Mining the biomarkers and associated-drugs for acute myeloid leukemia by bioinformatic methods
10.3760/cma.j.cn115356-20230414-00089
- VernacularTitle:生物信息学方法挖掘急性髓系白血病的生物标志物和相关药物
- Author:
Danxia LIN
1
;
Jiasheng HU
Author Information
1. 福建医科大学研究生院，福州 350100
- Keywords:
Leukemia, myeloid, acute;
Tumor markers, biological;
Molecular targeted therapy;
Computational biology;
ITGAL
- From:
Journal of Leukemia & Lymphoma
2024;33(5):288-293
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To screen the potential biomarkers and drugs in acute myeloid leukemia (AML) to improve the cure rate of leukemia.Methods:The GeneChip GSE90062 dataset (including 3 leukemia stem cell samples and 3 normal bone marrow hematopoietic stem cell samples) and GSE17054 dataset (including 9 leukemia stem cell samples and 4 normal bone marrow hematopoietic stem cell samples), which included AML patients and healthy donors, were downloaded from the Gene Expression Omnibus (GEO) database. Common differentially expressed gene (DEG) between bone marrow of AML patients and healthy donors in both GEO database datasets was analyzed by GEO2R software. Gene Ontology (GO) biofunction enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of common DEG were performed using DAVID online software. The protein-protein interaction (PPI) networks were constructed using the STRING database, and key genes in the top 10 of the correlation intensities were screened with the help of CytoHubba plug-in. Gene Expression Profiling Interaction Analysis (GEPIA) database was applied to reconfirm the key genes from the 10 key genes; Kaplan-Meier survival curves were used to assess the prognosis of AML patients with different levels of the 10 key genes, and comparisons were made using the log-rank test; the Drug-Gene Interaction Database (DGIdb) was used to screen for the key gene-related drugs.Results:A total of 75 up-regulated common DEG and 61 down-regulated common DEG were identified. In GO enrichment analysis, common DEG was mainly associated with cell apoptosis and cell migration. In KEGG pathway analysis, they were mainly related to apoptosis and hematopoietic pathway. The top 10 key genes with strong correlations screened by the PPI networks were ITGA4, ITGAL, HNRNPA3, CDC42, PRF1, SRSF3, HNRNPD, GTPBP4, CXCR4, and RPL35A, of which 6 genes (ITGA4, ITGAL, HNRNPA3, GTPBP4, CXCR4, and RPL35A) were reconfirmed by the GEPIA2 database. Kaplan-Meier survival curve analysis showed a statistically significant difference in poor overall survival of AML patients with high expression of ITGAL compared to patients with low expression of ITGAL ( P = 0.010); the difference in overall survival between patients with high and low expression of the remaining 9 key genes was not statistically significant (all P > 0.05). Four potential drugs which may be related to ITGAL were screened from DGIdb, they were efalizumab, odulimomab, lifitegrast, and rovelizumab. Conclusions:ITGAL is overexpressed in the bone marrow of AML patients compared to healthy individuals and it is an unfavorable prognostic factor for AML patients. The screened potential drugs targeting ITGAL (efalizumab, odulimomab, lifitegrast, and rovelizumab) provides new ideas for the treatment of AML.
