Identifying Biomarkers for Papillary Thyroid Carcinoma Using Machine Learning
R Geetanjali, D. Evangelin Geetha · 2024
This study aimed to identify potential biomarkers and gain insight into the underlying biological processes of papillary thyroid carcinoma (PTC), the most common form of thyroid cancer, using the GSE27155 dataset from the NCBI GEO database. By analyzing the data with three machine learning algorithms, the researchers identified 137 differentially expressed genes and several pathways associated with PTC. These genes and pathways may serve as targets for novel therapies for PTC and provide clinicians with potential biomarkers for early diagnosis and treatment. By examining the expression levels of these genes in patient samples, clinicians can identify patients at risk of developing PTC, leading to earlier diagnosis and treatment. The identified genes and pathways can also serve as potential targets for novel therapies for PTC. By targeting the biological pathways underlying PTC, clinicians can develop new treatments that are more effective and have fewer side effects than traditional treatments. Overall, the study highlights the importance of using machine learning to identify differentially expressed genes and potential biomarkers for PTC. By gaining a deeper understanding of the biological processes underlying PTC, clinicians can develop more accurate diagnostic tools and more effective treatments, ultimately improving patient outcomes.