Skip Navigation
Skip to contents

Cancer Res Treat : Cancer Research and Treatment

OPEN ACCESS

Search

Page Path
HOME > Search
1 "Ganghua Li"
Filter
Filter
Article category
Keywords
Authors
Original Article
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
Received April 3, 2024  Accepted July 9, 2024  Published online July 10, 2024  
DOI: https://doi.org/10.4143/crt.2024.343    [Epub ahead of print]
AbstractAbstract PDFSupplementary MaterialPubReaderePub
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.
  • 1,286 View
  • 93 Download
Close layer

Cancer Res Treat : Cancer Research and Treatment
Close layer
TOP