Development and Validation of a Risk Prediction Model for Breast Cancer Prognosis Based on Depression-Related Genes

Frontiers in Oncology

卷:12

Wei Yang¹  Zhiyu Wang²  Shengqi Wang²  Neng Wang³  Linda L. D. Zhong⁴  Kexin Su⁵  Yifeng Zheng⁸  Bo Pan⁸  Bowen Yang⁸  Xuan Wang⁸  Juping Zhang⁸ 

[1] Atrius Health, Harvard Vanguard Medical Associates, Burlington, MA, United States;

[2] Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangdong Provincial Academy of Chinese Medical Sciences, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China;

[3] Guangdong-Hong Kong-Macau Joint Lab on Chinese Medicine and Immune Disease Research, Guangzhou University of Chinese Medicine, Guangzhou, China;

[4] School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, Hong Kong SAR, China;

[5] School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, China;

[6] State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China;

[7] The Research Center for Integrative Medicine, School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, China;

[8] The Research Center of Integrative Cancer Medicine, Discipline of Integrated Chinese and Western Medicine, the Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China;

关键词: breast cancer;    depression;    predictive model;    overall survival;    nomogram;   

DOI  :  10.3389/fonc.2022.879563

来源: DOAJ

【 摘 要 】

BackgroundDepression plays a significant role in mediating breast cancer recurrence and metastasis. However, a precise risk model is lacking to evaluate the potential impact of depression on breast cancer prognosis. In this study, we established a depression-related gene (DRG) signature that can predict overall survival (OS) and elucidate its correlation with pathological parameters and sensitivity to therapy in breast cancer.MethodsThe model training and validation assays were based on the analyses of 1,096 patients from The Cancer Genome Atlas (TCGA) database and 2,969 patients from GSE96058. A risk signature was established through univariate and multivariate Cox regression analyses.ResultsTen DRGs were determined to construct the risk signature. Multivariate analysis revealed that the signature was an independent prognostic factor for OS. Receiver operating characteristic (ROC) curves indicated good performance of the model in predicting 1-, 3-, and 5-year OS, particularly for patients with triple-negative breast cancer (TNBC). In the high-risk group, the proportion of immunosuppressive cells, including M0 macrophages, M2 macrophages, and neutrophils, was higher than that in the low-risk group. Furthermore, low-risk patients responded better to chemotherapy and endocrine therapy. Finally, a nomogram integrating risk score, age, tumor-node-metastasis (TNM) stage, and molecular subtypes were established, and it showed good agreement between the predicted and observed OS.ConclusionThe 10-gene risk model not only highlights the significance of depression in breast cancer prognosis but also provides a novel gene-testing tool to better prevent the potential adverse impact of depression on breast cancer prognosis.

【 授权许可】

Unknown