IET Systems Biology最新文献

Colorectal cancer (CRC) occurs as the third most common cancer with high mortality across the world. Understanding the intratumoral immune cell heterogeneity and their responses to various therapies is crucial for enhancing patient outcomes. This study aimed to characterise and evaluate the immune microenvironment landscapes of CRC shaped by different therapies including CD73 inhibitor, PD-1 blockade and photothermal therapy (PTT). Our investigation revealed that three therapies could commonly modulate the down-regulation of Treg, M2 macrophage and Ptprj+ G4 granulocyte, up-regulation of effector/memory T cell, M1 macorphage and Hilpda+ G1 granulocyte. Moreover, we identified the uniquely dis-regulated cell types and pathway activities response to each therapy, such as CD73 inhibitor enriched more Cd8+ memory and central memory (CM) cell, PD-1 blockade with more Cd8+ CTL and Cxcl3+ G2 granulocyte, and PTT with more Cd8+ effector memory and Rethlg+ G3 granulocyte cell. These responses disordered the glycolysis, angiogenesis, phagocytosis functions and cellular communication to reshape the CRC tumour immune microenvironment. We provide the detail insights into the intratumoral immunomodulation preferences of CRC mice treated with CD73 inhibitor, PD-1 blockade and PTT therapies, which might contribute to the ongoing development of more effective anticancer strategies.

Gallbladder cancer (GBC) is the most common biliary tract neoplasm. Identifying biomarkers for GBC initiation and progression remains a challenge. This study aimed to identify GBC biomarkers using machine learning and bioinformatics. Differentially expressed genes (DEGs) were identified from two microarray datasets (GSE100363, GSE139682) from the GEO database. Gene Ontology and pathway analyses were performed using DAVID. A protein–protein interaction network was constructed using STRING, and hub genes were identified via three ranking algorithms (degree, MNC and closeness centrality). Feature selection methods (Pearson correlation, recursive feature elimination) were applied to extract key gene subsets. Machine learning models (SVM, NB and RF) were trained on GSE100363 and validated on GSE139682 to assess predictive performance. Biomarkers were further validated using the GEPIA database. A total of 146 DEGs were identified, including 39 upregulated and 107 downregulated genes. Eleven hub genes were identified, with SLIT3, COL7A1 and CLDN4 strongly correlated with GBC. Machine learning results confirmed their diagnostic potential. The study highlights NTRK2, COL14A1, SCN4B, ATP1A2, SLC17A7, SLIT3, COL7A1, CLDN4, CLEC3B, ADCYAP1R1 and MFAP4 as crucial genes associated with GBC. SLIT3, COL7A1 and CLDN4 serve as highly predictive biomarkers, and findings can improve early diagnosis and prognosis, aiding clinical decision-making.

Predicting the combined score in protein–protein interaction (PPI) networks represents a critical research focus in bioinformatics, as it contributes to enhancing the accuracy of PPI data and uncovering the inherent complexity of biological systems. However, existing intelligent algorithms encounter significant challenges in effectively integrating heterogeneous data sources, capturing the nonlinear dependencies within PPI networks, and improving model generalizability. To address these limitations, this study introduces an enhanced gene expression programming (DF-GEP) algorithm that incorporates dynamic factor optimization. The proposed DF-GEP framework integrates Spearman correlation analysis with kernel ridge regression (SC-KRR) to extract and assign refined weights to key PPI network features. Additionally, the algorithm adaptively regulates selection, crossover, mutation and fitness evaluation processes via dynamic factor adjustment, thereby improving adaptability and predictive precision. Experimental results show that the DF-GEP algorithm consistently outperforms baseline models in both predictive accuracy and stability. Beyond its application to PPI-combined score prediction, the proposed algorithm also exhibits strong potential for addressing complex nonlinear problems in other domains.

Breast cancer is a highly heterogeneous disease and it is generally divided into four subtypes in clinical practice. Common differentially expressed genes are always ignored. In fact, the regulatory associations of common differentially expressed genes exhibit significant differences among the four subtypes of breast cancer. A deep differential analysis in four subtype of breast cancer is proposed in this paper. The common differentially expressed genes among four subtypes of breast cancer are mainly considered. The miRNA-mRNA regulatory network is constructed as a bipartite network and the regulations of miRNA-mRNA for each subtype of breast cancer are predicted. The common differentially expressed genes for four subtypes of breast cancer are obtained. Breast cancer is classified into four subtypes by using Prediction Analysis of Microarray 50. The method of EdgeR is employed to obtain the common differentially expressed genes. A background network is designed by the common differentially expressed genes. MiRNA-mRNA bipartite network is constructed by the background network. A method of weighted similarity information (WSI) is proposed. Global similarity information of miRNA and mRNA are obtained by the WSI, respectively. The regulations of miRNA-mRNA in four subtypes of breast cancer are predicted by integrating the MiRNA-mRNA bipartite network and the global similarity information of miRNA and mRNA. In 5-fold cross-validation, this method performs well across the four subtypes of breast cancer. In addition, the predicted regulations of miRNA-mRNA have 85% ratio in the miRWalk2.0 database. This represents a 30% improvement over traditional methods.

Many studies have shown that microRNAs (miRNAs) play key roles in some important processes and human complicated diseases. In addition, they also have specific physiological roles at different cellular sites. Therefore, identifying their subcellular localisation is very urgent to systemically understand their physiological functions. In this study, we propose a computational method, called PMLocMSCAM, to predict miRNA subcellular localisation based on miRNA similarities and cross-attention mechanism. PMLocMSCAM implements a multimodal integration framework that systematically processes miRNA sequence data, miRNA-mRNA association networks with mRNA subcellular localisation annotations, miRNA-disease associations, and miRNA-drug association networks. The architecture initiates with intrinsic feature extraction through Smith-Waterman alignment for sequence similarity computation and disease ontology-based functional similarity derivation. Subsequent heterogeneous network embedding employs Node2vec for topological feature learning across three interaction modalities (miRNA-disease, miRNA-drug, and miRNA-mRNA networks), enhanced by hypergraph convolution to capture higher-order relationships through incidence matrix decomposition. Localisation-specific patterns are propagated via miRNA-mRNA interaction weights, culminating in a multi-head attention mechanism that dynamically fuses five feature matrices—miRNA sequence features, miRNA-disease association features, miRNA-drug association features, miRNA-mRNA association features, and miRNA-mRNA localisation features. These integrated representations are processed through residual-connected multilayer perceptrons to generate probabilistic predictions across seven subcellular compartments, establishing an end-to-end computational paradigm for multimodal miRNA localisation analysis. In order to assess the prediction performance of our method and compare it with other miRNA subcellular localisation computational methods, we conduct 10-fold cross validation (10-CV) and independent test dataset. The AUC (area of receiver operating characteristic curve) and AUPR (area of precision-recall curve) are used as metrics. The experiment results show that the average AUC and AUPR values exceed 0.9182 and 0.8487 on 10-CV, respectively. The AUC and AUPR values also reach 0.9157 and 0.8469 on independent test dataset, respectively. It is superior with compared methods. The ablation experiment results also further that PMLocMSCAM can effective predict miRNA subcellular localisations and provide help to understand their physiological functions.

In this paper, we propose a robust control method for the automatic treatment of targeted anti-angiogenic molecular therapy based on multi-input multi-output (MIMO) nonlinear fractional and non-fractional models using the backstepping (BS) approach. This protocol aims to eradicate tumour cells while preserving high levels of the body's natural effector cells and maintaining drug dosage within safe limits. The exponential stability of the controlled system is mathematically demonstrated using the Lyapunov theorem. Consequently, the tumour volume's convergence rate can be precisely controlled—a critical factor in cancer treatment. To fine-tune the controller gains, a soft actor-critic (SAC) algorithm within the framework of deep reinforcement learning (DRL) is employed, with a reward function designed based on the specific requirements of the system. Additionally, the Lyapunov theorem is used to mathematically verify the system's robustness against parametric uncertainty. Compared to state-of-the-art approaches, the proposed scheme demonstrates superior long-term performance, achieving complete tumour eradication and drug delivery convergence to zero within 50 days while preserving high effector cell levels.

ADAMTS5, a member of the ADAMTS family, exhibits crucial biological roles, including protein shedding, proteolysis, and cell migration. Its relevance in breast cancer (BC) was explored through an integrative approach combining high-throughput analyses, database validations, and experimental confirmation. ADAMTS5 expression was significantly reduced in BC samples, as verified by microarray analysis, qRT-PCR, and public database resources. A protein–protein interaction network revealed five proteins—COL10A1, COL11A1, COMP, MMP1 and SDC1—that interact with ADAMTS5 and are primarily associated with the ECM-receptor interaction pathway. These proteins also engage in cell cycle checkpoint signalling, emphasising their potential role in tumour progression. Survival analysis of BC samples identified a novel prognostic signature based on ADAMTS5-related proteins. The study extended to coding and noncoding RNA interactions, identifying lncRNAs as key regulators. CRNDE acts as a ceRNA for ADAMTS5, modulating its expression via hsa-miR-135b-3p. Meanwhile, BAIAP2-AS1 interacts directly with ADAMTS5, offering another layer of regulatory control and prognostic value. These findings position ADAMTS5 as a vital player in BC biology, with its low expression linked to critical pathways and survival outcomes. The identified lncRNA-mediated regulatory mechanisms add depth to understanding ADAMTS5's role and suggest potential targets for therapeutic development. This study underscores ADAMTS5's potential as a biomarker and its broader implications in unravelling BC molecular mechanisms.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterised by immune dysregulation leading to inflammation and organ damage. Despite the rising global incidence of SLE, its aetiology remains unclear. We applied Mendelian randomisation (MR), multi-omics integration, machine learning (ML), and SHAP to identify key metabolites and genes associated with SLE, revealing the crucial role of the glutathione pathway. MR analysis was performed on 1400 serum metabolites, revealing significant enrichment in the glutathione metabolic pathway. Single-cell RNA sequencing (scRNA-seq) data classified monocytes into Metabolism_high and Metabolism_low groups based on glutathione metabolism scores. Differentially expressed genes were analysed using GSEA, metabolic pathway activity assessment, transcription factor prediction, cellular communication analysis, and Pseudotime analysis. LASSO regression identified hub genes and machine learning models (CatBoost, XGBoost, NGBoost) were developed. The SHAP method was used to interpret these models. Expression of key genes was validated across multiple datasets. MR analysis confirmed that metabolites were enriched in the glutathione pathway, identifying nine hub genes. Machine learning models achieved AUCs of 0.85, 0.80, and 0.83 in the validation set. SHAP analysis highlighted LAP3 as the top contributing gene across all models. scRNA-seq data showed that LAP3 plays a significant role in the immune microenvironment of SLE. Validation across multiple datasets (training, validation, and GSE112087) revealed elevated LAP3 expression in PBMCs of SLE patients, with AUCs of 0.935, 0.795, and 0.817, respectively, suggesting strong diagnostic potential. Glutathione metabolism is closely associated with SLE development and LAP3 may play a key role in its progression. Both glutathione metabolism and LAP3 could serve as potential targets for SLE diagnosis and treatment.

SAE1, a key factor in tumour development, has not been thoroughly examined in pancreatic adenocarcinoma (PAAD), a cancer with high incidence and poor prognosis. We conducted a comprehensive study, integrating mRNA data, immunohistochemistry, CRISPR-modified cell line analysis and single-cell RNA sequencing to assess SAE1's role in PAAD. We also used ChIP-Seq to explore SAE1's transcriptional regulation and analysed clinical data, drug sensitivity and molecular docking models. SAE1 mRNA was significantly overexpressed in PAAD, with a substantial impact on cell proliferation and migration. Functional analyses linked SAE1 to cell cycle and DNA replication pathways, suggesting a role in PAAD development. Our study indicates that SAE1 may promote PAAD through cell cycle pathways, with FOXA1 potentially regulating SAE1's abnormal behaviour.

Mitochondrial dynamics (MD) play a crucial role in the genesis of Alzheimer's disease (AD); however, the molecular mechanisms underlying MD dysregulation in AD remain unclear. This study aimed to identify critical molecules of MD that contribute to AD progression using GEO data and bioinformatics approaches. The GSE63061 dataset comparing AD patients with healthy controls was analysed, WGCNA was employed to identify co-expression modules and differentially expressed genes (DEGs) and LASSO model was developed and verified using the DEGs to screen for potential biomarkers. A PPI network was built to predict upstream miRNAs, which were experimentally validated using luciferase reporter assays. A total of 3518 DEGs were identified (2209 upregulated, 1309 downregulated; |log₂FC| > 1.5, adjusted p < 0.05). WGCNA revealed 160 MD-related genes. LASSO regression selected HIBCH and MGME1 as novel biomarkers with significant downregulation in AD (fold change > 2, p < 0.001). KEGG enrichment analysis highlighted pathways associated with neurodegeneration. Luciferase assays confirmed direct binding of miR-922 to the 3′UTR of MGME1. HIBCH and MGME1 are promising diagnostic biomarkers for AD with AUC values of 0.73 and 0.74. Mechanistically, miR-922 was experimentally validated to directly bind MGME1 3′UTR.