Expression of RASSF1A , DIRAS3 , and AKAP9 Genes in Thyroid Lesions: Implications for Differential Diagnosis and Prognosis of Thyroid Carcinomas.
Kamila SoboskaMichał KusińskiKarol PawelczykMonika Migdalska-SękEwa Brzeziańska-LasotaKarolina Henryka Czarnecka-ChrebelskaPublished in: International journal of molecular sciences (2024)
Thyroid carcinoma is the primary endocrine malignancy worldwide. The preoperative examination of thyroid tissue lesion is often unclear. Approximately 25% of thyroid cancers cannot be diagnosed definitively without post-surgery histopathological examination. The assessment of diagnostic and differential markers of thyroid cancers is needed to improve preoperative diagnosis and reduce unnecessary treatments. Here, we assessed the expression of RASSF1A , DIRAS3 , and AKAP9 genes, and the presence of BRAF V600E point mutation in benign and malignant thyroid lesions in a Polish cohort (120 patients). We have also performed a comparative analysis of gene expression using data obtained from the Gene Expression Omnibus (GEO) database (307 samples). The expression of RASSF1A and DIRAS3 was decreased, whereas AKAP9 's was increased in pathologically changed thyroid compared with normal thyroid tissue, and significantly correlated with e.g., histopathological type of lesion papillary thyroid cancer (PTC) vs follicular thyroid cancer (FTC), patient's age, tumour stage, or its encapsulation. The receiver operating characteristic (ROC) analysis for the more aggressive FTC subtype differential marker suggests value in estimating RASSF1A and AKAP9 expression, with their area under curve (AUC), specificity, and sensitivity at 0.743 (95% CI: 0.548-0.938), 82.2%, and 66.7%; for RASSF1A , and 0.848 (95% CI: 0.698-0.998), 54.8%, and 100%, for AKAP9 . Our research gives new insight into the basis of the aggressiveness and progression of thyroid cancers, and provides information on potential differential markers that may improve preoperative diagnosis.
Keyphrases
- gene expression
- poor prognosis
- chronic kidney disease
- patients undergoing
- end stage renal disease
- emergency department
- binding protein
- dna methylation
- case report
- long non coding rna
- acute coronary syndrome
- risk assessment
- atrial fibrillation
- artificial intelligence
- electronic health record
- social media
- deep learning
- health information
- coronary artery bypass