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Background <p>Acute myeloid leukemia (AML) is a malignancy with high mortality and poor prognosis. Thus, exploring additional genetic biomarkers is crucial.</p> Methods and results <p>This study compared gene regulatory networks (GRN) between AML patients and healthy individuals using single-cell sequencing data and single-cell regulatory network inference and clustering (SCENIC) technology. Eight regulatory modules were identified, of which the M4 module showing higher transcription factor activity in AML. Enrichment analysis identified the activated endoplasmic reticulum stress (ERS) pathway. The computational tool Gene Set Density, based on graphical models, was used to calculate the activity score of the endoplasmic reticulum pathway in each cell. Based on the activity scores, AML bone marrow single cells were divided into high and low ERS activity score groups. Subsequently, the differentially expressed genes between these two groups were identified to construct a prognostic signature (including seven key genes, MPO, HOPX, CST3, ITM2A, CD96, IFITM1, and SPINK2) on bulk RNA transcriptome through univariate Cox analysis, as well as Lasso and stepwise Cox regression analysis. Additionally, the bulk cohort was stratified based on the signature to evaluate immune cell infiltration. Finally, a nomogram was constructed by integrating the signature and clinical data, with time-dependent area under the curve values of 0.720 for 1-year, 0.786 for 3-year and 0.798 for 5-year OS predictions, respectively.</p> Conclusions <p>In this study, we constructed a novel prognostic model based on GRN and ERS for AML patients, which would help predict the prognosis of AML and provide a new orientation to further research studies.</p>

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Integrated multi-omics profiling develops a prognostic model based on endoplasmic reticulum stress in acute myeloid leukemia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月8日

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  • Xiaoli Sun,
  • Runing Fu

摘要

Background

Acute myeloid leukemia (AML) is a malignancy with high mortality and poor prognosis. Thus, exploring additional genetic biomarkers is crucial.

Methods and results

This study compared gene regulatory networks (GRN) between AML patients and healthy individuals using single-cell sequencing data and single-cell regulatory network inference and clustering (SCENIC) technology. Eight regulatory modules were identified, of which the M4 module showing higher transcription factor activity in AML. Enrichment analysis identified the activated endoplasmic reticulum stress (ERS) pathway. The computational tool Gene Set Density, based on graphical models, was used to calculate the activity score of the endoplasmic reticulum pathway in each cell. Based on the activity scores, AML bone marrow single cells were divided into high and low ERS activity score groups. Subsequently, the differentially expressed genes between these two groups were identified to construct a prognostic signature (including seven key genes, MPO, HOPX, CST3, ITM2A, CD96, IFITM1, and SPINK2) on bulk RNA transcriptome through univariate Cox analysis, as well as Lasso and stepwise Cox regression analysis. Additionally, the bulk cohort was stratified based on the signature to evaluate immune cell infiltration. Finally, a nomogram was constructed by integrating the signature and clinical data, with time-dependent area under the curve values of 0.720 for 1-year, 0.786 for 3-year and 0.798 for 5-year OS predictions, respectively.

Conclusions

In this study, we constructed a novel prognostic model based on GRN and ERS for AML patients, which would help predict the prognosis of AML and provide a new orientation to further research studies.