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Identifying prognostic biomarker related to immune infiltration in acute myeloid leukemia

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Abstract

The immune cells of tumor microenvironment (TME) constitute a vital element of the tumor tissue. There is increasing evidence for their clinical significance in predicting prognosis and therapeutic outcomes. However, the TME immune cell infiltrating pattern of the bone marrow in acute myeloid leukemia (AML) patients remains unclear. Here, RNA-sequencing results of AML patients from TCGA database were used to quantify the abundance of 28 types of immune cells in the TME using the single-sample gene set enrichment analysis algorithm. We comprehensively evaluated the immune infiltration status in the TCGA-LAML cohort and defined two immunophenotypes: the immune hot and immune cold subtypes. Additionally, we constructed a TME score reflecting the immune infiltrating pattern of the patients using Cox regression algorithm. Subtypes with high TME score were characterized by over-activation of immune inflammation-related pathways, release of inflammatory factors, T-cell dysfunction, and poor prognosis. Subtypes with a low TME score were characterized by relatively low immune infiltration and immune exclusion. Our analysis indicated that patients in the low TME score group were more sensitive to chemotherapeutic drugs, and those in high TME score were more likely to respond to immunotherapy. Our study provides a new direction to evaluate anti-tumor therapy from immune infiltration of the TME, and the individualized scoring system in this study has important clinical significance in identifying patients who respond to immunotherapy.

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Data availability

The dataset TCGA-LAML analyzed during the current study is available in the Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/projects/TCGA-LAML). And the dataset GSE106291 analyzed during the current study is available in the Expression Omnibus (GEO) database under accession ID GSE106291 (http://www.ncbi.nlm.nih.gov/geo/). Associated codes are available at https://github.com/lwx0727/code-available.git.

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Acknowledgements

We sincerely appreciated the data provided by GEO and TCGA database.

Funding

The present study was funded by the National Natural Science Foundation of China (Grant No.: 82070133).

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Author notes

  1. Weixiang Lu and Guopan Yu have contributed equally to this work.

Authors and Affiliations

  1. Department of Hematology, Nanfang Hospital, Southern Medical University, No.1838 Guangzhou Avenue North, Guangzhou, 510515, China

    Weixiang Lu, Guopan Yu, Yanlin Li, Changxin Yin, Jiaxin Long, Xiaofan Chen, Yanxiao Chen, Zhongxin Zheng & Dan Xu

  2. The First Clinical Medical College, Guangdong Medical University, Zhanjiang, 524000, China

    Yujie Lai & Xinyu Zhou

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  1. Weixiang Lu
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Contributions

All authors were responsible for the conception and design of the manuscript. WXL and GPY contributed to draft the manuscript. GPY, WXL, XFC, YLL, JXL, YXC, ZXZ, YJL, XYZ, and CXY contributed to develop the methodology, analyze the data, and interpret the results. DX was responsible for preparing the figures, acquiring the funding, and revising critically to the manuscript. All authors have read and approved the final version.

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Correspondence to Dan Xu.

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Lu, W., Yu, G., Li, Y. et al. Identifying prognostic biomarker related to immune infiltration in acute myeloid leukemia. Clin Exp Med 23, 4553–4562 (2023). https://doi.org/10.1007/s10238-023-01164-4

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  • DOI: https://doi.org/10.1007/s10238-023-01164-4

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