Normal view
-
cs.AI, q-bio.NC updates on arXiv.org
-
Kolmogorov-Arnold Fourier Networks
arXiv:2502.06018v3 Announce Type: replace-cross Abstract: Although Kolmogorov-Arnold-based interpretable networks (KANs) possess strong theoretical expressiveness, they suffer from severe parameter explosion and limited ability to capture high-frequency features in high-dimensional tasks. To address these issues, we propose the Kolmogorov-Arnold Fourier Network (KAF), which fundamentally redefines the KAN paradigm through spectral reparameterization. Our key contributions include: (1) proposing
-
(Multiomics OR Omics) AND (Pancreatic)
-
Robust transcriptomic hallmarks targeting intratumor heterogeneity in intrahepatic cholangiocarcinoma
Cell Rep Med. 2026 Mar 30:102708. doi: 10.1016/j.xcrm.2026.102708. Online ahead of print.ABSTRACTIntratumor heterogeneity (ITH) undermines transcriptome-based stratification in intrahepatic cholangiocarcinoma (iCCA). Here, we integrate multi-omics data from multi-region, single-region, and single-cell RNA sequencing cohorts to systematically characterize gene expression ITH. We uncover that immune and stromal heterogeneity are primary drivers of ITH, leading to misclassification of a median 27.8
Robust transcriptomic hallmarks targeting intratumor heterogeneity in intrahepatic cholangiocarcinoma
Cell Rep Med. 2026 Mar 30:102708. doi: 10.1016/j.xcrm.2026.102708. Online ahead of print.
ABSTRACT
Intratumor heterogeneity (ITH) undermines transcriptome-based stratification in intrahepatic cholangiocarcinoma (iCCA). Here, we integrate multi-omics data from multi-region, single-region, and single-cell RNA sequencing cohorts to systematically characterize gene expression ITH. We uncover that immune and stromal heterogeneity are primary drivers of ITH, leading to misclassification of a median 27.8% of tumors by existing subtyping systems. To overcome this, we identify a low-intratumor-heterogeneity/high-intertumor-variability (LIHV) gene set and develop an ITH-insensitive classification system defining five subgroups: inflammatory (SI), metabolic (SII), atypical (SIII-1), immune-silent (SIII-2), and neurodegenerative (SIII-3). These subgroups exhibit distinct clinical outcomes, molecular features, immune landscapes, and therapeutic vulnerabilities. GPRC5A and VTCN1 serve as robust immunohistochemical biomarkers for SI and SIII tumors, while serum CEA and CA19-9 identify inflammatory iCCA. Therapeutically, HSP90 inhibition synergizes with anti-PD1 in inflammatory iCCA, whereas combined anti-PD1 and anti-TIM3 suppresses neurodegenerative iCCA. Collectively, our study provides a robust molecular framework and actionable therapeutic strategies for iCCA.
PMID:41916296 | DOI:10.1016/j.xcrm.2026.102708
-
Oncogene - Issue - nature.com science feeds
-
Dual function of DOT1L suppresses tumor cell-intrinsic immunogenicity in hepatocellular carcinoma
Oncogene, Published online: 31 March 2026; doi:10.1038/s41388-026-03744-6Dual function of DOT1L suppresses tumor cell-intrinsic immunogenicity in hepatocellular carcinoma
Dual function of DOT1L suppresses tumor cell-intrinsic immunogenicity in hepatocellular carcinoma
Oncogene, Published online: 31 March 2026; doi:10.1038/s41388-026-03744-6
Dual function of DOT1L suppresses tumor cell-intrinsic immunogenicity in hepatocellular carcinoma-
Omics in Hepatocellular
-
The Yin and Yang of tertiary lymphoid structures in primary liver cancer
Cancer Lett. 2026 Mar 27;648:218461. doi: 10.1016/j.canlet.2026.218461. Online ahead of print.ABSTRACTTertiary lymphoid structures (TLSs) have emerged as key regulators of anti-tumor immunity and biomarkers for immunotherapy response in liver cancer, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (iCCA), and combined hepatocellular-cholangiocarcinoma (cHCC-iCCA). Advances in single-cell and spatial multi-omics technologies have revealed unprecedented complexity in TLSs
The Yin and Yang of tertiary lymphoid structures in primary liver cancer
Cancer Lett. 2026 Mar 27;648:218461. doi: 10.1016/j.canlet.2026.218461. Online ahead of print.
ABSTRACT
Tertiary lymphoid structures (TLSs) have emerged as key regulators of anti-tumor immunity and biomarkers for immunotherapy response in liver cancer, including hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (iCCA), and combined hepatocellular-cholangiocarcinoma (cHCC-iCCA). Advances in single-cell and spatial multi-omics technologies have revealed unprecedented complexity in TLSs, challenging the traditional binary classification of TLSs as simply "good" or "bad". Their functional diversity appears to be shaped by spatiotemporal context, cellular composition, and maturation status. This review provides a comprehensive synthesis of TLSs in liver cancer, employing the Yin-Yang paradigm to navigate their functional dualism and prognostic contradictions through a detailed analysis of their identification, classification, and spatiotemporal interactions within the TME. Mechanistically, we elucidate how TLS functions are orchestrated by complex interactions between tumor cells, immune cell subsets, stromal components, and systemic factors. Within this framework, key metabolic drivers, notably ATP citrate lyase (ACLY), and signaling axes such as cGAS-STING/mTOR have emerged as pivotal regulators of TLS ontogeny. In addition, we evaluate current preclinical animal models and therapeutic strategies for clinical TLS induction. Furthermore, we have discussed the key unanswered questions in the field, including the three-dimensional architecture of TLSs and the mechanisms by which they establish durable immunological memory independent of the primary tumor. Clinically, TLSs exhibit great promise as prognostic and predictive biomarkers, particularly in the context of immune checkpoint blockade and locoregional therapies. Finally, we identify challenges in standardization, mechanistic understanding, and translational applications, providing directions for future research to harness TLSs for improving liver cancer outcomes.
PMID:41905709 | DOI:10.1016/j.canlet.2026.218461
-
cs.AI, q-bio.NC updates on arXiv.org
-
UniG2U-Bench: Do Unified Models Advance Multimodal Understanding?
arXiv:2603.03241v1 Announce Type: cross Abstract: Unified multimodal models have recently demonstrated strong generative capabilities, yet whether and when generation improves understanding remains unclear. Existing benchmarks lack a systematic exploration of the specific tasks where generation facilitates understanding. To this end, we introduce UniG2U-Bench, a comprehensive benchmark categorizing generation-to-understanding (G2U) evaluation into 7 regimes and 30 subtasks, requiring varying de