Analysis of Unfavorable Prognosis Gene Markers in Patients with Acute Myeloid Leukemia by Multiomics.
10.19746/j.cnki.issn.1009-2137.2019.02.004
- Author:
Xi-Meng CHEN
1
;
Hao-Min ZHANG
1
;
Bo YANG
2
;
Xue-Chun LU
3
;
Pei-Feng HE
4
Author Information
1. School of Management, Shanxi Medical University, Taiyuan 030001, Shanxi Province,China.
2. Department of Hematology, Second Medical Center, General Hospital of the People's Liberation Army, National Center for Clinical Research of Geriatric Diseases, Beijing 100853, China.
3. School of Management, Shanxi Medical University, Taiyuan 030001, Shanxi Province,China,Department of Hematology, Second Medical Center, General Hospital of the People's Liberation Army, National Center for Clinical Research of Geriatric Diseases, Beijing 100853, China,E-mail: luxuechun@126.com.
4. School of Management, Shanxi Medical University, Taiyuan 030001, Shanxi Province,China,E-mail: hepeifeng2006@126.com.
- Publication Type:Journal Article
- MeSH:
Gene Expression Profiling;
Gene Expression Regulation, Neoplastic;
Humans;
Leukemia, Myeloid, Acute;
Prognosis;
Transcriptome
- From:
Journal of Experimental Hematology
2019;27(2):331-338
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To analyze the molecular markers associated with occurrence, development and poor prognosis of acute myeloid leukemia (AML) by using the data of GEO and TCGA database, as well as multiomics analysis.
METHODS:The transcriptome data meeting requirements were down-loaded from GEO database, the differentially expressed genes were screened by using the R language limma package, and the GO function enrichment analysis and KEGG pathway analysis were performed for differentially expressed genes, at the same time, the protein interaction network was contracted by using STRING database and cytoscape software to screen out the hub gene, then the prognosis analysis was carried out for hub gene by combination with the clinical information affected in TCGA database.
RESULTS:620 differentially expressed genes were screened out, among which 162 differentially expressed genes were up-regulated, and 458 differentially expressed genes were down-regulated. Based on the results of GO functional enrichment, the KEGG pathway enrichment and protein interaction network, CXCL4, CXCR4, CXCR1, CXCR2, CCL5 and JUN were selected as hub genes. The survival analysis showed that the high expression of CXCL4, CXCR1, and CCL5 was a risk factor for poor prognosis of patiants.
CONCLUSION:CXCL4, CXCR1 and CCL5 can be used as biomarkers for the occurrence and development of AML, which relateds with the unfavorable prognosis and can provide a basis for further study.
