Identification and validation of a prognostic risk model based on caveolin family genes for breast cancer

• Published in: Frontiers in cell and developmental biology Vol. 10; p. 822187
• Main Authors: Tang, Qiang, Wang, Shurui, Di, Ziyang, Li, Huimin, Xu, Kailiang, Hu, Xin, Di, Maojun
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 06.09.2022
• Subjects: breast cancer; caveolins; Cell and Developmental Biology; expression; infiltrating immune; methylation; prognosis
• ISSN: 2296-634X; 2296-634X
• DOI: 10.3389/fcell.2022.822187

Background: Breast cancer (BC) is the most vicious killer of women’s health and is accompanied by increased incidence and mortality rates worldwide. Many studies have demonstrated that caveolins (CAVs) were abnormally expressed in a variety of tumors and implicated in tumorigenesis and cancer progression. However, the role of CAVs in BC remains somewhat contentious. Methods: We comprehensively explored the expression and prognostic value of CAVs (CAV1-3) in BC utilizing public databases (ONCOMINE, TIMER, UALCAN, and TCGA databases). Then we constructed a prognostic model based on the expression profiles. Also, a prognostic nomogram was built to predict the overall survival (OS). We further investigated the relationship between this signature and immune cell infiltration and the mutational landscape in BC. The R package “pRRophetic” was used to predict chemotherapeutic response in BC patients. Finally, we employed loss-of-function approaches to validate the role of CAVs in BC. Results: We found that CAVs were significantly downregulated in various cancer types, especially in BC. Low CAV expression was closely related to the malignant clinicopathological characteristics and worse OS and relapse-free survival (RFS) in BC. Then we constructed a prognostic model based on the expression profiles of CAVs, which divided BC patients into two risk groups. The Kaplan–Meier analysis showed that patients in the high-risk group tend to have a poorer prognosis than those in the low-risk group. Multivariate analysis indicated that the risk score and stage were both independent prognostic factors for BC patients, suggesting a complementary value. The clinical profiles and risk module were used to construct a nomogram that could accurately predict the OS in BC. In addition, we found that patients in the low-risk group tend to have a relatively high immune status and a lower mutation event frequency compared to the high-risk group. Furthermore, this signature could predict the response to chemotherapy and immunotherapy. Finally, CAV depletion promoted the colony formation, migration, and invasion of BC cells. Conclusion: CAVs may serve as novel biomarkers and independent prognostic factors for BC patients. Also, the constructed signature based on CAVs may predict immunotherapeutic responses and provide a novel nomogram for precise outcome prediction of BC.