IET Systems Biology最新文献

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.

Pancreatic adenocarcinoma (PAAD) remains highly lethal because of chemotherapy resistance and immunosuppressive microenvironments. Tertiary lymphoid structures (TLSs) were analysed in PAAD to develop personalised therapeutic strategies. Nine TLS-related genes (CCR6, CD1d, CD79B, CETP, EIF1AY, LAT, PTGDS, RBP5 and SKAP1) were selected for integrative analysis of TLS status in relation to clinical outcomes, immune cell infiltration, tumour mutational burden (TMB) and drug resistance. High TLS scores (TLS_H) were associated with improved overall survival (OS) and progression-free survival (PFS), independent of age or tumour grade. Twelve immune cell types differed across TLSs. Single-cell RNA-seq analysis revealed that the 9 TLS-related genes were enriched in distinct immune cell populations. Combining TLS and TMB improved survival prediction. Notably, the TLS_H group demonstrated enhanced sensitivity to chemotherapeutics including AZD8055, axitinib, vorinostat, nilotinib, camptothecin and paclitaxel. Real-time fluorescent quantitative PCR (RT-qPCR) validation in Mia PaCa2 and Jurkat cells indicated that LAT, RBP5 and SKAP1 may play important roles in modulating sensitivity to these chemotherapeutics. These findings establish TLS as a potential biomarker for PAAD, enabling personalised chemotherapy selection by integrating immune contexture and genomic drivers to improve clinical outcomes.

Accurate polyp segmentation is crucial for computer-aided diagnosis and early detection of colorectal cancer. Whereas feature pyramid network (FPN) and its variants are widely used in polyp segmentation, inherent limitations existing in FPN include: (1) repeated upsampling degrades fine details, reducing small polyp segmentation accuracy and (2) naive feature fusion (e.g., summation) inadequately captures global context, limiting performance on complex structures. To address limitations, we propose a cascaded aggregation network (CANet) that systematically integrates multi-level features for refined representation. CANet adopts PVT transformer as the backbone to extract robust multi-level representations and introduces a cascade aggregation module (CAM) that enriches semantic features without sacrificing spatial details. CAM adopts a top-down enhancement pathway, where high-level features progressively guide the fusion of multiscale information, enhancing semantic representation while preserving spatial details. CANet further integrates a multiscale context-aware module (MCAM) and a residual-based fusion module (RFM). MCAM applies parallel convolutions with diverse kernel sizes and dilation rates to low-level features, enabling fine-grained multiscale extraction of local details and enhancing scene understanding. RFM fuses these local features with high-level semantics from CAM, enabling effective cross-level integration. Experiments show that CANet outperforms SOTA methods in in- and out-of-distribution tests.

Accumulating evidence suggests that the TP53 mutation, intratumoral microbiome, and tumour microenvironment (TME) are closely linked to tumourigenesis, yet the biological mechanisms underlying these connections remain unclear. To explore this, we collected multi-omics data—including genome, transcriptome, and tumour microbiome data—from a wide range of cancer types in The Cancer Genome Atlas (TCGA). Through a pan-cancer analysis, we identified significant correlations between intratumoral microbiota diversity and TP53 mutation status, particularly in hepatocellular carcinoma (HCC) and endometrial cancer (EC). Despite notable differences in microbiota composition between these two cancer types, we consistently observed that TP53 mutations were associated with reduced alpha-diversity. Additionally, we found that TP53 mutation status significantly influenced stromal components within the TME, such as a strong correlation between decreased endothelial cell abundance and TP53 mutation. Our integrated approach reveals the complex interplay between TP53 and factors regulating the host TME, offering new insights into cancer progression and potential therapeutic targets for future research.

Magnetic resonance imaging (MRI) has a pivotal role in both pretreatment staging and post-treatment evaluation of rectal cancer. This study presents an innovative deep learning model, CAAFE-ResNet18*, based on the residual neural network ResNet18*. The model features an ingeniously designed feature extraction and complementation module (i.e., CAAFE), which leverages a multiscale dilated convolution parallel architecture combined with a channel attention mechanism (CAM) to achieve multilevel information fusion, spatial feature enhancement and channel feature optimisation. This enables in-depth exploration and augmentation of multilevel downsampled features, significantly improving feature representation capability and overall performance. Testing on rectal cancer MRI data demonstrates that the CAAFE-ResNet18* model significantly outperforms convolutional neural network (CNN) backbone networks and recent state-of-the-art (SOTA) models. This result indicates that the CAAFE model, by complementing and extracting MR images of patients with locally advanced rectal cancer (LARC) features at different scales from ResNet18*, may help to identify patients who will show complete response (CR) at the end of treatment and those who will not respond to therapy (NR) at an early stage of the treatment.

Primary liver cancer ranks as the third most lethal cancer globally, with hepatocellular carcinoma (HCC) being the most prevalent pathologic type. The liver plays a crucial role in maintaining normal coagulation function by synthesising, regulating and clearing coagulation factors and other bioactive substances involved in coagulation. Although several previous studies have proposed coagulation-associated prognostic models in HCC, the mechanisms at the single-cell level are not fully elucidated. In this study, the coagulation subtypes and their heterogeneity of HCC malignant cells were identified based on the coagulation-related genes collected from KEGG and GO databases. Through machine learning algorithms, we defined a coagulation gene signature at the single-cell level, based on which a coagulation-associated risk score (CARS) model was constructed in the TCGA-LIHC cohort. Integrating clinicopathological information and the CARS, a nomogram model was further developed for individualised prognostic assessment. Additionally, the mechanisms of prognostic differences among patients with divergent coagulation-associated risks were dissected through tumour signalling pathways, cellular communication and pseudotime trajectory analysis, while exploring the potential application of this risk assessment system in HCC treatment. In conclusion, the established CARS system accurately predicts prognosis, providing an important theoretical basis for precision treatment of HCC.

Automated polyp detection plays a critical role in the early diagnosis of colorectal cancer, ranking as the second leading cause of cancer-related mortality worldwide. However, existing segmentation methods face difficulties in handling complex polyp shapes, size variations, and generalising across diverse datasets. We propose a Multi-dimensional Residual Attention Network (MRANet) for the polyp segmentation task, focusing on enhancing feature representation and ensuring robust performance across diverse clinical scenarios. During encoding, MRANet employs residual self-attention to capture semantic information of high-level features, guiding the refinement of low-level information. In addition, convolutions with Multiple Kernel and Dilation rates (CMKD) are integrated with residual channel and spatial attentions to expand the model's receptive field, enhance encoder features, and accelerate convergence. In the decoding stage, MRANet uses the proposed Attention-based Scale Interaction Module (ASIM) to merge upsampled high-level features with low-level pixel information, enriching low-level layers using semantic knowledge. A Residual-based Scale Fusion Module (RSFM) is further designed to merge low-level features, which preserves high-frequency details including edges and textures. Experiments demonstrate that MRANet effectively segments polyps with varying sizes, indistinct boundaries, and scattered distributions, achieving the best overall performance. Our code is available at https://github.com/hpguo1982/MRANet.