Hepatocellular Carcinoma (HCC)
Conditions
Keywords
HCC, Liver cancer, tsRNA, Liquid Biopsy, Early Detection
Brief summary
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.
Detailed description
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.
Interventions
DIAGNOSTIC_TESTSmall RNA sequence
Comprehensive small RNA sequencing of serum or plasma-derived cf-tsRNAs to identify candidate biomarkers distinguishing HCC from NDC.
DIAGNOSTIC_TESTRt-qPCR
Construction of integrated cf- tsmiRNAs diagnostic classifier using machine learning
Sponsors
City of Hope Medical Center
Study design
Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Adults aged 18 to 100 years. * A histologically confirmed diagnosis of hepatocellular carcinoma. * A clinically diagnosed as chronic liver disease, including chronic hepatitis or liver cirrhosis, without evidence of hepatocellular carcinoma. * Healthy volunteers without known liver disease or malignancy. * Received standard diagnostic and staging procedures as per local guidelines * Availability of blood or other biospecimens. * Ability to provide written informed consent.
Exclusion criteria
* Lack of or inability to provide informed consent * History of other active malignancies within the past 5 years * Previous liver transplantation * Severe systemic infection or inflammatory disease at the time of enrollment * Inadequate sample quality or quantity
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Diagnostic performance of the integrated multi-omics signature for hepatocellular carcinoma detection | At the baseline | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Validation of the diagnostic model in independent cohorts | At the baseline | To assess the reproducibility and generalizability of the diagnostic model using independent validation cohorts. |
| Identification of etiology-associated omics profiles in hepatocellular carcinoma | At the baseline | To characterize differences in omics patterns among HBV-, HCV-, and NBNC-related hepatocellular carcinoma cases, using integrated high-throughput multi-omics analysis. |
Countries
United States
Contacts
CONTACTGoel Ajay, PhD
Outcome results
None listed