Identification of a Prognostic 3-Gene Risk Prediction Model for Thyroid Cancer.

Haiping Zhao,Haixing Fang,Shijie Shao,Shiwei Zhang

doi:10.3389/fendo.2020.00510

Haiping Zhao, Haixing Fang + Show 2 more

Open Access

https://doi.org/10.3389/fendo.2020.00510

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Abstract

Objective: We aimed to screen the genes associated with thyroid cancer (THCA) prognosis, and construct a poly-gene risk prediction model for prognosis prediction and improvement.Methods: The HTSeq-Counts data of THCA were accessed from TCGA database, including 505 cancer samples and 57 normal tissue samples. “edgeR” package was utilized to perform differential analysis, and weighted gene co-expression network analysis (WGCNA) was applied to screen the differential co-expression genes associated with THCA tissue types. Univariant Cox regression analysis was further used for the selection of survival-related genes. Then, LASSO regression model was constructed to analyze the genes, and an optimal prognostic model was developed as well as evaluated by Kaplan-Meier and ROC curves.Results: Three thousand two hundred seven differentially expressed genes (DEGs) were obtained by differential analysis and 23 co-expression genes (|COR| > 0.5, P < 0.05) were gained after WGCNA analysis. In addition, eight genes significantly related to THCA survival were screened by univariant Cox regression analysis, and an optimal prognostic 3-gene risk prediction model was constructed after genes were analyzed by the LASSO regression model. Based on this model, patients were grouped into the high-risk group and low-risk group. Kaplan-Meier curve showed that patients in the low-risk group had much better survival than those in the high-risk group. Moreover, great accuracy of the 3-gene model was revealed by ROC curve and the remarkable correlation between the model and patients' prognosis was verified using the multivariant Cox regression analysis.Conclusion: The prognostic 3-gene model composed by GHR, GPR125, and ATP2C2 three genes can be used as an independent prognostic factor and has better prediction for the survival of THCA patients.

Highlights

Thyroid cancer (THCA), derived from parafollicular cells or thyroid follicular cells, is the most common endocrine malignancy accounting for about 1% of all kinds of human cancers (1)
The mechanism of WGCNA is the research for co-expression modules and the exploration of the correlation between the gene network and the phenotypes, which is motivated by the analyses of scale-free clustering and dynamic tree cut on expression profiles
ROC curve was plotted to predict the 3-year survival and the results showed in Figure 2D revealed that area under the curve (AUC) of the 3-gene model was 0.854, which indicated the good performance of the risk score in survival prediction

Summary

Introduction

Thyroid cancer (THCA), derived from parafollicular cells or thyroid follicular cells, is the most common endocrine malignancy accounting for about 1% of all kinds of human cancers (1). A Prognostic 3-Gene Model for Thyroid Cancer (3), while anaplastic carcinoma is rare to be seen with extremely poor prognosis (4). The conventional prognostic model of THCA in clinical practice is constructed according to predictive factors like age, tumor size and lymph nodule metastasis (5). Microarray-based gene expression analysis enables us to identify the important genes during tumor progression and helps to define and diagnose prognostic characteristics (7). In this way, many THCA prognostic biomarkers have been verified. Many THCA prognostic biomarkers have been verified These markers are almost single genes and have not been widely accepted (8). Restricted by research methods, novel biological algorithm needs to be explored to construct more accurate diagnostic or prognosis models

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Conclusion