Abstract：
UNASSIGNED: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the USs.
UNASSIGNED: 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.
UNASSIGNED: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), 3 adenosarcomas, 2 carcinosarcomas, and 1 uterine tumor resembling an ovarian sex-cord tumor (UTROSCT). ESS (including high-grade ESS and low-grade ESS) 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 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 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), 3 adenosarcomas, 2 carcinosarcomas, and 1 uterine tumor resembling an ovarian sex-cord tumor (UTROSCT). ESS (including high-grade ESS and low-grade ESS) 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 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 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.
UNASSIGNED: 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.
摘要：
■子宫肉瘤的基因组特征尚未完全阐明。本研究旨在探索美国的基因组景观。
■通过RNA测序进行全面的基因组分析。基因融合,差异表达基因（DEG），信号通路富集，免疫细胞浸润，并对预后进行分析。构建了一个深度学习模型来预测美国患者的生存。
■共检查了71个美国样品，包括47个子宫内膜间质肉瘤（ESS），18子宫平滑肌肉瘤（uLMS），3个腺肉瘤，2癌肉瘤，和1个类似于卵巢性索肿瘤（UTROSCT）的子宫肿瘤。ESS（包括高等级ESS和低等级ESS）和uLMS显示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诊断标志物；在ESS与uLMS和HGESSvs.LGESS团体，分别。uDEGs在多个途径中富集。包括LAMB4在内的15个基因在USS中被证实具有预后价值；免疫浸润分析显示髓样树突状细胞的预后价值，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；名为MMN-MIL的深度学习模型在预测美国患者的生存率方面表现出令人满意的性能，接收器工作曲线曲线下面积达到0.909，精度达到0.804。
■USs在HGESS之间具有独特的基因融合特征和基因表达特征，LGESS,和uLMS。MMN-MIL模型可以有效预测美国患者的生存。
■通过RNA测序进行全面的基因组分析。基因融合,差异表达基因（DEG），信号通路富集，免疫细胞浸润，并对预后进行分析。构建了一个深度学习模型来预测美国患者的生存。
■共检查了71个美国样品，包括47个子宫内膜间质肉瘤（ESS），18子宫平滑肌肉瘤（uLMS），3个腺肉瘤，2癌肉瘤，和1个类似于卵巢性索肿瘤（UTROSCT）的子宫肿瘤。ESS（包括高等级ESS和低等级ESS）和uLMS显示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诊断标志物；在ESS与uLMS和HGESSvs.LGESS团体，分别。uDEGs在多个途径中富集。包括LAMB4在内的15个基因在USS中被证实具有预后价值；免疫浸润分析显示髓样树突状细胞的预后价值，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；名为MMN-MIL的深度学习模型在预测美国患者的生存率方面表现出令人满意的性能，接收器工作曲线曲线下面积达到0.909，精度达到0.804。
■USs在HGESS之间具有独特的基因融合特征和基因表达特征，LGESS,和uLMS。MMN-MIL模型可以有效预测美国患者的生存。
