潜在类别分析衍生分类改善淋巴瘤的癌症特异性死亡风险分层：一项大规模回顾性队列研究

Latent class analysis-derived classification improves the cancer-specific death stratification of lymphomas: A large retrospective cohort study

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INTERNATIONAL JOURNAL OF CANCER

影响因子:4.7

分区:医学2区 / 肿瘤学2区

发表日期:2025 Mar 15

作者: Xiaojie Liang, Yuzhe Wu, Weixiang Lu, Tong Li, Dan Liu, Bingyu Lin, Xinyu Zhou, Zhihao Jin, Baiwei Luo, Yang Liu, Shengyu Tian, Liang Wang

DOI: 10.1002/ijc.35219

keywords: latent class analysis; lymphoma; lymphoma‐specific death; non‐lymphoma‐related death; prognostic stratification

摘要

淋巴瘤具有多样的病因、治疗方式和预后。由于淋巴瘤患者对非淋巴瘤相关死亡的易感性增强，准确的生存期估计具有挑战性。为解决这一难题，我们提出了一种基于潜在类别分析（LCA）的新型淋巴瘤分类系统，该系统结合了人口统计学和临床病理因素作为指标。我们利用SEER（监测、流行病学及最终结果数据库）中221,812例原发性淋巴瘤患者的数据，进行LCA分析，识别出四个不同的类别。该基于LCA的分类方法高效地对患者进行分层，有效调整了非淋巴瘤相关死亡等竞争风险事件引起的偏差，即使在因果死亡信息有限的情况下也能保持效果，从而提高了淋巴瘤预后评估的准确性。我们还在外部队列中验证了该分类模型，发现其对分子亚型的预后分层也有显著改善。进一步分析了LCA亚组的分子特征，识别出每个亚组特异的潜在驱动基因。综上所述，我们的研究提出了一种新颖的基于LCA的淋巴瘤分类系统，通过考虑竞争风险事件，提供了更优的预后预测，增强了分子亚型的临床相关性，并为潜在的治疗靶点提供了线索。

Abstract

Lymphomas have diverse etiologies, treatment approaches, and prognoses. Accurate survival estimation is challenging for lymphoma patients due to their heightened susceptibility to non-lymphoma-related mortality. To overcome this challenge, we propose a novel lymphoma classification system that utilizes latent class analysis (LCA) and incorporates demographic and clinicopathological factors as indicators. We conducted LCA using data from 221,812 primary lymphoma patients in the Surveillance, Epidemiology, and End Results (SEER) database and identified four distinct LCA-derived classes. The LCA-derived classification efficiently stratified patients, thereby adjusting the bias induced by competing risk events such as non-lymphoma-related death. This remains effective even in cases of limited availability of cause-of-death information, leading to an enhancement in the accuracy of lymphoma prognosis assessment. Additionally, we validated the LCA-derived classification model in an external cohort and observed its improved prognostic stratification of molecular subtypes. We further explored the molecular characteristics of the LCA subgroups and identified potential driver genes specific to each subgroup. In conclusion, our study introduces a novel LCA-based lymphoma classification system that provides improved prognostic prediction by accounting for competing risk events. The proposed classification system enhances the clinical relevance of molecular subtypes and offers insights into potential therapeutic targets.