iCell is a newly established, peer-reviewed journal dedicated to the dissemination of cutting-edge research in cellular biology. The journal focuses on innovative studies that advance our understanding of cellular processes, mechanisms, and applications in medicine and biotechnology.
The widespread regulatory role of the phosphorylation of RNA-binding proteins (RBPs) in RNA abnormalities
Protein phosphorylation is a pervasive post-translational modification that orchestrates cellular signal transduction and functional control during tumorigenesis. By charting a pan-cancer phosphoproteomic atlas with a focus on RNA-binding proteins (RBPs), we found that differentially phosphorylated sites between tumors and matched non-tumor tissues are strongly enriched on RBPs. These data suggest that phosphorylation can amplify oncogenic signals through RBP-mediated RNA dysregulation. Among all RBPs, phosphorylation of Nucleolin (NCL) at threonine 76 (NCL T76) is upregulated across multiple cancer types and is tightly associated with poor patient prognosis and widespread abnormalities in RNA processing. We systematically mapped the upstream kinase–substrate landscape and actionable compounds for this key RBP phosphosite and the downstream classes of RNA dysregulation linked to these modifications. Integrative analyses and in vitro experiments identify cyclin-dependent kinase 2 (CDK2) as the upstream kinase for NCL T76. The small molecule inhibitor TG-02 effectively suppresses CDK2-mediated phosphorylation at NCL T76. Our study establishes RBP phosphorylation as a prevalent regulatory layer across cancers that reshapes RNA regulatory networks. It further defines NCL T76 as a critical functional site, elucidates its upstream control, and highlights the CDK2–NCL T76 axis as a tractable therapeutic avenue.
RNA-binding proteins (RBP), phosphorylation modification, cancer-related RNA abnormalities, Nucleolin(NCL), CDK2, small molecule inhibitor
Artificial Intelligence for Single-Cell Biology: From Representation Learning to Predictive Modeling
While single-cell sequencing technologies provide a high-resolution approach for dissecting cellular heterogeneity, their data are inherently high-dimensional, sparse, noisy, and strongly affected by batch effects and limited annotations. These properties make artificial intelligence (AI), particularly deep generative and probabilistic models, more suitable for analyzing single-cell data. Recent AI frameworks, including variational inference–based model scVI and its extensions, have supported unified pipelines for normalization, representation learning, batch correction, multimodal integration, and downstream analyses. Specialized downstream analyses, such as scalable cell-type annotation, trajectory and dynamic inference, cell–cell communication analysis, spatial mapping, and the prediction of genetic or pharmacological perturbation responses, can be extended by learning transferable latent representations. Emerging self-supervised foundation models promise reusable cellular representations across tasks, tissues, and species. Addressing challenges in benchmarking, interpretability, uncertainty quantification, and robust generalization highlights future frontiers in the development of predictive and causal single-cell models.
Dynamic Profiling of the Tumor Microenvironment in Lung Squamous Cell Carcinoma by Multidimensional Transcriptomics
Therapeutic options for lung squamous cell carcinoma (LSCC) remain limited and vary by TNM stage. Achieving precise, stage-dependent treatment requires an understanding of how the tumor microenvironment (TME) changes during disease progression. Therefore, we investigated stage-related TME changes and explored prognostic strategies based on multidimensional transcriptomic data. In this study, we comprehensively characterize the TME during different stages of LSCC progression by integrating single-cell RNA sequencing (scRNA-seq), spatial transcriptomics (ST), and bulk RNA sequencing (bulk RNA-seq). The TME of LSCC displayed marked heterogeneity across TNM stages. Notably, the proportion of SPP1⁺ macrophages increased progressively from early to late disease and was associated with heightened immunosuppressive activity. We constructed a prognostic model based on differentially expressed genes in SPP1⁺ macrophages between stage IV and stage I. We found SPP1⁺ macrophages and fibroblasts exhibited the most frequent ligand-receptor interactions at the tumor edge. Our study revealed stage-dependent remodeling of the LSCC TME and highlighted SPP1⁺ macrophage subgroup as key contributors to tumor progression. The SPP1⁺ macrophage–based prognostic model may facilitate risk stratification and inform future therapeutic strategies.
Lung squamous cell carcinoma; Macrophages; Osteopontin; RNA-Seq; Tumor Microenvironment.
Artificial Intelligence in Immunoinformatics: From Multi-Omics to Precision Immunology
The immune system is a multilayered, adaptive network whose behavior emerges across molecules, cells, tissues, and time. Contemporary immunology therefore generates high-dimensional, heterogeneous datasets that strain classical analytical assumptions. Artificial intelligence (AI), spanning machine learning and deep learning, is increasingly becoming a core paradigm for extracting structure, prediction, and actionable insight from these data. This review summarizes how AI is transforming key steps of modern immunology, from sequencing- and repertoire-based analysis to antigen specificity and epitope prioritization for vaccine design; from single-cell and spatial profiling to inference of immune states and cell–cell communication; and from multi-omics integration to prediction and optimization of immunotherapy responses, including immune checkpoint blockade and CAR-based therapies. We compare the strengths and limitations of major model families—convolutional and recurrent networks, graph neural networks, generative models, and transformer-based architectures—highlighting how their inductive biases map to immunological questions. We also discuss barriers to broad adoption, including data standardization and metadata quality, interpretability and uncertainty calibration, computational costs, and gaps between benchmark performance and clinical generalizability. Finally, we outline a roadmap toward interpretable and uncertainty-aware models, cross-center data sharing and benchmarking, closed-loop “dry–wet” validation with perturbation experiments, and clinically deployable pipelines for personalized immunodiagnosis and therapy.
Artificial Intelligence; Machine Learning; Deep Learning; Bioinformatics; Computational Immunology; Immunomics; Vaccine Design; Immunotherapy; Multi-omics Integration
BioPE-RV: A New Paradigm for Biomedical Relation Extraction Integrating Prompt Engineering and Explicit Rule Verification
Abstract: Infectious diseases remain a major public health challenge threatening human health and socioeconomic development. Understanding viral infection mechanisms is therefore critical for effective disease control. Biomedical literature contains a vast number of regulatory relationships between genes and proteins, and accurately extracting these relationships is a fundamental prerequisite for constructing molecular regulatory networks and supporting downstream biological research. In recent years, large language models have demonstrated considerable potential in biomedical information extraction tasks. However, when applied to regulatory relationship extraction, they still suffer from systematic limitations, including the misclassification of co-expression as regulation, excessive chain causal inference, and insufficient understanding of experimental contexts. These issues substantially compromise the reliability of extracted results. To address these challenges, this study proposes BioPE-RV, a regulatory relationship extraction framework that integrates large language model–based candidate generation with explicit biological logic verification. The framework first leverages a large language model to generate candidate regulatory relationships from text and subsequently applies multi-level logical constraints—including entity validity, explicit regulatory agency, semantic consistency, causal chain integrity, and experimental context rationality—to iteratively verify and filter candidate results. Experimental results on COVID-19-related biomedical literature demonstrate that combining generative models with interpretable biological logic constraints significantly improves both the reliability and reproducibility of relationship extraction.
Entity-Relationship Extraction; LLM;Prompt Engineering; Logical Constraint Verification
Crosstalk Between Gingival Mesenchymal Stem Cells and the Inflammatory Microenvironment
Gingival mesenchymal stem cells (GMSCs) hold similar biological features to other MSCs, but also have some advantages that include being easily accessible with relatively few technical and ethical constraints, stronger proliferative capacity, and more stable morphological characteristics. A close bidirectional interaction exists between the biological functions of GMSCs and their inflammatory microenvironment: inflammatory factors can regulate the immunomodulatory and tissue repair capabilities of GMSCs, while GMSCs, in turn, can reshape the local microenvironment, suppressing excessive inflammation and promoting tissue regeneration. Herein, we discuss the biological properties and functional alterations of GMSCs under various pathophysiological conditions, aiming to provide new insights into the immunoregulatory mechanisms of GMSCs in tissue regeneration and therapeutic processes.
Detection and genetic characterization of Densvirus from Oriental Pied hornbill in Guangxi, China
Densoviruses (DNVs), members of the family Parvoviridae, infect a broad range of invertebrate and vertebrate hosts, yet their presence in birds remains poorly understood. Densoviruses have been increasingly recognized for their ecological roles in controlling insect populations and potentially affecting vertebrate hosts through trophic interactions. In this study, we investigated DNVs in the Oriental Pied hornbill, a widespread species in Southeast Asia and Southern China. Thirteen fecal samples were collected and analyzed using viral metagenomics and molecular techniques. PCR screening detected DNVs in two samples, and complete genomes of two strains (GBF3 and GBF4) were successfully obtained. Phylogenetic analysis of conserved NS1 and VP1 regions showed that both strains belong to the genus Densovirus (subfamily Densovirinae) and cluster closely with PmDNV-JL previously identified in Parus major. These findings represent the first genomic evidence of DNVs in Oriental Pied hornbills, emphasizing the potential for cross-species transmission and the importance of birds as ecological reservoirs of arthropod-associated viruses. Our study also provides insights into viral diversity, host adaptation, and the evolutionary dynamics of DNVs in natural ecosystems. Future research should explore the replication capacity and pathogenic potential of DNVs in avian hosts to assess their epidemiological significance.
HiOmics 2.0: An All-in-One Intelligent Cloud Platform for Multi-Omics Analysis in Biomedical Research
Rapid advances in high-throughput sequencing and mass spectrometry have introduced substantial challenges for multi-omics data analysis, including fragmented workflows, slow interactive responsiveness, and limited reproducibility of results. To address these issues, we present HiOmics 2.0 (https://henbio.com), a substantially upgraded cloud platform. The core innovation is a hierarchical task-scheduling system that decouples lightweight interactive tasks from compute-intensive analytical pipelines and enables real-time responses via a dedicated API cluster. This redesign reduces the generation time of common analytical plots from over 30 seconds to below 5 seconds (an improvement exceeding 80%), delivering responsiveness comparable to local desktop software. Building on this foundation, the platform adds modules for single-cell transcriptomics, proteomics, metabolomics, pathogen detection, and Mendelian randomization. Accordingly, HiOmics has evolved from a collection of discrete tools into an integrated infrastructure that supports systematic life science research. The unified platform enables users to complete an end-to-end research workflow—from data preprocessing and multi-omics mining to model development and visualization. As of November 2025, HiOmics 2.0 integrates 453 analytical tools, has recorded 112,957 visits from users worldwide, and has completed 36,726 analytical tasks. Representative case studies demonstrate its utility in both basic research and clinical translation. Overall, through architectural innovation and functional integration, HiOmics 2.0 provides an efficient, user-friendly, and reproducible all-in-one solution for life science research.
Investigating the Role of Pangolin Pseudogenes in Host-Pathogen Immune Interactions: A BiLSTM-Based Approach
This study integrates a Bidirectional Long Short-Term Memory (BiLSTM) deep learning framework with bioinformatics approaches to elucidate the functional role of pangolin pseudogenes in the immune interactions between tick-borne pathogens and their hosts. We developed a novel BiLSTM-autoencoder model incorporating dynamic weight positional encoding and a multi-head self-attention mechanism, effectively capturing pseudogene sequence features while overcoming limitations inherent in traditional analytical methods. Genome-wide screening of the Manis pentadactyla (GCF_030020395.1) and Manis javanica (GCF_001685135.1) assemblies identified 3,209 and 2,035 pseudogenes, respectively. Subsequent filtration yielded 94 immune-related homologous genes, classified into ten distinct immune system pathways. Our analysis reveals significant species-specific variation and functional plasticity in pangolin pseudogenes: Chinese Pangolin LILRA6 Pseudogenes: Fourteen identified variants modulate bacterial recognition and inflammatory responses through specific amino acid deletions. Interferon Receptor Pseudogenes (e.g., XP_036870501.1): Critical mutations disrupt JAK-STAT signaling pathway functionality. Toll-like Receptor (TLR) Pseudogenes: Reconstruction error values (ranging from 28.149 to 68.957) correlate strongly with structural integrity. Notably, the disrupted LRR domain in the giant pangolin sequence KAK2502118.1 (error = 65.986) suggests an adaptive immune strategy. Furthermore, we identified a potential molecular interaction between a pangolin-derived 52 kDa sequence and the 8.9 kDa salivary protease of Amblyomma javanense, providing novel insights into tick-borne transmission mechanisms. This study represents the first application of deep learning to elucidate the functional role of pangolin pseudogenes, confirming their active involvement in immune resistance. It establishes a new paradigm for investigating host-pathogen interactions and provides a critical foundation for the analysis of underlying data in the surveillance and control of tick-borne diseases.
Pathogen; Pangolin; Immune interaction; Pseudogene
Advancing STEC Bioinformatics Training for the New Era
Bioinformatics, positioned at the nexus of life, data, and computational sciences, is pivotal for cultivating innovators and enabling technology translation. Its growing importance, driven by the quantitative shift in life sciences, is met with universal challenges: outdated curricula, siloed pedagogy, and inadequate lifelong learning frameworks. To address these, the Guangdong-Guangxi-Hainan Bioinformatics Education Consortium convened in Nanning, China (14–16 November 2025). Guided by the STEC principle (Science, Technology, Engineering, Collaboration), educators and industry experts presented practical cases aligned with global educational trends. This framework offers a concrete strategy to tackle seven core challenges in contemporary bioinformatics education, providing a roadmap for its sustainable advancement.
Circular RNA circR-4600 inhibits hepatocellular carcinoma progression by suppressing autophagy through DVL3 and DAB2 interaction
Circular RNA (circRNA) plays a crucial role in regulating autophagy in hepatocellular carcinoma (HCC); however, the specific mechanisms underlying this regulation remain poorly understood. This study investigates the role and mechanisms of circR-4600 in HCC, aiming to identify novel therapeutic targets to enhance patient prognosis. Differential expression of circRNA in HCC and adjacent non-tumor tissues from six patients was assessed using circRNA microarray analysis. RNA pulldown combined with ESI-FT-ICR-MS identified DVL3 as the RNA-binding protein that interacts with circR-4600. Functional assays, including overexpression and knockdown experiments in HCC cell lines, as well as in vivo studies, were conducted to evaluate the effects.The results demonstrated significantly lower circR-4600 expression in HCC tissues, which negatively correlated with poor prognosis. Overexpression of circR-4600 inhibited HCC cell proliferation, migration, and invasion in vitro, and reduced lung metastasis in vivo. Mechanistically, circR-4600 interacted with DVL3 to suppress ATG5-mediated autophagy, thereby diminishing DVL3’s inhibitory effect on DAB2, which led to a decreased LC3 II/I ratio and epithelial-mesenchymal transition, while increasing P62 accumulation. In conclusion, circR-4600 modulates autophagy through DVL3 and DAB2, thereby inhibiting HCC progression and providing insights into its potential as a therapeutic target.
Enhancing Undergraduate Academic Writing in the Context of Medical Research: A Comprehensive Training Framework
Objective: Scientific writing is essential for students to engage in research and contribute meaningfully to academic communities. Yet many undergraduates lack formal training in how to write clearly and rigorously. This often leads to work that falls short in logic, structure, and originality. This study asks how a structured, multi-component training framework can improve academic writing skills and research output among undergraduates. Methods: We present a long-term case study from Guangdong Medical University, where an undergraduate research group in computer-aided drug design implemented a comprehensive academic writing program. The framework combines four strategies: a student-led writing platform, peer mentorship and collaborative learning, regular outcome-focused seminars, and bilingual, practice-driven writing instruction. Results: Between 2015 and 2024, over 300 students participated. Many produced publishable manuscripts, with 30 Science Citation Index papers authored by undergraduates. These outcomes reflect both skill development and sustained research engagement. Conclusion: Our work offers a tested model for building academic writing capacity at scale. It may serve as a guide for institutions seeking to equip students especially non-native English speakers with the skills to write and publish scientific work.
A Compendium of ERVs in the Genomes of Major Primates
ERVs integrate into host DNA, forming vertically inherited proviral sequences. These sequences not only record the historical imprints of ancient retroviruses but also play significant biological roles in modern host genomes. The widespread distribution of ERVs in primate genomes suggests that constructing a unified system for ERV site distribution would facilitate the study of their impact on host genomic biology and evolution. In this study, we utilized LTR_harvest to predict candidate ERV sequences in primate genomes and further identified and annotated the characteristics of these candidate sequences using LTR_digest software. We have compiled a comprehensive and well-annotated database (http://ervsdb.omics.henbio.com/home/index/index.html?idx=1). This database enables the acquisition of ERV data and corresponding neighboring genes for 61 primate species, characterization of ERV proviral structures, assessment of integration sites, and prediction of potential regulatory gene functions associated with ERV-LTRs. Using the Trachypithecus francoisi as an example, we have preliminarily explored the potential influence of ERVs on host resistance or susceptibility to diseases, providing a theoretical reference for further research on ERVs in other animals.
Revealing Causal Genes in Nasopharyngeal Carcinoma: An Integrated Approach of Mendelian Randomization and Single-Cell Sequencing
Objective: This study employed Mendelian randomization (MR) to identify genes associated with nasopharyngeal carcinoma (NPC) and utilized single-cell sequencing to analyze their expression patterns within the tumor microenvironment. Methods: Transcriptomic datasets (GSE53819 and GSE12452) were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis was performed using R software to identify common differentially expressed genes. Summary-level data for expression quantitative trait loci (eQTLs) of these genes and for NPC genome-wide association studies (GWAS) were sourced from the OpenGWAS and FinnGen databases, respectively. Using gene-associated single nucleotide polymorphisms (SNPs) as instrumental variables (IVs), MR analyses—including the Wald ratio, inverse variance weighted (IVW), MR-Egger regression, and weighted median methods—were conducted to evaluate the causal effects of these genes on NPC, adhering to the three core assumptions of MR. Additionally, single-cell RNA sequencing datasets (GSE150825 and GSE120926) from GEO were processed for quality control, cell clustering, and annotation. The expression of MR-identified genes across cell subtypes was visualized using DotPlot. Results: Differential expression analysis identified 494 common differentially expressed genes, of which 313 were available for MR analysis. The MR results indicated that MFSD4 (Wald ratio) as well as PSPH and VPREB3 (IVW) were associated with an increased risk of NPC, whereas TMEM200A and THBS2 (IVW) were associated with a reduced risk. Single-cell expression analysis revealed that PSPH was predominantly expressed in epithelial cells, TMEM200A was highly expressed in fibroblasts and T cells, THBS2 was mainly expressed in fibroblasts, MFSD4 showed higher expression in endothelial cells, and VPREB3 was broadly highly expressed in tumor cells, plasma B cells, and B cells—with the highest expression observed in B cells. Conclusion: By integrating Mendelian randomization with single-cell transcriptomic analysis, this study identifies MFSD4, PSPH, and VPREB3 as potential risk genes for NPC, and TMEM200A and THBS2 as potential protective genes. These findings clarify their cellular expression characteristics within the NPC microenvironment and provide novel insights into the molecular mechanisms of NPC, suggesting potential targets for future gene-targeted therapeutic strategies.
Construction of an Integrated Intelligent Database Platform for Antimicrobial-Resistant Strains
Abstract:Antimicrobial resistance (AMR) poses a severe global public health threat, causing over 700,000 deaths annually. The fragmentation of AMR data hinders comprehensive research, clinical decision-making, and public health surveillance. Existing databases like CARD and CARSS often suffer from data silos, limited analytical tools, and insufficient localized data. This study aims to construct an integrated, operable database for drug-resistant bacterial strains to support AMR research and control.Methods:The study involved: (1) Systematic collection of data on resistant bacteria, genomes, and antimicrobial substances from public databases and literature; (2) Design of a relational database structure following normalization paradigms (1NF, 2NF, 3NF) and implementation using MySQL; (3) Data processing and batch import via Python and pandas; (4) Development of an interactive web platform using the Flask framework, with front-end interfaces built using HTML, CSS, JavaScript, and Layui for data overview, query, statistical visualization, and download.Results:A fully functional and locally deployable database website was successfully constructed. The system supports efficient data querying, dynamic statistical visualization (including pie charts for species distribution and bar charts for top proteins and drug classes), and data export. The platform integrates multi-source resistance data and offers a user-friendly interface for real-time data access and analysis, providing a practical tool for researchers and clinicians.Conclusion:This study developed a comprehensive and scalable database for drug-resistant bacteria, facilitating the centralized management and analysis of AMR data. The database aids in understanding resistance mechanisms, guiding clinical treatment, and informing public health strategies. Future work will focus on expanding data coverage, incorporating AI-driven analysis, and enhancing system automation and security to further support the global fight against antimicrobial resistance.
A bibliometric analysis of RNA methylation in prostate cancer and its complications from 2003 to 2024
Background: RNA methylation has emerged as an active research field in prostate cancer (PCa) and its complications, while few bibliometric analyses have been performed. This study aims to analyze the development trends and hotspots of RNA methylation research to provide a comprehensive and objective overview of the current search state in this field of prostate cancer using bibliometric methods. Methods: The articles and reviews regarding RNA methylation in PCa within 2003 to 2024, and its complications were from the Web of Science Core Collection databases. A retrospective bibliometric analysis and science mapping were performed using the CiteSpace software, the VOSviewer software, and the R package “bibliometrix” to plot the knowledge maps and predict the hotspots and trends. Results: One hundred and twenty-two qualified records were retrieved. The annual publications gradually increased over the past 22 years. These publications mainly came from 19 countries led by China and 138 institutions. Among top 10 funders, 8 were Chinese, with NSFC supporting 68 studies. The most common keywords were “prostate cancer”, “expression”, “messenger-RNA”, “progression”, “methyltransferase METTL3 promotes”, “N6-methyladenosine”, and “promotes”. Keyword trends shifted from “m6A”/“METTL3” to “m6A modification”/“androgen receptor”/“metastasis”. N6-methyladenosine (m6A) within prostate cancer and its associated complications is an emerging frontier, with “m6A score” and “cellular senescence” as current priorities. Conclusion: This research represents the inaugural bibliometric examination of the application of RNA methylation in PCa, utilizing three acknowledged bibliometric tools. It offers an impartial overview and holistic direction for subsequent pertinent studies.
From Virtual Cell Challenge to Virtual Organs: Navigating the Deep Waters of Medical AI Models
Recent advances in single-cell profiling and artificial intelligence have enabled the construction of virtual cells, computational entities that simulate cellular states and responses with unprecedented resolution. Initiatives such as the Virtual Cell World Challenge highlight the potential of large AI models to move beyond annotation toward predictive and generative simulations. Yet, the next frontier lies in scaling from virtual cells to virtual organs, where thousands of cell types interact across spatial, temporal, and biophysical dimensions. This transition exposes major challenges: incomplete cellular atlases, limited integration of spatial and longitudinal data, difficulties in cross-scale modeling, and the lack of robust validation frameworks. Addressing these obstacles requires embedding biological priors into foundation models, developing multi-modal integration strategies, and adopting graph-based and hybrid mechanistic-statistical approaches. The emergence of digital twins--organ- or patient-specific replicas--illustrates how virtual organ models can inform drug discovery, predict toxicity, and guide precision medicine. Ultimately, the trajectory from virtual cells to virtual organs points toward the vision of a virtual human, enabling in silico experimentation at scale. Realizing this goal will demand not only technical breakthroughs but also collaborative validation, ensuring that medical AI navigates these deep waters toward safe and transformative clinical application.
Virtual Cells; Virtual Organs; Medical AI Models; Digital Twins
JNK1 downregulation confers fibroblasts with a mammary epithelial cell fate
The fate of cells is determined by their specific transcription patterns. Previously, we demonstrated that chemical cocktails, including the single small molecule TGFβR1 inhibitor RepSox, can induce changes in transcription patterns and drive somatic cells to reprogram into chemically induced mammary epithelial cells (CiMECs) by downregulating TGFβR1 expression. However, the underlying mechanism of chemical induction for reprogramming remains unclear. Here, we employed single-cell sequencing (scRNA-seq and scATAC-seq) to uncover RepSox-induced reprogramming events and molecular trajectories and clarify the similarities and differences to the iMECs generated by chemical cocktails induction. Importantly, we found that JNK1 expression, a downstream gene of TGFβR1 signaling pathway, plays a critical role in iMEC conversion as its downregulation induces iMEC generation. Thus, the TGFβR1-JNK1 signaling may regulate fibroblast-to-mammary epithelial cell cell fate decision. These findings provide valuable insights into the mechanisms governing mammary epithelial cell fate decisions and may pave the way for developing novel strategies to generate a large number of milk-secreting mammary cells in vitro and facilitate mammary gland regeneration in vivo.
Induced mammary epithelial cells (iMECs)
Resveratrol Oligomers Trans-gnetin H Promotes Reactive Oxygen Species Generation and Rictor Degradation to Suppress Cancer Cell Viability
The function of resveratrol as an antioxidant is to scavenge reactive oxygen species (ROS) in the body. The effects of resveratrol on cellular activity have been widely reported. Trans-gnetin H is a trimer of resveratrol that is found in seeds of peonies. However, there is not enough research on the cellular effects of trans-gnetin H, and it is not clear if it has the same physiological functions as resveratrol. This study tries to answer this question by investigating the effect of trans-gnetin H on ROS, mammalian target of rapamycin (mTOR), cell viability, autophagy, and ferroptosis in several cancer cell lines. Trans-gnetin H was found to regulate cell viability, cell proliferation, and autophagy, but it did not affect ferroptosis. Molecular experiments showed that these effects were brought about via significant promotion of ROS generation and suppression of mTOR activation in an ROS-dependent manner. Further mechanistic experiments showed that trans-gnetin H inhibits mTOR activation by inducing FBXW7-mediated ubiquitination and degradation of Rictor, a key component of mTORC2. Thus, trans-gnetin H appears to control tumor cell viability by inhibiting activation of the mTORC1 and mTORC2 pathways through a novel regulatory mechanism involving the ROS-Rictor signaling axis.
Annotation and target analysis of human endogenous retroviruses
Endogenous retroviruses (ERVs) are important regulatory elements in the human genome. They are involved in the regulation of host gene expression and disease progression through long terminal repeats (LTR) and coding domains (gag, pol, env). Methods: Based on the implicit Markov model, this study integrated LTRharvest and LTRdigest software, combined with 55 ERV-related protein domain databases, systematically annotated ERVs elements in the human genome (GRCh38.p14), and analyzed their potential targets and functions by using STRING, GO and KEGG. Results: A total of 47,666 HERVs candidate sequences (11.05% of the genome) were identified in this study, of which 605 were complete structures, mainly concentrated in chromosomes 1 and 3. It was found that env accounted for the least among the three protein structures. Potential target genes in the upstream and downstream 20kb range of LTR were screened, and core targets such as histone family genes H4C6 and H2BC12 and immune-related genes TLR2 and CCR5 were found to be involved in disease regulation through chromatin remodeling or immune pathways. Enrichment results were significantly associated with nucleosome assembly, innate immune response, and cancer-related pathways (e.g., herpes simplex virus infection, systemic lupus erythematosus). Conclusion: This study constructed a comprehensive HERVs annotated database, revealing the potential regulatory ability of LTR, providing a theoretical basis for the application of HERVs in cancer, autoimmune diseases and evolutionary research, and laying a foundation for the development of targeted therapy strategies.
Exosomal miRNAs in Inflammatory Diseases
Exosomal miRNAs are a type of non-coding RNA molecules present in exosomes. MiRNAs are involved in the regulation of a variety of physiological and pathological processes by transmitting information between cells through the exosome, a carrier of intercellular communication. During inflammatory responses, exosomal miRNAs can be involved in regulating the activation of inflammatory cells and the release of inflammatory mediators, thus affecting the development of inflammatory diseases. Therefore, exosomal miRNAs may be promising biomarkers for monitoring disease progression based on their functions and changes. In addition, since exosome prevents miRNAs from being degraded by RNase, drug development targeting the release of exosomal miRNA contents lays the foundation for innovative targeted therapies in the future. This review focuses on exosomal miRNAs with the aim of combing and mastering its latest developments in the current research of inflammatory diseases.
Revealing PAM as a Prognostic Biomarker and Therapeutic Target in Clear Cell Renal Cell Carcinoma
Background: Accumulating evidence suggests that peptidylglycine α-amidating monooxygenase (PAM) is involved in vital physiological and pathological processes, including the development and progression of cancer. Nevertheless, the precise contributions of PAM-mediated pathways in clear cell renal cell carcinoma (ccRCC) remain poorly understood. Clarifying the role of PAM in ccRCC could yield novel insights into the disease's pathogenesis and offer potential therapeutic strategies. Methods: Using genome-wide association study (GWAS) data from the UK Biobank and whole-blood eQTL data, we screened for ccRCC-related genes and identified PAM as a potential oncogene. Bioinformatics analyses, including differential expression, prognostic, genomic, and methylation analyses, were conducted to characterize the role of PAM in ccRCC. In addition, functional pathways of PAM were explored using gene set enrichment analysis. The association between PAM expression, immune cell infiltration, and immunotherapy response was also evaluated. Subsequently, in vitro tumor phenotype experiments, such as cell viability, wound healing and modified Boyden chamber assays, were conducted to validate the bioinformatics predictions. Results: Our findings indicated that PAM expression was elevated in ccRCC tissues compared to adjacent normal ones, and is associated with unfavorable disease-free survival in ccRCC patients. Genomic alterations such as gene amplifications were detected in ccRCC, with PAM expression linked to multiple cancer pathways. Furthermore, PAM expression was positively correlated with immune cell infiltration and negatively with immune cell function in ccRCC. In vitro functional assays revealed that PAM downregulation reduced the proliferative and migratory capacity of ccRCC cells. Conclusions: Our studies reveal that PAM serves as a potential prognostic biomarker and therapeutic target in ccRCC. Further researches are warranted to validate its clinical utility and investigate its potential for guiding personalized treatment strategies in ccRCC patients. Understanding the role of PAM in ccRCC progression may provide novel insights for the development of targeted therapies and biomarker-based approaches for ccRCC management.
The Mechanism of RNA Virus-mediated Tumorigenesis
Various RNA viruses could lead to tumorigenesis. Since these viruses belong to different genera, they have evolved distinct mechanisms of tumorigenesis, with unique interactions among key oncoproteins, oncogenes and hosts factors. This article reviews the tumorigenic mechanism of two well-known human RNA tumor viruses, and explores the potential tumorigenic effects of SARS-CoV-2, which is also an RNA virus.
Long COVID: Current Status, Challenges and Future Directions
Long COVID refers to the persistent symptoms and health issues experienced by patients following infection with the SARS-CoV-2. As the pandemic continues, research into Long COVID has gradually become a focal point for the global medical community. Current studies suggest that the pathogenesis of Long COVID may be closely related to factors such as immune response, inflammatory reactions, neurological damage, and microvascular lesions. The clinical manifestations are diverse and include, but are not limited to, fatigue, dyspnea, cognitive impairment, and mental health issues. Epidemiological studies show that there are significant differences in the incidence of Long COVID among different populations, and its effects can be long-lasting, severely affecting patients' quality of life. Currently, diagnostic methods for Long COVID are still under exploration, with common assessment tools involving clinical interviews and questionnaires. In terms of treatment strategies, while no specific treatments exist yet, a comprehensive management approach including symptom relief, rehabilitation training, and psychological support has shown some efficacy. By analyzing the latest research findings, this paper aims to provide references for clinical practice and future research, promoting further understanding and response to Long COVID.
Long COVID; sequelae; SARS-CoV-2; clinical manifestations; mechanisms
RNA-Binding Proteins: Biological Mechanisms and Their Impact on Osteoporosis Development
Osteoporosis is a common bone disease characterized by reduced bone mass and increased fracture risk, largely driven by imbalances in osteoblast and osteoclast activity. Recent studies highlight the critical role of RNA-binding proteins (RBPs) in the regulation of bone metabolism, specifically in osteoblast and osteoclast function. These proteins have also been implicated in other bone diseases, suggesting a broader influence on skeletal health. This review explores the molecular mechanisms through which RBPs impact bone cells, discusses their potential as therapeutic targets in osteoporosis, and outlines future research directions and challenges in harnessing RBPs for clinical applications.
RNA-binding proteins (RBPs); Osteoporosis; Biological Mechanisms
Pan-Cancer Analysis for Identification of Tumor Antigens and Immune Subtypes in mRNA Vaccine Development
Purpose: Immunotherapy using vaccines proved effective for a variety of cancers, however, so far there is no vaccine against many types of cancers. The aim of this study was to identify potential tumor antigens for the development of antiall cancer mRNA vaccines and to map the immune landscape of pan-cancer to select suitable patients for vaccination. Methods: We collected normalized gene expression data and clinical data from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). Gene Expression Profiling Interactive Analysis (GEPIA) version 2 was used to analyze the differential expression of cancer genes and to calculate the prognostic index of each selected antigen. We used the cBio Cancer Genomics Portal (cBioPortal) to visualize the gene alterations of potential tumor antigens in the TCGA data and the Tumor Immune Estimation Resource (TIMER) to analyze and visualize the association between immune infiltrates and expression level of the identified potent antigens. Consistency matrix analysis was used to identify corresponding gene modules and immune subtypes. Univariate and multivariate Cox regression as well as log-rank tests were used to assess the prognostic distinctions of the GBM immune subtypes. We used the reduce Dimension function of the Monocle package to visualize the distribution of immune subtypes across TCGA and CGGA patients. Results: Different cancer have different potential RNA Antigens. In GBM, seven tumor antigens (DIRAS3, EGFLAM, FUCA1, LILRB2, MPZL2, OSMR, and STC1) were identified as promising candidates for developing anti-GBM mRNA vaccines. Based on the immune gene expression profiles, GBM was classified into four immune subtypes (IS1–IS4). IS1 and IS4 were immunologically “hot,” which means they are more likely to benefit from vaccines. In addition, six immune hub genes (CLCF1, CD54, SOCS3, PLAUR, LIF, and BCL3) were identified as potential biomarkers for GBM vaccine effectiveness. Conclusions: Different cancer types should have different potential RNA vaccine. For instance, DIRAS3, EGFLAM, FUCA1, LILRB2, MPZL2, OSMR and STC1 were identified to be related with poor survival and antigen presenting cell (APC) infiltration in GBM, and they are promising targets for mRNA vaccine development. GBM patients could be divided in to four immune subtypes based on the clustering of immune-related gene expression, and those in the IS1 and IS4 groups were candidates for vaccination.
TGDD: A Genomics-Based Database for Tumor Treatment Drugs
With advancements in cancer genomics, precision medicine is increasingly being applied in cancer treatment. However, the fragmentation of genetic mutation-related information and the lack of standardized interpretation tools present significant challenges to its effective implementation and clinical decision-making. To address these challenges, we have developed the Tumor Genomic Drug Database (TGDD), a comprehensive genomics-based cancer drug treatment resource. It integrates data on genes, molecular characteristics, cancer types, and therapeutic drugs from established cancer knowledge bases such as CIViC, COSMIC, and OncoKB, providing a unified and standardized clinical support tool. Currently, it encompasses 19,557 entries, including 391 gene datasets, 525 cancer types, 5,322 drug combinations, and 8,109 mutation coordinates. This resource assists clinicians in formulating personalized treatment plans and supports cancer research and drug development.
Prognostic Implications and Immune Infiltration in the Nod-like receptor signaling pathway: A Comprehensive Analysis across Pan-cancer
Purpose Nucleotide-binding oligosaccharide-like receptors (NOD) are pivotal molecules with crucial roles in the regulation of inflammation, tumor transformation, angiogenesis, tumor stem cells, and chemoresistance. This study aimed to assess the prognostic implications of NOD signaling in diverse cancer types and its relationship with immune infiltration. Methods Gene expression data from TCGA related to the NOD signaling pathway were integrated with clinical data. Prognostically relevant NOD pathway genes were analyzed using univariate and multivariate Cox regression and Kaplan-Meier survival analysis. The accuracy of our prediction model was validated through receiver operating characteristic (ROC) curve analysis.Single-cell analysis of genes associated with reduced survival in patients, and single-sample immunoinfiltration analysis revealed cell-level differences between different groups. Results Univariate Cox regression analysis, multivariate cox regression analysis and Kaplan-Meier analysis were used to identify prognostic genes in NOD pathway. TRAF5 is an important prognostic gene in multiple cancer types, and mutation analysis showed that patients with TRAF5 mutations had reduced survival. Immune infiltration analysis revealed differences in effector memory CD8 T cells and immature B cells between high- and low-risk groups, suggesting potential druggable targets. Single-cell analysis highlighted that reduced survival was associated with overexpression of TXN in both primary and metastatic tissues. Conclusion NOD signaling pathway, specifically TRAF5, plays a critical role in cancer prognosis across various cancer types. Immune infiltration disparities offer therapeutic opportunities, and TXN represents a promising target for novel anticancer treatments.
Nod-like receptor signaling pathway, pan-cancer, prognostic marker, Immune Infiltration