Cancer Research and Treatment

Original Article

Sarcoma

Integrated Transcriptomic Landscape and Deep Learning Based Survival Prediction in Uterine Sarcomas: Yaolin Song, Guangqi Li, Zhenqi Zhang, Yinbo Liu, Huiqing Jia, Chao Zhang, Jigang Wang, Yanjiao Hu, Fengyun Hao, Xianglan Liu, Yunxia Xie, Ding Ma, Ganghua Li, Zaixian Tai, Xiaoming Xing; Cancer Res Treat. 2025;57(1):250-266. Published online July 10, 2024; DOI: https://doi.org/10.4143/crt.2024.343

Abstract PDF Supplementary Material PubReader ePub

Purpose
The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs).
Materials and Methods
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.
Results
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.
Conclusion
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.

Citations

Citations to this article as recorded by

Mitochondrial Ribosomal Proteins and Cancer
Huiyi Wu, Xiaowei Zhu, Huilin Zhou, Min Sha, Jun Ye, Hong Yu
Medicina.2025; 61(1): 96. CrossRef
Molecular Insights in Endometrial Stromal Sarcomas: Exploring New Targets for Novel Therapeutic Approaches
Alice Costa, Annalisa Astolfi, Livia Gozzellino, Margherita Nannini, Gianandrea Pasquinelli, Maria Abbondanza Pantaleo
Biomolecules.2025; 15(2): 265. CrossRef
Uncertainty-driven hybrid-view adaptive learning for fully automated uterine leiomyosarcoma diagnosis
Qi Li, Jingxian Wu, Xiyu Liu, Dengwang Li, Jie Xue
Medical Image Analysis.2025; 105: 103692. CrossRef
2.5D deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma
Xiaoxin Huang, Xiaoxiao Huang, Kui Wang, Haosheng Bai, Xiuxian Lu, Guanqiao Jin
BMC Medical Imaging.2025;[Epub] CrossRef
A four-gene signature identified by integrated transcriptomic analysis for differential diagnosis and prognosis of uterine smooth muscle tumors
Huiyi Hu, Yuanqun Chen, Bo Hong, Jia Liu, Yuchen Jiang, Zhiying Yu, Zhi-Jie Xiao, Jing Li
Frontiers in Oncology.2025;[Epub] CrossRef

4,120 View
156 Download
4 Web of Science
5 Crossref

First
Prev
Page of 1
Next
Last

Search