response data for 81 HCC cell lines [52]. The SVM_10 model successfully classified the
cell lines into two subtypes, similar to the clinical patient subtypes (Fig 8A and 8B). CLASS B showed stronger sensitivity to cell
proliferation inhibitors, such as Temsirolimus, Camptothecin, and BX-912, possibly related to its strong proliferative characteristics.
(Fig 8C).
Fig 8. The drug sensitivity analysis of HCC subtypes.
(A) The SVM_10 model assigned subtypes to 81 HCC cell lines from the LIMORE dataset. (B) KEGG enrichment analysis of
the two subtypes. (C) Box plots illustrating drugs with differential activity area in the two subtypes.
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Discussion
This study expanded HCC driver genes using gene families and identified protein domains with significant mutation burden, through
permutation testing [12]. From the results, some of these domains were associated with classical cancer mutation events, such as
RTK and PI3K/AKT signaling pathways, along with the SET domain, known for its methyltransferase activity crucial for maintaining
the tumor-suppressive function of genes [53]. Moreover, the zf-H2C2_2 domain in zinc finger transcription factors also showed high
mutation frequency and entropy, potentially leading to widespread transcriptional dysregulation in tumors and conferring a selective
growth advantage to the tumor [10].
To overcome the discreteness inherent to mutation data for cancer stratification, we employed the NBS algorithm [27] to transform it
into continuous features. These features were subsequently integrated with gene expression data using the SNF algorithm [28] for
clustering analysis. The results demonstrated that SNF effectively captured the smoothed mutation features from NBS and utilized
them for clustering. Through consensus clustering, we classified HCC into two subtypes, CLASS A and CLASS B, with significant
differences in survival, with CLASS B displaying a lower survival probability. Moreover, compared with previous studies, our subtype
model exhibits a higher value in clinical prediction.
Interestingly, only driver genes showed poor stability in clustering and survival differences compared to the stratification results
obtained through gene family expansion. This may be attributed to insufficiently utilizing the entire stratification algorithm’s
extensive protein-protein interaction network information when focusing solely on driver genes. In the stratification algorithm, gene
family expansion allowed for a more comprehensive consideration of family members associated with driver genes. This approach
introduced more relevant information, aiding in the revelation of complex molecular relationships and regulatory mechanisms. By
incorporating family members into consideration, our understanding of the overall biological network was enhanced, resulting in
more biologically reasonable stratification outcomes.
Subsequently, we conducted further analysis of the two subtypes. GSVA results indicated that CLASS A exhibited prominent
metabolic features enriched in pathways such as organic acid metabolism, redox reactions, fatty acid metabolism, and glycolysis.
Conversely, CLASS B was enriched in proliferative pathways, including mitosis and the cell cycle. Interestingly, we did not explicitly
emphasize metabolism-related genes in the classifier genes. Therefore, the heightened metabolic features in CLASS A suggest that
aberrant changes in specific metabolic genes may contribute to the progression of HCC.
Moreover, the increased dependency on these metabolic pathways may lead to metabolic vulnerability, implying the potential
therapeutic efficacy of inhibiting these pathways in treating CLASS A tumors [54]. Meanwhile, CLASS B exhibited higher immune
cell abundance and overexpression of immune checkpoint molecules such as CTLA4 and HCVAR2, indicating its potential
suitability for immune checkpoint inhibitor therapy. Additionally, CLASS B had higher stemness scores, implying a more active
population of cancer stem cells and increased tumor cell dedifferentiation [38]. These findings were confirmed in subsequent single-
cell analysis.
Next, we compared genomic differences between the subtypes and observed significant chromosomal instability in CLASS B,
characterized by deletions in chromosomes 4p, 4q, 13q, 16p, 16q, and 17p. These deletions are frequently found in clinically
advanced, poorly differentiated, large, and metastatic HCC cases [55,56]. Additionally, our subtypes demonstrated a high degree of
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consistency with other subtypes. For example, CLASS A was enriched in several high metabolic subtypes with a better prognosis,
such as Hosdia S3, iHCC1, and hALDH2. Additionally, CLASS B was enriched in low metabolic flux, high TP53 mutation, and highly
proliferative subtypes, such as Hosdia S1, Hosdia S2, and iHCC3. In addition, referring to the findings of Benfeitas et al. [30], these
two subtypes may employ different mechanisms to resist reactive oxygen species (ROS). Notably, during the subtype stratification
of HCC, we incorporated mutation data. We observed significant differences in the mutation frequencies of classical oncogenes,
such as RB1, ALB, APOB, and NFE2L2, among others, between the two distinct subtypes, except for TP53 and CTNNB1. This
difference was not significant among other subtypes (p > 0.05).
RB1, a crucial tumor suppressor gene, regulates the cell cycle by inhibiting E2F transcription factors and cyclin-dependent kinases
during G1 to S phase transition [57,58]. RB1 mutations were predominantly found in CLASS B samples (Fisher’s exact test, p =
1.39×10–8), explaining the heightened proliferative features in CLASS B. The Wnt-β-catenin signaling pathway is frequently
activated in HCC [59]. Aggressive HCC subtypes, unlike well-prognosed ones, enhance the Wnt pathway by regulating intracellular
free β-catenin through TGF-β overexpression [31]. In CLASS B, we observed the RGS domain primarily occurring in AXIN1
(Fisher’s exact test, p = 0.012632, S11 Fig and S7 Table). This domain binds to APC protein, contributing to β-catenin degradation
[60]. Missense mutations in the RGS domain of AXIN1 promote AXIN1 aggregation, leading to impaired β-catenin degradation [61].
Thus, in this aggressive subtype, gene mutations may indirectly or directly affect β-catenin degradation, resulting in Wnt pathway
activation.
Finally, we developed a 10-gene SVM subtypes classifier between the subtypes. An intriguing finding was made regarding TTK,
one of the classifier genes with relevant drug records in Drugbank. TTK itself demonstrated excellent performance in subtype
classification. TTK, also known as Mps1, recruits other SAC proteins to unattached kinetochores during prophase to activate SAC-
related arrest [62]. Inhibiting TTK activity leads to premature chromosome segregation, severe chromosomal missegregation,
aneuploidy, and cell death [63]. TTK is overexpressed in various human tumors, including HCC. Studies indicate its role in
promoting HCC cell malignancy [64]. TTK inhibitors like BOS172722 [65] and CFI-402257 [66] have shown promise in cancer
treatment, making TTK a potential independent diagnostic biomarker and therapeutic target for specific HCC subtypes.
Although we have identified two HCC subtypes with distinct survival and biological characteristics using driver genes and their
family members, there are some limitations. Firstly, the lack of patient cohorts with comprehensive multi-omics and clinical data
restricts the validation of the subtype models across diverse populations. Secondly, this study needs to include relevant in vivo/in
vitro experiments to demonstrate the therapeutic effect of relevant inhibitors on specific subtypes.
Conclusion
In conclusion, we successfully constructed two HCC subtypes with significant differences in survival. We have delved into the
biological disparities underlying these subtypes by integrating diverse omics data. Our tailored prognostic and classification models
demonstrated robustness and accurately predicted overall survival in HCC patients. Notably, TTK emerged as a crucial diagnostic
biomarker and potential therapeutic target for specific HCC subtypes. Our study offers a fresh outlook on HCC subtypes, enhancing
our understanding of the disease’s pathogenesis and potential therapeutic strategies.
Methods
Data sources and preprocessing
This study included a cohort of 893 patients with primary tumors from three HCC datasets, namely TCGA_LIHC, ICGC_JP, and
ICGC_FR. Among these, 690 patients (S8 Table) were retained for subsequent analysis as they had both RNA-seq and mutation
data available.
Somatic mutations, miRNA expression, HM450 methylation, copy number variants, and clinical information for the TCGA_LIHC
cohort were obtained from the GDC Data Portal(https://portal.gdc.cancer.gov/), mRNA-Seq data were obtained from the
TCGAxGETx combined dataset from UCSC Xena (http://xena.ucsc.edu/). Somatic mutations, mRNA-Seq, and clinical data for
ICGC_JP and ICGC_FR cohorts were obtained from the ICGC database (https://dcc.icgc.org/).
We standardized them into TPM values to ensure consistency and comparability of RNA-seq data across the three cohorts. We
mitigated batch effects using the "ComBat" function within the R package "sva" [67]. After amalgamating these cohorts into a single
merged cohort, we filtered out genes exhibiting TPM values ≤ 0 in over 30% of the samples. This preliminary data processing step
effectively reduced noise, ensuring data quality by retaining approximately 17,660 genes for subsequent analyses.
Methylation probes were preprocessed and normalized using the R package "ChAMP" [68]. We removed null rate probes in more
than 30% of methylation and miRNA data samples, using KNN (K = 15) interpolation with the R package "impute".
For somatic mutation data, we retained only non-silent mutations in the coding region.
Protein domain with significant mutation burden
Before the domain mutation analysis, we further processed the somatic mutation data. Samples with tumor mutational burden
(TMB) values >30 mutations/Mb were excluded to eliminate the influence of hypermutated samples. We calculated TMB as the
count of non-silent mutations within coding region exons divided by 38 Mb. We also removed MAF entries with non-point mutations,
amino acid change positions larger than the protein length, and the reference amino acids not aligning with the protein reference
sequence.
A permutation test was used to identify protein domains with significant mutation burden, as described by Miller et al. [12] In this
test, we assumed that all amino acids within the protein have an equal chance of mutation. The permutation test assessed whether
the observed values significantly deviated from the empirical distribution obtained from random mutations in the gene. The P-value
after i permutations was defined as:
To quantify the information about the distribution of the mutation burden within a specific domain among its gene members, we
computed the entropy value (S). The entropy is defined as:
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In information entropy theory, the entropy value reaches its maximum when the distribution is uniform. Thus, the entropy value is
close to 1 if mutations in the domain are uniformly distributed across the family genes. Conversely, if the mutations are not
uniformly distributed, the entropy value will be closer to 0.
Differential analysis
We used the Mann-Whitney U test for miRNA and mRNA data to find genes with expression differences between different groups.
We corrected the p-values using the Benjamin-Hochberg method. We define differentially expressed miRNA (DMi) and differential
gene (DEG) for those with | Log2FC |>1 and p.adjust<0.05. Additionally, we excluded DMi and DEG with mean expression levels
less than 1 in both the test and control groups.
Differential methylation probes (DMPs) were identified using the R package "ChAMP" for DNA methylation data, considering probes
with |Δβ| > 0.2 and p.adjust < 0.05 as DMPs.
For copy number data, we employed the GISTIC2.0 function available in GenePattern(https://www.genepattern.org/) to identify
chromosomal regions and genes exhibiting significant copy number variations.
Define driver-dysregulated gene
We defined driver-dysregulated genes (DDGs) as genes that exhibit transcriptional dysregulation due to alterations in DNA
methylation, miRNA expression, or copy number variation. To comprehensively define DDGs, we further divide DMPs into three
groups: distal enhancers, promoters (TSS1500, TSS200, 5’UTR, and 1stExon), and gene body. Here, TSS1500 refers to the region
200–1500 bases upstream of the transcriptional start site (TSS), while TSS200 represents the region 0–200 bases upstream of the
TSS. The R packages "ELMERv.2" [69] and "ChAMP" were used to identify distal enhancer probes, promoter and gene body
methylation probes, and their target genes. For DMi, we used the R package "multiMiR" [70] to obtain target genes based on
experimental or computational predictions. Genes with a copy number gain or loss ratio greater than 20% were categorized as gain
or loss groups, respectively (CNV levels greater than 1 for gain and less than -1 for loss). The association between DMPs, DMi,
CNV, and DEG was analyzed using Spearman correlation (threshold: |ρ| > 0.2, p-value after Benjamin Hochberg correction < 0.05).
Venn diagrams were used for result filtering to illustrate the relationships between different omics layers and the transcriptome.
Subtype recognition model
In this paper, we aimed to construct HCC subtypes using somatic mutation data and mRNA expression data. First, we converted
the mutation MAF file into a binary matrix, where 0 represents no mutation, and 1 indicates a mutation presence in that sample. To
smooth the mutation data, we used pyNBS (https://github.com/idekerlab/pyNBS) [71], a Python version of the Network-based
stratification (NBS) algorithm [27]. This algorithm employs network propagation to smooth the mutation signals, enhancing their
classification capabilities like other continuous features. The following formula can represent the process of network propagation:
F represents the patient mutation matrix, A represents the normalized adjacency matrix of the gene interaction network, and α is
an adjustment parameter that controls the distance allowed for mutation signals to spread through the network during propagation.
The propagation function iterates until convergence (determined by the matrix norm of F
- F < 1×10
), with α set to 0.7 following
recommendations from the references for smoothing the mutation data.
The gene expression data underwent log (x+1) transformation and Z-score normalization. Next, we integrated the smoothed
mutation data with the gene expression data to form a similarity matrix W using the R package "SNFtool". For integration, we used
20 nearest neighbors, set the variance of the local model to 0.5, and performed 20 iterations of the diffusion process.
We conducted consensus clustering using the similarity matrix W with the R package "ConsensusClusterPlus." We set clustering
parameters as follows: maximum clusters = 6, iterations = 5000, item sample proportion = 0.8, distance metric = ’spearman’, and
clustering algorithm = ’hc’. We evaluated clustering quality using the silhouette coefficient (Sil), defined as follows
Here, a(i) represents the average distance between vector i and all other points within the same cluster. In contrast, b(i) represents
the minimum average distance between vector i and all points in a cluster that does not include it.
Gene enrichment analysis
The R package "clusterProfiler" [72] performs hypergeometric distribution tests on the annotations of specific gene sets in different
databases.
To convert gene expression data into scores for particular biological processes, we utilized the R packages "GSVA" [73] and
"msigdbr." Subsequently, a Student’s t-test was employed to identify significant differences in biological processes among different
subtypes. A differential biological process was defined as |ΔGSVA score| > 0.2 and Benjamini-Hochberg adjusted p < 0.05.
CIN ratio
Based on previous research, we assessed chromosomal instability (CIN) across different subtypes using the CIN ratio [74,75]. To
calculate the CIN ratio for each tumor sample, we extracted the "broad_values_by_arm.txt" file from the GISTIC2.0 results. In this
file, CNV scores exceeding 0.1 or falling below -0.1 were regarded as alterations. The CIN ratio was defined as:
0
t+1
t
−6
2
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Nomogram model of HCC subtypes
We used the R package "survival" to construct univariate and multivariate Cox prognostic models. The "forestplot" package was
employed to generate forest plots, while the "rms" package facilitated the creation of a nomogram for the multivariate model. To
assess the performance of the nomogram model, we employed calibration plots, decision curve analysis, and receiver operating
characteristic (ROC) curves, leveraging the R packages "rms," "dcurves," and "timeROC" respectively. Additionally, the R package
"DynNom" wasutilized to develop a dynamic nomogram model and an interactive webpage.
Single-cell data processing
We downloaded the HCC single-cell RNA sequencing (scRNA-seq) data from the GEO database (GEO accession: GSE149614
[51]). For further analysis, we selected 10 primary tumor samples. Quality control (QC) was performed using the R package
"Seurat" [76], involving cell-level QC and gene-level QC. Specifically, cells with UMIs greater than 500, expressing genes between
500 and 8000, and mitochondrial content less than 10% were retained. We kept genes with expression data in at least 10 cells for
gene-level QC. Subsequently, 22,298 genes and 31,490 cells were used for subsequent analyses.
Single-cell data dimensionality reduction clustering
We processed the scRNA-seq data using the R package "Seurat." Firstly, the "NormalizeData" function was applied for background
correction and normalization. We employed the "FindVariableFeatures" function (selection method = ’vst’, ’ x-axis cut off = (0.0125,
3), y-axis cut off > 0.5) to identify the top 2000 highly variable genes. Scaling of the data was carried out with the "ScaleData"
function, excluding mitochondrial contamination heterogeneity. Next, we reduced data dimensionality with the "RunPCA" function,
selecting the top 30 principal components based on the "ElbowPlot" analysis. Cell clustering was conducted with a resolution of 0.2
using the "FindClusters" function, and cell clusters were visualized with the "RunUMAP" and "DimPlot" functions. Significant marker
genes in different clusters were identified using the "FindAllMarkers" function. To annotate the cell clusters, we adopted a statistical-
based approach by performing enrichment analysis on the marker genes using the "clusterProfiler" R package. We downloaded the
Human cell markers dataset from CellMarker (http://biocc.hrbmu.edu.cn/CellMarker).
Subtype machine learning model
All machine learning classifiers were constructed using the "scikit-learn" package (version 1.0.2) in Python (version 3.9.12). Cross-
validation and grid search were used to obtain the best hyperparameters for the model. To assess the performance of the
classification results, we employed Confusion matrices, ROC curves, accuracy (ACC), sensitivity (SEN), specificity (SPE), and
Matthews correlation coefficient (MCC). These evaluation metrics are defined as follows:
Here, TP denotes the count of true positives, TN denotes the count of true negatives, FP denotes the count of false positives, and
FN denotes the count of false negatives. The machine learning model can be found at https://github.com/Mike-
W29/SVM_model_for_HCC_subtype.
Statistic analysis
Statistical analyses for this paper were conducted using R (version: 4.2.1). Kaplan-Meier curves were evaluated with the log-rank
test to determine significance. The chi-square and Fisher’s exact tests explored associations between clinical characteristics in
different groups. Unless otherwise specified, statistical significance was defined as p-values < 0.05 (two-tailed). In the paper,
significance levels were denoted as * (p < 0.05), ** (p < 0.01), *** (p < 0.001), **** (p < 0.0001), and "na" indicated no statistical
difference.
Supporting information
S1 Fig. Identification of driver gene-related subtypes in the TCGA cohort.
(A) Silhouette coefficients for different values of k. (B) Silhouette plot for k = 2. (C) Consensus matrix heatmap defining two
subtypes. (D) Five-year survival curves for the two subtypes, with CLASS A represented in red and CLASS B in blue.
https://doi.org/10.1371/journal.pcbi.1012113.s001
(TIF)
S2 Fig. The Sankey diagrams depicting the associations between the subtypes identified in our study and those from previous research studies.
(A) Hoshida 3-class subtypes. (B) Bidkhori 3-class subtypes. (C) Benfeitas 2-class subtypes. (D) TCGA 3-class subtypes.
https://doi.org/10.1371/journal.pcbi.1012113.s002
(TIF)
S3 Fig. C-index analysis comparing the subtypes identified in this study with previous research.
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(TIF)
S4 Fig. XCELL analysis results for the two subtypes.
(A) Analysis of different cell abundances between the two subtypes using XCELL. The statistical significance of the differences was
assessed using the Mann-Whitney U test. (B) Analysis of different immune scores, stromal scores, and microenvironment scores
between the two subtypes using XCELL. The statistical significance of the differences was assessed using the Mann-Whitney U
test.
https://doi.org/10.1371/journal.pcbi.1012113.s004
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(TIF)
S5 Fig. Box plots depicting the expression of KRT19 in the two subtypes.
Statistical significance of the differences was assessed using the Mann-Whitney U test to determine if these differences were
statistically significant.
https://doi.org/10.1371/journal.pcbi.1012113.s005
(TIF)
S6 Fig. Box plot showing the TMB in each subtype.
https://doi.org/10.1371/journal.pcbi.1012113.s006
(TIF)
S7 Fig. The classification results of the HCC subtypes using the SVM_TTK model.
(A) Predicted results of the SVM_TTK model on the validation set. (B) ROC curve of the SVM_TTK model in the training set. (C)
ROC curve of the SVM_TTK model in the validation set.
https://doi.org/10.1371/journal.pcbi.1012113.s007
(TIF)
S8 Fig. The validation of the classification and prognostic models.
(A) Survival analysis of the GSE14520 cohort based on the classification results using the SVM_TKK model. (B) Expression levels
of TTK in the two subtypes of the GSE14520 cohort, with statistical significance determined by the Mann-Whitney U test (C) ROC
curves depicting the predictive results of the prognostic model for 1-, 3-, and 5-year survival probabilities in the GSE14520 cohort.
https://doi.org/10.1371/journal.pcbi.1012113.s008
(TIF)
S9 Fig. The decision curve analysis for the prognostic model of the subtypes.
(A) Decision curve analysis for 1-year survival. (B) Decision curve analysis for 3-year survival. (C) Decision curve analysis for 5-
year survival.
https://doi.org/10.1371/journal.pcbi.1012113.s009
(TIF)
S10 Fig. The single-cell expression profiles of four immune checkpoint genes that exhibit differential expression at the bulk level.
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(TIF)
S11 Fig. Oncoplot illustrating the specific differences in protein domain mutation frequencies between the two subtypes (chi-square test).
https://doi.org/10.1371/journal.pcbi.1012113.s011
(TIF)
S1 Table. Driver gene and their family number.
https://doi.org/10.1371/journal.pcbi.1012113.s012
(XLS)
S2 Table. Protein domains with significant mutations.
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(XLS)
S3 Table. List of stratification genes.
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(XLS)
S4 Table. Differential clinical features between the two subtypes.
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(XLS)
S5 Table. HCC subtypes from other studies used in this paper.
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(XLS)
S6 Table. Differential enrichment analysis of two subtypes in REACTOME gene sets using GSVA.
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(XLS)
S7 Table. Protein domains with specific differences in mutation frequencies between the two subtypes.
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(XLS)
S8 Table. Description of the patient cohort.
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(XLS)
Acknowledgments
We thank Dr Jianming Zeng (University of Macau), and all the members of his bioinformatics team, biotrainee, for generously
sharing their experience and codes.
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