Guest Editors
Prof. Hung-Yu Lin
Email: linhungyu700218@gmail.com
Affiliation: 1. Department of Post-Baccalaureate Medicine, National Chung Hsing University, Taichung, 402, Taiwan
2. Research Assistant Center, Show Chwan Memorial Hospital, Changhua, 500, Taiwan
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Research Interests: Cancer Biology; Biomarker; Metabolism; Mitochondria; Immuno-oncology; Non-coding RNAs; Single-cell Omics; Targeted Therapy; machine learning
Prof. Hsing-Ju Wu
Email: hildawu09@gmail.com
Affiliation: 1. Research Assistant Center, Show Chwan Memorial Hospital, Changhua, 500, Taiwan
2. Department of Nursing, Nursing and Management, Jenteh Junior College of Medicine, Miaoli, 356, Taiwan
Homepage:
Research Interests: Cancer Biology, Molecular Biology, Medical Microbiology
Summary
Tumor biomarkers have become an essential foundation for advancements in cancer diagnosis, prognosis, and targeted therapy. As oncology moves towards precision medicine, significant progress in fields such as immuno-oncology, non-coding RNA research, and the application of pharmacogenetics, and machine learning has provided new opportunities to identify and utilize biomarkers for personalized treatment strategies. These biomarkers play a pivotal role in understanding tumor heterogeneity, predicting therapeutic responses, and assessing disease progression, thereby shaping modern oncology practice.
This Special Issue invites original research articles, comprehensive reviews, and advanced perspectives on the discovery, validation, and clinical implementation of tumor biomarkers. Topics of interest include single-cell omics approaches for tumor ecosystem profiling, biomarker-driven targeted therapy development, and the use of machine learning in biomarker analysis. Additionally, we seek contributions that explore the role of pharmacogenetics in optimizing cancer therapy and the utilization of immune-related biomarkers to enhance therapeutic efficacy in immuno-oncology. By showcasing interdisciplinary contributions, this Special Issue aims to advance biomarker research and provide actionable insights for their integration into clinical oncology, ultimately aiming to improve patient outcomes in the fight against cancer.
Keywords
Cancer Biology; Biomarker; Prognosis; Diagnosis; Therapy; Immuno-oncology; Non-coding RNAs; Single-cell Omics; Pharmacogenetics; Targeted Therapy; Machine Learning
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