Adv Sci (Weinh). 2026 Sep 29:e78039. doi: 10.1002/advs.78039. Online ahead of print.

ABSTRACT

Durable responses to current therapies remain limited in hepatocellular carcinoma (HCC), highlighting the need to identify regulators of malignant progression. By integrating multi-omics analyses, spatial transcriptomics, clinical specimens, and multiple models, we identified chromatin assembly factor 1B (CHAF1B) as a functional regulator of HCC phenotypes. Gain- and loss-of-function of CHAF1B altered proliferative, migratory, clonogenic, and tumorigenic phenotypes. LC-MS/MS, DIA proteomics, and cell-based assays revealed CHAF1B-associated lipogenic remodeling characterized by SREBP1C nuclear localization, lipogenic gene/protein induction, and lipid-droplet accumulation. Mechanistically, the WD40 repeat-containing region of CHAF1B contributed to its association with UHRF1 and SETDB1, supporting UHRF1-associated K63-linked ubiquitination and CRM1/exportin-1-dependent cytoplasmic redistribution of SETDB1. Conversely, CHAF1B depletion enhanced SETDB1 association with VHL and favored a predominantly K11-associated degradative ubiquitin state linked to proteasomal SETDB1 loss. SETDB1 redistribution and catalytic activity were associated with AKT T308-linked signaling. A focused CRISPR-based screen of deubiquitinases identified USP4 as an upstream regulator of CHAF1B protein homeostasis. USP4 depletion or Akebia saponin D (ASD) increased K48-linked ubiquitination of CHAF1B, reduced CHAF1B protein abundance, attenuated AKT T308-linked signaling, and suppressed malignant and lipogenic phenotypes. These findings reveal distinct ubiquitin-dependent states governing SETDB1 stability and identify USP4-dependent CHAF1B stabilization as an upstream regulatory node in HCC.

PMID:42811544 | PMC:PMC13624420 | DOI:10.1002/advs.78039

Adv Sci (Weinh). 2026 Sep 27:e78005. doi: 10.1002/advs.78005. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) patients show heterogeneous responses to immune checkpoint inhibitors (ICIs). This study developed ECOS-Net, a transformer-based multitask network integrating CT-derived habitat and 2.5-dimensional (2.5D) deep learning features for simultaneously predicting early disease control (DC) and overall survival (OS). Of 1,234 patients with HCC enrolled from eight institutions and public databases, 832 ICI-treated patients were used for model development. ECOS-Net fused features using multi-head attention and generated early DC probabilities and OS risk scores. ECOS-DC achieved AUCs of 0.836, 0.822, and 0.817 in training, internal validation, and external test sets, outperforming clinical models (all p values < 0.05). ECOS-OS yielded C-indices of 0.730, 0.722, and 0.720, respectively. Integrated models also showed favorable external performance (early DC AUC: 0.825; OS C-index: 0.741). Patients with higher ECOS-DC probabilities had a higher likelihood of early DC, whereas those with higher ECOS-OS risk had shorter OS, with directionally consistent associations across most subgroups. Exploratory biological analyses suggested that the higher ECOS-DC probability and lower ECOS-OS risk groups were associated with immune-active tumor microenvironment features. Therefore, ECOS-Net shows potential as a non-invasive imaging-based risk stratification framework for simultaneously predicting early DC and OS in ICI-treated HCC patients.

PMID:42801546 | PMC:PMC13616327 | DOI:10.1002/advs.78005