Department of General Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Department of Thyroid and Breast Surgery, Wuzhong People's Hospital of Suzhou City, Suzhou, China.
J Clin Lab Anal. 2022 Jun;36(6):e24384. doi: 10.1002/jcla.24384. Epub 2022 Apr 20.
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.
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 | > 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.
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).
The prognostic model based on lipid metabolism lncRNAs possessed an important value in survival prediction of breast cancer patients.
脂质代谢与乳腺癌的发生发展密切相关。本研究旨在建立一种基于脂质代谢相关长非编码 RNA（lncRNA）的新型模型，并评估其在预测乳腺癌患者预后方面的潜在临床价值。
从癌症基因组图谱（TCGA）数据库中获取乳腺癌的 RNA 数据和临床信息。通过相关系数 |R|>0.4 和 p<0.001 的标准来识别脂质代谢相关的 lncRNA，并通过 Cox 回归分析来确定预后 lncRNA 以建立模型。使用训练集和验证集来验证模型的可行性，并将所有样本分为高风险组或低风险组。通过基因本体论（GO）和基因集富集分析（GSEA）来评估潜在的生物学功能，并通过 Cibersortx 数据库来探索免疫浸润水平。
共鉴定出 14 个 lncRNA 作为保护性基因（AC022150.4、AC061992.1、AC090948.3、AC092794.1、AC107464.3、AL021707.8、AL451085.2、AL606834.2、FLJ42351、LINC00926、LINC01871、TNFRSF14-AS1、U73166.1 和 USP30-AS1），其 HR<1，而 10 个 lncRNA（AC022150.2、AC090948.1、AC243960.1、AL021707.6、ITGB2-AS1、OTUD6B-AS1、SP2-AS1、TOLLIP-AS1、Z68871.1 和 ZNF337-AS1）与 HR>1 相关。共选择了 24 个预后 lncRNA 构建模型。低风险组患者在训练集（p<0.001）和验证集（p<0.001）中均具有更好的预后。单因素和多因素 Cox 回归分析显示，风险评分是训练集（p<0.001）和验证集（p<0.001）的独立预后因素。GO 和 GSEA 分析表明，这些 lncRNA 与代谢相关信号通路和免疫细胞信号通路有关。风险评分与 B 细胞（r=-0.097，p=0.002）、NK 细胞（r=-0.097，p=0.002）、浆细胞（r=-0.111，p=3.329e-04）、CD4 阳性 T 细胞（r=-0.064，p=0.039）和 CD8 阳性 T 细胞（r=-0.322，p=2.357e-26）呈负相关，与树突状细胞（r=0.077，p=0.013）和单核细胞（r=0.228，p=1.107e-13）呈正相关。
基于脂质代谢 lncRNA 的预后模型在预测乳腺癌患者生存方面具有重要价值。
