IET Systems Biology

Parkinson's disease (PD), a degenerative disorder affecting the nervous system, manifests as unbalanced movements, stiffness, tremors, and coordination difficulties. Its cause, believed to involve genetic and environmental factors, underscores the critical need for prompt diagnosis and intervention to enhance treatment effectiveness. Despite the array of available diagnostics, their reliability remains a challenge. In this study, an innovative predictor PADG‐Pred is proposed for the identification of Parkinson's associated biomarkers, utilising a genomic profile. In this study, a novel predictor, PADG‐Pred, which not only identifies Parkinson's associated biomarkers through genomic profiling but also uniquely integrates multiple statistical feature extraction techniques with ensemble‐based classification frameworks, thereby providing a more robust and interpretable decision‐making process than existing tools. The processed dataset was utilised for feature extraction through multiple statistical moments and it is further involved in extensive training of the model using diverse classification techniques, encompassing Ensemble methods; XGBoost, Random Forest, Light Gradient Boosting Machine, Bagging, ExtraTrees, and Stacking. State‐of‐the‐art validation procedures are applied, assessing key metrics such as specificity, accuracy, sensitivity/recall, and Mathew's correlation coefficient. The outcomes demonstrate the outstanding performance of PADG‐RF, showcasing accuracy metrics consistently achieving ∼91% for the independent set, ∼94% for 5‐fold, and ∼96% for 10‐fold in cross‐validation.

As an economically important tree species, mulberry (Morus spp.) has exhibited a remarkable tolerance for salinity, drought and heavy metals. However, the precise mechanism of metabolome‐mediated drought adaptation is unclear. In this study, two new mulberry varieties—‘drought‐sensitive guisangyou62 (GSY62) and highly drought‐tolerant guiyou2024 (GY2024)’—after three days (62F or 2024F) and six days (62B or 2024B) of drought–stress conditions were subjected to transcriptome and metabolome analyses. The enrichment analysis demonstrated that the differentially expressed genes (DEGs) were mainly enriched in carbohydrate metabolism, amino acid metabolism, energy metabolism and secondary metabolite biosynthesis under drought–stress conditions. Notably, compared with the CK group (without drought treatment), 60 and 70 DEGs in GY2024 and GSY62 were involved in sucrose and starch biosynthesis, respectively. The genes encoding sucrose phosphate synthase 2 and 4 were downregulated in GY2024, with a lower expression. The genes encoding key enzymes in starch biosynthesis were upregulated in GY2024 and the transcriptional abundance was significantly higher than in GSY62. These results indicated that drought stress reduced sucrose synthesis but accelerated starch synthesis in mulberry.

With the development of high‐throughput sequencing technology, the analysis of single‐cell RNA sequencing data has become the focus of current research. Matrix analysis and processing of downstream gene expression after preprocessing is a hot topic for researchers. This paper proposed an iterative block matrix completion algorithm, called SeqBMC, based on matrix factorisation. The algorithm is used to complete the missing value of the gene expression matrix caused by the defect of sequencing technology. The gene frequency of the matrix is used to block the matrix, and then the matrix factorisation algorithm is used to complete the small matrix after the block, and then the biological zeros that may exist in the block matrix are retained. Experimental results show that the matrix completion algorithm can significantly improve the classification performance of the gene expression matrix after completion with 86.81% F1 score, which is conducive to the recognition of cell types in sequencing data. Moreover, this completion method can be completed only by the machine learning method without too much prior knowledge related to biology and has good effects. Compared with ALRA, SeqBMC increased 5.47% accuracy and 5.03% F1 score. It indicates that SeqBMC has significant advantages in the matrix completion of single‐cell RNA sequencing data.

Hypertension, often known as high blood pressure, is a major concern to millions of individuals globally. Recent studies have demonstrated the significant efficacy of naturally derived peptides in reducing blood pressure. Hypertension is one of the risks associated with cardiovascular disorders and other health problems. Naturally sourced bioactive peptides possessing antihypertensive properties provide considerable potential as viable substitutes for conventional pharmaceutical medications. Currently, thorough examination of antihypertensive peptide (AHTPs), by using traditional wet‐lab methods is highly expensive and labours. Therefore, in‐silico approaches especially machine‐learning (ML) algorithms are favourable due to saving time and cost in the discovery of AHTPs. In this study, a novel ML‐based predictor, called StackAHTP was developed for predicting accurate AHTPs from sequence only. The proposed method, utilise two types of feature descriptors Pseudo‐Amino Acid Composition and Dipeptide Composition to encode the local and global hidden information from peptide sequences. Furthermore, the encoded features are serially merged and ranked through SHapley Additive explanations (SHAP) algorithm. Then, the top ranked are fed into three different ensemble classifiers (Bagging, Boosting, and Stacking) for enhancing the prediction performance of the model. The StackAHTPs method achieved superior performance compare to other ML classifiers (AdaBoost, XGBoost and Light Gradient Boosting (LightGBM), Bagging and Boosting) on 10‐fold cross validation and independent test. The experimental outcomes demonstrate that our proposed method outperformed the existing methods and achieved an accuracy of 92.25% and F1‐score of 89.67% on independent test for predicting AHTPs and non‐AHTPs. The authors believe this research will remarkably contribute in predicting large‐scale characterisation of AHTPs and accelerate the drug discovery process. At https://github.com/ali‐ghulam/StackAHTPs you may find datasets features used.

Metal ions are significant ligands that bind to proteins and play crucial roles in cell metabolism, material transport, and signal transduction. Predicting the protein‐metal ion ligand binding residues (PMILBRs) accurately is a challenging task in theoretical calculations. In this study, the authors employed fused amino acids and their derived information as feature parameters to predict PMILBRs using three classical machine learning algorithms, yielding favourable prediction results. Subsequently, deep learning algorithm was incorporated in the prediction, resulting in improved results for the sets of Ca²⁺ and Mg²⁺ compared to previous studies. The validation matrix provided the optimal prediction model for each ionic ligand binding residue, exhibiting the capability of effectively predicting the binding sites of metal ion ligands for real protein chains.

The herbal sitz bath formula, as a complementary therapy, effectively alleviates postoperative wound pain and accelerates healing time in patients with perianal abscesses. To investigate its mechanism of action, this study conducted 16S rRNA gene sequencing and bioinformatics analysis on wound exudate samples from patients after perianal abscess surgery. Patients were randomly divided into two groups: one receiving the herbal sitz bath as an adjunctive therapy and the other without this adjunctive therapy. Samples were collected on the first and eighth days after surgery to compare the differences in microbial community composition between the two groups on the eighth day and between the first and eighth days within each group. The study revealed that the herbal sitz bath significantly altered the structure of the microbial community, increasing its diversity and abundance. By reducing Enterococcus and increasing Bifidobacterium, Faecalibacterium, and Ruminococcus, the therapy enhanced the wound's anti‐infective capacity and accelerated healing. This study explored the potential mechanism of the herbal sitz bath formula as an adjunctive therapy in promoting postoperative recovery from perianal abscesses, providing valuable data for further research on the role of microorganisms in wound care. These findings contribute to optimising postoperative treatment regimens and facilitating patient recovery.

One of the challenges that beset modelling complex biological networks is to relate networks to function to dynamics. A further challenge is deciphering the cellular function and dynamics that can change drastically when the network edge is tinkered with by adding or removing it. To illustrate this, the authors took a well‐studied three‐variable Goodwin oscillatory motif with only a negative feedback loop. To this motif, an edge was added that results in an emergent structure consisting of new feedforward and feedback loops while retaining Goodwin's original negative feedback loop. To relate emergent structure to oscillatory dynamics, the authors took all the combinations of edge signs in the interlocked motif. Bifurcation analysis reveals that all the structural combinations can be grouped into two categories based on their unique dynamics. These two groups also exhibit unique amplitude‐frequency (amp‐freq) plots. These two categories are attributed to the emergence of interlocked motifs with specific edge signs. To support the ideas, a well‐studied plant circadian model of Arabidopsis thaliana was taken to illustrate the importance of interlocked motifs in fine‐tuning amplitude and frequency in circadian oscillators. The authors briefly discuss its implications for central oscillators' adaptation to different environmental cues.

Spatially resolved transcriptomics technologies potentially provide the extra spatial position information and tissue image to better infer spatial cell–cell interactions (CCIs) in processes such as tissue homeostasis, development, and disease progression. However, methods for effectively integrating spatial multimodal data to infer CCIs are still lacking. Here, the authors propose a deep learning method for integrating features through co‐convolution, called SpaGraphCCI, to effectively integrate data from different modalities of SRT by projecting gene expression and image feature into a low‐dimensional space. SpaGraphCCI can achieve significant performance on datasets from multiple platforms including single‐cell resolution datasets (AUC reaches 0.860–0.907) and spot resolution datasets (AUC ranges from 0.880 to 0.965). SpaGraphCCI shows better performance by comparing with the existing deep learning‐based spatial cell communication inference methods. SpaGraphCCI is robust to high noise and can effectively improve the inference of CCIs. We test on a human breast cancer dataset and show that SpaGraphCCI can not only identify proximal cell communication but also infer new distal interactions. In summary, SpaGraphCCI provides a practical tool that enables researchers to decipher spatially resolved cell–cell communication based on spatial transcriptome data.

Oral squamous cell carcinoma (OSCC) is a common head and neck malignant tumour with high incidence and poor prognosis. Arsenic trioxide (ATO) has therapeutic effects on solid tumours. Microwave ablation (MWA) has unique advantages in the treatment of solid tumours. However, the therapeutic mechanism of ATO and MWA, as well as their combined effect on OSCC were largely unelucidated. Cal‐27 cell‐bearing nude mice were treated with ATO and/or MWA, respectively. RNA sequencing was used to obtain gene expression profiles in tumour tissues of mice treated by ATO or MWA. RNA sequencing results were verified by real‐time polymerase chain reaction (PCR). The lncRNA‐miRNA‐mRNA co‐expression network was constructed based on the competitive endogenous RNA (ceRNA) theory. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed using differentially expressed genes. The combined effect of ATO and MWA on OSCC was evaluated. Finally, CCK‐8 assay, EdU assay and transwell migration assay were performed to detect the effect of HSPA6 on the proliferation and migration of OSCC cells. The reduced volume of tumour tissues was observed in both ATO‐ and MWA‐treated groups. 37.8% decreased in the ATO group and 35.0% in the MWA group. A total of 207 and 539 differentially expressed mRNAs and lncRNAs were identified in the ATO group. And a total of 200 and 522 differentially expressed mRNAs and lncRNAs in the MWA group were identified. The expression levels of 8 genes were verified by real‐time PCR. The differentially expressed genes were closely related to “chemical carcinogenesis”, “herpes simplex infection”, “porphyrin and chlorophyll metabolism”, and “MAPK signalling pathway”. The lncRNA‐miRNA‐mRNA co‐expression networks were constructed. The combined treatment with ATO and MWA showed a better inhibitive effect on OSCC than either of them. The synergistic effect of ATO and MWA was related to the upregulation of HSPA6. The downregulation of HSPA6 could promote the proliferation and migration of OSCC cells. This study detected the long non‐coding RNA and mRNA expression profiles related to the treatment of OSCC and constructed corresponding ceRNA networks. Arsenic trioxide and MWA have a synergistic effect on OSCC, which was related to the upregulation of HSPA6.

Klebsiella pneumoniae, an opportunistic pathogen, is pervasively distributed across the world. Its escalating antibiotic resistance poses a serious threat to global public health. The mechanisms behind this resistance remain largely elusive. In this study, we performed antibiotic susceptibility testing on several clinical isolates of Klebsiella pneumoniae, and a reference strain ATCC13883, and then analysed their transcriptomic profiles to identify genes and pathways associated with antibiotic resistance. Our results showed that a clinical isolate DY16KPN may counteract antibiotics by enhancing the biosynthesis of building blocks of bacterial cell, such as fatty acids, proteins, and DNA, and reducing transmembrane transport. Increased butanoate metabolism and lipopolysaccharide biosynthesis may also contribute to the drug‐resistance of Klebsiella pneumoniae. Additionally, we identified resistance‐promoting mutations in gene promoter regions, which regulate the activity of downstream drug‐resistant genes and pathways. Our results also demonstrated that DY16KPN counterbalances the trimethoprim/sulfamethoxazole‐mediated inhibitory effect on the synthesis of tetrahydrofolates and DNA by up‐regulating the expression of targeted enzymes of trimethoprim/sulfamethoxazole, dihydrofolate reductase and dihydropteroate synthase.

Complex network is an effective approach to studying complex diseases, and provides another perspective for understanding their pathological mechanisms by illustrating the interactions between various factors of diseases. Type 2 diabetes mellitus (T2DM) is a complex polygenic metabolic disease involving genetic and environmental factors. By combining the complex network approach with biological data, this study constructs a pathway‐based weighted network model of T2DM‐related genes to explore the interrelationships between genes, here a weight is assigned to each edge in terms of the number of the same pathways in which the two nodes (genes) connected to the edge are involved. The edge weights can reflect differences in the strength of connections (interactions) between nodes (genes), which intuitively reflect the extent of biological correlations between genes and contribute to the importance of the nodes. Analysis of statistical and topological characteristics shows that the edge weights are correlated to the network topology, and the edge weight distribution decays as a power‐law. The disparity of the weights indicates that the edge weight distribution for the nodes with the same degree is of approximately equal weights; and most edges with the higher weights tend to connect with the higher degree nodes. To determine the key hub genes of the weighted network, an integrated ranking index is used to comprehensively reflect the contribution of the three indices (strength, degree and number of pathways) of nodes; by taking the threshold of integrated ranking index greater than 0.56, 12 key hub genes are identified: MAPK1, PIK3CD, PIK3CA, PIK3R1, AKT2, AKT1, KRAS, TNF, MAPK8, PRKCA, IL6 and MTOR. These genes should play an important role in the occurrence and development of T2DM, and can be regarded as potential therapeutic targets for further biological and medical research on their functions in T2DM. It can be expected that combining complex network approach with other data analysis techniques can provide more clues for exploring the pathogenesis and treatment of T2DM and other complex diseases in the future.

Abdominal aortic aneurysm (AAA) is a severe vascular condition, marked by the progressive dilation of the abdominal aorta, leading to rupture if untreated. The objective of this study was to identify key biomarkers and decipher the immune mechanisms underlying AAA utilising multi‐omics data analysis and machine learning techniques. Single‐cell RNA sequencing disclosed a heightened presence of macrophages and CD8‐positive alpha‐beta T cells in AAA, highlighting their critical role in disease pathogenesis. Analysis of cell–cell communication highlighted augmented interactions between macrophages and dendritic cells derived from monocytes. Enrichment analysis of differential expression gene indicated substantial involvement of immune and metabolic pathways in AAA pathogenesis. Machine learning techniques identified CCR7 and CBX6 as key candidate biomarkers. In AAA, CCR7 expression is upregulated, whereas CBX6 expression is downregulated, both showing significant correlations with immune cell infiltration. These findings provide valuable insights into the molecular mechanisms underlying AAA and suggest potential biomarkers for diagnosis and therapeutic intervention.

The interaction of transcription factors (TFs) with DNA precisely regulates gene transcription. In mammalian cells, thousands of TFs often interact with DNA cis‐regulatory elements in a combinatorial manner rather than act alone. The identification of cooperativity between TFs can help to explore the mechanism of transcriptional regulation. However, little is known about the cooperative patterns of TFs in the genome. To identify which TFs prefer co‐localisation, the authors conducted a paired motif analysis in the accessible regions of the human genome based on the Poisson background model. Especially, the authors distinguish the cooperative binding TFs and the competitive binding TFs according to the distance between TF motifs. In the K562 cell line, the authors find that TFs from a same family are always competing the same binding sites, such as FOS_JUN family, whereas KLF family TFs show significant cooperative binding in the adjacency region. Furthermore, the comparative analysis across 16 human cell lines indicates that most TF combination patterns are conserved, but there are still some cell‐line‐specific patterns. Finally, in human prostate cancer cells (PC‐3) and human prostate normal cells (RWPE‐2), the authors investigate the specific TF combination patterns in the disease cell and normal cell. The results show that the cooperative binding TF pairs shared by PC‐3 and RWPE‐2 account for over 90%. Simultaneously, the authors also identify 26 specific TF combination pairs in PC‐3 cancer cells.

Intracranial haemorrhage (ICH) is an urgent and potentially fatal medical condition caused by brain blood vessel rupture, leading to blood accumulation in the brain tissue. Due to the pressure and damage it causes to brain tissue, ICH results in severe neurological impairment or even death. Recently, deep neural networks have been widely applied to enhance the speed and precision of ICH detection yet they are still challenged by small or subtle hemorrhages. The authors introduce DDANet, a novel haematoma segmentation model for brain CT images. Specifically, a dilated convolution pooling block is introduced in the intermediate layers of the encoder to enhance feature extraction capabilities of middle layers. Additionally, the authors incorporate a self‐attention mechanism to capture global semantic information of high‐level features to guide the extraction and processing of low‐level features, thereby enhancing the model's understanding of the overall structure while maintaining details. DDANet also integrates residual networks, channel attention, and spatial attention mechanisms for joint optimisation, effectively mitigating the severe class imbalance problem and aiding the training process. Experiments show that DDANet outperforms current methods, achieving the Dice coefficient, Jaccard index, sensitivity, accuracy, and a specificity of 0.712, 0.601, 0.73, 0.997, and 0.998, respectively. The code is available at https://github.com/hpguo1982/DDANet.

Essential genes are necessary to sustain the life of a species under adequate nutritional conditions. These genes have attracted significant attention for their potential as drug targets, especially in developing broad‐spectrum antibacterial drugs. However, studying essential genes remains challenging due to their variability in specific environmental conditions. In this study, the authors aim to develop a powerful prediction model for identifying essential genes in humans. The authors first obtained the essential gene data from human cancer cell lines and characterised gene sequences using 7 feature encoding methods such as Kmer, the Composition of K‐spaced Nucleic Acid Pairs, and Z‐curve. Subsequently, feature fusion and feature optimisation strategies were employed to select the impactful features. Finally, machine learning algorithms were applied to construct the prediction models and evaluate their performance. The single‐feature‐based model achieved the highest area under the Receiver Operating Characteristic curve (AUC) of 0.830. After fusing and filtering these features, the classical machine learning models achieved the highest AUC at 0.823 while the deep learning model reached 0.860. Results obtained by the authors show that compared to using individual features, feature fusion and feature optimisation strategies significantly improved model performance. Moreover, the study provided an advantageous method for essential gene identification compared to other methods.

Sepsis is a severe systemic inflammatory syndrome triggered by infection and is a leading cause of morbidity and mortality in intensive care units (ICUs). Immune dysfunction is a hallmark of sepsis. In this study, the authors investigated cell‐cell communication among lymphoid‐derived leucocytes using single‐cell RNA sequencing (scRNA‐seq) to gain a deeper understanding of the underlying mechanisms in late‐stage sepsis. The authors’ findings revealed that both the number and strength of cellular interactions were elevated in septic patients compared to healthy individuals, with several pathways showing significant alterations, particularly in conventional dendritic cells (cDCs) and plasmacytoid dendritic cells (pDCs). Notably, pathways such as CD6‐ALCAM were more activated in sepsis, potentially due to T cell suppression. This study offers new insights into the mechanisms of immunosuppression and provides potential avenues for clinical intervention in sepsis.

Small interfering RNA (siRNA) has revolutionised biomedical research and drug development through precise post‐transcriptional gene silencing technology. Despite its immense potential, siRNA therapy still faces technical challenges, such as delivery efficiency, targeting specificity, and molecular stability. To address these challenges and facilitate siRNA drug development, we have developed siRNAEfficacyDB, a comprehensive database that integrates experimentally validated siRNA efficacy data. This database contains 3544 siRNA records, covering 42 target genes and 5 cell lines. It provides detailed information on siRNA sequences, target genes, inhibition efficiencies, experimental techniques, cell lines, siRNA concentrations, and incubation times. siRNAEfficacyDB offers a user‐friendly web interface that makes it easy to query, browse and analyse data, enabling efficient access to siRNA‐related information. In summary, siRNAEfficacyDB provides a useful data foundation for siRNA drug design and optimisation, serving as a valuable resource for advancing computer‐aided siRNA design research and nucleic acid drug development. siRNAEfficacyDB is freely available at https://cellknowledge.com.cn/siRNAEfficacy for non‐commercial use.

Globular proteins (GPs) play vital roles in a wide range of biological processes, encompassing enzymatic catalysis and immune responses. Enzymes, among these globular proteins, facilitate biochemical reactions, while others, such as haemoglobin, contribute to essential physiological functions such as oxygen transport. Given the importance of these considerations, accurately identifying Globular proteins is essential. To address the need for precise GP identification, this research introduces an innovative approach that employs a hybrid‐based deep learning model called Deep‐GP. We generated two datasets based on primary sequences and developed a novel feature descriptor called, Consensus Sequence‐based Trisection‐Position Specific Scoring Matrix (CST‐PSSM). The model training phase involved the application of deep learning techniques, including the bidirectional long short‐term memory network (BiLSTM), gated recurrent unit (GRU), and convolutional neural network (CNN). The BiLSTM and CNN were hybridised for ensemble learning. The CST‐PSSM‐based ensemble model achieved the most accurate predictive outcomes, outperforming other competitive predictors across both training and testing datasets. This demonstrates the potential of harnessing deep learning for precise GB prediction as a robust tool to expedite research, streamline drug discovery, and unveil novel therapeutic targets.

Artificial Intelligence is playing a crucial role in healthcare by enhancing decision‐making and data analysis, particularly during the COVID‐19 pandemic. This virus affects individuals across all age groups, but its impact is more severe on the elderly and those with underlying health issues like chronic diseases. This study aimed to develop a machine learning model to improve the prediction of COVID‐19 in patients with acute respiratory symptoms. Data from 915 patients in two hospitals in Saudi Arabia were used, categorized into four groups based on chronic lung conditions and COVID‐19 status. Four supervised machine learning algorithms—Random Forest, Bagging classifier, Decision Tree, and Logistic Regression—were employed to predict COVID‐19. Feature selection identified 12 key variables for prediction, including CXR abnormalities, smoking status, and WBC count. The Random Forest model showed the highest accuracy at 99.07%, followed by Decision Tree, Bagging classifier, and Logistic Regression. The study concluded that machine learning algorithms, particularly Random Forest, can effectively predict and classify COVID‐19 cases, supporting the development of computer‐assisted diagnostic tools in healthcare.

This article used network pharmacology, molecular docking, GEO analysis, and Gene Set Enrichment Analysis to obtain 38 main chemical components and 66 corresponding targets involved in Alzheimer's disease (AD) treatment in "Cistanche deserticola‐Polygala". Through further Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes enrichment analysis, we obtained AD signalling pathways, calcium signalling pathways, and other signalling pathways related to the treatment of AD with “Cistanche deserticola‐Polygala”. Molecular docking showed that most of the core chemical components had good binding ability with the core targets. This article aims to reveal the mechanism of “Cistanche deserticola‐Polygala” in treating AD and provide a basis for the treatment of AD with “Cistanche deserticola‐Polygala”.

Synaptojanin 2 (SYNJ2) has crucial role in various tumors, but its role in papillary thyroid carcinoma (PTC) remains unexplored. This study first detected SYNJ2 protein expression in PTC using immunohistochemistry method and further assessed SYNJ2 mRNA expression through mRNA chip and RNA sequencing data and its association with clinical characteristics. Additionally, KEGG, GSVA, and GSEA analyses were conducted to investigate potential biological functions, while single‐cell RNA sequencing data were used to explore SYNJ2's underlying mechanisms in PTC. Meanwhile, immune infiltration status in different SYNJ2 expression groups were analyzed. Besides, we investigated the immune checkpoint gene expression and implemented drug sensitivity analysis. Results indicated that SYNJ2 is highly expressed in PTC (SMD = 0.66 [95% CI: 0.17–1.15]) and could distinguish between PTC and non‐PTC tissues (AUC = 0.74 [0.70–0.78]). Furthermore, the study identified 134 intersecting genes of DEGs and CEGs, mainly enriched in the angiogenesis and epithelial‐mesenchymal transition (EMT) pathways. Subsequent analysis showed the above pathways were activated in PTC epithelial cells. PTC patients with high SYNJ2 expression showed higher sensitivity to the six common drugs. Summarily, SYNJ2 may promote PTC progression through angiogenesis and EMT pathways. High SYNJ2 expression is associated with better response to immunotherapy and chemotherapy.

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.

Objectives Acute ischemic stroke (AIS) is caused by cerebral ischemia due to thrombosis in the blood vessel. The purpose of this study is to identify key genes related to metabolism to aid in the mechanism research and management of AIS. Materials and Methods Gene expression data were downloaded from the Gene Expression Omnibus database. Weighted gene co‐expression network analysis, Gene Ontology and kyoto encyclopedia of genes and genomes analysis were used to identify metabolism‐related genes that may be involved in the regulation of AIS. A protein protein interaction network was mapped using Cytoscape based on the STRING database. Subsequently, hub metabolism‐related genes were identified based on Cytoscape‐CytoNCA and Cytoscape‐MCODE plug‐ins. Least absolute shrinkage and selection operator algorithm and differential expression analysis. In addition, drug prediction, molecular docking, ceRNA network construction, and correlation analysis with immune cell infiltration were performed to explore their potential molecular mechanisms of action in AIS. Finally, the expression of hub gene was verified by real‐time PCR. Results Metabolism‐related genes FBL, HEATR1, HSPA8, MTMR4, NDUFC1, NDUFS8 and SNU13 were identified. The AUC values of FBL, HEATR1, HSPA8, MTMR4, NDUFS8 and SNU13 were all greater than 0.8, suggesting that they had good diagnostic accuracy. Correlation analysis found that their expression levels were also related to the infiltration levels of multiple immune cells, such as Activated.CD8.T.cell and Activated.dendritic.cell. It was found that only HSPA8 was successfully matched to drugs with literature support, and these drugs were acetaminophen, bupivacaine, dexamethasone, gentamicin, tretinoin and cisplatin. Moreover, it was also identified that the ENSG000000218510‐hsa‐miR‐330‐3p‐HEATR1 axis may be involved in regulating AIS. Conclusions The identification of FBL, HEATR1, HSPA8, MTMR4, NDUFC1, NDUFS8 and SNU13 provides a new research direction for exploring the molecular mechanisms of AIS, which can help in clinical management and diagnosis.