About this Research Topic
Cancer is a complex and heterogeneous disease, and traditional diagnostic and prognostic markers often fall short of accurately characterizing its intricacies. Multi-omics approaches, combining data from genomics, transcriptomics, epigenomics, proteomics, metabolomics, and other omics domains, have emerged as powerful tools to comprehensively study cancer. By integrating multi-dimensional molecular profiles, these approaches offer unprecedented insights into cancer subtypes, prognosis, and personalized treatment strategies.
This Research Topic invites authors to contribute original research articles, reviews, and methodological papers focusing on multi-omics approaches in cancer subtyping, prognosis, and diagnosis. The following themes are of particular interest, but not limited to:
1. Multi-omics Integration for Cancer Subtyping: Explore novel strategies and computational methods for integrating diverse omics data to identify robust and biologically meaningful cancer subtypes, enabling improved disease classification and patient stratification.
2. Prognostic and Predictive Biomarkers: Present studies leveraging multi-omics data to identify biomarkers associated with cancer prognosis and treatment response, facilitating personalized medicine approaches and guiding therapeutic decisions.
3. Machine Learning and Data Mining in Multi-omics Analysis: Discuss advanced machine learning algorithms, data mining techniques, and network-based approaches applied to multi-omics data to uncover hidden patterns and biological interactions relevant to cancer subtyping, prognosis, and diagnosis.
4. Translational Applications of Multi-omics in Cancer Research: Highlight studies demonstrating the translation of multi-omics analysis into clinical practice, including the development of diagnostic assays, prognostic models, and therapeutic targets for improved patient outcomes.
Authors are encouraged to provide comprehensive examples of multi-omics studies in cancer, including preclinical and clinical datasets, to showcase the potential of these approaches in elucidating cancer biology and advancing precision medicine. Methodological papers introducing innovative analytical tools and pipelines for multi-omics data integration and analysis are also highly welcomed.
Join us in this Research Topic as we unravel the transformative power of multi-omics approaches in cancer subtyping, prognosis, and diagnosis. By fostering collaboration and knowledge exchange, we aim to drive advancements in cancer research and pave the way for personalized interventions that will ultimately benefit cancer patients worldwide.
Keywords: multi-omics approaches in cancer research, cancer subtyping, prognosis, diagnosis, integrative strategies, computational methods, biomarkers, machine learning, data mining, translational applications, clinical practice, personalized therapeutic interventi
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.