Constructing a neural network model based on tumor-infiltrating lymphocytes (TILs) to predict the survival of hepatocellular carcinoma patients
This study included a total of 511 patients, divided into a training cohort of 331 cases (from Qingdao University Hospital between January 2013 and December 2016) and a validation cohort of 180 cases (TCGA). The results revealed that tumor-infiltrating lymphocytes (TILs) have a protective effect and successfully predicted the survival risk of liver cancer patients using machine learning and neural network technology. The discovery of TNFSF4 provides a new potential target for immunotherapy.
Authors
- Bing Han (ORCID: https://orcid.org/0000-0001-7559-434X)
- Yanbing Wei
- Fengda Li (ORCID: https://orcid.org/0000-0002-7322-422X)
- Jin Cheng (ORCID: https://orcid.org/0000-0002-3141-3460)
- Xin Fang (ORCID: https://orcid.org/0000-0003-2098-6623)
- Ziyin Zhao (ORCID: https://orcid.org/0000-0002-7358-6124)
- Jingyi Sun (ORCID: https://orcid.org/0000-0002-0316-1198)
- Wenqing Zhong
Institutions
- Qingdao University (CN)
- Shanghai Jiao Tong University (CN)
- Affiliated Hospital of Qingdao University (CN)
Publication Details
- Journal
- PeerJ
- Published
- 2025-04-23
- DOI
- https://doi.org/10.7717/peerj.19351
- Primary Topic
- Cancer Immunotherapy and Biomarkers
- Type
- article
- Field-Weighted Citation Impact
- 0.00