IET Systems Biology最新文献

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.

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.