High-throughput technologies have produced vast multi-omics datasets (genomics, transcriptomics, epigenomics, proteomics) from large cancer initiatives such as The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). These resources offer great potential, but a key challenge remains finding biomarkers that accurately diagnose, prognosticate, or predict therapeutic response in diverse cancers, with robust and reproducible results. Many biomarkers identify only a single type of cancer, which limits their broader clinical use. Integrating different omics layers to reveal complex, system-level biomarkers require advanced computational approaches that manage high dimensionality, noise, and biological heterogeneity. This Research Topic seeks studies on new computational and statistical methods for discovering and validating next-generation, pan-cancer biomarkers, with an emphasis on integrative models over single-omics analyses.

The primary goal of this Research Topic is to spotlight innovative computational approaches that advance cancer mutation and biomarker discovery. We seek methods that enhance sensitivity, specificity, and clinical utility by leveraging integrated multi-omics data. We are particularly interested in contributions that address the challenges of pan-cancer analysis, aiming to identify biomarkers and biological pathways that are conserved across multiple cancer types, which could lead to more universal diagnostic tools or therapeutic strategies.
The scope of this topic includes, but is not limited to, the following themes:
• Novel AI/ML Models: Development of deep learning, graph neural networks, or other machine learning frameworks for multi-omics data fusion and feature selection.
• Network-Based Approaches: Algorithms that leverage biological networks to identify robust, pathway-level biomarkers with strong functional implications.
• Liquid Biopsy and Minimally Invasive Biomarkers: Computational methods for analyzing circulating tumor DNA (ctDNA), exosomes, or other liquid biopsy data for early detection and monitoring.
• Spatial Transcriptomics/Proteomics Integration: Develop computational tools that add spatial context to biomarker discovery. These tools should enable the linking of molecular profiles to the structure of the tumor microenvironment.
• Robustness and Reproducibility: Studies focusing on the validation of computational pipelines across independent cohorts and their application to real-world clinical data.
• Explainable AI in Biomarker Discovery: Methods that provide biological interpretation for predictions made by complex models, building trust for clinical adoption.

We welcome Original Research, reviews, mini-reviews, Systematic Reviews, Perspectives, Methods, hypotheses and theories, Clinical Trials, Technology and Code, Study Protocols, Brief Research Reports, Data Reports, General Commentaries, and Opinion papers that link genetic mutations and clinical biomarkers for solid tumors and blood cancers.

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Bioinformatics and big data driving the discovery of clinical biomarkers for solid tumors and blood malignancies