Abstract
Acute Leukaemia (AL) is a neoplasm of WBCs (white blood cells). Being an important class of metabolites, alteration in free fatty acids (FFAs) levels play a key role in cancer development and progression. As they involve in cell signaling, maintain membrane integrity, regulate homeostasis and effect cell and tissue functions. Considering this fact, a comprehensive analysis of FFAs was conducted to monitor their alteration in AL, pre-leukaemic diseases and healthy control. Fifteen FFAs were analyzed in 179 serum samples of myelodysplastic syndrome (MDS), aplastic anemia (APA), acute lymphoblastic leukaemia (ALL), acute myeloid leukaemia (AML) and healthy control using gas chromatography-multiple reaction monitoring-mass spectrometry (GC-MRM-MS). A multivariate statistical method of random forest (RF) was employed for chemometric analysis. Serum level of two FFAs including C18:0 and C14:0 were found discriminative among all five groups, and between ALL and AML, respectively. Moreover, C14:0 was identified as differentiated FFAs for systematic progression of pre-leukaemic conditions towards AML. C16:0 came as discriminated FFAs between APA and MDS/AML. Over all it was identified that FFAs profile not only become altered in leukaemia but also in pre-leukaemic diseases.
Highlights
Leukaemia, among the 10th foremost roots of cancer-related decease throughout the world, can be subdivided into acute lymphoblastic leukaemia (ALL) and AML13
There have been no studies on quantification of free fatty acids (FFAs) profiles of ALL, acute myeloid leukaemia (AML) and pre-leukaemic conditions that are aplastic anemia (APA) and myelodysplastic syndrome (MDS) to exploit any distinctive FFA that may serve as differentiated biomarker among these groups
We have analyzed FFAs profiles in Acute Leukaemia (AL) patients (AML and ALL), pre-leukaemic (APA, MDS) and healthy subjects using Gas chromatography-mass spectrometry (GC-MS) followed by chemometric analysis using random forest (RF) algorithm that serve as a new and powerful classifying model that use to discriminate groups and provide useful information about distinguishing metabolites that may serve as biomarkers
Summary
Among the 10th foremost roots of cancer-related decease throughout the world, can be subdivided into ALL and AML13. It has been observed that 10–30% of the survived patients from APA or MDS would eventually develop leukeamia mainly AML in late stages of their life. There have been no studies on quantification of FFAs profiles of ALL, AML and pre-leukaemic conditions that are APA and MDS to exploit any distinctive FFA that may serve as differentiated biomarker among these groups. We have analyzed FFAs profiles in AL patients (AML and ALL), pre-leukaemic (APA, MDS) and healthy subjects using GC-MS followed by chemometric analysis using RF algorithm that serve as a new and powerful classifying model that use to discriminate groups and provide useful information about distinguishing metabolites that may serve as biomarkers. The aliquots of sera were made and kept in −80 °C freezer until further analysis was performed
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