A novel prognostic model associated with the overall survival in patients with breast cancer based on lipid metabolism‐related long noncoding RNAs. Issue 6 (20th April 2022) - Shi, Guo‐Jian; Zhou, Qin; Zhu, Qi; Wang, Li; Jiang, Guo‐Qin
Abstract: Background: Lipid metabolism is closely related to the occurrence and development of breast cancer. Our purpose was to establish a novel model based on lipid metabolism‐related long noncoding RNAs (lncRNAs) and evaluate the potential clinical value in predicting prognosis for patients suffering from breast cancer. Methods: RNA data and clinical information for breast cancer were obtained from the cancer genome atlas (TCGA) database. Lipid metabolism‐related lncRNAs were identified via the criteria of correlation coefficient | R 2 | > 0.4 and p < 0.001, and prognostic lncRNAs were identified to establish model through Cox regression analysis. The training set and validation set were established to certify the feasibility, and all samples were separated into high‐risk group or low‐risk group. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were conducted to evaluate the potential biological functions, and the immune infiltration levels were explored through Cibersortx database. Results: A total of 14 lncRNAs were identified as protective genes (AC022150.4, AC061992.1, AC090948.3, AC092794.1, AC107464.3, AL021707.8, AL451085.2, AL606834.2, FLJ42351, LINC00926, LINC01871, TNFRSF14−AS1, U73166.1 and USP30−AS1) with HRs < 1 while 10 lncRNAs (AC022150.2, AC090948.1, AC243960.1, AL021707.6, ITGB2−AS1, OTUD6B−AS1, SP2−AS1, TOLLIP−AS1, Z68871.1 and ZNF337−AS1) were associated with increased risk with HRs >1. A total of 24 prognostic lncRNAs were selected to construct the model. The patients in low‐risk group were associated with better prognosis in both training set ( p < 0.001) and validation set ( p < 0.001). The univariate and multivariate Cox regression analyses revealed that risk score was an independent prognostic factors in both training set ( p < 0.001) and validation set ( p < 0.001). GO and GSEA analyses revealed that these lncRNAs were related to metabolism‐related signal pathway and immune cells signal pathway. Risk score was negatively correlated with B cells ( r = −0.097, p = 0.002), NK cells ( r = −0.097, p = 0.002), Plasma cells ( r = −0.111, p = 3.329e‐04), T‐cells CD4 ( r = −0.064, p = 0.039) and T‐cells CD8 ( r = −0.322, p = 2.357e‐26) and positively correlated with Dendritic cells ( r = 0.077, p = 0.013) and Monocytes ( r = 0.228, p = 1.107e‐13). Conclusion: The prognostic model based on lipid metabolism lncRNAs possessed an important value in survival prediction of breast cancer patients. Abstract : (A) Kaplan–Meier curve of samples in high‐risk group and low‐risk groups in validation set. (B) Distribution of risk score in validation set. (C) The relationship between survival status and risk score in validation set. (D) Forest plot of Cox univariate analysis in validation set. (E) Forest plot of Cox multivariate analysis in validation set. (F) ROC curve of risk score and clinical features in validation set.