Robust Rank Aggregation Method Research Articles

Common Molecular Signatures Between Coronavirus Infection and Alzheimer's Disease Reveal Targets for Drug Development.

Cognitive decline is a common consequence of COVID-19, and studies suggest a link between COVID-19 and Alzheimer's disease (AD). However, the molecular mechanisms underlying this association remain unclear. To understand the potential molecular mechanisms underlying the association between COVID-19 and AD development, and identify the potential genetic targets for pharmaceutical approaches to reduce the risk or delay the development of COVID-19-related neurological pathologies. We analyzed transcriptome datasets of 638 brain samples using a novel Robust Rank Aggregation method, followed by functional enrichment, protein-protein, hub genes, gene-miRNA, and gene-transcription factor (TF) interaction analyses to identify molecular markers altered in AD and COVID-19 infected brains. Our analyses of frontal cortex from COVID-19 and AD patients identified commonly altered genes, miRNAs and TFs. Functional enrichment and hub gene analysis of these molecular changes revealed commonly altered pathways, including downregulation of the cyclic adenosine monophosphate (cAMP) signaling and taurine and hypotaurine metabolism, alongside upregulation of neuroinflammatory pathways. Furthermore, gene-miRNA and gene-TF network analyses provided potential up- and downstream regulators of identified pathways. We found that downregulation of cAMP signaling pathway, taurine metabolisms, and upregulation of neuroinflammatory related pathways are commonly altered in AD and COVID-19 pathogenesis, and may make COVID-19 patients more susceptible to cognitive decline and AD. We also identified genetic targets, regulating these pathways that can be targeted pharmaceutically to reduce the risk or delay the development of COVID-19-related neurological pathologies and AD.

Construction of lncRNA-miRNA-mRNA regulatory network in severe asthmatic bronchial epithelial cells: A bioinformatics study.

Asthma is a chronic respiratory disease caused by environment-host interactions. Bronchial epithelial cells (BECs) are the first line of defense against environmental toxins. However, the mechanisms underlying the role of BECs in severe asthma (SA) are not yet fully understood. Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) have been shown to play important roles in the regulation of gene expression in the pathogenesis of SA. In this study, bioinformatics was used for the first time to reveal the lncRNA-miRNA-mRNA regulatory network of BECs in SA. Five mRNA datasets of bronchial brushing samples from patients with SA and healthy controls (HC) were downloaded from the Gene Expression Omnibus (GEO) database. A combination of the Venn diagram and robust rank aggregation (RRA) method was used to identify core differentially expressed genes (DEGs). Protein-protein interaction (PPI) analysis of core DEGs was performed to screen hub genes. The miRDB, miRWalk, and ENCORI databases were used to predict the miRNA-mRNA relationships, and the ENCORI and starBase v2.0 databases were used to predict the upstream lncRNAs of the miRNA-mRNA relationships. Four core DEGs were identified: carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5), interleukin-1 receptor type 2 (IL1R2), trefoil factor 3 (TFF3), and vascular endothelial growth factor A (VEGFA). These 4 core DEGs indicated that SA was not significantly associated with sex. Enrichment analysis showed that the MAPK, Rap1, Ras, PI3K-Akt and Calcium signaling pathways may serve as the principal pathways of BECs in SA. A lncRNA-miRNA-mRNA regulatory network of the severe asthmatic bronchial epithelium was constructed. The top 10 competing endogenous RNAs (ceRNAs) were FGD5 antisense RNA 1 (FGD5-AS1), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), X inactive specific transcript (XIST), HLA complex group 18 (HCG18), small nucleolar RNA host gene 16 (SNHG16), has-miR-20b-5p, has-miR-106a-5p, hsa-miR-106b-5p, has-miR-519d-3p and Fms related receptor tyrosine kinase 1 (FLT1). Our study revealed a potential mechanism for the lncRNA-miRNA-mRNA regulatory network in BECs in SA.

Integrated multiple-microarray analysis and mendelian randomization to identify novel targets involved in diabetic nephropathy.

Diabetic nephropathy (DN), which is the main cause of renal failure in end-stage renal disease, is becoming a common chronic renal disease worldwide. Mendelian randomization (MR) is a genetic tool that is widely used to minimize confounding and reverse causation when identifying the causal effects of complex traits. In this study, we conducted an integrated multiple microarray analysis and large-scale plasma proteome MR analysis to identify candidate biomarkers and evaluate the causal effects of prospective therapeutic targets in DN. Five DN gene expression datasets were selected from the Gene Expression Omnibus. The robust rank aggregation (RRA) method was used to integrate differentially expressed genes (DEGs) of glomerular samples between patients with DN and controls, followed by functional enrichment analysis. Protein quantitative trait loci were incorporated from seven different proteomic genome-wide association studies, and genetic association data on DN were obtained from FinnGen (3676 cases and 283,456 controls) for two-sample MR analysis. External validation and clinical correlation were also conducted. A total of 82 DEGs (53 upregulated and 29 downregulated) were identified through RRA integrated analysis. The enriched Gene Ontology annotations and Kyoto Encyclopedia of Genes and Genomes pathways of the DEGs were significantly enriched in neutrophil degranulation, neutrophil activation, proteoglycan binding, collagen binding, secretory granule lumen, gluconeogenesis, tricarboxylic acid cycle, and pentose phosphate pathways. MR analysis revealed that the genetically predicted levels of MHC class I polypeptide-related sequence B (MICB), granzyme A (GZMA), cathepsin S (CTSS), chloride intracellular channel protein 5, and ficolin-1 (FCN1) were causally associated with DN risk. Expression validation and clinical correlation analysis showed that MICB, GZMA, FCN1, and insulin-like growth factor 1 may participate in the development of DN, and carbonic anhydrase 2 and lipoprotein lipase may play protective roles in patients with DN. Our integrated analysis identified novel biomarkers, including MICB and GZMA, which may help further understand the complicated mechanisms of DN and identify new target pathways for intervention.

MiR-203a-5p Inhibits Multiple Myeloma Cell Proliferation and Cell Cycle Progression via Targeting JAG1

To investigate the biological function of miR-203a-5p and the underlying mechanism in multiple myeloma (MM). Three miRNA expression profiles (GSE16558, GSE24371 and GSE17498) were downloaded from the GEO database. The three miRNA expression profiles contained 131 MM samples and 17 normal plasmacyte samples. The robust rank aggregation (RRA) method was used to identify the differentially expressed miRNAs between MM and normal plasmacytes. In order to carry out cytological experiments, MM cell line with stable over-expression of miR-203a-5p was constructed with lentivirus. Expression levels of miR-203a-5p in MM cells were quantified by qRT-PCR. The effects of miR-203a-5p on MM cells were investigated using assays of cell viability and cell cycle. Cell proliferation was measured using the Cell Counting kit (CCK)8 assay. The percentage of cells in each cell cycle was measured with a FACSCalibur system. Xenograft tumor models were established to evaluate the role of miR-203a-5p in tumorigenesis in vivo . To elucidate the underlying molecular mechanisms of miR-203a-5p in mediating cell proliferation inhibition and cell cycle arrest in MM, we used TargetScan and miRanda to predict the candidate targets of miR-203a-5p. The potential target of miR-203a-5p in MM cells was explored using the luciferase reporter assay, qRT-PCR, and Western blot. An integrated analysis of three MM miRNA expression datasets showed that the levels of miR-203a-5p in MM were notably downregulated compared with those in normal plasmacytes. Accordingly, the relative expression levels of miR-203a-5p were decreased in MM cell lines. In addition, overexpression of miR-203a-5p inhibited the proliferation and cell cycle progression of RPMI8226 and U266 cells. In vivo experiments demonstrated that upregulation of miR-203a-5p expression could significantly inhibit the tumorigenesis of subcutaneous myeloma xenografts in nude mice. Mechanistic investigation led to the identification of Jagged 1 (JAG1) as a novel and direct downstream target of miR-203a-5p. Interestingly, the reintroduction of JAG1 abrogated miR-203a-5p-induced MM cell growth inhibition and cell cycle arrest. Our data demonstrate that miR-203a-5p inhibits cell proliferation and cell cycle progression in MM cells by targeting JAG1, supporting the utility of miR-203a-5p as a novel and potential therapeutic agent for miRNA-based MM therapy.

Integrated interactome and transcriptome analysis reveals key host factors critical for SARS-CoV-2 infection

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has seriously threatened global public health and caused huge economic losses. Omics studies of SARS-CoV-2 can help understand the interaction between the virus and host, thereby providing a new perspective in guiding the intervention and treatment of the SARS-CoV-2 infection. Since large amount of SARS-CoV-2 omics data have been accumulated in public databases, this study aimed to identify key host factors involved in SARS-CoV-2 infection through systematic integration of transcriptome and interactome data. By manually curating published studies, we obtained a comprehensive SARS-CoV-2-human protein-protein interactions (PPIs) network, comprising 3591 human proteins interacting with 31 SARS-CoV-2 viral proteins. Using the RobustRankAggregation method, we identified 123 multiple cell line common genes (CLCGs), of which 115 up-regulated CLCGs showed host enhanced innate immunity and chemotactic response signatures. Combined with network analysis, co-expression and functional enrichment analysis, we discovered four key host factors involved in SARS-CoV-2 infection: IFITM1, SERPINE1, DDX60, and TNFAIP2. Furthermore, SERPINE1 was found to facilitate SARS-CoV-2 replication, and can alleviate the endoplasmic reticulum (ER) stress induced by ORF8 protein through interaction with ORF8. Our findings highlight the importance of systematic integration analysis in understanding SARS-CoV-2-human interactions and provide valuable insights for future research on potential therapeutic targets against SARS-CoV-2 infection.

Identification of Signature Genes of Dilated Cardiomyopathy Using Integrated Bioinformatics Analysis.

Dilated cardiomyopathy (DCM) is characterized by left ventricular or biventricular enlargement with systolic dysfunction. To date, the underlying molecular mechanisms of dilated cardiomyopathy pathogenesis have not been fully elucidated, although some insights have been presented. In this study, we combined public database resources and a doxorubicin-induced DCM mouse model to explore the significant genes of DCM in full depth. We first retrieved six DCM-related microarray datasets from the GEO database using several keywords. Then we used the "LIMMA" (linear model for microarray data) R package to filter each microarray for differentially expressed genes (DEGs). Robust rank aggregation (RRA), an extremely robust rank aggregation method based on sequential statistics, was then used to integrate the results of the six microarray datasets to filter out the reliable differential genes. To further improve the reliability of our results, we established a doxorubicin-induced DCM model in C57BL/6N mice, using the "DESeq2" software package to identify DEGs in the sequencing data. We cross-validated the results of RRA analysis with those of animal experiments by taking intersections and identified three key differential genes (including BEX1, RGCC and VSIG4) associated with DCM as well as many important biological processes (extracellular matrix organisation, extracellular structural organisation, sulphur compound binding, and extracellular matrix structural components) and a signalling pathway (HIF-1 signalling pathway). In addition, we confirmed the significant effect of these three genes in DCM using binary logistic regression analysis. These findings will help us to better understand the pathogenesis of DCM and may be key targets for future clinical management.

Identification of key genes in bovine mammary epithelial cells challenged with Escherichia coli and Staphylococcus aureus by integrated bioinformatics analysis

This study integrated four microarray datasets by Robust Rank Aggregation (RRA) method to identify the differentially expressed genes (DEG) in bovine mammary epithelial (BME) cells in response to Escherichia coli and Staphylococcus aureus infection. Furthermore, the GO function and KEGG pathway enrichment analysis of the integrated DEG were performed. Finally, the protein-protein interaction (PPI) network was constructed. A total of 72 integrated DEG were identified from the four datasets. The most significantly enriched terms within the integrated DEG were [...]

Integrated multiple microarray studies by robust rank aggregation to identify immune-associated biomarkers in Crohn's disease based on three machine learning methods

Crohn's disease (CD) is a complex autoimmune disorder presumed to be driven by complex interactions of genetic, immune, microbial and even environmental factors. Intrinsic molecular mechanisms in CD, however, remain poorly understood. The identification of novel biomarkers in CD cases based on larger samples through machine learning approaches may inform the diagnosis and treatment of diseases. A comprehensive analysis was conducted on all CD datasets of Gene Expression Omnibus (GEO); our team then used the robust rank aggregation (RRA) method to identify differentially expressed genes (DEGs) between controls and CD patients. PPI (protein‒protein interaction) network and functional enrichment analyses were performed to investigate the potential functions of the DEGs, with molecular complex detection (MCODE) identifying some important functional modules from the PPI network. Three machine learning algorithms, support vector machine-recursive feature elimination (SVM-RFE), random forest (RF), and least absolute shrinkage and selection operator (LASSO), were applied to determine characteristic genes, which were verified by ROC curve analysis and immunohistochemistry (IHC) using clinical samples. Univariable and multivariable logistic regression were used to establish a machine learning score for diagnosis. Single-sample GSEA (ssGSEA) was performed to examine the correlation between immune infiltration and biomarkers. In total, 5 datasets met the inclusion criteria: GSE75214, GSE95095, GSE126124, GSE179285, and GSE186582. Based on RRA integrated analysis, 203 significant DEGs were identified (120 upregulated genes and 83 downregulated genes), and MCODE revealed some important functional modules in the PPI network. Machine learning identified LCN2, REG1A, AQP9, CCL2, GIP, PROK2, DEFA5, CXCL9, and NAMPT; AQP9, PROK2, LCN2, and NAMPT were further verified by ROC curves and IHC in the external cohort. The final machine learning score was defined as [Expression level of AQP9 × (2.644)] + [Expression level of LCN2 × (0.958)] + [Expression level of NAMPT × (1.115)]. ssGSEA showed markedly elevated levels of dendritic cells and innate immune cells, such as macrophages and NK cells, in CD, consistent with the gene enrichment results that the DEGs are mainly involved in the IL-17 signaling pathway and humoral immune response. The selected biomarkers analyzed by the RRA method and machine learning are highly reliable. These findings improve our understanding of the molecular mechanisms of CD pathogenesis.

Identification of essential genes and immune cell infiltration in rheumatoid arthritis by bioinformatics analysis

Rheumatoid arthritis (RA) is a common autoimmune disease that can lead to severe joint damage and disability. And early diagnosis and treatment of RA can avert or substantially slow the progression of joint damage in up to 90% of patients, thereby preventing irreversible disability. Previous research indicated that 50% of the risk for the development of RA is attributable to genetic factors, but the pathogenesis is not well understood. Thus, it is urgent to identify biomarkers to arrest RA before joints are irreversibly damaged. Here, we first use the Robust Rank Aggregation method (RRA) to identify the differentially expressed genes (DEGs) between RA and normal samples by integrating four public RA patients’ mRNA expression data. Subsequently, these DEGs were used as the input for the weighted gene co-expression network analysis (WGCNA) approach to identify RA-related modules. The function enrichment analysis suggested that the RA-related modules were significantly enriched in immune-related actions. Then the hub genes were defined as the candidate genes. Our analysis showed that the expression levels of candidate genes were significantly associated with the RA immune microenvironment. And the results indicated that the expression of the candidate genes can use as predictors for RA. We hope that our method can provide a more convenient approach for the early diagnosis of RA.

Development and validation of a 13-gene signature associated with immune function for the detection of Alzheimer's disease

Current knowledge of Alzheimer's disease (AD) etiology and effective therapy remains limited. Thus, the identification of biomarkers is crucial to improve the detection and treatment of patients with AD. Using robust rank aggregation method to analyze the microarray data from Gene Expression Omnibus database, we identified 1138 differentially expressed genes in AD. We then explored 13 hub genes by weighted gene co-expression network analysis, least absolute shrinkage, and selection operator, and logistic regression in the training dataset. The detection model, which composed of CD163, CDC42SE1, CECR6, CSF1R, CYP27A1, EIF4E3, H2AFJ, IFIT2, IL10RA, KIAA1324, PSTPIP1, SLA, and TBC1D2 genes, along with APOE gene, showed that the area under the curve for detecting AD was 0.821 (95% confidence interval [CI] = 0.782–0.861) and the model was validated in ADNI dataset (area under the curve = 0.776; 95%CI = 0.686–0.865). Notably, the 13 genes in the model were highly enriched in immune function. These findings have implications for the detection and therapeutic target of AD.

Abstract 14674: Identifying CXCL12 as a Potential Marker for AF Subset Classification Based on Bioinformatic Tools

Introduction: An improved understanding of the pathogenesis of AF and atrial substrate remodeling is necessary for development of novel therapeutic approaches and new management strategies. Hypothesis: CXCL12 could be involved in the regulation of AF inflammatory microenvironment and have the potential to be a marker for AF subset classification. Methods: In this study, we utilized the Robust Rank Aggregation method to integrate AF microarray datasets. Potential compounds were identified by mining the Connectivity Map database. Functional modules and closely-interacted clusters were identified using WGCNA analysis and PPI network, respectively. Biological functions were further analysed. Moreover, a reliable Machine Learning-based diagnostic model was constructed and visualized to clarify the diagnostic features of these genes. Results: Differentially expressed genes were identified. We showed that mitogen-activated protein kinase and epidermal growth factor receptor inhibitors were likely to mitigate AF based on Connectivity Map analysis. Four genes were identified as hub genes. CXCL12 was shown to play an important role in regulation of local inflammatory response and immune cell infiltration. Conclusions: Key genes involving in the pathogenesis of AF were identified. The biological features of CXCL12 in AF were investigated using integrative bioinformatics tools. The results suggested that CXCL12 might be a biomarker that could be used for distinguishing subsets of AF, and indicated that CXCL12 might be an important intermediate in the development of AF by increasing the infiltration of mast cells, neutrophils, and γδ T cells, and reducing infiltration of regulatory T cells. A reliable Machine Learning-based diagnostic model was constructed. Our work improved understanding of the mechanisms of AF predisposition and progression, and identified potential therapeutic avenues for treatment of AF.

Identification of hub genes and transcription factor regulatory network for heart failure using RNA-seq data and robust rank aggregation analysis.

Heart failure (HF) is the end stage of various cardiovascular diseases with a high mortality rate. Novel diagnostic and therapeutic biomarkers for HF are urgently required. Our research aims to identify HF-related hub genes and regulatory networks using bioinformatics and validation assays. Using four RNA-seq datasets in the Gene Expression Omnibus (GEO) database, we screened differentially expressed genes (DEGs) of HF using Removal of Unwanted Variation from RNA-seq data (RUVSeq) and the robust rank aggregation (RRA) method. Then, hub genes were recognized using the STRING database and Cytoscape software with cytoHubba plug-in. Furthermore, reliable hub genes were validated by the GEO microarray datasets and quantitative reverse transcription polymerase chain reaction (qRT-PCR) using heart tissues from patients with HF and non-failing donors (NFDs). In addition, R packages "clusterProfiler" and "GSVA" were utilized for enrichment analysis. Moreover, the transcription factor (TF)-DEG regulatory network was constructed by Cytoscape and verified in a microarray dataset. A total of 201 robust DEGs were identified in patients with HF and NFDs. STRING and Cytoscape analysis recognized six hub genes, among which ASPN, COL1A1, and FMOD were confirmed as reliable hub genes through microarray datasets and qRT-PCR validation. Functional analysis showed that the DEGs and hub genes were enriched in T-cell-mediated immune response and myocardial glucose metabolism, which were closely associated with myocardial fibrosis. In addition, the TF-DEG regulatory network was constructed, and 13 significant TF-DEG pairs were finally identified. Our study integrated different RNA-seq datasets using RUVSeq and the RRA method and identified ASPN, COL1A1, and FMOD as potential diagnostic biomarkers for HF. The results provide new insights into the underlying mechanisms and effective treatments of HF.

A potential biomarker of cognitive impairment: The olfactory dysfunction and its genes expression.

Accumulation evidence has reported that olfactory impairment may be an essential clinical marker and predictor of mild cognitive impairment or Alzheimer's disease. Participants were enrolled in the population-based, prospective study in Fuxin county, Liaoning province, China between 2019 and 2021. An inverse probability weighting logistic regression and mixed-effect models were performed to explore the association between dysosmia and cognition and rate of change in cognition, respectively. Besides, we utilized the Robust Rank Aggregation method to integrated three eligible datasets from the Gene Expression Omnibus to identify differential expressed genes. A total of 4695 participants were enrolled and 4221 of those were eligible for our cross-sectional study. The mean (SD) age was 59.93(9.78) years, 64.8% were men. Over a 2-year follow-up, of the 2088 participants who completed follow-up, 1559 participants were eligible for our longitude cohort study. We observed an association between dysosmia and an increased risk of cognitive impairment (OR, 0.47, [95% CI, 0.35-0.64]; p < 0.001). The OR (95% CI) for cognition in females with dysosmia was higher than (OR, 0.73[0.51, 1.05], p=.007) that for males with dysosmia (OR, 0.25[0.15, 0.42], p < 0.001; P for interaction <0.001). Dysosmia was also associated with more rapid decline in calculation ability (p < 0.001). Besides, several DEGs were identified, which are mainly associated with olfactory transduction, detection of chemical stimulus involved in sensory perception of smell, sensory perception of smell, olfactory receptor activity and odorant binding. These findings proved novel insight into identifying olfactory dysfunction as potential biomarker for diagnosis of cognitive impairment.

Prognostic biomarkers and immune cell infiltration characteristics in small cell lung cancer

BackgroundSmall cell lung cancer (SCLC) is a highly malignant and aggressive neuroendocrine tumor. With the rise of immunotherapy, it has provided a new direction for SCLC. However, due to the lack of prognostic biomarkers, the median overall survival of SCLC is still to be improved. This study aimed to explore novel biomarkers and tumor-infiltrating immune cell characteristics that may serve as potential diagnostic and prognostic markers in SCLC. MethodsGene expression profiles from patients with SCLC were downloaded from the Gene Expression Omnibus (GEO) database, and tumor microenvironment (TME) infiltration profile data were obtained using CIBERSORT. The robust rank aggregation (RRA) method was utilized to integrate three SCLC microarray datasets downloaded from the GEO database and identify robust differentially expressed genes (DEGs) between normal and tumor tissue samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the functions of the robust DEGs. Subsequently, protein–protein interaction networks and key modules were constructed by Cytoscape, and hub genes were selected from the whole network using the plugin cytoHubba. Survival analysis of hub genes was performed by Kaplan–Meier plotter in 18 patients with extensive-stage SCLC. ResultsA total of 312 robust DEGs, including 55 upregulated and 257 downregulated genes, were screened from 129 SCLC tissue samples and 44 normal tissue samples. GO and KEGG enrichment analyses revealed that the robust DEGs were predominantly involved in human T-cell leukemia virus 1 infection, focal adhesion, complement and coagulation cascades, tumor necrosis factor (TNF) signaling pathway, and ECM-receptor interaction, which are closely associated with the development and progression of SCLC. Subsequently, three DEGs modules and six hub genes (ITGA10, DUSP12, PTGS2, FOS, TGFBR2, and ICAM1) were identified through screening with the Cytoscape plugins MCODE and cytoHubba, respectively. Immune cell infiltration analysis by the CIBERSORT algorithm revealed that resting memory CD4+ T cells were the predominant infiltrating immune cells in SCLC. In addition, Kaplan–Meier plotter revealed that the gene prostaglandin-endoperoxide synthase 2 (PTGS2) was a potential prognostic biomarker of SCLC. ConclusionsHub genes and tumor-infiltrating immune cells may be the molecular mechanisms underlying the development of SCLC, and this finding could contribute to the formulation of individualized immunotherapy strategies for SCLC.

High iodine promotes autoimmune thyroid disease by activating hexokinase 3 and inducing polarization of macrophages towards M1.

Autoimmune thyroid disease (AITD), the most common autoimmune disease, includes Graves' disease (GD) and Hashimoto's thyroiditis (HT). Currently, the pathogenesis of AITD is not fully understood. Our study aimed to examine the presence of macrophage polarization imbalance in AITD patients, to investigate whether high iodine can cause macrophage polarization imbalance, and to investigate the role of key genes of metabolic reprogramming in macrophage polarization imbalance caused by high iodine. We synergistically used various research strategies such as systems biology, clinical studies, cell culture and mouse disease models. Gene set enrichment analysis (GSEA) revealed that M1 macrophage hyperpolarization was involved in the pathogenesis of AITD. In vitro and in vivo experiments showed that high iodine can affect the polarization of M1 or M2 macrophages and their related cytokines. Robust rank aggregation (RRA) method revealed that hexokinase 3 (HK3) was the most aberrantly expressed metabolic gene in autoimmune diseases. In vitro and in vivo studies revealed HK3 could mediate macrophage polarization induced by high iodine. In summary, hyperpolarization of M1-type macrophages is closely related to the pathogenesis of AITD. High iodine can increase HK3 expression in macrophages and promote macrophage polarization towards M1. Targeting HK3 can inhibit M1 polarization induced by high iodine.

Identification of core genes as potential biomarkers for predicting progression and prognosis in glioblastoma.

Background: Glioblastoma is a common malignant neuroepithelial neoplasm with poor clinical outcomes and limited treatment options. It is extremely important to search and confirm diverse hub genes that are effective in the advance and prediction of glioblastoma. Methods: We analyzed GSE50161, GSE4290, and GSE68848, the three microarray datasets retrieved from the GEO database. GO function and KEGG pathway enrichment analyses for differentially expressed genes (DEGs) were performed using DAVID. The PPI network of the DEGs was analyzed using the Search Tool for the Retrieval of Interacting Genes database and visualized by Cytoscape software. Hub genes were identified through the PPI network and a robust rank aggregation method. The Cancer Genome Atlas (TCGA) and the Oncomine database were used to validate the hub genes. In addition, a survival curve analysis was conducted to verify the correlation between the expression of hub genes and patient prognosis. Human glioblastoma cells and normal cells were collected, and then RT-PCR, Western blot, and immunofluorescence were conducted to validate the expression of the NDC80 gene. A cell proliferation assay was used to detect the proliferation of glioma cells. The effects of NDC80 expression on migration and invasion of GBM cell lines were evaluated by conducting scratch and transwell assays. Results: A total of 716 DEGs were common to all three microarray datasets, which included 188 upregulated DEGs and 528 downregulated DEGs. Furthermore, we found that among the common DEGs, 10 hub genes showed a high degree of connectivity. The expression of the 10 hub genes in TCGA and the Oncomine database was significantly overexpressed in glioblastoma compared with normal genes. Additionally, the survival analysis showed that the patients with low expression of six genes (BIR5C, CDC20, NDC80, CDK1, TOP2A, and MELK) had a significantly favorable prognosis (p < 0.01). We discovered that NDC80, which has been shown to be important in other cancers, also has an important role in malignant gliomas. The RT-PCR, Western blot, and immunofluorescence results showed that the expression level of NDC80 was significantly higher in human glioblastoma cells than in normal cells. Moreover, we identified that NDC80 increased the proliferation and invasion abilities of human glioblastoma cells. Conclusion: The six genes identified here may be utilized to form a panel of disease progression and predictive biomarkers of glioblastoma for clinical purposes. NDC80, one of the six genes, was discovered to have a potentially important role in GBM, a finding that needs to be further studied.

Hub Genes Identification, Small Molecule Compounds Prediction for Atrial Fibrillation and Diagnostic Model Construction Based on XGBoost Algorithm.

BackgroundAtrial fibrillation (AF) is the most common sustained cardiac arrhythmia and engenders significant global health care burden. The underlying mechanisms of AF is remained to be revealed and current treatment options for AF have limitations. Besides, a detection system can help identify those at risk of developing AF and will enable personalized management.Materials and MethodsIn this study, we utilized the robust rank aggregation method to integrate six AF microarray datasets from the Gene Expression Omnibus database, and identified a set of differentially expressed genes between patients with AF and controls. Potential compounds were identified by mining the Connectivity Map database. Functional modules and closely-interacted clusters were identified using weighted gene co-expression network analysis and protein–protein interaction network, respectively. The overlapped hub genes were further filtered. Subsequent analyses were performed to analyze the function, biological features, and regulatory networks. Moreover, a reliable Machine Learning-based diagnostic model was constructed and visualized to clarify the diagnostic features of these genes.ResultsA total of 156 upregulated and 34 downregulated genes were identified, some of which had not been previously investigated. We showed that mitogen-activated protein kinase and epidermal growth factor receptor inhibitors were likely to mitigate AF based on Connectivity Map analysis. Four genes, including CXCL12, LTBP1, LOXL1, and IGFBP3, were identified as hub genes. CXCL12 was shown to play an important role in regulation of local inflammatory response and immune cell infiltration. Regulation of CXCL12 expression in AF was analyzed by constructing a transcription factor-miRNA-mRNA network. The Machine Learning-based diagnostic model generated in this study showed good efficacy and reliability.ConclusionKey genes involving in the pathogenesis of AF and potential therapeutic compounds for AF were identified. The biological features of CXCL12 in AF were investigated using integrative bioinformatics tools. The results suggested that CXCL12 might be a biomarker that could be used for distinguishing subsets of AF, and indicated that CXCL12 might be an important intermediate in the development of AF. A reliable Machine Learning-based diagnostic model was constructed. Our work improved understanding of the mechanisms of AF predisposition and progression, and identified potential therapeutic avenues for treatment of AF.

Integrated analysis of multiple bioinformatics studies to identify microRNA-target gene-transcription factor regulatory networks in retinoblastoma.

BackgroundIn children, retinoblastoma (RB) is one of the most common primary malignant ocular tumors and has a poor prognosis and high mortality. To understand the molecular mechanisms of RB, we identified microRNAs (miRNAs), key genes and transcription factors (TFs) using bioinformatics analysis to build potential miRNA-gene-TF networks.MethodsWe collected three gene expression profiles and one miRNA expression profile from the Gene Expression Omnibus (GEO) database. We used the limma R package to identify overlapping differentially expressed genes (DEGs) and differentially expressed miRNAs in RB tissues compared to noncancer tissues. The robust rank aggregation (RRA) method was implemented to identify key genes among the DEGs. Then, miRNA-key gene-TF networks were built using the online tools TransmiR and miRTarBase. Next, we used RT-qPCR to confirm the results.ResultsWe identified 180 DEGs in RB tissues compared to nontumor tissues using integrative analysis, among which 109 genes were upregulated and 71 were downregulated. Gene ontology (GO) analysis revealed that these DEGs were primarily involved with chromosome segregation, condensed chromosome and DNA replication origin binding. The most highly enriched pathways obtained in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were cell cycle, DNA replication, homologous recombination, P53 signaling pathway and pyrimidine metabolism. Furthermore, two key differentially expressed miRNAs (DEMs) were also established: let-7a and let-7b. Finally, the potential regulatory networks of miRNA-target gene-TFs were examined.ConclusionsThis study identified key genes and built miRNA-target gene-TF regulatory networks in RB, which will deepen our understanding of the molecular mechanisms involved in the development of RB. These key genes and miRNAs may be potential targets and biomarkers for RB diagnosis and therapy.

Discovering Gene Signature Shared by Prostate Cancer and Neurodegenerative Diseases Based on the Bioinformatics Approach.

Background Prostate cancer (PCa) is one of the highest frequent malignant tumors with very complicated pathogenesis. Genes of neurodegenerative diseases can influence tumor progression. But its role in the progression of PCa remains unclear. The purpose of the present academic work was to identify significant genes with poor outcome and their underlying mechanism. Methods The GSE70768, GSE88808, and GSE134051 datasets were downloaded to screen the differentially expressed genes (DEGs). The DEG screening criteria were as follows: P < 0.05 and differential fold change |logFC| ≥ 1. The common DEGs (co-DEGs) of the three datasets were obtained by the Robust Rank Aggregation (RRA) method. Gene Ontology (GO) function annotation and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis were performed using R software. Protein-protein interaction (PPI) network analysis was performed for co-DEGs using STRING to screen critical genes. Differential expression and prognosis of key genes were analyzed by the online tool Gene Expression Profiling Interactive Analysis 2 (GEPIA2). The intersection gene between key genes and neurodegenerative genes was identified by constructing a Venn diagram. Results A total of 263 co-DEGs were identified from the three datasets. GO analysis showed that co-DEGs were mainly involved in muscle contraction and blood circulation regulation. The top ten key genes were ACTG2, APOE, F5, CALD1, MYH11, MYL9, MYLK, TPM1, TPM2, and CALM1. GEPIA2 analysis showed that APOE, MYH11, and MYLK differ dramatically between tumor and normal tissues. These key genes are related to disease-free survival (DFS) in PCa. APOE was the intersection gene between key genes and Alzheimer-related genes. Conclusion The neurodegenerative gene APOE may be a potential prognostic and diagnostic biomarker for PCa.

Identification of hub genes and biological pathways in glioma via integrated bioinformatics analysis.

ObjectiveGlioma is the most common intracranial primary malignancy, but its pathogenesis remains unclear.MethodsWe integrated four eligible glioma microarray datasets from the gene expression omnibus database using the robust rank aggregation method to identify a group of significantly differently expressed genes (DEGs) between glioma and normal samples. We used these DEGs to explore key genes closely associated with glioma survival through weighted gene co-expression network analysis. We then constructed validations of prognosis and survival analyses for the key genes via multiple databases. We also explored their potential biological functions using gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA).ResultsWe selected DLGAP5, CDCA8, NCAPH, and CCNB2, as four genes that were abnormally up-regulated in glioma samples, for verification. They showed high levels of isocitrate dehydrogenase gene mutation and tumor grades, as well as good prognostic and diagnostic value for glioma. Their methylation levels were generally lower in glioma samples. GSEA and GSVA analyses suggested the genes were closely involved with glioma proliferation.ConclusionThese findings provide new insights into the pathogenesis of glioma. The hub genes have the potential to be used as diagnostic and therapeutic markers.