Bioinformatics Analysis of Core Genes and Key Pathways in Myelodysplastic Syndrome.
10.19746/j.cnki.issn.1009-2137.2022.03.023
- Author:
Yan WANG
1
;
Ying-Shao WANG
1
;
Nai-Bo HU
1
;
Guang-Shuai TENG
1
;
Yuan ZHOU
2
;
Jie BAI
3
Author Information
1. Department of Hematology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China.
2. State Key Laboratory of Experimental Hematology, Institute of Hematology and Blood Diseases Hospital, National Clinical Research Center for Blood Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China.
3. Department of Hematology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China,E-mail: Janebai86@126.com.
- Publication Type:Journal Article
- Keywords:
LEF1 gene;
bioinformatics;
differentially expressed gene;
myelodysplastic syndrome;
pathogenesis
- MeSH:
Computational Biology;
Gene Expression Profiling;
Gene Expression Regulation, Neoplastic;
Gene Ontology;
Humans;
Myelodysplastic Syndromes/genetics*
- From:
Journal of Experimental Hematology
2022;30(3):804-812
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To screen differentially expressed gene (DEG) related to myelodysplastic syndrome (MDS) based on Gene Expression Omnibus (GEO) database, and explore the core genes and pathogenesis of MDS by analyzing the biological functions and related signaling pathways of DEG.
METHODS:The expression profiles of GSE4619, GSE19429, GSE58831 including MDS patients and normal controls were downloaded from GEO database. The gene expression analysis tool (GEO2R) of GEO database was used to screen DEG according to | log FC (fold change) |≥1 and P<0.01. David online database was used to annotate gene ontology function (GO). Metascape online database was used to enrich and analyze differential genes in Kyoto Encyclopedia of Genes and Genomes (KEGG). The protein-protein interaction network (PPI) was constructed by using STRING database. CytoHubba and Mcode plug-ins of Cytoscape were used to analyze the key gene clusters and hub genes. R language was used to diagnose hub genes and draw the ROC curve. GSEA enrichment analysis was performed on GSE19429 according to the expression of LEF1.
RESULTS:A total of 74 co-DEG were identified, including 14 up-regulated genes and 60 down regulated genes. GO enrichment analysis indicated that BP of down regulated genes was mainly enriched in the transcription and regulation of RNA polymerase II promoter, negative regulation of cell proliferation, and immune response. CC of down regulated genes was mainly enriched in the nucleus, transcription factor complexes, and adhesion spots. MF was mainly enriched in protein binding, DNA binding, and β-catenin binding. KEGG pathway was enriched in primary immunodeficiency, Hippo signaling pathway, cAMP signaling pathway, transcriptional mis-regulation in cancer and hematopoietic cell lineage. BP of up-regulated genes was mainly enriched in type I interferon signaling pathway and viral response. CC was mainly enriched in cytoplasm. MF was mainly enriched in RNA binding. Ten hub genes and three important gene clusters were screened by STRING database and Cytoscape software. The functions of the three key gene clusters were closely related to immune regulation. ROC analysis showed that the hub genes had a good diagnostic significance for MDS. GSEA analysis indicated that LEF1 may affect the normal function of hematopoietic stem cells by regulating inflammatory reaction, which further revealed the pathogenesis of MDS.
CONCLUSION:Bioinformatics can effectively screen the core genes and key signaling pathways of MDS, which provides a new strategy for the diagnosis and treatment of MDS.
