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At least 19 recordsLinked to original sources

PubMed2025

MiNEApy: enhancing enrichment network analysis in metabolic networks.

MOTIVATION: Modeling genome-scale metabolic networks (GEMs) helps understand metabolic fluxes in cells at a specific state under defined environmental conditions or perturbations. Elementary flux modes (EFMs) are powerful tools for simplifying complex metabolic networks into smaller, more manageable pathways. However, the enumeration of all EFMs, especially within GEMs, poses significant challenges due to computational complexity. Additionally, traditional EFM approaches often fail to capture essential aspects of metabolism, such as co-factor balancing and by-product generation. The previously developed Minimum Network Enrichment Analysis (MiNEA) method addresses these limitations by enumerating alternative minimal networks for given biomass building blocks and metabolic tasks. MiNEA facilitates a deeper understanding of metabolic task flexibility and context-specific metabolic routes by integrating condition-specific transcriptomics, proteomics, and metabolomics data. This approach offers significant improvements in the analysis of metabolic pathways, providing more comprehensive insights into cellular metabolism. RESULTS: Here, I present MiNEApy, a Python package reimplementation of MiNEA, which computes minimal networks and performs enrichment analysis. I demonstrate the application of MiNEApy on both a small-scale and a genome-scale model of the bacterium Escherichia coli, showcasing its ability to conduct minimal network enrichment analysis using minimal networks and context-specific data. AVAILABILITY AND IMPLEMENTATION: MiNEApy can be accessed at: https://github.com/vpandey-om/mineapy.

Metabolic Networks and Pathways↗

PubMed2025

Integrated proteomic network analysis reveals PTPRC as a central hub protein orchestrating co-expression modules and metabolic dysregulation in renal carcinoma: PTPRC protein molecular action.

The occurrence of renal carcinoma is closely related to a variety of molecular mechanisms and metabolic disorders. PTPRC (protein tyrosine phosphatase receptor C), as an important regulatory protein, was studied to reveal the role of PTPRC in renal carcinoma through comprehensive proteomic network analysis, especially its core position in the coordination of co-expression modules and metabolic disorders. This study was the first to download and process multiple publicly available renal cancer transcriptome data to conduct differential gene expression analysis across datasets. Functional enrichment and disease ontology analysis were performed on the transcriptome of renal cancer, and weighted gene co-expression network (WGCNA) was constructed. The results showed that comprehensive principal component analysis revealed significant differences in the transcriptome of renal cancer, and functional annotation revealed specific pathways associated with renal cancer. WGCNA analysis identified tumor-associated co-expression modules, while multi-omics analysis further identified core regulatory networks including PTPRC. As a central hub protein, PTPRC plays an important coordinating role in the co-expression module and metabolic dysregulation of renal carcinoma. This discovery provides a new perspective for understanding the molecular mechanism of kidney cancer.

Humans↗

PubMed2025

Exploring prognostic genes in the immune microenvironment of acute myeloid leukemia via weighted gene co-expression network analysis.

BACKGROUND: Acute myeloid leukemia (AML) is a heterogeneous blood cancer that arises from transformed myeloid precursor cells in a compromised bone marrow microenvironment. This environment is essential for AML initiation, progression, and relapse. Alongside oncogenic changes in hematopoietic cells, immunological dysregulation also contributes to leukemogenesis. The present study is aimed to identify prognostic genes in stromal and immune cells associated with AML using the weighted gene co-expression network analysis (WGCNA). METHODS: Gene expression profiles were retrieved from The Cancer Genome Atlas database, and immune and stromal cell scores were calculated using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) method. These scores helped identify differentially expressed genes (DEGs), which were then used to create gene clusters through WGCNA. To explore the functions of genes linked to AML subtypes, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed. A protein-protein interaction network was developed to identify hub genes. The top 18 hub genes were identified using the cytoHubba plug-in in Cytoscape software, and survival analysis was conducted with the Gene Expression Profiling Interactive Analysis 2 online tool. RESULTS: A total of 1097 DEGs were identified, with 601 being upregulated and 496 downregulated. WGCNA analysis indicated that the gray module, comprising 165 genes, had the strongest association with AML subtypes (Cor > 0.3; P < .05). Gene Ontology enrichment analysis demonstrated that the 18 identified hub genes were predominantly associated with neutrophil activation, immune response, secretory granule membrane, and pattern recognition receptor activity. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that the DEGs were mainly involved in pathways related to phagosome, lysosome, tuberculosis, leishmaniasis, and neutrophil extracellular trap formation. Kaplan-Meier survival analysis of the top 18 hub genes indicated that ITGAM, IL10, and CD163 were significantly correlated with survival outcomes in AML. CONCLUSION: Key stromal and immune-related genes influencing AML patient outcomes were identified, highlighting their potential as therapeutic targets. These discoveries provide deeper insights into the molecular mechanisms driving AML pathogenesis and subtype differentiation.

Leukemia, Myeloid, Acute↗

PubMed2024

Comparison of the antibiotic resistance mechanisms in a gram-positive and a gram-negative bacterium by gene networks analysis.

Nowadays, the emergence of some microbial species resistant to antibiotics, both gram-positive and gram-negative bacteria, is due to changes in molecular activities, biological processes and their cellular structure in order to survive. The aim of the gene network analysis for the drug-resistant Enterococcus faecium as gram-positive and Salmonella Typhimurium as gram-negative bacteria was to gain insights into the important interactions between hub genes involved in key molecular pathways associated with cellular adaptations and the comparison of survival mechanisms of these two bacteria exposed to ciprofloxacin. To identify the gene clusters and hub genes, the gene networks in drug-resistant E. faecium and S. Typhimurium were analyzed using Cytoscape. Subsequently, the putative regulatory elements were found by examining the promoter regions of the hub genes and their gene ontology (GO) was determined. In addition, the interaction between milRNAs and up-regulated genes was predicted. RcsC and D920_01853 have been identified as the most important of the hub genes in S. Typhimurium and E. faecium, respectively. The enrichment analysis of hub genes revealed the importance of efflux pumps, and different enzymatic and binding activities in both bacteria. However, E. faecium specifically increases phospholipid biosynthesis and isopentenyl diphosphate biosynthesis, whereas S. Typhimurium focuses on phosphorelay signal transduction, transcriptional regulation, and protein autophosphorylation. The similarities in the GO findings of the promoters suggest common pathways for survival and basic physiological functions of both bacteria, including peptidoglycan production, glucose transport and cellular homeostasis. The genes with the most interactions with milRNAs include dpiB, rcsC and kdpD in S. Typhimurium and EFAU004_01228, EFAU004_02016 and EFAU004_00870 in E. faecium, respectively. The results showed that gram-positive and gram-negative bacteria have different mechanisms to survive under antibiotic stress. By deciphering their intricate adaptations, we can develop more effective therapeutic approaches and combat the challenges posed by multidrug-resistant bacteria.

Anti-Bacterial Agents↗

PubMed2024

Decoding the genetic landscape of allergic rhinitis: a comprehensive network analysis revealing key genes and potential therapeutic targets.

BACKGROUND: Allergic Rhinitis (AR), an inflammatory affliction impacting the upper respiratory tract, has been registering a substantial surge in incidence across the globe. METHODS: We embarked on examination of differentially expressed genes (DEGs) and the Weighted Gene Co-Expression Network Analysis (WGCNA). With this armory of genes identified, we engaged the tools of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Our study continued with the establishment of a protein-protein interaction (PPI) network and the application of LASSO regression. Finally, we leveraged a docking model to elucidate potential drug-gene interactions involving these key genes. RESULTS: Through WGCNA and different express genes screening, PPI network was performed, identifying top 20 key genes, including CD44, CD69, CD274. LASSO regression identified three independent factors, STARD5, CST1, and CHAC1, that were significantly associated with AR. A predictive model was developed with an AUC value over 0.75. Also, 105 potential therapeutic agents were discovered, including Fluorouracil, Cyclophosphamide, Doxorubicin, and Hydrocortisone, offering promising therapeutic strategies for AR. CONCLUSION: By fuzing DEGs with key genes derived from WGCNA, this study has illuminated a comprehensive network of gene interactions involved in the pathogenesis of AR, paving the way for future biomarker and therapeutic target discovery in AR.

Humans↗

PubMed1975

Network analysis of dendritic fields of pyramidal cells in neocortex and Purkinje cells in the cerebellum of the rat.

The connectivity within the dendritic array of Purkinje cells in the cerebellum and pyramidal cells of the neocortex of the rat, stained by the Golgi-Cox method, has been quantified by the method of network analysis. Connectivity was characterized either by applying the system of Strahler ordering, which assigns a relative order of magnitude to each branch of the arborescence or by the identification of unique topological branching patterns within the tree. The former method has been used to define the entire dendritic array of the Purkinje cell and the apical system of neocortical pyramids. It has been shown that the relation between the numbers of branches of successive Strahler order in Purkinje cells form an inverse geometric series in which the highest order is unity and the ratio between successive orders approximates to 3. On the other hand, the apical dendrites of neocortical pyramids exhibit two bifurcation ratios, i.e. a ratio of 3 between low orders and a ratio of 4 between higher orders. A computer simulation technique was used to generate networks of a size comparable with the Purkinje cell networks and grown according to two hypotheses namely, a 'terminal growth model' in which additional segments were added randomly to the terminal branches only and a 'segmental growth model' in which additional segments were added randomly to any branch within the array including terminal branches. Subsequent ordering of the simulated trees revealed that the relation between the numbers of successive orders for networks generated according to the 'segmental model' tended towards an inverse geometric series with a ratio of 4 and that generated according to the 'terminal model' tended towards a ratio of 3. This result showed that the dendritic tree of Purkinje cells grow in a manner indistinguishable from a system adding branches to random terminal segments and that neocortical apical dendrites add their collateral branches to random segments of the apical shaft but that the collateral branches themselves grow by random terminal branching. The possibility that such conclusions may be influenced by loss of branches incurred by either a failure of impregnation, by sectioning, or by environmental influences was investigated by means of a computer technique...

Animals↗

PubMed2026

Nested co-expression network analysis identifies compact gene clusters in a black box.

MOTIVATION: Digital analysis of biological systems requires methods capable of identifying both broad and nested gene modules reflecting complex biological processes. Existing transcriptomic methods often miss compact gene sets corresponding to subprocesses in specialized cell types, limiting insights into functional heterogeneity. RESULTS: We present Nested-WGCNA, a two-stage unsupervised network analysis algorithm designed to identify coarse-grained and fine-grained gene modules. Applied to bulk RNA-Seq data, Nested-WGCNA reveals stable modules reproducible across datasets. When validated against scRNA-Seq data, these modules correspond to both major and minor immune cell subtypes. Application to immunotherapy response datasets uncovers predictive and prognostic biomarkers, highlighting its utility in treatment stratification and biomarker discovery. AVAILABILITY: The NestedWGCNA source code and analysis pipeline are available on GitHub (https://github.com/ilyada/NestedWGCNA) and archived on Zenodo (https://doi.org/10.5281/zenodo.18959244).

Algorithms↗

PubMed2026

Crosstalk mediators implicated in the Stevens-Johnson Syndrome through gene regulatory network analysis.

Stevens-Johnson syndrome (SJS) is a rare and severe mucocutaneous disorder often triggered by medications or infections. Our previous research identified that four key genes, Ikzf1, Ptger3, Mavs, and Tlr3 are involved in SJS susceptibility and the conjunctival epithelial innate immune response, demonstrating their role in regulating interferon-stimulated genes. However, the interplay among these regulatory factors remains unclear. This study aimed to elucidate the crosstalk mechanisms between the pathways regulated by these four genes in conjunctival epithelial cells. We constructed a comprehensive gene regulatory network using transcriptomic data from murine conjunctival epithelial cells under 16 distinct conditions, including polyI:C stimulation across wild-type, knockout, and transgenic backgrounds for the key genes. A targeted network analysis systematically identified numerous candidate genes mediating the crosstalk between the regulatory pathways initiated by Ikzf1, Ptger3, Mavs, and Tlr3. The identified candidates suggest the involvement of diverse signaling pathways previously unlinked to SJS pathology. Our findings suggest that the pathogenesis of SJS may arise not from the dysfunction of isolated genes but from the disruption of a balance maintained by intricate pathway crosstalk.

Animals↗

PubMed2026

Functional and Nutritional Potential of Chickpea Protein Hydrolysates: A Systematic Review and Plant-protein Network Analysis.

Chickpea is a protein-rich legume increasingly explored as a substrate for functional plant-based ingredients. Chickpea protein hydrolysates (CPHs) and chickpea-derived peptides (CPs), obtained through enzymatic hydrolysis or simulated gastrointestinal digestion, may provide technological and biological properties while supporting the valorization of chickpea fractions and by-products. This review integrates a network analysis of title-abstract terms from 5,728 unique Scopus and PubMed records on plant protein hydrolysates with a systematic review of 72 studies focused on CPH production, peptide characterization, bioactivity, and translational gaps. The evidence indicates that CPHs and CPs show promising antioxidant, antihypertensive, antidiabetic, anti-inflammatory, lipid-lowering, immunomodulatory, antimicrobial, and anticancer-related activities, mainly supported by biochemical assays, cell models, and animal studies. However, heterogeneous hydrolysis protocols, incomplete peptide characterization, inconsistent bioactivity methods, limited scale-up evidence, and the absence of human intervention trials restrict translation. Future studies should prioritize standardized protocols, mechanistic validation, bioavailability, sensory and regulatory assessment, food-matrix validation, and clinical trials.

Cicer↗

PubMed2026

Genetic dissection of cardiac iron regulation using transcriptome network analysis and systems genetics in BXD mice.

Cardiac iron homeostasis is essential for myocardial energy metabolism and contractile function, yet the genetic and molecular mechanisms governing iron levels within the heart remain poorly understood. We used a systems genetics approach to dissect the transcriptional regulation of cardiac iron homeostasis. Myocardial iron level varies substantially across BXD strains (40-112 μg/g) and is under heritable genetic control (H2 = 0.38). Elevated cardiac iron is associated with reduced ventricular mass, increased ventricular ectopy, and prolonged atrioventricular conduction in the BXD population. Weighted gene co-expression network analysis of the BXD heart transcriptome identified a co-expression module that was significantly and negatively correlated with cardiac iron levels in both young and old BXD mice and enriched for pathways related to metabolic regulation, cyclic AMP (cAMP) signaling, circadian entrainment, and cardiovascular physiology. The module showed substantial overlap with a curated cardiac iron gene set, and cross-species enrichment analysis confirmed its conservation in human cardiomyopathy differentially expressed genes (enrichment ratio = 1.49; false discovery rate [FDR] = 0.0342). Quantitative trait locus (QTL) mapping of the first principal component of the overlapping module iron genes (n = 38), corroborated by individual gene mapping, identified trans-eQTL hotspots on multiple chromosomes, implicating Fcho2, Gcc2, and Rmdn1 as candidate upstream regulators operating through sequential steps of intracellular iron trafficking. Together, these findings establish a systems-level map of cardiac iron gene regulation, identify candidate genetic regulators, and provide a molecular framework linking disruption of iron-related transcriptional networks to structural and electrical cardiac dysfunction with implications for iron-related heart diseases.

BXD mouse population↗

PubMed2026

Protamine gene expression is associated with sperm motility in rams: An integrative experimental and gene network analysis.

Protamine 1 (PRM1) and protamine 2 (PRM2) are essential regulators of sperm chromatin condensation and genome integrity, and their dysregulation has been associated with impaired male fertility. However, their role in rams remains insufficiently characterized. This study investigated the relationship between protamine gene expression and semen quality in rams and explored their potential upstream regulatory mechanisms using gene regulatory network (GRN) analysis. Fifteen ejaculates from five rams were analyzed. Based on total sperm motility using computer-assisted sperm analysis (CASA), ejaculates were classified into a high-motility group (n = 8) and a low-motility group (n = 7). PRM1 and PRM2 expression levels were quantified by RT-qPCR. Following normality confirmation (p > 0.05), parametric tests were applied using the ejaculate as the biological experimental unit. Samples with reduced motility showed significantly lower expression of both protamines (p < 0.01). Moreover, progressive sperm motility was strongly correlated with both PRM1 (r = 0.71, p = 0.019) and PRM2 (r = 0.69, p = 0.03) transcript levels. Cross-species GRN inference using scGeneRAI and a reference human spermatogenesis dataset identified several hypothesis-generating candidate transcription factors, including HMGB4, HMGB1, H2AFZ, NKX6-1, and SMC3, consistently supported across multiple bootstrap resampling runs. These findings demonstrate a strong association between protamine expression and sperm motility in rams. While the identified candidate regulators provide a valuable framework for future species-specific validation, they also highlight promising candidate molecular biomarkers of male fertility in livestock.

Gene regulatory networks↗

PubMed2026

Decoding protein signatures and protein interactions in oral potentially malignant disorders: a systematic review and network analysis.

BACKGROUND: Proteomic profiling offers thorough insights into protein structure and function, as well as it acts as an essential approach for analyzing molecular changes at the tissue level. However, because of the proteome's diversity and dynamic nature, biomarker discovery remains challenging. By combining proteomics with bioinformatics, the level of understanding in relation to molecular interactions and disease processes can be improved. Through an integrative approach, few limitations can be addressed, thereby promoting proteomic profiling for the discovery of new therapeutic targets and novel biomarkers for a variety of disorders. AIM: To identify differentially expressed protein markers and their key molecular pathways associated with Oral Potentially Malignant Disorders. METHODS: Systematic Review was conducted following the PRISMA guidelines and the protocol registered in the International Prospective Register of Systematic Reviews (PROSPERO) with the registration ID number CRD42024557545. A comprehensive literature review was performed using electronic databases, yielding 12,797, studies from which 15 eligible articles were selected. The Newcastle-Ottawa Scale was used to assess the risk of bias. Vote counting was performed to identify proteins reported in more than one study. A bipartite network was constructed using Cytoscape to identify shared and disease-specific protein markers. Lesion-wise protein-protein interaction networks were generated using STRING and analysed in Cytoscape to identify highly interconnected hub proteins, and pathway enrichment analysis for these hubs was performed using Reactome. RESULTS: A total of fifteen studies (Leukoplakia (LK) - n = 1, Proliferative Verrucous Leukoplakia (PVL) - n = 2, Oral Submucous Fibrosis (OSMF) - n = 7, and Oral Lichen Planus (OLP) - n = 5) were included. The Newcastle-Ottawa Scale was used to evaluate methodological quality and the quality of studies included in this systematic review was high for 4 articles and moderate in the remaining 11. The most commonly employed technique was mass spectrometry. A total of 318 candidate proteins (LK - 14, PVL - 82, OSMF - 172, and OLP - 50) were identified across the oral potentially malignant disorders. Key markers identified through vote counting included ERO1A, NUCB1, RHOA, and IL36A for PVL; LUM, KRT1, KRT9, ALB, and VIM for OSMF; and ALB, LYZ, HP, HBB, and AMY1A for OLP. The bipartite network showed that OSMF and OLP shared the highest number of proteins, indicating the strongest overlap among lesions. Network analysis further highlighted distinct hub proteins for each lesion: for LK- AMY1A, AMY1B and APOA1; for PVL- CFL1, RHOA and CDC42; for OSMF- HSP90AA1, ENO1 and SERPINA1; and for OLP- HP, B2M, and ORM1. Lesion-specific pathway enrichment revealed that LK was associated with epithelial differentiation, PVL with oncogenic signaling, OSMF with stress-driven fibrosis, and OLP with immune-mediated inflammation. CONCLUSIONS: Proteomic expression offers insights into disease pathogenesis by identifying important molecular changes across OPMDs. However, the majority of biomarkers are still in the exploratory stage due to the considerable variation in lesion types, sample sources, proteomic techniques, and reporting systems. In order to create reliable and clinically applicable biomarkers, future studies should concentrate on combining multi-omics techniques with large-scale, standardized cohorts.

Humans↗

PubMed2026

Examining early-phase symptom trajectories in interpersonal psychotherapy versus antidepressant medication for adults with depression: A dynamic time warp network analysis.

BACKGROUND: Depression is characterized by substantial symptom heterogeneity, which is often concealed when examining total severity scores. Analyzing symptom-level change can improve our understanding of treatment effects and recovery processes. This study, therefore, examined dynamic symptom networks during early-phase interpersonal psychotherapy (IPT) and selective serotonin reuptake inhibitor (SSRI) antidepressant treatment, assessing patterns of symptom change across as well as differences between treatments. METHODS: Using weekly item-level Hamilton Depression Rating Scale (HAM-D) data from a randomized clinical trial comparing IPT and SSRIs for adults with depression, this preregistered study examined symptom trajectories in the first six weeks of treatment with Dynamic Time Warping (DTW). RESULTS: Depressive symptom trajectories and DTW-based symptom networks were largely similar for IPT and SSRI. In both conditions, changes in somatic symptoms of anxiety and middle insomnia tended to precede improvements in depressed mood. CONCLUSIONS: Early symptom change may occur outside the core affective domain, underscoring the importance of monitoring symptoms broadly. Symptom-level patterns may reflect patients' stage of recovery and provide clinically relevant information beyond total severity scores. The absence of differences in improvement patterns between IPT and SSRI suggest few indications for treatment selection based on baseline symptom profiles. Future research should replicate and extend these findings to subsequent treatment phases using more frequent assessments and a broader range of interventions.

Humans↗

PubMed2026

Integrative Network Analysis of Bioactive Compounds from Punica granatum L. Peel: Multi-Target Mechanisms in Wound Healing.

BACKGROUND: Wound-healing agents often have limited efficacy and require prolonged recovery times, prompting growing interest in developing herbal-based formulations. Among these, Punica granatum L. has attracted considerable attention because of its high polyphenolic content. Despite its widespread use, the precise pharmacological targets underlying its wound-healing effects remain poorly understood and require systematic investigation. OBJECTIVES: This study aimed to elucidate the underlying pharmacological mechanisms of the topical wound-healing properties of P. granatum L. using a network pharmacology approach. METHODS: Bioactive compounds of P. granatum L. and their potential target genes were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Similarity Ensemble Approach (SEA), and SwissTargetPrediction databases. Wound healing-related genes were retrieved from the GeneCards database. Genes intersecting P. granatum L. targets and wound healing-associated genes were subjected to functional enrichment analyses, including protein-protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The PPI network was further analyzed using Cytoscape, and the phytoconstituent-target interaction network was visualized using Gephi. These findings were validated using molecular docking. RESULTS: A total of 40 intersecting genes were identified as potential P. granatum L. targets involved in wound healing. Among these, EGFR, PTPN11, HRAS, IGF1R, and ESR1 were identified as key hub genes. Functional enrichment analysis indicated that the most significantly enriched signaling pathways included the MAPK, PI3K-Akt, EGFR tyrosine kinase inhibitor resistance, focal adhesion, and FoxO signaling pathways. Molecular docking analysis confirmed favorable binding of quercetin and ellagic acid to the hub targets EGFR, IGF1R, and ESR1. CONCLUSIONS: These findings elucidate the pharmacological pathways underlying P. granatum-mediated wound healing and suggest that P. granatum L. acts as a multi-target modulator in the wound-healing process.

Focal Adhesion↗

PubMed2025

DigiNet: Optimizing personalized care for patients with stage IV non-small cell lung cancer (NSCLC) through a digitally connected provider network-analysis plan of a prospective multicenter cohort trial.

PURPOSE: The German sector-based healthcare system poses a major challenge to continuous patient monitoring and long-term follow-up, both essential for generating high-quality, longitudinal real-world data. The national Network for Genomic Medicine (nNGM) bridges the inpatient and outpatient care sectors to provide comprehensive molecular diagnostics and personalized treatment for non-small cell lung cancer (NSCLC) patients in Germany. Building on the established nNGM infrastructure, the DigiNet study aims to evaluate the impact of digitally integrated, personalized care on overall survival (OS) and the optimization of treatment pathways, compared to routine care. METHODS: DigiNet is a prospective, controlled, non-randomized multicenter cohort study including patients with stage IV NSCLC in two study regions (East and West) in Germany. The results of molecular diagnostics and clinical information, along with the entire treatment data are documented in a shared database. A board of lung cancer specialists monitors critical events. Patients digitally complete quality of life questionnaires, with results visualized for physicians. To assess the impact of this personalized digital care, a population-based control group will be identified by matching cohorts within the involved cancer registries. The primary endpoint is OS, and secondary endpoints comprise time on first-line treatment and hospitalization rates. Furthermore, a health economic and business economic evaluation will be conducted. Qualitative interviews with patients and physicians will be performed to assess barriers and facilitating factors for implementing the DigiNet intervention. ETHICS: The study protocol was reviewed and approved by the Ethics Committee of the University Hospital of Cologne (21-1521). TRIAL REGISTRATION: NCT05818449, registered retrospectively on December 12, 2022.

Humans↗

PubMed2026

Enzyme-Metabolite Network Analysis of Endometrial Cancer-Derived Extracellular Vesicles Through Integrated Proteomics and Metabolomics.

Endometrial cancer (EC) is the most common gynecological malignancy in high-income countries. Extracellular vesicles (EVs) are key mediators of intercellular communication and metabolic reprogramming, but their molecular cargo in EC remains poorly characterized. EVs were isolated from four EC cell lines representing Type I and Type II subtypes (AN3CA, ISHIKAWA, HEC1A, and KLE). Untargeted metabolomics was performed by HILIC-LC-MS/MS, proteomics by data-independent acquisition (DIA) mass spectrometry, and multi-omics integration using MetaboAnalyst and OmicsNet. Metabolomic profiling identified 1463 annotated features and revealed significant differences among EC cell lines (PERMANOVA, p = 0.002). Twenty-eight differentially abundant metabolites, including lactic acid, succinic acid, and uric acid, were identified. Proteomic analysis quantified 8513 proteins with subtype-specific expression patterns. Integrated analysis revealed seven significantly enriched pathways, including glycolysis/gluconeogenesis, central carbon metabolism in cancer, and the pentose phosphate pathway. Increased LDHA abundance in metastatic AN3CA-derived EVs was confirmed by Western blot (p = 0.047). EC-derived EVs display subtype- and metastatic-status-specific metabolo-proteomic signatures, with glycolysis, TCA cycle remodeling, and central carbon metabolism as convergent pathway signatures of molecular reprogramming. These findings establish a multi-omics framework for characterizing EV cargo in EC and identify candidate enzyme-metabolite nodes for future biomarker validation in patient-derived specimens.

Female↗

PubMed1979

Clinical use of network analysis for psychiatric and aged populations.

Explore the source record for details and available documents.

Adult↗

PubMed2025

Genome-wide association study meta-analysis provides insights into the etiology of heart failure and its subtypes.

Heart failure (HF) is a major contributor to global morbidity and mortality. While distinct clinical subtypes, defined by etiology and left ventricular ejection fraction, are well recognized, their genetic determinants remain inadequately understood. In this study, we report a genome-wide association study of HF and its subtypes in a sample of 1.9 million individuals. A total of 153,174 individuals had HF, of whom 44,012 had a nonischemic etiology (ni-HF). A subset of patients with ni-HF were stratified based on left ventricular systolic function, where data were available, identifying 5,406 individuals with reduced ejection fraction and 3,841 with preserved ejection fraction. We identify 66 genetic loci associated with HF and its subtypes, 37 of which have not previously been reported. Using functionally informed gene prioritization methods, we predict effector genes for each identified locus, and map these to etiologic disease clusters through phenome-wide association analysis, network analysis and colocalization. Through heritability enrichment analysis, we highlight the role of extracardiac tissues in disease etiology. We then examine the differential associations of upstream risk factors with HF subtypes using Mendelian randomization. These findings extend our understanding of the mechanisms underlying HF etiology and may inform future approaches to prevention and treatment.

Humans↗