%A Jiang, Chenglu 
%A Zhang, Shengke 
%A Jiang, Lai 
%A Chen, Zipei 
%A Chen, Haiqing 
%A Huang, Jinbang 
%A Zhou, Xuancheng 
%A Tang, Jingyi 
%A Gao, Xinrui 
%A Chi, Hao 
%A Yang, Guanhu 
%A Huang, Shangke 
%D 2024
%T Precision prognostication in breast cancer: unveiling a long non-coding RNA-based model linked to disulfidptosis for tailored immunotherapeutic strategies
%! Precision prognostication in breast cancer: unveiling a long non-coding RNA-based model linked to disulfidptosis for tailored immunotherapeutic strategies
%K breast cancer, immune marker, prognosis, tumor microenvironment, immunotherapy
%X Background: Breast cancer, comprising 15% of newly diagnosed malignancies, poses a formidable global oncological challenge for women. The severity of this malady stems from tumor infiltration, metastasis, and elevated mortality rates. Disulfidptosis, an emerging cellular demise mechanism, presents a promising avenue for precision tumor therapy. Our aim was to construct a prognostic framework centered on long non-coding RNAs (lncRNAs) associated with disulfidptosis, aiming to guide the strategic use of clinical drugs, enhance prognostic precision, and advance immunotherapy and clinical prognosis assessment.
Methods: We systematically analyzed the TCGA-BRCA dataset to identify disulfidptosis-linked lncRNAs. Employing co-expression analysis, we discerned significant relationships between disulfidptosis-associated genes and lncRNAs. Identified lncRNAs underwent univariate Cox regression and validation through LASSO regression, culminating in the identification of eight signature lncRNAs using a multivariate Cox proportional risk regression model. Then, we utilized the selected genes to build prognostic prediction models.
Results: The DAL model exhibited outstanding prognostic efficacy, establishing itself as an autonomous determinant for breast cancer prognosis. It adeptly differentiated low and high-risk patient cohorts, with high-risk individuals experiencing significantly abbreviated survival durations. Notably, these cohorts displayed marked discrepancies in clinical markers and tumor microenvironment attributes.
Conclusions: The DAL model has performed well in clinical prognostic assessment by combining it with other clinical traditional indicators to construct Nomogram plots and use gene expression data to calculate patients' disease risk scores. This approach provides new ideas for clinical decision support and personalized treatment decisions for patients with different risk levels.
%U https://doi.org/10.18632/aging.205946
%J Aging
%0 Journal Article
%R 10.18632/aging.205946
%@ 1945-4589