Luciano, Neila (2025) Insight into novel biomarkers and diagnostic approaches for thyroid carcinoma. [Tesi di dottorato]

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Abstract

Thyroid cancer is the most common endocrine tumor and its prevalence is constantly rising. This rise can be partly attributed to advancements in diagnostic imaging and molecular testing, which have enhanced the early detection of both benign and aggressive forms of the disease. Thyroid cancer is a highly heterogeneous condition, comprising multiple subtypes with distinct genetic profiles and clinical behaviors, as highlighted in the 2022 World Health Organization classification. Despite advances in diagnostic tools, the clinical management of thyroid cancer patients remains challenging due to the reliance on procedures like ultrasound, fine-needle aspiration (FNA), and liquid biopsies, which can sometimes generate imprecise results. Consequently, there is a growing need to identify non-invasive biomarkers that could help to improve early diagnosis and prognosis. To address this topic, my research was focused on molecular markers involved in thyroid tumor progression and their potential as biomarkers. In details, my work was focused on exploring the role of selected non-coding RNAs and protein-coding genes in thyroid cancer progression. Among the potential candidates, microRNAs (miRNAs), small non-coding RNAs that regulate gene expression, have emerged as potential biomarkers in oncology. In detail, my PhD research focused on the characterization of the role of miR-331-5p in thyroid cancer and on assessing its potential as a novel biomarker. Through in silico analysis, miR-331-5p was statistically downregulated in thyroid cancer tissues compared to normal samples. Through in vitro experiments, I analyzed the effect of miR-331-5p on cellular behavior in both papillary and anaplastic thyroid cancer cell lines. Functional studies demonstrated that the overexpression of miR-331-5p significantly reduced thyroid cancer cell motility, whereas silencing of this miRNA increased cellular migration. These findings highlight the crucial role of miR-331-5p in regulating cellular migration and invasion. Proteomic screening identified eight potential targets down-regulated by miR-331-5p, with BID being validated as a direct target. Data from The Cancer Genome Atlas-Thyroid Cancer (TCGA-THCA) further revealed an inverse correlation between BID levels and miR-331-5p expression in thyroid cancer tissues. These results suggest that the dysregulation of the miR-331-5p/BID axis may enhance tumor aggressiveness, providing novel insight into the biological mechanisms driving thyroid cancer progression. To further investigate the signaling pathways underlying thyroid cancer and to identify novel potential biomarkers, my research also focused on protein-coding gene, i.e., KCTD15, a member of the KCTD protein family. Data from TCGA-THCA database revealed that KCTD15 expression is significantly reduced in thyroid cancer tissues compared to non-cancerous thyroid tissues, and is negatively correlated with cancer stage and aggressive histological subtypes. Notably, KCTD15 down-regulation was found to be associated with the presence of the BRAFV600E mutation, a key oncogenic driver in thyroid cancer. Functional experiments in papillary thyroid cancer cells further demonstrated that over-expression of KCTD15 reduced cell proliferation, supporting its role as a potential tumor suppressor. These findings highlight the potential of KCTD15 as biomarker of poor outcome and as candidate for therapeutic targeting in thyroid cancer. Finally, my PhD research also explored, through a comprehensive analysis of the literature, the integration of radiomic features and genomic data in thyroid cancer as a novel approach to improve diagnosis of thyroid cancer patients. Radiomics, which involves extracting quantitative features from medical images, has shown promise in identifying patterns linked to genetic and molecular tumor characteristics. By correlating radiomic features with genomic data, this innovative, non-invasive approach aims to enhance both the diagnosis and prognosis of thyroid cancer, potentially improving clinical decision-making and patient management. Overall, the findings presented in this thesis shed light on promising novel diagnostic biomarkers for thyroid cancer. Future research should aim to validate these results through in vivo studies and to explore their integration with imaging-derived features to enhance personalized approaches to thyroid cancer management.

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