IET Systems Biology最新文献

{"title":"Identifying driver modules based on multi-omics biological networks in prostate cancer","authors":"Zhongli Chen,&nbsp;Biting Liang,&nbsp;Yingfu Wu,&nbsp;Haoru Zhou,&nbsp;Yuchen Wang,&nbsp;Hao Wu","doi":"10.1049/syb2.12050","DOIUrl":"10.1049/syb2.12050","url":null,"abstract":"<p>The development of sequencing technology has promoted the expansion of cancer genome data. It is necessary to identify the pathogenesis of cancer at the molecular level and explore reliable treatment methods and precise drug targets in cancer by identifying carcinogenic functional modules in massive multi-omics data. However, there are still limitations to identifying carcinogenic driver modules by utilising genetic characteristics simply. Therefore, this study proposes a computational method, NetAP, to identify driver modules in prostate cancer. Firstly, high mutual exclusivity, high coverage, and high topological similarity between genes are integrated to construct a weight function, which calculates the weight of gene pairs in a biological network. Secondly, the random walk method is utilised to reevaluate the strength of interaction among genes. Finally, the optimal driver modules are identified by utilising the affinity propagation algorithm. According to the results, the authors’ method identifies more validated driver genes and driver modules compared with the other previous methods. Thus, the proposed NetAP method can identify carcinogenic driver modules effectively and reliably, and the experimental results provide a powerful basis for cancer diagnosis, treatment and drug targets.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e8/7f/SYB2-16-187.PMC9675413.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10848807","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":"CHAC1 as a novel biomarker for distinguishing alopecia from other dermatological diseases and determining its severity","authors":"Hassan Karami,&nbsp;Samira Nomiri,&nbsp;Mohammad Ghasemigol,&nbsp;Niloufar Mehrvarzian,&nbsp;Afshin Derakhshani,&nbsp;Mohammad Fereidouni,&nbsp;Masoud Mirimoghaddam,&nbsp;Hossein Safarpour","doi":"10.1049/syb2.12048","DOIUrl":"10.1049/syb2.12048","url":null,"abstract":"<p>Alopecia Areata (AA) is characterised by an autoimmune response to hair follicles (HFs) and its exact pathobiology remains unclear. The current study aims to look into the molecular changes in the skin of AA patients as well as the potential underlying molecular mechanisms of AA in order to identify potential candidates for early detection and treatment of AA. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to identify key modules, hub genes, and mRNA–miRNA regulatory networks associated with AA. Furthermore, Chi2 as a machine-learning algorithm was used to compute the gene importance in AA. Finally, drug-target construction revealed the potential of repositioning drugs for the treatment of AA. Our analysis using four AA data sets established a network strongly correlated to AA pathogenicity based on <i>GZMA</i>, <i>OXCT2</i>, <i>HOXC13</i>, <i>KRT40</i>, <i>COMP</i>, <i>CHAC1</i>, and <i>KRT83</i> hub genes. Interestingly, machine learning introduced these genes as important in AA pathogenicity. Besides that, using another ten data sets, we showed that <i>CHAC1</i> could clearly distinguish AA from similar clinical phenotypes, such as scarring alopecia due to psoriasis. Also, two FDA-approved drug candidates and 30 experimentally validated miRNAs were identified that affected the co-expression network. Using transcriptome analysis, suggested <i>CHAC1</i> as a potential diagnostic predictor to diagnose AA.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469792/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10831174","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":"Positive input observer-based controller design for blood glucose regulation for type 1 diabetic patients: A backstepping approach","authors":"Mohamadreza Homayounzade","doi":"10.1049/syb2.12049","DOIUrl":"10.1049/syb2.12049","url":null,"abstract":"<p>In practice, there are many physical systems that can have only positive inputs, such as physiological systems. Most conventional control methods cannot ensure that the main system input is positive. A positive input observer-based controller is designed for an intravenous glucose tolerance test model of type 1 diabetes mellitus (T1DM). The backstepping (BS) approach is employed to design the feedback controller for artificial pancreas (AP) systems, based on the Extended Bergman's Minimal Model (EBMM). The EBMM represents the T1DM in terms of the blood glucose concentration (BGC), insulin concentration, and plasma level and the disturbance of insulin during medication due to either meal intake or burning sugar by doing some physical exercise. The insulin concentration and plasma level are estimated using observers, and these estimations are applied as feedback to the controller. The asymptotic stability of the observer-based controller is proved using the Lyapunov theorem. Moreover, it is proved that the system is bounded input-bounded output (BIBO) stable in the presence of uncertainties generated by uncertain parameters and external disturbance. For realistic situations, we consider only the BGC to be available for measurement and additionally inter-and intra-patient variability of system parameters is considered.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/36/21/SYB2-16-157.PMC9469794.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40704075","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":"Deep learning-based microarray cancer classification and ensemble gene selection approach","authors":"Khosro Rezaee,&nbsp;Gwanggil Jeon,&nbsp;Mohammad R. Khosravi,&nbsp;Hani H. Attar,&nbsp;Alireza Sabzevari","doi":"10.1049/syb2.12044","DOIUrl":"10.1049/syb2.12044","url":null,"abstract":"<p>Malignancies and diseases of various genetic origins can be diagnosed and classified with microarray data. There are many obstacles to overcome due to the large size of the gene and the small number of samples in the microarray. A combination strategy for gene expression in a variety of diseases is described in this paper, consisting of two steps: identifying the most effective genes via soft ensembling and classifying them with a novel deep neural network. The feature selection approach combines three strategies to select wrapper genes and rank them according to the k-nearest neighbour algorithm, resulting in a very generalisable model with low error levels. Using soft ensembling, the most effective subsets of genes were identified from three microarray datasets of diffuse large cell lymphoma, leukaemia, and prostate cancer. A stacked deep neural network was used to classify all three datasets, achieving an average accuracy of 97.51%, 99.6%, and 96.34%, respectively. In addition, two previously unreported datasets from small, round blue cell tumors (SRBCTs)and multiple sclerosis-related brain tissue lesions were examined to show the generalisability of the model method.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c5/71/SYB2-16-120.PMC9290776.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40585256","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 TNFAIP6 as a hub gene associated with the progression of glioblastoma by weighted gene co-expression network analysis","authors":"Dongdong Lin,&nbsp;Wei Li,&nbsp;Nu Zhang,&nbsp;Ming Cai","doi":"10.1049/syb2.12046","DOIUrl":"10.1049/syb2.12046","url":null,"abstract":"<p>This study aims to discover the genetic modules that distinguish glioblastoma multiforme (GBM) from low-grade glioma (LGG) and identify hub genes. A co-expression network is constructed using the expression profiles of 28 GBM and LGG patients from the Gene Expression Omnibus database. The authors performed gene ontology (GO) and Kyoto encyclopaedia of genes and genomes (KEGG) analysis on these genes. The maximal clique centrality method was used to identify hub genes. Online tools were employed to confirm the link between hub gene expression and overall patient survival rate. The top 5000 genes with major variance were classified into 18 co-expression gene modules. GO analysis indicated that abnormal changes in ‘cell migration’ and ‘collagen metabolic process’ were involved in the development of GBM. KEGG analysis suggested that ‘focal adhesion’ and ‘p53 signalling pathway’ regulate the tumour progression. TNFAIP6 was identified as a hub gene, and the expression of TNFAIP6 was increased with the elevation of pathological grade. Survival analysis indicated that the higher the expression of TNFAIP6, the shorter the survival time of patients. The authors identified TNFAIP6 as the hub gene in the progression of GBM, and its high expression indicates the poor prognosis of the patients.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40404350","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":"Dynamic changes of serum cytokines in acute paraquat poisoning and changes in patients' immune function","authors":"Huimin Yuan,&nbsp;Qian Liu,&nbsp;Yulan Yu","doi":"10.1049/syb2.12045","DOIUrl":"10.1049/syb2.12045","url":null,"abstract":"<p>Acute paraquat poisoning is due to the extremely severe toxicity of paraquat. After paraquat enters the human body, it will cause rapid changes in the human body system. Since paraquat poisoning will quickly invade the organs of the whole body, it may cause damage to the functions of multiple organs in the poisoned patient. The liver organ is the most important detoxification site for the human body, so the damage to the liver of the patient is more obvious. This article discovers and observes the structure of paraquat and the dynamic changes of serum cytokines in patients with paraquat poisoning through the clinical phenomenon of paraquat poisoning, and the related changes of human serum cells after the subjects took paraquat and the changes of cell dynamic factors after different doses of paraquat entered the human body were analysed. At the same time, the changes in the immune function of the body of different groups of people were also observed. The experimental results in this article show that according to the intake of paraquat, the severity of poisoning patients will be mild, moderate, severe and outbreak poisoning. Among them, the dose for adults who cannot be treated for prognosis is 10 ml.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/92/4b/SYB2-16-132.PMC9290778.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40401313","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":"Disrupted myelination network in the cingulate cortex of Parkinson's disease","authors":"Song Xie,&nbsp;Jiajun Yang,&nbsp;Shenghui Huang,&nbsp;Yuanlan Fan,&nbsp;Tao Xu,&nbsp;Jiangshuang He,&nbsp;Jiahao Guo,&nbsp;Xiang Ji,&nbsp;Zhibo Wang,&nbsp;Peijun Li,&nbsp;Jiangfan Chen,&nbsp;Yi Zhang","doi":"10.1049/syb2.12043","DOIUrl":"10.1049/syb2.12043","url":null,"abstract":"<p>The cingulate cortex is part of the conserved limbic system, which is considered as a hub of emotional and cognitive control. Accumulating evidence suggested that involvement of the cingulate cortex is significant for cognitive impairment of Parkinson's disease (PD). However, mechanistic studies of the cingulate cortex in PD pathogenesis are limited. Here, transcriptomic and regulatory network analyses were conducted for the cingulate cortex in PD. Enrichment and clustering analyses showed that genes involved in regulation of membrane potential and glutamate receptor signalling pathway were upregulated. Importantly, myelin genes and the oligodendrocyte development pathways were markedly downregulated, indicating disrupted myelination in PD cingulate cortex. Cell-type-specific signatures revealed that myelinating oligodendrocytes were the major cell type damaged in the PD cingulate cortex. Furthermore, downregulation of myelination pathways in the cingulate cortex were shared and validated in another independent RNAseq cohort of dementia with Lewy bodies (DLB). In combination with ATACseq data, gene regulatory networks (GRNs) were further constructed for 32 transcription factors (TFs) and 466 target genes among differentially expressed genes (DEGs) using a tree-based machine learning algorithm. Several transcription factors, including Olig2, Sox8, Sox10, E2F1, and NKX6-2, were highlighted as key nodes in a sub-network, which control many overlapping downstream targets associated with myelin formation and gliogenesis. In addition, the authors have validated a subset of DEGs by qPCRs in two PD mouse models. Notably, seven of these genes,TOX3, NECAB2 NOS1, CAPN3, NR4A2, E2F1 and FOXP2, have been implicated previously in PD or neurodegeneration and are worthy of further studies as novel candidate genes. Together, our findings provide new insights into the role of remyelination as a promising new approach to treat PD after demyelination.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.12043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46356053","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":"SRAS-net: Low-resolution chromosome image classification based on deep learning","authors":"Xiangbin Liu,&nbsp;Lijun Fu,&nbsp;Jerry Chun-Wei Lin,&nbsp;Shuai Liu","doi":"10.1049/syb2.12042","DOIUrl":"10.1049/syb2.12042","url":null,"abstract":"<p>Prenatal karyotype diagnosis is important to determine if the foetus has genetic diseases and some congenital diseases. Chromosome classification is an important part of karyotype analysis, and the task is tedious and lengthy. Chromosome classification methods based on deep learning have achieved good results, but if the quality of the chromosome image is not high, these methods cannot learn image features well, resulting in unsatisfactory classification results. Moreover, the existing methods generally have a poor effect on sex chromosome classification. Therefore, in this work, the authors propose to use a super-resolution network, Self-Attention Negative Feedback Network, and combine it with traditional neural networks to obtain an efficient chromosome classification method called SRAS-net. The method first inputs the low-resolution chromosome images into the super-resolution network to generate high-resolution chromosome images and then uses the traditional deep learning model to classify the chromosomes. To solve the problem of inaccurate sex chromosome classification, the authors also propose to use the SMOTE algorithm to generate a small number of sex chromosome samples to ensure a balanced number of samples while allowing the model to learn more sex chromosome features. Experimental results show that our method achieves 97.55% accuracy and is better than state-of-the-art methods.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9290780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9178707","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 and validation of a seven-gene prognostic marker in colon cancer based on single-cell transcriptome analysis","authors":"Yang Zhou,&nbsp;Yang Guo,&nbsp;Yuanhe Wang","doi":"10.1049/syb2.12041","DOIUrl":"10.1049/syb2.12041","url":null,"abstract":"<p>Colon cancer (CC) is one of the most commonly diagnosed tumours worldwide. Single-cell RNA sequencing (scRNA-seq) can accurately reflect the heterogeneity within and between tumour cells and identify important genes associated with cancer development and growth. In this study, scRNA-seq was used to identify reliable prognostic biomarkers in CC. ScRNA-seq data of CC before and after 5-fluorouracil treatment were first downloaded from the Gene Expression Omnibus database. The data were pre-processed, and dimensionality reduction was performed using principal component analysis and t-distributed stochastic neighbour embedding algorithms. Additionally, the transcriptome data, somatic variant data, and clinical reports of patients with CC were obtained from The Cancer Genome Atlas database. Seven key genes were identified using Cox regression analysis and the least absolute shrinkage and selection operator method to establish signatures associated with CC prognoses. The identified signatures were validated on independent datasets, and somatic mutations and potential oncogenic pathways were further explored. Based on these features, gene signatures, and other clinical variables, a more effective predictive model nomogram for patients with CC was constructed, and a decision curve analysis was performed to assess the utility of the nomogram. A prognostic signature consisting of seven prognostic-related genes, including <i>CAV2</i>, <i>EREG</i>, <i>NGFRAP1</i>, <i>WBSCR22</i>, <i>SPINT2</i>, <i>CCDC28A</i>, and <i>BCL10</i>, was constructed and validated. The proficiency and credibility of the signature were verified in both internal and external datasets, and the results showed that the seven-gene signature could effectively predict the prognosis of patients with CC under various clinical conditions. A nomogram was then constructed based on features such as the RiskScore, patients' age, neoplasm stage, and tumor (T), nodes (N), and metastases (M) classification, and the nomogram had good clinical utility. Higher RiskScores were associated with a higher tumour mutational burden, which was confirmed to be a prognostic risk factor. Gene set enrichment analysis showed that high-score groups were enriched in ‘cytoplasmic DNA sensing’, ‘Extracellular matrix receptor interactions’, and ‘focal adhesion’, and low-score groups were enriched in ‘natural killer cell-mediated cytotoxicity’, and ‘T-cell receptor signalling pathways’, among other pathways. A robust seven-gene marker for CC was identified based on scRNA-seq data and was validated in multiple independent cohort studies. These findings provide a new potential marker to predict the prognosis of patients with CC.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/syb2.12041","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43690326","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":"Construction and validation of m6A RNA methylation regulators associated prognostic model for gastrointestinal cancer","authors":"Yandong Miao,&nbsp;Bin Su,&nbsp;Xiaolong Tang,&nbsp;Jiangtao Wang,&nbsp;Wuxia Quan,&nbsp;Yonggang Chen,&nbsp;Denghai Mi","doi":"10.1049/syb2.12040","DOIUrl":"10.1049/syb2.12040","url":null,"abstract":"<p>N6-methyladenosine (m⁶A) RNA methylation is correlated with carcinogenesis and dynamically possessed through the m⁶A RNA methylation regulators. This paper aimed to explore 13 m⁶A RNA methylation regulators' role in gastrointestinal cancer (GIC) and determine the risk model and prognosis value of m⁶A RNA methylation regulators in GIC. We used several bioinformatics methods to identify the differential expression of m⁶A RNA methylation regulators in GIC, constructed a prognostic model, and carried out functional enrichment analysis. Eleven of 13 m⁶A RNA methylation regulators were differentially expressed in different clinicopathological characteristics of GIC, and m⁶A RNA methylation regulators were nearly associated with GIC. We constructed a risk model based on five m⁶A RNA methylation regulators (METTL3, FTO, YTHDF1, ZC3H13, and WTAP); the risk score is an independent prognosis biomarker. Moreover, the five m⁶A RNA methylation regulators can also forecast the 1-, 3- and 5-year overall survival through a nomogram. Furthermore, four hallmarks of oxidative phosphorylation, glycolysis, fatty acid metabolism, and cholesterol homoeostasis gene sets were significantly enriched in GIC. m⁶A RNA methylation regulators were related to the malignant clinicopathological characteristics of GIC and may be used for prognostic stratification and development of therapeutic strategies.</p>","PeriodicalId":50379,"journal":{"name":"IET Systems Biology","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2022-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/16/3b/SYB2-16-59.PMC8965361.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39930575","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}