ArticleTechnology in cancer research & treatment
A Novel 85-Gene Expression Signature Predicts Unfavorable Prognosis in Acute Myeloid Leukemia.
Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 15 citations in OpenAlex.
- Article
- Identifying a prognostic model and screening of potential natural compounds for acute myeloid leukemia.Translational cancer research · 2023Article
- Establishing a novel Fanconi anemia signaling pathway-associated prognostic model and tumor clustering for pediatric acute myeloid leukemia patients.Open medicine (Warsaw, Poland) · 2023Article
- HERVs characterize normal and leukemia stem cells and represent a source of shared epitopes for cancer immunotherapy.American journal of hematology · 2022Article
- Identification and validation of a novel 16-gene prognostic signature for patients with breast cancer.Scientific reports · 2022Article
- Survival prediction in acute myeloid leukemia using gene expression profiling.BMC medical informatics and decision making · 2022Article
- A six-gene prognostic signature for both adult and pediatric acute myeloid leukemia identified with machine learning.American journal of translational research · 2022Article
- Venetoclax plus azacitidine and donor lymphocyte infusion in treating acute myeloid leukemia patients who relapse after allogeneic hematopoietic stem cell transplantation.Annals of hematology · 2022Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimAcute myeloid leukemia (AML) is a heterogeneous disorder with complex genetic basis and adverse prognosis. Cytogenetics risk, somatic mutations and gene expression profiles are important prognostic factors for AML patients. However, accurate stratification of patient prognosis remains an unsolved problem in AML. This study was to to develop a novel gene profile to accurately classify AML patients into subgroups with different survival probabilities.
methodsSurvival-related genes were determined by Kaplan-Meier survival analysis and multivariate analysis using the expression and clinical data of 405 AML patients from Oregon Health & Science University (OHSU) dataset and validated in The Cancer Genome Atlas (TCGA) database. Feature selection was performed by using the Least Absolute Shrinkage and Selection Operator (LASSO) method. With the LASSO model, a prognostic 85-gene score was established and compared with 2 known gene-expression risk scores. The stratification of AML patients was performed by unsupervised hierarchical clustering of 85 gene expression levels to identify clusters of AML patients with different survival probabilities.
resultsThe LASSO model comprising 85 genes was considered as the optimal model based on relatively high area under curve value (0.83) and the minimum mean squared error. The 85-gene score was associated with increased mortality in AML patients. Hierarchical clustering analysis of the 85 genes revealed 3 subgroups of AML patients in the OHSU dataset. The cluster1 AML patients were associated with more female cases, higher percent of bone marrow blast cells, 85-gene score, cytogenetics risk, more frequent FLT3-ITD,
conclusionsThe 85-gene score is superior to the 2 established prognostic gene signatures in the prediction of prognosis of AML patients.
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