Xiong Dan, Wei Xiaolei, Huang Weiming, Zheng Jingxia, Feng Ru
Department of Hematology, Nanfang Hospital, Southern Medical University or the First School of Clinical Medicine, Southern Medical University, Guangzhou 510515, China.
Department of Hematology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan 528308, Guangdong, China.
Aging (Albany NY). 2024 Jan 17;16(2):1049-1076. doi: 10.18632/aging.205282.
BACKGROUND/AIMS: Diffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin lymphoma, has significant prognostic heterogeneity. This study aimed to generate a prognostic prediction model based on autophagy-related genes for DLBCL patients.
Utilizing bioinformatics techniques, we analyzed the clinical information and transcriptome data of DLBCL patients from the Gene Expression Omnibus (GEO) database. Through unsupervised clustering, we identified new autophagy-related molecular subtypes and pinpointed differentially expressed genes (DEGs) between these subtypes. Based on these DEGs, a prognostic model was constructed using Cox and Lasso regression. The effectiveness, accuracy, and clinical utility of this prognostic model were assessed using numerous independent validation cohorts, survival analyses, receiver operating characteristic (ROC) curves, multivariate Cox regression analysis, nomograms, and calibration curves. Moreover, functional analysis, immune cell infiltration, and drug sensitivity analysis were performed.
DLBCL patients with different clinical characterizations (age, molecular subtypes, ECOG scores, and stages) showed different expression features of autophagy-related genes. The prediction model was constructed based on the eight autophagy-related genes (ADD3, IGFBP3, TPM1, LYZ, AFDN, DNAJC10, GLIS3, and CCDC102A). The prognostic nomogram for overall survival of DLBCL patients incorporated risk level, stage, ECOG scores, and molecular subtypes, showing excellent agreement between observed and predicted outcomes. Differences were noted in the proportions of immune cells (native B cells, Treg cells, CD8 T cell, CD4 memory activated T cells, gamma delta T cells, macrophages M1, and resting mast cells) between high-risk and low-risk groups. LYZ and ADD3 exhibited correlations with drug resistance to most chemotherapeutic drugs.
This study established a novel prognostic assessment model based on the expression profile of autophagy-related genes and clinical characteristics of DLBCL patients, explored immune infiltration and predicted drug resistance, which may guide precise and individualized immunochemotherapy regimens.
背景/目的:弥漫性大B细胞淋巴瘤(DLBCL)是非霍奇金淋巴瘤最常见的亚型,具有显著的预后异质性。本研究旨在为DLBCL患者建立基于自噬相关基因的预后预测模型。
利用生物信息学技术,我们分析了来自基因表达综合数据库(GEO)的DLBCL患者的临床信息和转录组数据。通过无监督聚类,我们确定了新的自噬相关分子亚型,并找出了这些亚型之间的差异表达基因(DEG)。基于这些DEG,使用Cox和Lasso回归构建了一个预后模型。使用多个独立验证队列、生存分析、受试者工作特征(ROC)曲线、多变量Cox回归分析、列线图和校准曲线评估了该预后模型的有效性、准确性和临床实用性。此外,还进行了功能分析、免疫细胞浸润和药物敏感性分析。
具有不同临床特征(年龄、分子亚型、ECOG评分和分期)的DLBCL患者表现出自噬相关基因的不同表达特征。基于八个自噬相关基因(ADD3、IGFBP3、TPM1、LYZ、AFDN、DNAJC10、GLIS3和CCDC102A)构建了预测模型。DLBCL患者总生存的预后列线图纳入了风险水平、分期、ECOG评分和分子亚型,观察到的和预测的结果之间显示出极好的一致性。高危组和低危组之间免疫细胞(天然B细胞、调节性T细胞、CD8 T细胞、CD4记忆激活T细胞、γδ T细胞、M1巨噬细胞和静息肥大细胞)的比例存在差异。LYZ和ADD3与大多数化疗药物的耐药性相关。
本研究基于DLBCL患者自噬相关基因的表达谱和临床特征建立了一种新的预后评估模型,探索了免疫浸润并预测了耐药性,这可能指导精确和个体化的免疫化疗方案。