Dataset Information

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

ACCESS DATA

Publications

Risk stratification of papillary thyroid cancers using multidimensional machine learning.

Li Yuanhui Y Wu Fan F Ge Weigang W Zhang Yu Y Hu Yifan Y Zhao Lingqian L Gou Wanglong W Shi Jingjing J Ni Yeqin Y Li Lu L Fu Wenxin W Lin Xiangfeng X Yu Yunxian Y Han Zhijiang Z Chen Chuanghua C Xu Rujun R Zhang Shirong S Zhou Li L Pan Gang G Peng You Y Mao Linlin L Zhou Tianhan T Zheng Jusheng J Zheng Haitao H Sun Yaoting Y Guo Tiannan T Luo Dingcun D

International journal of surgery (London, England) 20240101 1

<h4>Background</h4>Papillary thyroid cancer (PTC) is one of the most common endocrine malignancies with different risk levels. However, preoperative risk assessment of PTC is still a challenge in the worldwide. Here, the authors first report a Preoperative Risk Assessment Classifier for PTC (PRAC-PTC) by multidimensional features including clinical indicators, immune indices, genetic feature, and proteomics.<h4>Materials and methods</h4>The 558 patients collected from June 2013 to November 2020 ...[more]

PMID: 37916932

Similar Datasets

Project description:Well-differentiated tumours (WDT) of the thyroid gland can be difficult to diagnose. Focal nuclear clearing can be suggestive of a papillary thyroid carcinoma (PTC), while questionable vascular or capsular penetration may raise the possibility of diagnosis of a follicular thyroid carcinoma (FTC). The recently proposed term “thyroid tumours of uncertain malignant potential” (TT-UMP) defines cases showing inconclusive morphological evidence of malignancy. We use microRNA (miRNA) expression profiling to analyze 42 well differentiated thyroid tumours including 7 follicular tumours of uncertain malignant potential (FT-UMP), 6 well differentiated tumours of uncertain malignant potential (WDT-UMP), 7 follicular thyroid adenomas (FTA), 5 follicular variant of papillary thyroid carcinoma (FV-PTC) 6 follicular thyroid carcinoma (FTC), and 11 conventional papillary thyroid carcinoma (C-PTC) with 6 C-PTC mutated for BRAFV600E (C-PTC-mut) and 5 not mutated: wild type (C-PTC-wt). Comparison of these 13 tumours of uncertain malignant potential (7 FT-UMP and 6 WDT-UMP) with those obtained from 16 PTC (11 C-PTC and 5 FV-PTC), 6 FTC and 7 FTA is performed in order to clarify the relationships between TT-UMP and the morphologically well characterized categories of thyroid tumours (i.e. C-PTC, FV-PTC, FTC and FTA). In first, each pathological sample (“L” for Lesional tissue) is compared with its matched control (“S” for Safe tissue) for the 42 patients (84 miRNA microarray slides). This control was taken from the same patient at a large distance from the tumour. Secondly, the perilesional tissue from the same patients but 2 (1 PTC and 1 adenoma, without enough RNA left) is compared to normal thyroid tissue (safe tissue reference) obtained from a patient who underwent total thyroidectomy for a laryngeal carcinoma that partially invaded the thyroid gland, to search for microRNA signatures of perilesional tissues (80 miRNA microarray slides).Experiments is performed with a miRNA microarray, referenced in GEO under the accession number GPL4717 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL4717). 42 Patients: 7 FT-UMP, 6 WDT-UMP, 7 FTA (+1 duplicate sample), 5 FV-PTC, 6 FTC (+ 2 duplicate samples), and 11 C-PTC. 44 dye-swap pairs and 1 with no dye swap pair for a total of 89 samples.

Project description:Context: Papillary thyroid cancer (PTC) is the most common thyroid cancer with dramatically increasing worldwide. Accurate diagnosis is crucial to avoid unnecessary overtreatment for low-risk patients, but the currently available markers are still insufficient. tRNA-derived fragments (tRFs) are emerging as a new class of small non-coding RNAs due to their potential roles as novel biomarkers and therapeutic targets in cancer. However, their involvement in papillary thyroid carcinoma (PTC) is still unclear. Objectives: This study was performed to identify differentially expressed tRFs in PTC and investigate the role of serum tRFs to discriminate PTC from nodular goiters (NGs).Methods and Materials: tRF expression profiles were measured in pooled sera from patients with PTCs (n=6), NGs (n=6) and healthy subjects (n=6) using high-throughput sequencing (cohort1). One selected tRF candidate was validated in the same individual samples (cohort1) by qRT-PCR. The confirmed tRF was further validated in a larger second cohort including patients with PTCs (n=30), NGs (n=20) and healthy individuals (n=18).Results: Our sequencing results showed that circulating tRFs were differentially expressed in patients with PTC compared with those with NG and healthy people. After the validation in individual samples, tRF-Pro-AGG-018 was confirmed as differentially elevated in serum samples of patients with PTC. These results were further confirmed in a larger second cohort. Moreover, tRF-Pro-AGG-018 exhibited good diagnostic performance with a sensitivity of 0.7222 and a specificity of 0.8846 for PTC and it was associated with several targeted genes of PTC. Conclusions: Our study identified that the serum tRFs were differentially expressed in PTC compared with NG and healthy individuals. Notably, circulating tRF-Pro-AGG-018 was found to have a significant elevation in patients with PTC and exhibited good diagnostic performance with relatively high sensitivity and specificity for PTC. Our findings suggest tRFs might serve as novel circulating biomarkers in predicting patients with PTC, which may prevent unnecessary thyroid surgeries for low-risk and benefit from optimal

Dataset Information

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

Publications

Risk stratification of papillary thyroid cancers using multidimensional machine learning.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets