Frontiers in immunology · 2025
Integrated bioinformatic analysis and machine learning developed a prognostic model based on mitochondrial function for acute myeloid leukemia.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Chen Xingbiao, Ling Weijun, Yang Zhehan, Chen Xinyi, Lu Ziyuan
Аннотация
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.
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.