Development of a prognostic index based on an immunogenomic landscape analysis of papillary thyroid cancer

Abstract BACKGROUND: Papillary thyroid cancer (PTC) is the most common pathological type of thyroid cancer and its incidence is still on the rise worldwide. Our study aimed to provide an understanding of papillary thyroid cancer in immunological aspects and prompt its clinical diagnosis and treatment.METHODS: We obtained RNA-FPKM data and clinical data of papillary thyroid cancer (PTC) samples from the TCGA data portal. Immune-related genes (IRGs) were selected from Molecular Signatures Database v7.0. The immune-related lncRNAs were identified by constructing DELs-IRGs co-expression networks. Cox regression and Kaplan-Meier methods were used to construct immune-related lncRNAs prognosis index (IRLPI) and analyze the impact of IRLPI on overall survival. Gene Set Enrichment Analysis (GSEA) was performed by clusterProfiler R package to explore the potential molecular mechanism in TCGA dataset. The relationship between immune cell types and IRLPI were explored by using Pearson correlation analysis.RESULTS: A total of 478 primary PTC samples were collected for the analysis. 162 immune-related lncRNAs were identified in immune-lncRNAs co-expression network. And 7 lncRNAs were extracted to develop the IRLPI. Patients with high-IRLPI showed poor survival probability (P =0.00052) and IRLPI was identified as an independent prognostic factor (HR: 2.329, 95%CI: 1.481-2.664, p =2.54e-4). ROC curve declared a promising prognosis ability of IRLPI. PCA proved that IRLPI reflected different immune environment and biological processes. GSEA indicated that immune system development is the main different biological process between high- and low-IRLPI group. Furthermore, IRLPI was proved that it related with multiple immune cells and immune responses by Pearson correlation analysis.CONCLUSION: Our results showed that immune-related lncRNAs prognosis index (IRLPI) could serve as a potential prognostic model in papillary thyroid cancer and play an important role in the immunotherapies and surveillance of PTC.

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

Cancers ◽

10.3390/cancers13205110 ◽

2021 ◽

Vol 13 (20) ◽

pp. 5110

Author(s):

Jaehyung Kim ◽

Soo Young Kim ◽

Shi-Xun Ma ◽

Seok-Mo Kim ◽

Su-Jin Shin ◽

...

Keyword(s):

Thyroid Cancer ◽

Papillary Thyroid Cancer ◽

Monozygotic Twins ◽

Prognostic Index ◽

Machine Learning Algorithms ◽

Small Subset ◽

Papillary Thyroid ◽

Normal Tissues ◽

Diagnosis And Prognosis ◽

Perfect Performance

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.

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