Guest Editor(s)
Dr. Xudong Zhu
Email: xdzhu@cmu.edu.cn
Affiliation: Liaoning Provincial Key Laboratory of Precision Medicine for Malignant Tumors
Department of Hepatopancreatobiliary Surgery, Liaoning Cancer Hospital & Institute, Cancer Hospital of Dalian University of Technology
Shenyang, Shenyang, China
Homepage:
Research Interests: malignant progression of solid cancer, targeted therapy of solid cancer
Dr. Yadong Guo
Email: guoya.dong@tongji.edu.cn
Affiliation: Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
Homepage:
Research Interests: immune evasion mechanisms in genitourinary malignancies, including bladder cancer and prostate cancer, tumor microenvironment and cancer immunotherapy, translational oncology, risk of second primary cancers (spcs) in cancer survivors, metabolic regulation of cancer and combined metabolic–immunotherapy strategies
Dr. Shantanu Gupta
Email: shantanu.gupta@imd.ufrn.br
Affiliation: Universidade Federal do Rio Grande do Norte, Instituto Metrópole Digital, Bioinformatics Multidisciplinary Environment - BioME, Rua do Horto, Lagoa Nova, 59076-550 - Natal, RN, Brasil
Homepage:
Research Interests: bioinformatics and systems biology
Summary
Cancer research is undergoing a transformative shift driven by the integration of artificial intelligence (AI), multi-omics technologies, and precision medicine. High-throughput genomic, transcriptomic, epigenomic, proteomic, metabolomic, spatial, and single-cell datasets have generated unprecedented opportunities to decipher tumor heterogeneity, identify therapeutic vulnerabilities, and develop individualized treatment strategies.
At the same time, advances in machine learning, deep learning, foundation models, and large language models are revolutionizing biomedical data interpretation, biomarker discovery, treatment prediction, digital pathology, radiomics, and clinical decision support. The convergence of AI and multi-omics is accelerating the transition from descriptive oncology toward predictive and actionable cancer medicine.
This Special Issue aims to provide a comprehensive platform for cutting-edge research exploring how AI-enabled analytical frameworks and integrative multi-omics approaches can improve cancer diagnosis, prognostic assessment, therapeutic stratification, drug discovery, treatment response prediction, and precision immunotherapy.
We welcome original research articles, systematic reviews, meta-analyses, methodological studies, perspectives, and translational investigations spanning both experimental and clinical oncology. Studies involving molecular mechanisms, tumor microenvironment, cancer metabolism, immune regulation, digital pathology, radiogenomics, liquid biopsy, and next-generation therapeutic strategies are particularly encouraged.
By bringing together experts from computational biology, oncology, immunology, pathology, bioinformatics, and translational medicine, this Special Issue seeks to highlight emerging technologies and innovative concepts that will shape the future of precision cancer care.
Keywords
precision oncology, artificial intelligence, multi-omics integration, foundation models, large language models, cancer genomics, single-cell sequencing, spatial transcriptomics, digital pathology, radiomics, radiogenomics, cancer immunotherapy, tumor microenvironment, immune evasion, cancer metabolism, biomarker discovery, personalized therapy, drug resistance, liquid biopsy, translational oncology
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