IET Systems Biology最新文献

{"title":"SAE1 May Play a Pro-Carcinogenic Role in Pancreatic Adenocarcinoma: A Comprehensive Study Integrating Multiple Pieces of Evidence","authors":"Yi Chen,&nbsp;Tong Wu,&nbsp;Qi Li,&nbsp;Ming-Jie Li,&nbsp;Na Yu,&nbsp;Li-Jueyi Meng,&nbsp;Xian-Jin Chen,&nbsp;Bang-Teng Chi,&nbsp;Shi-De Li,&nbsp;Su-Ning Huang,&nbsp;Gang Chen,&nbsp;Yu-Ping Ye,&nbsp;Dan-Ming Wei","doi":"10.1049/syb2.70017","DOIUrl":"https://doi.org/10.1049/syb2.70017","url":null,"abstract":"<p>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.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70017","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of HIBCH and MGME1 as Mitochondrial Dynamics-Related Biomarkers in Alzheimer's Disease Via Integrated Bioinformatics Analysis","authors":"Hailong Li,&nbsp;Fei Feng,&nbsp;Shoupin Xie,&nbsp;Yanping Ma,&nbsp;Yafeng Wang,&nbsp;Fan Zhang,&nbsp;Hongyan Wu,&nbsp;Shenghui Huang","doi":"10.1049/syb2.70018","DOIUrl":"https://doi.org/10.1049/syb2.70018","url":null,"abstract":"<p>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| &gt; 1.5, adjusted <i>p</i> &lt; 0.05). WGCNA revealed 160 MD-related genes. LASSO regression selected HIBCH and MGME1 as novel biomarkers with significant downregulation in AD (fold change &gt; 2, <i>p</i> &lt; 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.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictor-Based Output Feedback Control of Tumour Growth With Positive Input: Application to Antiangiogenic Therapy","authors":"Mohamadreza Homayounzade","doi":"10.1049/syb2.70005","DOIUrl":"https://doi.org/10.1049/syb2.70005","url":null,"abstract":"<p>Controlling tumour growth systems presents significant challenges due to the inherent restriction of positive input in biological systems, along with delays in system output and input measurements. Traditional control methods struggle to address these issues effectively, as they rely heavily on real-time feedback from system outputs. The delays in output measurements can lead to instability in closed-loop systems, whereas the inability of conventional approaches to manage the positive input constraint often results in ineffective control. In this study, the authors propose a novel control system designed to overcome these challenges. First, a system state prediction observer that utilises delayed output measurements was developed. Next, a backstepping technique was utilized to develop a feedback controller that ensures the control input stays positive, thereby guaranteeing the system's asymptotic stability. Furthermore, numerical comparisons with previous research validate the effectiveness of the proposed strategy. Overall, the approach offers a promising solution to the issues of delays and positive input constraints in tumour growth control systems.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved in Silico Identification of Protein-Protein Interactions Using Deep Learning Approach","authors":"Irfan Khan,&nbsp;Muhammad Arif,&nbsp;Ali Ghulam,&nbsp;Somayah Albaradei,&nbsp;Maha A. Thafar,&nbsp;Apilak Worachartcheewan","doi":"10.1049/syb2.70008","DOIUrl":"https://doi.org/10.1049/syb2.70008","url":null,"abstract":"<p>Protein–protein interactions (PPIs) perform significant functions in many biological activities likewise gene regulation, metabolic pathways and signal transduction. The deregulation of PPIs may cause deadly diseases, such as cancer, autoimmune, pernicious anaemia etc. Detecting PPIs can aid in elucidating the cellular process's underlying molecular mechanisms and contribute to facilitating the discovery of new proteins for the development of novel drugs. Although high-throughput wet-lab technologies have been matured to identify large scale PPI identification; however, the traditional experimental methods are costly and slow and resource intensive. To support experimental techniques, numerous computational approaches have been emerged for identifying PPIs solely from protein sequences. However, the performance of available PPI tools are unsatisfactory and gaps remain for further improvement. In this study, a novel deep learning-based model, Deep_PPI, was developed for predicting multiple species PPIs. To extract the biological features, the authors used 21D vector representing 20 kinds' native and one special amino acid residue and implemented the Keras binary profile encoding technique to formulate each residue in proteins. The binary profile use the PaddVal strategy to equalise the length of positive and negative PPIs. After extracting the features, the authors fed them into one dimension convolutional neural network to build the final prediction model. The proposed Deep_PPI model, which consider the protein pairs into two convolutional heads. Finally, the authors concatenated the two outputs were concatenated from two branches concatenated by fully connected layer. The efficiency of the proposed predictor was demonstrated both on the cross validation and tested on various species datasets, for example, that is (Human, <i>C. elegans</i>, <i>E. coli</i>, and <i>H. sapiens</i>). The proposed model surpassed both the machine-learning models and existing state-of-the-art PPI methods. The proposed Deep_PPI will serve as valuable tool in the discovery of large-scale PPIs in particular and provide insights for drugs development in general.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Eight Histone Methylation Modification Regulators Associated With Breast Cancer Prognosis","authors":"Yan-Ni Cao,&nbsp;Xiao-Hui Li,&nbsp;Xing-Jie Chen,&nbsp;Kang-Cheng Xu,&nbsp;Jun-Yuan Zhang,&nbsp;Hao Lin,&nbsp;Yu-Xian Liu","doi":"10.1049/syb2.70012","DOIUrl":"https://doi.org/10.1049/syb2.70012","url":null,"abstract":"<p>Histone methylation is an important epigenetic modification process coordinated by histone methyltransferases, histone demethylases and histone methylation reader proteins and plays a key role in the occurrence and development of cancer. This study constructed a risk scoring model around histone methylation modification regulators and conducted a multidimensional comprehensive analysis to reveal its potential role in breast cancer prognosis and drug sensitivity. First, 144 histone methylation modification regulators (HMMRs) were subjected to differential analysis and univariate Cox regression analysis, and nine differentially expressed HMMRs associated with survival were screened out. Next, a risk scoring model consisting of eight HMMRs was constructed using the LASSO regression algorithm, exhibiting independent predictive values in training and validation cohorts. Then, immune analysis shows that patients in the high-risk group divided by the risk scoring model has weakened the immune response. In addition, through functional analysis of differentially expressed genes (DEGs) between high-risk and low-risk groups, we confirmed that the DEGs mainly affected the nucleoplasm and tumour microenvironment. Finally, drug sensitivity analysis demonstrated that our model could be useful for drug screening and identify potential drugs for treating BRCA patients. In conclusion, these eight HMMRs may be key factors in the prognosis and drug sensitivity of BRCA patients.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"scRSSL: Residual semi-supervised learning with deep generative models to automatically identify cell types","authors":"Yanru Gao,&nbsp;Hongyu Duan,&nbsp;Fanhao Meng,&nbsp;Conghui Zhang,&nbsp;Xiyue Li,&nbsp;Feng Li","doi":"10.1049/syb2.12107","DOIUrl":"https://doi.org/10.1049/syb2.12107","url":null,"abstract":"<p>Single-cell sequencing (scRNA-seq) allows researchers to study cellular heterogeneity in individual cells. In single-cell transcriptomics analysis, identifying the cell type of individual cells is a key task. At present, single-cell datasets often face the challenges of high dimensionality, large number of samples, high sparsity and sample imbalance. The traditional methods of cell type recognition have been challenged. The authors propose a deep residual generation model based on semi-supervised learning (scRSSL) to address these challenges. ScRSSL creatively introduces residual networks into semi-supervised generative models. The authors take advantage of its semi-supervised learning to solve the problem of sample imbalance. During the training of the model, the authors use a residual neural network to accomplish the inference of cell types so that local features of single-cell data can be extracted. Because of the semi-supervised learning approach, it can automatically and accurately predict individual cell types in datasets, even with only a small number of cell labels. Experimentally, the authors’ method has proven to have better performance compared to other methods.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.12107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transcriptome Analyses Reveal the Important miRNAs Involved in Immune Response of Gastric Cancer","authors":"Wen Jin,&nbsp;Jianli Liu,&nbsp;Tingyu Yang,&nbsp;Zongqi Feng,&nbsp;Jie Yang,&nbsp;Lei Cao,&nbsp;Chengyan Wu,&nbsp;Yongchun Zuo,&nbsp;Lan Yu","doi":"10.1049/syb2.70014","DOIUrl":"https://doi.org/10.1049/syb2.70014","url":null,"abstract":"<p>MicroRNAs (miRNAs) are crucial factors in gene regulation, and their dysregulation plays important roles in the immunity of gastric cancer (GC). However, finding specific and effective miRNA markers is still a great challenge for GC immunotherapy. In this study, we computed and analysed miRNA-seq, RNA-seq and clinical data of GC patients from the TCGA database. With the comparison of tumour and normal tissues in GC, we identified 2056 upregulated and 2311 downregulated protein-coding genes. Based on the miRNet database, more than 2600 miRNAs interact with these genes. Several key miRNAs, including hsa-mir-34a, hsa-mir-182 and hsa-mir-23b, were identified to potentially play important regulatory roles in the expression of most upregulated and downregulated genes in GC. Based on bioinformation approaches, the expressions of hsa-mir-34a and hsa-mir-182 were closely linked to the tumour stage, and high expression of hsa-mir-23b was correlated with poor survival in GC. Moreover, these three miRNAs are involved in immune cell infiltration (such as activated memory CD4 T cells and resting mast cells), particularly hsa-mir-182 and hsa-mir-23b. GSEA suggested that the changes in their expression may possibly activate/inhibit immune-related signal pathways, such as chemokine signalling pathway and <i>CXCR4</i> pathway. These results will provide possible miRNA markers or targets for combined immunotherapy of GC.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SVM-LncRNAPro: An SVM-Based Method for Predicting Long Noncoding RNA Promoters","authors":"Guohua Huang,&nbsp;Taigan Xue,&nbsp;Weihong Chen,&nbsp;Liangliang Huang,&nbsp;Qi Dai,&nbsp;JinYun Jiang","doi":"10.1049/syb2.70013","DOIUrl":"https://doi.org/10.1049/syb2.70013","url":null,"abstract":"<p>Long non-coding RNAs (lncRNAs) are closely associated with the regulation of gene expression, whose promoters play a crucial role in comprehensively understanding lncRNA regulatory mechanisms, functions and their roles in diseases. Due to limitations of the current techniques, accurately identifying lncRNA promoters remains a challenge. To address this challenge, we propose a support vector machine (SVM)–based method for predicting lncRNA promoters, called SVM-LncRNAPro. This method uses position-specific trinucleotide propensity based on single-strand (PSTNPss) to encode the DNA sequences and employs an SVM as the learning algorithm. The SVM-LncRNAPro achieves state-of-the-art performance with reduced complexity. Additionally, experiments demonstrate that this method exhibits a strong generalisation ability. For the convenience of academic research, we have made the source code of SVM-LncRNAPro publicly available. Researchers can download the code and perform the prediction of the lncRNA promoter via the following link: https://github.com/TG0F7/Prom/tree/master.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TNFR-LSTM: A Deep Intelligent Model for Identification of Tumour Necroses Factor Receptor (TNFR) Activity","authors":"Faisal Binzagr,&nbsp;Ansar Naseem,&nbsp;Muhammad Umer Farooq,&nbsp;Nashwan Alromema","doi":"10.1049/syb2.70007","DOIUrl":"https://doi.org/10.1049/syb2.70007","url":null,"abstract":"<p>Tumour necrosis factors (TNFs) are key players in processes such as inflammation, cancer development, and autoimmune diseases. However, accurately identifying TNFs remains challenging because of their complex interactions with other cytokines. Although existing machine learning models offer some potential, they often fall short in reliably distinguishing TNFs. To address this issue, the authors developed DEEP-TNFR, a more advanced model designed specifically to predict TNFR activity. The approach incorporates features such as relative and reverse positions, along with statistical moments, and is tested on a recognised benchmark dataset. The authors explored six different deep learning classifiers, including fully connected networks (FCN), convolutional neural networks (CNN), simple RNN (RNN), long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), and gated recurrent units (GRU). The model's effectiveness was evaluated through multiple methods: self-consistency, independent set testing, and 5- and 10-fold cross-validation, using metrics, such as accuracy, specificity, sensitivity, and Matthews correlation coefficient. Among these classifiers, LSTM proved to be the most effective, outperforming the others and setting a new standard compared to previous studies. DEEP-TNFR is poised to significantly support ongoing research by enhancing the accuracy of TNFR identification.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the Impact of Antibiotics on Environmental Microbiota Through Machine Learning Models","authors":"Yiheng Du,&nbsp;Khandaker Asif Ahmed,&nbsp;Md Rakibul Hasan,&nbsp;Md Zakir Hossain","doi":"10.1049/syb2.70009","DOIUrl":"https://doi.org/10.1049/syb2.70009","url":null,"abstract":"<p>Antibiotic pollution in the environment can significantly impact soil microorganisms, such as altering the soil microbial community or emerging antibiotic-resistant bacteria. We propose three machine learning (ML) methods to investigate antibiotics' impact on microorganisms and predict microbial abundance. We examined the microbial abundances of various environmental soil samples treated with antibiotics. We developed 3 ML models: (Model 1) for predicting the most abundant bacterial classes in a specific treatment group; (Model 2) for predicting antibiotic treatment effects based on bacterial abundances; and (Model 3) for using data from short-term incubations to predict the data of community structure after stabilisation. In Model 1, the Random Forest model achieved the highest average accuracy, with a Coefficient of Variation mean of 0.05 and 0.14 in the training and test set. In Model 2, the accuracy of the random forest and SVM models have the highest accuracy (nearly 0.90). Model 3 demonstrates that the Random Forest can use data from short-term incubations to predict the abundance of bacterial communities after long-term stabilisation. This study highlights the potential of ML models as powerful tools for understanding microbial dynamics in response to antibiotic treatments. The code is publicly available at - https://github.com/DeweyYihengDu/ML_on_Microbiota.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":"19 1","pages":""},"PeriodicalIF":1.9,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143717410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}