Machine Learning‐Derived Immune Gene Signature Predicts Prognosis and Therapeutic Vulnerabilities in Multiple Myeloma
Kai Wang, Chenfei Zhao, Jingru Shi, Shiwei Liu, Yi Chen, Jujuan Wang, Zhengxu Sun, Sanmei Wang, Lei Fan, Jin Fan, Xiaoyan Qu · 2026
Read Machine Learning‐Derived Immune Gene Signature Predicts Prognosis and Therapeutic Vulnerabilities in Multiple Myeloma by Kai Wang, Chenfei Zhao, Jingru Shi, Shiwei Liu, Yi Chen, Jujuan Wang, Zhengxu Sun, Sanmei Wang, Lei Fan, Jin Fan, Xiaoyan Qu (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-13546800.txt.
Description
A twelve-gene immune signature, built by machine learning, sorts bone marrow cancer patients by expected survival across several cohorts. Kai Wang and co-authors developed that signature in this 2026 study of multiple myeloma. The immune surroundings of the tumor shape how this cancer behaves, yet immune markers with steady prognostic value have been scarce.
The signature held its sorting power after standard clinical risk factors were taken into account. It also pointed to treatment weak spots that differ between risk groups. A marker that is reproducible across cohorts is the exception in this field, where many signatures fail outside their home dataset. The authors position their score as a tool for both prognosis and therapy matching.
Catalog record
| CREATOR | Kai Wang, Chenfei Zhao, Jingru Shi, Shiwei Liu, Yi Chen, Jujuan Wang, Zhengxu Sun, Sanmei Wang, Lei Fan, Jin Fan, Xiaoyan Qu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 51 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |