A Five-Gene Prognostic Nomogram Predicting Disease-Free Survival of Differentiated Thyroid Cancer.

Pan Ruchong,Wang Xiang,Tang Haiping,Chiara Nicolazzo

doi:10.1155/2021/5510780

Pan Ruchong, Wang Xiang + Show 2 more

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https://doi.org/10.1155/2021/5510780

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Journal: Disease markers	Publication Date: Jun 15, 2021
Citations: 7	License type: CC BY 4.0

Affiliation: Chongqing University

Abstract

Background Differentiated thyroid cancer (DTC) is the most common type of thyroid tumor with a high recurrence rate. Here, we developed a nomogram to effectively predict postoperative disease-free survival (DFS) in DTC patients. Methods The mRNA expressions and clinical data of DTC patients were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Seventy percent of patients were randomly selected as the training dataset, and thirty percent of patients were classified into the testing dataset. Multivariate Cox regression analysis was adopted to establish a nomogram to predict 1-year, 3-year, and 5-year DFS rate of DTC patients. Results A five-gene signature comprised of TENM1, FN1, APOD, F12, and BTNL8 genes was established to predict the DFS rate of DTC patients. Results from the concordance index (C-index), area under curve (AUC), and calibration curve showed that both the training dataset and the testing dataset exhibited good prediction ability, and they were superior to other traditional models. The risk score and distant metastasis (M) of the five-gene signature were independent risk factors that affected DTC recurrence. A nomogram that could predict 1-year, 3-year, and 5-year DFS rate of DTC patients was established with a C-index of 0.801 (95% CI: 0.736, 0.866). Conclusion Our study developed a prediction model based on the gene expression and clinical characteristics to predict the DFS rate of DTC patients, which may be applied to more accurately assess patient prognosis and individualized treatment.

Highlights

Differentiated thyroid cancer (DTC) is the most common tumor in the head and neck area and accounts for approximately 90% of all cases
DTC is composed of papillary thyroid carcinoma (PTC) and follicular thyroid carcinoma (FTC), which both originate from follicular cells of the thyroid [1, 2]
Gene Ontology (GO) analysis demonstrated that changes in biological processes of differentially expressed genes (DEGs) were mainly enriched in interactions between extracellular matrix (ECM), angiogenesis, BMP signaling pathway, transforming growth factor-beta receptor signaling pathway, and the regulation of the mitogen-activated protein kinase (MAPK) cascade (Figure 2(c))

Summary

Introduction

Differentiated thyroid cancer (DTC) is the most common tumor in the head and neck area and accounts for approximately 90% of all cases. The commonly used risk stratification includes the American Joint Committee on Cancer (AJCC) staging system,the American Thyroid Association (ATA) staging system, and the European Thyroid Association (ETA) staging system [6,7,8] These traditional risk stratification systems are useful for predicting overall patient outcome, it is difficult to apply them for individualized and accurate prediction. Multivariate Cox regression analysis was adopted to establish a nomogram to predict 1-year, 3-year, and 5-year DFS rate of DTC patients. Our study developed a prediction model based on the gene expression and clinical characteristics to predict the DFS rate of DTC patients, which may be applied to more accurately assess patient prognosis and individualized treatment

Methods

Results

Conclusion