ArticleJournal of cancer research and clinical oncology2023
Analysis of prognostic biomarker models and immune microenvironment in acute myeloid leukemia by integrative bioinformatics.
Article in Journal of cancer research and clinical oncology, 2023. 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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Who cites it
8 citing papers in PubMed, 8 citations in OpenAlex.
- Artificial intelligence-based prognostic models in acute myeloid leukemia: systematic review and meta-analysis.Blood neoplasia · 2026Article
- Expression Analysis, Diagnostic Significance and Biological Functions of BAG4 in Acute Myeloid Leukemia.Medicina (Kaunas, Lithuania) · 2025Article
- Ribosomal protein S3A (RPS3A), as a transcription regulator of colony-stimulating factor 1 (CSF1), promotes glioma progression through regulating the recruitment and autophagy-mediated M2 polarization of tumor-associated macrophages.Naunyn-Schmiedeberg's archives of pharmacology · 2025Article
- Acute Myeloid Leukemia Genome Characterization Study and Subtype Classification Employing Feature Selection and Bayesian Networks.Biomedicines · 2025Article
- PSMA2 promotes chemo- and radioresistance of oral squamous cell carcinoma by modulating mitophagy pathway.Cell death discovery · 2025Article
- The Proteasome-Family-Members-Based Prognostic Model Improves the Risk Classification for Adult Acute Myeloid Leukemia.Biomedicines · 2024Article
- Establishment and verification of a TME prognosis scoring model based on the acute myeloid leukemia single-cell transcriptome.Scientific reports · 2024Article
- Effective Prognostic Model for Therapy Response Prediction in Acute Myeloid Leukemia Patients.Journal of personalized medicine · 2023Article
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute myeloid leukemia (AML) is a hematological cancer driven on by aberrant myeloid precursor cell proliferation and differentiation. A prognostic model was created in this study to direct therapeutic care.
methodsDifferentially expressed genes (DEGs) were investigated using the RNA-seq data from the TCGA-LAML and GTEx. Weighted Gene Coexpression Network Analysis (WGCNA) examines the genes involved in cancer. Find the intersection genes and construct the PPI network to discover hub genes and remove prognosis-related genes. A nomogram was produced for predicting the prognosis of AML patients using the risk prognosis model that was constructed using COX and Lasso regression analysis. GO, KEGG, and ssGSEA analysis were used to look into its biological function. TIDE score predicts immunotherapy response.
resultsDifferentially expressed gene analysis revealed 1004 genes, WGCNA analysis revealed 19,575 tumor-related genes, and 941 intersection genes in total. Twelve prognostic genes were found using the PPI network and prognostic analysis. To build a risk rating model, RPS3A and PSMA2 were examined using COX and Lasso regression analysis. The risk score was used to divide the patients into two groups, and Kaplan-Meier analysis indicated that the two groups had different overall survival rates. Univariate and multivariate COX studies demonstrated that risk score is an independent prognostic factor. According to the TIDE study, the immunotherapy response was better in the low-risk group than in the high-risk group.
conclusionsWe eventually selected out two molecules to construct prediction models that might be used as biomarkers for predicting AML immunotherapy and prognosis.
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