A novel exosome-derived prognostic signature and risk stratification for breast cancer based on multi-omics and systematic biological heterogeneity.

Abstract

Tumor heterogeneity remains a major challenge for disease subtyping, risk stratification, and accurate clinical management. Exosome-based liquid biopsy can effectively overcome the limitations of tissue biopsy, achieving minimal invasion, multi-point dynamic monitoring, and good prognosis assessment, and has broad clinical prospects. However, there is still lacking comprehensive analysis of tumor-derived exosome (TDE)-based stratification of risk patients and prognostic assessment for breast cancer with systematic dissection of biological heterogeneity. In this study, the robust corroborative analysis for biomarker discovery (RCABD) strategy was used for the identification of exosome molecules, differential expression verification, risk prediction modeling, heterogenous dissection with multi-ome (6101 molecules), our ExoBCD database (306 molecules), and 53 independent studies (481 molecules). Our results showed that a 10-molecule exosome-derived signature (exoSIG) could successfully fulfill breast cancer risk stratification, making it a novel and accurate exosome prognostic indicator (Cox P = 9.9E-04, HR = 3.3, 95% CI 1.6-6.8). Interestingly, HLA-DQB2 and COL17A1, closely related to tumor metastasis, achieved high performance in prognosis prediction (86.35% contribution) and accuracy (Log-rank P = 0.028, AUC = 85.42%). With the combined information of patient age and tumor stage, they formed a bimolecular risk signature (Clinmin-exoSIG) and a convenient nomogram as operable tools for clinical applications. In conclusion, as an extension of ExoBCD, this study conducted systematic analyses to identify prognostic multi-molecular panel and risk signature, stratify patients and dissect biological heterogeneity based on breast cancer exosomes from a multi-omics perspective. Our results provide an important reference for in-depth exploration of the "biological heterogeneity - risk stratification - prognosis prediction".

• Authors: Fei Long, Haodong Ma, Youjin Hao, Luyao Tian, Yinghong Li, Bo Li, Juan Chen, Ying Tang, Jing Li, Lili Deng, Guoming Xie, Mingwei Liu
• Journal: Computational and structural biotechnology journal (Comput Struct Biotechnol J) Vol. 21 Pg. 3010-3023 ( 2023) ISSN: 2001-0370 [Print] Netherlands
• PMID: 37273850 (Publication Type: Journal Article)
• Copyright: © 2023 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.