Plasma proteomic signature for preoperative prediction of microvascular invasion in HCC

Microvascular invasion (MVI) is a major determinant of poor prognosis in hepatocellular carcinoma (HCC). However, reliable noninvasive biomarkers for the preoperative evaluation and diagnosis of MVI are urgently needed in clinical practice.

Plasma samples were collected from 160 HCC patients (80 MVI-positive and 80 MVI-negative patients) from four medical centers. Plasma proteomic profiling was obtained using data-independent acquisition mass spectrometry (DIA-MS). Principal component analysis and differential protein abundance analysis were used to assess the proteomic changes between the two groups of patients. Protein biomarker candidates were further quantitatively validated by enzyme-linked immunosorbent assay (ELISA).

Proteomic analysis of 50 HCC patients (25 MVI-positive and 25 MVI-negative) identified three plasma protein biomarkers (TALDO1, PDIA3, and PGK1) which are significantly upregulated in MVI-positive patients (FDR-adjusted p< 0.05) and subsequently were cross-validated by ELISA. A machine learning-based Plasma pRotein MVI risk Model (PRIM) was developed for the preoperative prediction of MVI. The PRIM model demonstrated excellent discriminatory ability, with areas under the receiver operating characteristic curve (AUROC) values ranging from 0.78 to 0.99 across three independent cohorts. Single-cell RNA sequencing of five HCC tumors provided a cell type-resolved atlas of biomarker expression, showing their predominant presence in malignant cells and macrophages within the MVI+ tumor microenvironment compared to MVI- tumors.

This study provides a comprehensive analysis of the plasma proteomic landscape in HCC and presents a promising blood-based tool for preoperative MVI risk stratification.

This study highlights the transformative potential of plasma proteomic profiling in improving the preoperative prediction of microvascular invasion in hepatocellular carcinoma. By integrating data-independent acquisition mass spectrometry and with machine learning, we identified three plasma protein biomarkers (TALDO1, PDIA3, and PGK1) and developed the Plasma pRotein MVI risk Model (PRIM), which demonstrated robust diagnostic accuracy across multicenter validation cohorts. These findings pave the way for preoperative risk stratification and personalized therapeutic strategies in HCC management.