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At least 19 recordsLinked to original sources

PubMed2003

Cytoscape: a software environment for integrated models of biomolecular interaction networks.

Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

Algorithms↗

PubMed2005

BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks.

The Biological Networks Gene Ontology tool (BiNGO) is an open-source Java tool to determine which Gene Ontology (GO) terms are significantly overrepresented in a set of genes. BiNGO can be used either on a list of genes, pasted as text, or interactively on subgraphs of biological networks visualized in Cytoscape. BiNGO maps the predominant functional themes of the tested gene set on the GO hierarchy, and takes advantage of Cytoscape's versatile visualization environment to produce an intuitive and customizable visual representation of the results.

Algorithms↗

PubMed2006

GenePro: a Cytoscape plug-in for advanced visualization and analysis of interaction networks.

MOTIVATION: Analyzing the networks of interactions between genes and proteins has become a central theme in systems biology. Versatile software tools for interactively displaying and analyzing these networks are therefore very much in demand. The public-domain open software environment Cytoscape has been developed with the goal of facilitating the design and development of such software tools by the scientific community. RESULTS: We present GenePro, a plugin to Cytoscape featuring a set of versatile tools that greatly facilitates the visualization and analysis of protein networks derived from high-throughput interactions data and the validation of various methods for parsing these networks into meaningful functional modules. AVAILABILITY: The GenePro plugin is available at the website http://genepro.ccb.sickkids.ca.

Algorithms↗

PubMed2006

GOlorize: a Cytoscape plug-in for network visualization with Gene Ontology-based layout and coloring.

UNLABELLED: We have implemented a graph layout algorithm that exposes Gene Ontology (GO) class structure on the network nodes. It can be used in conjunction with BiNGO plug-in to Cytoscape, which finds the GO categories over-represented in a given network. Our plug-in, named GOlorize, first highlights the class members with category-specific color-coding and then constructs an enhanced visualization of the network using a class-directed layout algorithm. AVAILABILITY: http://www.cytoscape.org/plugins2.php. SUPPLEMENTARY INFORMATION: Installation instructions and tutorial at http://www.cytoscape.org/plugins/GOlorize/GOlorizeUserGuide.pdf.

Algorithms↗

PubMed2005

Decomposing protein networks into domain-domain interactions.

UNLABELLED: The application of novel experimental techniques has generated large networks of protein-protein interactions. Frequently, important information on the structure and cellular function of protein-protein interactions can be gained from the domains of interacting proteins. We have designed a Cytoscape plugin that decomposes interacting proteins into their respective domains and computes a putative network of corresponding domain-domain interactions. To this end, the network graph of proteins has been extended by additional node and edge types for domain interactions, including different node and edge shapes and coloring schemes used for visualization. An additional plugin provides supplementary web links to Internet resources on domain function and structure. AVAILABILITY: Both Cytoscape plugins can be downloaded from http://www.cytoscape.org

Algorithms↗

PubMed2004

System-based proteomic analysis of the interferon response in human liver cells.

BACKGROUND: Interferons (IFNs) play a critical role in the host antiviral defense and are an essential component of current therapies against hepatitis C virus (HCV), a major cause of liver disease worldwide. To examine liver-specific responses to IFN and begin to elucidate the mechanisms of IFN inhibition of virus replication, we performed a global quantitative proteomic analysis in a human hepatoma cell line (Huh7) in the presence and absence of IFN treatment using the isotope-coded affinity tag (ICAT) method and tandem mass spectrometry (MS/MS). RESULTS: In three subcellular fractions from the Huh7 cells treated with IFN (400 IU/ml, 16 h) or mock-treated, we identified more than 1,364 proteins at a threshold that corresponds to less than 5% false-positive error rate. Among these, 54 were induced by IFN and 24 were repressed by more than two-fold, respectively. These IFN-regulated proteins represented multiple cellular functions including antiviral defense, immune response, cell metabolism, signal transduction, cell growth and cellular organization. To analyze this proteomics dataset, we utilized several systems-biology data-mining tools, including Gene Ontology via the GoMiner program and the Cytoscape bioinformatics platform. CONCLUSIONS: Integration of the quantitative proteomics with global protein interaction data using the Cytoscape platform led to the identification of several novel and liver-specific key regulatory components of the IFN response, which may be important in regulating the interplay between HCV, interferon and the host response to virus infection.

Cell Line, Tumor↗

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↗

PubMed2024

Bioinformatics analysis of ferroptosis in frozen shoulder.

OBJECTIVES: Frozen shoulder is a common shoulder disease that significantly affects the patient's life and work. Ferroptosis is a new type of programmed cell death, which is involved in many diseases. However, there have been no studies reporting the relationship between frozen shoulders and ferroptosis. This study identified potential molecular markers of ferroptosis in frozen shoulders to provide more effective strategies for the treatment of frozen shoulders. METHODS: GSE238053 was downloaded from the Gene Expression Omnibus (GEO) dataset and intersected with ferroptosis genes to obtain differentially expressed genes (DEGs). The signaling pathways and biological functions of DEGs were performed by WebGestalt and Metascape. The interactions related to these DEGs and the key genes between frozen shoulders and ferroptosis was performed by STRING and Cytoscape. A frozen shoulders rat model was used to validate our predicted genes, Western Blot and qRT-PCR was used to assess the expression levels of our genes of interest. RESULTS: A total of 34 DEGs between GSE238053 and Ferroptosis Database were obtained, most of which were involved in the HIF-1 signaling pathway and inflammatory response. A protein-protein interaction network was obtained by Cytoscape and the key genes (IL-6, HMOX1 and TLR4) were screened by MCODE. Our results of Western Blot showed that the protein expression level of TLR4 and HMOX1 were elevated, and the protein level of IL-6 decreased in frozen shoulders rat model. The mRNA level after frozen shoulders showed that IL-6 was upregulated, whereas TLR4 and HMOX1were downregulated. CONCLUSIONS: The results demonstrated that ferroptosis may affect the pathological process of frozen shoulders through these signaling pathways and genes. The identification of IL-6, HMOX1 and TLR4 genes can provide new therapeutic targets for frozen shoulders.

Ferroptosis↗

PubMed2026

Screening of core targets for Di(2-ethylhexyl) Phthalate-related gastric cancer based on machine learning, molecular docking, and SHAP analysis.

PURPOSE: Given the existing uncertainties regarding the link between Di(2-ethylhexyl) phthalate (DEHP) exposure and gastric cancer (GC) progression, this study aimed to clarify their association, identify the toxic targets of DEHP, and elucidate the underlying molecular mechanisms. METHODS: Multiple integrated approaches were employed, including Gene Expression Omnibus (GEO) data analysis, network toxicology, molecular docking, and machine learning. STRING and Cytoscape tools were utilized to identify key targets, while Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the functional enrichment of intersecting targets. Machine learning and SHAP analysis were applied to screen core targets in GC. Molecular docking was performed to evaluate the binding affinity of DEHP toward core targets, and 200 ns molecular dynamics simulations were further conducted for representative complexes to validate their dynamic stability. RESULTS: A total of 18 key targets were identified using STRING and Cytoscape. GO and KEGG enrichment analyses demonstrated that these intersecting targets were primarily enriched in the extracellular region, as well as the Calcium signaling pathway and cAMP signaling pathway. Through machine learning analyses, 7 key genes (ADRB2, ESRRG, GRIA4, IL13RA2, NR3C2, PLA2G1B, and SULT2A1) were identified as core targets in GC through machine learning analyses. Molecular docking simulations revealed strong binding specificity between DEHP and the target proteins. Among them, NR3C2 and ADRB2 exhibited relatively high predictive importance in the machine learning models. DEHP showed favorable binding affinity toward these core targets, and molecular dynamics simulations further confirmed that ADRB2-DEHP and NR3C2-DEHP complexes maintained stable conformations throughout the simulation. CONCLUSIONS: Our findings identified GC associated genes that were computationally predicted as potential targets of DEHP. These results indicated structural compatibility between DEHP and its target proteins but did not prove that DEHP exposure accounts for the gene expression changes in GC.

Molecular Docking Simulation↗

PubMed2024

Functional Analysis of MS-Based Proteomics Data: From Protein Groups to Networks.

Mass spectrometry-based proteomics allows the quantification of thousands of proteins, protein variants, and their modifications, in many biological samples. These are derived from the measurement of peptide relative quantities, and it is not always possible to distinguish proteins with similar sequences due to the absence of protein-specific peptides. In such cases, peptide signals are reported in protein groups that can correspond to several genes. Here, we show that multi-gene protein groups have a limited impact on GO-term enrichment, but selecting only one gene per group affects network analysis. We thus present the Cytoscape app Proteo Visualizer (https://apps.cytoscape.org/apps/ProteoVisualizer) that is designed for retrieving protein interaction networks from STRING using protein groups as input and thus allows visualization and network analysis of bottom-up MS-based proteomics data sets.

Proteomics↗

PubMed2026

Exploring the Mechanism of Zhigancao Decoction in the Treatment of Chronic Heart Failure via Modulation of Oxidative Stress.

BACKGROUND: Zhigancao decoction has shown therapeutic potential in the management of chronic heart failure (CHF); however, the molecular mechanisms underlying its pharmacological effects remain incompletely understood. This study aimed to investigate its potential mechanisms, with a particular focus on oxidative stress-related pathways. METHODS: The chemical profile of Zhigancao decoction was characterized by LC-MS/MS, and putative targets were predicted using SwissTargetPrediction. A protein-protein interaction (PPI) network was established using the STRING database and Cytoscape software, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Differentially expressed genes from two GEO datasets (GSE9128 and GSE84796) were integrated with reactive oxygen species (ROS)-related genes to identify candidate targets. Network pharmacology and molecular docking were subsequently performed to investigate compound-target interactions. RESULTS: A total of 66 chemical constituents and 818 putative targets were characterized and collected, respectively. Among these targets, MMP9 emerged as a central candidate associated with the therapeutic effects of Zhigancao decoction. GO and KEGG enrichment analyses demonstrated that the core targets were significantly enriched in oxidative stress-related pathways, inflammatory signaling cascades, and cell fate regulatory pathways. Computational deconvolution of bulk transcriptomic data suggested marked alterations in the estimated immune cell composition of the CHF microenvironment. Network pharmacology analysis further indicated that multiple chemical constituents of Zhigancao decoction converge on MMP9 and its associated pathways. Molecular docking analysis demonstrated favorable binding affinities between 10 representative compounds and MMP9, with binding energies below -7.0 kcal/mol. CONCLUSIONS: In silico predictions suggest that Zhigancao decoction may exert potential therapeutic effects against CHF through computationally predicted targeting of MMP9 and associated oxidative stress- and immune-related pathways. These computational findings provide a theoretical foundation for future experimental investigations into the mechanisms of Zhigancao decoction in CHF, though clinical application would require confirmation through rigorous in vivo and clinical studies.

Oxidative Stress↗

PubMed2026

Integrative pooled transcriptomic analysis reveals shared and distinct molecular signatures in adult T-cell leukemia/lymphoma and peripheral T-cell lymphoma.

Adult T-cell leukemia/lymphoma (ATLL) and peripheral T-cell lymphomas (PTCLs) are aggressive neoplasms of mature T cells with poor prognosis and limited therapies. ATLL originates from HTLV-1 infection, while PTCL comprises heterogeneous subtypes without a defined etiologic factor. Comparative molecular profiling of these malignancies remains limited. We conducted an integrative pooled transcriptomic analysis of publicly available Gene Expression Omnibus (GEO) microarray datasets to compare ATLL, PTCL, and normal T-cell samples. Differential expression, functional enrichment, and protein-protein interaction (PPI) network analyses were performed using STRING, Cytoscape, and Gephi. Key hub genes and functional modules were further analyzed through KEGG and Enrichr databases. Comparative analyses revealed upregulation of extracellular matrix (ECM) components (COL1A1, COL3A1, FN1, SPARC, THBS1) and immune-regulatory molecules (CD163, CXCL12-CXCR4, complement subunits). Shared pathways included ECM-receptor interaction, focal adhesion, and PI3K-Akt signaling. PTCL showed enrichment in coagulation and angiogenesis, while ATLL displayed distinct enrichment of cytoskeletal, chemokine, immune-regulatory, and signaling-associated pathways. PPI networks identified ECM and chemokine signaling as key hubs, with subtype-specific modules related to immune regulation, proliferation, and metabolism. This integrative approach uncovers common and distinct oncogenic programs in ATLL and PTCL, emphasizing ECM remodeling and immune modulation as shared hallmarks. Hub genes such as COL1A1, FN1, and CXCL12-CXCR4 may represent candidate molecular signatures that warrant validation in independent patient cohorts and functional studies before their clinical utility can be established.

Humans↗

PubMed2026

Exploring shared biomarkers and their mechanisms in thyroid cancer and systemic lupus erythematosus via bioinformatics analysis.

BACKGROUND: Systemic lupus erythematosus (SLE), an autoimmune disorder, is linked to a heightened risk of multiple malignancies, including thyroid cancer. Thyroid cancer is the most prevalent malignancy of the endocrine system, and its autoimmune-related pathological features render it an optimal subject for investigating the mechanisms of their comorbidity. The molecular mechanisms underlying this comorbidity are still ambiguous. The accurate diagnosis and treatment of thyroid cancer urgently necessitate innovative molecular targets that extend beyond conventional pathological characteristics. This study seeks to employ integrated bioinformatics approaches to elucidate potential shared molecular mechanisms and immunological features between thyroid cancer and systemic lupus erythematosus (SLE), aiming to enhance understanding of their comorbidity and identify novel intervention targets. METHODS: This study initially acquired gene expression data for TC and SLE from the GEO database and subsequently screened and identified differentially expressed genes (DEGs) shared by both diseases. Subsequently, we conducted Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome functional enrichment analyses on these 46 shared differentially expressed genes (DEGs) and further assessed the activation status of pertinent pathways using Gene Set Enrichment Analysis (GSEA). Subsequently, we employed CIBERSORTx to examine immune infiltration patterns and developed protein-protein interaction networks utilising the STRING database. We identified hub genes utilising the MCODE and cytoHubba plugins and visualised the findings with Cytoscape software. We additionally assessed the diagnostic efficacy of these core hub genes in an independent dataset utilising ROC curves and investigated their prognostic relevance in thyroid cancer through Kaplan-Meier survival analysis and multivariate Cox proportional hazards regression. Ultimately, we employed the Network Analyst platform to forecast transcription factor-gene and miRNA-gene regulatory networks and identified potential targeted therapeutic compounds utilising the DSigDB database. RESULTS: This study identified 46 differentially expressed genes (DEGs) commonly linked to thyroid cancer and systemic lupus erythematosus (SLE), which were significantly enriched in signalling pathways associated with immune-inflammatory activation, type I interferon responses, and complement pathway activation. Moreover, GSEA findings validated that immune-inflammatory and autoimmune-related pathways are markedly activated in both conditions. Twelve hub genes were discerned through protein-protein interaction networks. Analysis of immune infiltration indicated that thyroid cancer and systemic lupus erythematosus exhibit a shared characteristic of innate immune dysregulation, marked by the infiltration of myeloid cells (neutrophils, M0/M2 macrophages). Receiver operating characteristic (ROC) curve analysis identified six significant core hub genes with substantial diagnostic value: C1QB, LCN2, C1QC, LTF, VSIG4, and C3AR1. Univariate survival analysis indicated that elevated expression of C1QC and C3AR1 significantly enhances overall survival in thyroid cancer patients; however, multivariate COX regression analysis revealed that their independent prognostic significance necessitates further validation. This study predicted the interaction networks of transcription factors and miRNAs regulating key genes, with LCN2 demonstrating the highest connectivity to miRNAs, and identified candidate therapeutic compounds linked to it. CONCLUSION: This study employed bioinformatics analysis to identify critical shared hub genes and molecular pathways connecting thyroid cancer and systemic lupus erythematosus, offering novel insights into their shared pathogenesis and the advancement of targeted biomarkers and therapeutic strategies.

Bioinformatics analysis↗

PubMed2026

Zhiling Jiangya decoction treats hypertension in rats: An integrative study of network pharmacology, immune infiltration, molecular simulation, and 16S rDNA sequencing.

OBJECTIVE: This study integrated network pharmacology, immune infiltration analysis, molecular docking, molecular dynamics simulation, ADMET prediction, 16S rDNA sequencing, and rat experiments to elucidate the potential mechanisms underlying the antihypertensive effects of Zhiling Jiangya Decoction (ZLJYD). METHODS: Active compounds and their potential targets were screened from the PubChem, TCMSP, NovoPro, and SwissTargetPrediction databases. Hypertension-related targets were retrieved from the OMIM and GeneCards databases, and overlapping targets were identified. The STRING database and Cytoscape 3.10.1 software were used to construct a protein-protein interaction network and a herb-component-target-disease network. Gene Ontology functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed to identify the key biological processes and signaling pathways involved. Using the CIBERSORT algorithm combined with correlation analysis, we investigated the association between key targets and immune cell infiltration. Molecular docking, molecular dynamics simulations, and ADMET predictions were performed to assess the binding stability and pharmacokinetic properties of the main compounds with their corresponding targets. Finally, the antihypertensive efficacy of ZLJYD was validated using a spontaneously hypertensive rat model, and alterations in gut microbiota were analyzed using 16S rDNA sequencing. RESULTS: A total of 123 active compounds and 267 hypertension-related targets of ZLJYD were identified. Enrichment analysis revealed that these targets were primarily associated with the PI3K-Akt signaling pathway and lipid and atherosclerosis pathways. Immune infiltration analysis suggested that the therapeutic effects of ZLJYD may involve the regulation of follicular helper T cells, naïve B cells, and naïve CD4⁺ T cells. Molecular docking and dynamics simulations supported the stable binding of key compounds to their target proteins, while ADMET predictions indicated favorable pharmacokinetic properties and safety profiles. Rat experiments demonstrated that ZLJYD significantly reduced blood pressure in spontaneously hypertensive rats, partially alleviated gut microbiota dysbiosis, and altered microbial community structure and phylogenetic diversity. CONCLUSION: This study systematically elucidates the potential mechanisms underlying the antihypertensive effects of ZLJYD through multiple components, targets, and pathways, particularly immune regulation and gut microbiota remodeling. These findings provide mechanistic insights into its potential therapeutic application.

16S rDNA sequencing↗

PubMed2026

Comprehensive bioinformatics analysis identifies candidate ciliogenesis-related genes preferentially associated with N0-stage lung squamous cell carcinoma.

PURPOSE: There is few research on which genes play an important role in tumors without lymph metastasis. This study aimed to identify candidate molecular alterations preferentially associated with N0-stage LUSC. METHODS: we conducted a comprehensive bioinformatics analysis using publicly available The Cancer Genome Atlas (TCGA) data. Differentially expressed genes (DEGs) were identified separately by comparing N0 tumors and N+ tumors with normal lung tissues. Genes dysregulated in both N0 and N+ tumors were excluded to identify candidate N0-associated genes PPI networks were constructed using STRING and Cytoscape, with module analysis performed via MCODE. Hub genes were identified using multiple Cytohubba algorithms. Functional enrichment analyses were conducted using GO, and KEGG pathways using DAVID. Gene interaction networks were further explored using GeneMANIA. Immune cell infiltration was evaluated with TIMER. Associations with pathological stage and patient survival were assessed using GEPIA and other relevant tools. RESULTS: A total of 1103 candidate N0-associated DEGs were identified, including 748 upregulated and 355 downregulated genes. The PPI network contained five major MCODE clusters. One cluster (MCODE 4) included TTC30A, TTC30B, BBS7, and KIF3B genes implicated in ciliogenesis. TTC30B showed significant differential expression across pathological stages in the overall LUSC cohort. Seven consensus hub genes (ERBB2, CHUK, CASP8, NOTCH1, HNF4A, CREBBP, and IRS1) were identified based on their consistent ranking across multiple CytoHubba algorithms. Upregulated candidate N0-associated genes were primarily enriched in immune-related processes, including B-cell-mediated immunity and humoral responses, whereas downregulated genes were enriched in lysosomal and trans-Golgi network-related pathways. Exploratory immune infiltration analyses identified associations between the four ciliogenesis-related genes and several immune cell populations. CONCLUSIONS: This study identified candidate molecular signatures preferentially associated with N0-stage LUSC, including ciliogenesis-related genes and consensus hub genes. These findings provide hypotheses regarding molecular features of N0-stage LUSC and warrant further validation in independent cohorts and experimental studies.

Humans↗

PubMed2026

Key hub genes and pathways associated with HCV-related hepatocellular carcinoma as potential diagnostic biomarkers.

BACKGROUND: Hepatitis C virus (HCV)-related hepatocellular carcinoma (HCC) remains a major global health challenge, with high morbidity and mortality despite recent therapeutic advances. Early detection and identification of reliable molecular biomarkers are essential to improve patient outcomes. Therefore, the present study aimed to investigate key hub genes and pathways associated with HCV-related HCC as potential diagnostic biomarkers. METHODS: The datasets GSE69715 and GSE62232 were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were recognized according to an adjusted p-value and a log fold change (logFC). The GEO2R tool facilitated the identification of common DEGs across the two datasets. Pathways were explored using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases. Furthermore, protein-protein interactions (PPIs) were assessed through Cytoscape. The target genes were confirmed through a GEPIA analysis. RESULTS: A total of 421 common DEGs were identified, and 80 hub genes were subsequently determined through GEO and PPI network analyses, respectively. The GO and KEGG pathways analysis presented DEGs were enhanced in metabolic pathways, cellular components, extracellular exosome, detoxification of copper ion and monooxygenase activity. The GEPIA analysis indicated a notable variation in the expression levels of four specific genes -CDKN2A, CDK1, CCNB1, and TOP2A-when comparing normal samples to tumor samples. CONCLUSION: The present study discovered novel genes by expression variation in HCV-related hepatocellular carcinoma development. These findings suggest that CDKN2A, CDK1, CCNB1, and TOP2A are promising candidates for diagnostic biomarkers and present a valuable opportunity for the early identification of HCV-HCC, which could lead to improved treatment outcomes.

Bioinformatics↗

PubMed2026

Transpulmonary proteomic gradient analysis in women with pulmonary arterial hypertension associated with systemic sclerosis.

This study investigated proteomic alterations in the pulmonary circulation of patients with pulmonary arterial hypertension associated with systemic sclerosis (PAH-SSc) by analyzing the transpulmonary protein gradient and comparing the proteomic profiles with systemic sclerosis (SSc) without PAH. Twenty women were included (10 PAH-SSc, 64.6 ± 10.8 years; 10 SSc, 62.8 ± 11.5 years). The transpulmonary gradient was defined as the difference in biomarker concentrations between wedge-position and pulmonary artery blood samples. Peptides were analysed using liquid chromatography-mass spectrometry, and differentially abundant proteins were identified with Proteome Discoverer. Protein-protein interaction networks were generated with STRING and visualized in Cytoscape. A total of 270 proteins were detected, with no significant transpulmonary gradient alterations. However, patients with PAH-SSc showed distinct proteomic profiles compared to SSc. Multivariate analysis identified 48 differentially abundant proteins in pulmonary artery plasma, with 15 overrepresented and 33 downregulated in PAH-SSc. Among these, the downregulation of transforming growth factor-beta-induced protein ig-h3 (TGFβI/ig-h3) points to a potential involvement of the TGF-β-related extracellular matrix remodelling pathway in PAH-SSc. However, further validation in larger and independent cohorts is required before its relevance as a biomarker or therapeutic target can be established. In conclusion, while no transpulmonary proteomic gradient was observed, the proteomic profiles of PAH-SSc and SSc were different. The profile in PAH-SSc was characterized by differences in immune response, lipid metabolism, and hemostatic proteins. SIGNIFICANCE: This study offers the first proteomic characterization of the transpulmonary gradient in PAH-SSc and SSc. Although no differences in the gradient were found, the pulmonary artery plasma proteome of PAH-SSc patients showed a distinct pattern compared to SSc. Several proteins associated with immune function, haemostasis, and cellular processes were altered, which may indicate specific pathophysiological features of PAH-SSc or suggest how lung dysfunction develops in SSc. Targeting dysregulated proteins like TGFβI/ig-h3 or addressing immune-coagulation imbalances may support future research studies. Overall, these findings refine the molecular profile of PAH-SSc and provide a basis for future large-scale studies aimed at clarifying disease mechanisms and identifying clinically relevant molecular signatures.

Humans↗

PubMed2026

Enterococcus faecalis GP1764 induces an early differential gene expression in the intestine on key pathways related to cellular immune response and gut barrier function in chickens.

The aim of the present study was to elucidate the mode of action of Enterococcus faecalis GP1764 in improving performance traits during the starter phase by analyzing genome-wide gene expression and its interaction with microbial populations in the intestine of chickens challenged with an NSP-rich diet. At day 7, microbiota populations from ileal and cecal contents and transcriptomics from jejunal and cecal mucosa were analyzed between Control (Ctrl) and Enterococcus faecalis GP1764 (EntF) groups. Results from microbiota analysis demonstrated that EntF shifted β-diversity indices in ileum (neutral (p= 0.006) and phylogenetic (p= 0.006)) and caecum (phylogenetic (p= 0.017)). Transcriptomics revealed 43 differentially expressed genes for EntF vs. Ctrl in the jejunal mucosa. Of these, MHCY-36 (MHC-I-Related), RAG2 and MUC19-like genes were upregulated in EntF vs. Ctrl, protein-coding genes with immunomodulatory capacities as supported by GSEA and Cytoscape-ClueGo pathway analyses. Results suggest an intestinal immunomodulation induced through presentation of B vitamins metabolites, synthetized by EntF, to an undescribed subset of innate-like unconventional T lymphocytes in chickens, similar to MAIT cells in mammals. These cells could contribute to antibacterial responses and repair of damaged barrier tissue after inflammatory processes. The upregulation of the MUC19-like gene expression observed in the jejunal mucosa can protect gut integrity via the promotion of mucus production by goblet cells. Finally, RAG2, involved in V(D)J coding segments recombination in B- and T-cells may provide a greater recognition of foreign invaders, allowing the animals to efficiently fight against pathogenic infections. Collectively, these results suggest an important role of EntF in promoting the capacity of animals to rapidly act against pathogenic challenges, herein, inducing resilience towards dietary ingredients with anti-nutritional activity that impart moderate inflammation in chickens.

Enterococcus faecalis↗