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

Identification of disease-specific cell subtypes (DSCSs) has profound implications for understanding disease mechanisms, preoperative diagnosis, and precision therapy. However, achieving unified annotation of DSCSs in heterogeneous single-cell datasets remains a challenge. In this study, we developed the gPRINT algorithm (generalized approach for cell subtype identification with single cell's voicePRINT). Inspired by the principles of speech recognition in noisy environments, gPRINT transforms gene position and gene expression information into voiceprints based on ordered and clustered gene expression phenomena, obtaining unique "gene print" patterns for each cell. Then, we integrated neural networks to mitigate the impact of background noise on cell identity label mapping. We demonstrated the reproducibility of gPRINT across different donors, single-cell sequencing platforms, and disease subtypes, and its utility for automatic cell subtype annotation across datasets. Moreover, gPRINT achieved higher annotation accuracy of 98.37% when externally validated based on the same tissue, surpassing other algorithms. Furthermore, this approach has been applied to fibrosis-associated diseases in multiple tissues throughout the body, as well as to the annotation of fibroblast subtypes in a single tissue, tendon, where fibrosis is prevalent. We successfully achieved automatic prediction of tendinopathy-specific cell subtypes, key targets, and related drugs. In summary, gPRINT provides an automated and unified approach for identifying DSCSs across datasets, facilitating the elucidation of specific cell subtypes under different disease states and providing a powerful tool for exploring therapeutic targets in diseases.
Humans ; Algorithms ; Single-Cell Analysis ; Databases, Genetic ; Molecular Sequence Annotation

The chloroplast genome is an important tool for studying plant classification, evolution, and the heterologous production of secondary metabolites and protein drugs. With advancements in sequencing technology and reductions in sequencing costs, chloroplast genome data have rapidly accumulated. However, existing chloroplast genome databases suffer from issues such as incomplete data, inadequate management, and inconsistent, inaccurate information, posing significant challenges for the development and utilization of the chloroplast genome. Therefore, it is urgently necessary to establish a database that provides comprehensive and reliable chloroplast genome information. This article provides a brief introduction to the Chloroplast Genome Information Resource(CGIR), the most comprehensive chloroplast genome database globally in terms of species coverage. The database, consisting of five modules, i.e.,(1) genomes,(2) genes,(3) simple sequence repeats(SSRs),(4) DNA barcodes, and(5) DNA signature sequences(DSSs), currently includes 34 923 chloroplast genome assemblies from 16 717 species. Based on the functionalities of these modules, the article systematically summarizes the progress in the application of the database in plant phylogenetic analysis, species identification, and chloroplast genetic engineering. The chloroplast genome database will be continuously updated in the future to provide a solid and reliable data foundation for chloroplast genome research, further promoting studies on traditional Chinese medicine(TCM)identification, resource conservation, and germplasm innovation.
Genome, Chloroplast ; Plants/classification* ; Databases, Genetic ; Phylogeny ; Chloroplasts/genetics*

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

Metabolic reprogramming is a common phenomenon in cancer, with aerobic glycolysis being one of its important characteristics. Hypoxia-inducible factor-1α (HIF1Α) is thought to play an important role in aerobic glycolysis. Meanwhile, naringin is a natural flavanone glycoside derived from grapefruits and many other citrus fruits. In this work, we identified glycolytic genes related to HIF1Α by analyzing the colon cancer database. The analysis of extracellular acidification rate and cell function verified the regulatory effects of HIF1Α overexpression on glycolysis, and the proliferation and migration of colon cancer cells. Moreover, naringin was used as an inhibitor of colon cancer cells to illustrate its effect on HIF1Α function. The results showed that the HIF1Α and enolase 2 (ENO2) levels in colon cancer tissues were highly correlated, and their high expression indicated a poor prognosis for colon cancer patients. Mechanistically, HIF1Α directly binds to the DNA promoter region and upregulates the transcription of ENO2; ectopic expression of ENO2 increased aerobic glycolysis in colon cancer cells. Most importantly, we found that the appropriate concentration of naringin inhibited the transcriptional activity of HIF1Α, which in turn decreased aerobic glycolysis in colon cancer cells. Generally, naringin reduces glycolysis in colon cancer cells by reducing the transcriptional activity of HIF1Α and the proliferation and invasion of colon cancer cells. This study helps to elucidate the relationship between colon cancer progression and glucose metabolism, and demonstrates the efficacy of naringin in the treatment of colon cancer.
Glycolysis ; Colonic Neoplasms/metabolism* ; Humans ; Hypoxia-Inducible Factor 1, alpha Subunit/metabolism* ; Phosphopyruvate Hydratase/metabolism* ; Flavanones/pharmacology* ; Cell Line, Tumor ; Databases, Genetic ; Cell Proliferation/drug effects* ; Transfection ; Warburg Effect, Oncologic

This paper aimed to explore the antidepressant effect of the essential oil from Schizonepeta tenuifolia Briq.(EOST) on the treatment of depression and its mechanism by using a combination of network pharmacology and the mouse model of lipopolysaccharide(LPS)-induced depression. The chemical components in EOST were identified using gas chromatography-mass spectrometer(GC-MS), and 12 active components were selected as the study objects. The targets related to EOST were obtained by Traditional Chinese Medicines Systems Pharmacology(TCMSP) and SwissTargetPrediction database. The targets related to depression were screened out through GeneCards, Therapeutic Target Database(TTD), and Online Mendelian Inheritance in Man(OMIM) database. The Venny 2.1 was applied to screen out the common targets of EOST and depression. The targets were imported into Cytoscape 3.7.2 to generate "drug-active component-diease-target" network diagram. The protein-protein interaction(PPI) network was constructed using STRING 11.5 database and Cytoscape 3.7.2, and the core targets were screened out. DAVID 6.8 database was used for Gene Ontology(GO) func-tional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis, and subsequently the enrichment results were visualized through the bioinformatics platform. The mouse model of depression was induced by intraperitoneally injecting with LPS in mice. Before modeling, mice were administrated orally with EOST. The antidepressant effect of EOST was evalua-ted by tail suspension test(TST), forced swimming test(FST), and novelty suppressed feeding test(NSFT) after modeling. The content of interleukin(IL)-1β was determined by enzyme-linked immunosorbent assay(ELISA), and the protein expression levels of IL-1β and pro IL-1β in the hippocampus were determined by Western blot. There were 12 main components and 179 targets in EOAT, of which, 116 targets were related to depression, mainly involved in neuroactive ligand-receptor interaction, calcium signaling pathway, and cyclic adenosine monophosphate(cAMP) signaling pathway. Biological processes such as synaptic signal transduction, G-protein coupled receptor signaling pathway, and chemical synaptic transmission were involved. Molecular functions such as neurotransmitter receptor activity, RNA polymerase Ⅱ transcription factor activity, and heme binding were involved. In mice experiments, the results showed that EOST at 100 mg·kg~(-1) and 50 mg·kg~(-1) significantly shortened the immobility time in TST and FST as well as the feeding latency in NSFT compared with the model group, decreased the levels of serum IL-1β and NO, and reduced the protein expression levels of IL-1β and pro IL-1β in the hippocampus. In conclusion, EOST shows a good antidepressant effect in a multi-component, multi-target, and multi-pathway manner. The mechanism may be attributed to the fact that EOST can down-regulate the protein expression levels of IL-1β and pro IL-1β, decrease the release of inflammatory factors, and reduce neuroinflammation response.
Animals ; Mice ; Oils, Volatile ; Depression ; Lipopolysaccharides ; Network Pharmacology ; Databases, Genetic ; Calcium Signaling ; Disease Models, Animal

The present study aimed to explore the main active components and potential mechanisms of Panax notoginseng saponins(PNS) and osteopractic total flavone(OTF) in the treatment of osteoporosis(OP) through network pharmacology, molecular docking and in vitro cell experiments, which was expected to provide a theoretical basis for clinical applications. The blood-entering components of PNS and OTF were obtained from literature search and online database, and their potential targets were obtained from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP) and SwissTargetPrediction. The OP targets were obtained by means of searching Online Mendelian Inheritance in Man(OMIM) and GeneCards. The common targets of the drug and disease were screened by Venn. Cytoscape was used to construct a "drug-component-target-disease" network, and the core components were screened according to the node degree. The protein-protein interaction(PPI) network of the common targets was constructed by STRING and Cytoscape, and the core targets were screened according to the node degree. GO and KEGG enrichment analysis of potential therapeutic targets were carried out by R language. Molecular docking was used to determine the binding activity of some active components to key targets by AutoDock Vina. Finally, HIF-1 signaling pathway was selected for in vitro experimental verification according to the results of KEGG pathway analysis. Network pharmacology showed that there were 45 active components such as leachianone A, kurarinone, 20(R)-protopanaxatriol, 20(S)-protopanaxatriol, and kaempferol, and 103 therapeutic targets such as IL6, AKT1, TNF, VEGFA and MAPK3 involved. PI3K-AKT, HIF-1, TNF and other signaling pathways were enriched. Molecular docking revealed that the core components had good binding ability to the core targets. In vitro experiments found that PNS-OTF could up-regulate the mRNA expression levels of HIF-1α, VEGFA and Runx2, indicating that the mechanism of PNS-OTF in treating OP may be related to the activation of HIF-1 signaling pathway, and thus PNS-OTF played a role in promoting angiogenesis and osteogenic differentiation. In conclusion, this study predicted the core targets and pathways of PNS-OTF in treating OP based on network pharmacology and carried out in vitro experimental verification, which reflected the characteristics of multi-component, multi-target and multi-pathway synergy of PNS-OTF, and provided new ideas for the future clinical treatment of OP.
Humans ; Molecular Docking Simulation ; Network Pharmacology ; Osteogenesis ; Phosphatidylinositol 3-Kinases ; Osteoporosis ; Databases, Genetic

The present study aimed to explore the main active components and underlying mechanisms of Marsdenia tenacissima in the treatment of ovarian cancer(OC) through network pharmacology, molecular docking, and in vitro cell experiments. The active components of M. tenacissima were obtained from the literature search, and their potential targets were obtained from SwissTargetPrediction. The OC-related targets were retrieved from Therapeutic Target Database(TTD), Online Mendelian Inheritance in Man(OMIM), GeneCards, and PharmGKB. The common targets of the drug and the disease were screened out by Venn diagram. Cytoscape was used to construct an "active component-target-disease" network, and the core components were screened out according to the node degree. The protein-protein interaction(PPI) network of the common targets was constructed by STRING and Cytoscape, and the core targets were screened out according to the node degree. GO and KEGG enrichment analyses of potential therapeutic targets were carried out with DAVID database. Molecular docking was used to determine the binding activity of some active components to key targets by AutoDock. Finally, the anti-OC activity of M. tenacissima extract was verified based on SKOV3 cells in vitro. The PI3K/AKT signaling pathway was selected for in vitro experimental verification according to the results of GO function and KEGG pathway analyses. Network pharmacology results showed that 39 active components, such as kaempferol, 11α-O-benzoyl-12β-O-acetyltenacigenin B, and drevogenin Q, were screened out, involving 25 core targets such as AKT1, VEGFA, and EGFR, and the PI3K-AKT signaling pathway was the main pathway of target protein enrichment. The results of molecular docking also showed that the top ten core components showed good binding affinity to the top ten core targets. The results of in vitro experiments showed that M. tenacissima extract could significantly inhibit the proliferation of OC cells, induce apoptosis of OC cells through the mitochondrial pathway, and down-regulate the expression of proteins related to the PI3K/AKT signaling pathway. This study shows that M. tenacissima has the characteristics of multi-component, multi-target, and multi-pathway synergistic effect in the treatment of OC, which provides a theoretical basis for in-depth research on the material basis, mechanism, and clinical application.
Humans ; Female ; Marsdenia ; Molecular Docking Simulation ; Network Pharmacology ; Phosphatidylinositol 3-Kinases ; Proto-Oncogene Proteins c-akt ; Ovarian Neoplasms/genetics* ; Databases, Genetic ; Plant Extracts ; Drugs, Chinese Herbal/pharmacology*

The acupoint application of Euodiae Fructus at Yongquan(KI1) can significantly improve the sleep quality of patients with insomnia with berberine as the main effective component for the efficacy. Nineteen active compounds and 203 drug targets were screened out from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP). After comparison with GeneCards and Online Mendelian Inheritance in Man(OMIM), 24 common genes of diseases and drugs were obtained. STRING 11.0 was used to construct a protein-protein interaction(PPI) network of the overlapping genes, and Matthews correlation coefficient(MCC) was employed to screen the core genes, which were then subjected to enrichment analysis with gene ontology(GO) and Kyoto encyclopedia of genes and genomes(KEGG). The results revealed that the main compounds of Euodiae Fructus, such as berberine and rutaecarpine, participated in the biological processes(such as neurotransmitter receptor activity) by regulating C-reactive protein(CRP), estrogen receptor 1(ESR1), 5-hydroxytryptamine(5-HT) receptor, and interleukin-6(IL-6) to exert sedative, anxiolytic, and antidepressant effects. Sixty 4-week-old SPF mice were randomly divided into a control group, a model group, a positive drug(diazepam tablets) group, and low-, medium-, and high-dose berberine groups. Medication with corresponding drugs was performed for one week. The results demonstrated that berberine was potent in reducing the activities and standing times of mice, down-regulating the levels of CRP and IL-6 mRNA in the hypothalamus, and up-regulating the expression of 5-HT(P<0.01); however, no significant effect on ESR1 was observed. The network of Euodiae Fructus in treating insomnia was constructed by network pharmacology and verified by tests. The findings indicated that the therapeutic efficacy of Euodiae Fructus in treating insomnia was achieved by participating in multiple biological processes, such as neurotransmitter receptor activity, which provided a scientific basis for its clinical application.
Animals ; Databases, Genetic ; Drugs, Chinese Herbal/pharmacology* ; Gene Ontology ; Humans ; Medicine, Chinese Traditional ; Mice ; Sleep Initiation and Maintenance Disorders/genetics*

As one of the most commonly diagnosed cancers in the world, female breast cancer is induced by the high level of estrogen. Saussureae Involucratae Herba(SIH), a gynecological medicinal, regulates estrogen-induced diseases. However, the therapeutic effect of SIH on breast cancer has not been reported. Therefore, this study aims to explore the potential efficacy of SIH on breast cancer based on in vitro experiment and network pharmacology. The inhibitory effect of SIH water extract on proliferation and migration of breast cancer MDA-MB-231 cells was examined. The result demonstrated SIH water extract significantly suppressed the proliferation of breast cancer cells(IC_(50)=6.47 mg·mL~(-1)) and also restricted the migration. A total of 39 components of SIH were retrieved from traditional Chinese medicine database(TCMD) and 160 targets of SIH were screened by target fishing with the PharmaDB database. The Online Mendelian Inheritance in Man(OMIM) was used to establish a 1 001-targets data set of breast cancer. Based on the overlaps(45) of targets between SIH and breast cancer, a protein-protein interaction(PPI) network was built to analyze the interactions among these targets with STRING platform and Cytoscape. Finally, through topology and GO and KEGG analysis, 8 targets, 101 pathways and 85 biological processes were found to involve the treatment of breast cancer by SIH. SIH may exert the anti-breast cancer effect by regulating cell cycle, inhibiting proliferation, migration and adhesion of cancer cells, and modulating estrogen receptor. This study clarified the mechanism of SIH in treating breast cancer, which lays a foundation for the further development of SIH.
Breast Neoplasms/genetics* ; Databases, Genetic ; Drugs, Chinese Herbal ; Female ; Humans ; Medicine, Chinese Traditional