An AI-driven multi-omics framework identifies lactylation-mediated therapeutic targets to overcome drug resistance in ovarian cancer
Lijia Zhang, Qi Guo, Xue Lei, Xinyu Yin, Yun Ling, Ye Liu, Songjiang Liu · 2025
Read An AI-driven multi-omics framework identifies lactylation-mediated therapeutic targets to overcome drug resistance in ovarian cancer by Lijia Zhang, Qi Guo, Xue Lei, Xinyu Yin, Yun Ling, Ye Liu, Songjiang Liu (2025) 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-12804988.txt.
Description
Lijia Zhang and co-authors built an AI framework that hunts drug-resistance targets in ovarian cancer through a sugar-linked gene mark, in a 2025 study. The mark, lactylation, grows where tumors ferment sugar and pile up lactate, and it rewires genes toward survival and drug failure. The team fed transcript, mutation, drug response, and outcome data from public cancer cohorts into deep learning models, including autoencoders and memory networks.
The models sorted patients, predicted survival, and estimated drug sensitivity with reported precision near ninety-five percent. Two lactylation genes stood out, tied to immune exhaustion and resistance to the standard drug cisplatin. A combined score predicted survival and drug response across cohorts. Drug repurposing analysis nominated fresh attack points. The authors frame the work as precision oncology groundwork; the targets now need bench testing.
Catalog record
| CREATOR | Lijia Zhang, Qi Guo, Xue Lei, Xinyu Yin, Yun Ling, Ye Liu, Songjiang Liu |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 59 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |