Integration of Graph Neural Networks and multi-omics analysis identify the predictive factor and key gene for immunotherapy response and prognosis of bladder cancer.

Journal: Journal of translational medicine

PMID: 39716185

Abstract

OBJECTIVE: The evaluation of the efficacy of immunotherapy is of great value for the clinical treatment of bladder cancer. Graph Neural Networks (GNNs), pathway analysis and multi-omics analysis have shown great potential in the field of cancer diagnosis and treatment.

Authors

Shuai Ren

Department of Orthopedics, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150001 Heilongjiang, China.
Yongjian Lu

Shantou University Medical College, Shantou, Guangdong, 515041, People's Republic of China.
Guangping Zhang

Shantou University Medical College, Shantou, Guangdong, 515041, People's Republic of China.
Ke Xie
Danni Chen

School of Computer Science and Engineering, South China University of Technology, China. Electronic address: conniechen9469@gmail.com.
Xiangna Cai

Medical Cosmetic Center, First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, 515041, People's Republic of China.
Maodong Ye

Medical Cosmetic Center, First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, 515041, People's Republic of China. 624683069@qq.com.

Keywords

Apoptosis Cell Line, Tumor Cell Movement Cell Proliferation Computational Biology Gene Expression Regulation, Neoplastic Humans Immunotherapy Multiomics Neural Networks, Computer Prognosis Proteasome Endopeptidase Complex Reproducibility of Results Single-Cell Analysis Treatment Outcome Tumor Microenvironment Urinary Bladder Neoplasms

External Resources

View on PubMed Access via DOI PubMed (39716185)

Integration of Graph Neural Networks and multi-omics analysis identify the predictive factor and key gene for immunotherapy response and prognosis of bladder cancer.

Abstract

Authors

Keywords

External Resources

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