Cell‑cycle reactivation and hepatocyte identity loss in hepatocellular carcinoma: Transcriptomic hallmarks and validation strategies (Review)

Introduction

Background

Hepatocellular carcinoma (HCC) still accounts for a large share of cancer-related mortality, even though surveillance, locoregional treatment, systemic therapy and immunotherapy have improved the outlook for selected patients (1–6). Treatment decisions depend on tumour burden, liver reserve, performance status, molecular heterogeneity and tolerability, and current clinical guidelines continue to emphasize that biological interpretation must be integrated with liver-function context (2–6). Biologically, most HCCs arise in a chronically injured liver. Chronic viral hepatitis, metabolic injury, inflammation, fibrosis and repeated regeneration create a background in which malignant transformation is difficult to describe by mutation status alone (2,7,8). Non-coding RNA regulation and transcriptomic remodeling further add regulatory layers that are relevant to HCC biology and precision interpretation (7–13). Transcriptomic profiles therefore remain useful, because they record malignant-cell programs together with residual signals from the diseased liver environment (7–10).

Transcriptomic cell-state remodeling in HCC

Across many HCC transcriptomic datasets, the most recognizable signal is not a single hub gene. It is a paired shift: Mitotic, DNA-replication and replication-stress programs become stronger, whereas transporter, detoxification, metabolic and defense-related transcripts become weaker (11,12,14–27). Representative proliferation-associated examples include DNA topoisomerase IIα (TOP2A), cyclin B1 (CCNB1) and ribonucleotide reductase regulatory subunit M2 (RRM2), whereas hepatocyte-identity-related examples include solute carrier family 22 member 1 (SLC22A1), solute carrier organic anion transporter family member 1B3 (SLCO1B3), glutaminase 2 (GLS2) and cytochrome P450 (CYP) family genes (14–27). Reading these two arms together is often more informative than ranking isolated differentially expressed genes (DEGs), because the paired signal links tumour growth, differentiation state, model suitability and validation design. Representative transcriptomic modules relevant to this proliferation-identity imbalance are summarized in Table I.

Table I. Transcriptomic modules relevant to HCC cell-state remodeling.

Table I.

Transcriptomic modules relevant to HCC cell-state remodeling.

Module	Representative genes/programs	Biological interpretation	Main caution
Cell cycle/mitosis	TOP2A, CCNB1, AURKA, CDK1	Active cycling and mitosis (14–19)	May mainly mark cycling fraction
DNA replication/repair	RRM2, MCM-family genes, PCNA-related programs	Nucleotide demand and replication stress (14–19,42)	Non-specific if read alone
Hepatocyte transport	SLC22A1, SLCO1B3	Reduced liver transport and drug handling (20–22)	Transporter biology varies by model
Metabolism/detoxification	GLS2, CYP-family genes	Remodeled liver metabolic identity (23–27)	May reflect tissue context or purity
Immune/defense	Complement and defense-response genes	Innate-defense and microenvironment signal (9,28–34)	Bulk origin may be non-malignant

[i] The listed genes are examples of broader programs and should not be read as individually validated targets. AURKA, aurora kinase A; CCNB1, cyclin B1; CDK1, cyclin-dependent kinase 1; CYP, cytochrome P450; GLS2, glutaminase 2; HCC, hepatocellular carcinoma; MCM, minichromosome maintenance; PCNA, proliferating cell nuclear antigen; RRM2, ribonucleotide reductase regulatory subunit M2; SLC22A1, solute carrier family 22 member 1; SLCO1B3, solute carrier organic anion transporter family member 1B3; TOP2A, DNA topoisomerase IIα.

This view builds on earlier HCC molecular-classification studies. Transcriptome-based subclassifications separated proliferative, poorly differentiated and more hepatocyte-like tumour groups, showing that HCC expression states have biological structure rather than forming random marker collections (11,12). The present review uses those state-level observations for a different purpose: To connect recurrent expression patterns with model selection, assay design and claim strength.

No connectivity-mapping platform was used to generate data, perform compound ranking or support the conclusions of the present narrative review. Connectivity Map (https://clue.io/cmap), the Library of Integrated Network-based Cellular Signatures (https://lincsproject.org/) and L1000 Fireworks Display (https://maayanlab.cloud/l1000fwd/) are mentioned only to distinguish the present state-interpretation framework from platform-centred drug-repositioning studies.

Aim and objectives of this review

This review aims to clarify how the recurring proliferation-identity pattern in HCC transcriptomes should be interpreted and translated into validation strategies without overstating what expression data can prove. Specifically, it will: i) Summarize recurrent transcriptomic hallmarks involving cell-cycle reactivation and hepatocyte identity loss; ii) explain how these paired signals can guide experimental model selection and validation design; iii) distinguish module-level hypotheses from mechanistic or therapeutic claims; and iv) demonstrate the practical use of the framework through a worked example based on published HCC transcriptomic evidence.

Novelty and contribution

The recurring HCC transcriptomic signal is considered to be a cell-state problem. In that reading, cell-cycle activation and erosion of hepatocyte identity are not two unrelated findings; together, they help decide which model is appropriate, which reverse transcription-quantitative PCR (RT-qPCR) or RNA sequencing (RNA-seq) readout is worth using and when a result is still only a hypothesis rather than evidence for a mechanism or therapy.

Scope, conceptual approach and bioinformatics considerations

Scope and conceptual approach

Across bulk and integrated HCC transcriptomic studies, proliferative tumours commonly show enrichment of cell-cycle and DNA-replication programs, whereas differentiated hepatocyte transport, metabolic and defense-related programs are frequently reduced (11,12,14–27). Single-cell and spatial studies further indicate that the cellular origin of these signals can vary across malignant, immune, stromal and adjacent-liver compartments (28–34). These observations motivate the practical questions addressed in this review: Which patterns repeat in bulk or integrated datasets; whether proliferative and hepatocyte-identity genes move together or apart; what single-cell and spatial studies add to bulk signatures; which experimental model preserves the state being discussed; and at what point a module-level observation becomes an overclaim about mechanism or treatment. Table I summarizes representative modules and cautions, whereas Table II links selected modules to model features and claim boundaries.

Table II. Linking transcriptomic modules to testable vulnerabilities.

Table II.

Linking transcriptomic modules to testable vulnerabilities.

Transcriptomic signal	Possible vulnerability	Useful model feature	Claim boundary
High cell-cycle/mitotic module	Cell-cycle kinase or mitotic dependency (19,41)	Proliferative HCC line with measurable cycling fraction	Needs perturbation assays
High DNA replication/RRM2 module	Replication-stress or nucleotide-synthesis dependency (42)	Model with robust S-phase fraction	Needs functional assays
Low hepatocyte transporter module	Drug uptake/exposure limitation or differentiation loss (20–22)	Model preserving transporter biology	Needs exposure testing
Metabolic gene rewiring	Nutrient/redox/lipid metabolic dependency (23–27)	Model matched to metabolic phenotype	Avoid broad metabolism claims
Immune/defense shift	Microenvironmental or inflammatory interaction (9,28–34)	Co-culture, organoid or spatial context	Needs cellular-source evidence

[i] The table separates plausible hypotheses, model features and claim boundaries. AURKA, aurora kinase A; CCNB1, cyclin B1; CYP, cytochrome P450; EdU, 5-ethynyl-2′-deoxyuridine; HCC, hepatocellular carcinoma; PI, propidium iodide; RRM2, ribonucleotide reductase regulatory subunit M2; RT-qPCR, reverse transcription-quantitative PCR; SLC22A1, solute carrier family 22 member 1; SLCO1B3, solute carrier organic anion transporter family member 1B3.

Review design rationale

This article is a structured narrative review rather than a systematic review or meta-analysis and was not prepared according to PRISMA 2020 (35). The evidence comes from different literatures, including bulk transcriptomics, single-cell and spatial studies, pathway papers, model systems and validation reports. These materials do not share a common effect measure that could be pooled usefully; therefore, no PRISMA flow diagram or pooled estimate was prepared. The aim is an interpretive framework with explicit limits.

Search strategy and selection criteria

Searches were updated on 14 May 2026 in PubMed (https://pubmed.ncbi.nlm.nih.gov/), Web of Science (https://www.webofscience.com/) and Scopus (https://www.scopus.com/). Google Scholar (https://scholar.google.com/) was used as a secondary tool to follow citations from key HCC transcriptomic, single-cell, spatial-omics and model-system papers. Search terms combined HCC-related words (‘hepatocellular carcinoma’, ‘HCC’, ‘liver cancer’), transcriptomic terms (‘transcriptome’, ‘gene signature’, ‘cell cycle’, ‘DNA replication’, ‘hepatocyte identity’, ‘single-cell’, ‘spatial transcriptomics’) and translational terms (‘organoid’, ‘xenograft’, ‘drug response’, ‘transporter’, ‘SLC22A1’, ‘OCT1’, ‘metabolic reprogramming’). Original studies, relevant reviews and papers informative for experimental model choice were prioritized. Purely prognostic studies were retained only when they helped interpret cell state, model suitability or validation logic. Articles were excluded from narrative emphasis when they lacked direct relevance to HCC or HCC-relevant experimental models, provided insufficient methodological detail, focused solely on prognostic associations without informing cell-state interpretation or validation design, or made mechanistic or therapeutic claims unsupported by functional evidence.

Reporting boundary

This review synthesizes published evidence without generating a new dataset, pooling effect estimates or performing de novo statistical analyses. Reported findings were interpreted according to the original study design, tissue source, analytical platform, model system and validation depth. Therefore, the conclusions represent evidence-informed narrative guidance rather than meta-analytic estimates of effect.

Bioinformatics considerations for interpreting HCC transcriptomic signatures

For HCC transcriptomic interpretation, the first task is not only to rank genes by fold change, but also to determine whether the signal represents a coherent biological state. A stable proliferative module should usually include several concordant markers of DNA replication, mitotic entry or chromosome segregation, such as TOP2A, RRM2, CCNB1, cyclin-dependent kinase 1 (CDK1), marker of proliferation Ki-67 (MKI67), proliferating cell nuclear antigen (PCNA) and aurora kinase A (AURKA), rather than one isolated gene. A hepatocyte-identity module should likewise be evaluated as a coordinated liver-function program involving transporters, metabolic enzymes and liver-enriched transcriptional or secretory markers, rather than a single downregulated transcript (11,12,14–27).

A second task is to consider cellular origin. Bulk RNA sequencing (RNA-seq) can detect strong recurrent signals, but it cannot by itself determine whether a signal originates from malignant hepatocytes, cycling stromal or immune cells, or endothelial cells, or whether it reflects differences between tumour and adjacent chronically injured liver. Single-cell and spatial evidence should therefore be used, where available, to test whether proliferation-associated and hepatocyte-identity signals map to the malignant epithelial compartment or to the microenvironment (28–34).

A third task is to align the analysis with validation. If the computational result is a proliferation-identity imbalance, then model choice and assay choice should preserve and test that state. This means that a cell line, organoid or xenograft should be selected because it retains the relevant proliferative, metabolic or transporter phenotype. The same logic applies to validation readouts: reverse transcription-quantitative PCR (RT-qPCR) or RNA-seq can confirm coordinated transcript changes, whereas protein assays, cell-cycle profiling, 5-ethynyl-2′-deoxyuridine (EdU) incorporation and rescue or perturbation experiments are needed before mechanistic language becomes defensible (14–19,36–39).

Cell-cycle reactivation as a recurrent HCC transcriptomic hallmark

Cell-cycle and DNA-replication programs are among the most consistently upregulated signals in HCC. TOP2A, CCNB1, RRM2, AURKA and CDK1 often appear together within mitotic, chromosome-segregation and replication-stress modules (14–19). Their recurrence makes them useful markers of a proliferative state. It does not, however, mean that each gene is a primary driver or a stand-alone therapeutic target.

TOP2A participates in resolving topological stress during DNA replication and chromosome segregation. In HCC and other cancers, high TOP2A expression generally tracks with proliferation and aggressive biology, which provides a rationale for considering topoisomerase-related mechanisms in an appropriate experimental setting (40). CCNB1 and cyclin B2 mark cell-cycle transition and mitotic entry. AURKA is involved in centrosome and spindle regulation, and cyclin-dependent kinase 4/6-related evidence illustrates that cell-cycle perturbation can intersect with immune-related effects in HCC models (41). RRM2 links DNA-replication demand with nucleotide metabolism and replication stress and has been examined as a candidate HCC vulnerability in integrated in silico and in vitro work (42). Read as a module, these genes describe a cycling tumour state more reliably than any single marker.

The risk is familiar in bioinformatics studies: a frequently upregulated cell-cycle gene can be quickly promoted into an independent target, even though high expression may instead reflect tumour purity, the fraction of cycling cells, sampling of an aggressive region or a shared proliferation program. A more defensible use of the signal is to select assays and models. EdU incorporation, colony formation, propidium iodide (PI)-based cell-cycle profiling and protein-level assessment of CCNB1, AURKA, TOP2A or RRM2 provide stronger evidence than expression differences alone (14–19,28–34).

For that reason, cell-cycle genes should first be treated as state markers. Therapeutic language is justified only after functional dependency, pathway engagement and model-consistent effects have been shown.

Hepatocyte identity loss and downregulated liver-function programs

The other arm of the paired pattern is loss of hepatocyte identity. Downregulated transcripts often include transporters, detoxification enzymes, complement and coagulation genes, metabolic regulators and other liver-enriched functions. Representative examples include SLC22A1, SLCO1B3, CYP-family genes and GLS2. These signals matter because they influence tumour classification, drug uptake, metabolic competence and model interpretation (20–27).

SLC22A1 encodes organic cation transporter 1 (OCT1), which is relevant to hepatic uptake of endogenous metabolites and several drugs (20–22). CYP enzymes and SLCO-family transporters add a second pharmacological layer by linking the expression state to metabolism and liver-specific exposure. GLS2, a glutaminase associated with mitochondrial metabolism and redox balance, may represent a liver-metabolic component that should be interpreted separately from generic proliferation-associated nutrient demand (23–27).

This matters clinically because loss of transporter and metabolic programs can change how a candidate intervention is interpreted. Reduced OCT1/SLC22A1 or SLCO expression may limit intracellular drug exposure, whereas altered CYP and detoxification programs can shift assumptions about dose relevance and toxicity. In HCC, where cirrhosis and impaired liver function are common, transporter competence and metabolic identity are part of the pharmacological context rather than background annotation (20–27).

The same point applies to experimental interpretation. A compound may reduce proliferation in a poorly differentiated monolayer cell line without testing transporter-dependent uptake, hepatocyte metabolism or liver-specific toxicity. Conversely, restoration of one liver-function transcript is not enough to claim restored hepatocyte identity; stronger language requires coordinated changes across transporter, metabolic and functional readouts in models suited to the question (36–39).

Taken together, loss of hepatocyte identity is not only a diagnostic or descriptive signal. It changes the meaning of drug exposure, model suitability and liver-relevant pharmacology. A downregulated signature can be listed in a table, but its practical value lies in deciding what the model can and cannot test.

Proliferation-identity coupling: A more useful framework than single-gene interpretation

Proliferation-identity imbalance is a convenient name for a recurring, but not universal, HCC contrast. Replication and mitotic programs tend to rise as differentiated liver functions weaken. The phrase is useful because it puts two findings that are often discussed separately into the same frame and slows the common move from ‘gene is differentially expressed’ to ‘gene is a mechanism or target’.

The concept does not flatten HCC heterogeneity. A tumour with intense cell-cycle activity and marked loss of liver-function transcripts may behave differently from a tumour with moderate proliferation but stronger immune or metabolic remodeling. The model is therefore a guide for comparison, model choice and claim wording, not a new universal class of HCC.

Fig. 1 gives a schematic version of the idea. It should not be read to mean that proliferation and hepatocyte identity always change in lockstep. Its main use is more modest: Examining both arms together is usually more informative than judging either arm in isolation.

Figure 1. Conceptual summary of proliferation-identity imbalance in HCC. The schematic illustrates a recurrent transcriptomic pattern in HCC in which cell-cycle progression and DNA-replication programs increase, whereas hepatocyte-associated metabolic and transporter programs decline. The lower interpretation panel emphasizes that this paired pattern is intended as a cell-state hypothesis that can guide module-level interpretation, model selection and validation planning, rather than as direct evidence of a specific mechanism or therapeutic efficacy. HCC, hepatocellular carcinoma.

Used in that limited sense, the proliferation-identity frame helps organize recurring expression patterns while leaving room for subtype, etiology and microenvironmental variation (11,12,14–19,28–34).

Single-cell and spatial transcriptomics refine bulk signatures

Bulk transcriptomics is useful for detecting reproducible signals, but it averages over cell types. A change seen in a bulk HCC sample may come from malignant hepatocytes, stromal cells, immune cells, endothelial cells or the injured non-tumour liver. Single-cell and spatial approaches can separate some of these sources, which is particularly important for immune, complement, matrix and endothelial programs (28–34).

Recent single-cell and spatial studies make this point concrete. Some prognostic or candidate therapeutic signals are concentrated in malignant-cell subsets, immune interfaces or tumour-host metabolic niches rather than spread evenly through the tumour mass (28–34). For bulk-signature work, this means that the cellular source of a signal should be checked before it is used to justify a target, model or validation assay.

Spatial location also matters. HCC arises in a diseased organ, and the tumour core, invasive margin and cirrhotic liver around the tumour may carry different expression states. A tumour-vs.-adjacent comparison compresses these differences into one average. In translational work, that average can be misleading if the candidate signal is absent from malignant cells or is mainly stromal or immune (28–34).

A more defensible workflow is therefore iterative: start with stable bulk or multi-cohort patterns; use single-cell or spatial evidence to ask where the signal originates; then choose a validation model because it preserves the relevant state rather than because it is convenient (14–19,28–34,36–39). Fig. 2 presents this high-level model-selection logic.

Figure 2. Module-aware model selection framework for HCC transcriptomic programs. The schematic presents the high-level relationship between transcriptomic modules and state-matched model-selection principles. Module-specific hypotheses, useful model features and claim boundaries are detailed in Table II, whereas assay categories and validation evidence are detailed in Table III. HCC, hepatocellular carcinoma.

Single-cell and spatial data are valuable when they change the interpretation or the experiment. If they are presented without a link to the cell type, tissue location or assay being tested, their contribution is limited (28–34).

Candidate therapeutic vulnerabilities inferred from transcriptomic modules

Transcriptomic patterns can suggest candidate vulnerability hypotheses, but the evidence level must remain explicit. Cell-cycle activation points toward cyclin-dependent kinase, Aurora kinase, checkpoint, mitotic-spindle and replication-stress biology. An expression pattern alone is still not a validated target. A vulnerability claim requires a functional phenotype, dose-response behaviour, pathway or protein readout and, preferably, consistency across more than one HCC model (19,41,42).

Metabolic remodeling in HCC encompasses altered glycolysis, lipid synthesis, amino-acid metabolism, redox homeostasis and mitochondrial function, although the contribution of each program varies across tumours and experimental contexts (23–27). These changes can intersect with proliferation because a dividing tumour requires nucleotides, lipids, amino acids and reducing power. Metabolic signals should therefore be interpreted together with proliferative state. A useful translational question is whether the signal implies nutrient dependence, redox vulnerability, transporter-mediated exposure or immune suppression that can be tested (23–27).

Model selection is the third translational axis. Huh7, PLC/PRF/5, Hep3B, MHCC97H and other HCC models differ in differentiation state, TP53 status, viral background, transporter expression and proliferation rate (43). HepG2 is frequently used in liver-cancer experiments, but it has been shown to be a hepatoblastoma-derived cell line rather than a hepatocellular carcinoma cell line (44); it should therefore not be regarded as a representative HCC model. Model choice should follow the expression state under study rather than convenience alone. For example, Table II distinguishes a high cell-cycle module, for which a proliferative model and functional perturbation assays are required, from a low-transporter module, for which transporter-competent models and uptake testing are needed before altered drug exposure can be inferred.

Patient-derived tumour organoids, patient-derived xenograft models and related patient-derived systems can partly bridge this gap because they retain more genomic, histological or treatment-response context than conventional monolayer cultures (36–39,43). They are particularly useful when the question involves transporter competence, metabolic phenotype, intratumoural heterogeneity or drug-sensitivity ranking. Even these models need caution: organoid culture success, stromal and immune loss, medium composition and growth-factor dependence can alter the cell state being tested.

A model suitable for testing a mitotic phenotype may therefore be inadequate for evaluating transporter-dependent drug exposure, hepatocyte-identity restoration or microenvironment-dependent biology (36–39,43).

A minimal validation package for HCC transcriptomic modules

A practical RT-qPCR panel needs both arms of the proposed state. TOP2A, CCNB1, RRM2 and AURKA can represent proliferation, whereas SLC22A1, SLCO1B3, GLS2 and CYP1A2 can represent hepatocyte identity or metabolic-function loss. The panel does not replace RNA-seq; its purpose is to test, at a manageable scale, whether a perturbation shifts a coordinated expression state rather than one isolated gene (14–27).

Validation should be scaled to the claim. A viability assay alone supports only preliminary growth inhibition. If EdU incorporation and cell-cycle profiling also change, the interpretation can move toward proliferation or cell-cycle modulation. If selected module genes shift in the expected direction by RT-qPCR or RNA-seq, it becomes reasonable to discuss a module-level transcriptional response. Mechanistic or translational wording should wait for protein or pathway readouts, target perturbation, model extension and, where relevant, in vivo support (14–19,36–43).

Fig. 3 and Table III present a validation ladder rather than a mandatory checklist. Fig. 3 provides the stage sequence, whereas Table III specifies the assay categories, the evidence each stage can support and the limitations that remain. The purpose is to prevent a common shortcut: treating altered expression or a short viability experiment as if it already proved a mechanism (14–19,36–43).

Figure 3. Stepwise validation ladder for HCC transcriptomic modules. The figure displays only the sequence of five validation levels from phenotype screening to model extension. Detailed assay categories, evidentiary support and remaining limitations are provided in Table III. HCC, hepatocellular carcinoma; IF, immunofluorescence; PDX, patient-derived xenograft; RNA-seq, RNA sequencing; RT-qPCR, reverse transcription-quantitative PCR.

Table III. Minimal validation ladder for HCC transcriptomic cell-state hypotheses.

Table III.

Minimal validation ladder for HCC transcriptomic cell-state hypotheses.

Validation step	Core assays	What it supports	What it does not prove
Phenotype screen	Cell Counting Kit-8, CellTiter-Glo and colony formation	Growth or survival effect	Mechanism or module reversal
Proliferation and cell cycle	EdU, PI cell-cycle flow cytometry	DNA synthesis and cell-cycle redistribution	Target engagement
Module RT-qPCR	TOP2A, CCNB1, RRM2, AURKA, SLC22A1, GLS2 and selected context genes	Partial transcriptional support for module shift	Whole-transcriptome reversal
Pathway/protein readout	Western blot, immunofluore-scence and kinase/pathway markers	Protein-level pathway modulation	Clinical relevance
Model extension	Second HCC cell line, organoid, xenograft or PDX model	Context robustness and translational plausibility	Patient benefit

[i] Each step indicates what the assay can support and what remains unproven without further evidence. The staged framework is informed by published HCC transcriptomic, cell-cycle and preclinical-model studies (14–19,36–43). AURKA, aurora kinase A; CCNB1, cyclin B1; EdU, 5-ethynyl-2′-deoxyuridine; GLS2, glutaminase 2; HCC, hepatocellular carcinoma; IF, immunofluorescence; PDX, patient-derived xenograft; PI, propidium iodide; RNA-seq, RNA sequencing; RRM2, ribonucleotide reductase regulatory subunit M2; RT-qPCR, reverse transcription-quantitative PCR; SLC22A1, solute carrier family 22 member 1; TOP2A, DNA topoisomerase IIα.

In practical terms, a claim should move stepwise. First comes phenotype screening, followed by proliferation and cell-cycle assessment, then a coordinated transcriptional response, and only then protein, pathway or target-level support. A single viability assay, or one gene measured alone, can justify a preliminary observation, not a mechanistic conclusion (14–19,36–43).

Worked example: Applying the proliferation-identity framework to The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC)

A concrete example can be drawn from the TCGA-LIHC cohort, a widely used bulk transcriptomic resource for HCC (7). In this dataset-level setting, the first step is not to rank a single gene as the most important finding, but to ask whether the tumour profile shows a coordinated state shift. Published TCGA-LIHC and integrated HCC analyses consistently identify TOP2A, CCNB1 and RRM2 as components of proliferation-associated programmes (11,12,14–16). In contrast, reduced albumin and transthyretin expression reflects diminished hepatocyte differentiation (14,15), whereas reduced SLC22A1/OCT1 and SLCO-family transporter expression has been documented in HCC (20–22). Under the proposed framework, this pattern is interpreted as a proliferation-identity imbalance rather than as evidence that any one highly ranked gene is automatically a driver or drug target.

The second step is to interrogate cellular origin. A TCGA-LIHC tumour-sample bulk signal averages malignant hepatocytes, immune cells, endothelial cells and fibroblasts, In tumour-versus-adjacent tissue analyses, adjacent diseased liver constitutes a separate comparator and may influence the observed differential signal. Therefore, the proliferation-identity pattern should be cross-checked against single-cell or spatial evidence. If proliferative genes are concentrated in malignant epithelial compartments and hepatocyte-identity transcripts are reduced in those same compartments, the bulk signal supports tumour-cell state remodeling. If the apparent signal is mainly stromal, immune or derived from adjacent cirrhotic tissue, the validation question changes and a conventional HCC cell-line experiment may not test the relevant biology (28–34).

The third step is state-matched model selection. If the TCGA-LIHC-like signal represents a high-proliferation and low-hepatocyte-identity tumour state, the first experimental model should preserve strong cycling activity and allow cell-cycle readouts. Such a model can be used to test EdU incorporation, PI-based cell-cycle distribution, colony formation and protein expression of CCNB1, CDK1, TOP2A, RRM2 or PCNA. By contrast, if the working hypothesis concerns transporter-dependent drug exposure or restoration of hepatocyte metabolism, the model should retain measurable transporter and metabolic competence, such as selected differentiated models, organoids or patient-derived systems (36–39,43). The model is therefore chosen because it matches the state being tested, not simply because it is available.

The fourth step is to apply the validation ladder. For TCGA-LIHC example, a first tier would assess phenotype, including growth or survival. A second tier would examine proliferation and cell-cycle readouts, such as EdU incorporation, PI-based cell-cycle distribution or colony formation assays. A third tier would confirm the coordinated mRNA pattern by RT-qPCR or RNA-seq using both proliferation markers and hepatocyte-identity markers. A fourth tier would examine protein or pathway readouts, such as CCNB1, CDK1, PCNA, TOP2A, RRM2, OCT1/SLC22A1 or selected CYP proteins when appropriate. Stronger mechanistic claims would require perturbation or rescue experiments, and translational claims would require extension across additional models or patient-derived systems (14–19,36–43). This example shows how a bulk TCGA-LIHC-type DEG list can be converted into a state-aware experimental plan rather than a direct therapeutic conclusion.

Pitfalls in interpreting HCC transcriptomic modules

Interpretation can drift for several reasons. Adjacent liver is not a neutral control in many HCC datasets; it may be cirrhotic, inflamed, steatotic, fibrotic or virally injured. Tumour purity also changes bulk immune, stromal, endothelial, complement and matrix signals. Platform differences add another layer because microarray and RNA-seq studies differ in probe design, normalization, dynamic range and annotation. Original studies can use meta-analysis, robust rank aggregation or direction-consistency filters to reduce noise, but none of these steps removes the underlying biology and sampling issues (7,11,12,14–19,28–34).

Hub genes create another trap. Genes that sit near the centre of protein-interaction or co-expression networks are often essential, abundant or well studied. That does not make them uniquely disease-driving. Similarly, a high-risk prognostic signature may describe aggressive tumours without defining a core HCC mechanism or a general therapeutic vulnerability (14–19).

Validation can be too thin as well. One cell line, one dose and one or two genes may be enough for a first observation, but not for a coordinated mechanism. These limits do not weaken transcriptomics; they mark the point where interpretation should stop (36–39,43).

Expression patterns are most useful when readers can see the cohort context, platform constraints, cell-type origin and validation depth. Without that information, stable-looking signatures can easily harden into therapeutic stories that the data do not yet support (7,14–19,28–34,36–39,43).

Recommendations and future perspectives

Future work should join robust bulk signatures with cell-type resolution more deliberately. Multi-cohort analyses can show which patterns recur, whereas single-cell and spatial studies can show where those patterns arise. Stronger experimental designs will then choose a cell line, organoid, co-culture or xenograft because it retains the state under study, not because it is the nearest available model (14–19,28–34,36–39,43).

A first recommendation is to report transcriptomic claims in evidence tiers. A recurrent DEG module should be described as a state hypothesis. A coordinated RT-qPCR or RNA-seq response can support module-level transcriptional modulation. Protein, pathway and phenotype assays are needed before mechanistic language is used. Perturbation, rescue and model extension are needed before translational vulnerability claims are justified (14–19,36–43).

A second recommendation is to make model-selection rationale explicit. For proliferation-associated modules, the model should retain measurable cell-cycle activity. For hepatocyte-identity or transporter modules, the model should preserve relevant uptake, detoxification or metabolic features. For immune, complement or stromal modules, the model should include the cellular context required to test the signal. This recommendation is especially important because HCC cell lines, organoids and xenografts preserve different aspects of tumour biology (28–34,36–39,43).

For translation, background liver disease and drug handling need to be part of the same discussion. A proliferative module may point to cell-cycle or replication-stress testing, but transporter loss, CYP remodeling and weak hepatocyte identity can affect intracellular exposure and liver-specific toxicity. Future studies should therefore report not only whether tumour cells are inhibited, but also why the chosen model can support the pharmacological interpretation (20–27,36–39,43).

Future translational strategies should remain hypothesis-generating until they are tested in HCC-relevant models with state-matched readouts. Transcriptomic modules can prioritize questions about cell-cycle, replication-stress or metabolic dependencies, but they do not establish therapeutic efficacy by themselves (19,23–27,40–42).

Reporting habits also need to improve. Gene lists, preprocessing choices, ranking methods, cohort composition, model-selection rationale and validation status should be visible to readers. Negative validation results are worth reporting because they show where public transcriptomic predictions fail. The language should follow the evidence: a recurrent cell-cycle signal supports a proliferative-state hypothesis; it does not make every gene in the module a target. Loss of transporter or metabolic transcripts supports hepatocyte-identity erosion; it does not by itself prove drug resistance (7,14–19,28–34,36–39,43).

Conclusions

Across HCC transcriptomes, a recurring paired pattern is evident: Cell-cycle and DNA-replication programs often rise while differentiated hepatocyte transport, metabolic and defense functions decline. The proliferation-identity frame offers a practical way to read that pattern, select experimental models and plan validation without overstating what expression data can prove.

Expression data are most useful when they lead to testable biological questions. They are weakest when treated as proof of mechanism or therapeutic effect. A state-aware reading asks a limited set of questions before stronger claims are made: Which cell state is being represented, which model retains it, what phenotype is expected to change and what evidence would be sufficient for the claim.

Acknowledgements

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Funding

Funding: No funding was received.

Availability of data and materials

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Authors' contributions

JH and LL developed the review concept, including the proliferation-identity pairing as the central focus of the manuscript. JH performed the literature search and synthesis and wrote the first draft. BZ and LL reviewed and edited the manuscript. LL supervised the work. Data authentication is not applicable. All authors read and approved the final manuscript. All authors take responsibility for the accuracy of the literature synthesis and interpretation presented.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Use of artificial intelligence tools

During the preparation of this work, AI tools were used to improve the readability and language of the manuscript, and subsequently, the authors revised and edited the content produced by the AI tools as necessary, taking full responsibility for the ultimate content of the present manuscript.

Glossary

Abbreviations

Abbreviations:

References