2026/02/27 by Hewa Dikkumburage Tharindu Madhuranga, Rani Joseph, G. Madhan +2 · 1 voice
Medicine · Immunology and Microbiology · #Cancer Cells and Metastasis #Lung Cancer Research Studies #Biomarkers in Disease Mechanisms
paper · pdf · doi:10.1016/j.premed.2026.100032
openalex publication_date 2026/02/27 · openalex created_date 2026/02/28 · openalex updated_date 2026/06/21
Cancer biomarkers are measurable biological indicators whose clinical utility spans diagnosis, prognosis, and therapeutic prediction, and whose rigorous validation is prerequisite to meaningful precision oncology. Despite exponential growth in biomarker discovery, translational failure remains pervasive, driven by inadequate validation standards, poor assay reproducibility, insufficient population diversity in discovery studies, and conflation of prognostic with predictive functions. We review diagnostic, prognostic, predictive, and therapeutic biomarkers; the multi-omics and liquid biopsy platforms that underlie their discovery; and the clinical applications and translational challenges that define the current state of the field. Two seminal biomarkers, estrogen receptor expression in breast cancer and the Philadelphia chromosome in CML, set the standard for sensitivity, specificity, and clinical actionability against which all subsequent biomarkers must be measured. Legacy biomarkers including CEA, CA-125, and PSA demonstrate the paradox of clinically entrenched tests with limited performance: CEA sensitivity for early recurrence is only 40–60%, and PSA carries a 25–40% false-positive biopsy rate. Multi-omics platforms and AI-driven biomarker discovery hold genuine promise but introduce new reproducibility challenges; liquid biopsy for population-wide cancer screening remains premature pending completion of multi-institutional validation studies. Realizing the vision of integrated, AI-guided, multi-omics cancer management requires not more biomarker candidates, but rigorous pre-specified validation frameworks, representative patient diversity, and the scientific discipline that the field has historically underemphasized.