Search bioRxiv⌕ Search

Biology subjects

Lai, P. B. S.

Publications and source records attributed to Lai, P. B. S..

2 recordsLinked to original sources

Epigenomic analysis of hepatocellular carcinoma reveals aberrant cis-regulatory changes and dysregulated retrotransposons with prognostic potentials

Hepatocellular carcinoma (HCC) exhibits widespread epigenetic alterations, yet their impact on cis-regulatory elements (CREs) and retrotransposons remains poorly understood. Here, we present an integrated epigenomic and transcriptomic analysis of HCC tumors and matched tumor-adjacent normal tissues. We identified extensive DNA hypomethylation coupled with changes in histone modifications at partially methylated domains, CREs, and retrotransposons. These epigenetic aberrations were associated with dysregulated expression of genes involved in cell cycle regulation, immune response, and extracellular matrix organization. Notably, our findings revealed a novel mechanism for the transcriptional dysregulation of GPC3, a key HCC biomarker and immunotherapeutic target. We observed that GPC3 upregulation is driven by both the reactivation of a fetal liver super enhancer and hypomethylation of GPC3-associated CpG islands. Moreover, we found that DNA hypomethylation-driven aberrant expression of retrotransposons carries prognostic significance in HCC. Patients with high expression of a long non-coding RNA driven by a HERVE-int element exhibited more aggressive tumors, poorer clinical outcomes, and molecular features associated with favorable immunotherapy response. Together, our study provides a comprehensive resource for understanding the role of epigenetic dysregulation in HCC and identifies retrotransposon-associated transcripts as potential biomarkers.

genomics↗

Accurate identification of structural variations from cancer samples

Structural variations (SVs) are commonly found in cancer genomes. They can cause gene amplification, deletion, and fusion, among other functional consequences. With an average read length of hundreds of kilobases, nano-channel-based optical DNA mapping is powerful in detecting large SVs. However, existing SV calling methods are not tailored for cancer samples, which have special properties such as mixed cell types and sub-clones. Here we propose the COMSV method that is specifically designed for cancer samples. It shows high sensitivity and specificity in benchmark comparisons. Applying to cancer cell lines and patient samples, COMSV identifies hundreds of novel SVs per sample.

genomics↗