S12micro: a machine learning-derived serum miRNA panel for high-performance pan-cancer diagnosis via liquid biopsy
S12micro is a promising, noninvasive serum signature that discriminates cancers from healthy controls across diverse cohorts. While encouraging, real-world specificity versus benign diseases and cross-platform robustness require prospective, multicenter validation with harmonized preanalytics and normalization.
Authors
- Yingke Liang (ORCID: https://orcid.org/0000-0001-6247-8294)
- Zhenyu Jia (ORCID: https://orcid.org/0000-0002-1428-904X)
- Lu Jc
- Xiangming Mao
- Jianheng Ye
- Ruidong Li (ORCID: https://orcid.org/0000-0003-1356-2427)
- Yongding Wu
- Yuxiang Liang
- Jianjiang Xie (ORCID: https://orcid.org/0000-0002-8454-7753)
- Zhouda Cai
- Shanshan Mo
- Weide Zhong
- Yangjia Zhuo
- Zhaodong Han
- Zining Long
- Zitao He
- Le Zhang
- Chuanfan Zhong
- Chao Cai
Institutions
- University of California, Riverside (US)
- Macau University of Science and Technology (MO)
- First Affiliated Hospital of Guangzhou Medical University (CN)
- Guangzhou First People's Hospital (CN)
- Zhujiang Hospital (CN)
- Guangdong Academy of Medical Sciences (CN)
- Southern Medical University (CN)
- South China University of Technology (CN)
- Guangzhou Medical University (CN)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2026-01-12
- DOI
- https://doi.org/10.1097/js9.0000000000004721
- Primary Topic
- MicroRNA in disease regulation
- Type
- article
- Field-Weighted Citation Impact
- 0.00