ArticleFrontiers in oncology2022
Construction of a solid Cox model for AML patients based on multiomics bioinformatic analysis.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Artificial intelligence-based prognostic models in acute myeloid leukemia: systematic review and meta-analysis.Blood neoplasia · 2026Article
- Immune reprogramming in the bone marrow microenvironment: a new perspective on the bone immune microenvironment of postmenopausal osteoporosis.Frontiers in immunology · 2026Review
- Integrative profiling of lactylation reveals prognostic biomarkers and an immunosuppressive niche in acute myeloid leukemia.Frontiers in immunology · 2026Article
- Pan-cancer analysis revealing that PTPN2 is an indicator of risk stratification for acute myeloid leukemia.Scientific reports · 2023Article
- Aggrephagy-related patterns in tumor microenvironment, prognosis, and immunotherapy for acute myeloid leukemia: a comprehensive single-cell RNA sequencing analysis.Frontiers in oncology · 2023Article
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7 authors.
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Abstract
Acute myeloid leukemia (AML) is a highly heterogeneous hematological malignancy. The bone marrow (BM) microenvironment in AML plays an important role in leukemogenesis, drug resistance and leukemia relapse. In this study, we aimed to identify reliable immune-related biomarkers for AML prognosis by multiomics analysis. We obtained expression profiles from The Cancer Genome Atlas (TCGA) database and constructed a LASSO-Cox regression model to predict the prognosis of AML using multiomics bioinformatic analysis data. This was followed by independent validation of the model in the GSE106291 (n=251) data set and mutated genes in clinical samples for predicting overall survival (OS). Molecular docking was performed to predict the most optimal ligands to the genes in prognostic model. The single-cell RNA sequence dataset GSE116256 was used to clarify the expression of the hub genes in different immune cell types. According to their significant differences in immune gene signatures and survival trends, we concluded that the immune infiltration-lacking subtype (IL type) is associated with better prognosis than the immune infiltration-rich subtype (IR type). Using the LASSO model, we built a classifier based on 5 hub genes to predict the prognosis of AML (risk score = -0.086×ADAMTS3 + 0.180×CD52 + 0.472×CLCN5 - 0.356×HAL + 0.368×ICAM3). In summary, we constructed a prognostic model of AML using integrated multiomics bioinformatic analysis that could serve as a therapeutic classifier.
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