Construction of a cancer-associated fibroblast-related lncRNA signature for forecasting the prognosis, immune infiltration and drug response of breast cancer

Cancer-associated fibroblasts (CAFs)-associated long-non coding RNAs (lncRNAs) are key players in cancer progression. This study intended to develop a CAF-related lncRNA signature to forecast breast cancer prognosis, immune infiltration and drug response. The breast cancer-related public data were downloaded. Key CAF-related lncRNAs were screened for establishing the CAF-related lncRNA prognostic signature. The clinical utility of this signature was evaluated, and a nomogram was established by combining prognostic signature with clinical factors. Furthermore, immune cell infiltration analysis, drug sensitivity analysis, and Gene Set Enrichment Analysis (GSEA) of different risk groups were conducted. Besides, the expression of signature lncRNAs was validated in five breast cancer cell lines utilizing quantitative PCR (qPCR). CAF levels were increased in breast cancer samples and samples with high CAF levels had poor prognosis. A prognostic signature was built by eight CAF-related prognostic lncRNAs, including LINC00844, SENCR, NR2F2-AS1, HAND2-AS1, MIR31HG, CACNA1G-AS1, LINC01120, and LINC00626, which had a high prognostic performance, with AUC value of 0.844 (0.949, 0.705) and 0.742 (0.828, 0.614) in training and validation datasets, respectively. Then, a prognostic nomogram was established, which had high survival prediction accuracy and clinically useful. The prognostic signature was related to immune cell infiltration, particularly CD8 + T cell and M2 macrophage, response to several drugs like AZD7762 and cisplatin, and differential pathways, including cell cycle. Besides, qPCR confirmed the reduced expression of LINC00844, SENCR, NR2F2-AS1, HAND2-AS1, MIR31HG, and CACNA1G-AS1 and elevated expression of LINC01120 and LINC00626 in breast cancer cells. A robust CAF-related lncRNA signature was developed, offering valuable insights into forecasting prognosis and optimizing therapeutic strategies for breast cancer.