{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T16:11:07Z","timestamp":1792858267608,"version":"4.5.0"},"reference-count":91,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T00:00:00Z","timestamp":1763942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:p>The global burden of cancer continues to rise despite significant advances in conventional oncology, underscoring the urgent need for novel approaches to prevention and early detection. While cancer has traditionally been regarded as a genetic disease, mounting evidence highlights the role of metabolic dysfunction as a precursor to malignant transformation. Altered glucose utilization, amino acid metabolism, lipid synthesis, mitochondrial function, and disrupted methylation pathways contribute to oxidative stress, epigenetic instability, immune evasion, and tumor initiation. This paper discusses key metabolic markers such as homocysteine, lactate dehydrogenase, HbA1c, insulin, cortisol, neutrophil-to-lymphocyte ratio, C-reactive protein, vitamin B12, parathyroid hormone, ionized calcium, estrogen and progesterone, and their potential as early indicators of cancer risk. Drawing on insights from integrative oncology practice, we highlight how metabolic markers can serve as both predictive and prognostic tools, complementing standard genetic and imaging diagnostics. Importantly, these markers should not be viewed in isolation but collectively, as they interact through overlapping biochemical pathways that foster tumorigenesis. Early identification of metabolic abnormalities may enable timely interventions to restore balance and mitigate cancer risk. However, cumulative and multicentric data are needed to validate their translational utility across diverse clinical settings.<\/jats:p>","DOI":"10.3389\/fendo.2025.1693142","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T05:14:40Z","timestamp":1763961280000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Decoding metabolic dysfunction in cancer: foundations for early detection and personalized therapeutics"],"prefix":"10.3389","volume":"16","author":[{"given":"Pradeep M. 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