Construction of a Pyroptosis-Related Signature for Prognostic Prediction and Characterization of Immune Microenvironment in Acute Myelogenous Leukemia

Songyang Liu,1,&ast; Dongmei Luo,2,&ast; Jie Luo,2 Hanyin Liang,2 Yunfei Zhi,1 Dong Wang,3 Na Xu2 1The First School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, Guangdong, People’s Republic of China; 2Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, People’s Republic of China; 3Department of Bioinformatics, Basic Medical College of Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Dong Wang, Department of Bioinformatics, Basic Medical College of Southern Medical University, 1838 Guangzhou Da Dao North, Guangzhou, Guangdong, 510515, People’s Republic of China, Email wangdong79@smu.edu.cn Na Xu, Department of Hematology, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Da Dao North, Guangzhou, 510515, Guangdong, People’s Republic of China, Email sprenaa@163.comBackground: Acute myelogenous leukemia (AML) is a common and fatal disease in hematology with frequent relapses and a poor prognosis. Pyroptosis, a programmed cell death mediated by inflammasomes, has been shown to be associated with leukemia recently. However, the role of pyroptosis for diagnosis and prognosis in AML remained less understood.Methods: We downloaded three public datasets and constructed a signature of TCGA cohort using the least absolute shrinkage and selection operator (LASSO) Cox regression model to predict the overall survival of AML patients. Samples from the GEO database were treated as a validation cohort. Gone through LASSO-Cox regression analysis, an 8-PRG-related signature was developed. Used the median score from the signature, we classified patients in two subgroups. Subsequently, we employed univariate COX, multivariate Cox regression, ROC analysis and constructed a nomogram, Finally, differential analysis, GO and KEGG functional analysis, ESTIMATE algorithm and CIBERSORT algorithm were used to explore the difference between two groups.Results: The expression levels of 90.9% pyroptosis-related genes (PRGs) had significant difference compared AML with normal tissues. The results of univariate COX regression analysis demonstrated 10 differentially expressed genes (DEGs) were associated with patients’ OS (p < 0.05). Then, we found OS of patients in the low-risk group was more likely to be lengthened compared with their high-risk counterparts (P < 0.05 both in the TCGA and GEO cohort). After controlling clinical factors, the risk score could still remain an independent predictive element (HR > 1, P < 0.001) of OS in multivariate Cox regression analysis. Furthermore, a nomogram with prognostic value for AML was thus established. Time-dependent ROC analysis proved the predictive power of the signature. Functional analysis suggested that DEGs were mainly concentrated in immune-related pathways, such as humoral immune response and T cell proliferation. TME scores and risk scores were strongly correlated and immune status differed between the risk subgroups.Conclusion: A novel PRG-related signature was established to forecast the prognosis in AML, and pyroptosis may be a potential therapeutic target for AML.Keywords: acute myelogenous leukemia, pyroptosis, gene signature, tumor immune micro-environment, prognosis