IET Systems Biology最新文献

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.

Glioblastoma is a highly aggressive and devastating brain malignancy with dismal prognosis and extremely limited therapeutic options. Identification of prognostic biomarkers and therapeutic targets from multi-omics data is critical for improving patient outcomes. In this study, we investigated the clinical significance of cellular heterogeneity and super-enhancer-driven regulatory networks, which are critically implicated in glioblastoma progression and treatment resistance. We first performed scRNA-seq to dissect tumour microenvironment heterogeneity, identifying 16 distinct cell clusters, including astrocytes, macrophages, and CD8+ T cells. CellChat analysis revealed key intercellular signalling pathways, with astrocytes and macrophages acting as central communication hubs. To integrate bulk RNA sequencing data, we applied the Scissor algorithm to identify survival-associated cell states. By combining single-cell and bulk transcriptomic data, we uncovered 642 survival-related genes, including QKI and RBM47, which robustly predicted patient survival and immunotherapy response. Furthermore, WGCNA analysis identified seven co-expression modules and super enhancer-regulated networks orchestrated by transcription factors (RFX2, RFX4) and hub genes (NEAT1, CFLAR). These networks stratified patients into high- and low-risk groups with significant survival differences. Collectively, our findings elucidate the intricate interplay between cellular heterogeneity and super enhancer-driven gene regulation in glioblastoma, providing a translational framework for targeting oncogenic hubs and modulating microenvironment interactions.

Since instantaneous large changes in blood pressure (BP) values would cause the stroke or even death, continuous BP estimation is essential and crucial. Nevertheless, traditional cuffed BP estimation devices are unable to perform continuous BP estimation. Therefore, there has been a growing interest in developing continuous cuffless BP estimation devices. In order to reduce hardware costs, photoplethysmograms (PPGs) are acquired and their integer order derivative signals are computed to extract features related to BP. Then, conventional machine learning models are developed to estimate BP values. However, the nonlinear characteristics of the heart and blood vessels introduce fractional delays to blood flow. Hence, the traditional integer order derivatives of PPGs may not yield high accuracy. To address this issue, this paper proposes a cuffless BP estimation method based on fractional order derivatives (FODs) of PPGs. First, singular spectrum analysis (SSA) is employed to preprocess the PPGs. Then, the fractional order derivatives of the preprocessed PPGs are calculated. Second, a multi-channel Gramian angular field (GAF)-based image encoding method is applied to both the integer order and fractional order derivatives of the PPGs to generate two-dimensional (2D) images. Then, the encoded images from each individual channel are combined to form a multi-channel encoded image. Third, a residual neural network with 18 layers (ResNet-18) and a U-architecture convolutional network (U-Net) are respectively used for BP estimation. To evaluate the effectiveness of our proposed method, computer numerical simulations are conducted using the Queensland dataset. The results show that our proposed method yields the lower errors and higher correlation coefficients compared to existing methods. Furthermore, our proposed method outperforms both the single-channel and three-channel image encoding methods in terms of errors and correlation coefficients.

Kushen decoction (KSD), a traditional Chinese medicine, is extensively utilised for haemorrhoid treatment, yet its underlying mechanisms remain elusive. This study employs a systematic approach to elucidate the therapeutic mechanisms of KSD in haemorrhoid treatment by integrating network pharmacology, molecular docking and molecular dynamics simulation. A total of 788 active ingredients were identified from KSD, among which 623 intersected with 99 targets associated with haemorrhoids. Network pharmacology revealed quercetin, rhodionin and luteolin as key ingredients targeting 10 hub targets (CRP, PTGS2, ALB, CYP3A4, KLK3, TNF, MMP9, CYP1A2, CYP3A5 and CYP2C8) implicated in haemorrhoid pathology. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analyses indicated the involvement of these targets in pathways such as cGMP-PKG signalling, tryptophan metabolism, steroid hormone biosynthesis and drug metabolism-cytochrome P450. Moreover, molecular docking and molecular dynamics simulations confirmed the binding solid affinity of key ingredients to hub targets. These findings suggest that KSD's therapeutic effects on haemorrhoids are mediated through symptom alleviation, anti-inflammatory actions and immune enhancement.

Recent studies have reported that gut microbiota may play a role in the occurrence and development of digestive system cancers. Periampullary cancer is a relatively rare digestive system cancer which lacks effective targeted therapy and specific drugs. The purpose of this study is to elucidate the relationship between periampullary cancer and gut microbiota. This work collected public genome-wide association study (GWAS) data from 211 gut microbial taxa and three types of cancer related to periampullary cancer, which were used for two-sample Mendelian randomisation (MR) analysis. Based on the analysis of differentially expressed genes between periampullary cancer and adjacent normal tissue, extracellular matrix proteins were selected for further multivariable MR analysis. Finally, the Connectivity Map was used to screen potential therapeutic drugs for periampullary cancer. Two-sample MR results confirmed that nine microbial taxa, Tyzzerella, Alloprevotella, Holdemania, LachnospiraceaeUCG010, Terrisporobacter, Alistipes, Rikenellaceae, Anaerofilum and Dialister, were associated with periampullary cancer risk. Multivariable MR discovered extracellular matrix-related proteins [Collagen alpha-1(I) chain, Laminin, Fibronectin and Mucin] that may play a role in the association between gut microbiota and periampullary cancer. Finally, the Connectivity Map identified 27 potential candidate drugs. This study can provide theoretical basis for future prevention and diagnostic research on this rare cancer.

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.