# Plasma methylated HIST1H3G as a non-invasive biomarker for diagnostic modeling of hepatocellular carcinoma

## TL;DR

This study explores plasma methylated HIST1H3G as a non-invasive biomarker for diagnosing and predicting outcomes in hepatocellular carcinoma.

## Contribution

The study introduces a novel diagnostic and prognostic model using plasma HIST1H3G methylation for hepatocellular carcinoma.

## Key findings

- Plasma HIST1H3G methylation was significantly elevated in hepatocellular carcinoma patients.
- A diagnostic model combining HIST1H3G with clinical indicators outperformed alpha-fetoprotein in detecting HCC.
- A prognostic model using HIST1H3G and albumin effectively predicted survival outcomes in HCC patients.

## Abstract

DNA methylation carrying epigenetic aberrations could potentially serve as a non-invasive tool for revolutionizing cancer diagnosis and monitoring. Here, we comprehensively evaluated the diagnostic value of plasma methylated HIST1H3G, and constructed diagnostic and prognostic models aimed at facilitating early detection and improving the prognosis of hepatocellular carcinoma (HCC).

The level of HIST1H3G promoter methylation in HCC tissues was evaluated based on the UALCAN database, followed by validation through serum samples collected from HCC patients. We recruited 205 participants, encompassing 70 HCC patients, 79 liver cirrhosis (LC) patients, 46 hepatitis patients and 10 HCC patients before and after treatment with either transarterial chemoembolization (TACE) or radiofrequency ablation (RFA). Analysis of plasma HIST1H3G was performed using methylation-specific quantitative polymerase chain reaction (qPCR). Diagnostic and prognostic prediction models were formulated using the random forest algorithm, and the performance of these models was rigorously evaluated through receiver operating characteristics curve (ROC) analysis.

The methylation level of HIST1H3G was markedly elevated in both HCC tissues and plasma samples derived from HCC patients. HIST1H3G, PIVKA-II, total bilirubin (TBIL) and age were selected as the optimal markers and were included in the development of a diagnostic model. This model demonstrated superior accuracy in distinguishing HCC from high-risk populations, outperforming alpha-fetoprotein (AFP) in both the training cohort consisting of LC patients and the validation cohort comprising hepatitis patients. Additionally, HIST1H3G and albumin (Alb) were chosen to establish a prediction model for early HCC diagnosis, and this model exhibited a remarkable ability to identify early HCC. Furthermore, our prognostic prediction model proved effective in predicting the prognosis and survival outcomes of HCC patients.

Together, we identified and validated a diagnostic model that incorporated methylated HIST1H3G and clinically applicable serological indicators in HCC. The findings of our study established a pivotal foundation for the development of a non-invasive approach to identification and management in HCC.