Identification of PANoptosis-based signature for predicting the prognosis and immunotherapy response in AML
Lu Zhang,
Yanan Yu,
Guiqing Li,
Jiachun Li,
Xiaolin Ma,
Jiao Ren,
Na Liu,
Songyue Guo,
Jiaqiu Li,
Jinwei Cai
Affiliations
Lu Zhang
Department of Oncology, Affiliated Hospital of Shandong Second Medical University, School of Clinical Medicine, Shandong Second Medical University, Weifang, Shandong, 261053, China; Clinical Research Center, Affiliated Hospital of Shandong Second Medical University, Weifang, Shandong, 261031, China
Yanan Yu
Department of Oncology, Affiliated Hospital of Shandong Second Medical University, School of Clinical Medicine, Shandong Second Medical University, Weifang, Shandong, 261053, China; Clinical Research Center, Affiliated Hospital of Shandong Second Medical University, Weifang, Shandong, 261031, China
Guiqing Li
Department of Oncology, Affiliated Hospital of Shandong Second Medical University, School of Clinical Medicine, Shandong Second Medical University, Weifang, Shandong, 261053, China
Jiachun Li
Department of Information Engineering, Weifang Vocational College of Food Science and Technology, Weifang, 262100, Shandong, China
Xiaolin Ma
Department of Oncology, Affiliated Hospital of Shandong Second Medical University, School of Clinical Medicine, Shandong Second Medical University, Weifang, Shandong, 261053, China
Jiao Ren
Department of Oncology, Affiliated Hospital of Shandong Second Medical University, School of Clinical Medicine, Shandong Second Medical University, Weifang, Shandong, 261053, China; Clinical Research Center, Affiliated Hospital of Shandong Second Medical University, Weifang, Shandong, 261031, China
Na Liu
Department of Oncology, Affiliated Hospital of Shandong Second Medical University, School of Clinical Medicine, Shandong Second Medical University, Weifang, Shandong, 261053, China; Clinical Research Center, Affiliated Hospital of Shandong Second Medical University, Weifang, Shandong, 261031, China
Songyue Guo
Department of Oncology, Affiliated Hospital of Shandong Second Medical University, School of Clinical Medicine, Shandong Second Medical University, Weifang, Shandong, 261053, China; Clinical Research Center, Affiliated Hospital of Shandong Second Medical University, Weifang, Shandong, 261031, China
Jiaqiu Li
Department of Oncology, Affiliated Hospital of Shandong Second Medical University, School of Clinical Medicine, Shandong Second Medical University, Weifang, Shandong, 261053, China; Clinical Research Center, Affiliated Hospital of Shandong Second Medical University, Weifang, Shandong, 261031, China; Corresponding author. Department of Oncology, Affiliated Hospital of Shandong Second Medical University, School of Clinical Medicine, Shandong Second Medical University, Weifang, Shandong, 261053, China.
Jinwei Cai
Department of Oncology, People's Hospital of Kecheng District, Quzhou, 324000, Zhejiang, China; Corresponding author.
Background: In recent years, the incidence of acute myeloid leukemia (AML) has increased rapidly with a suboptimal prognosis. In AML, cell death is independent of tumorigenesis, tumor invasion, and drug resistance. PANoptosis is a newly discovered form of cell death that combines pyroptosis, apoptosis, and necroptosis. However, no studies have explored the role of PANoptosis-based signatures in AML. Methods: We screened for PANoptosis-related genes and established a PANoptosis-risk signature using the least absolute shrinkage and selection operator (LASSO) and Cox regression analysis. We combined TCGA, bulk RNA sequencing, and single-cell sequencing to investigate the correlation between candidate genes and the AML tumor microenvironment. Results: The PANoptosis risk signature effectively predicted prognosis with good sensitivity and specificity. The risk score emerged as an independent prognostic factor. Functional enrichment analysis of PANoptosis-related differentially expressed genes suggested that the risk score may be related to cell immunity. Patients with high-risk scores exhibited increased immune cell infiltration, implying a hot tumor immune microenvironment. The risk score was positively correlated with the immune scores and expression levels of immune checkpoints. Therefore, we identified three model factors, BIRC3, PELI1, and PRKACG, as predictors for immunotherapy efficacy. Single-cell sequencing analysis demonstrated that PELI1 and BIRC3 may participate in the regulation of the AML immune microenvironment. Finally, we performed a drug sensitivity analysis to target BIRC3 and PELI1 using molecular docking and molecular dynamics simulations. Conclusion: Our study established and verified a PANoptosis risk signature to predict the survival and immunological treatment response in AML.
