Search Results (863)

MicroRNAs are important regulators of melanoma progression; however, their relationship with cellular senescence remains poorly understood. To address this gap, a systematic review was conducted following PRISMA 2020 guidelines and prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO) under registration number CRD420251155760. A comprehensive search of PubMed, Scopus, Embase, and Dimensions identified studies evaluating melanoma-associated microRNAs and their effects on cell cycle regulation. Risk of bias was assessed using the SYRCLE tool and an adapted version of ToxRTool, with the included studies classified as having low, moderate, or high risk of bias. Fifteen studies met the eligibility criteria. Most studies reported that microRNA modulation reduced melanoma proliferation through cell cycle arrest; however, only two directly assessed senescence-associated markers. Of the fifteen identified microRNAs, seven had predicted targets and were included in the bioinformatic analysis. Integration of these predictions with genes downregulated in high-risk melanoma and underexpressed during cellular senescence identified 158 shared genes. Subsequent analysis identified predicted targets within this gene set only for hsa-miR-195-5p, and hsa-miR-425-5p, highlighting RNF138, and SYNCRIP as candidate regulatory genes. Collectively, these findings suggest a potential link between microRNA-mediated regulation and senescence-associated pathways during melanoma progression. Full article

Background: Atherosclerosis (AS) is a chronic inflammatory vascular disease lacking reliable biomarkers for early diagnosis and risk stratification. This study aimed to identify hub genes with diagnostic potential and characterize immune microenvironment remodeling in AS. Methods: GSE43292 and GSE100927 were integrated as the training cohort (n = 168), while GSE41571, GSE120521, and GSE28829 served as independent validation cohorts (n = 48). Batch effects were corrected using the ComBat algorithm. Differentially expressed genes (DEGs) were identified using limma, followed by GO/KEGG enrichment analysis. LASSO regression and Random Forest analysis were performed to identify hub genes. A logistic regression diagnostic model was constructed and evaluated using ROC analysis, 5-fold cross-validation, and external validation. Immune infiltration was assessed using ssGSEA, and correlations between hub genes and immune cells were analyzed using Spearman correlation. Results: A total of 1349 DEGs (870 upregulated and 479 downregulated) were identified. GO and KEGG analyses demonstrated significant enrichment of immune- and inflammation-related biological processes and pathways. Seven hub genes (IBSP, XAF1, SCAMP5, SAMD9L, MYBL1, PCDH12, and CDH19) were identified through the combined application of LASSO regression and Random Forest analysis. The 7-gene logistic model achieved excellent performance in the training cohort (AUC = 0.992, 95% CI: 0.981–1.000), with a mean 5-fold cross-validation AUC of 0.984 ± 0.014, and maintained robust performance in three independent validation cohorts (AUC range: 0.952–1.000). Immune infiltration analysis revealed extensive immune microenvironment remodeling, with significantly increased infiltration of 22 of the 24 immune cell types, particularly monocytes, macrophages, and myeloid cells. Spearman correlation analysis demonstrated strong associations between hub genes, particularly SAMD9L and IBSP, and immune cell infiltration. Conclusions: This study identified a robust 7-gene diagnostic signature for AS and revealed its close association with the inflammatory immune microenvironment, providing potential biomarkers for early diagnosis and risk stratification. Full article

Background/objectives: Rheumatoid arthritis (RA) is a chronic autoimmune disease that causes progressive joint damage and systemic complications. Despite multiple treatment options, many patients fail to achieve sustained remission. Our study aimed to integrate bioinformatics and single-cell RNA-seq analyses to identify potential biomarkers and therapeutic targets and explore bioactive compounds from traditional Chinese medicine (TCM). Methods: We integrated gene expression quantitative trait loci (eQTL), protein quantitative trait loci (pQTL), and genome-wide association study (GWAS) data for RA using two-sample Mendelian randomization to identify causal druggable genes. Bulk transcriptomics and machine learning were used for candidate gene screening and validation, while single-cell RNA-seq analysis characterized cell type-specific expression and functional relevance. TCM compound screening, molecular docking, and molecular dynamics (MD) simulations were subsequently performed. Results: CXCL6, IFNG, and SLAMF1 were identified as RA-associated candidate targets with distinct cell type-specific expression patterns, strong immune associations, and favorable diagnostic performance. Functional analyses linked these genes to immune activation and intercellular communication. In silico analyses prioritized sesamin, (+)-Ganoderic acid Mf, and (24R)-saringosterol as candidate compounds, with the IFNG–(+)-Ganoderic acid Mf complex showing stable behavior during MD simulation. Conclusions: This integrative framework identified CXCL6, IFNG, and SLAMF1 as candidate biomarkers and druggable targets for RA. Sesamin, (+)-Ganoderic acid Mf, and (24R)-saringosterol warrant further experimental evaluation. These findings provide a basis for future mechanistic and translational studies. Full article

Background: Chikungunya virus (CHIKV) is a re-emerging alphavirus that has caused millions of cases worldwide, yet its molecular epidemiology in Saudi Arabia remains poorly understood. This study integrates bioinformatic analysis of the envelope gene (E1) gene sequences from Saudi isolates with global genotypes to characterize circulating lineages, selection pressures and stability effects of endemic mutations. Methods: A total of 109 CHIKV E1 sequences (1155 bp) representing the East/Central/South African (ECSA), Asian, and West African genotypes were retrieved from GenBank and GISAID. Phylogenetic relationships were reconstructed using maximum likelihood (IQ-TREE). Codon-based selection analyses were performed with MEME, FEL, SLAC, and FUBAR. The structural effects of nine missense mutations were assessed using DynaMut and consensus predictors (DUET, mCSM). Results: All seven Saudi isolates clustered within the ECSA-Indian Ocean Lineage (IOL) subclade with strong bootstrap support (≥95%). Short branch lengths among Saudi strains indicated recent common ancestry and limited local divergence, suggesting possible repeated introductions or limited local circulation. No codon showed robust evidence of positive selection across multiple methods. However, episodic diversifying selection was detected at codon 99 (MEME, p = 0.01), while pervasive purifying selection acted on numerous sites (e.g., codons 135, 307, 344; strong signals across FEL, SLAC, and FUBAR). A single conserved N-linked glycosylation site was present at residue 141 (NITV motif) in all Saudi and most global strains. Three mutations unique to or prominent in Saudi isolates (N20H, L136F, A249T) were identified; consensus stability predictions (DUET) classified them as destabilizing. Conclusions: Saudi CHIKV strains belong exclusively to the ECSA-IOL lineage and exhibit strong purifying selection on the E1 gene, consistent with functional constraints on this essential fusion protein. The identified Saudi-associated mutations appear to be non-adaptive, tolerated changes. These findings underscore the value of continued genomic surveillance to monitor potential adaptive evolution, particularly in the context of mass gatherings and competent Aedes vectors. Full article

Background: Ischemic stroke remains a leading cause of mortality and long-term disability worldwide, with complex and heterogeneous pathophysiological processes. Single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) has been increasingly applied to investigate cellular heterogeneity at high resolutions. Methods: We systematically searched PubMed, Web of Science, and Embase to identify studies that applied sc/snRNA-seq in ischemic stroke research. Based on the retrieved literature, we summarized the bioinformatic analytical methods and application strategies reported in these studies, focusing on how sc/snRNA-seq has been utilized across different research contexts. Results: The application of sc/snRNA-seq in ischemic stroke has expanded rapidly across species and sample types. A wide range of downstream bioinformatic analyses have been employed, including clustering, differential expression analysis, trajectory inference, gene regulatory network analysis, and cell–cell communication analysis. These approaches have been applied to investigate diverse biological processes in ischemic stroke. In addition, these analytical strategies have been extended to multiple biological contexts, including extracerebral tissues, stroke-related modifiers, and their associated complications. Furthermore, integrative analytical approaches that combine multiple datasets, bulk transcriptomics, and other omics data have been increasingly utilized. Advances in temporal and spatial resolutions have enabled analyses across different stages and anatomical regions. Conclusions: This review systematically summarizes the analytical methods and application strategies of sc/snRNA-seq in ischemic stroke. These approaches provide a structured perspective for understanding the application of single-cell technologies in this field. Future studies may benefit from standardized designs and coordinated analytical strategies to facilitate more systematic investigations. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a major global health threat that requires the discovery of new antimicrobial agents. Environmental microbiomes from understudied regions represent a valuable source of antimicrobial peptide (AMP) candidates. This study aimed to identify and prioritize AMP candidates from South American genomic and metagenomic datasets and to investigate the antimicrobial potential of bioactive secretomes obtained through bacterial co-culture. Methods: A total of 853 genomes and 360 metagenomes were analyzed using a reproducible genome- and metagenome-mining pipeline combined with machine learning-based AMP prediction. Predicted AMP candidates were further characterized using complementary bioinformatic tools to assess physicochemical, structural, hemolytic, toxicological, anti-inflammatory, and anticancer properties. Selected environmental isolates were subjected to bacterial co-culture, followed by SPE-C18 and HPLC fractionation. Antimicrobial activity, antioxidant activity, hemolysis, minimum inhibitory concentration (MIC), and LC-MS/MS peptidomic analyses were performed on bioactive secretome fractions. Results: Genome and metagenome mining identified diverse AMP candidate sequences associated with bacterial genera including Streptomyces, Bacillus, Burkholderia, and Shewanella. Structural predictions revealed a predominance of α-helical conformations among prioritized candidates. Several secretome fractions obtained from co-cultures displayed antimicrobial activity against Gram-positive and Gram-negative bacteria, including methicillin-resistant Staphylococcus aureus (MRSA). Active fractions showed no detectable hemolytic activity and exhibited antioxidant activity in DPPH assays. MIC analyses indicated broad-spectrum activity against Escherichia coli ATCC 11229, Pseudomonas aeruginosa ATCC 27853, Klebsiella pneumoniae, carbapenem-resistant Acinetobacter baumannii, and MRSA, with an apparent MIC of 10,000 mg/L. LC-MS/MS analysis of bioactive fractions identified peptide sequences by de novo sequencing, including KTESHHK, KRVGPRR, GLFPRLGVSPR, and HHAEHLVHFR. Conclusions: Integrated genome mining, bacterial co-culture activation, and peptidomic analyses provide a useful framework for prioritizing antimicrobial peptide candidates from environmental microbiomes. The identification of peptide-containing bioactive fractions with antimicrobial and antioxidant activities highlights the potential of South American bacterial resources for the discovery of novel antimicrobial compounds. Further purification, peptide synthesis, and biological validation will be required to determine the contribution of individual peptides to the observed activities. Full article

Cadmium (Cd) is a widespread environmental pollutant associated with neurotoxicity, but its underlying mechanisms remain unclear. Disulfidptosis is a regulated cell death driven by disulfide stress under conditions of impaired cellular reducing capacity. This study investigated the potential involvement of disulfidptosis-associated molecular alterations in Cd-induced neurotoxicity. Male Sprague Dawley (SD) rats were exposed to cadmium chloride (Low-Dose Group: CdCl₂: 0.036 mg/kg bw; High-Dose Group: CdCl₂: 3.6 mg/kg bw) by oral gavage for 30 days. Neurobehavioral performance was assessed using the open field test, elevated plus maze, and Morris water maze. Hippocampal ultrastructure, redox-related metabolites, and disulfidptosis-associated genes were analyzed. In addition, bioinformatics analysis was performed by integrating cadmium-related, neurodegenerative disease-related, and disulfidptosis-related genes. The results showed that high-dose Cd exposure impaired locomotor activity, increased anxiety-like behavior, and disrupted spatial learning and memory (p < 0.05), accompanied by mitochondrial damage in hippocampal neurons. Bioinformatics analysis identified seven overlapping genes and enrichment of ferroptosis and oxidative phosphorylation pathways. Biochemically, cadmium exposure significantly increased the NADP⁺/NADPH ratio ([Control: 1.07 ± 0.044] vs. [High-dose: 3.80 ± 0.059], p < 0.05) and decreased the GSH/GSSG ratio ([Control: 2.80 ± 0.059] vs. [High-dose: 1.14 ± 0.091], p < 0.05), indicating severe redox imbalance. At the molecular level, cadmium exposure upregulated SLC7A11 mRNA expression by 1.48 ± 0.12-fold (p < 0.01) and SLC3A2 by 1.91 ± 0.55-fold (p < 0.05), while downregulating NDUFS1 expression to 0.84 ± 0.01-fold of control levels (p < 0.01) in hippocampal tissues. These findings suggest that high-dose Cd exposure induced neurotoxicity is associated with mitochondrial dysfunction, redox imbalance, and disulfidptosis-associated molecular alterations. Full article

Background: Cerebral venous sinus thrombosis (CVST) is a critical cause of brain injury and intracranial hypertension. However, its underlying molecular mechanisms remain poorly understood, limiting the development of targeted therapies. This study aims to systematically identify key molecular targets and signaling pathways involved in CVST-induced brain lesions using multi-omics approaches in a modified rat model of CVST. Methods: An optimized rat CVST model was established. Cortical tissues were collected from Sham-operated, 2-day post-CVST, and 7-day post-CVST groups for transcriptomic, proteomic, and single-cell transcriptomic sequencing. Bioinformatics analyses were performed to identify differentially expressed genes/proteins, followed by functional enrichment, protein–protein interaction network construction, and hub-gene screening. Further investigations included drug enrichment analysis, molecular docking, and molecular dynamics, as well as the prediction of competing endogenous RNA networks, transcription factor analysis, and expression profiling of potential edema-related therapeutic targets. Results: Multi-omics analyses revealed dynamic changes in gene and protein expression in the brain after CVST, along with associated pathways involved in immune inflammatory responses and tissue repair. Integrative analysis identified 12 core genes (Cd44, Cd40, Sdc1, Myd88, Icam1, Stat3, Jak2, Ptgs2, Aldh1a1, Hspb1, Pxdn, and Casp3). Single-cell RNA sequencing validated their expression and delineated cell-type specificity. Molecular docking hinted at the high binding potential of glucocorticoids such as dexamethasone and methylprednisolone to several core targets (JAK2, PTGS2, and CD44), with all docked complexes showing binding energies below −8.2 kcal/mol. Further molecular dynamics simulations indicated that methylprednisolone forms a stable complex with CD44, driven primarily by van der Waals and electrostatic interactions. Additionally, dynamic levels of several potential edema-related targets (Kcnn4, Piezo1, Trpv4, and Atp1a2) were observed. Conclusions: In summary, by applying integrated multi-omics profiling to a modified rat model, this study systematically mapped the molecular landscape of CVST-induced brain injury. A number of candidate targets and signaling pathways emerged from our analysis, along with several compounds of potential therapeutic interest. Collectively, these results provide a basis for further investigation into the mechanisms underlying CVST and for the design of novel treatment approaches. Full article

Next-generation sequencing (NGS) has improved HIV-1 genotypic resistance testing (GRT) by enabling the detection of minority drug-resistance variants, although interpretation of low-frequency mutations remains challenging because of sequencing artifacts. This study compared the analytical performance and workflow efficiency of two Ion Torrent platforms, GeneStudio S5 (S5) and Genexus (GX), for routine HIV-1 GRT. A total of 134 plasma samples from people with HIV were prospectively collected, and 100 samples successfully sequenced on both platforms were included in the comparative analysis. Overall concordance for resistance-associated mutations was 88.0%, with agreement rates of 97.0% for protease, 90.0% for reverse transcriptase, and 100% for integrase. Both platforms generated clinically interpretable resistance profiles; however, 13 discordant mutations were identified. Application of a standardized confirmation algorithm, integrating Stanford HIVdb analysis with manual read-level inspection in Geneious software (version 2025.2.2), reclassified several discordant mutations as low-confidence or non-confirmed variants. Operationally, GX provided a fully automated workflow with approximately 24 h turnaround time and minimal hands-on processing, whereas S5 required approximately 72 h and substantially greater operator involvement. These findings support both platforms for routine HIV-1 GRT while emphasizing the importance of standardized bioinformatic review for reliable variant interpretation. Full article

Hyperuricemia (HUA) is a common immunometabolic disorder associated with gout, renal dysfunction, and systemic inflammation, yet the molecular targets through which non-starch polysaccharides (NSPs) may modulate HUA-related inflammation remain unclear. Here, we applied an integrative bioinformatics and computational workflow combining public transcriptomic datasets, curated NSP-related targets, protein–protein interaction analysis, enrichment analysis, single-cell RNA sequencing, and Mendelian randomization. We further included GutMGene-based orthogonal support analysis, guided docking, structural dynamics analysis, exploratory ADMET profiling, and in silico TLR4 knockout to extend target prioritization. This approach prioritized a TLR4-centered inflammatory module, with TLR4, MSR1, TIRAP, and CXCL8 emerging as candidate genes. Enrichment analyses linked these genes to innate immune and NF-$\kappa$B-related pathways, whereas single-cell analyses localized the prioritized signals mainly to myeloid compartments during gout flares. Mendelian randomization suggested positive associations between genetically predicted expression of TLR4-axis genes and serum uric acid levels. Under electrostatic-guided docking conditions, fucoidan and alginate yielded plausible interaction models with TLR4, and normal mode and RMSF analyses suggested altered flexibility in the MD-2 region. In silico Tlr4 knockout further perturbed urate-handling programs in renal proximal tubule-enriched cells. Together, these findings do not establish TLR4 as a newly discovered hyperuricemia gene or confirm direct receptor antagonism by NSPs, but they provide an NSP-oriented integrative framework that prioritizes the TLR4 axis, highlights myeloid-cell relevance, and nominates fucoidan and alginate for experimental follow-up. Full article

Background/Objectives: Clear cell renal cell carcinoma (ccRCC), the predominant malignant subtype of kidney cancer, is the leading cause of death among renal cell carcinoma patients. Although a subset of ccRCC patients benefit from select immune checkpoint inhibitors (ICIs), prognosis remains poor. While PD-1 and PD-L1 have been extensively studied, the prevalence and distribution of other immune checkpoints (ICs) and their relationship with epithelial-to-mesenchymal transition (EMT) remain poorly characterised. Here, we investigated the interplay between twenty ICs and EMT markers and assessed their combined prognostic relevance in ccRCC patients. Methods: Transcriptomic profiling and integrated bioinformatic analyses were performed, including differential expression, correlation analyses, survival analyses, forest plot analyses, ROC curve evaluation, and OncoPrint visualisation, complemented by analysis of single-cell RNA sequencing data, immunohistochemistry, and multiplex secretory IC (LegendPlex) assays. Results: Transcriptomic profiling of over 500 ccRCC tumours versus normal kidney tissue revealed dysregulation of ICs, particularly LAG3 and NT5E. Notably, expression of ICs, including LAG3 and NT5E, was associated with poor overall survival in 415 ccRCC patients. ICs that synergised with the EMT phenotype provided improved prognostic discrimination compared to individual ICs. Correlation analyses, single-cell RNA sequencing, and immunohistochemistry demonstrated an association between EMT-associated tumours and expression of LAG3 and NT5E. ROC analysis indicated modest prognostic performance of LAG3 and NT5E. Conclusions: Collectively, this study identifies an EMT–IC axis in ccRCC and demonstrates its relevance to tumour biology and patient outcomes, highlighting LAG3 and NT5E as potential prognostic markers and therapeutic targets that warrant further investigation. Full article

Objective: Impaired fatty acid oxidation (FAO) is considered an important metabolic mechanism underlying skeletal muscle aging and sarcopenia; however, the key regulatory molecules involved in this process remain incompletely defined. This study aimed to identify candidate biomarkers associated with impaired FAO in aged skeletal muscle, characterize their potential biological functions and regulatory features through integrated bioinformatics and machine learning analyses, and preliminarily validate their expression patterns in in vivo and in vitro aging models. Methods: Skeletal muscle aging transcriptomic datasets GSE1428 and GSE674 were obtained from the Gene Expression Omnibus database. FAO-related genes were retrieved from GeneCards. Differentially expressed FAO-related genes (DE-FAOGs) were identified through differential expression analysis and were further analyzed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Random forest, Boruta, and protein–protein interaction (PPI) network analyses were used to screen hub genes, and an artificial neural network (ANN) model was constructed. Single-cell RNA sequencing analysis, gene set enrichment analysis, ceRNA network construction, drug prediction, molecular docking, and molecular dynamics simulation were further performed. Hub gene expression was validated by qRT-PCR in naturally aged mice and D-galactose-induced senescent C2C12 cells. Results: A total of 69 DE-FAOGs were identified and were mainly enriched in mitochondrial function, electron transport chain, and energy metabolism-related pathways. Three hub genes, creatine kinase, mitochondrial 2 (CKMT2), actin alpha cardiac muscle 1 (ACTC1), and forkhead box O3 (FOXO3), were identified by random forest, Boruta, and PPI analyses. Receiver operating characteristic (ROC) analysis showed good discriminatory performance for these genes. The three-gene ANN model achieved area under the curve (AUC) values of 0.992 and 0.964 in the training and validation datasets, respectively. Gene set enrichment analysis (GSEA) suggested that the hub genes were closely associated with mitochondrial energy metabolism, lipid metabolism, and stress regulation. qRT-PCR confirmed decreased Ckmt2 expression and increased Actc1 and Foxo3 expression under aging conditions, consistent with the bioinformatics results. Conclusions: CKMT2, ACTC1, and FOXO3 are potential biomarkers associated with impaired FAO in aged skeletal muscle. The ANN model based on these three genes showed good predictive performance and may provide new insights into the metabolic mechanisms and therapeutic targets of sarcopenia. Full article

Molecular characterization of the transferred DNA (T-DNA) insertion sites is required for the safety assessment of genetically modified (GM) crops, yet conventional PCR-based methods are labor-intensive and limited in their ability to resolve complex structural variations. We present T-DNA Analyzer, an integrated bioinformatics pipeline that transforms long-read sequencing data (PacBio HiFi or Oxford Nanopore) into a comprehensive insertion site report. The pipeline implements a host-derived read filter that subtracts host-homologous vector regions to eliminate false-positive chimeric read calls; a multi-segment fusion detection algorithm that resolves complex T-DNA integration architectures; and a deletion gap gene impact analysis that identifies genes affected by host genome deletions at the integration site. Validation on maize and cotton datasets demonstrated that the host-derived filter excluded 86.4% of false-positive reads while retaining all true chimeric reads, and the fusion detection algorithm successfully reconstructed a two-copy tandem T-DNA repeat within a single long read. T-DNA Analyzer provides automated, reproducible molecular characterization designed to support regulatory molecular characterization and is freely available as open-source software. Full article

Alzheimer’s disease (AD) is one of the most common neurodegenerative disorders; however, available treatments majorly offer symptomatic relief without delaying disease progression and are associated with various adverse effects, highlighting the need for development of alternative therapies. Acalypha indica has previously showed neuroprotective effects in aging-related animal models, yet its mechanisms against AD were not fully understood. In this study, we employed an integrated bioinformatics approach combining network pharmacology, transcriptomic analysis, and molecular docking to investigate the anti-AD potential of this herb. A total of 282 overlapping targets between A. indica compounds and AD were identified. Network pharmacology analysis indicated chrysin, daidzein, galangin, kaempferol, and quercetin as the key bioactive components. Enrichment analyses suggested that targets of these compounds are mainly associated with phosphoinositide 3-kinase/protein kinase B (PI3K/Akt) and mitogen-activated protein kinase (MAPK) signaling pathways. Protein–protein interaction (PPI) analysis identified AKT1, epidermal growth factor receptor (EGFR), interleukin 6 (IL6), tumor necrosis factor (TNF), and p53 protein (TP53) as crucial hub targets. These targets were significantly upregulated in AD brain samples and were closely associated with pathways related to neurodegeneration, inflammation, as well as alterations in immune cell infiltration. Among the compounds, quercetin exhibited the strongest binding affinity to these target proteins. Overall, these findings provide a strong foundation for the multi-target therapeutic potential of A. indica in AD. Full article

Plant endophytes can regulate host plant growth, improve stress resistance, and facilitate the biosynthesis of secondary metabolites, with great research value and application potential. However, traditional approaches for the isolation, cultivation and identification of plant endophytes are constrained by low culturability, limited species diversity, and loss of their original ecological functions inside host tissues. In recent years, integrated multi-omics strategies combining metagenomics, metatranscriptomics, and metaproteomics have exhibited the greatest potential to mitigate culturability limitations by enabling genome-guided targeted strain isolation and in situ functional activity profiling, among which the cultivation and targeted isolation of endophytes benefit most from omics integration. These approaches drive a paradigm shift from conventional blind screening to precise targeted isolation, and from generic medium culture to omics-guided rational cultivation, greatly improving the accuracy of strain identification and functional characterization. Nevertheless, current omics-based strategies still face inherent limitations including high experimental costs, complex operational procedures, and challenging data interpretation. The most critical future direction lies in establishing standardized experimental protocols and shared resource databases, combined with microfluidic platforms and artificial intelligence-assisted bioinformatics analysis, to address the core bottlenecks restricting endophyte isolation, cultivation and identification. This review is the first to systematically summarize research progress on traditional approaches, omics technologies and emerging strategies for plant endophyte isolation, cultivation and identification, highlights prevailing challenges and developmental trends in this field, and provides methodological references for the efficient exploitation and sustainable utilization of plant endophyte resources. Full article