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

Weiwei Zhu ^1,2,3,4 Huifen Wang ^3,4 Yudie Cai ^3,4 Jun Lei ^3,4 Jia Yu ^3,4 Li Ang ⁴ Zujiang Yu ^3*

• ¹ Zhengzhou University, Zhengzhou, China
• ² First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
• ³ Department of Infectious Diseases, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China
• ⁴ Gene Hospital of Henan Province, Center for Precision Medicine, First Affiliated Hospital of Zhengzhou University, Zhengzhou city, China

Background: DNA methylation carrying epigenetic aberrations could potentially serve as a noninvasive 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.