AUTOIMMUNE AND ONCOGENIC DISORDERS OF THE THYROID: A COMPREHENSIVE LITERATURE REVIEW ON GRAVES DISEASE, HASHIMOTOS THYROIDITIS, AND PAPILLARY THYROID CARCINOMA.

Two autoimmune thyroid diseases—Graves' disease and Hashimoto's thyroiditis—are intimately connected to general health and, in some situations, malignant alterations such as papillary thyroid carcinoma (PTC). This comprehensive research integrates two comprehensive academic studies into a single analysis, including the genesis, diagnosis, clinical presentation, and treatment strategies across several connected disorders. Driven by the activation of thyroid-stimulating hormone receptor antibodies (TRAb), Graves' disease (GD) remains a significant cause of hyperthyroidism, predominantly affecting middle-aged women. Management challenges are provided by high relapse rates, particularly following antithyroid drug treatment, and the development of extrathyroidal symptoms such as Graves' Orbitopathy (GO). Simultaneously, Hashimoto's thyroiditis (HT) is being linked increasingly to thyroid cancer, especially PTC, and is becoming a significant autoimmune cause of hypothyroidism. By means of this synthesis, the review explores overlapping molecular mechanisms—including CASC9-mediated chemoresistance, the PI3K/AKT pathway, and SFRP1's regulatory role in cancer stem cell persistence. Among other fresh diagnoses, it also provides GREAT+ rating for GD and artificial intelligence-assisted imaging for thyroid malignancies. The clinical change from HT to GD, diagnostic ambiguity, and treatment resistance are also studied, hence offering knowledge of evolving therapeutic and prognostic strategies. The study supports a paradigm shift toward precision medicine—where immunological, genetic, and metabolic indicators can direct therapy and improve patient outcomes. This review concludes with recommendations for biomarker standardization, targeted immunomodulation, and interdisciplinary collaboration to enhance early diagnosis, customize treatment, and reduce systemic effects. Future studies should investigate molecular-genetic interactions more thoroughly and apply artificial intelligence for risk prediction and individualized therapy.