ArticleClinical and experimental medicine2026
Identification and validation of lymphangiogenesis-related genes for predicting acute myeloid leukemia prognosis: insights from bulk RNA sequencing and single-cell RNA sequencing analyses.
Article in Clinical and experimental medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.