five

Circulating Cell-free DNA Methylation Biomarkers for Hepatocellular Carcinoma Risk Prediction in HIV-Positive Nigerian Population

收藏
NIAID Data Ecosystem2026-05-02 收录
下载链接:
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE298812
下载链接
链接失效反馈
官方服务:
资源简介:
Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality globally, with particularly high burdens among people living with HIV (PLWH) in low-resource settings like Nigeria. Effective early detection remains a major challenge due to limited access to imaging-based surveillance and the low sensitivity of current biomarkers such as alpha-fetoprotein (AFP). We conducted an epigenome-wide association study (EWAS) of circulating cell-free DNA (ccfDNA) methylation in a Nigerian cohort of HIV-positive individuals (n = 245), spanning HCC, cirrhosis, fibrosis, and HCC-free groups. Using random forest modeling, we developed and evaluated a ccfDNA methylation classifier (ccfDNAmRF) for HCC risk prediction. We identified 73 CpG sites significantly associated with HCC (false discovery rate <0.01). The ccfDNAmRF model demonstrated strong discriminatory power, achieving 100% sensitivity and 81–91% specificity for distinguishing HCC from cirrhosis, fibrosis, and HCC-free groups (area under the curve [AUC]: 92–97%). Combining ccfDNA methylation risk scores with AFP further improved classification accuracy (AUC up to 98.5%). Notably, ccfDNA methylation patterns displayed clear dose–response relationships across the disease spectrum, supporting their utility for early-stage detection and risk stratification. Our findings highlight the promise of ccfDNA methylation biomarkers as a minimally invasive, blood-based screening tool for improving early HCC detection among PLWH in resource-limited settings. These biomarkers may help address critical gaps in current surveillance strategies, offering scalable solutions adaptable to high-risk populations. In this study, we focus on identifying circulating cell-free epigenetic markers for HCC among Nigerian population living with HIV, and then build and evaluate a machine-learning-based model to predict the risk of HCC. The study encompassed four groups of participants: HIV Positive without Hepatocellular Carcinoma HIV Positive Hepatocellular Carcinoma HIV Positive Cirrhosis HIV Positive Fibrosis
创建时间:
2025-06-26
二维码
社区交流群
二维码
科研交流群
商业服务