Patel, Tushar P

ABSTRACT Introduction Hepatocellular carcinoma (HCC) development in patients with metabolic dysfunction‐associated steatotic liver disease (MASLD) is a growing health concern, but the underlying mechanisms are not fully understood. Epigenetic aging biomarkers, reflecting cellular and tissue aging, have been linked to various age‐related pathologies, but their association with MASLD‐HCC is unknown. We investigated associations between five epigenetic aging biomarkers and MASLD‐HCC risk. Methods We performed whole blood DNA methylation assay (Infinium 850k array) and calculated principal components‐based (PC) versions of HorvathAge, HannumAge, PhenoAge and GrimAge and the DunedinPACE aging rate. We further calculated relative age accelerations for PCHorvathAge, PCHannumAge, PCPhenoAge and PCGrimAge. The aging biomarkers were modelled as continuous variables and categorised into tertiles based on distributions among controls. Associations between each aging biomarker and MASLD‐HCC were examined using logistic regression, calculating odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for covariates. Results Data on 272 MASLD‐HCC cases and 316 cancer‐free MASLD controls recruited from six sites and matched on chronological age, sex and study site were analysed. Higher relative age accelerations of PCPhenoAge (OR T3 vs. T1 = 2.25, 95% CI: 1.45–3.50; OR continuous = 1.04, 95% CI: 1.02–1.07, p = 0.009), PCGrimAge (OR T3 vs. T1 = 3.97, 95% CI: 2.41–6.64; OR continuous = 1.16, 95% CI: 1.10–1.24, p = 8.76 × 10 −07 ) and DunedinPACE (OR T3 vs. T1 = 3.45, 95% CI: 2.17–5.55; OR continuous = 1.72, 95% CI: 1.43–2.10, p = 2.58 × 10 −08 ) were associated with MASLD‐HCC, but not PCHorvathAge or PCHannumAge. Conclusion Higher relative age accelerations of PCPhenoAge, PCGrimAge and DunedinPACE aging rate are associated with risk of MASLD‐HCC. These aging biomarkers could improve HCC risk assessment and facilitate risk stratification in patients with MASLD.

Abstract Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects about 32% of adults worldwide and is projected to increase in many parts of the world. Liver cancer is a recognized complication of end-stage MASLD. An estimated 20% of patients with MASLD will progress to cirrhosis, a premalignant state in liver cancer evolution. Because MASLD is usually asymptomatic, its progression to cirrhosis can be difficult to detect early for timely intervention. There are currently no known biological markers for predicting cirrhosis risk in patients with MASLD, making it challenging to identify patients at risk of disease progression. The aim of this study was to identify plasma protein biomarkers that can predict future cirrhosis development in patients with MASLD. Methods: We performed a nested case-control study among patients with MASLD at baseline who later progressed to cirrhosis and control patients with MASLD at baseline who never progressed to cirrhosis, matched on age at MASLD diagnosis (± 1-year), sex, race, year of blood sample collection, and length of follow-up (± 1-year). An aptamer-based array (SOMAscan™) platform was used to measure over 7000 proteins in baseline plasma samples obtained from the participants. The study sample was split 70/30 for training and validation analyses. We identified plasma protein biomarkers significantly differentially expressed in the training sample, adjusting for multiple testing with Bonferroni-corrected p-values. The significant set of proteins from the training set was used for prediction modeling in the validation sample, computing prediction metrics (area under the curve [AUC], sensitivity, specificity, and accuracy). Results: A total of 64 MASLD to cirrhosis cases matched to 64 MASLD controls were included in the analysis. Six protein biomarkers (PTPRU, ATL2, MFAP4, C7, CSTN2, and COLEC11) were overexpressed in cases, compared to controls, in the training data. Prediction modeling of the six-protein biomarker panel in the validation sample yielded an AUC = 0.85, sensitivity = 0.72, specificity = 0.78, and accuracy = 0.75. Conclusions: We have identified a panel of six plasma protein biomarkers that predict risk of cirrhosis development in patients with MASLD. These proteins could serve as candidates for future mechanistic studies on MASLD progression to advanced liver disease. If validated in larger studies and with assays suitable for clinical laboratory testing (e.g., ELISA), these protein biomarkers could be useful clinically for cirrhosis risk prediction in MASLD patients, and hence those at risk of liver cancer, enabling timely surveillance or interventions to prevent disease progression. Citation Format: Pasang Sherpa, Sebastian M. Armasu, Jacob A. Frank, Irene K. Yan, Kirk J. Wangensteen, Tushar Patel, Samuel O. Antwi. Predicting cirrhosis risk in patients with metabolic dysfunction-associated steatotic 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 2335.

The American Joint Committee on Cancer (AJCC) has increasingly recognized the need for more personalized probabilistic predictions than those delivered by ordinal staging systems, particularly through the use of accurate risk models or calculators. However, judging the quality and acceptability of a risk model is complex. The AJCC Precision Medicine Core conducted a 2‐day meeting to discuss characteristics necessary for a quality risk model in cancer patients. More specifically, the committee established inclusion and exclusion criteria necessary for a risk model to potentially be endorsed by the AJCC. This committee reviewed and discussed relevant literature before creating a checklist unique to this need of AJCC risk model endorsement. The committee identified 13 inclusion and 3 exclusion criteria for AJCC risk model endorsement in cancer. The emphasis centered on performance metrics, implementation clarity, and clinical relevance. The facilitation of personalized probabilistic predictions for cancer patients holds tremendous promise, and these criteria will hopefully greatly accelerate this process. Moreover, these criteria might be useful for a general audience when trying to judge the potential applicability of a published risk model in any clinical domain. CA Cancer J Clin 2016;66:370–374. © 2016 American Cancer Society.