cfDNA-derived gene signatures as surrogate for microvascular invasion in HCC.

BACKGROUND & AIMS: Microvascular invasion (MVI) is a critical prognostic risk factor in hepatocellular carcinoma (HCC). We evaluated the performance of 5-hydroxymethylcytosine (5hmC) modifications in circulating cell-free DNA (cfDNA) in preoperative assessment of MVI.

METHODS

A total of 907 patients with HCC were enrolled from two centers, including 671 in the training cohort, 152 in the internal validation cohort, and 84 in the external validation cohort. Preoperative clinical data, laboratory parameters, and cfDNA-derived 5hmC profiles were collected. Feature selection was performed using XGBoost, and modeling was conducted using a multilayer perceptron neural network. Survival analyses were performed to evaluate the prognostic significance of the MVI prediction model. RNA sequencing analysis was performed to explore the potential mechanism underlying the proposed model.

RESULTS

The 181-5hmC-modification signature demonstrated strong discriminatory performance, achieving an area under the curve of 0.852, 0.862, and 0.864 in the training, internal validation, and external validation cohorts, respectively. Univariate and multivariate analyses identified the alpha-fetoprotein level (odds ratio [OR] 1.576, p = 0.039), Barcelona Clinic Liver Cancer stage (OR 3.051, p <0.001), and the 5hmC signature (OR 46.891, p <0.001) as independent predictors of MVI. The 5hmC signature demonstrated significantly higher predictive accuracy than alpha-fetoprotein levels or BCLC stage alone. Survival analysis showed that the 5hmC signature significantly stratified both recurrence-free and overall survival in patients with resectable HCC. Furthermore, interpretability analysis based on RNA sequencing revealed that lower MVI prediction scores were associated with immune-related pathways and immune infiltration levels.

CONCLUSIONS

We developed and validated a circulating cfDNA-derived 5hmC signature that non-invasively predicts preoperative MVI status, with potential clinical utility in the management of resectable HCC.

IMPACT AND IMPLICATIONS: In this study, we present the first integration of cfDNA-derived 5hmC profiling with machine learning for preoperative MVI prediction in resectable HCC. The proposed 5hmC signature demonstrates potential for predicting MVI status and prognosis before surgery. Integration of RNA sequencing analysis provides biological support for the model's predictions, strengthening its clinical relevance. As a blood-based assay, this approach offers practical advantages for potential routine clinical implementation.