Transcriptome analysis creates a new era of precision medicine for managing recurrent hepatocellular carcinoma

Table 1 Comparisons of real-time quantitative reverse transcription PCR, microarrays, and RNA-sequencing and their applications in hepatocellular carcinoma recurrence

	RT-qPCR	Microarrays	RNA-seq
Basic steps	RNA isolation, genome DNA removal	RNA isolation, mRNA extraction	RNA isolation, mRNA extraction
	cDNA preparation with RT	cDNA library preparation	Quality and quantity check
	Use of primers for amplification	Labeling with fluorescence	cDNA library preparation
	Data analysis	Hybridization with transcript probes on slides	Sequencing
		Scanning	Data analysis
		Image processing and data analysis	Validation
		Validation
Throughput	Low	High	High
Dynamic range/sensitivity	Widest/high	Narrow/low	Wide (compared to microarrays)/high
Need for reference genome	No	No	Yes
Known sequences of genes of interest	Required	Required	Not required
Cost	Low	Low	High
Advantages	Low cost, simple	High throughput	Ability to detect novel differential transcripts
	Highest dynamic range	Relatively low cost	Splice junctions, SNP, non-coding RNA
	Gold standard	Good bioinformatics and statistical practices
Downsides	Dependence on pre-existing knowledge of genes of interest	Difficulty to detect novel transcripts, non-coding RNA, splicing, or other dynamic natures of transcriptome	Large data storage
			High cost
	Low throughput
		Need for designing probes
		Low dynamic range
Applications and main achievements in HCC recurrence-related research	Commonly used as a validation tool for confirming DGE results yielded from other high throughput analyses[56]	Providing abundant information on carcinogenicity of primary HCC cells and carcinogenic stimuli; laid the foundation for our current understanding of the pathogenesis of HCC recurrence[18]	Prospectively discovering DGE as potential novel classifiers for the carcinogenic profile of recurrent HCC cells; elucidating how HBV triggers HCC recurrence by interrupting the human genome[92,94,96]

cDNA: Complementary DNA; DGE: Differential gene expression; HBV: Hepatitis B virus; HCC: Hepatocellular carcinoma; RNA-seq: RNA-sequencing; RT: Reverse transcription; RT-q: Real-time quantitative reverse transcription; SNP: Single nucleotide polymorphism.

Table 2 Representative transcriptomic studies in recurrence of hepatocellular carcinoma

Ref.	Method	Sample comparison	Major findings	Featured research domain
Jiang et al[29], 2000	RT-qPCR	Nontumorous liver vs tumor samples; peripheral blood from HCC patients	MMP9 in tumors was related to recurrence. mRNA of AFP in blood samples was associated with recurrence	Primary cancer cells
Morimoto et al[30], 2005	RT-qPCR	Peripheral blood and bone marrow samples from patients with HCC vs benign diseases	AFP mRNA level in blood, but not bone marrow, could be useful for predicting postoperative tumor recurrence	Primary cancer cells
Cheung et al[56], 2005	Microarray	HCC tumors from patients with post-OP recurrence vs without recurrence	CLDN10, along with the pTNM stage, were independent predictors for HCC recurrence	Primary cancer cells
Matoba et al[57], 2005	Microarray	HCC tumors from patients with vs without post-OP early (< 1 yr) recurrence	HLA-DRA, HLA-DRB1, HLA-DG, and HLA-DQA had significantly lower expression in the early IHR group	Primary cancer cells
Iizuka et al[58], 2006	Microarray	HCC tumors from patients with post-OP IHR vs EHR	46 cell adhesion-related genes, including ITGA6 and SPP1, had higher expression levels in HCC with early IHR	Primary cancer cells
Ho et al[62], 2006	Microarray	HCC tumors from patients with vs without PVI	Differential expression of 14 genes related to the human melanoma gene family, cell growth, DNA glycosylation, and thrombin inhibitors, can be used to predict recurrence	Primary cancer cells
Chen et al[63], 2002	Microarray	HCC tumor and corresponding nontumorous tissue with vs without PVI	ARHGAP8 and ARHGEF6 were PVI-associated.	Primary cancer cells
Okabe et al[64], 2001	Microarray	HCC tumor from patients with vs without PVI	Upregulation of MMP14 and downregulation of two CYP genes, ADAMTS1, and ITGA7 were associated with PVI	Primary cancer cells
Okamoto et al[76], 2006	Microarray	Multicentric vs single nodular recurrent HCV-related HCC	36 marker genes were associated with multicentric recurrence and were used to develop a predictive scoring system	Carcinogenic stimulants
Mas et al[78], 2007	Microarray	HCV-related HCC from patients with vs without disease progression	Upregulation of FAIM3 and USP18, and downregulation of TFP1, HIST1H4E, and NRG1 were related to disease-free survival after curative treatment	Carcinogenic stimulants
Nagalakshmi et al[80], 2008	Microarray	MIM vs MAM	HLA-DPA1, HLA-DRA, PRG1, and ANXA1 were associated with a metastatic phenotype (Th2-predominant), for which CSF1 may be responsible	Microenvironment
Yoshioka et al[66], 2009	Microarray	HCC tumors from patients with multiple early (< 2 yr) IHR vs with DFS > 3 yr	Informative gene sets including PPARBP, RREB-1, BCL2, HDAC1, and BIRC5 were yielded and used for a predictive model, which was validated in independent cases	Primary cancer cells
Kim et al[74], 2012	Predictive model construction using microarray database	DGE in 65 genes from pre-existing databases were used for a predictive model for early HCC recurrence and validated in independent HBV-related HCC cohorts	A risk scoring system with 65 differentially expressed genes identified from microarray data successfully predicted overall survival < 3 yr post-OP	Carcinogenic stimulants
Kim et al[75], 2014	Predictive model construction using microarray database	DGE of 233 HIR-related genes from preexisting databases were used for a predictive model for late HCC recurrence and validated in independent HBV-related HCC cohorts	Genes related to STAT3/Notch signaling activation were related to late (> 1 yr) recurrence of HCC. RALGDS, IER3, CEBPD, and SLC2A3 were independent predictors of recurrence.	Carcinogenic stimulants
Nakagawa et al[65], 2021	Predictive model construction using microarray database	Validation of intrahepatic metastasis risk signatures created based on a preexisting microarray database in an independent patient cohort	STC1, FOXK2, MMP1, and LOXL2 that promote either cell cycle advancement or histone modulation could predict the incidence of early recurrence	Primary cancer cells
Liu et al[87], 2022	RNA-seq	HCC tumors from patients with vs without recurrence	Most altered expression genes are related to DNA synthesis (MCM8, MCM6, TOP2A, and CDC7), chromatin segregation (BUB1 and CDC6), and mitosis (NDC80 and PPP2R3C)	Primary cancer cells
Ng et al[88], 2021	RNA-seq	Paired tumor tissues vs nontumorous tissues from HCC patients	GSTA2 expression was associated with early-phase systemic injury and reactive oxygen species levels and could serve as a predictor of recurrence	Primary cancer cells
Lachmann et al[90], 2018	RNA-seq	Paired primary vs recurrent HCC tumor tissues	Mutations of GOLGB1 and SF3B3 are potential key drivers for the aggressive phenotype in recurrent HCC	Primary cancer cells
Okrah et al[97], 2018	RNA-seq	HBV-related HCC tumor vs distant nontumorous liver tissues	More HBV gene integrations correlated with a higher recurrence rate	Carcinogenic stimulants
Wang et al[98], 2021	Validation of RNA-seq database	HCC tumors vs matched cirrhotic tissues; CD8+ CTL-infiltrated vs T cell-excluded tumor tissues	Local tumor immunosuppression coincided with disease progression. Association was found between elevated fibrosis and the T cell-excluded immune phenotype	Microenvironment
Ho et al[99], 2021	Predictive model construction using RNA-seq database	Validation of recurrence-associated lncRNAs identified by regression analysis of TCGA database	9 immune-related lncRNAs were tightly associated with recurrence	Microenvironment
Zheng et al[120], 2018	scRNA-seq	CSC vs non-CSC populations defined by triple+ or triple− surface expression of CD133, CD24, EpCAM	286 signature genes linked to triple+ CSC could predict tumor recurrence in 240 HCC cases with multivariable Cox regression survival risk prediction analysis	Primary cancer cells
Sun et al[122], 2021	scRNA-seq	Tumors from primary vs early-relapse HCC patients	Decreased Treg and T cell proliferation with an increased proportion of CD8+- T cells and DC were found in early-relapse tumors compared to primary tumors. CD8+ T cells with overexpression of KLRB1 revealed an innate dysfunctional state with immunosuppressive phenotypes in recurrent tumors	Microenvironment
Fu and Lei[123], 2022	scRNA-seq	Primary vs early-relapsed HCC samples	ScRNA-seq analysis of primary vs relapsed HCC identified 645 genes with DGE across three T cell types. Univariate and multivariate analysis identified 15 prognostic genes (AP000866.1, ATIC, CAPN10, EDC3, EID3, NCKIPSD, OXLD1, PHOSPHO2, POLE2, POLR3G, SEPHS1, SRXN1, TIMM9, ZNF487, and ZSCAN9)	Microenvironment

AFP: Alpha-fetoprotein; CSC: Cancer stem cell; CTL: Cytotoxic T lymphocyte; CYP: Cytochrome P450; DC: Dendritic cell; DFS: Disease-free survival; DGE: Differential gene expression; EHR: Extrahepatic recurrence; EpCAM: Epithelial cellular adhesion molecule; GSTA2: Glutathione S-transferase A2; HBV: Hepatitis B virus; HCC: Hepatocellular carcinoma; HCV: Hepatitis C virus; HIR: Hepatic injury and regeneration; IHR: Intrahepatic recurrence; lncRNA: Long non-coding RNA; MAM: Metastasis-averse microenvironment; MIM: Metastasis-inclined microenvironment; MMP9: Matrix metalloproteinase 9; post-OP: Postoperative; pTNM: Pathological tumor-node-metastasis; PVI: Portal vein invasion; RNA-seq: RNA-sequencing; RT-qPCR: Real-time quantitative reverse transcription; scRNA-seq: Single-cell RNA sequencing; STAT3: Signal transducer and activator of transcription 3; TCGA: The Cancer Genome Atlas; Th2: T helper 2 cell; Treg: T-regulatory cell.