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

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.