ObjectiveThis study utilizes 16S rDNA gene sequencing technology and LC-MS/MS technology to examine the gut microbiota composition and serum metabolites of AML (acute myeloid leukemia) patients pre- and post-chemotherapy, in comparison to a control group of healthy individuals. Through screening, distinct microbiota and metabolites were identified in AML patients.Through the implementation of correlation analysis, a regulatory network linking gut microbiota and body metabolism in patients with AML was established, elucidating the pathophysiological mechanisms through which gut microbiota contribute to the metabolism of individuals with AML. These findings offer novel empirical evidence to support the implementation of gut microbiota-based intervention strategies for individuals diagnosed with AML.

MethodsFecal and serum samples were obtained from 46 newly diagnosed acute myeloid leukemia (AML) patients at two distinct time points: prior to chemotherapy initiation and during the granulocytopenia phase following chemotherapy (neutrophil count <0.5×10^9/L), in addition to samples from 20 healthy adult controls. Subsequent analyses involved 16S rDNA sequencing and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Differential microbiota and metabolites were identified using multivariate statistical methods, with functional annotation of the identified metabolites carried out using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.Moreover, Pearson correlation analysis was utilized to examine the co-metabolic patterns between the host and gut microbiota, with a specific emphasis on identifying the gut microbiota and metabolites that have a significant impact on the pathophysiological processes in patients with acute myeloid leukemia (AML) and investigating their interactions.

Results 1. Based on multivariate statistical analysis, the alpha and beta diversity of the intestinal microbiota in AML patients was significantly reduced compared with the healthy control group, and remission-induction chemotherapy further reduced the abundance and diversity of the intestinal microbiota in AML patients.

  • LC-MSmetabolomics analysis of all serum samples identified 430 differentially expressed metabolites between newly diagnosed AML patients and healthy controls, and 226 differentially expressed metabolites between AML patients in the neutropenic stage after induction chemotherapy and the newly diagnosed AML group.

  • KEGG functional enrichment analysis of differential metabolites showed that significantly altered pathways in newly diagnosed AML patients included amino acid metabolism pathways such as valine, leucine, and isoleucine degradation, arginine and proline metabolism, phenylalanine metabolism, and histidine metabolism. Functional enrichment analysis in patients with neutropenic AML revealed significantly altered pathways such as bile secretion and bile acid metabolism, pyrimidine-glutathione metabolism, fatty acid biosynthesis, propionate metabolism, and the PI3K-Akt signaling pathway.

  • Pearson correlation analysis revealed that AML patients possessed a unique interaction network between gut microbiota and serum metabolites.

ConclusionThis study used 16S rDNA high-throughput sequencing and LC-MS/MS to analyze the gut microbiota and metabolites of healthy controls and newly diagnosed AML patients before and after chemotherapy. We found significant differences in gut microbial composition and metabolites between newly diagnosed AML patients and healthy controls. Remission-induction chemotherapy further reduced gut microbial diversity in newly diagnosed AML patients, exacerbating metabolic disorders. In addition, the differential metabolites and differential microbiota between the groups were correlated based on the Pearson correlation coefficient, indicating that AML patients have a unique intestinal microbiota and metabolite regulatory network. The correlation between related metabolic pathways and intestinal microbiota was analyzed, providing a basis for revealing the pathogenesis of AML and providing a new perspective for clinical auxiliary disease diagnosis, treatment and prognosis.

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2025
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