Genes最新文献

Background/objectives: Triple-negative breast cancer (TNBC) exhibits high immune infiltration yet remains clinically aggressive. Although immune checkpoint blockade benefits a subset of patients, the molecular programs enabling concurrent immune activation and immune evasion in TNBC are not fully defined. This study aimed to identify TNBC-specific tumor-intrinsic and tumor-extrinsic molecular features that may explain this paradox.

Methods: Publicly available single-cell RNA-sequencing data from primary breast tumors were analyzed to characterize subtype-specific transcriptional programs across epithelial and stromal compartments. Tumor-intrinsic findings were independently validated using bulk transcriptomic and clinical data from the METABRIC cohort. Tumor microenvironment remodeling was evaluated using multiplexed tissue imaging of TNBC tumors. Functional analyses were done included Gene Ontology enrichment, Hallmark gene set enrichment analysis, and SERPINB3-centered protein-protein interaction network analysis using STRING.

Results: Single-cell analysis identified SERPINB3 as a TNBC-enriched epithelial gene relative to ER+ and HER2+ tumors. This subtype-restricted pattern was validated in the METABRIC cohort and associated with pathways related to epithelial-mesenchymal transition, interferon signaling, and antigen presentation. TNBC tumors also displayed a humoral immune signature characterized by B-cell and plasmablast enrichment, as well as ectopic immunoglobulin gene expression in cancer-associated fibroblasts, endothelial cells, and myeloid populations. Multiplex imaging revealed coordinated associations between immune suppression, stromal activation, and tumor proliferation. Network analysis placed SERPINB3 within interconnected immune-regulatory and stromal signaling modules.

Conclusions: Together, these data indicate that TNBC exhibits co-existing immune activation and immune-suppressive features. The identified epithelial and stromal signatures represent candidate biomarkers that may inform future studies of immune regulation and therapeutic stratification in TNBC.

Over the past several decades, rapid advances in molecular genomics have transformed our understanding of thyroid malignancies and are increasingly integrated into international clinical guidelines. Mutational profiles and epigenetic events are now recognized not only as diagnostic and prognostic tools but also as predictors of therapeutic response. Papillary, follicular, oncocytic, medullary, and anaplastic thyroid carcinomas harbor distinct early driver mutations, such as BRAFV600E, RAS, and fusion events (RET, NTRK, and ALK), that cooperate with secondary alterations (TERT promoter, TP53, PIK3CA, and CDKN2A/B loss) to drive dedifferentiation, metastasis, and therapeutic resistance. Insights from The Cancer Genome Atlas (TCGA) and transcriptomic scoring systems (e.g., BRAF-RAS score) now link genotype to tumor morphology, metastatic tropism, and radioactive iodine refractoriness. These molecular insights have been incorporated into updated risk stratification frameworks, preoperative surgical planning, and treatment algorithms, informing the selection of kinase inhibitors, redifferentiation strategies, and enrollment in genotype-directed clinical trials for radioiodine-refractory disease. This review synthesizes recent evidence connecting genomic alterations to clinical behavior and highlights their translation into evolving approaches for thyroid cancer management.

Background: Although circular RNAs are increasingly implicated in host responses, their longitudinal behaviors to predict outcomes in severe COVID-19 remain unclear. The purpose of this study is to distinguish the circRNA signature associated with COVID-19 outcome.

Method: Public total RNA-seq data from GEO (GSE273149) were used to assess circRNA differences among COVID-19 non-survivors, COVID-19 survivors, and patients with acute respiratory distress syndrome (ARDS) serving as severity-matched disease controls at two timepoints: Early (Day 3) and Late (Days 7 to 10). Differential expression was assessed after quality filtering, with the results reported as significant (FDR < 0.05) or suggestive (0.05-0.10); |log₂FC| ≥ 1 was used as a guide for interpretation. Early and Late effects were combined using a two-timepoint, precision-weighted approach to prioritize time-consistent signals.

Results: A distinction between non-survivors and survivors was observed, with nine significant and four suggestive candidates identified in the combined analysis; in addition, some candidates indicated a difference between survivors and ARDS controls. Early and Late effects primarily occurred in the same direction, and several circRNAs that were borderline at one timepoint became significant when the two timepoints were combined.

Conclusion: This time-resolved, precision-weighted analysis of public RNA-seq data reveals stable circRNA differences between key clinical groups (patients with severe COVID-19 and those with ARDS), improving detection and interpretability relative to single-timepoint tests and yielding a concise set of candidates suitable for mechanistic follow-up and potential biomarker development.

Background/objectives: Prenatal Exome Sequencing (pES) has revolutionized prenatal diagnosis in fetuses with congenital anomalies. Although its performance is very promising, previous pES studies have mainly focused on diagnostic yield, often without considering the actual impact on ongoing pregnancies. In this study, we aim to (1) assess whether a prenatal molecular diagnosis can reliably predict the clinical features of the unborn child and (2) determine the gestational age (gw) at which ultrasound (US) findings are sufficient to support the pathogenicity of genetic variants detected by pES.

Methods: We retrospectively selected 47 cases complicated by US anomalies that underwent Exome Sequencing (ES) and for which complete clinical assessment was available. A blinded reanalysis of ES data was performed, considering only prenatal features.

Results: In our cohort, standard ES led to a molecular diagnosis in 43% of cases. The blinded reanalysis revealed that a complete or partial retrospective prenatal diagnosis was achievable in 95% of diagnosed cases. The mean gestational week at which US data would have supported molecular diagnosis was 22 + 5 weeks. The clinical follow-up confirmed a syndromic presentation in 21 out of 23 newborns and in all terminated pregnancies.

Conclusions: Our study further confirms that pES is a valuable diagnostic tool for detecting genetic etiology in fetuses with congenital malformations. In most cases, pES results accurately predict the postnatal phenotype. However, the prenatal setting requires specific adjustments and precautions, and a negative pES result cannot be considered reassuring.

Background/objectives: Common vetch (Vicia sativa L.) is a globally cultivated leguminous crop, valued for its high nutritional content and role in sustainable agriculture.

Methods: To identify loci or genes significantly associated with salt tolerance, we conducted a genome-wide association study (GWAS) using 172 common vetch accessions primarily from diverse geographic regions. Single-nucleotide polymorphisms (SNPs) were obtained through re-sequencing, and five salt tolerance-related traits, including the germination rate (GR), germination potential (GP), germination index (GI), shoot length (SL), and root length (RL), were evaluated under salt stress conditions. We have identified 20 loci significantly associated with salt tolerance-related traits, and explaining 9.7-21.8% of the phenotypic variation. Notably, 13 loci exhibited pleiotropic effects on multiple traits; include qST1.1 (associated with SL, GR, GI), qST1.3 (RL, SL, GP), qST2.5 (SL, GR, GI, GP), and qST2.7 (SL, RL, GP, GI), and should be prioritized in future breeding programs. All 20 loci are novel compared to previous reports. Furthermore, we identified 7 candidate genes encoding key regulatory proteins, including a zinc finger MYM-type protein, ubiquitin-like domain-containing protein, transcription factor bHLH, ethylene-responsive transcription factor, auxin-responsive protein, and serine/threonine-protein kinase, as potential regulators of salt tolerance.

Conclusions: This study advances our understanding of the genetic basis of salt tolerance in common vetch and provides valuable loci, molecular tools, and elite accessions. HZMC1352, GLF303, GLF301, HZMC1387, GLF306, GLF368, GLF342, HZMC1384, HZMC1355, GLF307, HZMC1366 are used for improving salt tolerance in breeding programs.

Background/objectives: The poultry industry faces severe heat-stress challenges that threaten both economic sustainability and animal welfare. Embryonic thermal manipulation (ETM) has been proposed as a thermal programming strategy to enhance chick heat tolerance, yet its efficacy in layers requires verification, and its effects on growth performance and neurodevelopment remain unclear.

Methods: White Leghorn embryos at embryonic days 13 to 18 (ED 13-18) were exposed to 39.5 °C (ETM). Hatch traits and thermotolerance were recorded, and morphometric and histopathological analyses were performed on brain sections. Transcriptome profiling of the whole brains and hypothalami was conducted to identify differentially expressed genes (DEGs). Representative pathway genes responsive to ETM were validated by RT-qPCR.

Results: ETM reduced hatchability, increased deformity rate, and decreased hatch weight and daily weight gain. During a 37.5 °C challenge, ETM chicks exhibited delayed panting and lower cloacal temperature. Histopathology revealed impaired neuronal development and myelination. Transcriptomic analysis of ED18 whole brains showed DEGs enriched in neurodevelopment, stimulus response, and homeostasis pathways. RT-qPCR confirmed hypothalamic sensitivity to ETM: up-regulation of heat-shock gene HSP70, antioxidant gene GPX1, the inflammatory marker IL-6, and apoptotic genes CASP3, CASP6, CASP9; elevated neurodevelopmental marker DCX, indicative of a stress-responsive neuronal state; and reduced orexigenic neuropeptide AGRP.

Conclusions: ETM improves heat tolerance in layers but compromises hatching performance and brain development, with widespread perturbation of hypothalamic stress responses and neurodevelopmental gene networks. These findings elucidate the mechanisms underlying ETM and provide a reference for enhancing thermotolerance in poultry.

The burgeoning global population and the increasing recognition of seafood's nutritional value place unprecedented pressure on aquatic food systems [...].

Background: Single-cell RNA sequencing (scRNA-seq) enables high-resolution characterization of cellular heterogeneity and provides unique opportunities to identify rare cell populations that may be obscured in bulk transcriptomic data. However, despite the growing interest in rare-cell discovery, most existing detection methods were originally developed for single-sample datasets, and their behavior in multi-sample settings-where batch effects, sample imbalance, and heterogeneous cell-type compositions are common-remains poorly understood. This study aims to systematically evaluate representative rare cell detection methods under multi-sample settings and identify the most effective analytical strategies. Methods: We performed a comprehensive benchmarking analysis of five widely used rare cell detection tools, CellSIUS, GapClust, GiniClust, scCAD, SCISSORS and a scGPT-based rare cell detection method using Isolation Forest. Each method was evaluated under three analytical strategies: individual sample detection, pooled sample detection, and batch-corrected pooled sample detection. Performance was assessed across multiple publicly available scRNA-seq datasets using standardized evaluation metrics. Results: Batch-corrected pooled sample detection consistently achieved the highest overall performance across methods and datasets, whereas individual sample detection produced the weakest results. Among the evaluated tools, scCAD demonstrated the most robust and stable performance across dataset types and analytical conditions. Conclusions: This study provides strategy-level comparison in multi-sample settings. Our findings highlight the importance of batch correction and pooled analysis for improving rare cell detection accuracy and offer practical guidance for selecting optimal methods and analytical workflows in large-scale single-cell transcriptomic studies.

Background: Enterococcus faecalis is an opportunistic pathogen that is capable of causing bacterial encephalitis under specific pathological conditions. MicroRNAs (miRNAs) are a class of small, single-stranded non-coding RNAs, typically approximately 21 nucleotides in length. As master regulators of gene expression, they orchestrate critical pathways across diverse organisms and a broad spectrum of diseases; however, their role during E. faecalis neuro-invasion remains unexplored.

Methods: A lamb model of E. faecalis-induced encephalitis was established. Integrated analysis of high-throughput sequencing data identified oar-miR-29b as a key differentially expressed miRNA during infection. To first verify its association with inflammation, primary SBMECs were stimulated with lipoteichoic acid (LTA), confirming that oar-miR-29b expression was significantly upregulated under inflammatory conditions. Subsequently, independent gain- and loss-of-function experiments in SBMECs were performed, with inflammatory cytokine expression assessed by qPCR and tight-junction protein levels evaluated by Western blotting.

Results: Functional studies demonstrated that oar-miR-29b acts as a pro-inflammatory mediator, significantly upregulating IL-1β, IL-6, and TNF-α while degrading tight-junction proteins (ZO-1, occludin, and claudin-5), thereby compromising endothelial barrier integrity. Mechanistically, bioinformatic prediction and dual-luciferase reporter assays confirmed C1QTNF6 as a direct target of oar-miR-29b. The oar-miR-29b/C1QTNF6 axis is thus defined as a novel regulatory pathway contributing to neuro-inflammation and blood-brain barrier disruption.

Conclusions: Collectively, our findings identify the oar-miR-29b/C1QTNF6 axis as a novel pathogenic mechanism that exacerbates E. faecalis-induced neuroinflammation and blood-brain barrier disruption.

Background/objectives: Polycystic ovary syndrome (PCOS) is a common endocrine-metabolic disorder in which skeletal muscle insulin resistance contributes substantially to cardiometabolic risk. Pioglitazone improves insulin sensitivity in women with PCOS, yet the underlying transcriptional changes and their potential as treatment-response biomarkers remain incompletely defined. We aimed to reanalyse skeletal muscle gene expression from pioglitazone-treated PCOS patients using modern machine learning and network approaches to identify candidate biomarkers and regulatory hubs that may support precision therapy.

Methods: Public microarray data (GSE8157) from skeletal muscle of obese women with PCOS and healthy controls were reprocessed. Differentially expressed genes (DEGs) were identified and submitted to Ingenuity Pathway Analysis to infer canonical pathways, upstream regulators, and disease functions. Four supervised machine learning algorithms (logistic regression, random forest, support vector machines, and gradient boosting) were trained using multi-step feature selection and 3-fold stratified cross-validation to provide superior Exploratory Gene Analysis. Gene co-expression networks were constructed from the most informative genes to characterize network topology and hub genes. A simulated multi-omics framework combined selected transcripts with representative clinical variables to explore the potential of integrated signatures.

Results: We identified 1459 DEGs in PCOS skeletal muscle following pioglitazone, highlighting immune and fibrotic signalling, interferon and epigenetic regulators (including IFNB1 and DNMT3A), and pathways linked to mitochondrial function and extracellular matrix remodelling. Within this dataset, all four machine learning models showed excellent cross-validated discrimination between PCOS and controls, based on a compact gene panel. Random forest feature importance scoring and network centrality consistently prioritized ITK, WT1, BRD1-linked loci and several long non-coding RNAs as key nodes in the co-expression network. Simulated integration of these transcripts with clinical features further stabilized discovery performance, supporting the feasibility of multi-omics biomarker signatures.

Conclusions: Reanalysis of skeletal muscle transcriptomes from pioglitazone-treated women with PCOS using integrative machine learning and network methods revealed a focused set of candidate genes and regulatory hubs that robustly separate PCOS from controls in this dataset. These findings generate testable hypotheses about the immunometabolism and epigenetic mechanisms of pioglitazone action and nominate ITK, WT1, BRD1-associated loci and related network genes as promising biomarkers for future validation in larger, independent PCOS cohorts.