OBJECTIVE: To develop a user-friendly visualization application for the automatic annotation of Human Phenotype Ontology (HPO) terms based on large language models and retrieval-augmented generation (RAG) technology, and to validate its performance in an authoritative case dataset. METHODS: By integrating the domestic open-source large language model DeepSeek-V3 with RAG technology, an interactive web application was deployed on the Streamlit cloud platform. Using only the latest official HPO dataset as the data source, the lightweight sentence-embedding model BAAI/bge-small-en-v1.5 was employed to construct a FAISS vector index. During the online phase, a four-step closed-loop process is automatically completed: multilingual translation, phenotype phrase extraction, RAG candidate retrieval, term mapping, and official database validation. 121 English case reports publicly released by BMJ Case Reports and Oxford Medical Case Reports (with a gold-standard HPO set of 1 794 terms) were selected for application validation. Precision, recall, and F1 score were calculated and compared horizontally with traditional dictionary tools, standalone large language models, and the similar application "RAG-HPO". Finally, replace the model with the more advanced ChatGPT-5 and evaluate its performance on the newly extracted dataset. RESULTS: An HPO term automatic annotation visualization application named PhenoRAG, based on large language models and RAG technology, was successfully developed. Users can access it directly via a web link. Across the 112 cases, a total of 2 150 HPO terms were generated; 2,064 (96.0%) were fully validated by the official database, with a hallucination rate of 1.3% and an HPO ID-name mismatch rate of 2.7%. After deduplication, 1,906 terms remained for testing. The overall precision was 63.65%, recall was 67.34%, and F1 was 65.44%, significantly outperforming traditional annotation tools (F1: 0.45-0.49, P < 0.001). Although PhenoRAG's F1 was lower than that of RAG-HPO (F1 = 0.78, P < 0.001), which relies on a manually constructed synonym database of 54 000 entries plus the HPO dataset, it requires no additional dictionary maintenance and can be used without any background in computer programming. Moreover, after switching to the GPT-5 model, PhenoRAG exhibited no hallucination rate on the new dataset, and its F1 score significantly increased (P = 0.038). CONCLUSION Without constructing a synonym database, the PhenoRAG achieved high-accuracy automatic mapping from clinical text to standard HPO terms. It features a low usage threshold, free access, and a Chinese-language interface, and can directly serve rare disease diagnosis, genetic counseling, and research scenarios in China and worldwide, warranting further clinical promotion and multicenter validation.
Humans ; Phenotype ; Biological Ontologies ; Language ; Software ; Large Language Models

Through the investigation of relevant literature, the concepts, methods, languages and tools of ontology were explored, and the suitable methods and tools for the ontology construction of acupoint anatomy were selected. The current mainstream anatomical ontology and related ontology of TCM were investigated so as to provide the reference for the ontology construction of acupoint anatomy. According to the knowledge attributes of acupoint anatomy, the foundational model of anatomy (FMA) was served as the reusable ontology, and in association with the attribute classification of traditional Chinese medicine language system (TCMLS), the construction approach to acupoint anatomical ontology was explored. By taking "anatomical entity of acupoints" as the top-level concept, the demonstrative study on the anatomical ontology construction was conducted on the acupoints of lung meridian of hand-taiyin.
Acupuncture Points ; Humans ; Meridians ; Medicine, Chinese Traditional ; Biological Ontologies

Biomedical big data, characterized by its massive scale, multi-dimensionality, and heterogeneity, offers novel perspectives for disease research, elucidates biological principles, and simultaneously prompts changes in related research methodologies. Biomedical ontology, as a shared formal conceptual system, not only offers standardized terms for multi-source biomedical data but also provides a solid data foundation and framework for biomedical research. In this review, we summarize enrichment analysis and deep learning for biomedical ontology based on its structure and semantic annotation properties, highlighting how technological advancements are enabling the more comprehensive use of ontology information. Enrichment analysis represents an important application of ontology to elucidate the potential biological significance for a particular molecular list. Deep learning, on the other hand, represents an increasingly powerful analytical tool that can be more widely combined with ontology for analysis and prediction. With the continuous evolution of big data technologies, the integration of these technologies with biomedical ontologies is opening up exciting new possibilities for advancing biomedical research.
Deep Learning ; Biological Ontologies ; Humans ; Big Data ; Biomedical Research

OBJECTIVE: To explore the key target molecules and potential mechanisms of oridonin against non-small cell lung cancer (NSCLC). METHODS: The target molecules of oridonin were retrieved from SEA, STITCH, SuperPred and TargetPred databases; target genes associated with the treatment of NSCLC were retrieved from GeneCards, DisGeNET and TTD databases. Then, the overlapping target molecules between the drug and the disease were identified. The protein-protein interaction (PPI) was constructed using the STRING database according to overlapping targets, and Cytoscape was used to screen for key targets. Molecular docking verification were performed using AutoDockTools and PyMOL software. Using the DAVID database, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted. The impact of oridonin on the proliferation and apoptosis of NSCLC cells was assessed using cell counting kit-8, cell proliferation EdU image kit, and Annexin V-FITC/PI apoptosis kit respectively. Moreover, real-time quantitative PCR and Western blot were used to verify the potential mechanisms. RESULTS: Fifty-six target molecules and 12 key target molecules of oridonin involved in NSCLC treatment were identified, including tumor protein 53 (TP53), Caspase-3, signal transducer and activator of transcription 3 (STAT3), mitogen-activated protein kinase kinase 8 (MAPK8), and mammalian target of rapamycin (mTOR). Molecular docking showed that oridonin and its key target molecules bind spontaneously. GO and KEGG enrichment analyses revealed cancer, apoptosis, phosphoinositide-3 kinase/protein kinase B (PI3K/Akt), and other signaling pathways. In vitro experiments showed that oridonin inhibited the proliferation, induced apoptosis, downregulated the expression of Bcl-2 and Akt, and upregulated the expression of Caspase-3. CONCLUSION Oridonin can act on multiple targets and pathways to exert its inhibitory effects on NSCLC, and its mechanism may be related to upregulating the expression of Caspase-3 and downregulating the expressions of Akt and Bcl-2.
Diterpenes, Kaurane/chemistry* ; Carcinoma, Non-Small-Cell Lung/pathology* ; Humans ; Network Pharmacology ; Lung Neoplasms/pathology* ; Cell Proliferation/drug effects* ; Apoptosis/drug effects* ; Molecular Docking Simulation ; Protein Interaction Maps/drug effects* ; Cell Line, Tumor ; Signal Transduction/drug effects* ; Gene Expression Regulation, Neoplastic/drug effects* ; Reproducibility of Results ; Gene Ontology

OBJECTIVES: Hepatocellular carcinoma is one of the most common primary malignant tumors with the third highest mortality rate worldwide. This study aims to identify key genes associated with hepatocellular carcinoma prognosis using the Gene Expression Omnibus (GEO) database and provide a theoretical basis for discovering novel prognostic biomarkers for hepatocellular carcinoma. METHODS: Hepatocellular carcinoma-related datasets were retrieved from the GEO database. Differentially expressed genes (DEGs) were identified using the GEO2R tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and key genes were identified using Cytoscape software. The University of Alabama at Birmingham Cancer Data Analysis Resource (UALCAN) was used to analyze the expression levels of key genes in normal and hepatocellular carcinoma tissues, as well as their associations with pathological grade, clinical stage, and patient survival. The Human Protein Atlas (THPA) was used to further validate the impact of key genes on overall survival. Expression levels of key genes in the blood of hepatocellular carcinoma patients were evaluated using the expression atlas of blood-based biomarkers in the early diagnosis of cancers (BBCancer). RESULTS: A total of 78 DEGs were identified from the GEO database. GO and KEGG analyses indicated that these genes may contribute to hepatocellular carcinoma progression by promoting cell division and regulating protein kinase activity. Sixteen key genes were screened via Cytoscape and validated using UALCAN and THPA. These genes were overexpressed in hepatocellular carcinoma tissues and were associated with disease progression and poor prognosis. Finally, BBCancer analysis showed that ASPM and NCAPG were also elevated in the blood of hepatocellular carcinoma patients. CONCLUSIONS This study identified 16 key genes as potential prognostic biomarkers for hepatocellular carcinoma, among which ASPM and NCAPG may serve as promising blood-based markers for hepatocellular carcinoma.
Humans ; Carcinoma, Hepatocellular/mortality* ; Liver Neoplasms/pathology* ; Prognosis ; Computational Biology/methods* ; Protein Interaction Maps/genetics* ; Biomarkers, Tumor/genetics* ; Gene Expression Regulation, Neoplastic ; Gene Expression Profiling ; Gene Ontology ; Databases, Genetic

OBJECTIVES: Osteoarthritis (OA) and sarcopenia are significant health concerns in the elderly, substantially impacting their daily activities and quality of life. However, the relationship between them remains poorly understood. This study aims to uncover common biomarkers and pathways associated with both OA and sarcopenia. METHODS: Gene expression profiles related to OA and sarcopenia were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between disease and control groups were identified using R software. Common DEGs were extracted via Venn diagram analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify biological processes and pathways associated with shared DEGs. Protein-protein interaction (PPI) networks were constructed, and candidate hub genes were ranked using the maximal clique centrality (MCC) algorithm. Further validation of hub gene expression was performed using 2 independent datasets. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of key genes for OA and sarcopenia. Mouse models of OA and sarcopenia were established. Hematoxylin-eosin and Safranin O/Fast Green staining were used to validate the OA model. The sarcopenia model was validated via rotarod testing and quadriceps muscle mass measurement. Real-time reverse transcription PCR (real-time RT-PCR) was employed to assess the mRNA expression levels of candidate key genes in both models. Gene set enrichment analysis (GSEA) was conducted to identify pathways associated with the selected shared key genes in both diseases. RESULTS: A total of 89 common DEGs were identified in the gene expression profiles of OA and sarcopenia, including 76 upregulated and 13 downregulated genes. These 89 DEGs were significantly enriched in protein digestion and absorption, the PI3K-Akt signaling pathway, and extracellular matrix-receptor interaction. PPI network analysis and MCC algorithm analysis of the 89 common DEGs identified the top 17 candidate hub genes. Based on the differential expression analysis of these 17 candidate hub genes in the validation datasets, AEBP1 and COL8A2 were ultimately selected as the common key genes for both diseases, both of which showed a significant upregulation trend in the disease groups (all P<0.05). The value of area under the curve (AUC) for AEBP1 and COL8A2 in the OA and sarcopenia datasets were all greater than 0.7, indicating that both genes have potential value in predicting OA and sarcopenia. Real-time RT-PCR results showed that the mRNA expression levels of AEBP1 and COL8A2 were significantly upregulated in the disease groups (all P<0.05), consistent with the results observed in the bioinformatics analysis. GSEA revealed that AEBP1 and COL8A2 were closely related to extracellular matrix-receptor interaction, ribosome, and oxidative phosphorylation in OA and sarcopenia. CONCLUSIONS AEBP1 and COL8A2 have the potential to serve as common biomarkers for OA and sarcopenia. The extracellular matrix-receptor interaction pathway may represent a potential target for the prevention and treatment of both OA and sarcopenia.
Sarcopenia/genetics* ; Osteoarthritis/genetics* ; Computational Biology/methods* ; Humans ; Protein Interaction Maps/genetics* ; Animals ; Mice ; Gene Expression Profiling ; Gene Ontology ; Transcriptome ; Male ; Signal Transduction/genetics* ; Gene Regulatory Networks

BACKGROUND: PANoptosis has the features of pyroptosis, apoptosis, and necroptosis. Numerous studies have confirmed the diverse roles of various types of cell death in acute liver failure (ALF), but limited attention has been given to the crosstalk among them. In this study, we aimed to explore the role of PANoptosis in ALF and uncover new targets for its prevention or treatment. METHODS: Three ALF-related datasets (GSE14668, GSE62029, and GSE74000) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). Hub genes were identified through intersecting DEGs, genes obtained from weighted gene co-expression network analysis (WGCNA), and genes related to PANoptosis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein‍‒‍protein interaction (PPI) analyses and gene set enrichment analysis (GSEA) were performed to determine functional roles. Verification was performed using an ALF mouse model. RESULTS: Our results showed that expression of seven hub genes (B-cell lymphoma-2-modifying factor (BMF), B-cell lymphoma-2-interacting protein 3-like (BNIP3L), Caspase-1 (CASP1), receptor-interacting protein kinase 3 (RIPK3), uveal autoantigen with coiled-coil domains and ankyrin repeats protein (UACA), uncoordinated-5 homolog B receptor (UNC5B), and Z-DNA-binding protein 1 (ZBP1)) was up-regulated in liver samples of patients. However, in the ALF mouse model, the expression of BNIP3L, RIPK3, phosphorylated RIPK3 (P-RIPK3), UACA, and cleaved caspase-1 was up-regulated, while the expression of CASP1 and UNC5B was down-regulated. The expression of ZBP1 and BMF increased only during the development of ALF, and there was no significant change in the end stage. Immunofluorescence of mouse liver tissue showed that macrophages expressed all seven markers. Western blot results showed that pyroptosis, apoptosis, and necroptosis were always involved in lipopolysaccharide (LPS)/ d-galactosamine (d-gal)‍-induced ALF mice. The ALF cell model showed that bone marrow-derived macrophages (BMDMs) form PANoptosomes after LPS stimulation. CONCLUSIONS: Our results suggest that PANoptosis of macrophages promotes the development of ALF. The seven new ALF biomarkers identified and validated in this study may contribute to further investigation of diagnostic markers or novel therapeutic targets of ALF.
Animals ; Liver Failure, Acute/genetics* ; Computational Biology ; Mice ; Pyroptosis/genetics* ; Humans ; Protein Interaction Maps ; Apoptosis/genetics* ; Necroptosis/genetics* ; Gene Regulatory Networks ; Gene Ontology ; Gene Expression Profiling ; Disease Models, Animal

Biomanufacturing is an advanced manufacturing method that integrates biology, chemistry, and engineering. It utilizes renewable biomass and biological organisms as production media to scale up the production of target products through fermentation. Compared with petrochemical routes, biomanufacturing offers significant advantages in reducing CO₂ emissions, lowering energy consumption, and cutting costs. With the development of systems biology and synthetic biology and the accumulation of bioinformatics data, the integration of information technologies such as artificial intelligence, large models, and high-performance computing with biotechnology is propelling biomanufacturing into a data-driven era. This paper reviews the latest research progress on databases, knowledge bases, and large language models for biomanufacturing. It explores the development directions, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for scientific research in related areas.
Biotechnology/methods* ; Knowledge Bases ; Synthetic Biology ; Databases, Factual ; Artificial Intelligence ; Systems Biology ; Computational Biology ; Fermentation

OBJECTIVES: To identify cuproptosis- and ferroptosis-related genes involved in nonalcoholic fatty liver disease and to determine the diagnostic value of hub genes. METHODS: The gene expression dataset GSE89632 was retrieved from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) between the non-alcoholic steatohepatitis (NASH) group and the healthy group using the 'limma' package in R software and weighted gene co-expression network analysis. Gene ontology, kyoto encyclopedia of genes and genomes pathway, and single-sample gene set enrichment analyses were performed to identify functional enrichment of DEGs. Ferroptosis- and cuproptosis-related genes were obtained from the FerrDb V2 database and available literatures, respectively. A combined signature for cuproptosis- and ferroptosis-related genes, called CRF, was constructed using the STRING database. Hub genes were identified by overlapping DEGs, WGCNA-derived key genes, and combined signature CRF genes, and validated using the GSE109836 and GSE227714 datasets and real-time quantitative polymerase chain reaction. A nomogram of NASH diagnostic model was established utilizing the 'rms' package in R software based on the hub genes, and the diagnostic value of hub genes was assessed using receiver operating characteristic curve analysis. In addition, immune cell infiltration in NASH versus healthy controls was examined using the CIBERSORT algorithm. The relationships among various infiltrated immune cells were explored with Spearman's correlation analysis. RESULTS: Analysis of GSE89632 identified 236 DEGs between the NASH group and the healthy group. WGCNA highlighted 8 significant modules and 11,095 pivotal genes, of which 330 genes constituted CRF. Intersection analysis identified IL6, IL1B, JUN, NR4A1, and PTGS2 as hub genes. The hub genes were all downregulated in the NASH group, and this result was further verified by the NASH validation dataset and real-time quantitative polymerase chain reaction. Receiver operating characteristic curve analysis confirmed the diagnostic efficacy of these hub genes with areas under the curve of 0.985, 0.941, 1.000, 0.967, and 0.985, respectively. Immune infiltration assessment revealed that gamma delta T cells, M1 macrophages, M2 macrophages, and resting mast cells were predominantly implicated. CONCLUSIONS Our investigation underscores the significant association of cuproptosis- and ferroptosis-related genes, specifically IL6, IL1B, JUN, NR4A1, and PTGS2, with NASH. These findings offer novel insights into the pathogenesis of NASH, potentially guiding future diagnostic and therapeutic strategies.
Non-alcoholic Fatty Liver Disease/pathology* ; Humans ; Ferroptosis/genetics* ; Copper/metabolism* ; Gene Ontology ; Gene Expression Profiling

OBJECTIVES: To explore ferroptosis-related genes in osteoporosis through bioinformatic analysis and in vitro study. METHODS: Osteoporosis-related genes were identified from dataset GSE35958 in the Gene Expression Omnibus database; and the ferroptosis-related genes were identified from the FerrDb database. These were intersected with the differentially expressed genes in GSE35958 to obtain ferroptosis-related genes in osteoporosis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed for the differentially expressed genes. And Spearman correlation and protein-protein interaction network analysis were performed. Then, the hub genes of ferroptosis in osteoporosis were screened by Degree, MNC, EPC, MCC and DMNC in Cytoscape software CytoHubba plugin; and analyzed with receiver operating characteristic (ROC) curves. The bone marrow mesenchymal stem cells from osteoporosis patients (osteoporosis group) and non-osteoporosis patients (control group) were subjected to quantitative reverse transcription polymerase chain reaction to detect the messenger RNA expression of ferroptosis hub genes in both groups. RESULTS: A total of 32 differentially expressed genes related to ferroptosis in osteoporosis were identified, including 26 up-regulated genes and 6 down-regulated genes. GO enrichment analysis showed that the identified genes were mainly involved in intercellular adhesion, lipid metabolism and cytokine response. KEGG enrichment analysis showed that the genes were mainly involved in signaling pathways of adhesive plaques, MAPK, PI3K-Akt, and Wnt. Spearman correlation analysis showed correlation among differentially expressed genes. Six hub genes for ferroptosis in osteoporosis were obtained, namely MAPK3, CDKN1A, MAP1LC3A, TNF, RELA, and TGF-β1. ROC curve analysis showed that these hub genes had good diagnostic performance in osteoporosis and may become potential biomarkers of osteoporosis. In vitro experiments confirmed significant differences in these hub genes between the control group and the osteoporosis group (all P<0.05). CONCLUSIONS This study has identified six ferroptosis-related hub genes in osteoporosis, which may be used as novel biomarkers for the early diagnosis and treatment of osteoporosis.
Osteoporosis/genetics* ; Humans ; Computational Biology ; Ferroptosis/genetics* ; Protein Interaction Maps/genetics* ; Gene Ontology ; Mesenchymal Stem Cells/metabolism* ; Gene Expression Profiling ; Databases, Genetic