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Library-based, multiplexed strategy for mapping protein interaction networks via crosslinking.

BACKGROUND: Protein-protein interactions are fundamental to cellular function, yet resolving their interaction interfaces and dynamic behaviors in native biological contexts remains challenging, particularly for weak or transient interactions. Crosslinking strategies based on noncanonical amino acids offer an effective means to capture such interactions; however, traditional single-site incorporation provides limited coverage and may overlook critical interaction hotspots. RESULTS: By employing a mutagenesis library, multiple interaction partners and cross-linking sites of a target protein can be simultaneously screened in a single experiment, without prior knowledge of its precise structural or functional features, enabling effective and unbiased analysis of its interaction network. In this study, we constructed an amber codon-scanning mutagenesis library of PSMD10, facilitating independent incorporation of the photocrosslinking ncAA p-azido-phenylalanine at multiple distinct residues. This approach allowed us to systematically interrogate and precisely map potential interaction regions across the protein surface. Coupled with crosslinking mass spectrometry, we identified multiple residues involved in intermolecular interactions, as well as previously unreported interaction partners, including T2FA, TBA1C, and ATRIP. CONCLUSIONS: These findings expand our understanding of PSMD10-associated proteasome interactome, demonstrate a multiplexed strategy for in situ mapping of protein interaction interfaces with broad coverage, and offer a valuable platform for developing therapeutics that target protein-protein interactions.

Protein Interaction Mapping

The signed two-space proximity model for learning representations in protein-protein interaction networks.

MOTIVATION: Accurately predicting complex protein-protein interactions (PPIs) is crucial for decoding biological processes, from cellular functioning to disease mechanisms. However, experimental methods for determining PPIs are computationally expensive. Thus, attention has been recently drawn to machine learning approaches. Furthermore, insufficient effort has been made toward analyzing signed PPI networks, which capture both activating (positive) and inhibitory (negative) interactions. To accurately represent biological relationships, we present the Signed Two-Space Proximity Model (S2-SPM) for signed PPI networks, which explicitly incorporates both types of interactions, reflecting the complex regulatory mechanisms within biological systems. This is achieved by leveraging two independent latent spaces to differentiate between positive and negative interactions while representing protein similarity through proximity in these spaces. Our approach also enables the identification of archetypes representing extreme protein profiles. RESULTS: S2-SPM's superior performance in predicting the presence and sign of interactions in SPPI networks is demonstrated in link prediction tasks against relevant baseline methods. Additionally, the biological prevalence of the identified archetypes is confirmed by an enrichment analysis of Gene Ontology (GO) terms, which reveals that distinct biological tasks are associated with archetypal groups formed by both interactions. This study is also validated regarding statistical significance and sensitivity analysis, providing insights into the functional roles of different interaction types. Finally, the robustness and consistency of the extracted archetype structures are confirmed using the Bayesian Normalized Mutual Information (BNMI) metric, proving the model's reliability in capturing meaningful SPPI patterns. AVAILABILITY: S2-SPM is implemented and freely available under the MIT license at https://github.com/Nicknakis/S2SPM.

Protein Interaction Mapping

Integrative multi-omics analysis of metabolite-protein interaction networks across different stages of coronary heart disease.

To elucidate the molecular characteristics of synergistic interactions across the clinical stages of coronary heart disease (CHD)-specifically stable angina pectoris (SAP), unstable angina pectoris (UAP), and acute myocardial infarction (AMI)-through integrated metabolomic and proteomic analyses. Based on a cohort including SAP, UAP, AMI, and healthy controls, metabolomic and proteomic analyses were performed to identify differentially expressed molecules, followed by KEGG pathway enrichment analysis. Pathways co-enriched across both omics platforms were selected to construct metabolite-protein interaction networks. The number of pathways co-enriched in both metabolomic and proteomic analyses increased markedly with disease stage. Only two pathways (histidine metabolism and arginine and proline metabolism) were identified in the SAP stage; this number increased to five in the UAP stage (including ferroptosis and efferocytosis) and expanded to 25 in the AMI stage, encompassing three major functional modules: immune inflammation, metabolic reprogramming, and cell signaling. The core network exhibited a stepwise increase in connectivity, shifting from a sparse structure in the SAP stage to a highly interconnected architecture in the AMI stage, with L-glutamate and KNG1 identified as the central hubs in this cross-sectional network. In addition, CNDP1 exhibited a stage-dependent functional transition, shifting from downregulation in SAP to upregulation in AMI. In this cross-sectional analysis, metabolic dysregulation and immune activation exhibited stepwise increases in interconnectivity across the SAP, UAP, and AMI groups, with the most extensive crosstalk observed in the AMI stage-a network configuration consistent with a tightly coupled "molecular storm". These findings provide novel insights into stage-associated molecular signatures of CHD and identify candidate hub molecules for stage-oriented therapeutic investigation.

Humans

Kv11.1 (hERG) Protein Interaction Networks Connect Endocytic Trafficking to Polygenic Influences on Cardiac Repolarization.

Polygenic scores (PGS) capture the combined effect of many common genetic variants on quantitative traits and disease risk, yet their functional consequences at the protein level remain poorly defined. Here, we integrated quantitative and interaction proteomics to resolve how polygenic liability for cardiac repolarization manifests in human cells. We studied human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) from donors with extreme PGS for QT interval duration, a clinically relevant electrophysiologic trait associated with arrhythmia risk. Global quantitative proteomics revealed increased abundance of mitochondrial proteins in high-PGS cardiomyocytes. To define protein network-level effects on a key repolarizing ion channel, we performed multiplexed affinity purification-mass spectrometry (AP-MS) of Kv11.1. While mitochondrial changes did not directly explain Kv11.1-associated complexes, interactome analysis revealed increased association of Kv11.1 with myosin motor proteins and endosomal recycling machinery in high-PGS cells. These findings suggest altered channel trafficking dynamics of Kv11.1, distinct from the trafficking defects observed in monogenic Kv11.1 variants. Together, these data show that integrating global and interaction proteomics can resolve how polygenic variation reshapes protein networks. Future work using these methods could connect genomic risk to subcellular remodeling and our work provides a generalizable framework to probe the proteomic basis of complex traits. SIGNIFICANCE STATEMENT: Polygenic scores (PGS) predict disease risk, but how biological pathways are influenced by these common variants remains difficult to define. We generated human induced pluripotent stem cells from individuals with extreme high- and low- PGS for QT interval, a key electrocardiographic measure linked to arrhythmia risk. By combining global proteomics and interactomics for a common ion channel involved in regulating the QT interval (Kv11.1) we found potential mechanisms that are influenced by common genetic traits in patients. Our work provides an approach to connect polygenic scores to pathway-level molecular mechanisms in human cells and a general framework for uncovering how complex genetic architecture drives disease-relevant biology.

AP-MS

Comprehensive Analysis of miRNAs and Predicted Protein Interaction Networks in Skeletal Muscle Development of Myostatin-Deficient Rabbits.

Myostatin (MSTN), encoded by the MSTN gene, is a critical negative regulator of skeletal muscle mass. This study aims to identify and characterize the miRNAs involved in the development of the double-muscling phenotype in MSTN-deficient rabbits. We performed high-throughput sequencing to analyze the miRNA expression profiles in gluteus maximus tissue from wild type (MSTN+/+) and MSTN-KO (MSTN+/- and MSTN-/- inclusive) rabbits. Differentially expressed miRNAs (DEmiRNAs) were identified, and their potential target genes were predicted. Functional enrichment analysis of these target mRNAs was conducted using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database to elucidate the involved biological pathways and regulatory networks. A total of 25 DEmiRNAs (13 downregulated and 12 upregulated, |log2FC|&#x2009;&#x2265;&#x2009;1.0, adjusted p&#x2009;<&#x2009;0.05) and 1178 differentially expressed mRNAs (408 upregulated and 770 downregulated, |log2FC|&#x2009;&#x2265;&#x2009;2.0, adjusted p&#x2009;<&#x2009;0.05) were identified in MSTN-KO compared to MSTN+/+ rabbits. Bioinformatics analysis revealed that the target genes of these DEmiRNAs were significantly enriched in key pathways governing muscle growth and metabolism, including the PI3K-Akt signaling pathway, MAPK signaling pathway, and pathways related to ECM-receptor interaction and insulin signaling. Notably, many predicted target mRNAs are expressed by genes that encode key inhibitors of myogenesis (e.g., HDAC4) and major extracellular matrix components (e.g., COL4A3, POSTN). Our results demonstrate that MSTN deficiency induces a distinct and widespread change in the miRNA expression landscape of skeletal muscle.

Animals

Network pharmacology insights into the mechanistic basis of Taohe Chengqi Decoction in the treatment of constipation.

Constipation is a common gastrointestinal disorder associated with impaired motility, inflammation, and altered neuro-intestinal regulation. Taohe Chengqi Decoction, a classical prescription from Shang Han Lun, has been widely applied in the treatment of constipation, yet its pharmacological mechanisms remain insufficiently understood. We integrated systems pharmacology and network analysis to elucidate the therapeutic mechanisms of Taohe Chengqi Decoction against constipation. Active compounds and their putative targets were retrieved from traditional Chinese medicine systems pharmacology and PubChem, while constipation-related genes were collected from GeneCards and OMIM. Shared targets were identified and subsequently analyzed using STRING to construct a protein-protein interaction network. Hub proteins were ranked by degree centrality. A drug-disease-target network was built to map the interactions between Taohe Chengqi Decoction and constipation. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses were performed to uncover functional modules and signaling pathways. A total of 188 common targets were identified. Protein-protein interaction network analysis highlighted AKT1, interleukin-6 (IL6), IL1B, and JUN as hub proteins, suggesting central roles in regulating inflammation, apoptosis, and signal transduction. Additional nodes with high connectivity, such as caspase-3, PTGS2, signal transducer and activator of transcription 3, hypoxia-inducible factor-1&#x3b1;, estrogen receptor 1, and epidermal growth factor receptor, were implicated in apoptosis, oxidative stress, and transcriptional regulation. The drug-disease-target network revealed a dense and highly interconnected structure, reflecting the multicomponent, multi-target nature of Taohe Chengqi Decoction. Kyoto encyclopedia of genes and genomes enrichment indicated significant involvement of the advanced glycation end-product binding to their receptor signaling pathway, along with IL-17, TNF, and HIF-1 pathways, underscoring the contribution of inflammatory and oxidative stress-related processes. This study, based on computational pharmacology analysis, predicts that Taohe Chengqi Decoction may exert therapeutic effects on constipation through an integrated regulation involving multiple components, targets, and pathways. The potential mechanisms are likely associated with the modulation of inflammatory responses, apoptosis, and oxidative stress, with the advanced glycation end-product binding to their receptor signaling pathway possibly acting as a key mediator. These findings provide theoretical insights and future directions for elucidating the molecular mechanisms underlying the therapeutic effects of Taohe Chengqi Decoction against constipation.

Drugs, Chinese Herbal

Quantum computing-assisted validation of a conserved macrophage suppression module shared by ASFV and PEDV.

BACKGROUND: African swine fever virus (ASFV) and porcine epidemic diarrhea virus (PEDV) differ in viral biology and cellular tropism, yet both pathogens suppress macrophage-mediated immune responses in pigs. OBJECTIVE: To identify a conserved macrophage suppression module shared by ASFV and PEDV and evaluate quantum computing as an independent framework for biological network validation. METHODS: Integrated analysis of publicly available GEO datasets (GSE231435 for ASFV and GSE306895) identified 471 shared downregulated genes. A network- and multi-omics-informed 20-gene core was selected and encoded as a 20-qubit modularity-based Quadratic Unconstrained Binary Optimization (QUBO) problem. Community detection was benchmarked using the Quantum Approximate Optimization Algorithm (QAOA) on both the IBM Quantum Aer simulator and the 156-qubit IBM Fez (Heron r2) quantum processor and compared with brute-force enumeration and simulated annealing. RESULTS: A conserved macrophage suppression module shared by ASFV and PEDV was identified. For the STRING protein-protein interaction network, QAOA at circuit depth p&#x2009;=&#x2009;3 reproduced the brute-force optimum with an approximation ratio of 1.000. In contrast, performance progressively declined in the denser co-expression network with increasing circuit depth, consistent with noise accumulation under current Noisy Intermediate-Scale Quantum (NISQ) conditions. Multi-run consensus analysis identified stable hub genes, including MMP9 and SLA-DOA, as well as genes exhibiting variable community assignments. CONCLUSION: These findings reveal a conserved macrophage suppression module shared between ASFV and PEDV and demonstrate that quantum computing can serve as an independent validation framework for biologically meaningful host-response networks. Network topology emerged as a key determinant of QAOA performance on real NISQ hardware.

Animals

Uncovering ShuangZi Powder's Anti-Ovarian Cancer Mechanism: A Systems Biology and Experimental Approach.

INTRODUCTION: This study investigated the anti-ovarian cancer (OC) effects of Shuangzi Powder (SZP) and its regulatory impact on the tumor microenvironment. METHOD: This study employed systems biology approaches, integrating molecular docking and experimental validation, to explore the pharmacological mechanisms of SZP in OC treatment. To identify potential bioactive compounds and target genes of SZP, network pharmacology, protein- protein interaction network analysis,.Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were conducted. RESULTS: Among the 11 bioactive ingredients identified in SZP, 1,767 potential therapeutic targets were predicted, while 2,637 differentially expressed genes were found to be associated with OC. KEGG pathway analysis revealed significant enrichment in pathways related to cancer, apoptosis, the PI3K-Akt signaling pathway, and the PD-L1/PD-1 checkpoint pathway. Treatment of A2780 cells with &#x3b2;,&#x3b2;-Dimethylacrylshikonin (DMAS) inhibited cell viability, migration, and invasion. Moreover, DMAS downregulated the expression of cell cycle- and apoptosis-related genes (CCNB1, CHEK1, CCNE1, and PARP1) and upregulated the immune checkpoint gene PD-L1. DISCUSSION: These findings indicate that multiple components, targets, and pathways are involved in OC treatment by SZP. CONCLUSION: DMAS, one of the bioactive ingredients of SZP, was predicted and preliminarily validated to exert inhibitory effects on OC cells, mainly through the regulation of the cell cycle, apoptosis, and immune response, as demonstrated by molecular docking and experimental analyses.

Ovarian Neoplasms

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

Proteomic insights into Helicobacter pylori infection in stomach cells, revealing host response and host-targeted therapeutics repurposing.

BACKGROUND: Helicobacter pylori (H. pylori) is a globally prevalent gastric pathogen strongly associated with chronic gastritis, peptic ulcers, and gastric cancer. While bacterial factors have been extensively studied, host proteomic responses and their therapeutic potential remain largely underexplored. RESEARCH DESIGN AND METHODS: Current analyses employed a systematic proteomics-based data integration and harmonization approach (retrospective qualitative cohort study) to identify important differentially regulated host proteins. Proteomic datasets were curated from in vitro studies and analyzed for functional enrichment, protein-protein interaction networks, and hub protein identification. To explore therapeutic repurposing, drug repositioning was performed using the DrugBank database. RESULTS: Data summation describing protein differential regulation in human gastric cells as a result of the infection revealed 1672 perturbed host proteins. Bioinformatics analysis revealed 11 proteins including CSK, MET, RELA, MARK2, GRB2, FTO, PLCG1, CRKL, RPS5, RPS9, and RPS27A to be ideal host targets for therapeutic repurposing. Clinically approved drugs such as Dasatinib (targeting CSK) and Crizotinib (targeting MET) emerged as promising candidates due to favorable pharmacokinetics and known bioactivity. CONCLUSIONS: Host-directed therapeutics could offer alternative strategies to conventional antibiotic therapy, addressing challenges such as resistance and infection recurrence, providing a foundation for future experimental validation and development of host-targeted interventions for infection control.

Humans

Comprehensive Analysis of Differentially Expressed Genes and Immune Infiltration in Burn Injury: Key Biomarkers and Pathways.

BACKGROUND: Burn injuries trigger complex immune responses and gene expression changes, impacting wound healing and systemic inflammation. Understanding these changes is crucial for identifying biomarkers and therapeutic targets. METHODS: We analyzed two gene expression omnibus datasets (wound tissue [GSE8056] and blood [GSE37069]) to identify differentially expressed genes (DEGs) in burn injury samples versus controls. Immune cell proportions were assessed using CIBERSORT. Functional enrichment analyses (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes) and protein-protein interaction networks were constructed to identify key genes and pathways. RESULTS: We identified 1170 upregulated and 1227 downregulated DEGs. Gene Ontology analysis revealed enrichment in neutrophil activation, inflammatory response, and extracellular matrix organization. Kyoto Encyclopedia of Genes and Genomes analysis highlighted cytokine-cytokine receptor interaction, TNF, and IL-17 signaling pathways. Immune infiltration analysis showed significant changes in neutrophils, macrophages (M1/M2), and T-cell subsets. Protein-protein interaction network analysis identified five hub genes: JUN, STAT1, Bcl2, MMP9, and TLR2. CONCLUSIONS: This study provides a comprehensive bioinformatic analysis of gene expression and immune responses in burn injuries. The identified DEGs, hub genes, and pathways offer insights into the immune response mechanisms and suggest potential targets for diagnostic and therapeutic interventions in burn injury management.

Burns

Identification of key genes related to bone metastasis of breast cancer using bioinformatics methods and construction of a prognostic model.

Breast cancer (BC) ranks among the most prevalent cancers in females, with bone metastasis significantly compromising patients' quality of life and survival rates. Enhancing our comprehension of BC bone metastasis mechanisms at the molecular level holds promise for improving BC treatment and prognosis. Leveraging bioinformatics tools, we integrated multiple datasets, conducted comprehensive analyses across various databases, identified biomarkers associated with BC bone metastasis, and constructed a prognostic model. Firstly, 3 BC bone metastasis-related datasets were downloaded from gene expression omnibus, the data were merged, and batch effects were removed, followed by identification of differentially expressed genes (DEGs). Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the DEGs. A protein-protein interaction network was constructed using the STRING database to screen hub genes. Then, survival analysis of hub genes was performed using the Cancer Genome Atlas (TCGA) database. A prognostic model was constructed using key genes with survival differences, and the model was evaluated. Two hundred ninety-two DEGs were identified. Gene ontology and KEGG pathway enrichment analysis yielded 769 biological processes (BPs), 78 cellular components, 43 molecular functions, and 50 KEGG pathways. Fifteen hub genes were selected from the protein-protein interaction network. Survival analysis revealed 6 genes related to BC survival. The prognostic model identified 4 genes with important predictive value for BC prognosis. Our study utilized bioinformatics analysis to identify a series of DEGs related to BC bone metastasis. Based on further selection of hub genes, we constructed a relatively ideal prognostic model for BC, and identified 4 genes (DLGAP5, TPX2, PLK1, and CENPN) with valuable predictive value for BC prognosis.

Humans

Exploring the mechanism of Acanthopanax in treating vertigo: A network pharmacology and molecular docking study.

Acanthopanax has therapeutic efficacy against vertigo; however, the underlying mechanism remains unclear. This study aimed to elucidate the mechanism by which Acanthopanax treats vertigo through integrated network pharmacology and molecular docking techniques, and retrieved all target genes of Acanthopanax for vertigo treatment from July to October 2025. Vertigo-related target genes were subsequently identified from public databases, including GeneCards and Online Mendelian Inheritance in Man. The intersection between Acanthopanax-derived targets and vertigo-related targets was analyzed to identify candidate target genes. Using the STRING platform, we constructed protein-protein interaction networks for the identified candidate targets and mined the core functional modules within these networks. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed on candidate targets via the clusterProfiler package. A carp bile poisoning-liver injury target-pathway network was constructed via Cytoscape 3.8.2 software, network topology analysis was conducted, and the core components and targets were screened. The results found that A total of 295 candidate targets for the treatment of vertigo caused by Eleutherococcus senticosus were identified. Pathway enrichment analysis revealed that Eleutherococcus senticosus treatment for vertigo may be closely associated with pathways related to IL-17, TNF, phosphoinositide 3-kinase (PI3K)-Akt, p53, HIF-1, and Forkhead box O signaling. The core targets for the treatment of A. senticosus vertigo include TP53, AKT1, STAT3, TNF, and JUN. Network pharmacology and molecular docking studies suggest that A. senticosus may treat vertigo by regulating targets such as JUN, TNF, AKT1, STAT3, and STAT3 through pathways such as the IL-17, TNF, phosphoinositide 3-kinase-Akt, p53, HIF-1, and Forkhead box O signaling pathways. These mechanisms warrant further investigation in future o and in vitro studies.

Molecular Docking Simulation

Molecular Evolution and Expression Analysis of the ADH Gene Family in Apple Bud Mutants.

Alcohol dehydrogenase (ADH) catalyzes the reduction of aldehydes to alcohols, key precursor substrates for volatile ester biosynthesis, which determines the characteristic aroma of apple fruit. However, a comprehensive genome-wide investigation of the ADH gene family in apple has been lacking. In this study, we systematically identified ADH genes in the apple genome using integrated bioinformatics approaches, including phylogenetic analysis, synteny evaluation, promoter cis-element prediction, codon usage bias assessment, and protein interaction network modeling. Expression patterns were examined through transcriptomic data and validated by RT-qPCR analysis across different organs and among 'Red Delicious' and its four bud mutant lines. We identified 44 ADH genes, with 12 forming a prominent cluster on chromosome 1. RT-qPCR analysis revealed that MdADH20 was dramatically upregulated in the 'Red Chief' mutant (relative expression of 59.38), suggesting its pivotal role. Phylogenetic analysis revealed a close evolutionary relationship with wild strawberry. The encoded proteins were generally stable and predominantly localized to the cytoplasm. Promoter analysis showed enrichment of growth/development-related and ARE elements, while codon usage analysis identified AGA, GCU, GUU, and CUU as preferred codons. Protein interaction prediction suggested MdADH19 and MdADH20 as hub proteins. Expression profiling and RT-qPCR further identified MdADH20 as a core candidate gene, characterized by its stable and high expression, particularly in the 'Red Delicious' mutant. Its central position in the predicted protein-protein interaction network suggests a potential regulatory role in the aroma biosynthesis pathway of apple fruit. This study provides the first systematic genome-wide characterization of the apple ADH gene family, establishing a theoretical groundwork for deciphering aroma biosynthesis mechanisms and offering potential target genes for flavor improvement through bud mutation breeding strategies.

ADH gene family

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

Molecular mechanism of HaiZao-YuHu decoction in breast cancer treatment via network pharmacology and molecular docking: Computational pharmacology.

BACKGROUND: The molecular biological mechanisms of HaiZao-YuHu decoction were investigated using network pharmacology and molecular docking. METHODS: TCMSP database was used to collect the active ingredients and action targets of HaiZao-YuHu decoction, through the OMIM, PharmGkb, GeneCards, TDD, and DurgBank database query targets for breast cancer. Then, using the intersecting targets, the protein-protein interaction network of HaiZao-YuHu decoction was constructed using the STRING website. Network topology analysis was performed using Cytoscape 3.9.0 to identify the core targets. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed with the R package. The Autodock software was used for molecular docking. RESULTS: Thirty-four active ingredients, 219 intersection targets and 4 key targets were obtained. gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis get 2152 biological processes and 186 pathways. Molecular docking showed that the 4 core targets could combine well with the 5 main active components. CONCLUSION: HaiZao-YuHu decoction can play a role in the treatment of breast cancer through multi-targets, multi-components, and multi-pathways.

Molecular Docking Simulation

Exploring the treatment of liver cancer with Gehua Hugan Gao based on bioinformatics, network pharmacology, and molecular docking.

Gehua Hugan Gao (GHHGG) is a traditional Chinese medicine paste that is chiefly used to treat liver cancer. However, the potential impact of GHHGG on liver cancer remains unclear. We explored how GHHGG treats liver cancer using bioinformatics, network pharmacology, and molecular docking. Network pharmacology included GHHGG active ingredients, predicted targets, predicted targets for liver cancer, and differential gene collection. A protein-protein interaction network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins database, and crucial targets were ranked according to their degree values. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses of liver cancer targets were followed by survival, differential analysis, and molecular docking. Venn diagrams show 123 predicted GHHGG targets for the treatment of hepatocellular carcinoma (HCC). Enrichment analysis showed that GHHGG treats HCC through multiple targets and pathways. We also found that estrogen receptor 1, cytochrome P450 3A4, cyclin-dependent kinase 4, type IIA topoisomerase, aurora kinase A, and cyclin E1 targets were closely associated with HCC development through survival and differential analyses. Molecular docking confirmed GHHGG's strong affinity for liver cancer targets. This study helps us understand GHHGG ingredients and targets for liver cancer treatment. To a certain extent, the molecular mechanism of GHHGG in the treatment of liver cancer has been elucidated, thus providing a theoretical basis.

Molecular Docking Simulation

Beyond genes: EpiSwitch&#xae; and Orion platform-powered 3D genome architecture biomarkers reveal shared biology across ME/CFS, long COVID, PTSD, rheumatoid arthritis, and multiple sclerosis.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), Long COVID (LC19), post-traumatic stress disorder (PTSD), rheumatoid arthritis (RA), and multiple sclerosis (MS) are clinically distinct disorders that share substantial symptom overlap, including persistent fatigue, cognitive impairment, autonomic dysfunction, and immune dysregulation. Although these conditions differ in diagnosis and clinical presentation, their underlying biological mechanisms remain poorly understood and may involve convergent regulatory pathways. METHODS: The EpiSwitch&#xae; 3D genomics platform and Orion knowledgebase were used to integrate chromosome conformation signatures with genome-wide association study (GWAS)-derived datasets across ME/CFS, LC19, PTSD, RA, and MS. Three-dimensional genomic anchors were mapped to coding genes and analysed using STRING protein-protein interaction networks and Cytoscape-based systems biology approaches. Disease-specific anchor datasets were generated and compared at both gene and network levels to identify shared biological processes and regulatory mechanisms. RESULTS: Analysis of the ME/CFS dataset identified 552 unique 3D genomic anchors mapped to 567 genes, with analogous disease-specific anchor sets generated for LC19, PTSD, RA, and MS. Direct overlap between disease-associated genes was limited; however, higher-order network analyses revealed substantial interconnectivity and convergence across conditions. Shared biological pathways included immune and cytokine signalling, interferon responses, mitochondrial function, metabolic regulation, and neuroendocrine processes. Highly connected hub genes included immune regulatory nodes such as LAG3 and components of the mTOR signalling pathway, implicating T-cell exhaustion, chronic immune activation, and immunometabolic dysregulation as common mechanisms underlying these disorders. CONCLUSIONS: These findings support a systems-level model in which clinically overlapping fatigue-associated syndromes arise from perturbations of interconnected regulatory networks rather than discrete disease-specific pathways. Despite limited genetic overlap, substantial convergence at the network level suggests shared biological architecture across ME/CFS, LC19, PTSD, RA, and MS. The identification of common regulatory pathways provides a mechanistic framework for the development of cross-disease diagnostic and therapeutic strategies. By capturing dynamic regulatory states, 3D genomic biomarkers offer significant potential for objective blood-based diagnostics, patient stratification, and the identification of shared therapeutic targets across complex chronic disorders. These findings support the application of precision medicine approaches and may accelerate the development of novel interventions for fatigue-associated multisystem diseases.

Humans