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Received β€” 4 February 2026 ⏭ Favourites

Extrachromosomal DNA drives molecular and clinical heterogeneity in hepatocellular carcinoma: a multi-omics analysis and prognostic model development

βœ‡(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Kai Huang Β· Guangquan Zhang Β· Shuai Hu Β· Yongfei He Β· Hang Zhai Β· Junming Xu Β· Jicai Wang Β· Shengjie Hong Β· Fenfang Wu Β· Xianjie Shi
4 February 2026 at 19:00

Hum Genomics. 2026 Feb 3. doi: 10.1186/s40246-026-00927-w. Online ahead of print.

ABSTRACT

BACKGROUND: Extrachromosomal DNA (ecDNA) is an emerging hallmark of cancer that promotes tumor evolution and heterogeneity. However, the molecular characteristics and clinical significance of ecDNA in hepatocellular carcinoma (HCC) remain incompletely understood.

METHODS: The clinical outcomes, genomics, transcriptomics, proteomics, tumor microenvironment, and drug target landscapes of ecDNA-negative and ecDNA-positive HCC in the Cancer Genome Atlas (TCGA) were compared. Next, the least absolute shrinkage and selection operator (LASSO) and random survival forest (RSF) algorithms were used to screen the ecDNA gene signature. A nomogram was constructed and evaluated based on the risk score and clinicopathological features. Finally, the role of DNASE1L3 was validated through in vitro experiments.

RESULTS: EcDNA-positive tumors showed increased vascular invasion, higher AFP levels, and more TP53 mutations. These tumors displayed unique activation of proliferation pathways, decreased stromal infiltration, and heightened immune activation. Our validated six-gene signature (RNF186, BMP6, AOC1, FBLL1, MYBL2, and DNASE1L3) demonstrated strong prognostic value when combined with tumor stage in the nomogram. Notably, DNASE1L3 was downregulated in HCC, showed endothelial cell-specific expression, and suppressed the proliferation and migration of Hep3B2.1-7 cells.

CONCLUSION: Our study characterizes the molecular and clinical distinctions between ecDNA-negative and ecDNA-positive HCC and establishes a clinically applicable gene signature for patient prognosis. These findings advance our understanding of ecDNA-driven tumor heterogeneity and provide potential strategies for personalized HCC management.

PMID:41634868 | DOI:10.1186/s40246-026-00927-w

Received β€” 14 November 2025 ⏭ Favourites

MedFuse: Multiplicative Embedding Fusion For Irregular Clinical Time Series

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Yi-Hsien Hsieh Β· Ta-Jung Chien Β· Chun-Kai Huang Β· Shao-Hua Sun Β· Che Lin
13 November 2025 at 13:00
arXiv:2511.09247v1 Announce Type: new Abstract: Clinical time series derived from electronic health records (EHRs) are inherently irregular, with asynchronous sampling, missing values, and heterogeneous feature dynamics. While numerical laboratory measurements are highly informative, existing embedding strategies usually combine feature identity and value embeddings through additive operations, which constrains their ability to capture value-dependent feature interactions. We propose MedFuse, a framework for irregular clinical time series centered on the MuFuse (Multiplicative Embedding Fusion) module. MuFuse fuses value and feature embeddings through multiplicative modulation, preserving feature-specific information while modeling higher-order dependencies across features. Experiments on three real-world datasets covering both intensive and chronic care show that MedFuse consistently outperforms state-of-the-art baselines on key predictive tasks. Analysis of the learned representations further demonstrates that multiplicative fusion enhances expressiveness and supports cross-dataset pretraining. These results establish MedFuse as a generalizable approach for modeling irregular clinical time series.
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