Song Yaolin, Li Guangqi, Zhang Zhenqi, Liu Yinbo, Jia Huiqing, Zhang Chao, Wang Jigang, Hu Yanjiao, Hao Fengyun, Liu Xianglan, Xie Yunxia, Ma Ding, Li Ganghua, Tai Zaixian, Xing Xiaoming
Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China.
Department of IT Management, The Affiliated Hospital of Qingdao University, Qingdao, China.
Cancer Res Treat. 2025 Jan;57(1):250-266. doi: 10.4143/crt.2024.343. Epub 2024 Jul 10.
The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs).
Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients.
A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A-PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804.
USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.
子宫肉瘤的基因组特征尚未完全阐明。本研究旨在探索子宫肉瘤（USs）的基因组图谱。
通过RNA测序进行全面的基因组分析。分析基因融合、差异表达基因（DEGs）、信号通路富集、免疫细胞浸润及预后情况。构建深度学习模型以预测US患者的生存情况。
共检测了71例US样本，包括47例子宫内膜间质肉瘤（ESS）、18例子宫平滑肌肉瘤（uLMS）、3例腺肉瘤、2例癌肉瘤和1例类似卵巢性索肿瘤的子宫肿瘤。ESS（包括高级别ESS [HGESS]和低级别ESS [LGESS]）和uLMS表现出不同的基因融合特征；一个新的基因融合位点MRPS18A-PDC-AS1可能是uLMS和ESS病理鉴别诊断的潜在标志物；在ESS与uLMS组以及HGESS与LGESS组中分别鉴定出797个和477个子宫肉瘤DEGs（uDEGs）。这些uDEGs在多个通路中富集。包括LAMB4在内的15个基因在USs中被证实具有预后价值；免疫浸润分析揭示了髓样树突状细胞、浆细胞样树突状细胞、自然杀伤细胞、巨噬细胞M1、单核细胞和造血干细胞在USs中的预后价值；名为Max-Mean Non-Local多实例学习（MMN-MIL）的深度学习模型在预测US患者生存情况方面表现出令人满意的性能，受试者工作特征曲线下面积达到0.909，准确率达到0.804。
USs在HGESS、LGESS和uLMS之间具有不同的基因融合特征和基因表达特征。MMN-MIL模型可以有效预测US患者的生存情况。
