Xiao Mengmeng, Li Xiangji, Bu Fanqin, Ma Shixiang, Yang Xiaohan, Chen Jun, Zhao Yu, Cananzi Ferdinando, Luo Chenghua, Min Li
Department of Retroperitoneal Tumor Surgery, Peking University People's Hospital, Beijing, China.
Department of Retroperitoneal Tumor Surgery, Peking University International Hospital, Beijing, China.
Elife. 2025 May 23;14:RP100887. doi: 10.7554/eLife.100887.
Retroperitoneal liposarcoma (RPLS) is a critical malignant disease with various clinical outcomes. However, the molecular heterogeneity of RPLS was poorly elucidated, and few biomarkers were proposed to monitor its progression.
RNA sequencing was performed on a training cohort of 88 RPLS patients to identify dysregulated genes and pathways using clusterProfiler. The GSVA algorithm was utilized to assess signaling pathway levels in each sample, and unsupervised clustering was employed to distinguish RPLS subtypes. Differentially expressed genes (DEGs) between RPLS subtypes were identified to construct a simplified dichotomous clustering via nonnegative matrix factorization. The feasibility of this classification was validated in a separate validation cohort (n=241) using immunohistochemistry (IHC) from the REtroperitoneal SArcoma Registry (RESAR). The study is registered with https://clinicaltrials.gov/ under number NCT03838718.
Cell cycle, DNA damage and repair, and metabolism were identified as the most aberrant biological processes in RPLS, enabling the division of RPLS patients into two distinct subtypes with unique molecular signatures, tumor microenvironment, clinical features, and outcomes (overall survival [OS] and disease-free survival [DFS]). A simplified RPLS classification based on representative biomarkers (LEP and PTTG1) demonstrated high accuracy (area under the curve [AUC]>0.99), with patients classified as LEP+ and PTTG1-, showing lower aggressive pathological composition ratio and fewer surgery times, along with better OS (HR = 0.41, p<0.001) and DFS (HR = 0.60, p=0.005).
Our study provided an ever-largest gene expression landscape of RPLS and established an IHC-based molecular classification that was clinically relevant and cost-effective for guiding treatment decisions.
This work was supported by grants from the Beijing Municipal Science and Technology Project (Z191100006619081), National Natural Science Foundation of China (82073390), and Young Elite Scientists Sponsorship Program (2023QNRC001). The study sponsors had no role in the design and preparation of this manuscript.
NCT03838718.
腹膜后脂肪肉瘤（RPLS）是一种具有多种临床结局的严重恶性疾病。然而，RPLS的分子异质性尚未得到充分阐明，并且很少有生物标志物被提出用于监测其进展。
对88例RPLS患者的训练队列进行RNA测序，使用clusterProfiler识别失调的基因和通路。利用GSVA算法评估每个样本中的信号通路水平，并采用无监督聚类来区分RPLS亚型。识别RPLS亚型之间的差异表达基因（DEG），通过非负矩阵分解构建简化的二分聚类。使用来自腹膜后肉瘤登记处（RESAR）的免疫组织化学（IHC）在一个单独的验证队列（n = 241）中验证这种分类的可行性。该研究已在https://clinicaltrials.gov/注册，编号为NCT03838718。
细胞周期、DNA损伤与修复以及代谢被确定为RPLS中最异常的生物学过程，这使得RPLS患者能够被分为两种具有独特分子特征、肿瘤微环境、临床特征和结局（总生存期[OS]和无病生存期[DFS]）的不同亚型。基于代表性生物标志物（LEP和PTTG1）的简化RPLS分类显示出高准确性（曲线下面积[AUC]>0.99），分类为LEP +和PTTG1 -的患者显示出较低的侵袭性病理组成比例和较少的手术次数，以及更好的OS（HR = 0.41，p<0.001）和DFS（HR = 0.60，p = 0.005）。
我们的研究提供了迄今为止最大的RPLS基因表达图谱，并建立了一种基于IHC的分子分类，该分类在临床上具有相关性且具有成本效益，可用于指导治疗决策。
这项工作得到了北京市科技计划项目（Z191100006619081）、国家自然科学基金（82073390）和青年拔尖人才支持计划（2023QNRC001）的资助。研究赞助商在本稿件的设计和准备过程中没有参与。
NCT03838718。
