Lysosome-related genes predict acute myeloid leukemia prognosis and response to immunotherapy

作者全名：Wan, Peng; Zhong, Liang; Yu, Lihua; Shen, Chenlan; Shao, Xin; Chen, Shuyu; Zhou, Ziwei; Wang, Meng; Zhang, Hongyan; Liu, Beizhong

作者地址：[Wan, Peng; Shen, Chenlan; Shao, Xin; Chen, Shuyu; Zhou, Ziwei; Wang, Meng; Zhang, Hongyan; Liu, Beizhong] Chongqing Med Univ, Cent Lab, Yongchuan Hosp, Chongqing, Peoples R China; [Zhong, Liang; Liu, Beizhong] Chongqing Med Univ, Dept Lab Med, Minist Educ, Key Lab Lab Med Diagnost, Chongqing 400016, Peoples R China; [Yu, Lihua] Chongqing Med Univ, Yongchuan Hosp, Clin Lab, Chongqing, Peoples R China

通信作者：Liu, BZ (通讯作者)，Chongqing Med Univ, Cent Lab, Yongchuan Hosp, Chongqing, Peoples R China.; Liu, BZ (通讯作者)，Chongqing Med Univ, Dept Lab Med, Minist Educ, Key Lab Lab Med Diagnost, Chongqing 400016, Peoples R China.

来源：FRONTIERS IN IMMUNOLOGY

ESI学科分类：IMMUNOLOGY

WOS号：WOS:001230150800001

JCR分区：Q1

影响因子：5.7

年份：2024

卷号：15

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：acute myeloid leukemia; lysosome; prognostic model; immune infiltration; chemotherapy

摘要：Background Acute myeloid leukemia (AML) is a highly aggressive and pathogenic hematologic malignancy with consistently high mortality. Lysosomes are organelles involved in cell growth and metabolism that fuse to form specialized Auer rods in AML, and their role in AML has not been elucidated. This study aimed to identify AML subtypes centered on lysosome-related genes and to construct a prognostic model to guide individualized treatment of AML.Methods Gene expression data and clinical data from AML patients were downloaded from two high-throughput sequencing platforms. The 191 lysosomal signature genes were obtained from the database MsigDB. Lysosomal clusters were identified by unsupervised consensus clustering. The differences in molecular expression, biological processes, and the immune microenvironment among lysosomal clusters were subsequently analyzed. Based on the molecular expression differences between lysosomal clusters, lysosomal-related genes affecting AML prognosis were screened by univariate cox regression and multivariate cox regression analyses. Algorithms for LASSO regression analyses were employed to construct prognostic models. The risk factor distribution, KM survival curve, was applied to evaluate the survival distribution of the model. Time-dependent ROC curves, nomograms and calibration curves were used to evaluate the predictive performance of the prognostic models. TIDE scores and drug sensitivity analyses were used to explore the implication of the model for AML treatment.Results Our study identified two lysosomal clusters, cluster1 has longer survival time and stronger immune infiltration compared to cluster2. The differences in biological processes between the two lysosomal clusters are mainly manifested in the lysosomes, vesicles, immune cell function, and apoptosis. The prognostic model consisting of six prognosis-related genes was constructed. The prognostic model showed good predictive performance in all three data sets. Patients in the low-risk group survived significantly longer than those in the high-risk group and had higher immune infiltration and stronger response to immunotherapy. Patients in the high-risk group showed greater sensitivity to cytarabine, imatinib, and bortezomib, but lower sensitivity to ATRA compared to low -risk patients.Conclusion Our prognostic model based on lysosome-related genes can effectively predict the prognosis of AML patients and provide reference evidence for individualized immunotherapy and pharmacological chemotherapy for AML.

基金机构：Key Technology Innovation Special of Key Industries of the Chongqing Science and Technology Bureau [csct2022ycjh-bgzxm0034]; Joint Medical Research Project of Chongqing Municipal Science and Technology Commission and Health Commission [2022QNXM043]; Chongqing Education Commission Science and Technology Research Program Project [KJQN202100446]; Chongqing Natural Science Foundation Project [CSTB2023NSCQ-MSX0222]

基金资助正文：The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was supported by grants from the Key Technology Innovation Special of Key Industries of the Chongqing Science and Technology Bureau (Grant Number csct2022ycjh-bgzxm0034), Joint Medical Research Project of Chongqing Municipal Science and Technology Commission and Health Commission (Grant Number: 2022QNXM043), Chongqing Education Commission Science and Technology Research Program Project (Grant Number: KJQN202100446), and Chongqing Natural Science Foundation Project (Grant Number: CSTB2023NSCQ-MSX0222).