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Background. Alopecia areata (AA) is mainly a T cell-medicated autoimmune disease with non-scarring hair loss and limited treatment options. Of these, the patchy-type alopecia areata (AAP) is the most common and relatively easy to treat due to smaller areas of the scalp affected. To understand the pathogenesis of AAP and explore the therapeutic target, we focus on the molecular signatures by comparing AAP and normal subjects. Methods. The gene expression profile (GSE68801) was obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified between AAP patients and normal controls using the GEO2R. Then the Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Protein-Protein interaction (PPI) network analysis were performed for DEGs. Results. A total of 185 DEGs were identified, including 45 up-regulated genes and 140 down-regulated genes. The upregulated DEGs were related to the immune response and chemokine signaling pathway. Meanwhile, down-regulated DEGs were enriched in keratin filament and intermediate filament. Subsequently, the top 10 hub genes were picked out in the PPI network, among them, CD2 showed the highest connectivity degree and central roles. Conclusion. Our data suggest that the CD2 may be a new therapeutic target for AAP. Further study is needed to explore the value of CD2 in the treatment of alopecia areata.
Background: Competing endogenous RNAs (ceRNAs) render the functions of long non-coding RNAs (lncRNAs) more complicated during cancer processes. Potential lncRNA biomarkers as ceRNAs have not been clearly described for uterine corpus endometrial carcinoma (UCEC). In this research, we researched the functions and regulatory mechanisms of lncRNAs as ceRNAs in UCEC, and their potential applications in prognosis.Methods: The lncRNAs, mRNAs, and miRNAs expression profiles including 552 UCEC tissues and 35 non-tumor tissues were downloaded from The Cancer Genome Atlas (TCGA) portal. Differentially expressed mRNAs, miRNAs and lncRNAs were confirmed by R/Bioconductor package of edgeR (|log 2 FC| >2, FDR <0.01). GO enrichment and KEGG pathway enrichment analyses were accomplished utilizing DAVID online tool. The bioinformatics generated from miRcode and miRTarBase was used to construct the dysregulated lncRNA-associated ceRNA network. Kaplan-Meier curve analysis was used to predict the survival analysis of DERNAs.Results: A total of 1,102 lncRNAs, 2,612 mRNAs and 189 miRNAs were detected to be dysregulated in UCEC. The newly identified ceRNA network includes 27 UCEC-specific miRNAs, 90 lncRNAs, and 74 mRNAs. Eleven mRNAs, 3 miRNAs (has-mir-425, has-mir-211 and has-mir-301b) and 6 lncRNAs (AC11049.1, ADARB2-AS1, C10orf91, GLIS3-AS1, LINC00237 and LINC00261) were found to be significantly correlated with overall survival in UCEC (P value <0.05). Conclusions:In our research process, we successfully constructed a lncRNA-associated ceRNA network which will provide a novel perspective for improving the understanding of UCEC. This study also assists in the identification of new potential biomarkers to be used as candidate prognostic biomarkers or potential therapeutic targets.
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