ArticleBMC bioinformatics2022
Identification and validation of a siglec-based and aging-related 9-gene signature for predicting prognosis in acute myeloid leukemia patients.
Article in BMC bioinformatics, 2022. 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.
- Siglec-14-LGALS3BP glycoimmune axis shapes tumor-associated macrophage polarization and confers poor outcome in colorectal cancer.NPJ precision oncology · 2025Article
- Gene expression analysis of patients with chronic myeloid leukemia who discontinued tyrosine kinase inhibitor.Annals of hematology · 2025Article
- A novel perspective on survival prediction for AML patients: Integration of machine learning in SEER database applications.Heliyon · 2025Article
- MiRNA expression as outcome predictor in pediatric AML: systematic evaluation of a new model.NPJ genomic medicine · 2024Article
- Siglec15 in blood system diseases: from bench to bedside.Frontiers in immunology · 2024Review
- Article
- Mining Database to Identify Aging-Related Molecular Subtype and Prognostic Signature in Lung Adenocarcinoma.Journal of oncology · 2022Article
- Identification of Survival-Related Genes in Acute Myeloid Leukemia (AML) Based on Cytogenetically Normal AML Samples Using Weighted Gene Coexpression Network Analysis.Disease markers · 2022Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
backgroundAcute myeloid leukemia (AML) is a group of highly heterogenous and aggressive blood cancer. Despite recent progress in its diagnosis and treatment, patient outcome is variable and drug resistance results in increased mortality. The siglec family plays an important role in tumorigenesis and aging. Increasing age is a risk factor for AML and cellular aging contributes to leukemogenesis via various pathways.
methodsThe differential expression of the siglec family was compared between 151 AML patients and 70 healthy controls, with their information downloaded from TCGA and GTEx databases, respectively. How siglec expression correlated to AML patient clinical features, immune cell infiltration, drug resistance and survival outcome was analyzed. Differentially expressed genes in AML patients with low- and high-expressed siglec9 and siglec14 were analyzed and functionally enriched. The aging-related gene set was merged with the differentially expressed genes in AML patients with low and high expression of siglec9, and merged genes were subjected to lasso regression analysis to construct a novel siglec-based and aging-related prognostic model. The prediction model was validated using a validation cohort from GEO database (GSE106291).
resultsThe expression levels of all siglec members were significantly altered in AML. The expression of siglecs was significantly correlated with AML patient clinical features, immune cell infiltration, drug resistance, and survival outcome. Based on the differentially expressed genes and aging-related gene set, we developed a 9-gene prognostic model and decision curve analysis revealed the net benefit generated by our prediction model. The siglec-based and aging-related 9-gene prognostic model was tested using a validation data set, in which AML patients with higher risk scores had significantly reduced survival probability. Time-dependent receiver operating characteristic curve and nomogram were plotted and showed the diagnostic accuracy and predictive value of our 9-gene prognostic model, respectively.
conclusionsOverall, our study indicates the important role of siglec family in AML and the good performance of our novel siglec-based and aging-related 9-gene signature in predicting AML patient outcome.
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