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[C. Omics & Fusion Biology] C-1

PPARγ Targets-Derived Diagnostic and Prognostic Index for

Papillary Thyroid Cancer

Jaehyung Kim¹ , Soo Young Kim² , Shi-Xun Ma³, Su-Jin Shin⁵, Yong Sang Lee⁴, Hojin Chang⁴, Hang-Seok
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Chang⁴, Cheong Soo Park⁶, Seok-Mo Kim⁴*, Su Bin Lim¹*

¹Department of Biochemistry & Molecular Biology, Ajou University School of Medicine, Suwon 16499, Korea,

²Department of Surgery, Ajou University School of Medicine, Suwon 16499, Korea, ³Department of Neurology,
Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore MD 21205, USA,

⁴Department of Surgery, Yonsei University College of Medicine, Seoul 06273, Korea, ⁵Department of Pathology,
Yonsei University College of Medicine, Seoul 06273, Korea

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.