Identification of Potential Osteoporosis miRNA Biomarkers Using Bioinformatics Approaches
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ABSTRACT:
SUBMITTER: Lu W
PROVIDER: S-EPMC8579105 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Inflammatory reaction of pulp tissue plays a role in the pathogen elimination and tissue repair. The evaluation of severity of pulpitis can serve an instructive function in therapeutic scheme. However, there are many limitations in the traditional evaluation methods for the severity of pulpitis. Based on the Gene Expression Omnibus (GEO) database, our study discovered 843 differentially expressed genes (DEGs) related to pulpitis. Afterwards, we constructed a protein-protein interaction (PPI) network of DEGs and used MCODE plugin to determine the key functional subset. Meanwhile, genes in the key functional subset were subjected to GO and KEGG enrichment analyses. The result showed that genes were mainly enriched in inflammatory reaction-related functions. Next, we screened out intersections of PPI network nodes and pulpitis-related genes. Then, 20 genes were obtained as seed genes. In the PPI network, 50 genes that had the highest correlation with seed genes were screened out using random walk with restart (RWR). Furthermore, 4 pulpitis-related hub genes were obtained from the intersection of the top 50 genes and genes in the key functional subset. Finally, GeneMANIA was utilized to predict genes coexpressed with hub genes, and expression levels of the 4 hub genes in normal and pulpitis groups were analyzed based on GEO data. The result demonstrated that the 4 hub genes were mainly coexpressed with chemokine-related genes and were remarkably upregulated in the pulpitis group. In short, we eventually determined 4 potential biomarkers of pulpitis.
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Project description:Pneumonia is a lower respiratory tract infection that causes dramatic mortality worldwide. The present study aimed to investigate the pathogenesis of pneumonia and identify microRNA (miRNA) biomarkers as candidates for targeted therapy. RNA from the peripheral blood plasma of participants with pneumonia (severe, n=9; non-severe, n=9) and controls (n=9) was isolated and paired-end sequencing was performed on an Illumina HiSeq4000 system. Following the processing of raw reads, the sequences were aligned against the Genome Reference Consortium human genome assembly 38 reference genome using Bowtie2 software. Reads per kilobase of transcript per million mapped read values were obtained and the limma software package was used to identify differentially expressed miRNAs (DE-miRs). Then, DE-miR targets were predicted and subjected to enrichment analysis. In addition, a protein-protein interaction (PPI) network of the predicted targets was constructed. This analysis identified 11 key DE-miRs in pneumonia samples, including 6 upregulated miRNAs (including hsa-miR-34a and hsa-miR-455) and 5 downregulated miRNAs (including hsa-let-7f-1). All DE-miRs kept their upregulation/downregulation pattern in the control, non-severe pneumonia and severe pneumonia samples. Predicted target genes of DE-miRs in the subjects with non-severe pneumonia vs. the control and the subjects with severe pneumonia vs. the non-severe pneumonia group were markedly enriched in the adherens junction and Wnt signaling pathways. KALRN, Ras homolog family member A (RHOA), β-catenin (CTNNB1), RNA polymerase II subunit K (POLR2K) and amyloid precursor protein (APP) were determined to encode crucial proteins in the PPI network constructed. KALRN was predicted to be a target of hsa-mir-200b, while RHOA, CTNNB1, POLR2K and APP were predicted targets of hsa-let-7f-1. The results of the present study demonstrated that hsa-let-7f-1 may serve a role in the development of cancer and the Notch signaling pathway. Conversely, hsa-miR-455 may be an inhibitor of pneumonia pathogenesis. Furthermore, hsa-miR-200b might promote pneumonia via targeting KALRN.
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Project description:Background: Osteoporosis is one of the most common chronic diseases around the world, especially in postmenopausal women. Circular RNAs (circRNAs) are emerging as important regulators in human disease. The aim of the present study was to analyze circRNA expression profiles in osteoporosis and to explore the clinical significance. Methods: The serum samples were collected from osteoporosis patients and healthy people. The circRNA array was performed to screen for differentially expressed circRNAs. The mRNA expression levels of target genes were analyzed by quantitative real-time PCR (qRT-PCR). Results: Hsa_circ_0006859 was identified as one of the most upregulated circRNAs in the microarray analysis. Hsa_circ_0006859 in exosome was upregulated in osteoporosis patients compared with healthy controls. Hsa_circ_0006859 could differentiate osteopenia or osteoporosis patients from healthy controls with high sensitivity and specificity. Conclusion: Exosomal hsa_circ_0006859 is a potential biomarker for postmenopausal osteoporosis.
2020-11-13 | GSE161361 | GEO
Project description:Sepsis remains a major global concern and is associated with high mortality and morbidity despite improvements in its management. Markers currently in use have shortcomings such as a lack of specificity and failures in the early detection of sepsis. In this study, we aimed to identify key genes involved in the molecular mechanisms of sepsis and search for potential new biomarkers and treatment targets for sepsis using bioinformatics analyses. Three datasets (GSE95233, GSE57065, and GSE28750) associated with sepsis were downloaded from the public functional genomics data repository Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified using R packages (Affy and limma). Functional enrichment of the DEGs was analyzed with the DAVID database. Protein-protein interaction networks were derived using the STRING database and visualized using Cytoscape software. Potential biomarker genes were analyzed using receiver operating characteristic (ROC) curves in the R package (pROC). The three datasets included 156 whole blood RNA samples from 89 sepsis patients and 67 healthy controls. Between the two groups, 568 DEGs were identified, among which 315 were upregulated and 253 were downregulated in the septic group. These genes were enriched for pathways mainly involved in the innate immune response, T-cell biology, antigen presentation, and natural killer cell function. ROC analyses identified nine genes-LRG1, ELANE, TP53, LCK, TBX21, ZAP70, CD247, ITK, and FYN-as potential new biomarkers for sepsis. Real-time PCR confirmed that the expression of seven of these genes was in accordance with the microarray results. This study revealed imbalanced immune responses at the transcriptomic level during early sepsis and identified nine genes as potential biomarkers for sepsis.
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Project description:ObjectivesTo investigate potential genetic biomarkers of peri-implantitis and target genes for the therapy of peri-implantitis by bioinformatics analysis of publicly available data.MethodsThe GSE33774 microarray dataset was downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) between peri-implantitis and healthy gingival tissues were identified using the GEO2R tool. GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the DAVID database and the Metascape tool, and the results were expressed as a bubble diagram. The protein-protein interaction network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized using Cytoscape. The hub genes were screened by the cytoHubba plugin of Cytoscape. The potential target genes associated with peri-implantitis were obtained from the DisGeNET database and the Open Targets Platform. The intersecting genes were identified using the Venn diagram web tool.ResultsBetween the peri-implantitis group and the healthy group, 205 DEGs were investigated including 140 upregulated genes and 65 downregulated genes. These DEGs were mainly enriched in functions such as the immune response, inflammatory response, cell adhesion, receptor activity, and protease binding. The results of KEGG pathway enrichment analysis revealed that DEGs were mainly involved in the cytokine-cytokine receptor interaction, pathways in cancer, and the PI3K-Akt signaling pathway. The intersecting genes, including IL6, TLR4, FN1, IL1β, CXCL8, MMP9, and SPP1, were revealed as potential genetic biomarkers and target genes of peri-implantitis.ConclusionsThis study provides supportive evidence that IL6, TLR4, FN1, IL1β, CXCL8, MMP9, and SPP1 might be used as potential target biomarkers for peri-implantitis which may provide further therapeutic potentials for peri-implantitis.
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Project description:BackgroundMyopathies related to Ryanodine receptor 1 (RYR1) mutation are the most common nondystrophy muscle disorder in humans. Early detection and diagnosis of RYR1 mutation-associated myopathies may lead to more timely treatment of patients, which contributes to the management and preparation for malignant hyperthermia. However, diagnosis of RYR1 mutation-associated myopathies is delayed and challenging. The absence of diagnostic morphological features in muscle biopsy does not rule out the possibility of pathogenic variations in RYR1. Accordingly, it is helpful to seek biomarkers to diagnose RYR1 mutation-associated myopathies.MethodsSkeletal muscle tissue microarray datasets of RYR1 mutation-associated myopathies or healthy persons were built in accordance with the gene expression synthesis (GEO) database. Differentially expressed genes (DEGs) were identified on the basis of R software. Genes specific to tissue/organ were identified through BioGPS. An enrichment analysis of DEGs was conducted in accordance with the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We also built protein-protein interaction (PPI) networks to explore the function and enrichment pathway of DEGs and the identification of hub genes. Lastly, the ROC curve was drawn for hub genes achieving specific expressions within skeletal muscle. Moreover, the area under the curve (AUC) was obtained to calculate the predictive value of key genes. The transcription factors of hub genes achieving specific expressions within skeletal muscle were predicted with the use of the iRegulon plugin.ResultsWe identified 170 DEGs among 11 muscle biopsy samples of healthy subjects and 17 muscle biopsy samples of RYR1 mutation-associated myopathy patients in the dataset. Among the above DEGs, 30 genes achieving specific expressions within tissues/organs were found. GO and KEGG enrichment analysis of DEGs mainly focused on muscle contraction, actin-mediated cell contraction, actin filament-based movement, and muscular sliding. 12 hub genes were identified with the use of Cytoscape. Four hub genes were specifically expressed in skeletal muscle tissue, including MYH1 (AUC: 0.856), TNNT3 (AUC: 0.840), MYLPF (AUC: 0.786), and ATP2A1 (AUC: 0.765). The iRegulon predicted results suggested that the transcription factor MYF6 was found with the highest reliability.ConclusionsFour skeletal muscle tissue-specific genes were identified, including MYH1, TNNT3, MYLPF, and ATP2A1, as the potential biomarkers for diagnosing and treating RYR1 mutation-associated myopathies, which provided insights into the transcriptome-level development mechanism. The transcription factor MYF6 may be a vital upstream regulator of the above biomarkers.
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Project description:IntroductionRecurrent implantation failure (RIF) is a frustrating challenge because the cause is unknown. The current study aims to identify differentially expressed genes (DEGs) in the endometrium on the basis of immune cell infiltration characteristics between RIF patients and healthy controls, as well as to investigate potential prognostic markers in RIF.MethodsGSE103465, and GSE111974 datasets from the Gene Expression Omnibus database were obtained to screen DEGs between RIF and control groups. Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes Pathway analysis, Gene Set Enrichment Analysis, and Protein-protein interactions analysis were performed to investigate potential biological functions and signaling pathways. CIBERSORT was used to describe the level of immune infiltration in RIF, and flow cytometry was used to confirm the top two most abundant immune cells detected.Results122 downregulated and 66 upregulated DEGs were obtained between RIF and control groups. Six immune-related hub genes were discovered, which were involved in Wnt/-catenin signaling and Notch signaling as a result of our research. The ROC curves revealed that three of the six identified genes (AKT1, PSMB8, and PSMD10) had potential diagnostic values for RIF. Finally, we used cMap analysis to identify potential therapeutic or induced compounds for RIF, among which fulvestrant (estrogen receptor antagonist), bisindolylmaleimide-ix (CDK and PKC inhibitor), and JNK-9L (JNK inhibitor) were thought to influence the pathogenic process of RIF. Furthermore, our findings revealed the level of immune infiltration in RIF by highlighting three signaling pathways (Wnt/-catenin signaling, Notch signaling, and immune response) and three potential diagnostic DEGs (AKT1, PSMB8, and PSMD10).ConclusionImportantly, our findings may contribute to the scientific basis for several potential therapeutic agents to improve endometrial receptivity.
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Project description:BackgroundProstate cancer (PCa) is a common urinary malignancy, whose molecular mechanism has not been fully elucidated. We aimed to screen for key genes and biological pathways related to PCa using bioinformatics method.MethodsDifferentially expressed genes (DEGs) were filtered out from the GSE103512 dataset and subjected to the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein-protein interactions (PPI) network was constructed, following by the identification of hub genes. The results of former studies were compared with ours. The relative expression levels of hub genes were examined in The Cancer Genome Atlas (TCGA) and Oncomine public databases. The University of California Santa Cruz Xena online tools were used to study whether the expression of hub genes was correlated with the survival of PCa patients from TCGA cohorts.ResultsTotally, 252 (186 upregulated and 66 downregulated) DEGs were identified. GO analysis enriched mainly in "oxidation-reduction process" and "positive regulation of transcription from RNA polymerase II promoter"; KEGG pathway analysis enriched mostly in "metabolic pathways" and "protein digestion and absorption." Kallikrein-related peptidase 3, cadherin 1 (CDH1), Kallikrein-related peptidase 2 (KLK2), forkhead box A1 (FOXA1), and epithelial cell adhesion molecule (EPCAM) were identified as hub genes from the PPI network. CDH1, FOXA1, and EPCAM were validated by other relevant gene expression omnibus datasets. All hub genes were validated by both TCGA and Oncomine except KLK2. Two additional top DEGs (ABCC4 and SLPI) were found to be associated with the prognosis of PCa patients.ConclusionsThis study excavated the key genes and pathways in PCa, which might be biomarkers for diagnosis, prognosis, and potential therapeutic targets.
| S-EPMC6779116 | biostudies-literature
Project description:BackgroundAlternative splicing (AS) is an important mechanism for regulating gene expression and proteome diversity. Tumor-alternative splicing can reveal a large class of new splicing-associated potential new antigens that may affect the immune response and can be used for immunotherapy.MethodsThe RNA-seq transcriptome data and clinical information of stomach adenocarcinoma (STAD) cohort were downloaded from The Cancer Genome Atlas (TCGA) database data portal, and data of splicing events were obtained from the SpliceSeq database. Predicting genes were validated by Asian cancer research group (ACRG) cohort and Oncomine database. RT-qPCR was used to analysis the expression of ECT2 in STAD.ResultsA total of 32,166 AS events were identified, among which 2,042 AS events were significantly associated with patients survival. Biological pathway analysis indicated that these genes play an important role in regulating gastric cancer-related processes such as GTPase activity and PI3K-Akt signaling pathway. Next, we derived a risk signature, using alternate acceptor, that is an independent prognostic marker. Moreover, high ECT2 expression was associated with poorer prognosis in STAD. Multivariate survival analysis demonstrated that high ECT2 expression was an independent risk factor for overall survival. Gene set enrichment analysis revealed that high ECT2 expression was enriched for hallmarks of malignant tumors. The ACRG cohort and Oncomine also showed that high ECT2 expression was associated with poorer prognosis in gastric cancer patients. Finally, RT-qPCR showed ECT2 expression was higher in STAD compared to the normal tissues.ConclusionThis study excavated the alternative splicing events in gastric cancer, and found ECT2 might be a biomarkers for diagnosis and prognosis.
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