Liu Yang, Peng Fan, Wang Siyuan, Jiao Huanmin, Zhou Kaixiang, Guo Wenjie, Guo Shanshan, Dang Miao, Zhang Huanqin, Zhou Weizheng, Guo Xu, Xing Jinliang
State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and Department of Physiology and Pathophysiology, Fourth Military Medical University, Xi'an, China.
Department of Clinical Diagnosis, Tangdu Hospital, Fourth Military Medical University, Xi'an, China.
Clin Mol Hepatol. 2025 Jan;31(1):196-212. doi: 10.3350/cmh.2024.0527. Epub 2024 Oct 15.
BACKGROUND/AIMS: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA).
Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC).
The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively).
We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.
背景/目的：肝细胞癌（HCC）患者的早期检测和有效的预后预测为提高生存率提供了途径，但仍亟需更有效的方法。我们试图基于循环游离线粒体DNA（ccf-mtDNA）的片段组学特征开发具有超敏感性和低成本的检测及预后模型。
对1168名参与者的血浆游离DNA样本进行基于捕获的线粒体DNA测序，其中包括571例HCC患者、301例慢性乙型肝炎或肝硬化（CHB/LC）患者和296名健康对照（HC）。
系统分析显示，与CHB/LC组和HC组相比，HCC组中ccf-mtDNA的片段组学特征存在显著异常。此外，我们利用ccf-mtDNA片段组学特征构建了基于随机森林算法的HCC检测模型。内部验证队列和两个外部验证队列均表明，我们的模型在区分早期HCC患者与HC以及CHB/LC高危人群方面具有出色的能力，其曲线下面积（AUC）分别超过0.983和0.981，灵敏度超过89.6%和89.61%，特异性超过98.20%和95.00%，大大超过了甲胎蛋白（AFP）和线粒体DNA拷贝数的性能。我们还通过LASSO-Cox回归开发了一个HCC预后预测模型，以选择20个片段组学特征，该模型在预测1年、2年和3年生存率方面表现出卓越的能力（验证队列的AUC分别为0.8333、0.8145和0.7958）。
我们在一个大型临床队列中基于异常的ccf-mtDNA片段组学特征开发并验证了一种高性能、低成本的方法，在HCC患者的早期检测和预后预测方面具有良好的临床转化应用前景。
