Search Results (282)

Glioblastoma (GBM) is the most common malignant primary brain tumor in adults. Since numerous studies highlight the significance of cholinergic system components in tumor development, acetylcholine (ACh) and the differential activation of its receptors could play a crucial role in GBM progression. The aim of this study was to test this hypothesis by assessing the relevance of the cholinergic system in GBM cells and their microenvironment. We analyzed bulk RNA-seq expression data using the TIMER2.0 web server, focusing on the impact of patient survival in relation to muscarinic receptors (CHRM) and neutrophil infiltration in low-grade glioma (LGG) and GBM. Our analysis revealed a marked decrease in survival associated with all CHRMs, particularly in LGG. Moreover, GBM showed higher neutrophil infiltration and reduced survival, especially in relation to CHRM3. These findings were validated in the U251 cell line and in human GBM tumor biopsies (GBM-b), which also displayed CHRM3 expression. Additionally, we show that GBM cells exposed to cholinergic stimulation exhibited increased vascular endothelial growth factor (VEGF), IL-8 production, and PD-L1 expression, while the VEGF increase was blocked by tiotropium (Tio), a CHRM3 antagonist. Similarly, polymorphonuclear cells from GBM patients (PMN-p) displayed increased PD-L1 expression and IL-8 production upon cholinergic stimulation. Finally, as we previously reported on the relevance of thymic stromal lymphopoietin (TSLP) in GBM pathophysiology, here, we found that TSLP upregulated CHRM3 expression. Our findings highlight the importance of the cholinergic system in the tumor microenvironment, where it may act directly on tumor cells or influence neutrophil physiology, thereby modulating tumor progression. Full article

Background: Emerging evidence suggests that biological sex shapes glioma biology and therapeutic response. Methods: We performed a sex-stratified analysis of CGGA (Chinese Glioma Genome Atlas) RNA sequencing data comparing low-grade glioma (LGG) with high-grade glioma (HGG) and glioblastoma (GBM). Using the 3PodR framework, we integrated differential expression analysis with Gene Set Enrichment Analysis (GSEA), EnrichR, leading-edge analysis, and iLINCS drug repurposing. Results: These comparisons provide a proxy for biological processes underlying malignant transformation. In LGG vs. HGG, 973 significantly differentially expressed genes (DEGs) were identified in females and 1236 in males, with 15.5% and 33.5% unique to each sex, respectively. In LGG vs. GBM, 2011 DEGs were identified in females and 2537 in males, with 12.6% and 30.7% being unique. Gene-level contrasts included GLI1 upregulation in males and downregulation in females, GCGR upregulation in males, MYOD1 upregulation in females, and HIST1H2BH downregulation in males. Additional top DEGs included PRLHR, DGKK, DNMBP-AS1, HOXA9, CTB-1I21.1, RP11-47I22.1, HPSE2, SAA1, DLK1, H19, PLA2G2A, and PI3. In both sexes, LGG–HGG and LGG–GBM grade comparisons converged on neuronal and synaptic programs, with enrichment of glutamatergic receptor genes and postsynaptic modules, including GRIN2B, GRIN2A, GRIN2C, GRIN1, and CHRNA7. In contrast, collateral pathways diverged by sex: females showed downregulation of mitotic and chromosome-segregation programs, whereas males showed reduction of extracellular matrix and immune-interaction pathways. Perturbagen analysis nominated signature-reversing compounds across sexes, including histone deacetylase inhibitors, Aurora kinase inhibitors, microtubule-targeting agents such as vindesine, and multi-kinase inhibitors targeting VEGFR, PDGFR, FLT3, PI3K, and MTOR. Conclusions: Glioma grade comparisons reveal a shared neuronal–synaptic program accompanied by sex-specific transcriptional remodeling. These findings support sex-aware therapeutic strategies that pair modulation of neuron–glioma coupling with chromatin- or receptor tyrosine kinase/angiogenic-targeted agents, and they nominate biomarkers such as GLI1, MYOD1, GCGR, PRLHR, and HIST1H2BH for near-term validation. Full article

Background/Objectives: The 2021 WHO Classification of Central Nervous System (CNS) tumors emphasizes the integration of molecular data with histopathological features. Lower-grade gliomas (LGGs) represent a heterogeneous group of neoplasms with variable clinical behavior. This study aimed to explore the molecular landscape of a single-institution series of LGGs using targeted next-generation sequencing (NGS). Methods: Eleven adult patients diagnosed with LGG between 2015 and 2024 at Cattinara University Hospital (Trieste, Italy) were retrospectively analyzed. DNA and RNA were extracted from formalin-fixed, paraffin-embedded (FFPE) tissue and analyzed using the TruSight Oncology 500 panel (Illumina). Mutational, amplification, and transcriptomic profiles were evaluated. Results: IDH1 mutations were the most frequent alteration (75%), commonly co-occurring with TP53 and ATRX mutations, consistent with the canonical IDH-mutant astrocytoma profile. CDK4 amplification was found in four cases, while MYCN amplification and MET amplification were each identified in isolated cases. Two diffuse IDH-wild-type gliomas displayed aggressive clinical courses and shorter survival, and one was reclassified as glioblastoma (grade 4) based on EGFR amplification. The transcriptome analysis revealed heterogeneous expression signatures and distinct clustering of IDH1/ATRX-mutant tumors. Conclusions: Targeted NGS confirmed the key molecular features of diffuse gliomas and enabled precise WHO 2021 classification even in archival FFPE samples. Despite the exploratory nature of the analysis on a small population, the study underscores the biological and transcriptional heterogeneity of LGGs and highlights the limitations of tumor-only sequencing approaches. Broader genomic profiling and matched normal controls are warranted to refine the interpretation of rare or non-canonical variants. Full article

The high temperatures of the spray-drying process can cause thermal inactivation of probiotic bacteria. This study evaluated the effect of chia seed mucilage (CM) on the survival and viability of Lactobacillus rhamnosus GG (LGG) encapsulated by spray-drying in cross-linked alginate matrices (CLAM). Two types of microcapsules were used: CLAM without CM (M0-LGG) and with CM (M1-LGG). Viability was assessed under storage conditions (4 °C and 25 °C), heat treatments, and gastrointestinal simulations. The results show that LGG survival improved after spray drying in CLAM (M0-LGG), reaching levels above 92%. Microcapsules containing CM (M1-LGG) maintained high viability, exceeding 8 log CFU/g, under storage at 4 °C for 60 days. CM demonstrated the ability to preserve LGG viability during gastrointestinal digestion (above 6 log CFU/g) and to confer thermal stability under heat stress conditions at 80 °C for 5 min. This study can be a valuable reference for the food industry, as the incorporation of CM as an encapsulating agent for probiotics can improve their viability under adverse processing and storage conditions. Full article

Integration of multi-omics data provides a comprehensive perspective on complex biological systems, facilitating advances in disease classification and biomarker discovery. However, the heterogeneity and high dimensionality of omics data present significant analytical challenges. To achieve effective and interpretable multi-omics integration, we propose a novel deep learning framework named MOGOLA(Multi-Omics integration by Gating and Omics-Linked Attention). MOGOLA consists of three core components: (1) A hybrid graph learning module that integrates Graph Convolutional Networks and Graph Attention Networks for intra-omics feature extraction. (2) A gating and confidence mechanism that adaptively weighs feature importance across different omics types. (3) A cross-omics attention-based fusion module that captures inter-omics relationships. Comprehensive evaluations on four benchmark datasets (BRCA, KIPAN, ROSMAP, and LGG) demonstrate that MOGOLA consistently outperforms eleven state-of-the-art approaches. Ablation studies further validate the contribution of each module, while biomarkers identification highlight the framework’s clinical potential. These results show that MOGOLA is a robust and interpretable approach for multi-omics data integration and a contribution to advances in computational biology and precision medicine. Full article

Objectives: Correctly identifying Central Nervous System (CNS) tumors through MRI is complicated by utilization of divergent MRI acquisition protocols, unequal tumor morphology, and a difficulty in systematically combining imaging with clinical information. This study presents the Adaptive Vision–Language Transformer (AVLT), a multimodal diagnostic infrastructure designed to integrate multi-sequence MRI with clinical descriptions while improving robustness and interpretability to domain shifts. Methods: AVLT integrates the MRI sequence ($T1$, $T1c$, $T2$, FLAIR) and clinical note text in a joint process using normalized cross-attention to establish association of visual patch embeddings with clinical token representations. An Adaptive Normalization Module (ANM) functions to mitigate distribution shift across datasets by adapting the statistics of domain-specific features. Auxiliary semantic and alignment losses were incorporated to enhance stability of multimodal fusion. Results: On all datasets, AVLT provided superior classification accuracy relative to CNN-, transformer-, radiogenomic-, and multimodal fusion-based models. The AVLT model accuracy was 84.6% on BraTS (OS), 92.4% on TCGA-GBM/LGG, 89.5% on REMBRANDT, and 90.8% on GLASS. AvLT AUC values are at least above 90 for all domains. Conclusions: AVLT provides a reliable, generalizable, and clinically interpretable method for accurate diagnosis of CNS tumors. Full article

The intestinal epithelial barrier is critical for maintaining gut homeostasis, yet its integrity can be compromised by inflammation and microbial dysbiosis. Here, we demonstrate that Lactobacillus rhamnosus GG (LGG) and Lactobacillus paracasei IMPC2.1 (L. paracasei) show their effectiveness in enhancing epithelial barrier function and modulating autophagy, counteract the epithelial barrier dysfunction, induced by Lipopolysaccharide (LPS), in Caco-2 cells by modulating tight junction (TJ) protein expression through regulation of inflammation and apoptosis. LPS exposure significantly reduced transepithelial electrical resistance (TEER) and increased paracellular permeability, effects that were partially reversed by both probiotic strains. Western blot analysis revealed that LPS downregulated ZO-1, Occludin, and p-mTOR, while upregulating autophagy markers LC3-II and Beclin1, without affecting p62 levels. The latter finding indicated an impairment of autophagy flux, confirmed by immunofluorescence experiments. Co-treatment with LGG or L. paracasei restored TJ protein expression and alleviated the LPS-induced impairment of autophagic flux. Both probiotics suppressed LPS-induced cyclooxygenase-2 (Cox-2) and Bax upregulation, suggesting anti-inflammatory and anti-apoptotic effects. In the complex interplay between inflammation, autophagy, and apoptosis, these findings highlight a key regulatory mechanism in probiotic-mediated epithelial protection, underscoring the therapeutic potential of LGG and L. paracasei in mitigating gut barrier dysfunction. Full article

Background/Objectives: Lower-grade gliomas (LGGs) are a biologically and clinically heterogeneous group of brain tumors, for which molecular stratification plays essential role in diagnosis, prognosis, and therapeutic decision-making. Conventional unimodal classifiers do not necessarily describe cross-layer regulatory dynamics which entail the heterogeneity of glioma. Methods: This paper presents a protein–protein interaction (PPI)-informed hybrid model that combines multi-omics profiles, including RNA expression, DNA methylation, and microRNA expression, with a Graph Attention Network (GAT), Random Forest (RF), and logistic stacking ensemble learning. The proposed model utilizes ElasticNet-based feature selection to obtain the most informative biomarkers across omics layers, and the GAT module learns the biologically significant topological representations in the PPI network. The Synthetic Minority Over-Sampling Technique (SMOTE) was used to mitigate the class imbalance, and the model performance was assessed using a repeated five-fold stratified cross-validation approach using the following performance metrics: accuracy, precision, recall, F1-score, ROC-AUC, and AUPRC. Results: The findings illustrate that a combination of multi-omics data increases subtype classification rates (up to 0.984 ± 0.012) more than single-omics methods, and DNA methylation proves to be the most discriminative modality. In addition, analysis of interpretability using attention revealed the major subtype-specific biomarkers, including UBA2, LRRC41, ANKRD53, and WDR77, that show great biological relevance and could be used as diagnostic and therapeutic tools. Conclusions: The proposed multi-omics based on a biological and explainable framework provides a solid computational approach to molecular stratification and biomarker identification in lower-grade glioma, bridging between predictive power, biological clarification, and clinical benefits. Full article

Background: The interaction between probiotics and the vitamin D/vitamin D receptor (VDR) pathway has been increasingly explored as a potential mechanism for immune modulation in inflammatory bowel disease (IBD). Lactobacillus rhamnosus GG (LGG) has shown promising results in ulcerative colitis (UC) patients, but its effect on the VDR pathway remains unexplored in humans. Aim: To test the hypothesis that LGG can stimulate the vitamin D/VDR pathway and modulate mucosal-adherent microbiota. Methods: In this study, we analyzed a subgroup of 13 patients from the LGGinUC trial, in which UC patients with mild-to-moderate disease activity received LGG monotherapy for four weeks. Colonic biopsy samples were collected before and after treatment to evaluate VDR expression via RT-qPCR and immunohistochemistry. Mucosal-adherent microbiota was also analyzed by DNA extraction and next-generation sequencing (NGS). Results: LGG administration significantly increased VDR mRNA expression in colonic mucosa (p < 0.05), with a corresponding rise in VDR protein levels in both epithelial and sub-epithelial compartments. Microbiota analysis revealed a reduction in α-diversity, primarily due to a decrease in commensal bacterial species, while β-diversity remained largely unchanged. Conclusions: Although the present results have to be considered preliminary, this is the first human study demonstrating that probiotic supplementation can upregulate VDR expression in colonic mucosa. We propose that LGG may exert its beneficial effects in UC by stimulating the VDR pathway, which in turn modulates mucosal immunity and microbiota composition. Further studies with larger sample sizes and longer treatment durations are needed to validate these findings and explore their therapeutic implications. Full article

Cisplatin is a widely used antineoplastic agent whose therapeutic efficacy is often limited by its adverse effects on the central nervous system. In this exploratory study, we characterized the transcriptomic impact of a cumulative cisplatin regimen on the male Wistar rat brain using microarray technology. Differentially expressed genes were identified, and their functional roles were investigated through enrichment analyses (KEGG) and Gene Ontology (GO), and the construction of protein–protein interaction (PPI) networks. Our results revealed significant alterations in pathways related to synaptic signaling, neuroplasticity, and cellular metabolism. To generate translational hypotheses, these findings were subsequently correlated in silico with public human lower-grade glioma (LGG) datasets, which suggested a potential association between key cisplatin-regulated genes and clinical prognosis and immune cell infiltration patterns. This manuscript does not include RT-qPCR (or Western blot) validation; results should be interpreted as hypothesis-generating and require orthogonal confirmation. These findings provide a comprehensive transcriptomic map of cisplatin-induced neurotoxicity, offering novel insights into its underlying molecular mechanisms and identifying a rich set of candidate targets for future neuroprotective strategies. Full article

Objectives: Cancer, characterized by its profound complexity and heterogeneity, arises from a multitude of molecular disruptions. The pursuit of identifying distinct cancer subtypes is driven by the need to stratify patients into clinically coherent subgroups, each exhibiting unique prognostic outcomes. The integration of multi-omics datasets enhances the precision of subtyping and advances precision medicine. Methods: Considering the high-dimensional nature inherent to various multi-omics data types, we introduce an innovative deep learning framework, DAE-MKL, which integrates denoising autoencoders with multi-kernel learning for identifying cancer subtypes. Leveraging the capabilities of DAE, we extract non-linearly transformed features that retain pertinent information while mitigating noise and redundancy. These refined data representations are then funneled into the MKL framework, thereby enhancing the accuracy of subtype identification. We applied the DAE-MKL framework to both simulated studies and empirical datasets derived from two distinct cancer types, low-grade glioma (LGG, $n$ = 86) and kidney renal clear cell carcinoma (KIRC, $n$ = 285), thereby validating its utility and feasibility. Results: In simulations, DAE-MKL achieved superior performance with NMI gains up to 0.78 compared to other state-of-the-art methods. For real datasets, DAE-MKL identified three LGG subtypes and three KIRC subtypes, showing significant survival differences (KIRC log-rank $p$ = 3.33 × 10⁻⁸, LGG log-rank $p$ = 3.99 × 10⁻⁸). Additionally, we explored potential cancer-related biomarkers. Conclusions: The DAE-MKL effectively identifies molecular subtypes, reduces data dimensionality, and improves prognostic stratification in multi-omics cancer datasets, providing an effective tool for precision oncology. Full article

Autophagy (cellular self-eating) is a tightly regulated catabolic process of eukaryotic cells during which parts of the cytoplasm are sequestered and subsequently delivered into lysosomes for degradation by acidic hydrolases. This process is central to maintaining cellular homeostasis, the removal of aged or damaged organelles, and the elimination of intracellular pathogens. The nematode Caenorhabditis elegans has proven to be a powerful genetic model for investigating the regulation and mechanism of autophagy. To date, the fluorescent autophagy reporters developed in this organism have predominantly relied on multi-copy, randomly integrated transgenes. As a result, the interpretation of autophagy dynamics in these models has required considerable caution due to possible overexpression artifacts and positional effects. In addition, starvation-induced autophagy has not been characterized in detail using these reporters. Here, we describe the development of two endogenous autophagy reporters, gfp::mCherry::lgg-1/atg-8 and gfp::atg-5, both inserted precisely into their endogenous genomic loci. We demonstrate that these single-copy reporters reliably track distinct stages of the autophagic process. Using these tools, we reveal that (i) the transition from the earliest phagophore to the mature autolysosome is an exceptionally rapid event because the vast majority of the detected fluorescent signals are autolysosome-specific, (ii) starvation triggers autophagy only after a measurable lag phase rather than immediately, and (iii) the regulation of starvation-induced autophagy depends on the actual life stage, and prevents excessive flux that could otherwise compromise cellular survival. We anticipate that these newly developed reporter strains will provide refined opportunities to further dissect the physiological and pathological roles of autophagy in vivo. Full article

Impairment of the intestinal epithelial barrier, accompanied by local and systemic inflammation, underlies numerous human pathologies, including inflammatory bowel diseases, celiac disease, sepsis, as well as severe acute malnutrition. Bifidobacterium longum subsp. infantis and Lacticaseibacillus rhamnosus GG (LGG^®) have been shown in preclinical studies to strengthen the gut epithelial barrier and attenuate inflammation. This study aimed to compare the ability of four commercial strains of B. infantis, LGG, and their combination to mitigate inflammation-mediated epithelial damage using an in vitro immunocompetent intestinal model. A microfluidic mid-throughput platform OrganoPlate^® was used to co-culture intestinal epithelial cells (Caco-2) with peripheral blood mononuclear cells (PBMCs). Epithelial damage was induced by stimulating PBMCs with lipopolysaccharide (LPS), and probiotic-conditioned media were applied to the apical side of Caco-2 cells to assess effects on barrier integrity, cytokine secretion, and gene transcription. All tested probiotics significantly protected the epithelium by modulating tight junction protein expression and promoting transcription of homeostatic cytokines, resulting in a “leak-tight” phenotype. These findings indicate that metabolites produced by B. infantis and/or LGG can protect the intestinal epithelium in vitro, warranting further in vivo studies to evaluate the translational relevance of this effect. Full article

Immunogenic cell death (ICD) is a specialized form of cell death that triggers antitumor immune responses. In tumors, ICD promotes the release of tumor-associated and tumor-specific antigens, thereby reshaping the immune microenvironment, restoring antitumor immunity, and facilitating tumor eradication. However, the regulatory mechanisms of ICD and its immunological effects vary across tumor types, and a comprehensive understanding remains limited. We systematically analyzed the expression of 34 ICD-related regulatory genes across 33 tumor types. Differential expression at the RNA, copy number variation (CNV), and DNA methylation levels was assessed in relation to clinical features. Associations between patient survival and RNA expression, CNVs, single-nucleotide variations (SNVs), and methylation were evaluated. Patients were stratified into immunological subtypes and further divided into high- and low-risk groups based on optimal prognostic models built using a machine learning framework. We explored the relationships between ICD-related genes and immune cell infiltration, stemness, heterogeneity, immune scores, immune checkpoint and regulatory genes, and subtype-specific expression patterns. Moreover, we examined the influence of immunotherapy and anticancer immune responses, applied three machine learning algorithms to identify prognostic biomarkers, and performed drug prediction and molecular docking analyses to nominate therapeutic targets. ICD-related genes were predominantly overexpressed in ESCA, GBM, KIRC, LGG, PAAD, and STAD. RNA expression of most ICD-related genes was associated with poor prognosis, while DNA methylation of these genes showed significant survival correlations in LGG and UVM. Prognostic models were successfully established for 18 cancer types, revealing intrinsic immune regulatory mechanisms of ICD-related genes. Machine learning identified several key prognostic biomarkers across cancers, among which NT5E emerged as a predictive biomarker in head and neck squamous cell carcinoma (HNSC), mediating tumor–immune interactions through multiple ligand–receptor pairs. This study provides a comprehensive view of ICD-related genes across cancers, identifies NT5E as a potential biomarker in HNSC, and highlights novel targets for predicting immunotherapy response and improving clinical outcomes in cancer patients. Full article

Glioblastoma (GBM) remains the most lethal primary brain tumor, owing to profound intratumoral heterogeneity and the limited efficacy of standard treatments. The mesenchymal (MES) molecular subtype is particularly aggressive, exhibiting heightened invasiveness, therapy resistance, and dismal patient survival compared with the proneural (PN) subtype. Emerging evidence implicates the High Mobility Group Box 1 (HMGB1) protein and its cognate receptor, the Receptor for Advanced Glycation End Products (RAGE), as drivers of malignant progression, yet their contribution to the PN-to-MES transition is incompletely defined. We integrated transcriptomic analyses of TCGA-GBM and TCGA-LGG cohorts with immunohistochemistry on in-house patient specimens. Functional studies in patient-derived and established GBM cell lines included migration and invasion assays, tumorsphere formation assays, shRNA knockdowns, and Seahorse XF metabolic profiling to interrogate the HMGB1-RAGE axis. HMGB1 and RAGE expression was markedly elevated in MES GBM tissues and cell lines. Importantly, higher HMGB1 expression correlated with shortened overall survival (p < 0.009). HMGB1 silencing curtailed cell motility and downregulated core epithelial-to-mesenchymal transition markers (N-cadherin, Snail). RAGE knockdown diminished tumorsphere formation efficiency and reduced transcription of stemness genes (OCT4), underscoring its role in sustaining tumor-initiating capacity. Metabolically, HMGB1/RAGE activation boosted both mitochondrial respiration and glycolysis, conferring the bioenergetic flexibility characteristic of MES GBM. The HMGB1-RAGE signaling axis orchestrates mesenchymal identity, invasiveness, stem cell-like properties, and metabolic reprogramming in GBM. Targeting this pathway may disrupt the PN-to-MES transition, mitigate therapeutic resistance, and ultimately improve outcomes for glioblastoma patients. Full article