Machine learning-based identification of an immunotherapy-related signature to enhance outcomes and immunotherapy responses in melanoma.

Journal: Frontiers in immunology

Published Date: Sep 17, 2024

Abstract

BACKGROUND: Immunotherapy has revolutionized skin cutaneous melanoma treatment, but response variability due to tumor heterogeneity necessitates robust biomarkers for predicting immunotherapy response.

Authors

Zaidong Deng

Department of Neurology, Medical Research Institute, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China.
Jie Liu

School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China.
Yanxun V Yu

Department of Neurology, Medical Research Institute, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China.
Youngnam N Jin

Department of Neurology, Medical Research Institute, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China.

Keywords

Animals Biomarkers, Tumor Gene Expression Profiling Gene Expression Regulation, Neoplastic Gene Regulatory Networks Humans Immunotherapy Machine Learning Melanoma Mice Prognosis Skin Neoplasms Transcriptome Treatment Outcome Tumor Microenvironment

External Resources

View on PubMed Access via DOI PubMed (39355255)

Machine learning-based identification of an immunotherapy-related signature to enhance outcomes and immunotherapy responses in melanoma.

Abstract

Authors

Keywords

External Resources

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