Molecular feature-based classification of retroperitoneal liposarcoma: a prospective cohort study.

IF 6.4 1区生物学 Q1 BIOLOGY

eLife Pub Date : 2025-05-23 DOI:10.7554/eLife.100887

Mengmeng Xiao, Xiangji Li, Fanqin Bu, Shixiang Ma, Xiaohan Yang, Jun Chen, Yu Zhao, Ferdinando Cananzi, Chenghua Luo, Li Min

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引用次数: 0

Abstract

Background: 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.

Methods: 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.

Results: 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).

Conclusions: 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.

Funding: 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.

Clinical trial number: NCT03838718.

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基于分子特征的腹膜后脂肪肉瘤分类：一项前瞻性队列研究。

背景：腹膜后脂肪肉瘤（RPLS）是一种具有多种临床结局的恶性疾病。然而，RPLS的分子异质性尚不清楚，很少有生物标志物被提出来监测其进展。方法：对88名RPLS患者进行RNA测序，使用clusterProfiler识别失调基因和通路。使用GSVA算法评估每个样本的信号通路水平，并使用无监督聚类来区分RPLS亚型。通过非负矩阵分解，鉴定RPLS亚型之间的差异表达基因（DEGs），构建简化的二分类聚类。采用来自后腹膜肉瘤登记处（RESAR）的免疫组织化学（IHC），在一个单独的验证队列（n=241）中验证了这种分类的可行性。该研究在https://clinicaltrials.gov/注册，编号为NCT03838718。结果：细胞周期、DNA损伤和修复以及代谢被确定为RPLS中最异常的生物学过程，使RPLS患者分为两种不同的亚型，具有独特的分子特征、肿瘤微环境、临床特征和结局（总生存期[OS]和无病生存期[DFS]）。基于代表性生物标志物（LEP和PTTG1）的简化RPLS分类具有较高的准确性（曲线下面积[AUC]>0.99），将患者分类为LEP+和PTTG1-，具有较低的侵袭性病理组成比和较少的手术次数，以及较好的OS （HR = 0.41, p）。我们的研究提供了RPLS有史以来最大的基因表达图谱，并建立了基于免疫球蛋白的分子分类，这对指导治疗决策具有临床相关性和成本效益。基金资助：北京市科技计划项目（Z191100006619081）、国家自然科学基金项目（82073390）和青年精英科技人才资助计划（2023QNRC001）资助。研究发起者在本文的设计和准备中没有任何作用。临床试验号：NCT03838718。

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来源期刊

eLife BIOLOGY-

CiteScore

12.90

自引率

3.90%

发文量

3122

审稿时长

17 weeks

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