Data_Sheet_2_A Novel Framework to Predict Breast Cancer Prognosis Using Immune-Associated LncRNAs.PDF

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.

研究背景：乳腺癌（Breast Cancer，BC）是女性群体中最常见的确诊恶性肿瘤之一。由于恶性肿瘤具有高度异质性，探寻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）。
研究方法：本研究从癌症基因组图谱（The Cancer Genome Atlas，TCGA）数据库下载乳腺癌患者的长链非编码RNA表达数据与临床信息，以对候选基因开展全面分析。采用单因素Cox回归分析与迭代套索（Lasso）Cox回归分析，构建了一组在乳腺癌癌组织中富集的免疫相关长链非编码RNA模型。该模型的预后预测性能在两个独立队列（GSE21653与BC-KR）中得到验证，并与已报道的预后生物标志物进行了对比。我们整合免疫相关长链非编码RNA特征与临床病理因素，构建了列线图以精准评估该特征的预后价值。此外，本研究还分析了该特征与乳腺癌免疫细胞浸润之间的相关性。
研究结果：Kaplan-Meier分析结果显示，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回归分析结果显示，免疫相关长链非编码RNA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）以及免疫细胞综合分析均显示，该特征与免疫细胞浸润显著相关。
研究结论：本研究成功构建了一组免疫相关长链非编码RNA特征，可精准预测乳腺癌的预后情况。