Search Results (959)

Ulcerative colitis (UC) is a chronic inflammatory bowel disease associated with disrupted lipid metabolism. This study aimed to uncover novel molecular subtypes and biomarkers by integrating sphingolipid metabolism-related genes (SMGs) with machine learning approaches. Using data from the GEO and GeneCards databases, 29 UC-related SMGs were identified. Consensus clustering was employed to define distinct molecular subtypes of UC, and a diagnostic model was developed through various machine learning algorithms. Further analyses—including functional enrichment, transcription factor prediction, single-cell localization, potential drug screening, molecular docking, and molecular dynamics simulations—were conducted to investigate the underlying mechanisms and therapeutic prospects of the identified genes in UC. The analysis revealed two molecular subtypes of UC: C1 (metabolically dysregulated) and C2 (immune-enriched). A diagnostic model based on three key genes demonstrated high accuracy in both the training and validation cohorts. Moreover, the transcription factor FOXA2 was predicted to regulate the expression of all three genes simultaneously. Notably, mebendazole and NVP-TAE226 emerged as promising therapeutic agents for UC. In conclusion, SMGs are integral to UC molecular subtyping and immune microenvironment modulation, presenting a novel framework for precision diagnosis and targeted treatment of UC. Full article

Background: Huntington’s Disease (HD) is a monogenic neurodegenerative disease resulting in a CAG repeat expansion in the HTT gene. Despite this genetic simplicity, its molecular mechanisms remain highly complex. Methods: In this study, untargeted serum proteomics, bioinformatics analysis, biomarker filtering and ELISA validation were implemented to characterize the proteomic landscape across the three HD stages—asymptomatic, early symptomatic and symptomatic advanced—alongside gender/age-matched controls. Results: We identified 84 over-expressed and 118 under-expressed differentially expressed proteins. Enrichment analysis revealed dysregulation in pathways including the complement cascade, LXR/RXR activation and RHOGDI signaling. Biomarker analysis highlighted key proteins with diagnostic potential, including CAP1 (AUC = 0.809), CAPZB (AUC = 0.861), TAGLN2 (AUC = 0.886), THBS1 (AUC = 0.883) and CFH (AUC = 0.948). CAP1 and CAPZB demonstrated robust diagnostic potential in linear mixed-effects models. CAP1 decreased in the asymptomatic stage, suggesting early cytoskeletal disruption, while CAPZB was consistently increased across HD stages. Conclusions: Our findings illuminate the dynamic proteomic and molecular landscape of HD. Future studies should validate these candidates in larger, more diverse cohorts and explore their mechanistic roles in HD pathology and progression. Full article

Gestational diabetes mellitus (GDM) and iron deficiency anemia (IDA) are the most common pregnancy-related conditions resulting in adverse maternal and fetal complications. MicroRNAs (miRNAs), particularly miR-182-3p and miR-24-3p, are promising biomarkers as they act as regulatory elements in various diseases; however, their roles in GDM and IDA are unclear. The present study aimed to analyze the expression and functional relevance of miR-182-3p and miR-24-3p in GDM and IDA. Experimental validation via RT-PCR revealed significant upregulation of both miRNAs in GDM and IDA samples. We identified common target genes and signaling pathways associated with these miRNAs, using a combination of data mining, bioinformatic tools (miRDB, TargetScan, miRTarBase, and miRWalk), and differentially expressed gene (DEGs) analysis using the GEO, OMIM, MalaCards, and GeneCards datasets. GO and KEGG pathway analyses revealed that the shared miRNA–mRNA in target genes were enriched in insulin signaling, apoptosis, and inflammatory pathways—key mechanisms implicated in GDM and IDA. Furthermore, hub genes such as IRS1, PIK3CA, CASP3, MAPK7, and PDGFRB were identified, supporting their central role in metabolic dysregulation during pregnancy. These findings demonstrate the potential of miR-182-3p and miR-24-3p as diagnostic biomarkers and therapeutic targets in managing GDM and IDA, offering new insights into the molecular interplay underlying pregnancy complications. Full article

Chromosomal microarray analysis (CMA) was performed for 40 patients with B-ALL undergoing treatment according to the ALL-2016 protocol to investigate the copy number alterations (CNAs) and copy neutral loss of heterozygosity (cnLOH) associated with minimal residual disease (MRD)-positive remission. Aberrations involving over 20,000 genes were identified, and a random forest approach was applied to isolate a subset of genes whose CNAs and cnLOH are significantly associated with poor therapeutic response. We have assembled the triple matched healthy population data and used that data as a reference, but not as a matched control. We identified a recurrent cluster of cnLOH in the 19q13.2–19q13.31 region, significantly enriched in MRD-positive patients (70% vs. 47% in the reference group vs. 16% in MRD-negative patients). This region includes the pregnancy-specific glycoprotein (PSG) gene family and the oncogene ERF, suggesting a potential role in leukemic persistence and treatment resistance. Additionally, we observed significant deletions involving 7p22.3 and 16q13, often as part of large-scale losses affecting almost the entire chromosomes 7 and 16, indicative of global chromosomal instability. These findings highlight specific genomic regions potentially involved in therapy resistance and may contribute to improved risk stratification in B-ALL. Our findings emphasize the value of high-resolution CMA in diagnostics and risk stratification and suggest that PSG genes and other candidate genes could serve as biomarkers for predicting treatment outcomes. Full article

Posterior capsule opacification (PCO), a frequent complication of cataract surgery, arises from dysregulated wound healing and fibrotic transformation of residual lens epithelial cells. While transcriptomic and machine learning (ML) approaches have elucidated fibrosis-related pathways in other tissues, the molecular divergence between regenerative and fibrotic outcomes in the lens remains unclear. Here, we used an ex vivo chick lens injury model to simulate post-surgical conditions, collecting RNA from lenses undergoing either regenerative wound healing or fibrosis between days 1–3 post-injury. Bulk RNA sequencing data were normalized, log-transformed, and subjected to univariate filtering prior to training LASSO, SVM, and RF ML models to identify discriminatory gene signatures. Each model was independently validated using a held-out test set. Distinct gene sets were identified, including fibrosis-associated genes (VGLL3, CEBPD, MXRA7, LMNA, gga-miR-143, RF00072) and wound-healing-associated genes (HS3ST2, ID1), with several achieving perfect classification. Gene Set Enrichment Analysis revealed divergent pathway activation, including extracellular matrix remodeling, DNA replication, and spliceosome associated with fibrosis. RT-PCR in independent explants confirmed key differential expression levels. These findings demonstrate the utility of supervised ML for discovering lens-specific fibrotic and regenerative gene features and nominate biomarkers for targeted intervention to mitigate PCO. Full article

Background/Objectives: Alterations in DNA damage repair mechanisms can impair the therapeutic effectiveness of cisplatin. MicroRNAs (miRNAs), key regulators of DNA damage repair processes, have been proposed as promising biomarkers for predicting the response to platinum-based chemotherapy (CT) in non-small cell lung cancer (NSCLC). In this study, by using a bioinformatics approach, we identified six miRNAs, which were differentially expressed (DE) between NSCLC patients characterized as responders and non-responders to platinum-based CT. We further validated the differential expression of the selected miRNAs on tumor and matched normal tissues from patients with resected NSCLC. Methods: Two miRNA microarray expression datasets were retrieved from the Gene Expression Omnibus (GEO) repository, comprising a total of 69 NSCLC patients (N = 69) treated with CT and annotated data from their response to treatment. Differential expression analysis was performed using the Linear Models for Microarray Analysis (Limma) package in R to identify DE miRNAs between responders (N = 33) and non-responders (N = 36). Quantitative real-time PCR (qRT-PCR) was used to assess miRNA expression levels in clinical tissue samples (N = 20). Results: Analysis with the Limma package revealed 112 DE miRNAs between responders and non-responders. A random-effects meta-analysis further identified 24 miRNAs that were consistently up- or downregulated in at least two studies. Survival analysis using the Kaplan–Meier plotter (KM plotter) indicated that 22 of these miRNAs showed significant associations with prognosis in NSCLC. Functional and pathway enrichment analysis revealed that several of the identified miRNAs were linked to key pathways implicated in DNA damage repair, including the p53, Hippo, PI3K and TGF-β signaling pathways. We finally distinguished a six-miRNA signature consisting of miR-26a, miR-29c, miR-34a, miR-30e-5p, miR-30e-3p and miR-497, which were downregulated in non-responders and are involved in at least three DNA damage repair pathways. Comparative expression analysis on tumor and matched normal tissues from surgically treated NSCLC patients confirmed their differential expression in clinical samples. Conclusions: In summary, we identified a signature of six miRNAs that are suppressed in NSCLC and may serve as a predictor of cisplatin response in NSCLC. Full article

Background/Objectives: The RTK-RAS signaling cascade is a central axis in colorectal cancer (CRC) pathogenesis, governing cellular proliferation, survival, and therapeutic resistance. Somatic alterations in key pathway genes—including KRAS, NRAS, BRAF, and EGFR—are pivotal to clinical decision-making in precision oncology. However, the integration of these genomic events with clinical and demographic data remains hindered by fragmented resources and a lack of accessible analytical frameworks. To address this challenge, we developed AI-HOPE-RTK-RAS, a domain-specialized conversational artificial intelligence (AI) system designed to enable natural language-based, integrative analysis of RTK-RAS pathway alterations in CRC. Methods: AI-HOPE-RTK-RAS employs a modular architecture combining large language models (LLMs), a natural language-to-code translation engine, and a backend analytics pipeline operating on harmonized multi-dimensional datasets from cBioPortal. Unlike general-purpose AI platforms, this system is purpose-built for real-time exploration of RTK-RAS biology within CRC cohorts. The platform supports mutation frequency profiling, odds ratio testing, survival modeling, and stratified analyses across clinical, genomic, and demographic parameters. Validation included reproduction of known mutation trends and exploratory evaluation of co-alterations, therapy response, and ancestry-specific mutation patterns. Results: AI-HOPE-RTK-RAS enabled rapid, dialogue-driven interrogation of CRC datasets, confirming established patterns and revealing novel associations with translational relevance. Among early-onset CRC (EOCRC) patients, the prevalence of RTK-RAS alterations was significantly lower compared to late-onset disease (67.97% vs. 79.9%; OR = 0.534, p = 0.014), suggesting the involvement of alternative oncogenic drivers. In KRAS-mutant patients receiving Bevacizumab, early-stage disease (Stages I–III) was associated with superior overall survival relative to Stage IV (p = 0.0004). In contrast, BRAF-mutant tumors with microsatellite-stable (MSS) status displayed poorer prognosis despite higher chemotherapy exposure (OR = 7.226, p < 0.001; p = 0.0000). Among EOCRC patients treated with FOLFOX, RTK-RAS alterations were linked to worse outcomes (p = 0.0262). The system also identified ancestry-enriched noncanonical mutations—including CBL, MAPK3, and NF1—with NF1 mutations significantly associated with improved prognosis (p = 1 × 10⁻⁵). Conclusions: AI-HOPE-RTK-RAS exemplifies a new class of conversational AI platforms tailored to precision oncology, enabling integrative, real-time analysis of clinically and biologically complex questions. Its ability to uncover both canonical and ancestry-specific patterns in RTK-RAS dysregulation—especially in EOCRC and populations with disproportionate health burdens—underscores its utility in advancing equitable, personalized cancer care. This work demonstrates the translational potential of domain-optimized AI tools to accelerate biomarker discovery, support therapeutic stratification, and democratize access to multi-omic analysis. Full article

Military personnel deployed to Iraq and Afghanistan were exposed to emissions from open-air burn pits, where plastics, metals, and medical waste were incinerated. These exposures have been linked to deployment-related respiratory diseases (DRRD) and may also impact neurological health via the lung–brain axis. To investigate molecular mechanisms, adult male rats were exposed to filtered air, naphthalene (a representative volatile organic compound), or a combination of naphthalene and carbon black (surrogate for particulate matter; CBN) via whole-body inhalation (six hours/day, three consecutive days). Lung, brain, and plasma samples were collected 24 h after the final exposure. Pro-inflammatory biomarkers were assessed using multiplex electrochemiluminescence and western blot. Differentially expressed genes (DEGs) were identified by RNA sequencing, and elastic net modeling was used to define exposure-predictive gene signatures. CBN exposure altered inflammatory biomarkers across tissues, with activation of nuclear factor kappa B (NF-κB) signaling. In the lung, gene set enrichment revealed activated pathways related to proliferation and inflammation, while epithelial–mesenchymal transition (EMT) and oxidative phosphorylation were suppressed. In the brain, EMT, inflammation, and senescence pathways were activated, while ribosomal function and oxidative metabolism were downregulated. Elastic net modeling identified a lung gene signature predictive of CBN exposure, including Kcnq3, Tgfbr1, and Tm4sf19. These findings demonstrate that inhalation of a surrogate burn pit mixture induces inflammatory and metabolic gene expression changes in both lung and brain tissues, supporting the utility of this animal model for understanding systemic effects of airborne military toxicants and for identifying potential biomarkers relevant to DRRD and Veteran health. Full article

The uterine microbiota plays a crucial role in maintaining postpartum reproductive health in dairy cows, and its dysregulation is closely associated with uterine diseases. Vaginal discharge characteristics serve as important clinical indicators for assessing uterine status and guiding clinical decision-making. This study employed 16S rRNA gene sequencing to analyze uterine microbial diversity in cows with different discharge types. Results revealed significant microbial shifts associated with discharge severity. Notably, Caviibacter was highly enriched (up to 60.25%) in cows with mildly purulent discharge (<50%), suggesting its potential role in early-stage endometritis. In contrast, Fusobacterium and Helcococcus dominated when purulent discharge exceeded 50%, while Bacteroides, Porphyromonas, and Peptostreptococcus prevailed in cows with malodorous or discolored secretions, indicating severe inflammation. This study extends previous findings by uncovering stage-specific microbial transitions and proposing Caviibacter as a potential early biomarker of endometritis. These insights support early diagnosis and targeted interventions, contributing to improved reproductive management and sustainable dairy farming. Full article

Background and Objectives: A thorough comprehension of the essential molecules and related processes underlying the carcinogenesis, proliferation, and recurrence of acute myeloid leukemia (AML) is crucial. This study aimed to investigate the expression levels, diagnostic and prognostic significance and biological roles of Bcl-2-associated athanogene 4 (BAG4) in AML carcinogenesis. Materials and Methods: Gene expression profiles were analyzed using publicly available datasets, particularly GSE9476 and TCGA, using tools such as GEO2R, GEPIA2, UALCAN and TIMER2.0. The immune infiltration correlation was examined using the GSCA platform, while the function of BAG4 at the single-cell level was analyzed via CancerSEA. Protein–protein and gene–gene interaction networks were constructed using STRING and GeneMANIA, and enrichment analyses were performed using GO, KEGG and DAVID. Expression validation was performed using RT-qPCR in HL-60 (AML) and HaCaT (normal) cells, and ROC curve analysis evaluated the diagnostic accuracy. Results: BAG4 was significantly overexpressed in AML tissues and cell lines compared with healthy controls. High BAG4 expression was associated with poor overall survival and strong diagnostic power (AUC = 0.944). BAG4 was positively associated with immune cell infiltration and negatively associated with CD4+/CD8+ T and NK cells. At the single-cell level, BAG4 was associated with proliferation, invasion, and DNA repair functions. Functional network analysis showed that BAG4 interacted with apoptosis and necroptosis-related genes such as BCL2, BAG3 and TNFRSF1A and was enriched in pathways such as NF-κB, TNF signaling and apoptosis. Conclusions: BAG4 is overexpressed in AML and is associated with adverse clinical outcomes and immune modulation. It may play an important role in leukemogenesis by affecting apoptotic resistance and immune evasion. BAG4 has potential as a diagnostic biomarker and treatment target in AML, but further in vivo and clinical validation is needed. Full article

Brain morphological abnormalities are common in patients with neurodevelopmental disorders (NDDs) and other neuropsychiatric disorders, often reflecting abnormal brain development and function. Genetic studies have found common genetic factors in NDDs and other neuropsychiatric disorders, although the etiology of brain structural changes in these disorders remains poorly understood. In this study, we analyzed magnetic resonance imaging (MRI) and genetic data from more than 30K individuals from the UK Biobank to evaluate whether NDD-risk copy number variants (CNVs) are also associated with neuroanatomical changes in both patients and neurotypical individuals. We found that the size differences in brain regions such as corpus callosum and cerebellum were associated with the deletions of specific areas of the human genome, and that specific neuroanatomical changes confer a risk of neuropsychiatric disorders. Furthermore, we observed that gene sets located in these genomic regions were enriched for pathways crucial for brain development and for phenotypes commonly observed in patients with NDDs. These findings highlight the link between CNVs, brain structure abnormalities, and the shared pathophysiology of NDDs and other neuropsychiatric disorders, providing new insights into the underlying mechanisms of these disorders and the identification of potential biomarkers for better diagnosis. Full article

Background/Objectives: In gastric cancer, only a subset of patients benefit clinically from neoadjuvant chemoimmunotherapy, underscoring the need for robust biomarkers that can predict treatment responses and guide personalized immunotherapy. This study aimed to characterize the immune microenvironment of gastric tumors and identify predictive markers associated with therapeutic efficacy. Methods: We prospectively enrolled 16 patients with histologically confirmed, PD-L1–positive (CPS ≥ 1) gastric adenocarcinoma (T_2–4N_0–1M₀). All patients received eight cycles of FLOT chemotherapy combined with pembrolizumab. Treatment response was assessed by Mandard tumor regression grading. Spatial transcriptomic profiling (10x Genomics Visium) and multiplex immunofluorescence were used to evaluate tumor-infiltrating immune cell subsets and PD-1 expression at baseline and after treatment. Results: Transcriptomic analysis differentiated the immune landscapes of responders from non-responders. Responders exhibited elevated expression of IL1B, CXCL5, HMGB1, and IFNGR2, indicative of an inflamed tumor microenvironment and type I/II interferon signaling. In contrast, non-responders demonstrated upregulation of immunosuppressive genes such as LGALS3, IDO1, and CD55, along with enrichment in oxidative phosphorylation and antigen presentation pathways. Multiplex immunofluorescence confirmed a higher density of FoxP3⁺ regulatory T cells in non-responders (median 5.36% vs. 2.41%; p = 0.0032). Notably, PD-1⁺ CD8⁺ T cell and PD-1⁺ FoxP3⁺ Treg frequencies were significantly elevated in non-responders, suggesting that PD-1 expression within cytotoxic and regulatory compartments may contribute to immune evasion. No substantial differences were observed in PD-L1 CPS or PD-1⁺ B cells and PD-1⁺ macrophages. Conclusions: Our findings identify PD-1⁺ CD8⁺ T cells and PD-1⁺ FoxP3⁺ Tregs as potential biomarkers of resistance to neoadjuvant chemoimmunotherapy in gastric cancer. Transcriptional programs centered on IL1B/CXCL5 and LGALS3/IDO1 define distinct immune phenotypes that may guide future combination strategies targeting both effector and suppressive arms of the tumor immune response. Full article

Background: Previous studies have shown Radix Hedysari (RH)’s gastroprotective potential, but its active components and mechanisms remain uncharacterized. This study aimed to identify RH’s bioactive fractions, elucidate protection mechanisms, establish flavonoid dose-effect relationships, and determine the pharmacodynamic basis. Methods: Chemical profiling quantified eight flavonoids via HPLC. Network pharmacology screened targets/pathways using TCMSP, GeneCards databases. In vivo validation employed cisplatin–induced injury models in Wistar rats (n = 10/group). Assessments included: behavioral monitoring; organ indices; ELISA (MTL, VIP, IFN–γ, IgG, IL–6, TNF–α etc.); H&E; and Western blot:(SCF, c–Kit, p65). Dose–effect correlations were analyzed by PLS–DA. Results: Content determination indicated that Calycosin–7–glucoside and Ononin were notably enriched on both the n–BuOH part and the EtOAc part. Network pharmacology identified 5 core flavonoids and 8 targets enriched in IL–17/TNF signaling pathways. n–BuOH treatment minimized weight loss vs. MCG, increased spleen/thymus indices. n–BuOH and HPS normalized gastrointestinal, immune, inflammatory biomarkers (p < 0.01 vs. MCG). Histopathology confirmed superior mucosal protection in n–BuOH group vs. MCG. Western blot revealed n–BuOH significantly downregulated SCF, c–kit, and p65 expressions in both gastric and intestinal tissues (p < 0.001 vs. MCG). PLS–DA demonstrated Calycosin–7–glucoside had the strongest dose–effect correlation (VIP > 1) with protective outcomes. Conclusions: The n–BuOH fraction of RH is the primary bioactive component against chemotherapy–induced gastrointestinal injury, with Calycosin–7–glucoside as its key effector. Protection is mediated through SCF/c–Kit/NF–κB pathway inhibition, demonstrating significant dose–dependent efficacy. These findings support RH’s potential as a complementary therapy during chemotherapy. Full article

Urinary extracellular vesicles (U-EVs) are gaining increasing interest as non-invasive liquid biopsy tools for clinical use. Prostate cancer (PCa) is amongst the highest cancer-related cause of death in men, and therefore, the identification of non-invasive robust biomarkers is of high importance. This study assessed U-EV profiles from individuals affected by PCa at Gleason scores 6–9, compared with healthy controls. U-EVs were characterised and assessed for proteomic cargo content by LC-MS/MS analysis. The U-EV proteomes were compared for enrichment of gene ontology (GO), KEGG, and Reactome pathways, as well as disease–gene associations. U-EVs ranged in size from 50 to 350 nm, with the majority falling within the 100–200 nm size range for all groups. U-EV protein cargoes from the PCa groups differed significantly from healthy controls, with 16 protein hits unique to the GS 6–7 and 88 hits to the GS 8–9 U-EVs. Pathway analysis showed increased enrichment in the PCa U-EVs of biological process GO (5 and 37 unique to GS 6–7 and GS 8–9, respectively), molecular function GO (3 and 6 unique to GS 6–7 and GS 8–9, respectively), and cellular component GO (10 and 22 unique to GS 6–7 and GS 8–9, respectively) pathways. A similar increase was seen for KEGG pathways (11 unique to GS 8–9) and Reactome pathways (102 unique to GS 8–9). Enrichment of disease–gene associations was also increased in the PCa U-EVs, with highest differences for the GS 8–9 U-EVs (26 unique terms). The pathway enrichment in the PCa U-EVs was related to several key inflammatory, cell differentiation, cell adhesion, oestrogen signalling, and infection pathways. Unique GO and KEGG pathways enriched for the GS 8–9 U-EVs were associated with cell–cell communication, immune and stress responses, apoptosis, peptidase activity, antioxidant activity, platelet aggregation, mitosis, proteasome, mRNA stability oxytocin signalling, cardiomyopathy, and several neurodegenerative diseases. Our findings highlight U-EVs as biomarkers to inform disease pathways in prostate cancer patients and offer a non-invasive biomarker tool for clinical use. Full article

Background and Objectives: VPS26A, a core component of the retromer complex, is pivotal to endosomal trafficking and membrane protein recycling. However, its expression profile, prognostic significance, and clinical relevance in liver hepatocellular carcinoma (LIHC) remain unexplored. This study investigates the prognostic potential of VPS26A by extensively analyzing publicly available LIHC-related databases. Materials and Methods: Multiple databases, including TIMER, UALCAN, HPA, GSCA, KM Plotter, OSlihc, MethSurv, miRNet, OncomiR, LinkedOmics, GeneMANIA, and STRING, were used to evaluate VPS26A expression patterns, prognostic implications, correlations with tumor-infiltrating immune cells (TIICs), epigenetic modifications, drug sensitivity, co-expression networks, and protein–protein interactions in LIHC. Results: VPS26A was significantly overexpressed at both the mRNA and protein levels in LIHC tissues compared to that in normal tissues. This upregulation was strongly associated with a poor prognosis. Furthermore, VPS26A expression was both positively and negatively correlated with various TIICs. Epigenetic analysis indicated that VPS26A is regulated by promoter and regional DNA methylation. Additionally, VPS26A influences the sensitivity of LIHC cells to a broad range of anticancer agents. Functional enrichment and co-expression analyses revealed that VPS26A serves as a central regulator of the LIHC transcriptomic landscape, with positively correlated gene sets linked to poor prognosis. Additionally, VPS26A contributes to the molecular architecture governing vesicular trafficking, with potential relevance to diseases involving defects in endosomal transport and autophagy. Notably, miRNAs targeting VPS26A-associated gene networks have emerged as potential prognostic biomarkers for LIHC. VPS26A was found to be integrated into a highly interconnected signaling network comprising proteins in cancer progression, immune regulation, and cellular metabolism. Conclusions: Overall, VPS26A may serve as a potential prognostic biomarker and therapeutic target in LIHC. This study provides novel insights into the molecular mechanisms underlying LIHC progression, and highlights the multifaceted role of VPS26A in tumor biology. Full article