IET Systems Biology最新文献

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 CAV2, EREG, NGFRAP1, WBSCR22, SPINT2, CCDC28A, and BCL10, 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.

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.

Liver hepatocellular carcinoma (LIHC) comprises most cases of liver cancer with a poor prognosis. N⁶ -methyladenosine (m6A) plays important biological functions in cancers. Thus, the present research was aimed to determine biomarkers of m6A regulators that could effectively predict the prognosis of LIHC patients. Based on the data collected from the Cancer Genome Atlas (TCGA) database, the correlation between the mRNA expression levels and copy number variation (CNV) patterns were determined. Higher mRNA expression resulted from the increasing number of 9 genes. Using the univariate Cox regression analysis, 11 m6A regulators that had close correlations with the LIHC prognosis were identified. In addition, under the support of the multivariate Cox regression models and the least absolute shrinkage and selection operator, a 4-gene (YTHDF2, IGF2BP3, KIAA1429, and ALKBH5) signature of m6A regulators was constructed. This signature was expected to present a prognostic value in LIHC (log-rank test p value < 0.0001). The GSE76427 (n = 94) and ICGC-LIRI-JP (n = 212) datasets were used to validate the prognostic signature, suggesting strong power to predict patients' prognosis for LIHC. To sum up, genetic alterations in m6A regulatory genes were identified as reliable and effective biomarkers for predicting the prognosis of LIHC patients.

Identification of drug-target interactions (DTIs) has great practical importance in the drug discovery process for known diseases. However, only a small proportion of DTIs in these databases has been verified experimentally, and the computational methods for predicting the interactions remain challenging. As a result, some effective computational models have become increasingly popular for predicting DTIs. In this work, the authors predict potential DTIs from the local structure of drug-target associations' network, which is different from the traditional global network similarity methods based on structure and ligand. A novel method called PPDTS is proposed to predict DTIs. First, according to the DTIs' network local structure, the known DTIs are converted into a binary network. Second, the Resource Allocation algorithm is used to obtain a drug-drug similarity network and a target-target similarity network. Third, a Collaborative Filtering algorithm is used with the known drug-target topology information to obtain similarity scores. Fourth, the linear combination of drug-target similarity model and the target-drug similarity model are innovatively proposed to obtain the final prediction results. Finally, the experimental performance of PPDTS has proved to be higher than that of the previously mentioned four popular network-based similarity methods, which is validated in different experimental datasets. Some of the predicted results can be supported in UniProt and DrugBank databases.

RYR2 mutation is clinically frequent in non-small cell lung cancer (NSCLC) with its function being elusive. We downloaded lung squamous cell carcinoma and lung adenocarcinoma samples from the TCGA database, split the samples into RYR2 mutant group (n = 337) and RYR2 wild group (n = 634), and established Kaplan-Meier curves. The results showed that RYR2 mutant group lived longer than the wild group (p = 0.027). Weighted gene co-expression network analysis (WGCNA) of differentially expressed genes (DEGs) yielded prognosis-related genes. Five mRNAs and 10 lncRNAs were selected to build survival prognostic models with other clinical features. The AUCs of 2 models are 0.622 and 0.565 for predicting survival at 3 years. Among these genes, the AUCs of DKK1 and GS1-115G20.1 expression levels were 0.607 and 0.560, respectively, which predicted the 3-year survival rate of NSCLC sufferers. GSEA identified an association of high DKK1 expression with TP53, MTOR, and VEGF expression. Several target miRNAs interacting with GS1-115G20.1 were observed to show the relationship with the phenotype, treatment, and survival of NSCLC. NSCLC patients with RYR2 mutation may obtain better prognosis by down-regulating DKK1 and up-regulating GS1-115G20.1.

Stroke is one of the leading causes of patients' death and long-term disability worldwide, and ischaemic stroke (IS) accounts for nearly 80% of all strokes. Differential genes and weighted gene co-expression network analysis (WGCNA) in male and female patients with IS were compared. The authors used cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) to analyse the distribution pattern of immune subtypes between male and female patients. In this study, 141 up-regulated and 61 down-regulated genes were gathered and distributed into five modules in response to their correlation degree to clinical traits. The criterion for Gene Ontology (GO) term and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway indicated that detailed analysis had the potential to enhance clinical prediction and to identify the gender-related mechanism. After that, the expression levels of hub genes were measured via the quantitative real-time PCR (qRT-PCR) method. Finally, CCL20, ICAM1 and PTGS2 were identified and these may be some promising targets for sex differences in IS. Besides, the hub genes were further verified by rat experiments. Furthermore, these CIBERSORT results showed that T cells CD8 and Monocytes may be the target for the treatment of male and female patients, respectively.

Rectal cancer is an important cause of cancer-related deaths worldwide. In this study, the differentially expressed (DE) lncRNAs/mRNAs were first identified and the correlation level between DE lncRNAs and mRNAs were calculated. The results showed that genes of highly correlated lncRNA-mRNA pairs presented strong prognosis effects, such as GPM6A, METTL24, SCN7A, HAND2-AS1 and PDZRN4. Then, the rectal cancer-related lncRNA-mRNA network was constructed based on the ceRNA theory. Topological analysis of the network revealed that the network was maintained by hub nodes and a hub subnetwork was constructed, including the hub lncRNA MIR143HG and MBNL1-SA1. Further analysis indicated that the hub subnetwork was highly related to cancer pathways, such as ‘Focal adhesion’ and ‘Wnt signalling pathway’. Hub subnetwork also had significant prognosis capability. A closed lncRNA-mRNA module was identified by bilateral network clustering. Genes in modules also showed high prognosis effects. Finally, a core lncRNA-TF crosstalk network was identified to uncover the crosstalk and regulatory mechanisms of lncRNAs and TFs by integrating ceRNA crosstalks and TF binding affinities. Some core genes, such as MEIS1, GLI3 and HAND2-AS1 were considered as the key regulators in tumourigenesis. Based on the authors’ comprehensive analysis, all these lncRNA-mRNA crosstalks provided promising clues for biological prognosis of rectal cancer.

Prehypertension is a new risky disease defined in the seventh report issued by the Joint National Commission. Hence, detecting prehypertension in time plays a very important role in protecting human lives. This study proposes a method for categorising blood pressure values into two classes, namely the class of healthy blood pressure values and the class of prehypertension blood pressure values, as well as estimating the blood pressure values continuously only by employing photoplethysmograms. First, the denoising of photoplethysmograms is performed via a discrete cosine transform approach. Then, the features of the photoplethysmograms in both the time domain and the frequency domain are extracted. Next, the feature vectors are categorised into the two classes of blood pressure values by a multi-model fusion of the classifiers. Here, the support vector machine, the random forest and the K-nearest neighbour classifier are employed for performing the fusion. There are two types of blood pressure values. They are the systolic blood pressure values and the diastolic blood pressure values. For each class and each type of blood pressure values, support vector regression is used to estimate the blood pressure values. Since different classes and different types of blood pressure values are considered separately, the proposed method achieves an accurate estimation. The computed numerical simulation results show that the proposed method based on the multi-model fusion of the classifiers achieves both higher classification accuracy and higher regression accuracy than the individual classification methods.

The goal of this study is to reveal the hub genes and molecular mechanisms of the coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS) based on the genome-wide RNA sequencing dataset. The RNA sequencing dataset of COVID-19 ARDS was obtained from GSE163426. A total of 270 differentially expressed genes (DEGs) were identified between COVID-19 ARDS and control group patients. Functional enrichment analysis of DEGs suggests that these DEGs may be involved in the following biological processes: response to cytokine, G-protein coupled receptor activity, ionotropic glutamate receptor signalling pathway and G-protein coupled receptor signalling pathway. By using the weighted correlation network analysis approach to analyse these DEGs, 10 hub DEGs that may play an important role in COVID-19 ARDS were identified. A total of 67 potential COVID-19 ARDS targetted drugs were identified by a complement map analysis. Immune cell infiltration analysis revealed that the levels of T cells CD4 naive, T cells follicular helper, macrophages M1 and eosinophils in COVID-19 ARDS patients were significantly different from those in control group patients. In conclusion, this study identified 10 COVID-19 ARDS-related hub DEGs and numerous potential molecular mechanisms through a comprehensive analysis of the RNA sequencing dataset and also revealed the difference in immune cell infiltration of COVID-19 ARDS.

Diabetes mellitus type 1 occurs when $��\beta �-�$cells in the pancreas are destroyed by the immune system. As a result, the pancreas cannot produce adequate insulin, and the glucose enters the cells to produce energy. To elevate the glycaemic concentration, sufficient amount of insulin should be taken orally or injected into the human body. Artificial pancreas is a device that automatically regulates the level of body insulin by injecting the requisite amount of insulin into the human body. A finite-time robust feedback controller based on the Extended Bergman Minimal Model is designed here. The controller is designed utilizing the backstepping approach and is robust against the unknown external disturbance and parametric uncertainties. The stability of the system is proved using the Lyapunov theorem. The controller is exponentially stable and hence provides the finite-time convergence of the blood glucose concentration to its desired magnitude. The effectiveness of the proposed control method is shown through simulation in MATLAB/Simulink environment via comparisons with previous studies.