ORIGINAL RESEARCH article
Front. Bioinform.
Sec. Integrative Bioinformatics
Volume 5 - 2025 | doi: 10.3389/fbinf.2025.1606284
This article is part of the Research TopicClinical prediction models in cancer through bioinformaticsView all 10 articles
Integrated Single-cell and Bulk RNA Dequencing to Identify and Validate Prognostic Genes Related to T Cell Senescence in Acute Myeloid Leukemia
Provisionally accepted
- Suzhou Yongding Hospital, Suzhou, China
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Background: T-cell suppression in patients with Acute myeloid leukemia (AML) limits tumor cell clearance. This study aimed to explore the role of T-cell senescence-related genes in AML progression using single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing (RNA-seq), and survival data of patients with AML in the TCGA database. Methods: The Uniform Manifold Approximation and Projection (UMAP) algorithm was used to identify different cell clusters in the GSE116256, and differentially expressed genes (DEGs) in T-cells were identified using the FindAllMarkers analysis. GSE114868 was used to identify DEGs in AML and control samples. Both were crossed with the CellAge database to identify aging-related genes. Univariate and multivariate regression analyses were performed to screen prognostic genes using the AML Cohort in The Cancer Genome Atlas (TCGA) Database (TCGA-LAML), and risk models were constructed to identify high-risk and low-risk patients. Line graphs showing the survival of patients with AML were created based on the independent prognostic factors, and Receiver Operating Characteristic Curve (ROC) curves were used to calculate the predictive accuracy of the line graph. GSE71014 was used to validate the prognostic ability of the risk score model. Tumor immune infiltration analysis was used to compare differences in tumor immune microenvironments between high-and low-risk AML groups. Finally, the expression levels of prognostic genes were verified using polymerase chain reaction (RT-qPCR). Results: 31 AMLDEGs associated with aging identified 4 prognostic genes (CALR, CDK6, HOXA9, and PARP1) by univariate, multivariate, and stepwise regression analyses with risk modeling The ROC curves suggested that the line graph based on the independent prognostic factors accurately predicted the 1-, 3-, and 5-year survival of patients with AML. Tumor immune infiltration analyses suggested significant differences in the tumor immune microenvironment between low-and high-risk groups. Prognostic genes showed strong binding activity to target drugs (IGF1R and ABT737). RT-qPCR verified that prognostic gene expression was consistent with the data prediction results.
Keywords: Acute Myeloid Leukemia, T cell, cell senescence, single-cell RNA sequencing, prognostic risk model CALR, Cdk6, HOXA9, and PARP1
Received: 05 Apr 2025; Accepted: 09 Jun 2025.
Copyright: © 2025 Sha, Chen, Hou, Dou and 张. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: 燕 张, Suzhou Yongding Hospital, Suzhou, China
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