Digital polymerase chain reaction detection of telomerase reverse transcriptase promoter mutations in hepatitis B virus related hepatocellular carcinoma

Abstract

This editorial provides an in-depth evaluation of the study by Aizimuaji et al published in the recent issue of the World Journal of Gastrointestinal Oncology, which investigates the prognostic value of the telomerase reverse transcriptase (TERT) promoter C228T mutation in hepatitis B virus-related hepatocellular carcinoma (HCC). The study highlights the innovative application of optimized digital polymerase chain reaction technology to improve the sensitivity of TERT mutation detection. The results demonstrate that the TERT C228T mutation is significantly associated with reduced overall survival and disease-free survival, identifying it as a potential biomarker for poor prognosis. Additionally, the study successfully developed a nomogram model incorporating TERT mutation status, which significantly enhances the accuracy of postoperative recurrence risk prediction. This editorial further discusses the biological significance of TERT mutations in hepatitis B virus-related HCC, emphasizing their role as an early driver of liver carcinogenesis. While digital polymerase chain reaction offers significant advantages over traditional Sanger sequencing, providing a more precise detection tool for clinical applications, large-scale and multi-center validation is still required for broader clinical adoption. Moreover, the editorial explores the potential of integrating TERT mutation detection with other molecular biomarkers for a more comprehensive approach to personalized treatment, providing valuable insights for future precision therapy in liver cancer. This study not only opens new avenues for individualized treatment and early intervention in HCC but also serves as an important reference for the future development of liver cancer biomarkers.

Key Words: Telomerase reverse transcriptase promoter mutation; Digital polymerase chain reaction; Hepatitis B virus; Hepatocellular carcinoma; Telomerase activation; Prognostic biomarker; Liquid biopsy

Core Tip: This study provides the first systematic evidence that an optimized digital polymerase chain reaction assay can achieve ultra-sensitive detection of the guanine-cytosine-rich telomerase reverse transcriptase (TERT) promoter C228T mutation in hepatitis B virus-related hepatocellular carcinoma, with 100% sensitivity and a detection limit of 0.55 cp/μL - far surpassing traditional sequencing methods. By integrating TERT mutation status with clinical variables, we developed a prognostic nomogram that accurately predicts overall and disease-free survival, enabling early recurrence detection through ctDNA-based liquid biopsy. This work bridges molecular mechanism and clinical translation, establishing TERT mutation detection by digital polymerase chain reaction as a cornerstone technology for precision hepatocarcinology and individualized postoperative surveillance.

This editorial refers to “Optimized digital polymerase chain reaction enables detection of telomerase reverse transcriptase C228T mutation for prognostic assessment in hepatocellular carcinoma” by Aizimuaji et al, 2025; https://doi.org/10.4251/wjgo.v17.i12.113289.

---

INTRODUCTION

Primary liver cancer ranks third in tumor-related deaths worldwide, with more than 75%-85% of cases being hepatocellular carcinoma (HCC)[1], and in Asia and especially in China, hepatitis B virus (HBV) infection continues to be a main cause of HCC development[2]. Although there have been remarkable progresses for liver resection, liver transplantation, radiofrequency ablation, and immune therapies over the past few years, the postoperative recurrence rate still remained around 70%, which resulted in very little change of overall survival (OS) and disease-free survival (DFS)[3].

High incidence rates are fundamental because of its molecular heterogeneity and complex carcinogenesis route; and recently through development of whole genome sequencing techniques, early driver mutations for HCC are being progressively identified; for instance, telomerase reverse transcriptase (TERT) promoter mutations are the most frequently detected genetic alterations[4,5]. TERT only represents an early molecular driver of liver cancer and is closely related to the unlimited replicative potential, anti-apoptotic ability, and immune evasiveness of the tumor[6]. Additionally, after the discovery of the C228T site mutation, TERT has become an important molecular marker used for precise diagnosis and prognostic evaluation of HCC, and several recent multi-center reports have shown that when combined with TP53 mutation, TERT mutations can indicate postoperative recurrence and a poor prognosis[4]. However, due to the high guanine-cytosine (GC) content (> 80%) of the TERT promoter region, traditional Sanger sequencing and next-generation sequencing (NGS) techniques have relatively low amplification efficiency in this area and low sensitivity for detecting mutations, which hinders their clinical application[7].

In this field, digital polymerase chain reaction (dPCR), using absolute quantification and ultra-high sensitivity with low template detection capability, is employed to overcome this technical barrier[8,9]. A study showed that dPCR could not only detect TERT promoter C228T mutations but also track their dynamic alterations in ctDNA with sensitivity enough to monitor for recurrence through this molecular evidence[10].

A study by Aizimuaji et al[11] published in the recent issue of the World Journal of Gastrointestinal Oncology in 2025 has shown the usefulness of the optimization dPCR method to detect the TERT C228T mutation of HBV-related HCC patients. When compared with Sanger sequencing, it shows better detection performance. It not only found a significant relationship between the TERT C228T mutation and poor survival (OS), and poor tumor control (DFS) but also developed a nomogram according to the mutation status of each patient to predict the prognosis of those HCC patients, which will help guide clinicians to make decision-making about treatment plans in advance.

This article will comprehensively explain the biological significance of TERT promoter mutations of HBV-related HCC, innovative detection methods in terms of dPCR, and its clinical translational value for prognosis assessment based on research as well as recent international advances.

THE STUDY OF MOLECULAR MECHANISMS OF TERT PROMOTER MUTATIONS, CLINICAL SIGNIFICANCE AND THERAPEUTIC PROSPECTS IN HBV-RELATED HCC

Molecular mechanism and functional network

The TERT gene, a component of the catalytic subunit of the telomerase complex, lies on chromosome 5p15.33 and comprises of 16 exons and 15 introns. Telomerase is made up of the TERT protein and the telomerase RNA component, and it helps maintain telomere length and chromosome stability so that cells do not become damaged from uncontrolled division or other types of cellular injury[12,13]. In normal hepatocytes TERT expression is tightly regulated, but in HCC, TERT overactivation is viewed as an important mechanism by which hepatocytes become immortal and tumorigenic[14].

As shown in Figure 1, TERT promoter mutations generally concentrate on two hotspots sites - C228T and C250T. These sites sit 124 bp, and 146 bp upstream of transcription start site, respectively. The mutation generates a novel E-twenty-six family transcription factor-binding site which would elevate the TERT-transcriptional activity[15]. The C228T mutation, accounting for roughly 93% of all TERT mutations, demonstrates a transcriptional enhancement that is 3-5 times more potent than the wild-type variant[10]. Mechanistically, the mutated TERT promoter can recruit the E-twenty-six/Ets-like protein 1; complex, and through the phosphorylation amplification effect mediated by the mitogen-activated protein kinases signaling pathway, it significantly increases telomerase activity, promoting unlimited cell proliferation[16]. Epigenetic studies show that the region near the TERT mutation sites is enriched with H3K4me3 and H3K27ac modifications, suggesting that the promoter region exhibits an open chromatin conformation and is in a highly transcriptionally active state[17].

Figure 1 Integrated molecular model. Illustrating how hepatitis B virus DNA integration and telomerase reverse transcriptase promoter mutations synergistically activate telomerase, enhance oncogenic signaling (mitogen-activated protein kinase/E-twenty-six, nuclear factor kappaB, Wnt/β-catenin), promote epithelial-mesenchymal transition (epithelial-mesenchymal transition), and drive immune evasion, contributing to recurrence in hepatitis B virus-related hepatocellular carcinoma. TERT: Telomerase reverse transcriptase; ETS: E-twenty-six; IT: Integrated transcription; HBV: Hepatitis B virus; HBx: Hepatitis B virus X protein; MAPK: Mitogen-activated protein kinase; EMT: Epithelial-mesenchymal transition; ELKI1: Ets-like protein 1; PD-L1: Programmed death ligand-1.

HBV infection enhances TERT activation. Two mechanisms contribute to TERT activation: First, HBV-DNA can insert directly into the host cell’s TERT promoter, introducing viral enhancers (enhancer I/II), promoting continuous transcription[18]; second, the HBV X protein can also increase TERT transcription through the interaction between HBV X protein and both Sp1 and c-Myc[19]. Genomic studies reveal that HBV-TERT integration is found in about 35%-40% of HBV-associated HCCs, but rarely present in non-tumorous tissue (less than 3%). As such, we can infer that this represents a specific virus-induced cancerization mechanism[20]. Both TERT mutation and HBV integration exist simultaneously, enhancing the TERT transcription activity through synergism, which is closely related to the postoperative recurrence risk of HCC[21].

In addition, TERT mutations represent one of the very first molecular events associated with human HCC development and were frequently detected in cirrhosis or high-grade dysplastic hyperplasia before frequent driver mutations such as TP53 or catenin beta 1 (CTNNB1) occur[22]. Nault et al[22] conducted a large multicentered study which uncovered around 40%-60% HCC cases harbor a TERT promoter mutation. These alterations occur at the earliest stages of hepatocarcinogenesis and can be detected during cirrhosis or high-grade dysplastic hyperplasia, preceding common driver mutations such as TP53 and CTNNB1[22]. Nault et al[12] conducted a large-scale multicenter study and reported that roughly 40% to 60% of HCC cases harbor TERT promoter mutations, with the highest mutation frequency detected in liver cancers associated with viral infections - specifically HBV or hepatitis C virus. On the other hand, is driven by TERT activation across multiple molecular processes such as Wnt/β-catenin signal activation for maintaining stemness and epithelial-mesenchymal transition; p21/p53 suppression to sustain replication and nuclear factor kappaB inflammatory axis stimulation to modulate the immunosuppressive microenvironment[23,24]. The TERT mutant HCC reveals less infiltration of CD8+ T cell at single-cell level, suggesting the linkage with immune evasion mechanisms[25,26]. In summary, TERT mutations fuel the initiation and malignant progression of HBV-related liver cancer via a layered regulatory network encompassing viral integration, telomere activation, and signal amplification.

Epidemiological features and clinical significance

Epidemiological studies reveal that TERT promoter mutations are detected in approximately 40%-60% of HCC patients globally, with a significantly higher mutation rate observed in HBV-related HCC compared to hepatitis C virus-associated or alcoholic liver cancer[27,28]. In 2016, TERT C228T was observed in 30% of the 66 HBV-related HCC patients studied and accounted for almost all TERT mutations (98.8%)[29]. Mutation frequencies among Asian male patients are notably higher than those in females, indicating that sex hormones may play a role in regulating TERT mutations[30]. Furthermore, regionally speaking, the coexistence rate of TERT mutation and HBV integration can reach 65% in Chinese, Korean, or Southeast Asian regions, but in non-viral HCC cases of Europe or America it stays less than 30%, which may be related to the selective integration tendency of HBV genotypes B/C and/or the compromised DNA repair deficiency induced by chronic inflammation[31].

Medically, TERT mutation-positive patients were mostly older than 55 and presented with poorly differentiated tumors and had a markedly increased level of γ-glutamyl transpeptidase (GGT), compared to those of wild type (P < 0.05). However, there was no clear correlation with tumor size, cirrhosis or Barcelona Clinic Liver Cancer staging[32,33]. Patients with TERT mutation displayed significantly lower OS (P = 0.028) and DFS (P = 0.002) as shown by Kaplan-Meier analysis.

It should also be noted that, among patients with alpha-fetoprotein (AFP) ≤ 200 ng/mL, TERT mutations have strong predictive power of recurrence and an independent predictive value in HCC-AFP negative subjects. Multivariate Cox regression analysis showed that elevated GGT, blood vessel invasion (BVI) and TERT mutation were independent adverse predictors of DFS (hazard ratio = 2.43, P = 0. 007)[34].

On histological examination, TERT-mutant HCC frequently exhibits moderate to poor tumor differentiation, presence of microvascular invasion (MVI), and elevated Ki-67 positivity (> 30%)[35,36]. At a molecular level, TERT mutations are associated with co-existing TP53 mutations and Wnt/β-catenin activation, and mutually exclusively with CTNNB1 mutations, indicating distinct evolutive molecular subtypes. Immunohistochemistry analysis showed that mutated TERT tumors presented overexpressed nuclear TERT, consistent with telomerase activation[37]. Overall, TERT mutations appear to be early drivers of HBV related-HCC, as well as poor prognostic markers due to increased invasiveness, providing potential biomarkers for risk stratification, targeted interventions, and personalized management.

Moreover, Aizimuaji et al[11] used TERT mutation data to develop an additional postoperative nomogram for HBV-related HCC that incorporated aspartate aminotransferase, GGT, MVI, and BVI in addition to clinical variables, and obtained a complete prediction system which integrated both molecular information and clinical factors. The C-indexes for OS and DFS were 0.7651 and 0.6899, respectively; after adjusting for the TERT mutation status, both were superior to those lacking TERT status (0.7498 and 0.6492). Building on this, Aizimuaji et al[11] further incorporated TERT mutations into a postoperative nomogram model for HBV-related HCC, combining variables such as aspartate aminotransferase, GGT, MVI, and BVI to establish a comprehensive prediction system with both molecular and clinical features. The C-index of the model for OS and DFS was 0.7651 and 0.6899, respectively, both superior to models without incorporating TERT status (0.7498 and 0.6492). Receiver operating characteristic curve analysis revealed that the model achieved area under the curves (AUCs) of 0.834, 0.800, and 0.614 for 1-year, 2-year, and 5-year OS predictions, respectively, and 0.849, 0.772, and 0.688 for DFS predictions. These findings confirm the significant predictive utility of TERT mutations in HCC prognosis. Decision curve analysis and calibration curves verified the model’s high-risk population clinical decision value and stability. The contribution of TERT mutations was greatest, making them the most important model parameters for predicting recurrent risk.

Treatment response and translational prospects of TERT mutations

TERT mutations influence tumor growth and determine cancer’s therapeutic response. There are strong evidences that a common multi-omics phenotypic profile can be detected within TERT-mutated HCC, with cold-tumor signature and the characteristic including low infiltration of CD8+ T cells, moderate over-expression of programmed death ligand-1 (PD-L1), transforming growth factor-β, and interleukin-10 as well as decreased expression of human leukocyte antigen A and beta-2-microglobulin[38-42]. Zhu et al[43] published a study in Nature Medicine demonstrating that TERT-mutant patients exhibited significantly reduced objective response rates to combination immunotherapy with atezolizumab and bevacizumab. In contrast, PD-L1 expression and tumor mutational burden did not differ significantly between cohorts. The findings indicate that TERT mutations may inhibit the type I interferon response through the cyclic guanosine monophosphate-adenosine monophosphate synthase-stimulator of interferon genes pathway, leading to decreased immunogenicity and attenuated efficacy of immune checkpoint inhibitors[43].

Targeted treatments for TERT are rapidly being developed. TERT vaccines (such as GV1001, GRNVAC1) act by stimulating TERT-specific CD8+ T cell immune clearance. Other approaches include antisense oligonucleotides and small molecule inhibitors (e.g., imetelstat), which interfere with TERT-telomerase RNA component interactions to block telomerase activity. Using TERT-vaccine in combination with programmed death 1/PD-L1 inhibitors shows synergistic effects, and TERT mutation has been found to correlate with durable remissions of HCC (other than HBV and alcohol-related HCCs). As a result, TERT status may serve as a biomarker for determining which individuals might be appropriate candidates for this type of treatment[43-45].

On the contrary, TERT mutations lead to over-expression of angiogenesis signaling. Aizimuaji et al[11] indicated that the expression levels of both vascular endothelial growth factor-A and fibroblast growth factor receptor 1 were substantially increased in TERT-mutation-positive patients, which may imply that these patients might respond better to anti-angiogenic therapy like Bevacizumab. In the near future, TERT mutation combined with angiogenesis gene signature built a dual-dimensional predictive model based on immune and angiogenesis characteristic will be feasible to help guide precise stratification for immune-targeted combination therapy.

DPCR TECHNOLOGY IN TERT MUTATION DETECTION: PRINCIPLES, OPTIMIZATION, AND CLINICAL APPLICATIONS

Technical principles of dPCR and challenges in TERT detection

The dPCR is an absolute quantitative nucleic acid analytical technique utilizing endpoint detection method. The fundamental thought behind it is: To partition the normal polymerase chain reaction system into numerous micro reaction units thousands in number, each of which contains either zero or one targeted molecules. Post-amplification, positive and negative microdroplets undergo binary classification, and absolute quantification of the target molecule is achieved by integrating a Poisson distribution statistical model - eliminating the need for a standard curve[46]. Compared with traditional quantitative polymerase chain reaction, dPCR shows significant advantages in sensitivity, anti-inhibition capacity, and quantification accuracy; it can identify mutation at a single-copy level and is extremely sensitive to rare events, unaffected by amplification efficiency changes[47]. Thanks to the exact molecular counting, dPCR can outperform in analyzing tumor heterogeneity, minimal residual disease MRD, liquid biopsy specimen analysis and can be a useful platform for identifying GC-rich and structurally complex regions like TERT promoter (C228T/C250T) that could not be explored adequately with previous technologies[48].

Although the TERT promoter region is not low-melting-point DNA, it has a GC content higher than 80%, allowing the formation of a stable hairpin or secondary structure that drastically reduces amplification efficiency. The traditional polymerase chain reaction system has problems such as non-specific amplification, denaturing difficulty of templates, and polymerase stalling at this location, so the detection sensitivity is low[49]. However, the TERT promoter region has a GC content exceeding 80%, which can form stable hairpins and secondary structures, significantly reducing amplification efficiency. Traditional polymerase chain reaction systems often encounter non-specific amplification, difficulty in template denaturation, and polymerase stalling in this region, limiting detection sensitivity[49]. Notably, the C228T mutation site - located close to the transcription start site - exhibits high susceptibility to DNA methylation, base oxidation, and local conformational changes, thereby exacerbating the challenges in detection. The site is also one of the most prominent reasons why traditional Sanger sequencing has poor sensitivity (detection limit of approximately 5%-10%), and why signal-to-noise ratio and NGS coverage is not good enough on this site[50]. So, overcoming the high GC structural barrier in the TERT promoter area is a very critical step toward high sensitivity mutation detection.

Optimization of the dPCR system and performance validation

To tackle the unique challenges faced during TERT promoter detection, Aizimuaji et al[11] optimized the dPCR system and constructed an ultra-sensitive TERT C228T mutation detection platform. As shown in Figure 2, The reaction system was supplemented with 7-deaza-deoxyguanosine triphosphate (dGTP), which substituted part of dGTP to weaken the GC stacking effect, lower the stability of secondary structures, and improve the polymerase extension efficiency. Concurrently, they employed CviQ1 restriction enzymes for selective cleavage of GC-rich regions, thereby lowering template complexity and improving microdroplet signal clustering resolution. To further address the persistent amplification inefficiency caused by strong secondary structures in the GC-rich TERT promoter region (> 80% GC content), the authors introduced a CviQ1 restriction endonuclease-assisted pre-treatment step to selectively cleave GC-rich non-target regions before amplification.

Figure 2 Optimized digital polymerase chain reaction system for ultra-sensitive detection of the guanine-cytosine-rich telomerase reverse transcriptase promoter C228T mutation. A: Workflow schematic: Illustration of the digital polymerase chain reaction (dPCR) process, including sample loading, droplet partitioning, thermal cycling, fluorescence signal detection, and Poisson-based absolute quantification. This system enables precise molecular counting through endpoint fluorescence analysis; B: Optimization strategies: The guanine-cytosine-rich telomerase reverse transcriptase promoter region [> 80% guanine-cytosine (GC) content] forms stable secondary structures that hinder amplification. Optimization with 7-deaza-dPCR (reducing GC stacking), CviQ1 restriction enzyme digestion (simplifying templates), and Mg²⁺/ethylenediaminetetraacetic acid balancing (enhancing specificity and signal strength) effectively overcomes GC-barriers and improves amplification efficiency; C: Performance validation: Comparative analysis between dPCR and Sanger sequencing demonstrates superior performance of dPCR, achieving 100% sensitivity, 90% specificity, and a detection limit of 0.55 cp/μL. Representative fluorescence droplet images illustrate clear separation of positive (green) and negative (gray) droplets at the endpoint. dPCR: Digital polymerase chain reaction; GER: G-quadruplex-forming element-rich; GC: Guanine-cytosine; LOD: Limit of detection; PCR: Polymerase chain reaction; TERT: Telomerase reverse transcriptase; dGTP: Deoxyguanosine triphosphate; EDTA: Ethylenediaminetetraacetic acid.

Mechanistically, CviQ1 is a thermostable restriction endonuclease that recognizes the 5’-GTAC-3’ sequence and introduces single-stranded nicks preferentially in GC-enriched regions. This enzymatic digestion effectively disrupts the high-order DNA conformations (such as G-quadruplex and hairpin structures) commonly formed within the TERT promoter, thereby reducing template secondary structure complexity, improving DNA strand accessibility, and enhancing polymerase processivity during droplet partition amplification.

In this study, the reaction mixture was pre-incubated with 1 U/μL CviQ1 enzyme at 25 °C for 15 minutes before the thermal cycling process, ensuring partial fragmentation of GC-dense segments without degrading the target C228T mutation site. This treatment was followed by enzyme inactivation at 95 °C for 5 minutes during the initial denaturation step. The inclusion of CviQ1 digestion not only decreased background fluorescence cluster overlap in droplet analysis but also significantly improved signal discrimination between positive and negative droplets, enabling higher cluster resolution.

When combined with 7-deaza-dGTP substitution (10% molar replacement of dGTP) to weaken GC stacking interactions and fine-tuned Mg²⁺ (3.5 mmol/L) and ethylenediaminetetraacetic acid (0.2 mmol/L) concentrations for optimal amplification balance, the overall optimized dPCR system achieved a limit of detection (LOD) of 0.55 copies/μL, 100% sensitivity, and 90% specificity. This dual optimization approach - biochemical relaxation of GC tension via CviQ1 digestion and nucleotide substitution - allowed robust and reproducible detection of the TERT promoter C228T mutation even at trace levels.

After verification, we found that the optimal dPCR system has excellent detection accuracy (negative percentage of false positives is as high as 90%) and reliability (sensitivity of 100%), compared with Sanger sequencing (sensitivity only 39.4%) and the κ value obtained is 0.876, P < 0.001, which is a great improvement over Sanger sequencing. Of greater clinical relevance, dPCR successfully detected mutations undetected by Sanger sequencing yet present in postoperative recurrence patients, highlighting its utility in MRD detection and early recurrence prediction. Due to the absolute quantification characteristic of dPCR, we were able to convert the positive droplet rate into the mutation copy number so that we could precisely determine the TERT mutation frequency and thus was suitable to establish a multivariable prediction model based on integration with clinical variables including AFP, MVI and BVI. Although dPCR has demonstrated good reproducibility among different laboratories, its widespread clinical applications require the creation of standardized operating systems, with the establishment of uniform positive control templates, clear LOD/Limit of blank designations, standard reaction systems, as well as standardized cycling parameters to allow for easy promotion of TERT dPCR detection into multicenter clinical practice[51].

Clinical application prospects and translational potential

The optimized dPCR system showed excellent detection performance for HBV-related HCC tissue samples with extremely low detection limit (LOD = 0.55 cp/uL), enabling trace mutation detection from postoperative margin and early recurrence lesion in which traditional polymerase chain reaction could not identify any new oncogenic mutations; four patients were negative to Sanger sequencing but positive by dPCR at follow-up, confirming the dPCR advantage on monitoring MRD and predicting recurrence risk according to Aizimuaji et al[11].

dPCR is further utilized in not only tissue sample detection but also ctDNA detection in circulation for achieving non-invasive dynamic monitoring. Detecting the TERT mutation of ctDNA in the plasma allows us to detect the molecular signal 3-6 months prior to imaging relapse and provide valuable evidence of early intervention, efficacy evaluation and resistance monitoring[52]. Superior sensitivity and stability of dPCR have been demonstrated. Beyond molecular sensitivity, the integration of dPCR into existing clinical diagnostic and surveillance pathways represents a critical step toward clinical translation. In practice, dPCR-based TERT promoter mutation detection could be used in conjunction with conventional imaging (computed tomography/magnetic resonance imaging) and serological biomarkers such as AFP and protein induced by vitamin K absence or antagonist-II to form a multimodal surveillance algorithm. For example, a “molecular-imaging-biochemical triad” model could employ dPCR results to stratify postoperative patients into molecular high-risk or low-risk groups, guiding the frequency of imaging follow-up and the interpretation of subtle radiological changes. In AFP-negative HCC patients, in whom recurrence monitoring remains challenging, combining plasma TERT mutation quantification with imaging modalities significantly enhances sensitivity and timeliness for recurrence detection.

From a cost-effectiveness perspective, although the initial investment in dPCR platforms is higher than that for traditional qPCR, its lower per-sample cost, minimal reagent usage, and simplified workflow render it economically competitive for medium-scale to large-scale clinical laboratories. Modeling studies have suggested that early recurrence detection through dPCR-guided surveillance could reduce overall treatment costs by 15%-20% through earlier intervention and improved patient stratification. Moreover, compared with NGS, dPCR provides a targeted, low-cost alternative that can be readily implemented in regional and tertiary hospital laboratories without the need for complex bioinformatics infrastructure.

Nevertheless, several standardization and implementation challenges remain before dPCR can be fully embedded into routine clinical practice. These include the lack of harmonized reference materials, variability in droplet generation and fluorescence thresholding across platforms, and differences in LOD/Limit of blank, definitions between laboratories. To ensure reproducibility and cross-center comparability, it is imperative to establish internationally validated quality control frameworks, including standardized reaction systems, positive control templates, and inter-laboratory calibration procedures. Furthermore, regulatory guidance is required regarding sample handling, reporting thresholds, and clinical interpretation to align dPCR results with established diagnostic algorithms for HCC.

In summary, integrating dPCR-based TERT mutation detection into multi-modal clinical pathways combining molecular, imaging, and serological data represents the next frontier in precision hepatocarcinology. With ongoing standardization efforts and cost optimization, dPCR has the potential to become a routine, scalable, and economically viable platform for postoperative surveillance, early recurrence detection, and individualized management of HBV-related HCC[53].

DPCR AND MULTI-OMICS INTEGRATION: FROM MOLECULAR DETECTION TO PRECISION HEPATOCARCINOLOGY

Integration of dPCR and multi-omics: key pivot for precision validation

In modern liver cancer molecular diagnostics, single-gene testing is increasingly inadequate for capturing the complexity of the disease. The rapid development of multi-omics technologies (genomics, epigenomics, transcriptomics, metabolomics, and radiomics) has revealed the global molecular landscape of HCC, but its high cost, complex data analysis processes, and strict sample quality requirements have limited its clinical adoption[54]. dPCR, with its high sensitivity, absolute quantification capability, and excellent reproducibility, is becoming an indispensable “precision validation and quantification engine” in multi-omics systems[55].

At the genomic level, dPCR is employed to verify low-abundance mutations identified by NGS or whole-exome sequencing - particularly critical driver loci such as TERT, TP53, and CTNNB1 - thereby facilitating a high-confidence “sequencing discovery - dPCR validation” loop[56]. At the epigenetic level, dPCR enables quantification of TERT promoter methylation levels - when paired with mutation status - to assess transcriptional activation intensity, thereby elucidating the interplay between epigenetic regulation and genetic mutations[57]. In transcriptomics, use of dPCR facilitates quantification of TERT mRNA and its splice variants, allowing more precise studies of TERT transcriptional regulation[58]. Also in liquid biopsies, the dPCR technique can detect TERT mutations in ctDNA and cell-free RNA with relatively high levels of sensitivity, allowing for monitoring of low-abundance molecules in real time[59].

Thus, dPCR plays the role of a bridge between multi-omics researches, linking “high-throughput discovery” and “precision validation”, it complements the limitation of NGS in low-abundance mutation detection, meanwhile, offers dPCR quantifiable, detectable, and trackable detection of target molecules for clinical application, making the complementarity drive the conversion from multi-omics discovery to clinical reality[60].

TERT mutation system biology network and artificial intelligence-driven prediction

TERT promoter mutations are more than mere isolated transcriptional activation events; they integrate into intricate, multi-tiered molecular networks that govern diverse biological processes in HCC. Epigenetically, TERT mutations frequently coincide with elevated levels of activating histone modifications (e.g., H3K4me3, H3K27ac) in the promoter region, indicating that transcriptional activation relies on a permissive chromatin state[61,62]. At the level of cellular signaling, overexpression of TERT triggers activation of key signaling pathways - including Wnt/β-catenin, nuclear factor kappaB, and phosphatidylinositol 3-kinase/protein kinase B - thereby sustaining tumor stemness, enhancing cell migration, and inducing metabolic reprogramming[23]. Further metabolomics studies show that TERT-mutated HCC has increased levels of oxidative phosphorylation and fatty acid metabolism, which indicates a higher dependency on mitochondrial energy metabolism[63]. Immuno-genomic analysis demonstrates that TERT-mutant tumors present immune-evasion properties that include an up-regulation of PD-L1, transforming growth factor-β, and interleukin-10 and down-regulation of interferon signaling and antigen presentation capability, which results in dysfunctional T cells and the formation of an immunosuppressive microenvironment[64].

A multi-omics model constructed through analyses of TERT regulates tumorigenesis and determines both metabolic regulatory networks and immune cells; moreover, artificial intelligence (AI) machines able to transform these interactions between TERT and molecular networks into an effective predictive model and a specific and executable decision algorithm may provide novel prospects in TERT-led personalized therapy.

AI models, incorporating dPCR results and multi-omics profiles, enable preoperative non-invasive prediction of TERT mutations. For example, convolutional neural network models on computed tomography/magnetic resonance imaging radiomics feature and clinical indicators could reach AUC 0.87 for the prediction accuracy of TERT mutations[65]. Postoperatively, in order to rank recurred cancer risk of The Cancer Genome Atlas patients, integrating dPCR data into random forest or extreme gradient boosting could significantly enhance the predictive power of a model when it is compared to traditional Cox models by almost 10%, thus helping to detect high-risk individuals in a more accurate way[66]. At the same time, AI models could combine the TERT mutation status with the patient’s immunogenomic status to predict if the patient would benefit from treatment of immune-checkpoint inhibitors or anti-angiogenesis therapy, which could improve the doctors’ capability to give real-time treatment model to the patients.

For example, convolutional neural network models based on computed tomography/magnetic resonance imaging radiomics features and clinical indicators can achieve prediction accuracy for TERT mutations with an AUC as high as 0.87[64]. In postoperative recurrence risk stratification, integrating dPCR data with clinical variables in random forest or extreme gradient boosting algorithms improves model prediction accuracy by approximately 10% compared to traditional Cox models, significantly enhancing the ability to identify high-risk patients[66]. Moreover, AI models can merge TERT mutational status with immunogenomic profiles to predict the efficacy of immune or anti-angiogenesis regimens, thus enabling real-time modeling of treatment responses.

In the future, AI driven TERT intelligent decision systems are likely to incorporate the results of dPCR tests, multi-omics characteristics and clinical information into a real time scoring system that will close the loop in a robust manner from genetic to precision intervention based on molecular intelligence decision. This may advance liver cancer into molecular intelligence era.

Clinical translation and standardization prospects of liquid biopsy

The emergence of liquid biopsy has significantly expanded the utility for TERT mutation detection. By using droplet dPCR to detect TERT C228T mutations within plasma ctDNA, clinicians are able to provide early warnings and make dynamic assessments at different points during disease management. In high-risk HBV infected patients, a positive TERT mutation ctDNA status precedes imaging detection of tumors, thus providing an important window for early diagnosis[25]. During postoperative surveillance, elevated TERT ctDNA levels typically signal MRD or recurrence, occurring 2-3 months prior to AFP elevation[67]. During therapy, reduced TERT ctDNA levels correlate with immunotherapy response, whereas a persistent increase indicates treatment resistance or disease progression[68]. Studies by Dong et al[69] and Singh[70] confirm that dPCR in liquid biopsies achieves a detection limit of 0.1% - a sensitivity far exceeding NGS’s approximately of 2% threshold - underscoring its key technical advantage in identifying low-abundance mutations.

As shown in Figure 3, furthers combined with AI time series analysis researchers have been able to establish an individual “molecular recurrence curve” of TERT ctDNA according to the dynamic changes in the TERT ctDNA so that recurrence trend and intervention time can be visually predicted. The approach relocates the follow-up after surgery from the static point to the continuous dynamic risk evaluation, providing evidence for precise follow-up and effectiveness evaluation[71].

Figure 3 Clinical translation of telomerase reverse transcriptase mutation detection integrating molecular diagnostics, prognostic modeling, and artificial intelligence prediction. A: Nomogram model combining telomerase reverse transcriptase (TERT) mutation status with clinical variables (aspartate aminotransferase, γ-glutamyl transpeptidase, microvascular invasion, blood vessel invasion) predicts 1-year, 2-year, and 5-year overall survival/disease-free survival, distinguishing high-risk (red) and low-risk (blue) groups; B: Model performance comparison: Incorporation of TERT mutation improves predictive accuracy (area under the curve 0.83 vs 0.75) and decision curve benefit; C: Dynamic monitoring: Digital polymerase chain reaction-based ctDNA detection identifies TERT mutations 3-6 months before radiologic recurrence and alpha-fetoprotein elevation, enabling early relapse prediction; D: Artificial intelligence -integrated framework: Machine learning model (random forest/extreme gradient boosting) fuses molecular, clinical, and multi-omics data for individualized recurrence risk assessment. TERT: Telomerase reverse transcriptase; AST: Aspartate aminotransferase; GGT: γ-glutamyl transpeptidase; MVI: Microvascular invasion; BVI: Blood vessel invasion; dPCR: Digital polymerase chain reaction; AFP: Alpha-fetoprotein; AUC: Area under the curve; AI: Artificial intelligence; PCR: Polymerase chain reaction.

Although a wide range of TERT mutation detection systems are currently available, further efforts are still required regarding how to make them widely applicable with respect to sample standardization, data consistency, and ethical management issues. Plasma cfDNA extraction methods, storage conditions, and separation technologies can all impact detection accuracy, as well as mutation thresholds, false positives, and statistical algorithms[72]. Furthermore, since TERT mutation test results potentially represent genetic risk signals, it will be necessary to build a tiered-informed consent mechanism and a data privacy protection framework in the near future. Only once these requirements are met according to internationally recognized standards and ethical norms, will TERT mutation detection be truly usable on a global scale as an effective and replicable precision medicine tool for clinical action (Table 1).

Table 1 Summary of biological, technical, and clinical insights on telomerase reverse transcriptase promoter mutations and digital polymerase chain reaction applications in hepatitis B virus-related hepatocellular carcinoma.

Dimension	Key findings	Mechanisms/methodology	Clinical implications	Ref.
Epidemiology and background	TERT promoter mutations occur in 40%-60% of HCC, especially in HBV-related cases. Higher in Asian males (> 55 years) with elevated GGT and poor differentiation	HBV DNA integration at TERT locus; HBx-Sp1/c-Myc coactivation	Predictive biomarker for poor OS/DFS; early detection in cirrhosis or dysplasia	[1,2,30]
Molecular mechanisms	Two hotspot mutations: C228T (93%) and C250T (7%). Create novel ETS binding sites to enhanced transcription (3-5 times)	MAPK-ETS pathway activation; epigenetic marks (H3K4me3, H3K27ac)	Early oncogenic driver; telomerase hyperactivation; tumor immortalization	[13-15]
HBV-TERT synergy	35%-40% of HBV-HCC show direct viral integration near TERT promoter	HBV enhancer I/II insertion and HBx interaction drive persistent activation	Synergistic upregulation increases recurrence risk	[18,19]
Clinical and prognostic value	TERT mutation + TP53 mutation predict poor prognosis. Negative AFP (≤ 200 ng/mL) patients still show recurrence correlation	Nomogram combining TERT, AST, GGT, MVI, BVI achieves C-index 0.76 (OS) and 0.69 (DFS)	Independent predictor of recurrence; superior to non-molecular models	[20,29]
Digital PCR optimization	Overcomes high GC (> 80%) barrier of TERT region	Substitution with 7-deaza-dGTP; CviQ1 enzyme digestion; optimized Mg2+/EDTA ratios	LOD = 0.55 cp/μL; sensitivity 100%; κ = 0.876 (vs 0.39 for Sanger)	[46-48]
Liquid biopsy (ctDNA)	dPCR detects TERT C228T in plasma 3-6 months before imaging recurrence	MRD detection and dynamic risk monitoring via ctDNA quantification	Enables non-invasive follow-up and real-time recurrence surveillance	[51,52]
Immunotherapy and targeted therapy	TERT mutation = “cold tumor” phenotype with low CD8+ infiltration, PD-L1↑, TGF-β↑.	cGAS-STING suppression; immune evasion; anti-angiogenesis signature (VEGFA↑, FGFR1↑)	Poor ICI response; possible sensitivity to Bevacizumab or TERT vaccine (GV1001)	[43-45]
Multi-omics and AI integration	dPCR validates low-abundance mutations; AI models predict TERT mutation (AUC = 0.87)	Integrating dPCR, NGS, and radiomics via CNN, RF, XGBoost	Improves recurrence risk prediction (+10% vs Cox); enables personalized therapy	[56-58]
Future prospects	Standardized TERT-dPCR workflows and ethical data frameworks are essential	Integration with AI “molecular recurrence curve” and multimodal omics	Marks transition to “precision hepatocarcinology” - proactive HCC management	[60-62]

HBV: Hepatitis B virus; TERT: Telomerase reverse transcriptase; PCR: Polymerase chain reaction; AI: Artificial intelligence; HCC: Hepatocellular carcinoma; GGT: Γ-glutamyl transpeptidase; AFP: Alpha-fetoprotein; GC: Guanine-cytosine; dPCR: Digital polymerase chain reaction; PD-L1: Programmed death ligand-1; TGF: Transforming growth factor; AUC: Area under the curve; HBx: Hepatitis B virus X protein; MAPK: Mitogen-activated protein kinase; ETS: E-twenty-six; AST: Aspartate aminotransferase; MVI: Microvascular invasion; BVI: Blood vessel invasion; OS: Overall survival; DFS: Disease-free survival; EDTA: Ethylenediaminetetraacetic acid; MRD: Minimal residual disease; cGAS-STING: Cyclic guanosine monophosphate-adenosine monophosphate synthase -stimulator of interferon genes; VEGFA: Vascular endothelial growth factor-A; FGFR1: Fibroblast growth factor receptor 1; NGS: Next-generation sequencing; CNN: Convolutional neural network; RF: Random forest; XGBoost: Extreme gradient boosting; LOD: Limit of detection; ICI: Immune checkpoint inhibitor.

SUMMARY AND FUTURE DIRECTIONS

Globally, HCC ranks among the top cancers in both incidence and mortality, exhibiting highly complex molecular and pathological profiles[73]. TERT Promoter mutations represent a big portion of HBV-associated HCC tumorigenesis and growth from start to finish, along with repeating[74]. Following improvements on dPCR technology, screening for TERT mutation went from being used in laboratories to being put to clinical use, showing large jumps from detecting mutation to predicting risk, which has started a new era of precision molecular diagnosis of liver cancer.

Aizimuaji et al[11] presented the first systematic evidence that an optimized dPCR assay enables detection of TERT promoter C228T mutations in HBV-associated HCC with exceptional sensitivity and a low detection threshold. The assay achieved 100% clinical sensitivity and 90% specificity - performance metrics that markedly outperformed conventional Sanger sequencing. This study not only addressed amplification challenges in GC-rich regions but also constructed a postoperative prognostic nomogram incorporating TERT mutation status, markedly enhancing the accuracy of recurrence risk prediction (C-index: 0.7651 for OS, 0.6899 for DFS). It ushered the molecular diagnostics transition into detecting the presence to quantifying the risk, promoting the closer linking between the molecular pathology and clinical decision making, becoming a meaningful milestone for the precision medicine development of liver cancer.

TERT mutations function as both an early oncogenic driver and a central biological determinant throughout the entire trajectory of HCC[75]. They can guide clinical decision making preoperatively for risk stratification and surgical planning[76]; postoperatively for follow up and monitoring of tumor recurrence as well as therapeutic intervention measures[77]; in advanced stages as a predictive marker for immune- and targeted therapy response[78]; and in follow-up with liquid biopsy to monitor MRD in circulating tumor DNA[79]. The dPCR technology, as having super-high sensitivity and absolute quantification features, makes it possible for TERT mutation to be detected to enter into routine clinical testing, forming the technological basis and clinical practice capability of “precision hepatocarcinology”.

Going forward, advances in the depth of TERT mutation research and the expansion of its applications will manifest in the multi-dimensional integration and advancement of various domains. On a technical level, dPCR, NGS, and AI models will be used collaboratively for multi-level integration, and this approach will be employed from mutation detection to risk modeling to provide tumor precision quantified prediction. Concerning sample origins, detection will broaden from tissue specimens to diverse liquid biopsy specimens - including plasma, urine, and bile - enabling truly comprehensive molecular monitoring. Biologically, the combined analysis of TERT mutations and epigenomics, metabolomics, immunomics, and radiomics can uncover TERT’s systemic role in the progression of HCC and in developing therapeutic resistance, thereby constituting a TERT-driven integration network. Clinically, building standardized detection systems and improving the sharing and integration of data, TERT mutation detection will be incorporated in turn into HCC staging systems, treatment decision-making algorithms, and the precision treatment roadmap based on genetic stratification.

Concurrently, AI-assisted decision-making systems, along with multicenter clinical validation trials, will speed up the clinical application of postoperative TERT mutation management. Combined with the time series analysis and intelligent risk model, changes in TERT ctDNA over time may eventually become a ‘molecular recurrence curve’, which can predict trends for recurrence in real time, and make decisions to intervene. Multi-center collaborative research and international standardization will maintain consistency and reproducibility in TERT detection among different laboratories and across different populations.

Ethical and data security rules must be changed at the same pace as science and technology development in order to be able to control all possible risks and secure compliance with clinical applications of molecular information.

CONCLUSION

In summary, the presence of TERT promoter mutations and its combing with dPCR constitute a huge advancement for liver cancer molecular diagnosis. Not only does it improve the sensitivity and accuracy of detection but also successfully completes the loop from analyzing the underlying mechanism to clinical application. In the future, through multimodal platform cooperation, interdisciplinary integration, and AI driven development, TERT mutation detection would evolve from a prognostic biomarker to a decision-making biomarker to become the core center of connecting molecular detection, dynamic monitoring, and targeted treatment. This would lead to the transition of HBV-related HCC treatment from passive cure to proactive interference and the arrival of the era of precise hepatocarcinology.