Biology subjects

Li, R.

Publications and source records attributed to Li, R..

At least 181 records · Page 10Linked to original sources

bioRxiv2025

Pancreatic cancer cachexia is mediated by PTHrP-driven disruption of adipose de novo lipogenesis

Pancreatic cancer patients have the highest rates and most severe forms of cancer cachexia, yet cachexia etiologies remain largely elusive, leading to a lack of effective intervening therapies. Parathyroid hormone-related protein (PTHrP) has been clinically implicated as a putative regulator of cachexia, with serum PTHrP levels correlating with increased weight loss in PDAC patients. Here we show that cachectic PDAC patients have high expression of tumor PTHrP and use a genetically engineered mouse model to functionally demonstrate that loss of PTHrP blocks cachectic wasting, dramatically extending overall survival. The re-expression of PTHrP in lowly cachectic models is sufficient to induce wasting and reduce survival in mice, which is reversed by the conditional deletion of the PTHrP receptor, Pth1r, in adipocytes. Mechanistically, tumor-derived PTHrP suppresses de novo lipogenesis in adipocytes, leading to a molecular rewiring of adipose depots to promote wasting in the cachectic state. Finally, the pharmacological disruption of the PTHrP-PTH1R signaling axis abrogates wasting, highlighting that a targeted disruption of tumor-adipose crosstalk is an effective means to limit cachexia. STATEMENT OF SIGNIFICANCEPancreatic ductal adenocarcinoma (PDAC) is the prototypical cancer type associated with cancer cachexia, a debilitating wasting syndrome marked by adipose tissue loss and muscle atrophy. Herein, we establish that PTHrP is a tumor-derived factor that facilitates cachexia by downregulating de novo lipogenesis in adipocytes and that blocking PTHrP is an effective means to limit wasting in preclinical mouse models.

cancer biology↗

bioRxiv2025

A conditional generative model to disentangle morphological variation from batch effects in model organism imaging studies

Identifying organism-level phenotypic variation that arises from genetic variation is a longstanding problem in genetics. Model organisms such as zebrafish are frequently used for genotype to phenotype studies as they can be genetically manipulated, grown and phenotyped for morphological changes in a high throughput manner through automated systems and imaging. However, individuals from model organisms are typically grown in groups: clutches for zebrafish and frogs, and litter for rodents. These clutches act as strong confounders during image analysis, as individuals from different genotypes but grown in the same clutch tend to look more similar to each other than to individuals with the same genotype in other clutches. Existing approaches such as conditional image classification models and domain adaptation approaches perform poorly on addressing these technical batch effects. Here, we propose a conditional latent diffusion model (cLDM) that disentangles these technical batch effects from morphological features by explicitly conditioning on batch-specific variables during generation, enabling targeted separation of technical artifacts from biologically relevant data. This approach enables accurate classification of genotypes using morphological images of individual zebrafish from distinct mutant classes. Furthermore, this approach allows us to efficiently correct for batch effect and demonstrates the versatility of cLDM in tackling domain-specific problems. This work highlights the potential of cLDM to overcome batch effects and extract meaningful features.

bioinformatics↗

bioRxiv2025

Structural and temporal dynamics analysis on PANoptosis in sepsis: a bibliometric analysis

PANoptosis, as a new type of programmed cell death, is characterized by pyroptosis, apoptosis and necroptosis, and is a key mechanism causing a variety of inflammatory diseases. Despite the growing number of studies indicating the crucial role of PANoptosis in sepsis, there has been no bibliometric analysis of the research hotspots and trends in this field. Therefore, this study aims to explore the history, research hotspots and emerging trends of PANoptosis in sepsis related research in the past 20 years from the perspective of structure and temporal dynamics. The articles related to PANoptosis in sepsis were retrieved from the Web of Science Core Collection (WoSCC) database from 2000 to 2024. CiteSpace and HistCite were used to analyse the historical features, the evolution of active topics, and emerging trends about PANoptosis in sepsis. 6165 original articles and reviews on PANoptosis in sepsis were included in the bibliometric analysis. In the last 20 years, the number of published documents is increasing year by year and reaches a peak in 2022. At the same time, many activation themes have emerged at different times, as evidenced by a total of 96 categories, 865 keywords and 629 reference bursts. Keyword clustering anchored eight emerging research subfields, namely 0# oxidative stress, 1#pyroptosis, 2#sepsis-associated encephalopathy, 3#acute kidney injury, 4#immunosuppression, 5#necroptosis, 7#lung injury and 8#extracellular vesicles. The keyword alluvial map shows that the most persistent research concepts in this field are phosphatase. And the emerging keywords are toll_like_receptors, regulatory T cells, recognition, etc. In a timeline visualization based on the time span of the citations, we find five relevant emerging topics, namely 1# immunosuppression, 2# sepsis-induced cardiomyopathy, 3# pyroptosis, 4# acute kidney injury, and 13# COVID-19. Our study provides a comprehensive bibliometric analysis and summary about the current status and trends on PANoptosis in sepsis, which will aid researchers in conducting further scientific research in this field.

bioinformatics↗

bioRxiv2025

Lack of synergy between AR targeted therapies and PARP inhibitors in homologous recombination-proficient prostate cancer

Recent clinical trials have explored the combination of androgen receptor (AR) pathway inhibitors and poly (ADP-ribose) polymerase (PARP) inhibitors as a potential treatment for castration-resistant prostate cancer. This combination treatment is based on the premise that AR directly regulates expression of DNA repair genes, leading to synergy between PARP and AR inhibition. Despite some promising preclinical evidence, this combination therapy has shown limited efficacy in patients with homologous recombination (HR)-proficient tumors. To investigate this discrepancy between preclinical and clinical results, we profiled the effects of PARP inhibition in prostate cancer models in the presence or absence of AR inhibition. Surprisingly, AR inhibition impaired response to PARP inhibitors in castration-sensitive cells and had no effect on response in castration-resistant cells. AR inhibition also did not regulate DNA repair in either the castration-resistant or castration-sensitive setting. Instead, we find that cell cycle progression is required for response to PARP inhibition in homologous-recombination proficient prostate cancer. STATEMENT OF SIGNIFICANCEAndrogen deprivation does not inhibit DNA repair and does not synergize with PARP inhibition in prostate cancer with intact homologous recombination repair.

cancer biology↗

bioRxiv2025

Biomni: A General-Purpose Biomedical AI Agent

Biomedical research underpins progress in our understanding of human health and disease, drug discovery, and clinical care. However, with the growth of complex lab experiments, large datasets, many analytical tools, and expansive literature, biomedical research is increasingly constrained by repetitive and fragmented workflows that slow discovery and limit innovation, underscoring the need for a fundamentally new way to scale scientific expertise. Here, we introduce Biomni, a general-purpose biomedical AI agent designed to autonomously execute a wide spectrum of research tasks across diverse biomedical subfields. To systematically map the biomedical action space, Biomni first employs an action discovery agent to create the first unified agentic environment - mining essential tools, databases, and protocols from tens of thousands of publications across 25 biomedical domains. Built on this foundation, Biomni features a generalist agentic architecture that integrates large language model (LLM) reasoning with retrieval-augmented planning and code-based execution, enabling it to dynamically compose and carry out complex biomedical workflows - entirely without relying on predefined templates or rigid task flows. Systematic benchmarking demonstrates that Biomni achieves strong generalization across heterogeneous biomedical tasks - including causal gene prioritization, drug repurposing, rare disease diagnosis, micro-biome analysis, and molecular cloning - without any task-specific prompt tuning. Real-world case studies further showcase Biomnis ability to interpret complex, multi-modal biomedical datasets and autonomously generate experimentally testable protocols. Biomni envisions a future where virtual AI biologists operate alongside and augment human scientists to dramatically enhance research productivity, clinical insight, and healthcare. Biomni is ready to use at https://biomni.stanford.edu, and we invite scientists to explore its capabilities, stress-test its limits, and co-create the next era of biomedical discoveries.

bioinformatics↗

bioRxiv2025

Analysis of isobaric quantitative proteomic data using TMT-Integrator and FragPipe computational platform

Isobaric mass tags, such as iTRAQ and TMT, are widely utilized for peptide and protein quantification in multiplex quantitative proteomics. We present TMT-Integrator, a bioinformatics tool for processing quantitation results from TMT and iTRAQ experiments, offering integrative reports at the gene, protein, peptide, and post-translational modification site levels. We demonstrate the versatility of TMT-Integrator using five publicly available TMT datasets: an E. coli dataset with 13 spike-in proteins, the clear cell renal cell carcinoma (ccRCC) whole proteome and phosphopeptide-enriched datasets from the Clinical Proteomic Tumor Analysis Consortium, and two human cell lysate datasets showcasing the latest advances with the Astral instrument and TMT 35-plex reagents. Integrated into the widely used FragPipe computational platform (https://fragpipe.nesvilab.org/), TMT-Integrator is a core component of TMT and iTRAQ data analysis workflows. We evaluate the FragPipe/TMT-Integrator analysis pipelines performance against MaxQuant and Proteome Discoverer with multiple benchmarks, facilitated by the bioinformatics tool OmicsEV. Our results show that FragPipe/TMT-Integrator quantifies more proteins in the E. coli and ccRCC whole proteome datasets, identifies more phosphorylated sites in the ccRCC phosphoproteome dataset, and delivers overall more robust quantification performance compared to other tools.

bioinformatics↗

bioRxiv2025

Neonatal White Matter Microstructure Predicts Infant Attention Disengagement from Fearful Faces

Infants develop an attentional bias towards faces already at birth, with further specification towards fearful faces emerging at 6 months and diminishing around 11 months of age. However, the neurobiological origins of attentional bias to fear are still poorly understood. To understand the neural structures underlying perception of facial expressions, the current study utilized newborn diffusion magnetic resonance images (N = 86; 41 females; = 27.15 days) and eye tracking from the same infants at 8-months ( = 8.75 months) as a behavioural measure. An overlap paradigm was used to measure attention disengagement from fearful, happy, and neutral faces. Tract-based spatial statistics revealed that higher white matter (WM) mean diffusivity in widespread regions across the brain was associated with lower attention disengagement from fearful faces. The same association was found with happy faces but was limited to only the splenium of the corpus callosum and sensorimotor pathways. Variance in neonatal WM microstructure may reflect individual differences in growth that is related to attentional bias development later in infancy.

neuroscience↗

bioRxiv2025

CSFeatures improves identification of cell type-specific differential features in single-cell and spatial omics data

Recent advancements in single-cell and spatial omics have enabled the analysis of gene expression patterns in cells and tissues with unprecedented precision. Genes that exhibit significant variation across different cell types or states are typically closely linked to cellular functional states and disease processes. However, current differential analysis methods often fail to adequately account for cell type specificity when identifying differentially expressed genes. CSFeatures effectively addresses this issue by comprehensively considering the expression level, the smoothness of expression distribution, and the proportion of gene expression in the target cell population as well as in each of the other cell populations. Through comprehensive experiments on eight single-cell RNA-seq datasets, it was demonstrated that CSFeatures can effectively identify cell type-specific genes that are highly expressed in the target cell population while being lowly expressed in all other populations. Moreover, it was validated that the underlying principle of CSFeatures can be generalized to other omics data, including single-cell ATAC-seq, spatial transcriptomics, and spatial ATAC-seq data. Furthermore, the differential features identified by CSFeatures are enriched in pathways, functional categories, and regulatory regions that are highly relevant to the specific functional states of the target cell population, providing insights into its gene regulation.

bioinformatics↗

bioRxiv2025

Accounting for uncertainty in residual variances improves calibration of the Sum of Single Effects model for small sample sizes

The Sum of Single Effects (SuSiE) model is a widely adopted method for genetic fine-mapping. We show that, in small-sample studies, the original SuSiE fitting procedure produces substantially higher rates of false positive findings. We show that a simple modification to SuSiE improves performance, especially in small-sample studies. This modification is particularly important for emerging molecular QTL applications in rare cell types and primary tissues where sample sizes are inherently limited.

bioinformatics↗

bioRxiv2025

A close unicellular relative reveals aggregative multicellularity was key to the evolution of animals

How animals evolved complex multicellularity from their unicellular ancestors remains unanswered. Unicellular relatives of animals exhibit simple multicellularity through clonal division, formation of multinucleate coenocytes, or aggregation.1 Therefore, animal multicellularity may have evolved from one (or a combination) of these behaviours. Aggregation has classically been dismissed as a means to complex multicellularity.2 However, aggregation occurs in many extant animal cells and has also been recently described in three different unicellular relatives of animals (the choanoflagellates Salpingoeca rosetta and Choanoeca flexa, and the filasterean Capsaspora owczarzaki).3-6 It is unclear whether aggregation in these species is derived or ancestral, and its relevance for animal origins remains unknown. To fill this gap, we investigated whether an additional unicellular relative of animals can undergo aggregation. We discovered that the marine free-living bacterivorous filasterean Ministeria vibrans7 forms homogeneous aggregates with reproducible kinetics that have long-term stability when cultured with an alphaproteobacterium. We found that many multicellularity genes involved in animal cell adhesion, signaling, and transcriptional regulation were deployed during this process. Our findings suggest that the last unicellular ancestor of animals had the capacity to aggregate using key animal multicellularity genes and that improved feeding and sexual reproduction may be evolutionary drivers of this aggregation.

biochemistry↗

bioRxiv2025

Quantitative RNA pseudouridine landscape reveals dynamic modification patterns and evolutionary conservation across bacterial species

Pseudouridine ({Psi}) modifications are the most abundant RNA modifications; however, their distribution and functional significance in bacteria remain largely unexplored compared to eukaryotic systems. In this study, we present the first transcriptome-wide and quantitative mapping of {Psi} modifications across five diverse bacterial species (Bacillus cereus, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Pseudomonas syringae) at single-base resolution, utilizing the optimized BID-seq method for bacterial RNA. Our analysis revealed growth phase-dependent dynamics of pseudouridylation in bacterial tRNA and mRNA, particularly in genes enriched in core metabolic pathways. Comparative analysis demonstrated evolutionarily conserved features of {Psi} modifications, such as dominant motif contexts, {Psi} clustering within operons, etc. Functional analysis indicated {Psi} modifications influence bacterial mRNA stability, translation, and interactions with specific RNA-binding proteins (RBPs) in response to changing cellular demands during growth phase transitions. The integrated computational analysis on local RNA architecture was conducted to elucidate the structure-dependent {Psi} modifications in bacterial RNA. Furthermore, we developed an integrated deep learning framework, combining Transformer-GNN-based neural networks (pseU_NN) to capture both RNA sequence and structural features for effective prediction of {Psi}-modified sites. Overall, our study provides valuable insights into the landscapes of bacterial RNA {Psi} modifications and establishes a foundation for future mechanistic investigations into the functions of {Psi} in bacterial RNA regulation.

microbiology↗

bioRxiv2025

Identification of immune-mitophagy related gene in PD based on single-cell sequencing and Mendelian randomization

Parkinsons disease (PD) involves dysregulated mitophagy and immuneresponses, though the underlying mechanisms remain unclear. To identify key genes linking these pathways, we integrated single-cell RNA sequencing (GSE157783) with Mendelian randomization(MR) analysis, screening mitophagy-related genes (MRGs) and immune-related genes (IRGs). Differential expression analysis across PD cell subpopulations, combined with MR, revealed four causal genes: SLC11A1and DDX17(protective) and MRAS and PDIA3(risk). These genes were enriched in antigen presentation and calcium signaling pathways and exhibited dynamic expression in astrocytes and microglia during differentiation. Subsequent protein-protein interaction(PPI) network, regulatory (SCENIC), and drug-target analyses further characterized their roles. Validation in MPTP-induced PD mice confirmed behavioral deficits and altered expression of these genes, supporting their functional relevance. Our findings highlight SLC11A1, DDX17, MRAS, and PDIA3 as critical mitophagy-immune hubs in PD, offering mechanistic insights and therapeutic targets. Author SummaryPD is a neurodegenerative disorder linked to mitochondrial dysfunction (mitophagy) and immune dysregulation, yet the interplay between these mechanisms remains poorly understood. Using integrative single-cell sequencing and MR analyses, we identified four key genes-SLC11A1, DDX17, MRAS, and PDIA3-that bridge immune and mitophagy pathways in PD pathogenesis. These genes exhibit causal relationships with PD risk, offering novel insights into diagnostic and therapeutic strategies.

genetics↗

bioRxiv2025

Cell type-specific contributions to impaired blood-brain barrier and cerebral metabolism in presymptomatic 5XFAD mice

Altered cerebral metabolism and blood-brain barrier (BBB) dysfunction are emerging as critical contributors to the preclinical phase of Alzheimers disease (AD), underscoring their role in early pathogenesis. To identify sensitive biomarkers before irreversible neuronal loss and cognitive decline, we examined 5XFAD mice at 3 months of age by applying multiple advanced MRI techniques. Arterial spin tagging based MRI revealed increased BBB permeability and water extraction fraction, indicating compromised BBB integrity at the early stage of pathogenesis in 5xFAD mice. Despite preserved cerebral blood flow, a decreased unit mass cerebral metabolic rate of oxygen (CMRO2) was evident in the same cohorts of 5XFAD mice. Interestingly, a region-specific decrease of tissue pH values was detected in the hippocampus of these 5XFAD mice by creatine chemical exchange saturation transfer MRI. Elevated neuronal H4K12 lactylation in the hippocampus supports the reduced pH values. To further dissect the cellular and molecular mechanisms underlying these MRI-detectable changes in 5XFAD mice, we conducted single-nucleus RNA sequencing (snRNA-Seq) with optimized blood vessel enrichment protocols. Our results revealed cell type-specific transcriptomic changes in the hippocampus of 3-month-old 5XFAD mice, including downregulation of synaptogenesis and synaptic transmission genes in the CA1 and dentate gyrus excitatory neurons, impaired endothelial gene expression linked to brain barrier function and angiogenesis, altered innate immune response genes in astrocytes, as well as upregulation of cholesterol biosynthesis and metabolism genes in the CA1 excitatory neurons. These findings underlie the intricate interplay between BBB disruption and metabolic dysregulation before the onset of cognitive decline in AD. Our study demonstrates that BBB dysfunction and cerebral metabolic alterations preceded brain hypoperfusion and cognitive decline, emphasizing potential molecular pathways for early intervention. These findings, once validated in human studies, could significantly enhance early diagnosis and inform novel therapeutic strategies targeting early AD pathogenesis.

neuroscience↗

bioRxiv2025

Single cell mapping of the human endometrium and first trimester decidua identifies distinct epithelial and stromal cell contributions to fertility

The human endometrium undergoes dynamic changes across the menstrual cycle to establish a receptive state for embryo implantation. Using bulk and single-cell RNA sequencing, we characterized gene expression dynamics in the cycling endometrium and the decidua from early pregnancy. We are the first to demonstrate that during the mid-secretory phase--the period encompassing the window of implantation--secretory glandular epithelial cells undergo notable transcriptional changes in cell-cell communication. Through comprehensive analyses, we identified the Glandular Epithelium Receptivity Module (GERM) signature, comprising 556 genes associated with endometrial receptivity. This GERM signature was consistently perturbed across multiple datasets of endometrial samples from women with impaired fertility, validating its relevance as a marker of receptivity. In addition to epithelial changes, we observed shifts in stromal cell populations, notably involving decidual and senescent subsets, which also play key roles in modulating implantation. Together, these findings provide a high-resolution transcriptomic atlas of the receptive and early pregnant endometrium and shed light on key molecular pathways underlying successful implantation.

molecular biology↗

bioRxiv2025

Heat stress disrupts early development and photosymbiosis in Cassiopea jellyfish

Photosymbioses between Cnidarians and algae are widespread in marine ecosystems. The jellyfish Cassiopea-Symbiodinium symbiosis serves as a valuable model for studying host-symbiont interactions in photosymbiotic organisms. Despite its ecological similarity to coral symbiosis, the effects of rising sea surface temperatures on Cassiopea symbiosis, particularly during early developmental stages, remain unexplored. By exposing Symbiodinium cultures to heat stress and subsequently using these symbionts to colonize jellyfish polyps under ambient and elevated temperature conditions, we study the impact of heat on microbe-stimulating metamorphosis. We observed a significant reduction in chlorophyll concentration in heat-stressed Symbiodinium algae. Polyps colonized with these symbionts exhibited delayed metamorphosis under ambient conditions and failed to undergo metamorphosis under continued heat stress. Additionally, we found abnormal ephyra morphology and increased rates of asexual reproduction under heat stress. Our findings suggest that ocean warming may disrupt critical stages of Cassiopea metamorphosis and development by impairing symbiosis, ultimately threatening their population stability under warming marine environments.

physiology↗

bioRxiv2025

A multi-institutional investigation of psilocybin's effects on mouse behavior

Studies reporting novel therapeutic effects of psychedelic drugs are rapidly emerging. However, the reproducibility and reliability of these findings could remain uncertain for years. Here, we implemented a multi-institutional collaborative approach to define the robust and replicable effects of the psychedelic drug psilocybin on mouse behavior. Five laboratories performed the same experiments to test the acute and persistent effects of psilocybin (2 mg/kg, IP) on various behaviors that psychedelics have been proposed to affect, including anxiety-related approach-avoidance, exploration, sociability, depression-related behaviors, fear extinction, and social reward learning. Through this coordinated approach, we found that psilocybin had several robust and replicable acute effects on mouse behavior, including increased anxiety- and avoidance-related behaviors and decreased fear expression. Surprisingly, however, we found that psilocybin did not have replicable effects 24 hours post psilocybin administration on reducing anxiety- and depression-like behaviors or facilitating fear extinction learning. Additionally, we were unable to observe psilocybin-induced alterations in social preference or social reward learning. Overall, our comprehensive characterization of psilocybins acute and persistent behavioral effects using [~]200 total male and female mice per experiment spread across five independent labs demonstrates with unique certainty several acute drug effects and suggests that psilocybins persistent effects in mice may be more modest and inconsistent than previously suggested. We believe this unusual multi-laboratory, highly coordinated research effort serves as a model for facilitating the generation of replicable results and consequently will reduce efforts based on unreliable and spurious results.

neuroscience↗

bioRxiv2025

Decoding the Moving Mind: Multi-Subject fMRI-to-Video Retrieval with MLLM Semantic Grounding

Decoding dynamic visual information from brain activity remains challenging due to inter-subject neural heterogeneity, limited per-subject data availability, and the substantial temporal resolution gap between fMRI signals (0.5 Hz) and video dynamics (30 Hz). Current approaches face persistent challenges in addressing these temporal mismatches, demonstrate limited capacity to integrate subject-specific neural patterns with shared representational frameworks, and lack adequate semantic granularity for aligning neural responses with visual content. To bridge these gaps, we propose a framework addressing these limitations through three innovations: (1) a Dynamic Temporal Alignment module that resolves temporal mismatches via exponentially weighted multi-frame fusion with adaptive decay coefficients; (2) a Brain Mixture-of-Experts architecture that combines subject-specific extractors with shared expert layers through parameter-efficient tri-modal contrastive learning; and (3) a Multi perspective Semantic Hyper-Anchoring module that resolves cross-subject attention bias via multi-dimensional semantic decomposition, leveraging multimodal LLMs for fine-grained video semantic extraction--enabling the model to match individual attention patterns as different subjects naturally focus on distinct aspects of the same visual stimulus. This module boosts Top-10/Top-100 retrieval by 17.7%/6.6%. Experiments on two video-fMRI datasets demonstrate state-of-the-art performance, with 39%/30% improvements in Top-10/Top-100 accuracy over single-subject baselines and 27% gains against multi-subject models. The framework exhibits remarkable few-shot adaptability, retaining 97% performance when using only 10% training data for new subjects. Visualization analysis confirms this generalization capability stems from effective disentanglement of subject-specific and shared neural representations.

neuroscience↗

bioRxiv2025

EcDNA-borne PVT1 fusion stabilizes oncogenic mRNAs

Extrachromosomal DNA (ecDNA) amplifications are prevalent drivers of human cancers. We show that ecDNAs exhibit elevated structural variants leading to gene fusions that produce oncogene fusion transcripts. The long noncoding RNA (lncRNA) gene PVT1 is the most recurrent structural variant across cancer genomes, with PVT1-MYC fusions arising most frequently on ecDNA. PVT1 exon 1 is the predominant 5 partner fused to MYC or other oncogenes on the 3 end. Mechanistic studies demonstrate that PVT1 exon 1 confers enhanced RNA stability for fusion transcripts, which requires PVT1 exon 1 interaction with SRSF1 protein. Genetic rescue of MYC-addicted cancer models and isoform-specific single-cell RNA sequencing of tumors reveal that PVT1-MYC better supports MYC dependency and better activates MYC target genes in vivo. Thus, the mutagenic landscape of ecDNA contributes to genome instability and generates chimeric fusions of lncRNA and mRNA genes, selecting PVT1 5 region as a stabilizer of oncogene mRNAs.

cancer biology↗