Advancing personalized, predictive, and preventive medicine in bladder cancer: a multi-omics and machine learning approach for novel prognostic modeling, immune profiling, and therapeutic target discovery.

Journal: Frontiers in immunology

PMID: 40330458

Abstract

OBJECTIVE: This study aimed to identify and analyze immunogenic cell death (ICD)-related multi-omics features in bladder cancer (BLCA) using single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data. By integrating these datasets, we sought to construct a prognostic signature (ICDRS) and explore its clinical and biological implications, including its association with immune cell infiltration, tumor microenvironment (TME), and drug sensitivity.

Authors

Han Yan

Department of Pulmonary Medicine, Peking University People's Hospital, Beijing, 100044, China.
Xinyu Ji

Department of Thoracic Surgery, The First Hospital of China Medical University, Liaoning, Shenyang, China.
Bohan Li

Department of Minimally Invasive Gynecologic Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing 100006, China.

Keywords

Biomarkers, Tumor Computational Biology Female Gene Expression Profiling Gene Expression Regulation, Neoplastic Humans Machine Learning Male Multiomics Precision Medicine Prognosis Single-Cell Analysis Tumor Microenvironment Urinary Bladder Neoplasms

External Resources

View on PubMed Access via DOI PubMed (40330458)

Advancing personalized, predictive, and preventive medicine in bladder cancer: a multi-omics and machine learning approach for novel prognostic modeling, immune profiling, and therapeutic target discovery.

Abstract

Authors

Keywords

External Resources

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