IET Systems Biology最新文献

Long non-coding RNAs (lncRNAs) have emerged as significant contributors to the regulation of various biological processes, and their dysregulation has been linked to a variety of human disorders. Accurate prediction of potential correlations between lncRNAs and diseases is crucial for advancing disease diagnostics and treatment procedures. The authors introduced a novel computational method, iGATTLDA, for the prediction of lncRNA-disease associations. The model utilised lncRNA and disease similarity matrices, with known associations represented in an adjacency matrix. A heterogeneous network was constructed, dissecting lncRNAs and diseases as nodes and their associations as edges. The Graph Attention Network (GAT) is employed to process initial features and corresponding adjacency information. GAT identified significant neighbouring nodes in the network, capturing intricate relationships between lncRNAs and diseases, and generating new feature representations. Subsequently, the transformer captures global dependencies and interactions across the entire sequence of features produced by the GAT. Consequently, iGATTLDA successfully captures complex relationships and interactions that conventional approaches may overlook. In evaluating iGATTLDA, it attained an area under the receiver operating characteristic (ROC) curve (AUC) of 0.95 and an area under the precision recall curve (AUPRC) of 0.96 with a two-layer multilayer perceptron (MLP) classifier. These results were notably higher compared to the majority of previously proposed models, further substantiating the model's efficiency in predicting potential lncRNA-disease associations by incorporating both local and global interactions. The implementation details can be obtained from https://github.com/momanyibiffon/iGATTLDA.

For the multistage progression of prostate cancer (PCa) and resistance to immunotherapy, tumour-associated macrophage is an essential contributor. Although immunotherapy is an important and promising treatment modality for cancer, most patients with PCa are not responsive towards it. In addition to exploring new therapeutic targets, it is imperative to identify highly immunotherapy-sensitive individuals. This research aimed to establish a signature risk model, which derived from the macrophage, to assess immunotherapeutic responses and predict prognosis. Data from the UCSC-XENA, GEO and TISCH databases were extracted for analysis. Based on both single-cell datasets and bulk transcriptome profiles, a macrophage-related score (MRS) consisting of the 10-gene panel was constructed using the gene set variation analysis. MRS was highly correlated with hypoxia, angiogenesis, and epithelial-mesenchymal transition, suggesting its potential as a risk indicator. Moreover, poor immunotherapy responses and worse prognostic performance were observed in the high-MRS group of various immunotherapy cohorts. Additionally, APOE, one of the constituent genes of the MRS, affected the polarisation of macrophages. In particular, the reduced level of M2 macrophage and tumour progression suppression were observed in PCa xenografts which implanted in Apolipoprotein E-knockout mice. The constructed MRS has the potential as a robust prognostic prediction tool, and can aid in the treatment selection of PCa, especially immunotherapy options.

EMT dysfunction is a dominant mechanisms of hypospadias. Thus, identification of EMT-related lncRNAs based on transcriptome sequencing data of hypospadias might provide novel molecular markers and therapeutic targets for hypospadias. First, the microarray data related to hypospadias were downloaded from Gene Expression Omnibus (GEO). Besides, the differentially expressed lncRNAs and messenger RNAs (mRNAs) related to EMT were screened to construct lncRNA-mRNA co-expression interaction pairs. In addition, the microRNA (miRNA) prediction analysis was performed through bioinformatics methods to construct a ceRNA network. Moreover, function prediction and function enrichment and pathway analyses were also performed. Finally, the core EMT-related lncRNAs were verified based on mRNA expression changes and cell functions. A total of 6 EMT-related lncRNAs were identified and 123 mRNA-lncRNA co-expression interaction pairs were screened in this study. Additionally, a ceRNA regulatory network comprising 17 mRNAs, 4 lncRNAs, and 28 miRNAs was constructed based on the prediction of hypospadias-related miRNAs. The validation results of the dataset GSE121712 revealed that only BEX1 was positively correlated with the expression of the lncRNA GNAS-AS1 (r = 0.874, P < 0.01), both of which had high expression. The cell experiment results demonstrated that interfering with the expression of GNAS-AS1 significantly promoted the proliferation, migration, and EMT of cells. Importantly, it was confirmed that GNAS-AS1 can serve as a ceRNA and play an important role in the EMT of hypospadias. Hence, it may be considered as a potential target in the treatment of this disease.

Genes associated with endoplasmic reticulum stress (ERS) and mitophagy can be conducive to predicting solid tumour prognosis. The authors aimed to develop a prognosis prediction model for these genes in lung adenocarcinoma (LUAD). Relevant gene expression and clinical information were collected from public databases including Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). A total of 265 differentially expressed genes was finally selected (71 up-regulated and 194 downregulated) in the LUAD dataset. Among these, 15 candidate ERS and mitophagy genes (ATG12, CSNK2A1, MAP1LC3A, MAP1LC3B, MFN2, PGAM5, PINK1, RPS27A, SQSTM1, SRC, UBA52, UBB, UBC, ULK1, and VDAC1) might be critical to LUAD based on the expression analysis after crossing with the ERS and mitochondrial autophagy genes. The prediction model demonstrated the ability to effectively predict the 5-, 3-, and 1-year prognoses of LUAD patients in both GEO and TCGA databases. Moreover, high VDAC1 expression was associated with poor overall survival in LUAD (p < 0.001), suggesting it might be a critical gene for LUAD prognosis prediction. Overall, the prognosis model based on ERS and mitophagy genes in LUAD can be useful for evaluating the prognosis of patients with LUAD, and VDAC1 may serve as a promising biomarker for LUAD prognosis.

Cancer treatment often involves heat therapy, commonly administered alongside chemotherapy and radiation therapy. The authors address the challenges posed by heat treatment methods and introduce effective control techniques. These approaches enable the precise adjustment of laser radiation over time, ensuring the tumour's core temperature attains an acceptable level with a well-defined transient response. In these control strategies, the input is the actual tumour temperature compared to the desired value, while the output governs laser radiation power. Efficient control methods are explored for regulating tumour temperature in the presence of nanoparticles and laser radiation, validated through simulations on a relevant physiological model. Initially, a Proportional-Integral-Derivative (PID) controller serves as the foundational compensator. The PID controller parameters are optimised using a combination of trial and error and the Imperialist Competitive Algorithm (ICA). ICA, known for its swift convergence and reduced computational complexity, proves instrumental in parameter determination. Furthermore, an intelligent controller based on an artificial neural network is integrated with the PID controller and compared against alternative methods. Simulation results underscore the efficacy of the combined neural network-PID controller in achieving precise temperature control. This comprehensive study illuminates promising avenues for enhancing heat therapy's effectiveness in cancer treatment.

Trap formation is the key indicator of carnivorous lifestyle transition of nematode-trapping fungi (NTF). Here, the DNA methylation profile was explored during trap induction of Arthrobotrys oligospora, a typical NTF that captures nematodes by developing adhesive networks. Whole-genome bisulfite sequencing identified 871 methylation sites and 1979 differentially methylated regions (DMRs). This first-of-its-kind investigation unveiled the widespread presence of methylation systems in NTF, and suggested potential regulation of ribosomal RNAs through DNA methylation. Functional analysis indicated DNA methylation's involvement in complex gene regulations during trap induction, impacting multiple biological processes like response to stimulus, transporter activity, cell reproduction and molecular function regulator. These findings provide a glimpse into the important roles of DNA methylation in trap induction and offer new insights for understanding the molecular mechanisms driving carnivorous lifestyle transition of NTF.

Calcific aortic valve disease (CAVD) and osteoarthritis (OA) are common diseases in the ageing population and share similar pathogenesis, especially in inflammation. This study aims to discover potential diagnostic and therapeutic targets in patients with CAVD and OA. Three CAVD datasets and one OA dataset were obtained from the Gene Expression Omnibus database. We used bioinformatics methods to search for key genes and immune infiltration, and established a ceRNA network. Immunohistochemical staining was performed to verify the expression of candidate genes in human and mice aortic valve tissues. Two key genes obtained, leucine rich repeat containing 15 (LRRC15) and secreted phosphoprotein 1 (SPP1), were further screened using machine learning and verified in human and mice aortic valve tissues. Compared to normal tissues, the infiltration of immune cells in CAVD tissues was significantly higher, and the expressions of LRRC15 and SPP1 were positively correlated with immune cells infiltration. Moreover, the ceRNA network showed extensive regulatory interactions based on LRRC15 and SPP1. The authors’ findings identified LRRC15 and SPP1 as hub genes in immunological mechanisms during CAVD and OA initiation and progression, as well as potential targets for drug development.

Pancreatic ductal adenocarcinoma (PDAC) accounts for 95% of all pancreatic cancer cases, posing grave challenges to its diagnosis and treatment. Timely diagnosis is pivotal for improving patient survival, necessitating the discovery of precise biomarkers. An innovative approach was introduced to identify gene markers for precision PDAC detection. The core idea of our method is to discover gene pairs that display consistent opposite relative expression and differential co-expression patterns between PDAC and normal samples. Reversal gene pair analysis and differential partial correlation analysis were performed to determine reversal differential partial correlation (RDC) gene pairs. Using incremental feature selection, the authors refined the selected gene set and constructed a machine-learning model for PDAC recognition. As a result, the approach identified 10 RDC gene pairs. And the model could achieve a remarkable accuracy of 96.1% during cross-validation, surpassing gene expression-based models. The experiment on independent validation data confirmed the model's performance. Enrichment analysis revealed the involvement of these genes in essential biological processes and shed light on their potential roles in PDAC pathogenesis. Overall, the findings highlight the potential of these 10 RDC gene pairs as effective diagnostic markers for early PDAC detection, bringing hope for improving patient prognosis and survival.