Pan-cancer single cell and spatial transcriptomics analysis deciphers the molecular landscapes of senescence related cancer-associated fibroblasts and reveals its predictive value in neuroblastoma via integrated multi-omics analysis and machine learning.

Journal: Frontiers in immunology

PMID: 39703515

Abstract

INTRODUCTION: Cancer-associated fibroblasts (CAFs) are a diverse group of cells that significantly contribute to reshaping the tumor microenvironment (TME), and no research has systematically explored the molecular landscapes of senescence related CAFs (senes CAF) in NB.

Authors

Shan Li

College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao 266061, China. Electronic address: lishan5600@163.com.
Junyi Luo

University of Electronic Science and Technology of China, Chengdu, China.
Junhong Liu

Galixir, Beijing 100080, China.
Dawei He

Department of Urology, Children's Hospital of Chongqing Medical University, Chongqing, China.

Keywords

Biomarkers, Tumor Cancer-Associated Fibroblasts Cellular Senescence Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Janus Kinase 1 Machine Learning Multiomics Neuroblastoma Predictive Value of Tests Prognosis Single-Cell Analysis Transcriptome Tumor Microenvironment

External Resources

View on PubMed Access via DOI PubMed (39703515)

Pan-cancer single cell and spatial transcriptomics analysis deciphers the molecular landscapes of senescence related cancer-associated fibroblasts and reveals its predictive value in neuroblastoma via integrated multi-omics analysis and machine learning.

Abstract

Authors

Keywords

External Resources

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