Bioinformatics and Raman spectroscopy-based identification of key pathways and genes enabling differentiation between acute myeloid leukemia and T cell acute lymphoblastic leukemia

被引:0
作者
Liang, Haoyue [1 ,2 ]
Kong, Xiaodong [3 ]
Cao, Zhijie [1 ,2 ]
Wang, Haoyu [1 ,2 ]
Liu, Ertao [1 ,2 ]
Sun, Fanfan [1 ,2 ]
Qi, Jianwei [1 ,2 ]
Zhang, Qiang [3 ]
Zhou, Yuan [1 ,2 ]
机构
[1] Chinese Acad Med Sci & Peking Union Med Coll, Inst Hematol & Blood Dis Hosp, Haihe Lab Cell Ecosyst, Natl Clin Res Ctr Blood Dis,State Key Lab Expt Hem, Tianjin, Peoples R China
[2] Tianjin Inst Hlth Sci, Tianjin, Peoples R China
[3] Tianjin Med Univ, Gen Hosp, Tianjin Geriatr Inst, Dept Geriatr, Tianjin, Peoples R China
来源
FRONTIERS IN IMMUNOLOGY | 2023年 / 14卷
基金
中国国家自然科学基金;
关键词
AML; T-ALL; bioinformatics analysis; Raman spectroscopy; functional enrichment analysis; ADENOSINE-DEAMINASE; ACTIVATION; EXPRESSION; PROTEIN; LYMPHOCYTES; RESISTANCE; DOMAINS; BIOLOGY; CLONING; ANTIGEN;
D O I
10.3389/fimmu.2023.1194353
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
Acute myeloid leukemia (AML) and T cell acute lymphoblastic leukemia (T-ALL) are two of the most prevalent hematological malignancies diagnosed among adult leukemia patients, with both being difficult to treat and associated with high rates of recurrence and mortality. In the present study, bioinformatics approaches were used to analyze both of these types of leukemia in an effort to identify characteristic gene expression patterns that were subsequently validated via Raman spectroscopy. For these analyses, four Gene Expression Omnibus datasets (GSE13204, GSE51082, GSE89565, and GSE131184) pertaining to acute leukemia were downloaded, and differentially expressed genes (DEGs) were then identified through comparisons of AML and T-ALL patient samples using the R Bioconductor package. Shared DEGs were then subjected to Gene Ontology (GO) enrichment analyses and were used to establish a protein-protein interaction (PPI) network analysis. In total, 43 and 129 upregulated and downregulated DEGs were respectively identified. Enrichment analyses indicated that these DEGs were closely tied to immune function, collagen synthesis and decomposition, inflammation, the synthesis and decomposition of lipopolysaccharide, and antigen presentation. PPI network module clustering analyses further led to the identification of the top 10 significantly upregulated and downregulated genes associated with disease incidence. These key genes were then validated in patient samples via Raman spectroscopy, ultimately confirming the value of these genes as tools that may aid the differential diagnosis and treatment of AML and T-ALL. Overall, these results thus highlight a range of novel pathways and genes that are linked to the incidence and progression of AML and T-ALL, providing a list of important diagnostic and prognostic molecular markers that have the potential to aid in the clinical diagnosis and treatment of these devastating malignancies.
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页数:17
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