An Aging-Related lncRNA Signature Establishing for Breast Cancer Prognosis and Immunotherapy Responsiveness Prediction - Zhou Y; Yang Z; Zeng H (Dove Medical Press, 2024)
Yanshijing Zhou,1 Zihui Yang,2 Hong Zeng2 1Department of Plastic and Cosmetic Surgery, Maternal and Child Health Hospital of Hubei Province, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China; 2Department of Plastic and Cosmetic Surgery, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of ChinaCorrespondence: Zihui Yang; Hong Zeng, Department of Plastic and Cosmetic Surgery, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, Hubei, 430030, People's Republic of China, Email yzh_2017@hust.edu.cn; zenghong1109@163.comPurpose: Emerging evidence demonstrates the vital role of aging and long non-coding RNAs (lncRNAs) in breast cancer (BC) progression. Our study intended to develop a prognostic risk model based on aging-related lncRNAs (AG-lncs) to foresee BC patients' outcomes.Patients and Methods: 307 aging-related genes (AGs) were sequenced from the TCGA project. Then, 697 AG-lncs were identified by the co-expression analysis with AGs. Using multivariate and univariate Cox regression analysis, and LASSO, 6 AG-lncs, including al136531.1, mapt-as1, al451085.2, otud6b-as1, tnfrsf14-as1, and linc01871, were validated to compute the risk score and establish a risk signature. Expression levels of al136531.1, mapt-as1, al451085.2, tnfrsf14-as1, and linc01871 were higher in low-risk BC patients, whereas otud6b-as1 expression was higher in high-risk BC patients. In the training and testing set, high-risk patients performed shorter PFI, OS, and DFS than low-risk patients.Results: Our risk signature had the highest concordance index among other established prognostic signatures and displayed ideal predictive ability for 1-, 3- and 5-year patient OS in the nomogram. Additionally, BC patients with different risk score levels showed different immune statuses and responses to immunotherapy via GSEA, ssGSEA, ESTIMATE algorithm, and TIDE algorithm analysis. Of note, the qRT-PCR analysis validated that these 6 AG-lncs expressed quite differentially in BC tissues at various clinical stages.Conclusion: The risk signature of 6 AG-lncs might offer a novel prognostic biomarker and promisingly enhance BC immunotherapy's effectiveness.Keywords: risk model, senescence, immune, cancer therapy