Monday, May 12, 2025
5/12/2025
Inflammation and Hepatocellular Carcinoma: A Multiomics Approach to Improve Early Detection, Causal Inference, and Lifestyle Based Prevention - PROJECT SUMMARY/ABSTRACT Dr. Xinyuan (Cindy) Zhang’s long-term goal is to become an independent and active researcher in the field of cancer molecular epidemiology that contributes to the development of novel strategies for cancer prevention and early diagnosis. Hepatocellular carcinoma (HCC) is highly lethal, without an effective early detection method. Chronic inflammation has been observed in HCC, but neither the genomics nor proteomics signatures of inflammation have been examined in the context of HCC etiology or lifestyle-based prevention. Leveraging the wealth of resources from eight population-based prospective cohorts, Dr. Zhang will integrate molecular data into epidemiology studies to address gaps in understanding HCC risk and improving risk assessment. In Aim 1, Dr. Zhang will use computational biology and system biology skills to examine pre-diagnostic plasma proteomics of incident HCC cases compared to matched non-HCC controls. She will use machine learning methods to develop and validate Inflammatory Proteomics Signatures for HCC early detection. In Aim 2, Dr. Zhang will analyze genomics and proteomics data by mapping inflammatory protein quantitative trait loci and performing Mendelian Randomization analysis for HCC risk. Findings on inflammatory proteomics and genomics will be further compared against current tests in a clinical cohort of liver cirrhosis patients who are currently undergoing HCC screening. In Aim 3, Dr. Zhang will develop a hypothesis-driven empirically derived Anti-Inflammatory Lifestyle Score by regressing health behaviors and factors against inflammatory multiomics markers. In preliminary studies, Dr. Zhang has shown that a dietary score weighted by traditional inflammation markers was associated with HCC risk and has identified a protein biomarker model for HCC prediction, suggesting that further improvement in predictive performance is feasible. Successful completion of the novel research aims will be supported by a well-tailored training plan with three specific objectives in (1) advanced multiomics analytics, (2) cancer biomarkers, and (3) molecular epidemiology study design and management. The outstanding mentoring team with complementary expertise, rigorous training activities, and rich resources will equip Dr. Zhang with the knowledge, skills, and data necessary for research success and transition to independence under this NCI Early K99/R00 award.
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