Abstract 2350: Protein biomarkers of HCC development in patients with metabolic liver disease
Authors: Ampem Darko Jnr Siaw, Sebastian M. Armasu, Jacob A. Frank, Irene K. Yan, Kirk J. Wangensteen, Tushar Patel, Samuel O. Antwi
Journal: Cancer Research (2025)

Abstract Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a fast-rising global public health problem. About 10% of patients with MASLD will develop hepatocellular carcinoma (HCC), but molecular markers for predicting future risk of HCC development in patients with MASLD are lacking. We investigated whether protein biomarkers in pre-malignant plasma samples can predict susceptibility to HCC in the setting of MASLD. Methods: A nested case-control study was performed among patients with MASLD who later developed HCC (cases) and MASLD patients who never progressed to HCC (controls), matched on age at MASLD diagnosis (± 1-year), sex, the year of blood sample collection, and length of follow-up (± 1-year). MASLD at baseline was determined by the presence of hepatic steatosis (assessed by imaging or biopsy) plus at least one cardiometabolic condition (obesity, diabetes mellitus, hypertension, or dyslipidemia). Archival baseline plasma samples stored at -80oC were used to perform the SomaScan 7k v4.0 assay. We also performed genotyping of the well-established HCC susceptibility variant, PNPLA3-rs738409 (I148M), among the cases and controls. The study sample was split into discovery (70%) and validation (30%) sets, and we selected promising protein biomarkers in the discovery set using differential expression analysis with volcano plots and Bonferroni-corrected p-values. A final set of significant proteins was used for prediction modeling in the validation sample, calculating area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and accuracy. Results: The study sample included 54 MASLD to HCC cases and 54 MASLD controls, split into discovery and validation sets. We identified five significantly differentially expressed proteins in the discovery set, four overexpressed (TSP2, GPR56, MIC-1, TIMP-1) and one under-expressed (IGFALS) in the cases versus controls. Fitting the five proteins in the validation sample yielded an AUC=0.88, sensitivity=0.87, specificity=0.75, and accuracy=0.81. Adding PNPLA3-rs738409 to the model improved the model’s specificity and accuracy (AUC=0.88, sensitivity=0.87; specificity=0.81, accuracy=0.84). Conclusions: Our findings show that a five-protein biomarker panel can robustly predict future HCC development in patients with MASLD. Adding a well-established HCC genetic risk variant, PNPLA3-rs738409, slightly improved the model’s performance. These initial findings provide evidence of HCC prediction with baseline plasma biomarkers and genetic risk variants, toward improving risk stratification and enhancing early detection of HCC in the setting of metabolic perturbations. Citation Format: Ampem Darko Jnr Siaw, Sebastian M. Armasu, Jacob A. Frank, Irene K. Yan, Kirk J. Wangensteen, Tushar Patel, Samuel O. Antwi. Protein biomarkers of HCC development in patients with metabolic liver disease [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2350.