We read with great interest the large-scale, longitudinal study recently published in Clinical and Molecular Hepatology, which characterizes the kinetics of plasma pregenomics HBV RNA (pgRNA) in nucleoside analogue (NUC)-treated chronic hepatitis B patients over a five-year period [1]. This work provides valuable insights into the long-term transcriptional activity of covalently closed circular DNA (cccDNA) under sustained antiviral suppression, a topic of growing clinical relevance amid efforts to define biomarkers of “functional cure.” However, several methodological aspects and interpretive implications merit further consideration, particularly regarding the heterogeneity of pgRNA expression across subgroups and the role of assay design in shaping our understanding of its prognostic value.

A central finding is the discrepancy between the high suppression of HBV DNA (>93%) and persistently detectable pgRNA in most patients (approximately 84% at year five). The authors correctly attribute this to the cccDNA transcriptional origin of pgRNA and the limited upstream effect of NUCs. However, the implications extend beyond the pharmacological mechanism. Interestingly, pgRNA levels were lower in patients with classical high-risk hepatocellular carcinoma (HCC) features, such as older age, male sex, and high PAGE-B scores. This counterintuitive pattern raises an important question: Is pgRNA a passive marker of viral activity, or does it reflect a host-viral equilibrium shaped by immune aging and hepatocellular turnover? Existing literature suggests that immunosenescence and the epigenetic silencing of cccDNA may suppress viral transcription in older individuals without reflecting true disease quiescence [2]. In this context, a low pgRNA level may not uniformly denote low risk, especially in patients with impaired immune surveillance or reduced hepatocyte regeneration. Rather than applying a universal cutoff, pgRNA interpretation likely requires stratification by age, sex, fibrosis status, and NUC duration. The authors touch on this point, but the clinical implications—particularly in determining eligibility for “stop-to-cure” strategies—deserve further exploration. Could stopping NUCs prematurely in patients with low pgRNA but poor immune competence paradoxically increase the risk of relapse?

Another dimension that merits deeper reflection is the reported lack of association between pgRNA levels and HCC development. This finding contrasts with previous evidence and prompts a closer examination of the assay design. The current study employed an assay that targeted full-length pgRNA and omitted the detection of truncated or spliced variants. However, recent evidence suggests that these alternative RNA forms may play distinct roles in hepatocarcinogenesis, potentially through integration or immune modulation [3]. Could the absence of an association in this study be due more to incomplete molecular resolution than to biological irrelevance? Furthermore, given the relatively short HCC follow-up period (median 3.6 years) and the high baseline prevalence of cirrhosis (>25%), it is possible that the prognostic value of pgRNA was obscured by more significant risk factors. Baseline HBV DNA showed an inverse association with HCC risk, which is opposite to the classical REVEAL-HBV findings and reflects the complexity of viral dynamics in cirrhotic hosts.4 Perhaps it is time to reevaluate our understanding of viral biomarkers in chronic hepatitis B, especially when immune architecture, fibrotic remodeling, and treatment-induced transcriptional silencing all converge.

In sum, this study enhances our understanding of pgRNA kinetics under long-term NUC. However, it also highlights two key limitations. First, pgRNA levels require personalized interpretation rather than population-averaged interpretation. Second, advanced, standardized assays are necessary to capture the full spectrum of HBV RNA species. Without addressing these issues, efforts to use pgRNA for therapeutic guidance or cancer risk prediction could lead to over- or undertreatment of the chronic hepatitis B population. To overcome these limitations, future research may benefit from integrative frameworks that move beyond isolated biomarker quantification. Simulation-based models that incorporate intracellular viral intermediates—such as relaxed circular DNA, pgRNA, and complete virions— within hepatocytes could provide a more holistic view of HBV dynamics. Leveraging artificial intelligence or systems biology approaches, such models may capture the nonlinear host-virus interactions shaped by immune aging, cccDNA epigenetic regulation, and hepatocellular turnover. Ultimately, such tools may help improve risk prediction and therapeutic decision-making in chronic hepatitis B, especially in patients with atypical biomarker profiles.

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