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PPARγ Targets-Derived Diagnostic and Prognostic Index for Papillary Thyroid Cancer

Authors
 Jaehyung Kim  ;  Soo Young Kim  ;  Shi-Xun Ma  ;  Seok-Mo Kim  ;  Su-Jin Shin  ;  Yong Sang Lee  ;  Hojin Chang  ;  Hang-Seok Chang  ;  Cheong Soo Park  ;  Su Bin Lim 
Citation
 CANCERS, Vol.13(20) : 5110, 2021-10 
Journal Title
CANCERS
Issue Date
2021-10
Keywords
diagnosis ; machine learning ; prognosis
Abstract
In most cases, papillary thyroid cancer (PTC) is highly curable and associated with an excellent prognosis. Yet, there are several clinicopathological features that lead to a poor prognosis, underscoring the need for a better genomic strategy to refine prognostication and patient management. We hypothesized that PPARγ targets could be potential markers for better diagnosis and prognosis due to the variants found in PPARG in three pairs of monozygotic twins with PTC. Here, we developed a 10-gene personalized prognostic index, designated PPARGi, based on gene expression of 10 PPARγ targets. Through scRNA-seq data analysis of PTC tissues derived from patients, we found that PPARGi genes were predominantly expressed in macrophages and epithelial cells. Machine learning algorithms showed a near-perfect performance of PPARGi in deciding the presence of the disease and in selecting a small subset of patients with poor disease-specific survival in TCGA-THCA and newly developed merged microarray data (MMD) consisting exclusively of thyroid cancers and normal tissues.
Files in This Item:
T202104790.pdf Download
DOI
10.3390/cancers13205110
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Pathology (병리학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Surgery (외과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Seok Mo(김석모) ORCID logo https://orcid.org/0000-0001-8070-0573
Shin, Su Jin(신수진) ORCID logo https://orcid.org/0000-0001-9114-8438
Lee, Yong Sang(이용상) ORCID logo https://orcid.org/0000-0002-8234-8718
Chang, Hang Seok(장항석) ORCID logo https://orcid.org/0000-0002-5162-103X
Chang, Ho Jin(장호진) ORCID logo https://orcid.org/0000-0002-8940-3484
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/186889
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