Related Experiment Video
Updated: Apr 28, 2026
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Biomarker identification and pathway analysis by serum metabolomics of childhood acute lymphoblastic leukemia
Yunnuo Bai1, Haitao Zhang1, Xiaohan Sun1
1Department of Pediatrics, The 2nd Affiliated Hospital of Harbin Medical University, Xuefu Road 246, Nangang District, Harbin 150001, China.
Background:
Acute lymphoblastic leukemia (ALL) is a common hematological malignant neoplasm that typically affects children. Although intense chemotherapeutic regimens have been useful to combat the disease, approximately 20% of patients will relapse despite treatment. Diagnosing ALL requires bone marrow puncture procedure, which many parents do not consent to for it is invasive. Additionally, metabolic alterations associated with the disease are unclear.
Methods:
Metabolic alterations associated with ALL were investigated by performing serum metabolomics based on ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry and multivariate statistical analysis. Ingenuity Pathways Analysis (IPA) was also performed.
Results:
Thirty metabolites (17 detected in positive mode and 13 in negative mode) were differentially expressed between patients with ALL and control patients; these metabolites were selected as potential biomarkers. Based on IPA analysis, glycerophospholipid metabolism is deregulated in patients with ALL and may represent an underlying metabolic pathway associated with disease progression.
Conclusions:
Metabolomics can be used to analyze the metabolic activity of ALL patients compared to healthy controls. The data we provide here suggest that glycerophospholipid metabolism may be a key mechanism underlying disease progression and development.
