Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia
BackgroundT-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 patient...
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Frontiers Media S.A.
2025-06-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbinf.2025.1606284/full |
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| author | Mengyao Sha Jun Chen Haifeng Hou Huaihui Dou Yan Zhang |
| author_facet | Mengyao Sha Jun Chen Haifeng Hou Huaihui Dou Yan Zhang |
| author_sort | Mengyao Sha |
| collection | DOAJ |
| description | BackgroundT-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.MethodsThe 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).Results31 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.ConclusionCALR, CDK6, HOXA9, and PARP1 predicted disease progression and prognosis in patients with AML. Based on these, we developed and validated a new AML risk model with great potential for predicting patients’ prognosis and survival. |
| format | Article |
| id | doaj-art-d7b348b8fcfa4907b443c3ebc6d0a9aa |
| institution | Kabale University |
| issn | 2673-7647 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Bioinformatics |
| spelling | doaj-art-d7b348b8fcfa4907b443c3ebc6d0a9aa2025-08-20T03:24:07ZengFrontiers Media S.A.Frontiers in Bioinformatics2673-76472025-06-01510.3389/fbinf.2025.16062841606284Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemiaMengyao Sha0Jun Chen1Haifeng Hou2Huaihui Dou3Yan Zhang4Department of Laboratory Medicine, Suzhou Yongding Hospital, Suzhou, ChinaDepartment of Hematology, Suzhou Yongding Hospital, Suzhou, ChinaDepartment of Laboratory Medicine, Suzhou Yongding Hospital, Suzhou, ChinaDepartment of Laboratory Medicine, Suzhou Yongding Hospital, Suzhou, ChinaDepartment of Laboratory Medicine, Suzhou Yongding Hospital, Suzhou, ChinaBackgroundT-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.MethodsThe 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).Results31 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.ConclusionCALR, CDK6, HOXA9, and PARP1 predicted disease progression and prognosis in patients with AML. Based on these, we developed and validated a new AML risk model with great potential for predicting patients’ prognosis and survival.https://www.frontiersin.org/articles/10.3389/fbinf.2025.1606284/fullacute myeloid leukemiaT cellcell senescencesingle-cell RNA sequencingprognostic risk model |
| spellingShingle | Mengyao Sha Jun Chen Haifeng Hou Huaihui Dou Yan Zhang Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia Frontiers in Bioinformatics acute myeloid leukemia T cell cell senescence single-cell RNA sequencing prognostic risk model |
| title | Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia |
| title_full | Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia |
| title_fullStr | Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia |
| title_full_unstemmed | Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia |
| title_short | Integrated single-cell and bulk RNA dequencing to identify and validate prognostic genes related to T Cell senescence in acute myeloid leukemia |
| title_sort | integrated single cell and bulk rna dequencing to identify and validate prognostic genes related to t cell senescence in acute myeloid leukemia |
| topic | acute myeloid leukemia T cell cell senescence single-cell RNA sequencing prognostic risk model |
| url | https://www.frontiersin.org/articles/10.3389/fbinf.2025.1606284/full |
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