Table_2_A Signature of Autophagy-Related Long Non-coding RNA to Predict the Prognosis of Breast Cancer.DOCX

Background: A surge in newly diagnosed breast cancer has overwhelmed the public health system worldwide. Joint effort had beed made to discover the genetic mechanism of these disease globally. Accumulated research has revealed autophagy may act as a vital part in the pathogenesis of breast cancer.Objective: Aim to construct a prognostic model based on autophagy-related lncRNAs and investigate their potential mechanisms in breast cancer.Methods: The transcriptome data and clinical information of patients with breast cancer were obtained from The Cancer Genome Atlas (TCGA) database. Autophagy-related genes were obtained from the Human Autophagy Database (HADb). Long non-coding RNAs (lncRNAs) related to autophagy were acquired through the Pearson correlation analysis. Univariate Cox regression analysis as well as the least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify autophagy-related lncRNAs with prognostic value. We constructed a risk scoring model to assess the prognostic significance of the autophagy-related lncRNAs signatures. The nomogram was then established based on the risk score and clinical indicators. Through the calibration curve, the concordance index (C-index) and receiver operating characteristic (ROC) curve analysis were evaluated to obtain the model's predictive performance. Subgroup analysis was performed to evaluate the differential ability of the model. Subsequently, gene set enrichment analysis was conducted to investigate the potential functions of these lncRNAs.Results: We attained 1,164 breast cancer samples from the TCGA database and 231 autophagy-related genes from the HAD database. Through correlation analysis, 179 autophagy-related lncRNAs were finally identified. Univariate Cox regression analysis and LASSO regression analysis further screened 18 prognosis-associated lncRNAs. The risk scoring model was constructed to divide patients into high-risk and low-risk groups. It was found that the low-risk group had better overall survival (OS) than those of the high-risk group. Then, the nomogram model including age, tumor stage, TNM stage and risk score was established. The evaluation index (C-index: 0.78, 3-year OS AUC: 0.813 and 5-year OS AUC: 0.785) showed that the nomogram had excellent predictive power. Subgroup analysis showed there were difference in OS between high-risk and low-risk patients in different subgroups (stage I-II, ER positive, Her-2 negative and non-TNBC subgroups; all P < 0.05). According to the results of gene set enrichment analysis, these lncRNAs were involved in the regulation of multicellular organismal macromolecule metabolic process in multicellular organisms, nucleotide excision repair, oxidative phosphorylation, and TGF-β signaling pathway.Conclusions: We identified 18 autophagy-related lncRNAs with prognostic value in breast cancer, which may regulate tumor growth and progression in multiple ways.

背景：全球范围内新诊断的乳腺癌病例激增，已导致公共卫生体系不堪重负。全球范围内，各方共同努力，致力于揭示该疾病的遗传机制。累积的研究表明，自噬可能在乳腺癌的发病机制中扮演着至关重要的角色。目标：旨在构建一个基于自噬相关长非编码RNA（lncRNAs）的预后模型，并探究其在乳腺癌中的潜在作用机制。方法：收集乳腺癌患者的转录组数据和临床信息来自癌症基因组图谱（TCGA）数据库。自噬相关基因来源于人类自噬数据库（HADb）。通过皮尔逊相关性分析获取与自噬相关的长非编码RNA（lncRNAs）。使用单因素Cox回归分析和最小绝对收缩和选择算子（LASSO）回归分析识别具有预后价值的自噬相关lncRNAs。构建了一个风险评估模型以评估自噬相关lncRNAs特征组的预后意义。基于风险评分和临床指标建立了列线图。通过校准曲线、一致性指数（C-index）和受试者工作特征（ROC）曲线分析评估了模型的预测性能。进行亚组分析以评估模型的差异能力。随后，进行基因集富集分析以探究这些lncRNAs的潜在功能。结果：我们从TCGA数据库中获得1,164个乳腺癌样本和来自HAD数据库的231个自噬相关基因。通过相关性分析，最终确定了179个自噬相关lncRNAs。单因素Cox回归分析和LASSO回归分析进一步筛选出18个与预后相关的lncRNAs。构建了风险评估模型以将患者分为高风险组和低风险组。研究发现，低风险组的总生存率（OS）优于高风险组。然后，建立了包括年龄、肿瘤阶段、TNM阶段和风险评分的列线图模型。评估指标（C-index：0.78，3年OS AUC：0.813和5年OS AUC：0.785）显示，该列线图具有优异的预测能力。亚组分析显示，在不同亚组（I-II期、ER阳性、Her-2阴性和非TNBC亚组）中，高风险和低风险患者在OS方面存在差异（所有P < 0.05）。根据基因集富集分析的结果，这些lncRNAs参与了多细胞生物中多细胞有机大分子代谢过程的调节、核苷酸切除修复、氧化磷酸化和TGF-β信号通路。结论：我们确定了18个在乳腺癌中具有预后价值的自噬相关lncRNAs，这些lncRNAs可能通过多种方式调节肿瘤的生长和进展。