Table_2_A Novel Framework to Predict Breast Cancer Prognosis Using Immune-Associated LncRNAs.XLSX

Background: Breast cancer (BC) is one of the most frequently diagnosed malignancies among females. As a huge heterogeneity of malignant tumor, it is important to seek reliable molecular biomarkers to carry out the stratification for patients with BC. We surveyed immune- associated lncRNAs that may be used as potential therapeutic targets in BC.
Methods: LncRNA expression data and clinical information of BC patients were downloaded from the TCGA database for a comprehensive analysis of candidate genes. A model consisting of immune-related lncRNAs enriched in BC cancerous tissues was established using the univariate Cox regression analysis and the iterative Lasso Cox regression analysis. The prognostic performance of this model was validated in two independent cohorts (GSE21653 and BC-KR), and compared with known prognostic biomarkers. A nomogram that integrated the immune-related lncRNA signature and clinicopathological factors was constructed to accurately assess the prognostic value of this signature. The correlation between the signature and immune cell infiltration in BC was also analyzed.
Results: The Kaplan-Meier analysis showed that the OS of Patients in the low-risk group had significantly better survival than those in the high-risk group, Clinical subgroup analysis showed that the predictive ability was independent of clinicopathological factors. Univariate/multivariate Cox regression analysis showed immune lncRNA signature is an important prognostic factor and an independent prognostic marker. In addition, GSEA and GSVA analysis as well as comprehensive analysis of immune cells showed that the signature was significantly correlated with the infiltration of immune cells.
Conclusion: We successfully constructed an immune-associated lncRNA signature that can accurately predict BC prognosis.

背景：乳腺癌（Breast Cancer, BC）是女性群体中最常见的确诊恶性肿瘤之一。作为一类具有高度异质性的恶性肿瘤，寻找可靠的分子生物标志物以实现乳腺癌患者的分层诊疗至关重要。本研究聚焦于可作为乳腺癌潜在治疗靶点的免疫相关长链非编码RNA（long non-coding RNA, lncRNA）。
方法：从TCGA（The Cancer Genome Atlas）数据库下载乳腺癌患者的长链非编码RNA表达数据及临床信息，对候选基因开展全面分析。通过单因素Cox回归分析与迭代套索（Lasso）Cox回归分析，构建了在乳腺癌癌组织中富集的免疫相关lncRNA模型。随后在两个独立队列（GSE21653与BC-KR）中验证该模型的预后效能，并与已报道的预后生物标志物进行对比。本研究整合免疫相关lncRNA特征与临床病理因素构建列线图，以精准评估该特征的预后价值；同时分析了该特征与乳腺癌组织中免疫细胞浸润的相关性。
结果：卡普兰-迈耶（Kaplan-Meier）分析显示，低风险组患者的总生存期（Overall Survival, OS）显著优于高风险组患者。临床亚组分析表明，该模型的预测能力不受临床病理因素的影响。单因素/多因素Cox回归分析显示，免疫相关lncRNA特征是重要的预后因素，同时也是独立的预后标志物。此外，基因集富集分析（Gene Set Enrichment Analysis, GSEA）、基因集变异分析（Gene Set Variation Analysis, GSVA）以及免疫细胞的综合分析均显示，该特征与免疫细胞浸润程度显著相关。
结论：本研究成功构建了可精准预测乳腺癌预后的免疫相关lncRNA特征。