Integrative metabolome and transcriptome analyses provide insights into skeletal muscle development of two duck breeds during embryonic stage.

Canine hepatocellular carcinoma (HCC) requires further molecular characterization to identify diagnostic and therapeutic targets, and to establish whether dogs with this condition can model the human disease. Accordingly, we aimed to identify differentially expressed genes (DEGs) in canine HCC and evaluate cross-species transcriptomic dysregulation in canine and human HCC. Liver tissue samples from three dogs with HCC and three healthy dogs were subjected to next-generation sequencing, followed by RT-qPCR validation. Identified DEGs were then targeted in bioinformatics analyses (pathway enrichment, protein-protein interaction network, and hub gene analyses) for molecular characterization and comparison with human HCC datasets. We identified 975 DEGs (upregulated: 604; and downregulated: 371). Extracellular matrix-receptor interaction, focal adhesion, cell adhesion molecule, PI3K/Akt signaling, and cytokine/chemokine-related pathways were enriched. C1R, APOC3, C1QA, APOA1, C1QB, ACTG1, C1QC, CRP, ANXA5, and ANXA2 were identified as hub genes. Canine and human HCCs share 118 DEGs, highlighting conserved alterations in metabolic pathways, PI3K-Akt signaling, focal adhesion, and PPAR signaling pathways. Based on human HCC data, SPP1, NQO1, RRM2, APOA1, APOC3, ALDOB, and IGF1 were identified as prognosticators indicating poor overall survival. This study presents the first cross-species transcriptomic analysis of canine HCC, revealing significant molecular resemblances to human HCC, indicating it may be a promising comparative model for studying tumor biology, drug responses, and novel therapeutic interventions.

In silico and in vivo studies reveal that Theiler's murine encephalomyelitis virus induces upregulation of miR-155-5p in the hippocampus.

Overcoming the leptomeningeal seeding of medulloblastoma by targeting HSP70.

Gene expression and alternative splicing are observed to shift in different ways after binding of naringenin chalcone and HRH2 in 293T cells.

Comprehensive transcriptome and metabolome analyses reveal dynamic changes in quality and anti-stress strategy of Volvariella volvacea during storage.

Effects of overexpression of the human intestinal alkaline phosphatase gene on the expression of related genes in intestinal epithelium-like cells.

Some patients with myasthenia gravis (MG) are refractory to available treatments, highlighting the need to further understand the pathogenesis of the disease. This study aimed to determine whether components in the serum from patients with acetylcholine receptor (AChR) antibody-positive MG affect myotubes, to explore their possible role in disease pathogenesis beyond the inhibition of acetylcholine signal transmission. Serum was collected from 14 patients with AChR antibody-positive MG. The differentiated human myotubes were stimulated with 10% serum from healthy controls or patients with MG. After 24h, ribonucleic acid extraction/sequencing was performed, and differentially expressed genes (DEGs) were extracted. Pathway analysis was completed using DEGs that were downregulated by stimulation with serum from patients with MG. Expression of genes important for muscle contraction was measured and myotube diameter was determined by immunostaining. Approximately 1200 DEGs were extracted by comparing gene expression in cultured human myotube cells stimulated with serum from healthy controls and patients with MG. Gene ontology terms linked with muscle function were suppressed in myotube cells stimulated with patient serum. Suppression of pathways associated with muscle atrophy/weakness, decreased expression of genes associated with muscle contraction, and smaller myotube diameter were confirmed in myotube cells stimulated with serum from patients versus healthy controls. Factors other than acetylcholine signal transmission inhibition may contribute to the pathogenesis of AChR antibody-positive MG. Further research is needed to clarify the pathways involved, potentially leading to more tailored pharmacotherapies.

Integrated transcriptomics and miRNA-mRNA network analysis reveals Kisspeptin-10 mediated regulation of EMT and apoptosis in glioblastoma.

Idiopathic Pulmonary Fibrosis (IPF) is a progressive and fatal interstitial lung disease characterized by excessive extracellular matrix (ECM) deposition and tissue stiffening. Matrix stiffness is a key driver of fibrosis, yet diagnostic biomarkers directly linked to this physical property are lacking. This study aimed to identify robust matrix stiffness-related diagnostic biomarkers and potential therapeutic targets for IPF using an integrated machine learning approach. Gene expression profiles were obtained from the GEO database (Training set: GSE33566; Validation set: GSE93606). Differentially expressed genes (DEGs) were intersected with a matrix stiffness-related gene set. Three machine learning algorithms (SVM-RFE, LASSO, and Naive Bayes) were employed to screen diagnostic feature genes. A diagnostic nomogram was constructed and evaluated. Functional enrichment (GO/KEGG/GSEA), immune infiltration (ssGSEA), and molecular docking analyses were performed to explore biological functions and predict therapeutic drugs. Eighteen matrix stiffness-related DEGs were identified. Through machine learning screening, GSN and ARG1 were determined as robust key genes, exhibiting high diagnostic accuracy (AUC>0.7) in both training and validation cohorts. Functional analysis revealed that GSN is involved in actin cytoskeleton regulation, while ARG1 participates in immune response modulation. Both genes showed strong positive correlations with the infiltration of macrophages and neutrophils. Furthermore, molecular docking identified RA-2 as a potential therapeutic agent targeting ARG1 with high binding affinity (-9.2kcal/mol). We identified GSN and ARG1 as novel matrix stiffness-related diagnostic biomarkers for IPF, linking mechanotransduction to immune microenvironment remodeling. The diagnostic nomogram offers high clinical predictive value, and RA-2 emerged as a putative ARG1-targeting compound with favorable docking energy and warrants further experimental validation as a potential antifibrotic agent.

Periodontitis (PD) and oral squamous cell carcinoma (OSCC) frequently co‑occur in clinical populations. However, shared molecular determinants that could support risk assessment across diseases remain insufficiently defined. This study aimed to identify shared diagnostic biomarkers and potential mechanisms linking PD and OSCC using integrated bioinformatics and machine learning. PD and OSCC transcriptomic datasets were analysed to identify shared programmed cell death-related biomarkers using differentially expressed genes (DEGs), WGCNA and multiple machine learning algorithms (LASSO, Random Forest, GLM). Diagnostic performance was validated in external cohorts via ROC analysis, while immune landscapes and cellular interactions were characterised using CIBERSORT and single-cell analysis. At the cellular level, we evaluated SERPINA1 expression in an LPS-induced periodontal inflammation model and conducted siRNA-mediated knockdown in CAL27 oral cancer cells to examine its effects on proliferation, migration and invasion. Transcriptomic analyses of PD and OSCC revealed a shared immune-inflammatory signature with enrichment of programmed cell death pathways. SERPINA1 was consistently prioritised by LASSO, random forest and GLM and showed strong diagnostic performance across training and external validation cohorts (AUC > 0.8). CIBERSORT indicated remodelling of the immune microenvironment associated with SERPINA1 expression, with positive correlations to macrophage and neutrophil abundance. Single-cell analyses localised SERPINA1 to myeloid populations and suggested putative crosstalk with the pro-inflammatory mediator interleukin-1β (IL-1β). In the periodontal inflammation model, SERPINA1 expression was significantly upregulated upon LPS stimulation alongside increased inflammatory mediators such as IL-8 and IL-1β. In contrast, SERPINA1 knockdown in oral cancer cells led to reduced proliferative, migratory and invasive capacities. SERPINA1 emerges as a cross‑disease candidate biomarker linking chronic periodontal inflammation and oral cancer biology. The integrative framework provides a transparent roadmap for identifying clinically meaningful, reproducible biomarkers across oral diseases, going forwards.

Decrypting the impact of ferroptosis and cuproptosis in development of pancreatic ductal adenocarcinoma.

Comprehensive characterization of transcriptional regulation during HCG-induced follicle maturation in mandarin fish (Siniperca chuatsi): Insights from transcriptomics.

Transcriptional and Pathway Level Heterogeneity in Luminal A Breast Cancer: A Framework for Precision Therapy.

Therapeutic potential of Dahuang Mudan Decoction for severe acute pancreatitis: targeting oxidative stress and MAPK pathways through network pharmacology and experimental verification1.

Integrative bioinformatics identifies NSCLC biomarkers associated with LPS metabolism and circadian disruption.

ITLN1 suppresses colorectal cancer progression by inhibiting Wnt/β-catenin-mediated EMT and reprogramming macrophage polarization.

Integrative GWAS and RNA-seq identify MYL9 as a key regulator of pullorum disease resistance in chickens.

Integration of network pharmacology, lipidomics, and transcriptomics analysis to reveal the mechanisms underlying the synergy between susceptibility factor TNF-α and Polygonum multiflorum-induced idiosyncratic liver injury in mice.

Transcriptomic analysis identifies VvMYB90 and VvNAC92 as key regulators mediating anthocyanin biosynthesis and fruit color differentiation in three grape cultivars.