Establishment and Validation of a Gene Signature-Based Prognostic Model to Improve Survival Prediction in Adrenocortical Carcinoma Patients.

Xiaoqin Ge,Xiujie Wang,Xueyan Yin,Xuehua Jiao,Gengxu Li,Zhenzhen Liu,Giuseppe Reimondo

doi:10.1155/2021/2077633

Abstract

Background The current guideline for the management of adrenocortical carcinoma (ACC) is insufficient for accurate risk prediction to guide adjuvant therapy. Given frequent and severe therapeutic side effects, a better estimate of survival is warranted for risk-specific assignment to adjuvant treatment. We attempted to construct an integrated model based on a prognostic gene signature and clinicopathological features to improve risk stratification and survival prediction in ACC. Methods Using a series of bioinformatic and statistical approaches, a gene-expression signature was established and validated in two independent cohorts. By combining the signature with clinicopathological features, a decision tree was generated to improve risk stratification, and a nomogram was constructed to personalize risk prediction. Time-dependent receiver operating characteristic (tROC) and calibration analysis were performed to evaluate the predictive power and accuracy. Results A three-gene signature could discriminate high-risk patients well in both training and validation cohorts. Multivariate regression analysis demonstrated the signature to be an independent predictor of overall survival. The decision tree could identify risk subgroups powerfully, and the nomogram showed high accuracy of survival prediction. Particularly, expression of a gene hitherto unknown to be dysregulated in ACC, TIGD1, was shown to be prognostically relevant. Conclusion We propose a novel gene signature to guide decision-making about adjuvant therapy in ACC. The score shows unprecedented survival prediction and hence constitutes a huge step towards personalized management. As a secondary important finding, we report the discovery and validation of a new oncogene, TIGD1, which was consistently overexpressed in ACC. TIGD1 might shed further light on the biology of ACC and might give rise to targeted therapies that not only apply to ACC but potentially also to other malignancies.

Highlights

Adrenocortical carcinoma (ACC) is a rare but aggressive malignancy with a generally poor prognosis, with a 5-year overall survival (OS) rate less than 50% in most series [1,2,3].e current tumor-node-metastasis (TNM) stage at diagnosis has been developed by using a large patient cohort from the European Network for the Study of Adrenal Tumors (ENSAT) [4] and independently validated [5]
A total of 985 differentially expressed genes (DEGs) (579 downregulated and 406 upregulated) were identified in 77 ACC samples compared to 14 normal samples (Figure 1(a))
Hierarchical clustering analysis indicated that normal tissues were characterized by lower expression levels of MKI67 and TIGD1 and higher expression levels of SGK1 compared to ACC tissues in the training set (Figure 1(g))

Summary

Introduction

E current tumor-node-metastasis (TNM) stage at diagnosis has been developed by using a large patient cohort from the European Network for the Study of Adrenal Tumors (ENSAT) [4] and independently validated [5]. Variability in clinical outcomes even within the same tumor stage has stimulated the search for markers that harbor prognostic value. Histological tumor grade, resection status, expression of proliferation marker Ki-67, age, and symptoms (GRAS) have been shown to improve prognostication in advanced disease [6]. Molecular markers for an improved prognostication of ACC have been sought during recent years and models using targeted molecular marker assessment have been developed that could improve prediction of recurrence after complete resection but were of more limited value in ENSATstage IV patients [7]. Individual molecular markers have not yet changed treatment strategies in ACC [8, 9].

Methods

Results

Conclusion