ArticleClinical and experimental medicine2023
Immune-related lncRNAs pairs prognostic score model for prediction of survival in acute myeloid leukemia patients.
Article in Clinical and experimental medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 4 citations in OpenAlex.
- Prime-time for LncRNA signatures in acute myeloid leukemia?Annals of hematology · 2025Review
- The role of long noncoding RNAs in the diagnosis, prognosis and therapeutic biomarkers of acute myeloid leukemia.Annals of hematology · 2024Review
- Effective Prognostic Model for Therapy Response Prediction in Acute Myeloid Leukemia Patients.Journal of personalized medicine · 2023Article
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8 authors at 2 institutions in 1 country.
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Abstract
Acute myeloid leukemia (AML) is one of the most common malignant and aggressive hematologic tumors, and risk stratification is indispensable to ensure proper treatment. But immune-related long noncoding RNAs (ir-lncRNAs) pairs prognostic risk models used to stratify AML have yet to be reported. In this study, we established a prognostic risk model based on eight ir-lncRNAs pairs using LASSO-penalized Cox regression analysis and successfully validated the model in an independent cohort. According to risk scores, patients were divided into a high-risk group and a low-risk group. High-risk patients presented more tumor mutation frequency and higher expression of human leukocyte antigen (HLA)-related genes and immune checkpoint molecules. Gene Set Enrichment Analysis (GSEA) indicated that the transforming growth factors β (TGFβ) pathway was activated in the high-risk group; meanwhile, we found that TGFβ1 mRNA levels were significantly elevated in AML patients and correlated with poor prognosis, which is closely related to drug resistance. Consistently, in vitro studies found that exogenous TGFβ1 can protect AML cells from chemotherapy-induced apoptosis. Collectively, we developed an ir-lncRNA prognostic model that helps predict the prognosis of AML patients and provides valuable information about their response to immune checkpoint inhibitors, and we found that increased TGFβ1 levels resulting in chemoresistance may be one of the leading causes of treatment failure in high-risk AML patients.
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