Citations 2
Uncovering the genetic landscape of cholangiocarcinoma and its subtypes via GWAS and integrative analyses
Han, Younghun , Byun, Jinyoung, Cooley, Matthew A. , Shaw, Vikram R., Ahmed, Fowsiyo , Giama, Nasra H., Ali, Hawa M. , Campbell, Nellie A. , Hassan, Mohamed, Andersen, Jesper B., Ankoma-Sey, Victor, Bañales, Jesús M. , Bujanda, L. , Bathe, Oliver F. , Bekaii-Saab, Tanios S., Borad, Mitesh J., Chan, Carlos H.F. , Cleary, S., Folseraas, Trine , Galle, P R, MacInnis, Robert J., Gores, Gregory J., Houlston, Richard S., Ilyas, Sumera, Javle, Milind, Johansen, Julia S., Christensen, Troels Dreier , Kamath, Ashwin S. , Kelley, Robin Kate , Lamarca, Angela, Lammert, F, Lindsey, Stacie , Macias, Rocio I. R., Patel, Tushar, Permuth, Jennifer B., Rossi, Stephen, Sanchez, William, Smoot, Rory L., Swerdlow, Anthony J., Cao, H. Tran, Valle, Juan, Weinmann, A., Wong, Linda L. , Zimmer, Vincent, Zhu, A., Kn, Lazaridis, Juran, Brian D., Jalal, Prasun Kumar, Knox, J., Hutchinson, Amy, Hicks, Belynda, Chanock, Stephen J., Hassan, Manal M. , Amos, Christopher I., McGlynn, Katherine A., Roberts, Lewis R., Roberts, Lewis R. , Sánchez, William , Han, Younghun , Byun, Jinyoung , Byun, Jinyoung , VanLith, Caitlin J. , Roberts, Lewis R , Banales, Jesus M , Jalal, Prasun K , Chan, Carlos H F , Sanchez, William , Shaw, Vikram R. , Hatia, Rikita , Andersen, Jesper B , Ankoma‐Sey, Victor, Bekaii‐Saab, Tanios , Borad, Mitesh J. , Björkström, Niklas K. , Cleary, Sean P. , Cornillet, Martin , Galle, Peter R., Genkinger, Jeanine M. , MacInnis, Robert J. , Gores, Gregory J. , Goyal, Lipika , Houlston, Richard S. , Ilyas, Sumera I. , Javle, Milind , Johansen, Julia S., Khan, Shahid Ali , Kim, Richard D. , Kwee, Sandi Alexander , Lamarca, Angela , LaRusso, Nicholas F. , Malhi, Harmeet , Permuth, Jennifer B. , Reeves, Helen L. , Smoot, Rory L. , Swerdlow, Anthony J , Weinmann‐Menke, Julia , Zhang, Xuehong , Zimmer, Vincent , Zhu, Andrew X. , Jalal, Prasun K. , Hutchinson, Amy , Hicks, Belynda , Koshiol, Jill , Chanock, Stephen Jacob , Amos, Christopher I. , McGlynn, Katherine A.
Hepatology | February 2, 2026 | Volume: No volume available | Issue: No issue available | Pages: No page available
Background and Aims: Cholangiocarcinoma (CCA) is a rare but aggressive malignancy with poorly understood genetic susceptibility. To date, genome-wide association studies (GWAS) investigating germline variants associated with CCA risk remain limited. We aimed to identify genetic risk loci for CCA and its clinical subtypes through comprehensive GWAS and post-GWAS analyses. Approach and Results: We conducted a GWAS of 2366 CCA cases and 11,750 controls of European ancestry. Genome-wide significant loci ( p <5×10 −8 ) were identified and further examined through fine-mapping, functional annotation, and HLA imputation. Subgroup analyses were conducted by CCA subtypes and primary sclerosing cholangitis (PSC) status. Cross-trait linkage disequilibrium score regression and Mendelian randomization were employed to investigate the shared genetic architecture and potential causal relationships with a diverse range of traits. We identified 1 new genome-wide significant variant, rs535777 (OR=1.44), near HLA-DRB1/DQA1 associated with CCA, and 2 variants associated with extrahepatic CCA: rs116224263 (OR=0.17) in LINC02506 at 4p15.1 and rs6914950 (OR=1.63) near HLA-DRB1/DQB1 . Stratified analyses revealed rs2395184 (OR=3.51) near HLA-DRA/DRB5 associated with PSC-related CCA, and rs142674434 (OR=2.98) in THSD7A at 7p21.3 associated with non-PSC-related CCA. HLA imputation uncovered new amino acid residues associated with disease risk. Cross-trait analyses identified shared genetic signals between CCA and anthropometric, lipidemic, lifestyle, and medical traits. Mendelian randomization supported putative causal associations for 12 traits with CCA or its subtypes. Conclusions: Our large-scale GWAS highlights new genetic variants and HLA-linked mechanisms underlying CCA susceptibility. Integrating multi-step post-GWAS approaches enhances understanding of CCA pathogenesis and may facilitate the development of risk biomarkers for early detection and precision prevention strategies.
Circulating cell‐free DNA methylation biomarkers for hepatocellular carcinoma risk prediction in HIV ‐positive Nigerian population
Zheng, Yinan , Han, Jingzhe , Qu, Yishu , Wang, Jun , Okeke, Edith, Davwar, Pantong , Duguru, Mary, Nyam, David, Oyeleke, Ganiyat K. , Sulaimon, Akanmu A., Hawkins, Claudia , Badmos, Kabir, Gursel, Demirkan, Silas, Olugbenga A. , Kocherginsky, Masha , Sun, Zequn , Achenbach, Chad J. , Yang, Hushan , Sagay, Atiene S. , Roberts, Lewis R., Hou, Lifang , Roberts, Lewis R. , Yang, Hushan , Roberts, Lewis R , Zheng, Yinan , Hou, Lifang , Wang, Jun , Joyce, Brian , Odeghe, Emuobor , Lesi, Olufunmilayo , Imade, Godwin , Abdulkareem, Fatimah Biade , Wei, Jian‐Jun , Kim, Kwang‐Youn A. , Zhang, Xuehong , Ogunsola, Folasade Tolulope , Murphy, Robert L. , Roberts, Lewis R.
International Journal of Cancer | October 23, 2025 | Volume: 158 | Issue: 5 | Pages: 1281-1291
Abstract 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 (FibroScan ≥12.3 kPa), fibrosis (FibroScan ≥7.6 and <12.3 kPa), and HCC‐free without fibrosis (FibroScan <7.6 kPa) 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 80%–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 non‐invasive, blood‐based screening tool for improving early identification of HCC cases among PLWH.