Establishment of Prognostic Signature Based on Neutrophil Extracellular Traps-Related Genes in Acute Myeloid Leukemia: A Bioinformatics Analysis

Zhenglei Shen¹Jingying Zhu²， Ni Luo¹， Lei Feng¹， Heng Yue¹Liying Song¹Kunmei Liu¹Huaxian Li¹Honghua Cao¹Yasar Yousafzai³， Asad⁴Yeying Zhou¹Youyu Qiu⁵Shiwen Zhang^2*

• ¹Department of Hematology, The Third Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, China
• ²Department of the Head and Neck, Third Affiliated Hospital of Kunming Medical University, Kunming, China
• ³Institute of Pathology and Diagnostic Medicine Khyber Medical University, Peshawar, Pakistan
• ⁴Molecular Biologist Public Health Reference Laboratory, Peshawar, Pakistan, Peshawar, Khyber Pakhtunkhwa, Pakistan
• ⁵Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China., Guangzhou, China

Background: Acute myeloid leukemia (AML) is a hematological malignancy with a high incidence of febrile neutropenia during the first two treatment cycles. This study aims to develop a gene signature related to neutrophil extracellular traps (NETs) to enhance understanding of AML mechanisms and identify potential prognostic biomarkers. Methods: A consistent cluster analysis was conducted on 151 AML patients from the TCGA dataset. A differential analysis was performed to identify the differentially expressed genes (DEGs) specific to different subtypes and the training cohort (normal vs tumour). The NETs-related differentially expressed genes (NR-DEGs) were obtained through the overlapping of the two sets of differentially expressed genes. Univariate Cox and Least absolute shrinkage and selection operator (LASSO) regression analysis were employed to construct a NETs-related AML prognostic signature. Furthermore, an immune feature estimation and functional enrichment analysis was conducted between the two risk subgroups. Results: Two distinct AML subtypes were identified, exhibiting markedly disparate survival outcomes. A total of 1,700 and 1,941 DEGs were identified in the different subtypes and training cohort (normal vs. tumour), respectively. Thirteen NR-DEGs were identified. Subsequently, a NETs-related prognostic signature was constructed based on the 3 prognostic genes (MPO, CCL3, and TLR8). An independent prognostic analysis indicated that the risk score and age could be employed as independent prognostic factors. Our findings revealed the presence of five markedly differentially expressed immune cells between the two risk subgroups. Ultimately, it was determined that all three genes were associated with the 'chemokine signalling pathway'. Conclusion: The prognostic signature comprised of MPO, CCL3, and TLR8 based on NETs was established, which provided theoretical basis and reference value for the research of AML.