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<p>Acute myeloid leukemia (AML) is a hematologic malignancy, and lymphangiogenesis can affect the proliferation, invasion, and other biological behaviors of leukemia cells. This study explored lymphangiogenesis-associated mechanisms in AML. AML datasets were downloaded from public databases. Differential expression analysis, univariate Cox regression, and machine learning were used to identify prognostic lymphangiogenesis-related genes (LYMRGs) and build a risk model. Prognostic analyses included enrichment pathway, genetic mutation, immune microenvironment, and drug sensitivity analyses. Dataset GSE116256 explored LYMRG expression in key cells; GSE142698 and RT-qPCR verified prognostic LYMRG expression. A 6-LYMRG (ANGPT1, HGF, MAPK8, PCNA, TBL1XR1, TLR4) risk model was the optimal prognostic signature. Moreover, pathways like cytokine-cytokine receptor interaction and immune cells such as macrophages were found to be associated with risk stratification in AML patients. Mutational patterns differed between different risk AML patients. High-risk AML patients showed greater sensitivity to UMI.77, vorinostat, BI.2536, tozasertib, daporinad, carmustine, MIM1, and WEHI.539. Furthermore, significant changes in prognostic LYMRG expression were observed during key cell (including progenitor cells, monocyte-derived dendritic cells, and erythroblasts) differentiation. Importantly, GSE142698 and RT-qPCR confirmed that <i>HGF</i>, <i>MAPK8</i>, <i>PCNA</i>, and <i>TBL1XR1</i> were abundantly expressed, while <i>TLR4</i> showed low expression in AML patients. This study identified <i>ANGPT1</i>, <i>HGF</i>, <i>MAPK8</i>, <i>PCNA</i>, <i>TBL1XR1</i>, and <i>TLR4</i> as the key prognostic indicators for AML. The lymphangiogenesis-associated risk model provided an efficient tool for predicting patient survival and might facilitate the development of personalized treatment strategies for AML.</p> Graphical abstract <p></p>

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Identification and validation of lymphangiogenesis-related genes for predicting acute myeloid leukemia prognosis: insights from bulk RNA sequencing and single-cell RNA sequencing analyses

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月2日

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  • Wentao Qu,
  • Rui Wang,
  • Mohan Zhao,
  • Tao Zhang,
  • Xiaoxue Shi,
  • Mingfeng Zhao

摘要

Acute myeloid leukemia (AML) is a hematologic malignancy, and lymphangiogenesis can affect the proliferation, invasion, and other biological behaviors of leukemia cells. This study explored lymphangiogenesis-associated mechanisms in AML. AML datasets were downloaded from public databases. Differential expression analysis, univariate Cox regression, and machine learning were used to identify prognostic lymphangiogenesis-related genes (LYMRGs) and build a risk model. Prognostic analyses included enrichment pathway, genetic mutation, immune microenvironment, and drug sensitivity analyses. Dataset GSE116256 explored LYMRG expression in key cells; GSE142698 and RT-qPCR verified prognostic LYMRG expression. A 6-LYMRG (ANGPT1, HGF, MAPK8, PCNA, TBL1XR1, TLR4) risk model was the optimal prognostic signature. Moreover, pathways like cytokine-cytokine receptor interaction and immune cells such as macrophages were found to be associated with risk stratification in AML patients. Mutational patterns differed between different risk AML patients. High-risk AML patients showed greater sensitivity to UMI.77, vorinostat, BI.2536, tozasertib, daporinad, carmustine, MIM1, and WEHI.539. Furthermore, significant changes in prognostic LYMRG expression were observed during key cell (including progenitor cells, monocyte-derived dendritic cells, and erythroblasts) differentiation. Importantly, GSE142698 and RT-qPCR confirmed that HGF, MAPK8, PCNA, and TBL1XR1 were abundantly expressed, while TLR4 showed low expression in AML patients. This study identified ANGPT1, HGF, MAPK8, PCNA, TBL1XR1, and TLR4 as the key prognostic indicators for AML. The lymphangiogenesis-associated risk model provided an efficient tool for predicting patient survival and might facilitate the development of personalized treatment strategies for AML.

Graphical abstract