Establishment and Validation of a Gene Signature-Based Prognostic Model to Improve Survival Prediction in Adrenocortical Carcinoma Patients
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 treat...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Endocrinology |
| Online Access: | http://dx.doi.org/10.1155/2021/2077633 |
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| author | Xiaoqin Ge Zhenzhen Liu Xuehua Jiao Xueyan Yin Xiujie Wang Gengxu Li |
| author_facet | Xiaoqin Ge Zhenzhen Liu Xuehua Jiao Xueyan Yin Xiujie Wang Gengxu Li |
| author_sort | Xiaoqin Ge |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a0983767ec67427bbcabb0297c7e01a8 |
| institution | Kabale University |
| issn | 1687-8345 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Endocrinology |
| spelling | doaj-art-a0983767ec67427bbcabb0297c7e01a82025-02-03T01:02:37ZengWileyInternational Journal of Endocrinology1687-83452021-01-01202110.1155/2021/2077633Establishment and Validation of a Gene Signature-Based Prognostic Model to Improve Survival Prediction in Adrenocortical Carcinoma PatientsXiaoqin Ge0Zhenzhen Liu1Xuehua Jiao2Xueyan Yin3Xiujie Wang4Gengxu Li5Department of EndocrinologyDepartment of EndocrinologyDepartment of EndocrinologyDepartment of EndocrinologyDepartment of EndocrinologyDepartment of EndocrinologyBackground. 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.http://dx.doi.org/10.1155/2021/2077633 |
| spellingShingle | Xiaoqin Ge Zhenzhen Liu Xuehua Jiao Xueyan Yin Xiujie Wang Gengxu Li Establishment and Validation of a Gene Signature-Based Prognostic Model to Improve Survival Prediction in Adrenocortical Carcinoma Patients International Journal of Endocrinology |
| title | Establishment and Validation of a Gene Signature-Based Prognostic Model to Improve Survival Prediction in Adrenocortical Carcinoma Patients |
| title_full | Establishment and Validation of a Gene Signature-Based Prognostic Model to Improve Survival Prediction in Adrenocortical Carcinoma Patients |
| title_fullStr | Establishment and Validation of a Gene Signature-Based Prognostic Model to Improve Survival Prediction in Adrenocortical Carcinoma Patients |
| title_full_unstemmed | Establishment and Validation of a Gene Signature-Based Prognostic Model to Improve Survival Prediction in Adrenocortical Carcinoma Patients |
| title_short | Establishment and Validation of a Gene Signature-Based Prognostic Model to Improve Survival Prediction in Adrenocortical Carcinoma Patients |
| title_sort | establishment and validation of a gene signature based prognostic model to improve survival prediction in adrenocortical carcinoma patients |
| url | http://dx.doi.org/10.1155/2021/2077633 |
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