Integrative multi-omics and machine learning approach reveals tumor microenvironment-associated prognostic biomarkers in ovarian cancer
This study presents a groundbreaking exploration of the OC TME, offering vital insights into its molecular intricacies. By systematically deciphering the TME-associated gene signatures, the research illuminates the potential of these biomarkers in refining patient prognosis and guiding treatment strategies. Our findings underscore the necessity for personalized medicine in OC treatment, potentially enhancing patient survival rates and quality of life. This study marks a significant stride in understanding and combatting the complexities of OC.
Authors
- Wenzhi Jiao (ORCID: https://orcid.org/0009-0002-8083-5755)
- Minmin Yu (ORCID: https://orcid.org/0000-0003-4442-7586)
- Shasha Yang (ORCID: https://orcid.org/0000-0002-6646-5332)
- Yu Li (ORCID: https://orcid.org/0000-0003-3056-2680)
- Yue Li (ORCID: https://orcid.org/0000-0003-0994-8714)
- Shanshan Liu (ORCID: https://orcid.org/0000-0001-8400-8357)
- Jianwei Shi (ORCID: https://orcid.org/0000-0001-9887-044X)
Institutions
- Capital Medical University (CN)
- Soochow University (CN)
- Second Affiliated Hospital of Soochow University (CN)
Publication Details
- Journal
- Translational Cancer Research
- Published
- 2024-11-01
- DOI
- https://doi.org/10.21037/tcr-24-539
- Citations
- 6
- Primary Topic
- Ferroptosis and cancer prognosis
- Type
- article
- Field-Weighted Citation Impact
- 2.51