IET Systems Biology最新文献

Oral squamous cell carcinoma (OSCC) is an aggressive malignancy associated with high morbidity and mortality. RAD51 recombinase (RAD51), a central DNA repair protein, plays a crucial role in homologous recombination and has been implicated in cancer progression through mechanisms such as genomic instability, chemoresistance and immune modulation. However, its specific function and regulatory mechanisms in OSCC remain incompletely elucidated. We conducted an integrated multiomics analysis including differential expression, single-cell transcriptomics, prognostic evaluation, functional enrichment and immune infiltration profiling. Experimental validation was performed using siRNA-mediated RAD51 knockdown in OSCC cell line HSC-3, followed by functional assays to assess proliferation, migration, invasion, reactive oxygen species (ROS) accumulation and chemosensitivity. RAD51 was significantly overexpressed across multiple cancers, including OSCC, and exhibited high diagnostic accuracy for OSCC (AUC = 0.956). Single-cell RNA sequencing revealed elevated RAD51 expression in malignant and proliferating T cells, associating it with aggressive phenotypic traits. High RAD51 expression predicted poor prognosis in OSCC and other cancers. Functional analyses indicated its involvement in the Fanconi anaemia pathway, DNA damage repair and cell cycle regulation. Immune infiltration analysis revealed significant negative correlations with multiple immune cell subtypes and tumour microenvironment scores. Experimentally, RAD51 knockdown suppressed malignant behaviours and enhanced ROS production and chemosensitivity in HSC-3 cells. RAD51 drives OSCC progression by enhancing malignant phenotypes, suppressing immune infiltration, promoting aberrant DNA repair, elevating oxidative stress and promoting therapy resistance. These findings support RAD51's potential as both a prognostic biomarker and a therapeutic target in OSCC.

Although immunotherapy has revolutionised cancer treatment, its benefits remain restricted to a minority of patients with colon cancer. Emerging evidence implicates super-enhancer (SE) networks and ferroptosis dysregulation as key oncogenic drivers, though their synergistic prognostic and immune microenvironment implications are unexplored. Super-enhancer-related ferroptosis genes (SEFGs) were identified by intersecting SE-associated and ferroptosis-related genes. Using TCGA-COAD (training) and GSE39582 (validation) cohorts, we established an 8-gene prognostic signature via LASSO Cox regression. This signature formed the basis of a clinical nomogram with robust calibration and discrimination (C-index = 0.813). High-risk patients exhibited significantly reduced overall survival. Elevated risk scores correlated with advanced stage, consensus molecular subtypes (CMS1/CMS4), high tumour mutation burden (TMB), high-level microsatellite instability (MSI) and enhanced immune cell infiltration, paradoxically coupled with immunosuppressive phenotypes including increased immune checkpoint gene expression and reduced immunotherapy responsiveness, alongside increased sensitivity to SE inhibitors. JQ-1 RNA-Seq profiling revealed three core SE-driven genes, TRIB2, CAV1 and ENO3, which were significantly downregulated upon SE inhibition. Among them, TRIB2 was distinguished by its SE recurrence, tumour overexpression, prognostic value and JQ-1 suppression. The SEFG signature facilitates simultaneous prediction of prognosis and assessment of the immune microenvironment, providing a potential tool for colon cancer management.

Major Depressive Disorder (MDD) is linked to increased neurodegenerative risk. Emerging evidence implicates ferroptosis in neuropsychiatric disorders, prompting investigation of its role in MDD through key gene identification. Three microarray datasets from the GEO database were analysed. Weighted gene co-expression network analysis (WGCNA) identified MDD-related module genes (MRGs) while ferroptosis-related genes (FRGs) were extracted from the FerrDb database. Overlapping genes between MRGs and FRGs were prioritised for mechanistic exploration. Functional enrichment (GO/KEGG) and protein-protein interaction (PPI) network analyses (via Cytoscape and CytoHubba) highlighted hub genes. Machine learning algorithms were applied to develop a diagnostic model, validated through nomogram analysis, calibration curves, decision curve analysis (DCA), ROC curves (AUC evaluation), gene set enrichment analysis (GSEA), and DGIdb-based drug prediction. Differential expression analysis identified 1878 MDD-associated genes (715 downregulated, 1163 upregulated). Four FRGs—MAPK14, WIPI1, DUSP1, and ULK1—emerged as diagnostic biomarkers, showing significant immune cell infiltration correlations (e.g., neutrophils, dendritic cells) and enrichment in pathways like MAPK signalling. The study highlights ferroptosis-related genes (ULK1, MAPK14, WIPI1, DUSP1) as potential diagnostic and therapeutic targets in MDD, linked to neuroimmune interactions and cellular stress responses. These findings underscore MDD's pathophysiological complexity and may guide strategies for managing MDD and neurodegenerative comorbidities.

Immune cells in early atherosclerotic lesions promote inflammation and acute coronary syndrome (ACS), but the precise link between inflammation and ACS progression is still unclear. In this study, we analysed mRNA and miRNA expression profiles of ACS from GEO, identifying 98 mRNAs and 627 miRNAs by differentially expressed analysis. GSEA revealed abnormal activation of immune- and inflammation-related pathways, such as T cell receptor signalling pathway and cell adhesion molecules cams. The biomarkers ARG1, HECW2, and PFKFB3 were identified through WGCNA, LASSO, and SVM-RFE. Diagnostic performance and miRNA–mRNA interaction network were performed using ROC curves and Cytoscape. CIBERSORT analysis revealed that the levels of CD4 memory resting T cells were downregulated, whereas monocytes and neutrophils were upregulated. ARG1, HECW2 and PFKFB3 showed close relationships with specific immune cell types. These findings offer new avenues for ACS treatments and identify ARG1, HECW2 and PFKFB3 as potential biomarkers.

Mitochondrial dysfunction is increasingly recognised as a critical contributor to acinar cell injury and systemic inflammation in acute pancreatitis (AP). However, comprehensive screening of mitochondrial-related genes (MRGs) and their mechanistic roles in AP progression remains limited. We integrated transcriptomic data with MRGs from the MitoCarta database. A total of 34 differentially expressed MRGs were identified, enabling classification of AP samples into three molecular subtypes with distinct immune cell infiltration patterns and clinical severity. Three hub genes were consistently identified by three machine learning algorithms: LASSO, SVM-RFE, and RF. qRT-PCR validation in cellular models confirmed consistent expression trends. Multi-level functional annotation was conducted through GSVA, CIBERSORT, transcription factor prediction, subcellular localisation and single-cell analyses. Talniflumate and ABT-737 were predicted as potential therapeutic agents using the CMap and validated through molecular docking and 100-ns molecular dynamics simulations. This study establishes a mitochondria-related diagnostic model for AP and identifies candidate therapeutic agents, offering novel insights into the molecular pathogenesis and targeted intervention of AP.

Sarcopenia and osteoporosis share pathophysiological links, but their co-occurrence mechanisms remain unclear. This study aimed to identify molecular mediators of their co-development using bioinformatics. Datasets for sarcopenia (GSE56815) and osteoporosis (GSE9103) were retrieved from GEO. Differentially expressed genes (DEGs) were analysed via edgeR and limma. Gene ontology (GO), Kyoto encyclopaedia of genes and genomes (KEGG) and weighted gene co-expression network analysis (WGCNA) identified shared pathways and hub genes. Protein–protein interaction (PPI) networks were constructed using STRING and Cytoscape. We validated hub genes in independent datasets (GSE13850, GSE8479) and assessed via ROC curves. Immune infiltration, single-cell analysis and drug prediction were performed. We identified 134 common DEGs (30 upregulated, 104 downregulated). WGCNA and PPI analysis revealed 14 hub genes (APOE, CDK2, PGK1, HRAS, RUNX2 etc.), all with ROC-AUC > 0.6. PGK1 was consistently downregulated in both diseases and linked to 21 miRNAs and six transcription factors (HSF1, TP53, JUN etc.). Single-cell analysis localised PGK1 predominantly in skeletal muscle fibroblasts. DrugBank identified lamivudine as a potential PGK1-targeting therapeutic. PGK1 emerged as a central downregulated gene in sarcopenia and osteoporosis, enriched in fibroblasts and modulated by lamivudine. These findings highlight PGK1 as a shared diagnostic and therapeutic target, offering insights into musculoskeletal crosstalk.

Muscle disuse atrophy (MDA) is a debilitating condition caused by prolonged inactivity. Given the gender differences, mechanisms of MDA are often investigated separately for each gender. To better understand the similarities and differences between genders in MDA, we analysed transcriptomic data from the gene expression omnibus database, stratified by gender, to identify differentially expressed genes. Weighted gene co-expression network analysis was employed to construct co-expression modules and identify hub genes. Least absolute shrinkage and selection operator regression was used to select common hub genes, and their diagnostic potential was validated using ROC analysis. Additionally, immune cell infiltration analysis was performed to explore the role of immune dysregulation in MDA. This study identified that CD36 was a biomarker across genders, while C21ORF33 was a male MDA biomarker. WGCNA revealed gender-specific co-expression modules significantly correlated with MDA traits. Immune cell infiltration analysis showed upregulated immature B cells and downregulated eosinophils in female MDA, highlighting the role of immune dysregulation. CD36 and C21ORF33 demonstrated strong discriminatory power. Expression of these two biomarkers was validated in tenotomy mouse modelling. This study emphasised the roles of chronic inflammation and immune dysregulation in MDA. The nongender-specific expression of CD36 underscores its potential importance in MDA pathogenesis.

This research aimed to determine genes associated with M1 TAMs (tumour-associated macrophages) and to develop an M1 TAMs-related signature for predicting GC (Gastric cancer)’s prognosis and therapeutic effect. Based on the GC dataset in TCGA, we constructed a prognostic signature using M1 TAMs-related genes and validated it using data from the GEO dataset. To evaluate the predictive power of the signature, the survival curves, ROC curves, Cox regression analysis, nomograms and calibration curves were constructed. Differences in immune infiltration, immunotherapy response, and chemotherapy sensitivity between the two risk groups were also analysed. Furthermore, by jointly using the string database and Cytoscape software, we identified the hub gene that differed between the two risk groups. In the end, the expression and function of the identified hub gene were validated using fresh tissue specimens and GC cell lines. A six-gene risk signature was developed based on M1 TAMs-related genes. Furthermore, the ROC curve, nomogram, calibration plot of the nomogram and Cox regression analysis confirmed M1 TAMs co-expressed genes have a strong predictive performance of the six-gene risk signature. Immune infiltration analysis and the TIDE algorithm indicated that low-risk GC patients may be more suitable for immunotherapy. Finally, fibronectin 1 (FN1), the hub gene with the highest degree of interaction between high- and low-risk groups, indicated a significant correlation with survival differences in GC. Functional analysis demonstrated that FN1 promotes GC cell proliferation, invasion, migration and EMT. The risk signature of six M1 TAMs co-expressed genes can be used to evaluate the prognosis and treatment efficacy of patients with GC, providing a basis for selecting new therapies for patients. The FN1 gene is the hub gene with predictive value in this signature, and it is upregulated in GC and functions as an oncogene.

Polycystic ovary syndrome (PCOS) is a prevalent endocrine and metabolic disorder characterised by heterogeneous clinical and molecular phenotypes. Flaxseed, widely used in traditional Chinese medicine and as a nutritional supplement, has shown promising therapeutic potential for PCOS. In this study, we integrated transcriptomic data with machine learning-based analytical approaches and network pharmacology to investigate the molecular mechanisms underlying PCOS and to identify the potential targets and pathways modulated by flaxseed. Differentially expressed genes (DEGs) and PCOS-related targets were systematically identified from GEO, GeneCards and DisGeNet databases. Bioactive compounds in flaxseed were predicted using TCMSP, SwissTargetPrediction and INPUT2.0. Functional and pathway enrichment analyses were conducted to explore mechanistic insights. Core targets were prioritised using Centiscape network topology parameters and LASSO regression, followed by molecular docking validation using AutoDock. Our results revealed that flaxseed's therapeutic action may primarily involve modulation of immune regulation, insulin signalling, apoptosis and inflammation pathways. Key active compounds, notably β-sitosterol and stigmasterol, exhibited strong binding affinities with critical targets, such as IL1B, GSK3B and HMGCR, suggesting potential anti-inflammatory and antioxidant effects. The findings provide a theoretical foundation for future experimental studies and support the development of flaxseed-based therapeutic strategies for PCOS through precision medicine frameworks.