Plasma proteomic signature for preoperative prediction of microvascular invasion in HCC
Background & Aims: Microvascular invasion (MVI) is a major determinant of poor prognosis in hepatocellular carcinoma (HCC). However, reliable non-invasive biomarkers for the preoperative evaluation and diagnosis of MVI are urgently needed in clinical practice. Methods: Plasma samples were co...
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Elsevier
2025-09-01
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| Series: | JHEP Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589555925001594 |
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| author | Xinrui Shi Yunzheng Zhao Ke Li Qingyu Li Yifeng Cui Yuhang Sui Liang Zhao Haonan Zhou Yongsheng Yang Jiajun Li Meng Zhou Zhaoyang Lu |
| author_facet | Xinrui Shi Yunzheng Zhao Ke Li Qingyu Li Yifeng Cui Yuhang Sui Liang Zhao Haonan Zhou Yongsheng Yang Jiajun Li Meng Zhou Zhaoyang Lu |
| author_sort | Xinrui Shi |
| collection | DOAJ |
| description | Background & Aims: Microvascular invasion (MVI) is a major determinant of poor prognosis in hepatocellular carcinoma (HCC). However, reliable non-invasive biomarkers for the preoperative evaluation and diagnosis of MVI are urgently needed in clinical practice. Methods: Plasma samples were collected from 160 patients with HCC (80 MVI-positive and 80 MVI-negative) from four medical centers. Plasma proteomic profiling was obtained using data-independent acquisition mass spectrometry. 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 ELISA. Results: Proteomic analysis of 50 patients with HCC (25 MVI-positive and 25 MVI-negative) identified three plasma protein biomarkers (TALDO1, PDIA3, and PGK1) that are significantly upregulated in MVI-positive patients (FDR-adjusted p <0.05) and were subsequently cross-validated by ELISA. A machine learning-based Plasma pRotein MVI risk Model (PRIM) was developed for the preoperative prediction of MVI. PRIM demonstrated excellent discriminatory ability, with areas under the receiver operating characteristic curve 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-positive tumor microenvironment compared with MVI-negative tumors. Conclusions: 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. Impact and implications: This study highlights the transformative potential of plasma proteomic profiling in improving the preoperative prediction of microvascular invasion in hepatocellular carcinoma (HCC). By integrating data-independent acquisition mass spectrometry and 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. |
| format | Article |
| id | doaj-art-4ceaee091fbb463cb521cce43810732c |
| institution | Kabale University |
| issn | 2589-5559 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | JHEP Reports |
| spelling | doaj-art-4ceaee091fbb463cb521cce43810732c2025-08-20T03:59:37ZengElsevierJHEP Reports2589-55592025-09-017910148110.1016/j.jhepr.2025.101481Plasma proteomic signature for preoperative prediction of microvascular invasion in HCCXinrui Shi0Yunzheng Zhao1Ke Li2Qingyu Li3Yifeng Cui4Yuhang Sui5Liang Zhao6Haonan Zhou7Yongsheng Yang8Jiajun Li9Meng Zhou10Zhaoyang Lu11Institute of Genomic Medicine, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, P.R. ChinaDepartment of Hepatic Surgery, Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, ChinaInstitute of Genomic Medicine, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, P.R. ChinaDepartment of Hepatic Surgery, Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, ChinaDepartment of Hepatic Surgery, Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, ChinaDepartment of Hepatic Surgery, Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, ChinaDepartment of Hepatopancreatobiliary Surgery, Harbin Medical University Cancer Hospital, Harbin, ChinaDepartment of Hepatobiliary Surgery, The First Hospital of China Medical University, Shenyang, ChinaDepartment of Hepatobiliary and Pancreatic Surgery, The Second Hospital of Jilin University, Changchun, ChinaDepartment of Hepatic Surgery, Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, ChinaInstitute of Genomic Medicine, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, P.R. China; Corresponding authors: Addresses: Institute of Genomic Medicine, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, P.R. China (M. Zhou); Department of Hepatic Surgery, Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China (Z. Lu).Department of Hepatic Surgery, Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China; Corresponding authors: Addresses: Institute of Genomic Medicine, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, P.R. China (M. Zhou); Department of Hepatic Surgery, Key Laboratory of Hepatosplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China (Z. Lu).Background & Aims: Microvascular invasion (MVI) is a major determinant of poor prognosis in hepatocellular carcinoma (HCC). However, reliable non-invasive biomarkers for the preoperative evaluation and diagnosis of MVI are urgently needed in clinical practice. Methods: Plasma samples were collected from 160 patients with HCC (80 MVI-positive and 80 MVI-negative) from four medical centers. Plasma proteomic profiling was obtained using data-independent acquisition mass spectrometry. 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 ELISA. Results: Proteomic analysis of 50 patients with HCC (25 MVI-positive and 25 MVI-negative) identified three plasma protein biomarkers (TALDO1, PDIA3, and PGK1) that are significantly upregulated in MVI-positive patients (FDR-adjusted p <0.05) and were subsequently cross-validated by ELISA. A machine learning-based Plasma pRotein MVI risk Model (PRIM) was developed for the preoperative prediction of MVI. PRIM demonstrated excellent discriminatory ability, with areas under the receiver operating characteristic curve 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-positive tumor microenvironment compared with MVI-negative tumors. Conclusions: 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. Impact and implications: This study highlights the transformative potential of plasma proteomic profiling in improving the preoperative prediction of microvascular invasion in hepatocellular carcinoma (HCC). By integrating data-independent acquisition mass spectrometry and 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.http://www.sciencedirect.com/science/article/pii/S2589555925001594Plasma proteomicsMicrovascular invasionRisk stratificationHCC managementTumor microenvironment |
| spellingShingle | Xinrui Shi Yunzheng Zhao Ke Li Qingyu Li Yifeng Cui Yuhang Sui Liang Zhao Haonan Zhou Yongsheng Yang Jiajun Li Meng Zhou Zhaoyang Lu Plasma proteomic signature for preoperative prediction of microvascular invasion in HCC JHEP Reports Plasma proteomics Microvascular invasion Risk stratification HCC management Tumor microenvironment |
| title | Plasma proteomic signature for preoperative prediction of microvascular invasion in HCC |
| title_full | Plasma proteomic signature for preoperative prediction of microvascular invasion in HCC |
| title_fullStr | Plasma proteomic signature for preoperative prediction of microvascular invasion in HCC |
| title_full_unstemmed | Plasma proteomic signature for preoperative prediction of microvascular invasion in HCC |
| title_short | Plasma proteomic signature for preoperative prediction of microvascular invasion in HCC |
| title_sort | plasma proteomic signature for preoperative prediction of microvascular invasion in hcc |
| topic | Plasma proteomics Microvascular invasion Risk stratification HCC management Tumor microenvironment |
| url | http://www.sciencedirect.com/science/article/pii/S2589555925001594 |
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