INTRODUCTION
Precision medicine is a medical service that provides the individual patients with the prediction, diagnosis and prevention of disease, and the most optimized tailored treatment on the basis of the unique characteristics of each person in terms of genetics, environments and life style. In recent date, advances in genomics with available big data in the field of health have ushered in a new era of precision medicine. In particular, therapeutic paradigm for cancer has shifted from nonspecific cytotoxic treatment strategy toward precision medicine which takes into account individual genetic features. The human genome map has been constructed through the Human Genome Project, which was started in 1990 and completed in 2003, and the genetic information of the individual is the most important factor to realize such precision medical care, and can be used for various diseases. DNA sequencing was the basis for the human genome project, but since the next generation sequencing (NGS) has been introduced due to the development of molecular biology techniques, full-length genome sequencing analysis, Whole Genome Sequencing, and Whole Exome Sequencing (WES), which analyzes the entire exon region, are used for early diagnosis of specific diseases. With the development of genomics, gene analysis using Big Data will accelerate the realization of personalized treatment for cancer. In the last decades cDNA microarray-based comparative geneomic hybridization and expression profiling methods based on proteomics have not only been used to investigate the molecular mechanism of carcinogenesis,1,2 but also been used in clinical settings for evaluating tumor staging and predicting prognosis of cancer.2-4 However, these expression profiling methods have limitations in detecting such as alternative splicing or gene fusions. Recently, with advancement in NGS technology, RNA-sequencing (RNA-seq) enables unpredictable sensitivity and transcriptomic profiling to discover new RNA species and to further develop a comprehensive transcriptome study.5,6 In general, it is well known that the development of cancer is caused by genetic alterations such as gene mutations or gene translocation and epigenetic regulations including DNA methylation, histone modification or non-coding RNA. In particular, NGS-based transcriptome analysis (RNA-seq) is a powerful tool for both defining the transcriptomes of cells and delineating alternative splicing variants associated with cell function.5 In general, conventional molecular profiling of a solid tumor is based on an average molecular signatures and phenotype from a bulk sample composed of in a variety of cells population. However, there is significant difference in transcriptional profiling between bulk RNA-seq and scRNA-seq. Because each cell in the tumor is constantly differentiated and proliferated, and heterogeneous in existing cells, there are many limitations in understanding the pathogenesis of cancer, and many obstacles in the development of diagnostic biomarkers and the development of target therapeutics for cancer.7 In order to overcome these problems, the emerging single cell RNA-seq (scRNA-Seq) is able to dynamically analyze the genetic and cytologic heterogeneity of each cell in specific tumor tissues, which it is possible to comprehensively study the molecular mechanism of carcinogenesis and the process of cancer evolution, and resistance to chemotherapy.8-10 The appropriate targets for studying the heterogeneity of single cells include heterogeneous cells in the
tumor tumor tissue,11 cancer stem cells (CSC),12 circulating tumor DNA13 and cell-free DNA.14 The heterogeneity of single cells is manifested in cell morphology, cell phenotype and genomics and proteomics. This heterogeneity can be analyzed in a variety of ways: imaging for cell morphology, immunofluorescence for cell phenotype, single-cell sequencing including WES and single-cell Western blotting or single-cell proteomics.
1. Workflow of single-cell RNA sequencing
Human primary tumors can be divided into two main categories:
hematologic and solid tumor. Comprehensive characterization
of different cell types from heterogeneous bulk
tissues is essential for understanding of pathogenesis and progression
of any cancer. However, profile data from bulk sample
representing the average expression of all cells has limitations
on characterizing individual transcriptional profiles. For
this reason, scRNA-seq is generally performed on each cell
isolated from complex tissues composed of multiple distinct
cell types in cancer. In order to achieve single-cell sequencing
data, the first step is mechanical dissociation into cell suspension
from bulk samples including solid tumorous tissues and
bone marrow. Next, single-cell isolation is generated by several
methods such as Serial dilution, Mechanical micromanipulation,
Laser capture microdissection, Fluorescence activated
cell sorting, Microfluidics.15-17 After single-cell isolation,
complementary DNA (cDNA) is synthesized from RNA via reverse
transcription (RT). Subsequently, cDNA amplification
is generated via polymerase chain reaction (PCR) of synthesized
cDNA or RT-PCR of amplified RNA by in vitro transcription
of synthesized cDNA. These double stranded cDNA
fragments are further processed to mRNA sequencing library
by random primers or attaching adapters. Lastly, single-cell
DNA sequencing or single-cell RNA-seq is performed. Recently,
single-cell RNA-seq is more established in a variety of
methods than single-cell DNA sequencing. After single-cell sequencing,
bioinformatic and statistical analyses are conducted
to evaluate intra-tumor heterogeneity. In particular, singlecell
RNA-seq data show significant discrepancy in distributional
properties compared with average RNA-seq data de-rived from conventional bulk samples because of the cell cycle
effects.18 A schematic diagram of single-cell sequencing analysis
is shown in Figure 1.

Figure 1. Generation of single cell sequencing data from primary human cancer cells can be described through a set of fundamental process: (1) sample acquisition
from patient; (2) creation of single cell suspension; (3) temporary storage; (4) isolation of sincle cells and library preparation; (5) sequencing; and (6)
bioinformatics and statistical analysis. cDNA, complementary DNA; IVT, in vitro transcription; RT-PCR, reverse transcription polymerase chain reaction; PCR,
polymerase chain reaction.
2. Single-cell transcriptomic analysis in HCC
RNA-seq is a very effective tool for comprehensive characterization
of whole transcriptome at both gene and exon levels
and for identifying novel splicing variants. Recently Huang et
al.,19 firstly reported RNA-seq-based transcriptome analyses
in tumor and non-tumorous tissues of 10 HBV-related hepatocellular
carcinoma (HCC). They identified 1,378 significantly
differently expressed genes (DEGs) and 24,338 differentially
expressed exons (DEEs). Comprehensive functional
analyses demonstrated that DEGs were most significantly enriched
in cell growth-related, metabolism-related and immune-related
pathways suggesting a very complicated mechanism
for hepatocarcinogenesis. Furthermore, RNA-seq data
analyses at exon level revealed a much complex landscape of
transcript-specific differential expressions in HCC. In particular,
a novel, highly up-regulated exon-exon junction was detected
in ATAD2 gene.19 However, in recent date, NGS has moved bulk tissue sample analysis to scRNA-Seq because by
single-cell sequencing data is more specific and reliable for
identifying heterogeneity within cell populations, defining
rare cell populations and exploring dynamic interactions between
cell groups. In particular, scRNA-seq is recognized as a
very useful tool for investigating development and progression
of in a variety of cancers including HCC. Advances of single-cell
genome, transcriptome and DNA methylome sequencing
technologies have greatly contributed to the study of
intercellular and intracellular heterogeneities.20 After that,
combined genome and transcriptome analyses of a single-cell
based on either microarray or NGS have been developed to
execute in-depth research of tumor heterogeneity.21 This single-cell
dual-omics sequencing technique can reveal the relationship
between genome and transcriptome but has the limitation
of not being able to unveil epigenetic regulation of gene
expression.22 Consequently, in order to overcome this drawback,
single-cell triple omics sequencing (scTrio-se) techniques
including the genome, epigenome and transcriptome have
been developed.23 This technique will help explore the dynamic
mechanisms which simultaneously occur in genetic and
epigenetic regulation on a specific gene expression associated with heterogeneity in a single cell. Recently, Hou et al.24 firstly
revealed genetic, epigenetic, and transcriptomic heterogeneity
in 25 single cancer cells derived from a human HCC tissue
sample using scTrio-seq. In this study, they showed the correlations
among genomic (copy-number variations, CNVs),
transcriptomic, and methylomic data analyzed in the same
individual cells of HCC. Also, they revealed that changes in the
gene dosage of certain regions due to CNVs proportionally
affect the RNA expression levels of the corresponding regions,
whereas they do not significantly affect the DNA methylation
levels in these regions. Moreover, they demonstrated subpopulations
of cancer cells according to the genome (CNVs) information,
and infer malignancy and metastasis potentials of
the subpopulations based on triple-omic information.24 This
integrative omics technique can provide simultaneously comprehensive
information about genomic, epigenomic and transcriptomic
heterogeneities within a single-cell of HCC.
3. Single-cell analysis in cancer stem cell in HCC
Recent studies have described that a subpopulation of tumor
cells harboring the ability to propagate, called cancer
stem cells (CSCs), is responsible for tumor initiation, progression
and metastasis. In addition, studies have demonstrated
that CSCs in various human tumors play a key role in tumor
recurrence, chemoresistance and radioresistance25-27 in the clinical
aspect. There are two types of heterogeneity of cancer: intertumor
and intratumor heterogeneity. In HCC, intertumor
heterogeneity is known to be determined by various molecular
subtypes28,29 but the factors that cause intratumor heterogeneity
are not well understood yet. In particular, there have
been few reports on the heterogeneity of liver CSCs at the single-cell
level in HCC. However, recent study by Zheng et al.30 demonstrated that CSC heterogeneity may contribute to a
molecular and biological diversity of HCC cell groups and
consequently, patient prognosis. In this study, the authors revealed
that liver CSCs at the single-cell level exhibited phenotypic,
functional and transcriptional heterogeneities. Also,
different CSC subpopulations contain distinct molecular signatures,
which are associated with prognosis of patients. Accordingly,
heterogeneity at the single cell level of liver CSCs
may be critical for the tumor progression and prognosis in
HCC and might be an important for the development of target
agent for HCC.
4. Single-cell analysis of circulating tumor cells in HCC
Circulating tumor cells (CTCs) are cells that have shed into
the blood stream from a primary tumor and give rise to metastasis
to other organs in the body. The early detection of metastasis
of an advanced cancer is essential for determining
therapeutic strategy and predicting prognosis. CTCs can be
useful for monitoring metastatic spread of cancer without invasive
diagnostic procedure. With the advances of technology
for isolation of CTCs, rare cancer cells within blood streams
can be detected without conventional detection methods like
a microscopic imaging. Conventional CTC staining criteria
are as follows: the presence of cell surface epithelial cell adhesion
molecule (EpCAM) and cytoplasmic epithelial cytokeratins
but the absence of the hematopoietic CD45 marker.31 However, strict imaging criteria is required in order to select
pure CTCs from surrounding leukocytes from blood samples.
The microfluidic CTC isolation technologies could allow
researcher to manipulate tumor cells without contamination
of blood cells32 and preserve cell viability with highquality
RNA contents suitable scRNA-seq.12,33 Recent study of
meta-analysis in the relationships between CTC positivity and
clinical-pathological parameters showed that CTC positivity
in the blood was significantly correlated with the tumour,
node and metastasis (TNM) stage, tumor size, vascular invasion,
portal vein tumor thrombus or serum AFP level, but
not with number of tumor.34 Also, CTC positivity was significantly
increased risk of disease recurrence and death.34 However,
the characteristically low EpCAM expression in this
HCC has limited the clinical utility of standard CTC measurements.
As a strategy to solve this drawback, Kalinich et al.35, developed a high-throughput microfluidic CTC-iChip which
efficiently depletes hematopoietic cells from blood specimens
and enriches for CTCs with well-preserved RNA to detect
CTC-derived signatures. In this study, the HCC specific
RNA-based digital PCR (dPCR) CTC-scoring assay was es-tablished. To develop the dPCR assay, a total of 10 genes
were selected: albumin, alpha fetoprotein (AFP), glypican 3,
transferrin, alpha 2-HSglycoprotein, apolipoprotein H, fatty
acid binding protein 1, fibrinogen beta chain, fibrinogen
gamma chain and retinol binding protein 4. The authors established
cross-validated logistic model using a digital signature
of 10 liver-specific transcripts. The positivity of HCC-derived
CTCs was observed in nine of 16 (56%) untreated HCC
patients but in 1 of 31 (3%) patients with chronic liver disease
at risk for developing HCC. Positive CTC scores declined in 9
of 32 (28%) patients receiving therapy, but RNA-based digital
CTC scoring was not correlated with the standard HCC
serum AFP level. This study suggest that digital RNA quantitation
is useful tool as a sensitive and specific CTC readout
which enable high-throughput clinical screening and monitoring
applications for HCC patients and chronic liver diseases
at high risk of HCC.35
5. Single-cell analysis in T-cell in HCC
The tumor is a tissue expressing high antigenicity due to
various mutations in terms of immunology and thus are subject
to immune surveillance by the immune system. In this regards,
development and progression of cancer is closely linked
to failure of immune surveillance including elimination of
cancer cell in initial stage or immune defense to prevent immune
escape.36 Recently, immunotherapy is the new hot-issue
in cancer treatment. The main types of immunotherapy treating
cancer include monoclonal antibodies, immune checkpoint
inhibitors, cancer vaccines such as DNA or peptide vaccine.
T cells play an important role in anticancer immune
response. Studies have described that activated CD8+ effector
T cells mediate tumor elimination37 but regulatory T cells
(Tregs) mediate significant tumor immune dysfunction.38 However, anticancer immunity is declined owing to T cell exhaustion
by several factors including decrease of effector T cell
function caused by T cell dysfunction and abnormally increase
of check point inhibitors such as programmed cell death protein
1 (PD1), cytotoxic T lymphocyte antigen 4 (CTLA4),
lymphocyte activation gene 3 protein, and killer cell lectinlike
receptor G1.39,40 The response rates to targeted immunotherapy are not uniform among cancer patients41 and therefore
identification of new biomarkers to predict response is required.
The factors determining response to checkpoint inhibitors
such as anti-PD1 or anti-CTLA4 include mutational
loads, tumor-infiltrating lymphocytes (TILs) levels and the
expression of drug targets.41 Moreover, tumor infiltrating
Tregs in transcriptome analyses of several cancers are reported
to suppress tumor-specific effector T cell.42 The T cell receptor
(TCR) that is responsible for recognizing antigen as
peptides bound to major histocompatibility complex molecules,
plays an essential role in regulating the selection and activation
of T cells.43 The TCR repertoire for each person is so
diverse because of TCR composed of α- and β-chain genes
that are derived by somatic V(D)J recombination. The link
T-cell function and TCR specificity will enable one to determine
which functional subsets of T cells have undergone
clonal expansion.44 Currently, immunotherapy for various cancers is a potentially
attractive option. Previous studies on immunotherapy
for HCC have shown the feasibility and safety but the results
are not satisfactory.45,46 Recent studies demonstrated that high
numbers of CD8+ TILs predict better prognosis in colorectal
cancer47 and ovarian cancer48 but predict worse prognosis in
nasopharyngeal carcinoma.49 TILs in HCC are significantly exist
but their tumor killing effect is limited.50 Recently Zheng
et al.51, analyzed TCR sequences and transcriptomes using
scRNA-seq of single T cell isolated from HCC patients and
revealed distinct subtypes and clonal expansion of TILs. Using
the spectral clustering technique, 11 unique T cell subsets
including 5 clusters for CD8+ and 6 clusters for CD4+ cells,
were identified. The key genes for each cluster for CD8+ cells
are as follows: lymphoid enhancer-binding factor 1, CX3C
chemokine receptor 1, solute carrier family 4 member 10,
layilin (LAYN) and granzyme K whereas those for CD4+ T
cell clusters include C-C chemokine receptor type 7, forkhead
box P3 (FOXP3), CTLA4, Granzyme A, chemokine (C-X-C
motif) ligand 13 (CXCL13) and granulysin. In particular,
among a total of 401 genes highly expressed in tumor-infiltrating
Tregs, FOXP3, CTLA4, tumor necrosis factor receptor
superfamily member 18, tumor necrosis factor receptor superfamily
member 4 and chemokine (C-C motif) receptor 8 were also identified in other cancers such as in colon and lung
cancers.42 These findings suggest that Tregs are closely related
to the development and progression of HCC. In addition to
Tregs for hepatocarcinogenesis, the role of tumor-infiltrating
exhausted CD8+ T cells is also important. The genes associated
with tumor-infiltrating exhausted CD8+ T cells, such as
CTLA4, hepatitis A virus cellular receptor 2, ectonucleoside
triphosphate diphosphohydrolase-1, T-cell immunoreceptor
with Ig and ITIM domains, programmed cell death 1 and
tumor necrosis factor receptor superfamily member 9, and
CD27, are previously identified.42 In HCC, several novel genes
as novel exhaustion markers, such as myosin VIIA, tryptophanyl-TRNA
synthetase, and CXCL13 LAYN, pleckstrin homology
like domain family A member 1 (PHLDA1), synaptosome
associated protein 47 (SNAP47) are also identified.51 Among them, high expression of PHLDA1 and SNAP47
were significantly associated with poor prognosis of the HCC
patients. LAYN which encode layilin was highly expressed in
Tregs isolated from lung and colon cancers.42 Zheng et al.51, demonstrated that LYAN was highly expressed in both tumor
Tregs and exhausted CD8+ T cells from tumor tissue of
HCC, and revealed that it is upregulated on activated CD8+
T cells Tregs and repressed the CD8+ T cell function in vitro. Furthermore, the transcriptional profiles by scRNA-seq analyses
of individual T cells isolated from HCC, coupled with
assembled TCR sequences, revealed 11 T cell subsets associated
with immunopathogenesis for development of HCC,
and thus these findings could be used for further understanding
the role of TIL in a variety of tumors and for the development
of potential immunotherapy for cancers.
CONCLUSION AND FUTURE PERSPECTIVES
Recent advances of single-cell transcriptomics have made
remarkable progress for deeper understanding of biology by
providing information on gene expression at the level of single
cell. In particular of cancer, single-cell technology provides
a powerful and comprehensive tool for the characterization of
tumor profiles including intra-tumor heterogeneity and epigenetic
alterations. Compared to conventional bulk average
molecular phenotyping, single-cell technology has the advantage of characterizing molecular phenotype with a higher resolution
of cancer cells. Also, single-cell RNA sequencing allows
detection of rare cancer cells and CTCs, elucidation of mechanism
for drug resistance and prediction of prognosis of cancer
patients. HCC is a highly heterogeneous cancer and its molecular
mechanism is extremely complex associated with a poor prognosis.
To date, the molecular mechanisms of hepatocarcinogenesis
remain unclear. Immune dysregulation as well as chronic
inflammation induced by hepatitis virus B/C or alcohol
plays a key role in hepatocarcinogenesis. For this reason, highthroughput
analysis of single-cell transcriptome derived from
tumor cell or immune cells is required for better understanding
of molecular pathogenesis, developing novel therapeutic
agents against HCC, and tailoring an individual’s cancer therapy.
Although only a few studies on scRNA-seq of HCC have
reported, these studies provide new insights into mechanistic
research of development and progression of HCC. Thus, applications
of single-cell transcriptome analysis enable comprehensive
characterization of tumor and immune cell in
HCC can provide mechanistic insights into HCC progression
and metastasis, which ultimately will contribute to the development
of novel therapeutic strategies and improvement of
clinical outcomes. However, current scRNA-seq still has several
limitations such as low capture efficiency, a quantitative
accuracy by complex expression distributions and highly technical
noise, and a difficulty in validating data due to a complicated
analytical network inference. Therefore, further investigation
to minimize these shortcomings is required to achieve
more potential application of single-cell transcriptome analysis
in the clinical setting.
Conflicts of Interest
The authors have no conflicts to disclose.
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