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Telomerase reverse transcriptase promoter mutation (pTERT MT) promotes human carcinogenesis via aberrant expression of telomerase reverse transcriptase (TERT). However, the tumorigenic impact of TERT expression independent of pTERT MT remains unclear despite numerous mechanisms of TERT being suggested. To tackle this issue, we employed comprehensive bioinformatics to assess biological variations noticed among different TERT expression mechanisms. Papillary thyroid cancer (PTC) with pTERT MT (pTERT MT PTC) presented aggressive clinical behavior and exhibited biological profiles associated with cellular immortality and genomic instability. PTC with TERT expression but without pTERT MT (TERT (+) PTC), also exhibited poor clinicopathological characteristics and was enriched with immune responses. In accordance, c-MYC/E2F and nuclear factor kappa B (NFκB) were dominant transcription factors in pTERT MT PTC and TERT (+) PTC, respectively. Notably, we revealed TERT hypermethylated oncological region (THOR) as a potential TERT expressing mechanism in TERT (+) PTC patients. Furthermore, three unique subtypes of papillary thyroid cancer were deciphered using a combination of machine learning-based scoring systems. Our proposed scoring system was clinically significant, especially in microcarcinoma, predicting survival outcomes and inferring therapeutic responses to radioactive iodine therapy. Finally, our analysis was expanded to endocrine-related cancers, unveiling various regulatory mechanisms of TERT with poor clinical outcomes and biological behaviors.