Gene Microarray Data Research Articles

Mild magnetic hyperthermia is synergistic with an antibiotic treatment against dual species biofilms consisting of S. aureus and P. aeruginosa by enhancing metabolic activity

Objective The wound biofilm infections that develop tolerance to standard-of-care antimicrobial treatment has been increasing. The objective of this study was to demonstrate a proof-of-concept of mild magnetic nanoparticle (MNP)/alternating magnetic field (AMF) hyperthermia as an anti-biofilm therapy against multispecies biofilm infections. Methods Using both an in vitro cell culture and in vivo murine model of wound infection, we investigated whether MNP/AMF hyperthermia applied at a mild thermal dosage would be synergistically effective against dual species biofilm infection consisting of S. aureus and P. aeruginosa when combined with a broad-spectrum antibiotic, ciprofloxacin (CIP). Results The combined treatment of MNP/AMF hyperthermia and CIP to the wounds of diabetic mice (db/db mice) significantly reduced the CFU number of S. aureus and P. aeruginosa by 2-log and 3-log, respectively, compared to the untreated control group, whereas either mild MNP/AMF hyperthermia or CIP treatment alone had little effect on the eradication of both bacteria. Our gene microarray data obtained from the culture of S. aureus biofilm suggest that mild MNP/AMF could shift the expression of genes for cellular respiration from anaerobic fermentation to an aerobic glycolytic/tricarboxylic acid cycle (TCA) pathway, implicating that the beneficial effect of mild MNP/AMF hyperthermia on the increased susceptibility of biofilm bacteria to an antibiotic treatment is associated with an increased metabolic activity. Conclusion Our results support the translational potential of mild MNP/AMF as an adjunctive therapy that can be combined with a broad-spectrum antibiotic treatment for the management of wound biofilm infections associated with multispecies bacteria.

Integrated analyses delineate distinctive immunological pathways and diagnostic signatures for Behcet's disease by leveraging gene microarray data.

Behcet's disease (BD) is a chronic inflammatory vasculitis and clinically heterogeneous disorder caused by immunocyte aberrations. Comprehensive research on gene expression patterns in BD illuminating its aetiology is lacking. E-MTAB-2713 downloaded from ArrayExpress was analysed to screen differentially expressed genes (DEGs) using limma. Random forest (RF) and neural network (NN) classification models composed of gene signatures were established using the E-MTAB-2713 training set and subsequently verified using GSE17114. Single sample gene set enrichment analysis was used to assess immunocyte infiltration. After identifying DEGs in E-MTAB-2713, pathogen-triggered, lymphocyte-mediated and angiogenesis- and glycosylation-related inflammatory pathways were discovered to be predominant in BD episodes. Gene signatures from the RF and NN diagnostic models, together with genes enriched in angiogenesis and glycosylation pathways, well discriminated the clinical subtypes of BD manifesting as mucocutaneous, ocular and large vein thrombosis involvement in GSE17114. Moreover, a distinctive immunocyte profile revealed T, NK and dendritic cell activation in BD compared to the findings in healthy controls. Our findings suggested that EPHX1, PKP2, EIF4B and HORMAD1 expression in CD14+ monocytes and CSTF3 and TCEANC2 expression in CD16+ neutrophils could serve as combined gene signatures for BD phenotype differentiation. Pathway genes comprising ATP2B4, MYOF and NRP1 for angiogenesis and GXYLT1, ENG, CD69, GAA, SIGLEC7, SIGLEC9 and SIGLEC16 for glycosylation also might be applicable diagnostic markers for subtype identification.

TNF-α activates RELA expression via TNFRSF1B to upregulate OPA1 expression and inhibit chondrogenic differentiation of human adipose stem cells

BackgroundTumor necrosis factor-alpha (TNF-α), one of the pro-inflammatory cytokines mediating the local inflammatory process in joints, inhibits cartilage formation and has a detrimental effect on stem cell-based cartilage regeneration for the treatment of osteoarthritis (OA). However, the mechanisms behind this inhibitory effect are still poorly understood. Mitochondrial morphological changes mediated by mitochondrial fusion and fission are highly plastic, are quite sensitive to environmental stimuli and play a crucial role in maintaining cell structure and function. In our study, chondrogenic differentiated human adipose stem cells (hADSCs) were exposed to TNF-α and the effect of TNF-α on the ability of hADSCs to chondrogenic differentiate and on mitochondrial fusion and fission was observed and analyzed. The aim was to investigate the role and mechanisms of mitochondrial fusion and fission regulation in the chondrogenic differentiation of hADSCs under normal conditions and under exposure to TNF-α.MethodsWe used flow cytometry to identify hADSCs immunophenotypes CD29, CD44, CD34, CD45, and HLA-DR. Alcian blue staining and Sirius red staining were used to observe the formation of proteoglycans and collagen during the chondrogenic differentiation of hADSCs, respectively. The mRNA and protein expression levels of the cartilage formation marker SOX9, type II collagen (COL2A1), and Aggrecan were measured by real-time fluorescent quantitative PCR (RT-qPCR) and western blot, respectively. The fluorescent probes MitoTracker® Red CMXRos and JC-1 were used to visualize mitochondria morphology and detect mitochondrial membrane electricity (MMP). Affymetrix PrimeView™ chips were used for gene expression profiling.ResultsThe results showed that the chondrogenic differentiation of hADSCs was inhibited in the presence of TNF-α that optic atrophy 1 (OPA1) expression was significantly upregulated and mitochondria were prolonged and interconnected during this process. Gene microarray and RT-qPCR data showed that the presence of TNF-α led to increased expression of TNFα receptor 2 (TNFRSF1B) and RELA during chondrogenic differentiation of hADSCs.ConclusionsTNF-α inhibits chondrogenic differentiation of human adipose stem cells by activating RELA expression through TNFRSF1B upregulating OPA1 expression thereby increasing mitochondrial fusion.

Bioinformatic analysis of hub markers and immune cell infiltration characteristics of gastric cancer.

Gastric cancer (GC) is the fifth most common cancer and the second leading cause of cancer-related deaths worldwide. Due to the lack of specific markers, the early diagnosis of gastric cancer is very low, and most patients with gastric cancer are diagnosed at advanced stages. The aim of this study was to identify key biomarkers of GC and to elucidate GC-associated immune cell infiltration and related pathways. Gene microarray data associated with GC were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were analyzed using Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia, Gene Set Enrichment Analysis (GSEA) and Protein-Protein Interaction (PPI) networks. Weighted gene coexpression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to identify pivotal genes for GC and to assess the diagnostic accuracy of GC hub markers using the subjects' working characteristic curves. In addition, the infiltration levels of 28 immune cells in GC and their interrelationship with hub markers were analyzed using ssGSEA. And further validated by RT-qPCR. A total of 133 DEGs were identified. The biological functions and signaling pathways closely associated with GC were inflammatory and immune processes. Nine expression modules were obtained by WGCNA, with the pink module having the highest correlation with GC; 13 crossover genes were obtained by combining DEGs. Subsequently, the LASSO algorithm and validation set verification analysis were used to finally identify three hub genes as potential biomarkers of GC. In the immune cell infiltration analysis, infiltration of activated CD4 T cell, macrophages, regulatory T cells and plasmacytoid dendritic cells was more significant in GC. The validation part demonstrated that three hub genes were expressed at lower levels in the gastric cancer cells. The use of WGCNA combined with the LASSO algorithm to identify hub biomarkers closely related to GC can help to elucidate the molecular mechanism of GC development and is important for finding new immunotherapeutic targets and disease prevention.

Subject clustering by IF-PCA and several recent methods.

Subject clustering (i.e., the use of measured features to cluster subjects, such as patients or cells, into multiple groups) is a problem of significant interest. In recent years, many approaches have been proposed, among which unsupervised deep learning (UDL) has received much attention. Two interesting questions are 1) how to combine the strengths of UDL and other approaches and 2) how these approaches compare to each other. We combine the variational auto-encoder (VAE), a popular UDL approach, with the recent idea of influential feature-principal component analysis (IF-PCA) and propose IF-VAE as a new method for subject clustering. We study IF-VAE and compare it with several other methods (including IF-PCA, VAE, Seurat, and SC3) on 10 gene microarray data sets and eight single-cell RNA-seq data sets. We find that IF-VAE shows significant improvement over VAE, but still underperforms compared to IF-PCA. We also find that IF-PCA is quite competitive, slightly outperforming Seurat and SC3 over the eight single-cell data sets. IF-PCA is conceptually simple and permits delicate analysis. We demonstrate that IF-PCA is capable of achieving phase transition in a rare/weak model. Comparatively, Seurat and SC3 are more complex and theoretically difficult to analyze (for these reasons, their optimality remains unclear).

Antagonizing Effects of Chromium Against Iron-Decreased Glucose Uptake by Regulating ROS-Mediated PI3K/Akt/GLUT4 Signaling Pathway in C2C12.

To investigate the effect of chromium and iron on glucose metabolism via the PI3K/Akt/GLUT4 signaling pathway. Skeletal muscle gene microarray data in T2DM (GSE7014) was selected using Gene Expression Omnibus database. Element-gene interaction datasets of chromium and iron were extracted from comparative toxicogenomics database (CTD). Gene ontology (GO)and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using DAVID online tool. Cell viability, insulin-stimulated glucose uptake, intracellular reactive oxygen species (ROS) level, and protein expression level were measured in C2C12 cells. The bioinformatics research indicated that PI3K/Akt signaling pathway participated in the effects of chromium and iron associated with T2DM. Insulin-stimulated glucose uptake level was significantly higher in chromium picolinate (Cr group) and lower in ammonium iron citrate (FA group) than that for the control group (P < 0.05); chromium picolinate + ammonium iron citrate (Cr + FA group) glucose uptake level was higher than that for the FA group (P < 0.05). Intracellular ROS level was significantly higher in the FAC group than that for the control group (P < 0.05), and that for the Cr + FA group was lower than that for the FA group (P < 0.05). p-PI3K/PI3K, p-Akt/Akt, and GLUT4 levels were significantly lower in the FA group than that for the control group (P < 0.05), and the Cr + FA group had higher levels than the FA group (P < 0.05). Chromium might have a protective effect on iron-induced glucose metabolism abnormalities through the ROS-mediated PI3K/Akt/GLUT4 signaling pathway.

A gravity inspired clustering algorithm for gene selection from high-dimensional microarray data

ABSTRACT The problem of selecting a subset of genes relevant to a particular disease classification task from high-dimensional gene microarray data has been an area of active research in the scientific community. A wide variety of methods ranging from assigning scores to genes based on their interactions with the output to the Gravity inspired clustering algorithm with the hybrid approach have been proposed. The feature sets selected by this approach have been evaluated by using the Support Vector Machine (SVM) with Leave One Out Cross Validation (LOOCV). The method has been evaluated on twelve different publicly available microarray cancer datasets and the proposed method provided good accuracy of 90.16% to the colon dataset, 98.59% to the ALL-AML dataset, 97.03% to the lung cancer dataset, 93.07% to the prostate cancer dataset, 98.41% to the meningioma dataset, 83.72% to the brain tumour dataset and has been shown to compete with existing methods.

Multi-stage biomedical feature selection extraction algorithm for cancer detection

Cancer is a significant cause of death worldwide. Early cancer detection is greatly aided by machine learning and artificial intelligence (AI) to gene microarray data sets (microarray data). Despite this, there is a significant discrepancy between the number of gene features in the microarray data set and the number of samples. Because of this, it is crucial to identify markers for gene array data. Existing feature selection algorithms, however, generally use long-standing, are limited to single-condition feature selection and rarely take feature extraction into account. This work proposes a Multi-stage algorithm for Biomedical Deep Feature Selection (MBDFS) to address this issue. In the first, three feature selection techniques are combined for thorough feature selection, and feature subsets are obtained; in the second, an unsupervised neural network is used to create the best representation of the feature subset to enhance final classification accuracy. Using a variety of metrics, including a comparison of classification results before and after feature selection and the performance of alternative feature selection methods, we evaluate MBDFS's efficacy. The experiments demonstrate that although MBDFS uses fewer features, classification accuracy is either unchanged or enhanced.

Identification of Hub Biomarkers and Immune and Inflammation Pathways Contributing to Kawasaki Disease Progression with RT-qPCR Verification

Background. Kawasaki disease (KD) is characterized by a disordered inflammation response of unknown etiology. Immune cells are closely associated with its onset, although the immune-related genes’ expression and possibly involved immune regulatory mechanisms are little known. This study aims to identify KD-implicated significant immune- and inflammation-related biomarkers and pathways and their association with immune cell infiltration. Patients and Methods. Gene microarray data were collected from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) regression, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were used to find KD hub markers. GSEA was used to assess the infiltration by 28 immune cell types and their connections to essential gene markers. Receiver operating characteristic (ROC) curves were used to examine hub markers’ diagnostic effectiveness. Finally, hub genes’ expressions were validated in Chinese KD patients by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Results. One hundred and fifty-one unique genes were found. Among 10 coexpression modules at WGCNA, one hub module exhibited the strongest association with KD. Thirty-six overlapping genes were identified. Six hub genes were potential biomarkers according to LASSO analysis. Immune infiltration revealed connections among activated and effector memory CD4+ T cells, neutrophils, activated dendritic cells, and macrophages. The six hub genes’ diagnostic value was shown by ROC curve analysis. Hub genes were enriched in immunological and inflammatory pathways. RT-qPCR verification results of FCGR1B ( P &lt; 0.001 ), GPR84 ( P &lt; 0.001 ), KREMEN1 ( P &lt; 0.001 ), LRG1 ( P &lt; 0.001 ), and TDRD9 ( P &lt; 0.001 ) upregulated expression in Chinese KD patients are consistent with our database analysis. Conclusion. Neutrophils, macrophages, and activated dendritic cells are strongly linked to KD pathophysiology. Through immune-related signaling pathways, hub genes such as FCGR1B, GPR84, KREMEN1, LRG1, and TDRD9 may be implicated in KD advancement.

A two-stage hybrid biomarker selection method based on ensemble filter and binary differential evolution incorporating binary African vultures optimization

BackgroundIn the field of genomics and personalized medicine, it is a key issue to find biomarkers directly related to the diagnosis of specific diseases from high-throughput gene microarray data. Feature selection technology can discover biomarkers with disease classification information.ResultsWe use support vector machines as classifiers and use the five-fold cross-validation average classification accuracy, recall, precision and F1 score as evaluation metrics to evaluate the identified biomarkers. Experimental results show classification accuracy above 0.93, recall above 0.92, precision above 0.91, and F1 score above 0.94 on eight microarray datasets.MethodThis paper proposes a two-stage hybrid biomarker selection method based on ensemble filter and binary differential evolution incorporating binary African vultures optimization (EF-BDBA), which can effectively reduce the dimension of microarray data and obtain optimal biomarkers. In the first stage, we propose an ensemble filter feature selection method. The method combines an improved fast correlation-based filter algorithm with Fisher score. obviously redundant and irrelevant features can be filtered out to initially reduce the dimensionality of the microarray data. In the second stage, the optimal feature subset is selected using an improved binary differential evolution incorporating an improved binary African vultures optimization algorithm. The African vultures optimization algorithm has excellent global optimization ability. It has not been systematically applied to feature selection problems, especially for gene microarray data. We combine it with a differential evolution algorithm to improve population diversity.ConclusionCompared with traditional feature selection methods and advanced hybrid methods, the proposed method achieves higher classification accuracy and identifies excellent biomarkers while retaining fewer features. The experimental results demonstrate the effectiveness and advancement of our proposed algorithmic model.

Sparse clusterability: testing for cluster structure in high dimensions

BackgroundCluster analysis is utilized frequently in scientific theory and applications to separate data into groups. A key assumption in many clustering algorithms is that the data was generated from a population consisting of multiple distinct clusters. Clusterability testing allows users to question the inherent assumption of latent cluster structure, a theoretical requirement for meaningful results in cluster analysis.ResultsThis paper proposes methods for clusterability testing designed for high-dimensional data by utilizing sparse principal component analysis. Type I error and power of the clusterability tests are evaluated using simulated data with different types of cluster structure in high dimensions. Empirical performance of the new methods is evaluated and compared with prior methods on gene expression, microarray, and shotgun proteomics data. Our methods had reasonably low Type I error and maintained power for many datasets with a variety of structures and dimensions. Cluster structure was not detectable in other datasets with spatially close clusters.ConclusionThis is the first analysis of clusterability testing on both simulated and real-world high-dimensional data.

Integrated Bioinformatics Analysis to Screen Hub Gene Signatures for Fetal Growth Restriction.

Fetal growth restriction (FGR) is the impairment of the biological growth potential of the fetus and often leads to adverse pregnancy outcomes. The molecular mechanisms for the development of FGR, however, are still unclear. The purpose of this study is to identify critical genes associated with FGR through an integrated bioinformatics approach and explore the potential pathogenesis of FGR. We downloaded FGR-related gene microarray data, used weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs), and protein-protein interaction (PPI) networks to screen hub genes. The GSE24129 gene set was used for validation of critical gene expression levels and diagnostic capabilities. A weighted gene co-expression network was constructed, and 5000 genes were divided into 12 modules. Of these modules, the blue module showed the closest relationship with FGR. Taking the intersection of the DEGs and genes in the blue module as pivotal genes, 277 genes were identified, and 20 crucial genes were screened from the PPI network. The GSE24129 gene set verified the expression of 20 genes, and CXCL9, CXCR3, and ITGAX genes were identified as actual pivotal genes. The expression levels of CXCL9, CXCR3, and ITGAX were increased in both the training and validation sets, and ROC curve validation revealed that these three pivotal genes had a significant diagnostic ability for FGR. Single-gene GSEA results showed that all three core genes activated "hematopoietic cell lineage" and "cell adhesion molecules" and inhibited the "cGMP-PKG signaling pathway" in the development of FGR. CXCL9, CXCR3, and ITGAX may therefore be closely associated with the development of FGR and may serve as potential biomarkers for the diagnosis and treatment of FGR.

An Efficient Feature Selection Algorithm for Gene Families Using NMF and ReliefF.

Gene families, which are parts of a genome's information storage hierarchy, play a significant role in the development and diversity of multicellular organisms. Several studies have focused on the characteristics of gene families, such as function, homology, or phenotype. However, statistical and correlation analyses on the distribution of gene family members in the genome have yet to be conducted. Here, a novel framework incorporating gene family analysis and genome selection based on NMF-ReliefF is reported. Specifically, the proposed method starts by obtaining gene families from the TreeFam database and determining the number of gene families within the feature matrix. Then, NMF-ReliefF is used to select features from the gene feature matrix, which is a new feature selection algorithm that overcomes the inefficiencies of traditional methods. Finally, a support vector machine is utilized to classify the acquired features. The results show that the framework achieved an accuracy of 89.1% and an AUC of 0.919 on the insect genome test set. We also employed four microarray gene data sets to evaluate the performance of the NMF-ReliefF algorithm. The outcomes show that the proposed method may strike a delicate balance between robustness and discrimination. Additionally, the proposed method's categorization is superior to state-of-the-art feature selection approaches.

Wumei Wan attenuates angiogenesis and inflammation by modulating RAGE signaling pathway in IBD: Network pharmacology analysis and experimental evidence

BackgroundWumei Wan (WMW) has been used to address digestive disorder for centuries in traditional Chinese medicine. Previous studies have demonstrated its anti-colitis efficacy, but the underlying mechanism of its action remains to be further clarified. PurposeTo investigate the underlying mechanisms of WMW in the treatment of chronic ulcerative colitis (UC) through network pharmacology and experimental validation. MethodsTraditional Chinese Medicine Systems Pharmacology (TCMSP) platform were used to identify the ingredients and potential targets of WMW. The microarray gene data GSE75214 datasets from GEO database was used to define UC-associated targets. Cytoscape3.7.2 was employed to construct the protein-protein interaction (PPI) network and compounds-disease targets network. GO enrichment analysis and KEGG pathway analysis were performed by R software for functional annotation. UPLC-TOF-MS/MS method was used to quantitatively analyze the active ingredients of WMW. For experimental validation, three cycles of 2% dextran sulfate sodium salt (DSS) were used to construct chronic colitis model. The hub targets and signal pathway were detected by qPCR, ELISA, western blotting , immunohistochemical and immunofluorescence. ResultsThrough network analysis, 104 active ingredients were obtained from WMW, and 47 of these ingredients had potential targets for UC. A total of 41 potential targets of WMW and 13 hub targets were identified. KEGG analysis showed that WMW involved in advanced glycation end products-receptor of advanced glycation end products (AGE-RAGE) signaling pathway. Taxifolin, rutaecarpine, kaempferol, quercetin, and luteolin of WMW were the more highly predictive components related to the AGE-RAGE signaling pathway. In vivo validation, WMW improved DSS-induced colitis, reduced the expression of inflammatory cytokines and chemokines. Notably, it significantly decreased the mRNA expression of Spp1, Serpine1, Mmp2, Mmp9, Ptgs2, Nos2, Kdr and Icam1, which were associated with angiogenesis. In addition, we confirmed WMW inhibited RAGE expression and diminished DSS-induced epithelial barrier alterations ConclusionOur results initially demonstrated the effective components and the strong anti-angiogenic activity of WMW in experimental chronic colitis. Sufficient evidence of the satisfactory anti-colitis action of WMW was verified in this study, suggesting its potential as a quite prospective agent for the therapy of UC.

GA-NobRFE-SVM: A New Algorithm for Classification of Unbalanced Gene Data

As an important branch of bioinformatics, gene microarray data analysis has become one of the important frontier fields in life sciences. Because of the high cost of microarray experiment, gene expression profile data shows the characteristics of small samples size, high dimensionality and category imbalance between samples. In this case, the traditional feature selection method is difficult to obtain good results. this paper proposes unbiased SVM-RFE (NobSVM-RFE). Compared with traditional feature selection algorithm, NobSVM-RFE algorithm can obtain better feature subset and reduce computation cost. Combining GASMOTE with NobSVM-RFE, this paper proposes a three-stage algorithm GA-NobRFE-SVM, which includes balancing algorithm, feature selection algorithm and classifier. The experimental results show that GA-NobRFE-SVM can effectively improve the classification performance of unbalanced gene data.

To Explore the Mechanism of Hawthorn in the Intervention of Hyperlipidemia Based on the Gene Expression Omnibus and Network Pharmacology

A common clinical treatment for hyperlipidemia in China is hawthorn, a traditional Chinese herb. However, few researches have been done on its active ingredients and their possible mechanisms for treating hyperlipidemia. The active chemical constituents of hawthorn were obtained from the encyclopedia of traditional Chinese medicine, the bioavailability and druglikeness properties of each active ingredient of hawthorn were analyzed using SwissADME and their possible targets of action were predicted using SwissTargetPrediction and PharmMapper. Then, we used the Gene Expression Omnibus-related gene microarray data to mine the differentially expressed genes for hyperlipidemia. Meanwhile, we collected relevant targets of hyperlipidemia through the DrugBank, Online Mendelian Inheritance in Man, GeneCards, DisGeNET and Therapeutic Target Database. Based on this data, a component-target-disease pathway and a protein-protein interaction networks were constructed using search tool for the retrieval of interacting genes/proteins. Finally, Metascape was systematically used to analyze the targets of hawthorn intervention in hyperlipidemia. A total of 8 active components and 312 hawthorn targets were gathered and 343 prospective targets associated with hyperlipidemia were predicted, 25 of which shared targets with hawthorn-related targets. Gene ontology enrichment analysis revealed that the mechanism of action of hawthorn in intervention of hyperlipidemia lies in the regulation of lipid metabolism, nutrition level, hormone level and cholesterol transport process. The Kyoto encyclopedia of genes and genomes enrichment analysis revealed that the peroxisome proliferator activated receptor signaling pathway contained the majority of the core targets. Molecular docking verified that hawthorn could bind closely to the core targets. According to the findings of network pharmacology and enrichment analysis, we predicted that the main active components of hawthorn to intervene in hyperlipidemia are epicatechin, officinalic acid and quercetin, which mainly intervene in hyperlipidemia through the peroxisome proliferator activated receptor signaling pathway.

High expression of CETN2 is associated with platinum resistance and poor prognosis in epithelial ovarian cancer.

The poor prognosis of ovarian cancer is largely due to platinum resistance. It has been demonstrated that nucleotide excision repair (NER) involving centrin-2(CETN2) is connected to platinum resistance in ovarian cancer. The molecular mechanism of CETN2 in ovarian cancer and the mechanism affecting the outcome of chemotherapy are unknown. The protein-protein interaction (PPI) network was mapped after obtaining the interacting proteins of CETN2, and the interacting genes were subjected to enrichment analysis. To examine the relationship between CETN2 and platinum resistance, gene microarray data and clinical data related to platinum resistance in ovarian cancer were downloaded. The possible signaling pathway of CETN2 was investigated by Gene set enrichment analysis (GSEA). Immune infiltration analysis was performed. Immunohistochemistry (IHC) and quantitative real-time PCR (QRT-PCR) were used to examine the expression of CETN2 in clinical samples in relation to the effectiveness of chemotherapy. The capacity of CETN2 to predict chemotherapy results was proven by receiver operating characteristic (ROC) curves after the construction of two prediction models, the logistic regression model and the decision tree model. The impact of CETN2 on prognosis was examined using the Kaplan-Meier technique. CETN2 was associated with NER, oxidative phosphorylation (OXPHOS) and cell cycle pathways in ovarian cancer drug-resistant samples. In clinical samples, CETN2 showed its possible correlation with immune infiltration. The protein expression level of CETN2 was significantly higher in platinum-resistant patients than that in platinum-sensitive patients, and the expression level had some predictive value for chemotherapy outcome, and high CETN2 protein expression was associated with poorer progression-free survival. CETN2 protein had a significant effect on ovarian cancer platinum sensitivity and prognosis, which may be related to the activation of NER, OXPHOS and cell cycle pathways upon CETN2 upregulation. Further research is necessary to determine the therapeutic application value of CETN2, which may be a new biomarker of chemoresponsiveness.

Screening of atrial fibrillation diagnostic markers based on a GEO database chip and bioinformatics analysis.

Study have shown that atrial fibrillation (AF) is a disease with genetic risk, and its pathogenesis is still unclear. This study sought to screen the gene microarray data of AF patients and to perform a bioinformatics analysis to identify AF signature diagnostic genes. The AF gene sets from the Gene Expression Omnibus (GEO) database were screened, and the differentially expressed genes (DEGs) were identified after the normalization of the data set by R software. We conducted a gene set enrichment analysis, a protein-protein interaction (PPI) network analysis, a gene-gene interaction (GGI) network analysis, and an immuno-infiltration analysis. The core genes were identified from the DEGs, and base on receiver operating characteristic, the top 5 core genes in the 2 data sets were selected as diagnostic factors and a nomogram was constructed. The miRNA of the core genes were predicted and an immune cell correlation analysis was performed. A total of 20 DEGs were identified. The functions of these DEGs were mainly related to muscle contraction, autophagosome, and bone morphogenetic protein (BMP) binding, and focused on the calcium signaling pathway, ferroptosis, the extracellular matrix-receptor interaction, and other pathways. A total of 5 core genes [i.e., GPR22 (G protein-coupled receptor 22), COG5 (component of oligomeric golgi complex 5), GALNT16 (polypeptide N-acetylgalactosaminyltransferase 16), OTOGL (otogelin-like), and MCOLN3 (mucolipin 3)] were identified, and a linear model for risk prediction was constructed, which has good prediction ability. Plasma cells and Macrophages M2 were significantly increased in AF, while T cells follicular helper and Dendritic cells activated were significantly decreased. In our study, we identified 5 potential diagnostic key genes (i.e., GPR22, COG5, GALNT16, OTOGL, and MCOLN3). Our findings may provide a theoretical basis for susceptibility analyses and target drug development in AF.

A novel hybrid dimension reduction and deep learning-based classification for neuromuscular disorder

Correct classification of neuromuscular disorders is essential to provide accurate diagnosis. Presently, gene microarray technology is a widely accepted technology to monitor the expression level of a large number of genes simultaneously. The gene microarray data are a high dimensional data, which usually contains small samples having a large number of genes. Therefore, dimension reduction is a crucial task for correct classification of diseases. Dimension reduction eliminates those genes which are less expressive and enhances the efficiency of the classification model. In the present paper, we developed a novel hybrid dimension reduction method and a deep learning-based classification model for neuromuscular disorders. The hybrid dimension reduction method is deployed in three phase: in the first phase, the expressive genes are selected using F test method, and the mutual information method and the best one among them are selected for further processing. In second phase, the gene selected by the best model is further transformed to low dimension by PCA. In third phase, the deep learning-based classification model is deployed. For experimentation, two diseased and multi-diseased micro array data sets, which is publicly available, is used. The best accuracy by 50-100-50-25-13 deep learning architecture with hybrid dimension reduction, where 100 genes select by F test and PCA with 50 principal components is 89% for NMD data set. The best accuracy by 50-100-2 deep learning architecture with hybrid dimension reduction, where 100 genes select by F test and PCA with 50 principal components is 97% for FSHD data set. The proposed hybrid method gives better classification accuracy result and reduces the search space and time complexity as well for both two diseased and multi-diseased micro array data sets.

Network analysis for the identification of hub genes and related molecules as potential biomarkers associated with the differentiation of bone marrow-derived stem cells into hepatocytes.

The incidence of liver diseases has been increasing steadily. However, it has some shortcomings, such as high cost and organ donor scarcity. The application of stem cell research has brought new ideas for the treatment of liver diseases. Therefore, it is particularly important to clarify the molecular and regulatory mechanisms of differentiation of bone marrow-derived stem cells (BMSCs) into liver cells. Herein, we screened differentially expressed genes between hepatocytes and untreated BMSCs to identify the genes responsible for the differentiation of BMSCs into hepatocytes. GSE30419 gene microarray data of BMSCs and GSE72088 gene microarray data of primary hepatocytes were obtained from the Gene Expression Omnibus database. Transcriptome Analysis Console software showed that 1896 genes were upregulated and 2506 were downregulated in hepatocytes as compared with BMSCs. Hub genes were analyzed using the STRING and Cytoscape v 3.8.2, revealing that twenty-four hub genes, play a pivotal role in the differentiation of BMSCs into hepatocytes. The expression of the hub genes in the BMSCs and hepatocytes was verified by reverse transcription-quantitative PCR (RT-qPCR). Next, the target miRNAs of hub genes were predicted, and then the lncRNAs regulating miRNAs was discovered, thus forming the lncRNA-miRNA-mRNA interaction chain. The results indicate that the lncRNA-miRNA-mRNA interaction chain may play an important role in the differentiation of BMSCs into hepatocytes, which provides a new therapeutic target for liver disease treatment.