Dataset Information

Screening and identification of key candidate genes and pathways in myelodysplastic syndrome by bioinformatic analysis.

ABSTRACT: Myelodysplastic syndrome (MDS) is a heterogeneous hematologic malignancy derived from hematopoietic stem cells and the molecular mechanism of MDS remains unclear. This study aimed to elucidate potential markers of diagnosis and prognosis of MDS. The gene expression profiles GSE19429 and GSE58831 were obtained and downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in MDS were screened using GEO2R and overlapped DEGs were obtained with Venn Diagrams. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway functional enrichment analyses, protein-protein interaction network establishment and survival analyses were performed. Functional enrichment analysis indicated that these DEGs were significantly enriched in the interferon signaling pathway, immune response, hematopoietic cell lineage and the FOXO signaling pathway. Four hub genes and four significant modules including 25 module genes were obtained via Cytoscape MCODE. Survival analysis showed that the overall survival of MDS patients having BLNK, IRF4, IFITM1, IFIT1, ISG20, IFI44L alterations were worse than that without alterations. In conclusion, the identification of these genes and pathways helps understand the underlying molecular mechanisms of MDS and provides candidate targets for the diagnosis and prognosis of MDS.

SUBMITTER: Le Y

PROVIDER: S-EPMC6886488 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Screening and identification of key candidate genes and pathways in myelodysplastic syndrome by bioinformatic analysis.

Le Ying Y

PeerJ 20191129

Myelodysplastic syndrome (MDS) is a heterogeneous hematologic malignancy derived from hematopoietic stem cells and the molecular mechanism of MDS remains unclear. This study aimed to elucidate potential markers of diagnosis and prognosis of MDS. The gene expression profiles GSE19429 and GSE58831 were obtained and downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) in MDS were screened using GEO2R and overlapped DEGs were obtained with Venn Diagra ...[more]

PMID: 31803541

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Project description:Rheumatoid arthritis (RA), which normally manifests as a multi‑joint inflammatory reaction, is a common immunological disease in clinical practice. However, the pathogenesis of RA has not yet been fully elucidated. Rituximab (RTX) is an effective drug in the treatment of RA, however its therapeutic efficacy and mechanism of action require further investigation. Thus, the present study aimed to screen the candidate key regulatory genes and explain the potential mechanisms of RA. Gene chips of RA and normal joint tissues were analyzed and, gene chips of RTX before and after treatment were investigated. In the present study, strong evidence supporting the pathogenesis of RA and mechanism of action of RTX were also revealed. Differentially expressed genes (DEGs) were analyzed using the limma package of RStudio software. A total of 1,150 DEGs were detected in RA compared with normal joint tissues. The upregulated genes were enriched in 'interleukin‑12 production', 'I‑κB kinase/NF‑κB signaling', 'regulation of cytokine production involved in immune response' and 'cytokine metabolic process'. Functional enrichment analysis showed that RTX was primarily involved in the inhibition of 'adaptive immune response', 'B cell activation involved in immune response' and 'immune effector process'. Subsequently, leukocyte immunoglobulin‑like receptor subfamily B member 1 (LILRB1), a hub gene with high connectivity degree, was selected, and traditional Chinese medicine libraries were molecularly screened according to the structure of the LILRB1 protein. The results indicated that kaempferol 3‑O‑β‑D‑glucosyl‑(1→2)‑β‑D‑glucoside exhibited the highest docking score. In the present study, the DEGs and their biological functions in RA and the pharmacological mechanism of RTX action were determined. Taken together, the results suggested that LILRB1 may be used as a molecular target for RA treatment, and kaempferol 3‑O‑β‑D‑glucosyl‑(1→2)‑β‑D‑glucoside may inhibit the pathological process of RA.

Project description:Background: The molecular mechanism of tumorigenesis remains to be fully understood in breast cancer. It is urgently required to identify genes that are associated with breast cancer development and prognosis and to elucidate the underlying molecular mechanisms. In the present study, we aimed to identify potential pathogenic and prognostic differentially expressed genes (DEGs) in breast adenocarcinoma through bioinformatic analysis of public datasets. Methods: Four datasets (GSE21422, GSE29431, GSE42568, and GSE61304) from Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) dataset were used for the bioinformatic analysis. DEGs were identified using LIMMA Package of R. The GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses were conducted through FunRich. The protein-protein interaction (PPI) network of the DEGs was established through STRING (Search Tool for the Retrieval of Interacting Genes database) website, visualized by Cytoscape and further analyzed by Molecular Complex Detection (MCODE). UALCAN and Kaplan-Meier (KM) plotter were employed to analyze the expression levels and prognostic values of hub genes. The expression levels of the hub genes were also validated in clinical samples from breast cancer patients. In addition, the gene-drug interaction network was constructed using Comparative Toxicogenomics Database (CTD). Results: In total, 203 up-regulated and 118 down-regulated DEGs were identified. Mitotic cell cycle and epithelial-to-mesenchymal transition pathway were the major enriched pathways for the up-regulated and down-regulated genes, respectively. The PPI network was constructed with 314 nodes and 1,810 interactions, and two significant modules are selected. The most significant enriched pathway in module 1 was the mitotic cell cycle. Moreover, six hub genes were selected and validated in clinical sample for further analysis owing to the high degree of connectivity, including CDK1, CCNA2, TOP2A, CCNB1, KIF11, and MELK, and they were all correlated to worse overall survival (OS) in breast cancer. Conclusion: These results revealed that mitotic cell cycle and epithelial-to-mesenchymal transition pathway could be potential pathways accounting for the progression in breast cancer, and CDK1, CCNA2, TOP2A, CCNB1, KIF11, and MELK may be potential crucial genes. Further, it could be utilized as new biomarkers for prognosis and potential new targets for drug synthesis of breast cancer.

Dataset Information

Screening and identification of key candidate genes and pathways in myelodysplastic syndrome by bioinformatic analysis.

Publications

Screening and identification of key candidate genes and pathways in myelodysplastic syndrome by bioinformatic analysis.

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