Pparγ targets‐derived diagnostic and prognostic index for papillary thyroid cancer

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

doi:10.3390/cancers13205110

Pparγ targets‐derived diagnostic and prognostic index for papillary thyroid cancer

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

Department of Surgery

Research output: Contribution to journal › Article › peer-review

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 manage-ment. 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 pres-ence 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.

Original language	English
Article number	5110
Journal	Cancers
Volume	13
Issue number	20
DOIs	https://doi.org/10.3390/cancers13205110
Publication status	Published - 2021 Oct 2

Bibliographical note

Funding Information:
Funding: This research was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1F1A1064122 and 2017R1E1A1A03070345) and a faculty research fund of Ajou University School of Medicine and Yonsei University College of Med‐ icine (6‐2019‐0092).

Funding Information:
Acknowledgments: This research was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1F1A1064122 and 2017R1E1A1A03070345) and a faculty research fund of Ajou University School of Medicine and Yonsei University College of Medicine (6‐2019‐0092).

Publisher Copyright:
© 2021 by the authors. Li-censee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

Oncology
Cancer Research

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.3390/cancers13205110

Cite this

@article{3700ba87ef524de48c7a9cf1f1b8de06,

title = "Pparγ targets‐derived diagnostic and prognostic index for papillary thyroid cancer",

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 manage-ment. 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 pres-ence 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.",

author = "Jaehyung Kim and Kim, {Soo Young} and Ma, {Shi Xun} and Kim, {Seok Mo} and Shin, {Su Jin} and Lee, {Yong Sang} and Hojin Chang and Chang, {Hang Seok} and Park, {Cheong Soo} and Lim, {Su Bin}",

note = "Funding Information: Funding: This research was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1F1A1064122 and 2017R1E1A1A03070345) and a faculty research fund of Ajou University School of Medicine and Yonsei University College of Med‐ icine (6‐2019‐0092). Funding Information: Acknowledgments: This research was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1F1A1064122 and 2017R1E1A1A03070345) and a faculty research fund of Ajou University School of Medicine and Yonsei University College of Medicine (6‐2019‐0092). Publisher Copyright: {\textcopyright} 2021 by the authors. Li-censee MDPI, Basel, Switzerland.",

year = "2021",

month = oct,

day = "2",

doi = "10.3390/cancers13205110",

language = "English",

volume = "13",

journal = "Cancers",

issn = "2072-6694",

publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",

number = "20",

}

TY - JOUR

T1 - Pparγ targets‐derived diagnostic and prognostic index for papillary thyroid cancer

AU - Kim, Jaehyung

AU - Kim, Soo Young

AU - Ma, Shi Xun

AU - Kim, Seok Mo

AU - Shin, Su Jin

AU - Lee, Yong Sang

AU - Chang, Hojin

AU - Chang, Hang Seok

AU - Park, Cheong Soo

AU - Lim, Su Bin

N1 - Funding Information: Funding: This research was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1F1A1064122 and 2017R1E1A1A03070345) and a faculty research fund of Ajou University School of Medicine and Yonsei University College of Med‐ icine (6‐2019‐0092). Funding Information: Acknowledgments: This research was funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1F1A1064122 and 2017R1E1A1A03070345) and a faculty research fund of Ajou University School of Medicine and Yonsei University College of Medicine (6‐2019‐0092). Publisher Copyright: © 2021 by the authors. Li-censee MDPI, Basel, Switzerland.

PY - 2021/10/2

Y1 - 2021/10/2

N2 - 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 manage-ment. 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 pres-ence 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.

AB - 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 manage-ment. 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 pres-ence 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.

UR - http://www.scopus.com/inward/record.url?scp=85116808127&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85116808127&partnerID=8YFLogxK

U2 - 10.3390/cancers13205110

DO - 10.3390/cancers13205110

M3 - Article

AN - SCOPUS:85116808127

SN - 2072-6694

VL - 13

JO - Cancers

JF - Cancers

IS - 20

M1 - 5110

ER -

Pparγ targets‐derived diagnostic and prognostic index for papillary thyroid cancer

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this