Advances in Brain Tumor Biomarkers: From Molecular Profiling to Liquid Biopsy and AI-Driven Detection

Brain tumors in older adults are difficult to diagnose and manage due to nonspecific symptoms, overlapping with neurodegenerative diseases such as dementia, and significant tumor heterogeneity. Although molecular markers such as IDH1/2 mutations, MGMT promoter methylation, TERT alterations, and 1p/19q co-deletion have improved glioma classification and prognostic assessment, current care still relies on invasive tissue biopsies, which limit longitudinal monitoring and may not fully capture tumor complexity because of sampling bias, assay variability, and limited accessibility. Liquid biopsy offers a promising alternative that enables the detection of tumor-derived DNA, RNA, proteins, and extracellular vesicles, supporting earlier diagnosis and real-time monitoring of disease progression and treatment response. However, liquid biopsy for brain tumors is not yet clinically definitive due to low biomarker abundance, lack of standardization, and limited validation, and therefore, it cannot replace tissue diagnosis. Ongoing research focuses on multi-analyte biomarker panels, improved assay standardization, and integration with imaging and tissue-based data. In parallel, artificial intelligence and machine learning are advancing the field by integrating multi-omics and radiomic data to enhance detection, classify tumors, and predict key molecular alterations, supporting the emerging framework of radiogenomics. Together, these developments are driving a shift toward more precise and dynamic approaches to brain tumor diagnosis and management, with relevance for improving outcomes in older adults with brain cancer.