Inferring translational efficiency from transcriptomes improves noncanonical neoantigen prioritization and cancer patient stratification
Yingying Ma, Chao Gao, Kang Xu, Kangle Wang, Jing Ma, Yangyang Cai, Dezhong Lv, Si Li, Qinghua Jiang, Kun Wang, Yongsheng Li, Juan Xu · 2026
Read Inferring translational efficiency from transcriptomes improves noncanonical neoantigen prioritization and cancer patient stratification by Yingying Ma, Chao Gao, Kang Xu, Kangle Wang, Jing Ma, Yangyang Cai, Dezhong Lv, Si Li, Qinghua Jiang, Kun Wang, Yongsheng Li, Juan Xu (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/PMC13639028.txt.
Description
Yingying Ma and eleven colleagues present a 2026 study on predicting how efficiently cells turn RNA messages into protein. Direct measurement of that step is costly, so the team tested whether software estimates from standard RNA sequencing could stand in for it.
Across fifteen datasets, the inferred translation profiles matched true ribosome measurements better than RNA levels alone. The approach also uncovered hidden cancer antigens that expression-based filters had missed. Applied to glioma, a brain cancer, it split patients into sharper risk groups. The authors present translation inference as a cheap upgrade for precision cancer care.
Catalog record
| CREATOR | Yingying Ma, Chao Gao, Kang Xu, Kangle Wang, Jing Ma, Yangyang Cai, Dezhong Lv, Si Li, Qinghua Jiang, Kun Wang, Yongsheng Li, Juan Xu |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 85939 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |