❌

Normal view

Received — 27 May 2026 ⏭

FGFR1 Promotes Malignant Progression in Lung Squamous Cell Carcinoma Through Activation of Wnt/beta-Catenin Signaling

✇Omics In Lung
By: Xingyuan Li · Shuya Mu · Yinjia Sun · Yongfeng Yu
18 April 2026 at 18:00

Cancer Med. 2026 Apr;15(4):e71833. doi: 10.1002/cam4.71833.

ABSTRACT

OBJECTIVES: This study aims to elucidate the role of FGFR1 in activating the Wnt/β-catenin signaling pathway and the underlying mechanisms by which it promotes malignant progression in lung squamous cell carcinoma (LUSC). By integrating multi-omics analysis with functional experiments, the clinical heterogeneity of FGFR1 amplification, signaling crosstalk, and their regulatory networks governing tumor phenotypes were revealed.

METHODS: Using TCGA data (n = 490), we analyzed the relationship between FGFR1 copy number variation (CNV) and mRNA expression in LUSC, and validated the correlation with protein expression in a clinical cohort (n = 38). GSEA and single-gene GSEA were performed to identify signaling pathways associated with high FGFR1 expression. The interaction between FGFR1 and the Wnt/β-catenin pathway was investigated by immunohistochemistry, immunofluorescence, stable cell lines, Western blot, qPCR, and functional assays.

RESULTS: FGFR1 amplification correlated with increased mRNA and protein expression. The top 25% FGFR1 high-expression group enriched Wnt/β-catenin, PI3K-Akt, and cAMP pathways. Mechanistically, FGFR1 promoted β-catenin nuclear accumulation and enhanced β-catenin signaling through PKA-associated phosphorylation and Akt/GSK3β-related regulation of β-catenin stability, and these effects were attenuated by AKT inhibition. CTNNB1 knockdown significantly inhibited proliferation, migration, invasion, and tumor growth of LUSC cells.

CONCLUSIONS: Our findings indicate that FGFR1 activates Wnt/β-catenin signaling through coordinated regulation of β-catenin phosphorylation, stability, and subcellular localization, thereby promoting malignant progression in LUSC. These results provide a rationale for targeting the FGFR1-Wnt/β-catenin axis as a potential therapeutic strategy.

PMID:41998829 | DOI:10.1002/cam4.71833

Received — 4 April 2026 ⏭

GPX3 suppresses gallbladder cancer progression by modulating redox balance, glycolysis, and anti-tumor immunity

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Zuyi Ma · Jia Sun · Xin Wu · Changzhen Shang · Binglu Li
2 April 2026 at 18:00

Oncogenesis. 2026 Apr 2. doi: 10.1038/s41389-026-00603-7. Online ahead of print.

ABSTRACT

Gallbladder cancer (GBC) is an aggressive malignancy characterized by metabolic plasticity and profound immune evasion. However, the functional role of glutathione peroxidase 3 (GPX3), a secreted antioxidant enzyme, in these processes remains unclear. Multi-omics analyses of paired GBC and adjacent non-tumor tissues revealed consistent downregulation of GPX3, which correlated with reactive oxygen species (ROS) accumulation and enhanced glycolytic activity. Functional restoration of GPX3 in GBC cells reduced intracellular ROS levels, suppressed the expression of glycolysis-related enzymes, and consequently impaired tumor proliferation, migration, and invasion. In xenograft models, GPX3 overexpression markedly attenuated tumor growth and lung metastasis. Notably, GPX3 restoration also enhanced CD8+ T cell infiltration and elevated pro-inflammatory cytokine production, suggesting reversal of tumor-associated immunosuppression. These findings identify GPX3 as a critical tumor suppressor that integrates redox regulation, metabolic reprogramming, and immune activation to restrict malignant progression. Targeting GPX3 or its downstream pathways may represent a promising therapeutic strategy to simultaneously suppress gallbladder cancer aggressiveness and reinforce anti-tumor immunity.

PMID:41927557 | DOI:10.1038/s41389-026-00603-7

GPX3 suppresses gallbladder cancer progression by modulating redox balance, glycolysis, and anti-tumor immunity

✇Omics In Lung
By: Zuyi Ma · Jia Sun · Xin Wu · Changzhen Shang · Binglu Li
2 April 2026 at 18:00

Oncogenesis. 2026 Apr 2. doi: 10.1038/s41389-026-00603-7. Online ahead of print.

ABSTRACT

Gallbladder cancer (GBC) is an aggressive malignancy characterized by metabolic plasticity and profound immune evasion. However, the functional role of glutathione peroxidase 3 (GPX3), a secreted antioxidant enzyme, in these processes remains unclear. Multi-omics analyses of paired GBC and adjacent non-tumor tissues revealed consistent downregulation of GPX3, which correlated with reactive oxygen species (ROS) accumulation and enhanced glycolytic activity. Functional restoration of GPX3 in GBC cells reduced intracellular ROS levels, suppressed the expression of glycolysis-related enzymes, and consequently impaired tumor proliferation, migration, and invasion. In xenograft models, GPX3 overexpression markedly attenuated tumor growth and lung metastasis. Notably, GPX3 restoration also enhanced CD8+ T cell infiltration and elevated pro-inflammatory cytokine production, suggesting reversal of tumor-associated immunosuppression. These findings identify GPX3 as a critical tumor suppressor that integrates redox regulation, metabolic reprogramming, and immune activation to restrict malignant progression. Targeting GPX3 or its downstream pathways may represent a promising therapeutic strategy to simultaneously suppress gallbladder cancer aggressiveness and reinforce anti-tumor immunity.

PMID:41927557 | DOI:10.1038/s41389-026-00603-7

Received — 26 March 2026 ⏭

OmniDiT: Extending Diffusion Transformer to Omni-VTON Framework

✇cs.AI, q-bio.NC updates on arXiv.org
By: Weixuan Zeng · Pengcheng Wei · Huaiqing Wang · Boheng Zhang · Jia Sun · Dewen Fan · Lin HE · Long Chen · Qianqian Gan · Fan Yang · Tingting Gao
25 March 2026 at 12:00
arXiv:2603.19643v2 Announce Type: replace-cross Abstract: Despite the rapid advancement of Virtual Try-On (VTON) and Try-Off (VTOFF) technologies, existing VTON methods face challenges with fine-grained detail preservation, generalization to complex scenes, complicated pipeline, and efficient inference. To tackle these problems, we propose OmniDiT, an omni Virtual Try-On framework based on the Diffusion Transformer, which combines try-on and try-off tasks into one unified model. Specifically, we first establish a self-evolving data curation pipeline to continuously produce data, and construct a large VTON dataset Omni-TryOn, which contains over 380k diverse and high-quality garment-model-tryon image pairs and detailed text prompts. Then, we employ the token concatenation and design an adaptive position encoding to effectively incorporate multiple reference conditions. To relieve the bottleneck of long sequence computation, we are the first to introduce Shifted Window Attention into the diffusion model, thus achieving a linear complexity. To remedy the performance degradation caused by local window attention, we utilize multiple timestep prediction and an alignment loss to improve generation fidelity. Experiments reveal that, under various complex scenes, our method achieves the best performance in both the model-free VTON and VTOFF tasks and a performance comparable to current SOTA methods in the model-based VTON task.
Received — 25 February 2026 ⏭

The Story is Not the Science: Execution-Grounded Evaluation of Mechanistic Interpretability Research

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiaoyan Bai · Alexander Baumgartner · Haojia Sun · Ari Holtzman · Chenhao Tan
24 February 2026 at 13:00
arXiv:2602.18458v1 Announce Type: cross Abstract: Reproducibility crises across sciences highlight the limitations of the paper-centric review system in assessing the rigor and reproducibility of research. AI agents that autonomously design and generate large volumes of research outputs exacerbate these challenges. In this work, we address the growing challenges of scalability and rigor by flipping the dynamic and developing AI agents as research evaluators. We propose the first execution-grounded evaluation framework that verifies research beyond narrative review by examining code and data alongside the paper. We use mechanistic interpretability research as a testbed, build standardized research output, and develop MechEvalAgent, an automated evaluation framework that assesses the coherence of the experimental process, the reproducibility of results, and the generalizability of findings. We show that our framework achieves above 80% agreement with human judges, identifies substantial methodological problems, and surfaces 51 additional issues that human reviewers miss. Our work demonstrates the potential of AI agents to transform research evaluation and pave the way for rigorous scientific practices.
There are no more articles
❌