Study on the Potential Targets and Mechanism of Hypoxia and Chronic Myeloid Leukemia by WGCNA Combined with Machine Learning Method
Objective To identify the potential key genes of hypoxia and chronic myeloid leukemia(CML)by WGCNA combined with machine learning method,and to explore the composition and distribution of key genes and 22 immune cells in CML.Methods Two CML datasets(GSE5550 and GSE24739)in the high-throughput gene expression database(GEO)were integrated to eliminate batch effects.The microarray data linear model(LIMMA)was used to analyze the differences of 200 hypoxia genes in 25 CML patients and 16 controls.The related modules were screened by weighted gene co-expression network analysis(WGCNA),and the key genes were screened by SVM-RFE analysis and LASSO regression model.Three algorithms do Venn analysis.The CIBERSORT algorithm in immune infiltration was used to evaluate the composition pattern of 22 immune cell components in CML and the correlation between key genes and 22 immune cells.Results A total of 34 CML-related hypoxia genes were identified,of which 14 genes were up-regulated and 20 genes were down-regulated.GO enrichment showed that it was related to long-chain fatty acid transport pathway,vascular wound healing,B cell homeostasis,negative regulation of lymphocyte apoptosis and protease binding pathway.KEGG analysis showed that hypoxia-inducible factor 1(HIF-1)signaling pathway,shear stress and atherosclerosis signaling pathway were significantly enriched.ANXA2 was identified as a key gene in the progression of CML,and its expression level was significantly higher than that of normal blood samples(P<0.01).ANXA2 showed strong predictive ability in CML diagnostic model(AUC=0.820,95%CI=0.692-0.935).Immune cell infiltration analysis showed that ANXA2 was associated with plasma cells,cytotoxic T cells,macrophage M0 type and mast cell activation.Conclusion Hypoxia gene ANXA2 can be used as an immune checkpoint of CML,which may play a role as a biological marker in the progression of CML.
国家科技期刊平台
NSTL
万方数据
