Risk stratification of thyroid papillary cancer based on proteomics and machine learning

ABSTRACT: Papillary thyroid cancer (PTC) is the most common type of thyroid carcinoma accounting for 85%. Most PTCs have a low risk of recurrence and metastasizing and not all low-risk PTCs need immediate surgery. However, cases are often recognized as low-risk only after surgery. Our study aimed to find a preoperative stratification strategy to distinguish low- from intermediate- and higher-risk cases to avoid unnecessary thyroidectomies or delayed surgeries. We conducted a retrospective, multicenter study with 558 PTCs comprising 118 pre- (n=118) and post- (n=440) operative samples. PTC samples were classified into high-, intermediate-, or low-risk according to the traditional postoperative assessment. From each patient, we collected clinical information, immunological indices, and BRAFV600E mutation assessment. Proteomic profiling was conducted through data-independent acquisition mode by mass spectrometry.

ORGANISM(S): Homo Sapiens

SUBMITTER: Tiannan Guo  

PROVIDER: PXD050577 | iProX | Wed Mar 13 00:00:00 GMT 2024

REPOSITORIES: iProX