Cancer Bioinformatics and Data-Driven Discoveries of Novel Targets and Therapeutics

Cancer bioinformatics and data-driven discovery represent a rapidly advancing frontier in oncology, dedicated to deciphering the complex molecular architecture underlying tumor initiation, progression, metastasis, and therapeutic response. This interdisciplinary field integrates large-scale omics technologies, including genomics, transcriptomics, epigenomics, proteomics, metabolomics, single-cell sequencing, and spatial profiling, with advanced computational and statistical methodologies. By leveraging high-throughput datasets and systems-level analyses, researchers can uncover molecular alterations, regulatory networks, and functional pathways that shape tumor biology and clinical outcomes.Recent advances in big data analytics, machine learning, network modeling, and integrative multi-omics approaches have dramatically expanded our ability to identify novel biomarkers, therapeutic targets, prognostic signatures, and drug response predictors. Data-driven strategies are now instrumental in characterizing tumor heterogeneity, mapping the tumor microenvironment, defining immune landscapes, tracing cancer evolution, and elucidating mechanisms of resistance. These developments are reshaping precision oncology by transforming large-scale datasets into actionable biological and translational insights.This Research Topic aims to provide a comprehensive platform for high-quality studies that employ robust bioinformatics and data-driven approaches to generate biologically meaningful discoveries in cancer research. Its primary objectives are to deepen our understanding of cancer mechanisms through integrative omics analyses, accelerate the identification of clinically relevant biomarkers and therapeutic vulnerabilities, and promote the translation of computational findings into experimental and clinical applications. By emphasizing mechanistic interpretation and translational relevance, this section seeks to bridge computational oncology with functional and clinical validation, advancing precision medicine and innovative therapeutic strategies.To gather further insights within the defined scope of cancer bioinformatics and data-driven discovery, we welcome articles addressing, but not limited to, the following themes: Integrative multi-omics analyses in cancer development and progression Discovery and validation of cancer biomarkers (diagnostic, prognostic, predictive) Identification of therapeutic targets and actionable molecular vulnerabilities Genomic, epigenomic, transcriptomic, and proteomic alterations across tumor types Omics profiling of drug-treated models and mechanisms of therapy response or resistance Tumor heterogeneity, clonal evolution, and metastasis dynamics Immune landscape analysis and tumor microenvironment characterization Network biology, pathway modeling, and systems-level cancer analyses AI- and machine learning–based predictive modeling in oncology Public database–driven discoveries (e.g., TCGA, GEO, CPTAC) with biological or clinical integrationBy fostering collaboration among computational scientists, molecular biologists, clinicians, and translational researchers, this Research Topic aims to accelerate the transformation of large-scale cancer data into meaningful biological insight and tangible clinical impact.***Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal. Topic editors: Haifeng Zhang, Weina Chen, Wenbo Ma, Caiyue Li Source: https://www.frontiersin.org/research-topics/78306/cancer-bioinformatics-and-data-driven-discoveries-of-novel-targets-and-therapeutics