Prostate cancer remains a major global health challenge, with outcomes closely tied to precise clinical staging and individualized, response-adapted management. Standard options across stages include radical prostatectomy and contemporary radiotherapy for localized disease, and systemic approaches for advanced disease such as androgen-deprivation therapy, chemotherapy, targeted agents and immunotherapy. Important limitations persist in static risk models that emphasize Gleason grading and bulk Prostate-Specific Antigen (PSA). Emerging molecular tools—particularly PSA glycosylation readouts and composite PSA “glycan indices”—provide orthogonal, probabilistic information that can improve discrimination of malignant from benign conditions and offer insight into tumor biology, although these measures show broad inter-patient overlap and do not provide stage-defining cutoffs. A recent analytical study reported a glycan-based PSA score that; in an early 30-patient discovery cohort; demonstrated high specificity for distinguishing prostate cancer from Benign Prostatic Hyperplasia (BPH) under controlled experimental conditions; however, this requires validation in larger, independent populations. Other reports associate shifts toward α2,3-linked sialylation, increased core fucosylation, greater N-glycan branching, and truncated O-glycans on PSA with adverse pathological features. When interpreted alongside PSA kinetics, Grade Group, and imaging findings, glycan-aware measures may support improved risk stratification, treatment selection, and longitudinal surveillance. This review synthesizes treatments across clinical stages, integrates glycan-based biomarkers at stage-appropriate decision points within conceptual decision frameworks, and outlines research priorities spanning assay harmonization, prospective validation, artificial-intelligence-enabled decision tools, and patient-reported outcomes. A literature-derived composite glycosylation index was explicitly calculated across clinical stages using literature reported feature ranges, normalization, interpolation where necessary, and fixed heuristic weights.
Amulya Pantangi (Sun,) studied this question.