Integrated bioinformatic analysis and machine learning developed a prognostic model based on mitochondrial function for acute myeloid leukemia.

Background: The disease burden of acute myeloid leukemia (AML) continues to pose a significant public health challenge globally. Mitochondria play a critical role in tumor development and progression by influencing bioenergetics, biosynthesis, and signaling pathways. However, the prognostic significance and therapeutic implications of mitochondrial function in AML warrant further investigation. Methods: We integrated mitochondrial gene expression data with bulk RNA sequencing to identify key mitochondrial genes associated with AML. A total of fourteen machine learning algorithms were employed, yielding 148 unique combinations. The best-performing model was utilized to develop a MitoScore, which was then combined with clinical variables to establish a MitoScore-based nomogram. Additionally, single-cell sequencing data were analyzed to assess the impact of key mitochondrial genes on immune cells. Samples were classified into low-risk and high-risk groups based on MitoScore, allowing for a comparative analysis of clinical features, biological mechanisms, copy number variations, tumor burden, immune infiltration, immune function, and drug sensitivity between the two groups. Results: Specific expression patterns of mitochondrial genes were observed in T cell subsets and at various developmental stages of AML. Samples were classified into low-risk and high-risk groups based on MitoScore. The high-risk MitoScore group exhibited a worse prognosis, with enriched biological processes and molecular pathways associated with immune response, a higher frequency of gene mutations linked to poor outcomes, increased immune cell infiltration, enhanced immune function, upregulated immune checkpoint gene expression, and greater sensitivity to cyclophosphamide and venetoclax. Conclusions: This robust machine learning framework underscores the potential of MitoScore as a tool for stratified prognostic assessment and personalized treatment planning in AML patients.