Decoding the Sphingolipid Landscape of Clear Cell Renal Cell Carcinoma: A Single‐Cell‐Guided Prognostic Model Built With 101 Machine Learning
Jinbang Huang, Shunsheng Wang, Yaojun Zhou, Hui Zhang, Yanyuan Zhang, Shi Huang, Haiwei Su, Hongling Zhu, Yuhan Lin, Gang Deng · 2026
Read Decoding the Sphingolipid Landscape of Clear Cell Renal Cell Carcinoma: A Single‐Cell‐Guided Prognostic Model Built With 101 Machine Learning by Jinbang Huang, Shunsheng Wang, Yaojun Zhou, Hui Zhang, Yanyuan Zhang, Shi Huang, Haiwei Su, Hongling Zhu, Yuhan Lin, Gang Deng (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13181217.txt.
Description
Jinbang Huang and co-authors build a 2026 model to forecast outcomes for the most common type of kidney cancer. The cancer, clear cell renal cell carcinoma, varies widely in how fast it grows, spreads, and responds to care. A family of fat-like molecules called sphingolipids helps steer tumor growth, yet its role in single cells of this cancer was unclear.
The team joined data from many patient groups, including gene data from single cells and from whole tumors. They sorted cells into states and found the genes tied to sphingolipid use in each state. They then screened a large set of machine learning blends to build a forecast model. The model was checked in separate groups to see how well it sorts patients by risk.
Catalog record
| CREATOR | Jinbang Huang, Shunsheng Wang, Yaojun Zhou, Hui Zhang, Yanyuan Zhang, Shi Huang, Haiwei Su, Hongling Zhu, Yuhan Lin, Gang Deng |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 65 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |