Enhanced prognostic signature for lung adenocarcinoma through integration of adjacent normal and tumor gene expressions
Overall, the COM-derived model demonstrated the best generalization ability. The superior performance of the tumor-derived model in the TCGA LUAD dataset might be due to overfitting. Our results suggest that appropriate combinations of gene expression data from tumor and normal tissues can enhance the predictive power of prognostic signatures.
Authors
- Yuanyuan Qiao (ORCID: https://orcid.org/0000-0002-1178-3480)
- Wei Ma (ORCID: https://orcid.org/0000-0003-1044-5612)
- Dandan Li (ORCID: https://orcid.org/0000-0002-2100-4145)
- Ming Xiong (ORCID: https://orcid.org/0009-0006-2180-5889)
- Xinkun Wang (ORCID: https://orcid.org/0009-0004-8577-2989)
- Mingyue Hao
- Weihao Chen (ORCID: https://orcid.org/0000-0003-2993-0183)
Institutions
- Sun Yat-sen University (CN)
- Chinese PLA General Hospital (CN)
- Sun Yat-sen Memorial Hospital (CN)
Publication Details
- Journal
- Heliyon
- Published
- 2024-09-26
- DOI
- https://doi.org/10.1016/j.heliyon.2024.e38527
- Primary Topic
- Lung Cancer Treatments and Mutations
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- General Hospital of People’s Liberation Army
- People’s Liberation Army Navy General Hospital
- Foundation for Innovative Research Groups of the National Natural Science Foundation of China