Endocrine connections · 2025
Shared molecular mechanisms between type 2 diabetes and thyroid cancer: integrated bioinformatics insights for prognostic biomarker discovery.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Qi Jiahui, Chen Chuanzhi, Zhu Feng, Chen Chuankai, Wang Yue
Аннотация
BACKGROUND: Thyroid cancer (TC) is a prevalent endocrine malignancy with rising global incidence, particularly among women. Emerging evidence suggests a significant association between type 2 diabetes mellitus (T2D) and TC, potentially mediated by hyperinsulinemia, insulin resistance, and chronic inflammation. However, the molecular mechanisms linking these diseases remain poorly understood.
METHODS: We integrated transcriptomic datasets from the Gene Expression Omnibus (GEO) database (GSE33630, GSE35570, GSE60542 for TC; GSE86468 for T2D) to identify shared differentially expressed genes (DEGs). Functional enrichment, protein-protein interaction networks, and Cox regression analyses were employed to elucidate pathways and prognostic biomarkers.
RESULTS: We identified 28 shared DEGs between TC and T2D, with CD44, TGFBI, RUNX2, and GJA1 as key hub genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis highlighted pathways involving cell adhesion, extracellular matrix remodeling, and NF-κB signaling. A risk model incorporating seven genes (e.g., PRDM1 [protective] and ZFPM2 [risk]) stratified TC patients into high- and low-risk groups with distinct survival outcomes (P = 0.017).
CONCLUSION: T2D and TC exhibit overlapping genetic dysregulation, particularly in pathways governing metabolic reprogramming and tumor microenvironment crosstalk. Notably, PRDM1 and ZFPM2 may serve as therapeutic targets for TC in patients with concurrent diabetes.
METHODS: We integrated transcriptomic datasets from the Gene Expression Omnibus (GEO) database (GSE33630, GSE35570, GSE60542 for TC; GSE86468 for T2D) to identify shared differentially expressed genes (DEGs). Functional enrichment, protein-protein interaction networks, and Cox regression analyses were employed to elucidate pathways and prognostic biomarkers.
RESULTS: We identified 28 shared DEGs between TC and T2D, with CD44, TGFBI, RUNX2, and GJA1 as key hub genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis highlighted pathways involving cell adhesion, extracellular matrix remodeling, and NF-κB signaling. A risk model incorporating seven genes (e.g., PRDM1 [protective] and ZFPM2 [risk]) stratified TC patients into high- and low-risk groups with distinct survival outcomes (P = 0.017).
CONCLUSION: T2D and TC exhibit overlapping genetic dysregulation, particularly in pathways governing metabolic reprogramming and tumor microenvironment crosstalk. Notably, PRDM1 and ZFPM2 may serve as therapeutic targets for TC in patients with concurrent diabetes.