Hepatocellular carcinoma (HCC) is often diagnosed at an advanced stage, and early detection is critical for improving patient outcomes. Despite this, reliable non-invasive biomarkers for early-stage HCC are limited. This study seeks to develop a multi-omics-based liquid biopsy assay, especially focusing on ncRNAs (e.g. tsRNA, miRNA, circRNA, lncRNA, etc.) for accurate detection of early-stage HCC.
Liver cancer is a major global health challenge, ranking as the 5th leading cause of cancer-related deaths in the U.S. and 3rd worldwide, with hepatocellular carcinoma (HCC) accounting for \~75% of cases. Incidence has more than tripled since 1980, and death rates have risen by \~2% annually, highlighting the need for improved detection and treatment. Prognosis remains poor: over 50% of HCC cases are diagnosed at stage IV, with a 1-year survival below 30%, whereas early-stage HCC (stages I-II) can achieve up to 74% 5-year survival with curative interventions. Major risk factors include viral hepatitis (HBV, HCV), alcohol abuse, obesity, type 2 diabetes, and non-alcoholic fatty liver disease (NAFLD), with non-viral HCC increasing in prevalence, particularly in Western countries. Screening programs target high-risk populations but miss many asymptomatic or average-risk individuals, contributing to late-stage diagnoses. Biomarker discovery holds promise for improving early detection. Alpha-fetoprotein (AFP), the most widely used biomarker, has limited sensitivity for early-stage HCC (39-64%). This study seeks to validate a panel of more accurate and non-invasive biomarkers in preoperative blood samples. Accurate early detection of HCC would help provide clear criteria for treatment decisions, such as timely surgical intervention or the addition of adjuvant chemotherapy.
Study Type
OBSERVATIONAL
Enrollment
600
Comprehensive small RNA sequencing of serum or plasma-derived cf-tsRNAs to identify candidate biomarkers distinguishing HCC from NDC.
Construction of integrated cf- tsmiRNAs diagnostic classifier using machine learning
PCR-based validation of the tsRNA panel
City of Hope Medical Center
Duarte, California, United States
RECRUITINGDiagnostic performance of the integrated multi-omics signature for hepatocellular carcinoma detection
To evaluate the diagnostic accuracy of the model for distinguishing patients with hepatocellular carcinoma from chronic liver disease and healthy controls, as assessed by the area under the operating characteristic curve (AUC), sensitivity, and specificity.
Time frame: At the baseline
Validation of the diagnostic model in independent cohorts
To assess the reproducibility and generalizability of the diagnostic model using independent validation cohorts.
Time frame: At the baseline
Identification of etiology-associated omics profiles in hepatocellular carcinoma
To characterize differences in omics patterns among HBV-, HCV-, and NBNC-related hepatocellular carcinoma cases, using integrated high-throughput multi-omics analysis.
Time frame: At the baseline
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