A miRNA-Based Prognostic Model to Trace Thyroid Cancer Recurrence.
Eman Ali ToraihManal S FawzyBo NingMourad ZerfaouiYoussef ErramiEmmanuelle M RuizMohammad H HusseinMuhib HaidariMelyssa BrattonGiovane G TorteloteSylvia HilliardNaris NilubolJonathon O RussellMohamed A ShamaSamir S El-DahrKrzysztof MorozTony HuEmad KandilPublished in: Cancers (2022)
Papillary thyroid carcinomas (PTCs) account for most endocrine tumors; however, screening and diagnosing the recurrence of PTC remains a clinical challenge. Using microRNA sequencing (miR-seq) to explore miRNA expression profiles in PTC tissues and adjacent normal tissues, we aimed to determine which miRNAs may be associated with PTC recurrence and metastasis. Public databases such as TCGA and GEO were utilized for data sourcing and external validation, respectively, and miR-seq results were validated using quantitative real-time PCR (qRT-PCR). We found miR-145 to be significantly downregulated in tumor tissues and blood. Deregulation was significantly related to clinicopathological features of PTC patients including tumor size, lymph node metastasis, TNM stage, and recurrence. In silico data analysis showed that miR-145 can negatively regulate multiple genes in the TC signaling pathway and was associated with cell apoptosis, proliferation, stem cell differentiation, angiogenesis, and metastasis. Taken together, the current study suggests that miR-145 may be a biomarker for PTC recurrence. Further mechanistic studies are required to uncover its cellular roles in this regard.
Keyphrases
- cell proliferation
- long non coding rna
- lymph node metastasis
- long noncoding rna
- papillary thyroid
- signaling pathway
- data analysis
- free survival
- gene expression
- genome wide
- real time pcr
- squamous cell carcinoma
- pi k akt
- single cell
- newly diagnosed
- healthcare
- rna seq
- emergency department
- mental health
- high resolution
- endothelial cells
- high grade
- dna methylation
- prognostic factors
- heavy metals
- electronic health record
- risk assessment
- peritoneal dialysis
- artificial intelligence
- transcription factor