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Normal view

Received — 10 September 2026 ⏭

Transduction Efficiency in Clinical CAR T-Cell Products: A Retrospective Study at a Single Center

✇Molecular Therapy Advances
By: Lipei Shao · Michaela Prochazkova · Yihua Cai · Kyu Lee Han · Kathryn Martin · Rongye Shi · Ruyan Yang · Jinxia Ma · Yanyan Zheng · Pamela Garces · Sue-Ellen Frodigh · Victoria Pham · Nan Zhang · Robert P. Somerville · David F. Stroncek · Ping Jin
2 September 2026 at 08:00
Transduction efficiency is a critical determinant of CAR T-cell manufacturing quality. Analysis of 204 clinical CAR T-cell products revealed that transduction efficiency is shaped primarily by manufacturing workflows and protocol-dependent starting material composition. Higher transduction efficiency was associated with early memory-like cellular states, providing insights into optimizing CAR T-cell.

Ammonium tetrathiomolybdate improves auditory and vestibular function after gentamicin exposure via the NRF2–GPX4 axis

✇Molecular Therapy
By: Qiong Wu · Minchao Wu · Mingwei Xu · Tianyu Gong · Yuan Yao · Qianqian Zhang · Linrui Chen · Jianhui Qiu · Yulian Jin · Guisheng Zhong · Yu Zhao · Ce Zheng · Qing Zhang
2 September 2026 at 08:00
Zhang and colleagues reveal that GPX4 serves as a critical regulator of NRF2-mediated otoprotection against aminoglycoside-induced hair cell injury. Their findings identify a GPX4-dependent antioxidant mechanism that enables therapeutic activation of NRF2 and provides new insights into strategies for preventing drug-induced hearing loss.

MITF-SCD1 Lipid Metabolic Axis Prevents Ouabain-Induced Spiral Ganglion Neuron Ferroptosis and Hearing Loss

✇Molecular Therapy
By: Xue Wang · Fang Chen · Ziji Zhang · Man Zhang · Man Wang · Lei Chen · Anqi Suo · Xugang Zhuang · Tenghan Ling · Renjie Chai · Haibo Wang · Bo Chu · Lei Xu · Wenwen Liu
31 August 2026 at 08:00
Ouabain triggers cochlear spiral ganglion neuron (SGN) ferroptosis and hearing loss via SCD1 downregulation. MITF directly activates Scd1 transcription, and the MITF–SCD1 axis mitigates SGN ferroptosis and hearing impairment in ototoxic ouabain and cisplatin models, revealing a lipid metabolic vulnerability and therapeutic target for sensorineural hearing loss.

Antisense oligonucleotides against Il6ra ameliorate cancer cachexia in mice

✇Molecular Therapy
By: Yuanyuan Wang · Qingtao Sun · Wenqiang Zheng · Lijie Han · Qian Zhang · Adrian R. Krainer · Bo Li
19 August 2026 at 08:00
Cancer cachexia is a devastating metabolic syndrome for which there are no approved treatments. Li and colleagues developed an RNA-targeted therapy, which ameliorates cachectic symptoms, reduces inflammation, and extends survival in mouse cancer models. The study provides an approach for treating cancer cachexia and paves the road for clinical studies.

A complement C5-targeted GalNAc-conjugated siRNA with sustained efficacy in a non-human primate model of IgA nephropathy

✇Molecular Therapy
By: Xueyan Zhang · Xue Zhang · Qiuyu Yang · Bin Kang · Mo Dan · Chunlei Li · Yan Chen · Jianhua Gan · Xiaoye Su
13 August 2026 at 08:00
This study characterizes a GalNAc-C5 small interfering RNA with potent in vitro and in vivo activity. Single subcutaneous dosing sustains long-term C5 suppression in cynomolgus monkeys with IgA nephropathy, outperforming Nefecon in blocking glomerular complement deposition, supporting its standalone or combinational clinical application.

The DreAM-plus integrative RNA switch enhances transient AAV expression and reduces side effects of gene editing

✇Molecular Therapy
By: Yueyang Zhang · Yuhan Yang · Zhanzhao Liu · Yue Li · Yizhe Xue · Zihan Zhang · Gonglie Chen · Jin Liu · Mengying He · Tian Lu · Yaohua Zhang · Dongyu Zhao · Ke Yang · Lei Miao · Fei Gao · Yuxuan Guo
10 August 2026 at 08:00
This study developed a multi-layer inducible RNA switch that achieves transient expression of gene-delivery vectors in hepatic and non-hepatic tissues. As an exemplary application, this RNA switch triggers pulsive expression of gene editors that reduces the off-target effects and immunotoxicity of gene editing.

A helicase-fused Cas9 improves large-size fragment knockin

✇Molecular Therapy
By: Duowei Zhao · Xiangjie Zhao · Yuhang Gao · Sujie Yang · Xiaochen Guo · Caiyan Zhang · Chuanpei Li · Chengda Zhang · Huan Tian · Qianqian Xu · Zhi Yin · Zhonghua Liu · Jun Song
29 July 2026 at 08:00
By fusing MCM5, a subunit of the eukaryotic MCM2–7 helicase complex, to the N terminus of spCas9 (MCCas), the MCCas fusion protein enhances large-size fragment knockin via homologous recombination, reduces insertions and deletions (indels), and enables efficient large-size fragment insertions in human cells and rabbit embryos.

Talking to Itself While Coding: What Makes Comments Help Code Generation?

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dangfeng Pan · Zhensu Sun · Cenyuan Zhang · David Lo · Xiaoning Du
10 September 2026 at 12:00
arXiv:2609.09242v1 Announce Type: cross Abstract: Large Language Models (LLMs) often generate natural-language comments while writing code, and these comments become part of the context used to generate the code that follows. However, it remains unclear which properties of comments affect code-generation performance. We study this question through observational analyses and controlled interventions. On LiveCodeBench, neither comment frequency nor broad comment intent reliably predicts pass@1. We then prefill weaker recipient models with comment blocks written by stronger source models, allowing us to separate comment surface form from the solution content they convey. Comments from source solutions that pass the tests raise recipient pass@1 by 17.2% on average. In contrast, comments describing failed solutions provide no reliable gain, while comments written for a different problem reduce pass@1 by 20.8%. Finally, across a wide range of models and prompt variants, most recipient models show no significant recovery of the external-comment gain, and the best case recovers only 24%. These results show that comments help code generation not merely because they are comments, but because they can provide correct solution content that prompting cannot reliably elicit.

HiRAD: A Flexible Large-Scale AGV Routing System

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yunjie Huang · Ruizhong Wu · Mengxuan Zhang · Frodo Kin Sun Chan · Yan Nei Law · Lei Li
10 September 2026 at 12:00
arXiv:2609.09752v1 Announce Type: cross Abstract: Automatic Guided Vehicles (AGVs) substantially boost warehouse throughput, but routing large-scale AGV fleets remains challenging. Classical Multi-Agent Pathfinding solvers suffer from exploding combinatorial complexity and super-quadratic runtime, while relying on idealized grid or piecewise-linear motion models that mismatch real-world kinematics. Recent Reinforcement Learning (RL) solutions improve flexibility via decentralized agent policies but depend on discretized spatiotemporal representations, require millions of episodes to converge, and incur full-map observation at every step, which leads to large models, slow convergence, and high inference latency that violates real-time industrial control constraints. To address these bottlenecks, we propose HiRAD, a hierarchical RL framework for continuous-space AGV routing with real-time guarantees: (1) a step-level spatiotemporal representation that translates continuous motion into a differentiable RL problem, (2) a hierarchical strategy that splits heading choice from velocity control to reduce the action space, and (3) an asynchronous event-driven decision pipeline that lowers inference complexity from O(n^2) to O(n) and cuts per-step latency by as much as 71 percent. Across random graphs and two warehouse maps, HiRAD reduces makespan by 45 percent to 63 percent and shortens end-to-end runtime.

Dear Algo: A Precision-First Agentic Intent Layer for Unified Search and Recommendation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rui Wang · Jiazhou Wang · Zheng Wei · Chenglin Lu · Fangcheng Sun · Ivy Sun · Jin Sun · Hui Geng · Lillian Zhang · Chao Yang · Lei Chen · Shahin Sefati · Reem Helou · Joe Zhou · Babak Shakibi · Yiyi Pan · Bi Xue · Hong Yan · Shujian Bu
10 September 2026 at 12:00
arXiv:2608.15877v3 Announce Type: replace Abstract: Search and recommendation serve a shared discovery objective but encode intent differently. We study this boundary through Dear Algo on Threads, a deployed product where open-ended requests such as \emph{more NBA news} or \emph{less politics} steer subsequent feed recommendations rather than return a one-shot result list. Its agentic intent layer compiles explicit, inferred, negative, and compound intent into a grounded executable plan, then invokes conventional retrieval and optional semantic or multimodal reranking. The layer shares an intent-to-retrieval contract without requiring one model or serving path across search-like and recommendation-like modes. We evaluate Dear Algo under a precision-first objective. In a blinded audit of 300 public request-item pairs (296 evaluable), a strict categorical LLM-as-a-judge gate achieved 94.4\% exact-Relevant precision [88.8\%, 98.9\%]. Across 72 normalized request clusters, the full configuration produced 7.73 judge-qualified candidates per 20 slots versus 6.61 for an LLM-derived-query baseline, a gain of 1.11 [0.12, 2.12]. In a candidate-randomized serving-path study restricted to the reranker path's first 72 eligible hours, the user-weighted judge-Irrelevant share among judged admissions was 2.80\% versus 4.78\% off (-1.97 points [-3.02, -0.94]), while Exact-Relevant share was 2.24 points higher [0.08, 4.41]. Together, these studies show how explicit natural-language intent can be carried into feed recommendation under a precision-first evaluation framework

RAU: Reference-based Anatomical Understanding with Vision Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yiwei Li · Yikang Liu · Jiaqi Guo · Lin Zhao · Zheyuan Zhang · Xiao Chen · Boris Mailhe · Ankush Mukherjee · Terrence Chen · Shanhui Sun
10 September 2026 at 12:00
arXiv:2509.22404v2 Announce Type: replace-cross Abstract: Anatomical understanding, which is the ability to identify, localize, or segment anatomical structures, is critical in medical image analysis; however, its progress is constrained by the scarcity of expert-labeled data. A promising remedy is to leverage an annotated reference image to guide the interpretation of an unlabeled target. Although recent vision-language models (VLMs) exhibit non-trivial visual reasoning, their reference-based understanding and fine-grained localization remain limited. We introduce RAU, a framework for reference-based anatomical understanding with VLMs. We first show that a VLM learns to identify anatomical regions through relative spatial reasoning between reference and target images, trained on a moderately sized dataset. We validate this capability through visual question answering (VQA) and bounding box prediction. Next, we demonstrate that the VLM-derived spatial cues can be seamlessly integrated with the fine-grained segmentation capability of SAM2, enabling localization and pixel-level segmentation of small anatomical regions, such as vessel segments. Across two in-distribution and two out-of-distribution datasets, RAU consistently outperforms a SAM2 fine-tuning baseline using the same memory setup, yielding more accurate segmentations and more reliable localization. More importantly, its generalization ability to unseen modalities makes it scalable to unseen datasets, a property crucial for medical image applications. To the best of our knowledge, RAU is the first to explore the capability of VLMs for reference-based identification, localization, and segmentation of anatomical structures in medical images. Its promising performance highlights the potential of VLM-driven approaches for anatomical understanding in automated clinical workflows.
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • Generative AI for Analysts Jian Xue · Qian Zhang · Wu Zhu
    arXiv:2512.19705v2 Announce Type: replace-cross Abstract: We study how generative artificial intelligence (GenAI) reshapes financial analysts' information production. Using the 2023 integration of GenAI into FACTSET as a plausibly exogenous change in AI access, we find that FACTSET-associated reports become markedly richer--featuring 26% more distinct information sources, 24% broader topical coverage, and 21% more analytical methods--while also improving timeliness. However, these gains do not
     
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Generative AI for Analysts

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jian Xue · Qian Zhang · Wu Zhu
10 September 2026 at 12:00
arXiv:2512.19705v2 Announce Type: replace-cross Abstract: We study how generative artificial intelligence (GenAI) reshapes financial analysts' information production. Using the 2023 integration of GenAI into FACTSET as a plausibly exogenous change in AI access, we find that FACTSET-associated reports become markedly richer--featuring 26% more distinct information sources, 24% broader topical coverage, and 21% more analytical methods--while also improving timeliness. However, these gains do not uniformly improve decision quality: relative forecast accuracy declines when analysts face greater information-processing demands. Yet, a machine-learning benchmark processing the same observable inputs shows no analogous deterioration, pointing to a human processing constraint rather than poorer underlying information. Placebo tests using other data vendors make a common platform-wide technology trend unlikely. Overall, GenAI relaxes information-acquisition constraints while making human attention a more important bottleneck.

A programmed cell death learning signature predicts immunotherapy response and identifies AP1S1 as a regulator of immune exclusion in breast cancer

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Guixin Wang · Junming Cao · Parhat Kaysar · Chenglu Lu · Ziyi Chen · Zhaohui Chen · Han Zhang · Yue Yu · Yongjie Xie · Shan Cheng · Pengpeng Zhang · Xin Wang
9 September 2026 at 18:00

Chin J Cancer Res. 2026 Aug 30;38(4):480-500. doi: 10.21147/j.issn.1000-9604.2026.04.08.

ABSTRACT

OBJECTIVE: Breast cancer remains a leading cause of global cancer mortality, characterized by profound heterogeneity. While immune checkpoint blockade (ICB) has transformed oncology, its efficacy in breast cancer is often hindered by "immune-cold" microenvironments and immune exclusion. Programmed cell death (PCD) is a critical regulator of tumor immune microenvironment (TIME). However, its role in the breast cancer immune microenvironment remains poorly understood.

METHODS: We integrated multi-omics data from six breast cancer cohorts (N=3,764) to develop a programmed cell death learning signature (PCDsig) using over 100 machine learning combinations. The model was benchmarked against 29 published signatures. Single-cell transcriptomic analysis decoded the immune landscape and cellular crosstalk. The role of adaptor-related protein complex 1 subunit sigma 1 (AP1S1) was validated through a clinical cohort, in vitro functional assays, and in vivo syngeneic mouse models.

RESULTS: PCDsig significantly stratified patient prognosis across all cohorts, consistently outperforming 29 existing models. High PCDsig scores correlated with immune-excluded phenotypes, reduced CD8+ T cell infiltration, and lower immunophenoscores. Single-cell analysis revealed that high-PCDsig tumors utilize vascular endothelial growth factor A (VEGFA) signaling to foster an immunosuppressive microenvironment. AP1S1 was identified as the core driver of immune exclusion. And our clinical cohort supported the immune exclusion effect of AP1S1. AP1S1 knockdown impaired tumor progression in vitro and fundamentally remodeled the tumor immune ecosystem in vivo. Combining AP1S1 inhibition with anti-programmed cell death ligand 1 (anti-PD-L1) therapy exerted profound synergistic effects, driven by massive infiltration and functional activation of cytotoxic Granzyme B (GZMB)+CD8+ T cells.

CONCLUSIONS: Our study establishes the PCDsig we developed is a potential prognostic and predictive biomarker for breast cancer. We provide the first evidence of AP1S1 as a core immunomodulatory oncogene that mediates immune exclusion. Targeting AP1S1 represents a highly promising strategy to sensitize cold breast tumors to ICB, offering a new perspective for precision immunotherapy.

PMID:42712842 | PMC:PMC13551362 | DOI:10.21147/j.issn.1000-9604.2026.04.08

The redox architecture of gestational diabetes mellitus: from cellular stress engine to epigenetic and mitochondrial rewiring

✇(Multiomics OR Omics) AND (Pancreatic)
By: Haijuan Zhang · Shaoxiong Zhang · Yuanhao Liu · Jinjiang Shi · Aijun Yang · Ruirui Jin · Yuxia Jin · Xuya Wang · Yitao Fan · Qian Liu · Yanhua Zhang · Cheng Wang · Jianjun Mao · Zhiyuan Zheng · Ling Lv · Muzhou Teng
9 September 2026 at 18:00

Free Radic Biol Med. 2026 Sep 9;256:441-460. doi: 10.1016/j.freeradbiomed.2026.09.006. Online ahead of print.

ABSTRACT

Gestational diabetes mellitus (GDM) is a common pregnancy complication with a rising global prevalence, posing serious short-term and long-term health threats to both mothers and offspring. This review repositions GDM as a systemic disorder in which oxidative stress acts as a proposed mechanistic hub, linking upstream risk factors to downstream pathophysiology. We first examine how "upstream" factors-including genetic susceptibility, pre-conception status, and environmental exposures-converge to promote a state of pathological redox imbalance. We then examine key mechanistic pathways through which oxidative stress is thought to contribute to systemic insulin resistance and pancreatic β-cell failure, highlighting novel pathways involving intercellular communication via tunneling nanotubes and exosomes. Furthermore, we explore the downstream cascade, where oxidative stress may program maternal accelerated biological aging and multi-organ offspring disease trajectories through nuclear epigenetic programming and mitochondrial dysfunction programming, leaving what has been termed a persistent "metabolic memory". Consequently, this review evaluates emerging strategies that target oxidative stress for early prediction and precision intervention. Early prediction models based on direct redox biomarkers and multi-omics signatures hold potential to shift diagnosis from late-gestation oral glucose tolerance test (OGTT) to first-trimester risk stratification. Current supporting evidence draws from human epidemiological associations, ex vivo placental analyses, and experimental models. However, direct causal and interventional validation in pregnant women remains limited. Integrating targeted redox risk stratification and precision interventions into a life-course clinical framework may help interrupt the intergenerational transmission of metabolic disease initiated by GDM.

PMID:42716407 | DOI:10.1016/j.freeradbiomed.2026.09.006

A programmed cell death learning signature predicts immunotherapy response and identifies AP1S1 as a regulator of immune exclusion in breast cancer

✇(Multiomics OR Omics) AND (Pancreatic)
By: Guixin Wang · Junming Cao · Parhat Kaysar · Chenglu Lu · Ziyi Chen · Zhaohui Chen · Han Zhang · Yue Yu · Yongjie Xie · Shan Cheng · Pengpeng Zhang · Xin Wang
9 September 2026 at 18:00

Chin J Cancer Res. 2026 Aug 30;38(4):480-500. doi: 10.21147/j.issn.1000-9604.2026.04.08.

ABSTRACT

OBJECTIVE: Breast cancer remains a leading cause of global cancer mortality, characterized by profound heterogeneity. While immune checkpoint blockade (ICB) has transformed oncology, its efficacy in breast cancer is often hindered by "immune-cold" microenvironments and immune exclusion. Programmed cell death (PCD) is a critical regulator of tumor immune microenvironment (TIME). However, its role in the breast cancer immune microenvironment remains poorly understood.

METHODS: We integrated multi-omics data from six breast cancer cohorts (N=3,764) to develop a programmed cell death learning signature (PCDsig) using over 100 machine learning combinations. The model was benchmarked against 29 published signatures. Single-cell transcriptomic analysis decoded the immune landscape and cellular crosstalk. The role of adaptor-related protein complex 1 subunit sigma 1 (AP1S1) was validated through a clinical cohort, in vitro functional assays, and in vivo syngeneic mouse models.

RESULTS: PCDsig significantly stratified patient prognosis across all cohorts, consistently outperforming 29 existing models. High PCDsig scores correlated with immune-excluded phenotypes, reduced CD8+ T cell infiltration, and lower immunophenoscores. Single-cell analysis revealed that high-PCDsig tumors utilize vascular endothelial growth factor A (VEGFA) signaling to foster an immunosuppressive microenvironment. AP1S1 was identified as the core driver of immune exclusion. And our clinical cohort supported the immune exclusion effect of AP1S1. AP1S1 knockdown impaired tumor progression in vitro and fundamentally remodeled the tumor immune ecosystem in vivo. Combining AP1S1 inhibition with anti-programmed cell death ligand 1 (anti-PD-L1) therapy exerted profound synergistic effects, driven by massive infiltration and functional activation of cytotoxic Granzyme B (GZMB)+CD8+ T cells.

CONCLUSIONS: Our study establishes the PCDsig we developed is a potential prognostic and predictive biomarker for breast cancer. We provide the first evidence of AP1S1 as a core immunomodulatory oncogene that mediates immune exclusion. Targeting AP1S1 represents a highly promising strategy to sensitize cold breast tumors to ICB, offering a new perspective for precision immunotherapy.

PMID:42712842 | PMC:PMC13551362 | DOI:10.21147/j.issn.1000-9604.2026.04.08

Wuwei Huanglian Wan inhibits Helicobacter pylori and alleviates associated gastritis: host metabolic remodeling and altered IL-6/STAT3 signaling

✇Omics in Gastric
By: Wenxi Yang · Gengyuan Yu · Yanan Zhang · Dongsheng Ren · Yuying Li · Jin Yang · Meicun Yao
5 September 2026 at 18:00

J Ethnopharmacol. 2026 Sep 5;374(Pt 1):122370. doi: 10.1016/j.jep.2026.122370. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Wuwei Huanglian Wan (WWHLW) is a traditional Tibetan medicine formula developed by Tibetan physician Takpe Pingcuo and officially documented in the Ministry of Health Drug Standards for Tibetan Medicines (Volume I, 1995). It has long been used for the treatment of gastrointestinal disorders, particularly conditions associated with gastrointestinal discomfort and inflammation. Given the overlap between its traditional indications and the clinical manifestations of H. pylori-associated gastritis (HAG), WWHLW represents a promising candidate for the management of H. pylori infection and related gastric inflammation. In addition, several constituent herbs of WWHLW have demonstrated anti-H. pylori and anti-inflammatory activities, providing a pharmacological basis for further investigating its therapeutic effects.

AIM OF THE STUDY: This study aimed to systematically evaluate the therapeutic effects of WWHLW against H. pylori infection and HAG, and to explore the biological processes associated with these effects through integrated multi-omics and experimental validation.

MATERIALS AND METHODS: The therapeutic effects of WWHLW were evaluated through in vitro antibacterial assays and an H. pylori-infected mouse model. UHPLC-HRMS/MS was employed for chemical profiling and identification of serum-absorbed constituents. Serum metabolomics, 16S rRNA gene sequencing, network pharmacology analysis, molecular docking analysis, and molecular biological analyses were integrated to investigate the metabolic, microbial, and signaling changes associated with its therapeutic activity.

RESULTS: WWHLW exhibited anti-H. pylori activity, with minimum inhibitory concentrations (MICs) of 0.2-0.5 mg/mL against both standard strains and multidrug-resistant clinical isolates. At MIC concentrations, WWHLW treatment altered the expression of multiple virulence-associated genes and reduced gastric H. pylori colonization by 93.8% in infected mice. UHPLC-HRMS/MS analysis putatively annotated 121 compounds in the WWHLW extracts, of which 10 prototype constituents were detected in serum after oral administration. Integrated metabolomics and network pharmacology analyses revealed alterations in lipid and amino acid-related metabolic pathways following WWHLW treatment. Gut microbiota analysis showed that WWHLW was associated with less pronounced alterations in microbial diversity and composition than antibiotic treatment. Correlation analysis further revealed statistical associations between microbial taxa and lipid and amino acid-related features. Experimental validation showed that WWHLW reduced inflammatory cytokine expression and suppressed STAT3 phosphorylation, consistent with altered IL-6/STAT3-related molecular changes.

CONCLUSIONS: WWHLW exhibits therapeutic potential against H. pylori infection and HAG through combined antibacterial, anti-inflammatory and metabolic regulatory effects. The protective activity of WWHLW was associated with reduced IL-6/STAT3 signaling, providing pharmacological evidence supporting its traditional use in gastrointestinal disorders.

PMID:42700849 | DOI:10.1016/j.jep.2026.122370

Integrative Pan-Cancer Characterization of lncRNA UPK1A-AS1 and Its Role in Hypoxia-Associated Sorafenib Resistance in Hepatocellular Carcinoma

✇Omics in Hepatocellular
By: Ze-Kai Li · Min-Rui Luo · Shu-Jie Fang · Yi-Da Wu · Quan-Hui Hu · Dong-Yan Zhang
1 September 2026 at 18:00

Anal Cell Pathol (Amst). 2026;2026(1):e1554526. doi: 10.1155/ancp/1554526.

ABSTRACT

Long noncoding RNAs (lncRNAs) are emerging as critical regulators of tumor initiation and progression through transcriptional and posttranscriptional mechanisms. UPK1A antisense RNA 1 (UPK1A-AS1), a cancer-associated lncRNA, has been reported to participate in oncogenic processes; however, its overall landscape across human malignancies and its biological role in therapy resistance remain poorly understood. Given the increasing importance of identifying functional lncRNAs with prognostic and therapeutic potential, this study presents a comprehensive multiomics characterization of UPK1A-AS1 and its experimental validation in hepatocellular carcinoma (HCC). We integrated datasets from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression Project (GTEx), the cancer immunology data engine (CIDE), and the cBioPortal for cancer genomics (cBioPortal) to systematically assess its expression pattern, genomic alterations, clinical significance, and immunological associations. Our analyses revealed that UPK1A-AS1 is significantly upregulated in multiple tumor types, with copy-number amplification as the predominant genomic alteration driving its overexpression. Elevated UPK1A-AS1 expression was correlated with advanced disease stage, poor differentiation, immune exclusion, and unfavorable prognosis, supporting its potential as a cancer type-dependent biomarker. In parallel, functional studies demonstrated that hypoxia transcriptionally induces UPK1A-AS1 in HCC, where it promotes sorafenib resistance by suppressing apoptosis. Silencing UPK1A-AS1 restored apoptotic and enhanced sorafenib efficacy both in vitro and in vivo. Collectively, our findings suggest that UPK1A-AS1 is a hypoxia-inducible oncogenic lncRNA that plays dual roles in cancer, with cancer type-dependent associations with progression and immune modulation across malignancies and mechanistically mediating hypoxia-associated drug resistance in HCC.

PMID:42678131 | PMC:PMC13532061 | DOI:10.1155/ancp/1554526

PLCE1 exacerbates the development of atherosclerosis by driving endothelial dysfunction and macrophage inflammation via targeting CTNNB1

✇Cell Death Discovery nature.com science feeds
By: Wen-Lin Cheng · Yufang Shi · Quan Zhang · Zhiwei Cai · Fei-Xiang Jiang · Xiangjie Kong · Jian-Lei Cao · Li Peng · Ming Chen · Tao He · Hairong Wang
31 August 2026 at 08:00

Cell Death Discovery, Published online: 31 August 2026; doi:10.1038/s41420-026-03309-2

PLCE1 exacerbates the development of atherosclerosis by driving endothelial dysfunction and macrophage inflammation via targeting CTNNB1
Received — 27 May 2026 ⏭

A Non-Canonical Role of SMAD4 in Regulating 3D Genome Architecture to Inhibit Lung Squamous Cell Carcinoma Development

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Qian Tang · Chen Lian · Xinyan Han · Xuhan Zhang · Zihan Wang · Boyu Wang · Taoyu Zhu · Xinrui Lin · Xiaolei Wang · Yaping Xu · Manyu Xiao · Zijin Wang · Junming Li · Silin Chen · Yunze Wang · Yufei Liu · Songsong Li · Zuolin Shen · Xi Lu · Xueqi Han · Yilu Zhou · Mingyang Xiao · Jiayi Ran · Xiaoran Cao · Xinyi Xu · Hugo Sámano-Sánchez · Alfredo Rodríguez · Lian Wang · Shuifang Chen · Zhanyu Xu · Shirong Zhang · Nuo Yang · Yong Tang · Jian Liu
26 May 2026 at 18:00

Adv Sci (Weinh). 2026 May 26:e75839. doi: 10.1002/advs.75839. Online ahead of print.

ABSTRACT

Lung squamous cell carcinoma (LUSC) lacks clearly defined key drivers and effective targeted therapies, reflecting an incomplete understanding of its molecular pathogenesis. Here, we identify SMAD4 as a critical regulator of three-dimensional (3D) genome organization in LUSC and uncover a mechanistic link between tumor suppressor loss and oncogenic transcriptional activation. By integrating clinical datasets, genetically engineered mouse models, human and murine LUSC cell lines, and multi-omics analyses, we demonstrate that SMAD4 deficiency promotes LUSC progression by unleashing EP300-mediated enhancer-promoter looping at the SOX2 locus. Mechanistically, SMAD4 does not directly bind SOX2 regulatory elements but instead constrains chromatin looping by sequestering EP300 away from loop anchor regions. Loss of SMAD4 leads to enhanced H3K27ac deposition, aberrant SOX2 activation, and increased LUSC tumor cell proliferation. Together, these findings reveal a non-canonical role for a transcription factor (e.g., SMAD4) in regulating dysregulated 3D genome architecture to inhibit tumor development.

PMID:42189071 | DOI:10.1002/advs.75839

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