Thyroid cancer (TC) is the most common endocrine malignancy, with well-differentiated
thyroid carcinomas (DTCs)-papillary (PTC) and follicular (FTC)-comprising the majority of
cases. While DTCs generally have favorable prognoses, a subset progresses to poorly
differentiated or anaplastic thyroid carcinoma (ATC), which is highly aggressive. Tumor
classification is based on histopathology, invasiveness, and molecular characteristics,
with new entities like thyroid tumors of uncertain malignant potential (TT-UMP) and
non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP)
refining diagnostic criteria.
Current standard treatments include surgical resection, radioactive iodine therapy, and
thyroid hormone replacement. However, some patients develop radioiodine-refractory
disease with an increased risk of recurrence and progression. Molecular alterations in
the MAPK and PI3K pathways play critical roles in thyroid tumorigenesis, influencing
therapeutic response and prognosis. Identifying novel biomarkers for early detection and
risk stratification is crucial. Emerging evidence highlights the role of microRNAs
(miRNAs) in thyroid cancer progression, functioning as oncogenes or tumor suppressors.
This retrospective case-control study aims to identify novel molecular markers linked to
thyroid cancer aggressiveness. Archived formalin-fixed paraffin-embedded (FFPE) tissue
and blood samples will be analyzed from patients with varying degrees of PTC and FTC
invasiveness. Control samples will be histologically normal thyroid tissue from the same
patients.
Next Generation Sequencing (NGS), including RNA-seq and miRNA-seq, will be employed to
detect differentially expressed RNA molecules. Validation will be performed using
Real-Time PCR in an independent cohort. High-throughput genomic sequencing (Illumina
TruSight Oncology 500) will assess mutations, copy number variations, and tumor mutation
burden to correlate genetic alterations with malignancy. Variants will be prioritized
based on frequency differences in tumor vs. non-tumor populations and functional
relevance.
The study will enroll patients with follicular cell-derived thyroid carcinoma. A power
analysis indicates that 80 subjects provide >80% statistical power for biomarker
identification. Descriptive statistics, parametric/non-parametric tests, and machine
learning approaches will analyze transcriptomic and genomic data. Receiver operating
characteristic (ROC) curves will assess diagnostic biomarker accuracy, while logistic
regression will model associations between molecular alterations and disease severity.
This study aims to uncover molecular mechanisms driving thyroid cancer progression and
identify biomarkers for improved risk stratification, early diagnosis, and potential
therapeutic targeting. Findings may enhance personalized treatment approaches in thyroid
oncology.