IET Systems Biology最新文献

Bipolar disorder (BD) is a chronic psychiatric illness associated with significant cognitive and social dysfunction, contributing substantially to the global disease burden. Recent evidence suggests that the gut microbiota may play a role in the pathophysiology of BD through the microbiota–gut–brain axis. To clarify this potential link and explore diagnostic applications, we investigated gut microbial alterations in BD and evaluated their predictive value using 16S rRNA sequencing and machine learning approaches. We first assessed microbial diversity and composition, revealing significantly reduced α-diversity and altered β-diversity in BD compared to healthy controls (HC), alongside weakened microbial co-occurrence network connectivity. Given these compositional differences, we systematically benchmarked 12 classification algorithms to discriminate BD from HC. Ensemble-based models, particularly the random forest (RF) classifier, achieved the best diagnostic performance. To further improve predictive accuracy, we compared multiple feature selection methods: RF feature importance ranking, independent t-tests and MaAsLin2 analysis, identifying 35 optimal microbial biomarkers based on RF. This feature set demonstrated excellent classification performance (AUC = 0.9316, AUPR = 0.9497). Furthermore, based on the taxonomic findings, we applied PICRUSt2 functional prediction using KEGG and MetaCyc annotations, which revealed marked alterations in pathways related to neurodegeneration, lipid metabolism and heme biosynthesis. Finally, to capture both compositional and functional aspects of microbial dysbiosis, we combined these functional features with the selected microbial biomarkers in an RF model, achieving further improved diagnostic performance (AUC = 0.9499, AUPR = 0.9586). In conclusion, our results demonstrate substantial compositional and functional disturbances in the gut microbiota of BD and highlight the value of machine learning-driven, microbiome-based models for noninvasive BD diagnosis. The identified microbial and metabolic markers also provide mechanistic insights into the microbiota–gut–brain axis, offering promising directions for precision psychiatry and microbiome-targeted interventions.

Sepsis heterogeneity poses a challenge to accurate diagnosis and treatment. The impact of SUMOylation, a post-translational modification, on sepsis is largely unexplored. We integrated three GEO datasets to construct a large-scale sepsis cohort and applied three machine learning algorithms to screen hub genes from differentially expressed genes (DEGs) associated with SUMOylation in sepsis. Unsupervised consensus clustering was performed to identify sepsis subtypes. Using single-sample gene set enrichment analysis (ssGSEA) and gene set variation analysis (GSVA), we analysed the immunological and functional features of these subtypes. We assembled the regulatory network of hub genes and performed drug prediction analysis. The expression of hub genes was confirmed in a murine caecal ligation and puncture (CLP) sepsis model through qRT-PCR. Bioinformatics analysis identified a total of 43 SUMOylation-associated DEGs. The machine learning pipeline further pinpointed eight hub genes: TOP2B, HDAC4, NUP43, HNRNPK, BCL11A, RPA1, RORA and XRCC4. Each gene exhibited high diagnostic potential. Based on this eight-gene signature, sepsis patients were stratified into two subtypes. Subtype A, known as immune suppressive, was characterised by high infiltration of regulatory T cells, along with suppressed activity in immune pathways. The hyper-inflammatory subtype B displayed large infiltration of effector lymphocytes and extensive activation of inflammatory pathways. Drug prediction analysis revealed possible therapeutic compounds, particularly the epigenetic modulator vorinostat. Experimental validation ultimately confirmed the dysregulation of these hub genes. In conclusion, our study discovered a novel eight-gene signature associated with SUMOylation that supports new diagnostic strategies, and uncovers sepsis heterogeneity. The identification of two sepsis subtypes with different immunological and functional characteristics emphasises the role of SUMOylation in sepsis pathophysiology and provides a new strategy for advancing precision diagnostics and personalised therapy.

Colorectal cancer (CRC) is counted among the most widespread malignancies worldwide, characterised by elevated incidence and mortality rates. Conventional chemotherapy is frequently associated with severe toxic side effects and the development of drug resistance, which necessitates an urgent search for alternative therapeutic modalities. Traditional Chinese medicine (TCM), distinguished by its multi-component and multi-target synergistic actions, represents a promising prospect for the development of innovative anti-tumour therapies. Nitidine chloride (NC), a major bioactive component isolated from Zanthoxylum nitidum, has demonstrated notable anti-tumour activity in various cancer types. However, the specific molecular mechanisms underlying its anti-CRC effects remain unclear. Centromere-associated protein E (CENPE) exerts a pivotal function in the regulation of the cell cycle, and its aberrant expression has been documented in multiple malignancies. It may therefore serve as a potential therapeutic target. This study sought to clarify the interplay between NC and CENPE, with the aim of offering a scientific foundation for the advancement of precision therapeutic approaches for CRC utilising TCM-derived bioactive compounds. To comprehensively characterise the expression pattern of CENPE in CRC, we integrated a range of state-of-the-art technologies encompassing single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, large-scale mRNA cohort analyses and immunohistochemistry (IHC). The regulatory impact of NC on CENPE expression was verified through real-time quantitative polymerase chain reaction (RT-qPCR) and IHC. Additionally, molecular dynamics simulation (MDS) was employed to investigate the binding mode and stability of the NC-CENPE complex. Multi-dimensional analyses indicated that CENPE is significantly overexpressed in CRC tissues, with a standardised mean difference of 1.32, and its expression scores approach 1.0 in malignant regions. CRISPR screening data suggested that CENPE knockout is associated with markedly reduced proliferation of CRC cells. MDS data supported a plausible binding mode between NC and CENPE, with a predicted binding free energy of -8.2 kcal/mol, in which van der Waals interactions constituted a major component of the calculated binding energy. Furthermore, treatment with NC was associated with significant downregulation of CENPE mRNA and protein levels in CRC cells and xenograft models in this study, although these findings require further validation in additional experimental systems. NC exerts anti-colorectal cancer activity through targeting CENPE. This discovery lays a mechanistic foundation for the development of precision therapies based on active TCM ingredients, offering a new direction for CRC treatment.

The automated segmentation of thyroid nodules from ultrasound images holds significant value in clinical diagnosis and treatment. However, achieving precise segmentation remains a substantial challenge due to issues such as blurred nodule boundaries, variable scales, image noise, and inaccurate annotations. To address these difficulties, this paper proposes a novel medical image segmentation network named MFS-Unet. The network introduces three innovative modules to enhance segmentation performance. First, we designed the multi-path vision mamba (MPV) module, which leverages the advantages of state space models (SSMs) to efficiently capture global contextual information and multi-scale features with linear computational complexity, effectively addressing the problem of significant variations in nodule size. Second, a feature gating (FG) module is deployed in the skip connections between the encoder and decoder. Through an attention mechanism, it dynamically screens and enhances features transmitted from the encoder, suppressing background noise and reinforcing key boundary information of the nodules. Finally, we propose a supervised label rectification (SLR) module, aimed at proactively handling the prevalent issue of label noise in training data. By dynamically adjusting loss weights during training, it guides the model to learn more robust feature representations. We conducted extensive experiments on three public thyroid ultrasound datasets: DDTI, TG3K, and TN3K. The results demonstrate that MFS-Unet achieves superior performance across all evaluation metrics compared with various state-of-the-art segmentation methods, proving its effectiveness and significant potential for precise thyroid nodule segmentation in complex ultrasound environments.

Hepatocellular carcinoma (HCC) is a cancer with high morbidity and mortality, and effective biomarkers for indicating prognosis and treatment have still not been fully investigated. Forkhead box O1 (FOXO1), as a crucial transcription factor, its role remains to be elucidated in HCC. Herein, by combining bioinformatics techniques and basic experiments, the expression and function of FOXO1 in HCC were preliminarily explored. The expression profile, prognostic analysis, mutation landscape, immune infiltration abundance and tumour stemness index of FOXO1 were determined in TCGA and GEO databases. Moreover, a FOXO1-related nomogram was constructed and validated in the HCC cohort. Ultimately, the expression and function of FOXO1 in HCC were verified through basic experiments, such as western blotting, RT-qPCR, immunohistochemical analysis, CCK8 assay and clone formation assay. The expression of FOXO1 was decreased and was associated with a favourable prognosis in majority of cancers. Mutation landscapes of FOXO1 in various cancers were described and revealed the significant association between FOXO1 expression and TMB/MSI. A FOXO1-based Nomogram was constructed and verified in HCC cohort. The expression and function of FOXO1 were closely related to the HCC immune microenvironment. Moreover, there was a negative correlation between the FOXO1 expression and tumour stemness index. Finally, the expression pattern of FOXO1 in HCC and its association with tumour proliferation ability were verified through basic experiments. FOXO1 was identified to regulate the immune microenvironment and the tumour proliferation ability in HCC, demonstrating its potential as a therapeutic target for HCC.

Sarcopenia is an ageing-related disease characterised primarily by skeletal muscle functional decline. Despite of fatty acid metabolism (FAM) affecting oxidative stress within muscle tissue, the key roles of critical genes linking FAM and sarcopenia are unclear. The GSE8479, GSE1428, and GSE136344 datasets were downloaded and intersected for identifying FAM-related differentially expressed genes (FAMRDEGs) screened by enrichment analysis, LASSO regression, and Support Vector Machine (SVM) analyses. Cytoscape software was used for visualising mRNA-transcription factor (TF) and mRNA-miRNA networks. In addition, ROC curves of key genes were plotted to evaluate their diagnostic significance. A Fatty Acid Metabolism Score (FAM-Score) was conducted and immune cell infiltration analysis was conducted. The qPCR assay was performed to analyse the levels of screened critical genes. A total of 109 FAMRDEGs were obtained, and the LASSO regression and SVM models screened 14 of these genes. The network included 7 key genes with 54 miRNAs and 9 hub genes with 102 TFs. There were 6 types of immune cell infiltration showing statistical significance. The FABP3 (P < 0.001), PECR (P < 0.01), and OPN3 (P < 0.001) mRNA expression markedly increased in sarcopenia versus control groups. In contrast, sarcopenia group showed remarkably reduced PCTP (P < 0.001), SREBF2 (P < 0.001), and PPARGC1A (P < 0.05) levels. This study provides reference indicators for FAM-associated auxiliary biomarkers of sarcopenia and preliminarily establishes effective machine learning models for further mechanistic exploration.

Medical image segmentation is crucial for clinical diagnosis and treatment planning. Although methods based on CNN, particularly U-Net and its variants, have achieved remarkable success in automated segmentation tasks, they still face challenges in effectively capturing long-range dependencies, refining multi-level features, and efficiently integrating cross-level information. To address these issues, we propose a novel U-Net architecture incorporating a multi-scale feature refinement mechanism (MFR-UNet). This network enhances segmentation accuracy and robustness by integrating three innovative modules. First, we designed a wavelet transform convolution (WtConv) module. By decomposing, processing, and reconstructing features in the frequency domain, this module enables the model to learn high-frequency details and low-frequency contours with greater precision. Second, we introduce a large receptive field attention (LRFA) module in the encoder. Combining deep separable convolutions with multi-head attention, LRFA efficiently captures global contextual information at low computational cost. Finally, in the skip connections and decoding path, our weighted contextual fusion module (WCF) module dynamically generates channel attention weights for one feature stream to another, achieving efficient adaptive feature fusion. Simulation experiments on multiple public medical image segmentation datasets demonstrate that our MFR-UNet outperforms several existing mainstream methods in key metrics such as Dice coefficient and IoU, proving its effectiveness in enhancing segmentation accuracy and boundary clarity.

Ruptured intracranial aneurysms (IAs) are the leading cause of aSAH. There are limitations in combining traditional imaging methods (CTA and DSA) and clinical scores (PHASES) to predict IAs rupture risk, whereas artificial intelligence (AI) algorithms show potential. This meta-analysis evaluated AI algorithm performance for predicting IAs rupture risk based on CTA and DSA. As of February 2025, we searched Web of Science, PubMed, Scopus, and Embase, extracting TP, FP, FN, and TN from included studies. The combined sensitivity, specificity, and AUC were synthesised with a bivariate random-effects model. Subgroup analyses were performed. PROSPERO: CRD420251008866. Twenty studies (13,232 patients, 14,344 IAs) reported pooled sensitivity 0.84 (95% CI: 0.80–0.87), specificity 0.82 (95% CI: 0.78–0.86), and AUC 0.90 (95% CI: 0.87–0.92) with substantial heterogeneity. Subgroup analyses showed DOR in the DSA versus CTA groups (DSA 23.55, CTA 22.21) with persistent heterogeneity. The clinical-morphological-radiomics group had DOR 18.76 without heterogeneity. By publication year, 2021 group had a lower DOR (12.99) versus 2022 (23.03) versus 2023 (26.98), with low heterogeneity. AI algorithms predicting IAs rupture risk based on CTA and DSA demonstrate high diagnostic accuracy and have potential to advance the field.

The gut microbiome is crucial for paediatric intestinal development and holds therapeutic potential for inflammatory bowel disease (IBD). This review explores the link between gut microbiome dysbiosis and paediatric IBD pathogenesis. Microbial colonisation during early developmental windows establishes immune tolerance, reinforces epithelial barrier integrity and regulates metabolic functions. Dysbiosis contributes to disease through reduced beneficial microbial metabolites, impaired mucosal barriers and aberrant immune activation. Distinct dysbiosis signatures in paediatric patients correlate with clinical phenotypes and treatment responses, suggesting potential biomarkers. Emerging therapies include targeted nutritional therapies, designed microbial consortia, microbiota transplantation and tailored diets. By correcting underlying microbial imbalances, these approaches may offer more sustainable disease control with fewer side effects than conventional anti-inflammatory treatments. However, challenges persist, such as limited paediatric cohort sizes, a lack of causal mechanistic data and variability in microbiome profiles due to diet, geography and developmental stage. Future research requires larger longitudinal studies to develop paediatric-specific interventions that restore microbial equilibrium, ultimately transforming IBD management in children.

Adaptive therapy seeks to use intra-tumoral competition to avoid or delay the emergence of drug resistance in cancer treatment. Driven by clinical trials of metastatic castrate-resistant prostate cancer, people are increasingly interested in extending this approach to other tumors. A mathematical model that includes two cell populations of sensitive cells and drug-resistant cells has been studied in this article. The data of patients with metastatic melanoma is calibrated and the outcome of adaptive therapy is predicted. Studies have shown that the progress time of adaptive therapy depends on the initial tumor density, initial resistance level, drug-induced drug resistance rate and baseline size of tumor treatment. For adaptive therapy to provide a benefit, the tumor burden must undergo a sufficient decline to allow for treatment withdrawal, competition within the tumor must be sufficiently strong and the rate of drug-induced resistance must be reduced as much as possible. Prolonging the tumor treatment holiday can enhance intra-tumoral competition and improve the effect of adaptive therapy. This work provides a practical and effective treatment for metastatic melanoma, and provides a possible idea for patients with melanoma to design adaptive treatment. This article is protected by copyright. All rights reserved.