ArticleTranslational cancer research2022
Screening diagnostic markers for acute myeloid leukemia based on bioinformatics analysis.
Article in Translational cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
- Disrupted miRNA Biogenesis Machinery Reveals Common Molecular Pathways and Diagnostic Potential in MDS and AML.Biomedicines · 2025Article
- Article
- Two machine learning-derived nomogram for predicting the occurrence and severity of acute graft-versus-host disease: a retrospective study based on serum biomarkers.Frontiers in genetics · 2024Article
- Identification of prognostic and driver gene mutations in acute myeloid leukemia by a bioinformatics analysis.Translational cancer research · 2023Article
- Comprehensive genetic analyses of childhood acute leukemia in Iraq using next-generation sequencing.Translational pediatrics · 2023Article
- Research on the oncogenic role of the house-keeping geneTranslational cancer research · 2023Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: An in-depth understanding of the key molecules and associated mechanisms involved in acute myeloid leukemia (AML) carcinogenesis, proliferation, and relapse is critical. This provides a basis for disease screening, early diagnosis, and development of effective treatment strategies and prognosis. Methods: We downloaded AML transcription data sets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) were screened by R software and limma packages. Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on DEGs by public databases. In the DEG set, a random forest algorithm was used to identify characteristic genes of AML. The receiver operator characteristic (ROC) curve was used to evaluate the diagnostic efficacy of selected characteristic genes, which provided clues for the discovery of early diagnostic markers. The Estimate score was calculated using the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm. Spearman's correlation test was used to explore the correlation between characteristic genes and Estimate Score, which provided clues for clarifying the potential pathogenic mechanism of key genes. Results: A total of 1,494 DEGs were identified from AML samples and normal samples, among which 1,181 genes were upregulated and 313 genes were downregulated in AML. There were 2 genes with a mean decrease Gini >2, namely, Conclusions: The genes
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