Table_6_Identification of a Three-RNA Binding Proteins (RBPs) Signature Predicting Prognosis for Breast Cancer.xlsx

BackgroundTo date, breast cancer remains the primary cause of tumor-related death among women, even though some leap-type developments of oncology have been done to slash the mortality. Considering the tumor heterogeneity and individual variation, the more reliable biomarkers are required to be identified for supporting the development of precision medicine in breast cancer.
MethodsBased on the TCGA-BRCA and METABRIC databases, the differently expressed RNA binding proteins (RBPs) between tumor and normal tissues were investigated. In this study, we focused on the communal differently expressed RBPs in four subtypes of breast cancer. Lasso-penalized Cox analysis, Stepwise-multivariate Cox analysis and Kaplan–Meier survival curve were performed to identify the hub RBP-coding genes in predicting prognosis of breast cancer, and a prognostic model was established. The efficiency of this model was further validated in other independent GSE20685, GSE4922 and FUSCC-TNBC cohorts by calculating the risk score and performing survival analysis, ROC and nomogram. Moreover, pathologic functions of the candidate RBPs in breast cancer were explored using some routine experiments in vitro, and the potential compounds targeting these RBPs were predicted by reviewing the Comparative Toxicogenomics Database.
ResultsHere, we identified 62 RBPs which were differently expressed between the tumor and normal tissues. Thereinto, three RBPs (MRPL12, MRPL13 and POP1) acted as independent risk factors, and their expression pattern also correlated with poor prognosis of patients. A prognostic model, built with these 3-RBPs, possessed statistical significance to predict the survival probability of patients with breast cancer. Furthermore, experimental validations showed that down-regulating the expression of endogenous MRPL12, MRPL13 or POP1 could dramatically suppress the cellular viability and migration of breast cancer cells in vitro. Besides, some compounds (such as the Acetaminophen, Urethane and Tunicamycin) were predicted for curing breast cancer via targeting MRPL12, MRPL13 and POP1 simultaneously.
ConclusionThis study identified and established a 3-RBPs-based signature and nomogram for predicting the survival probability of patients with breast cancer. MRPL12, MRPL13 and POP1 might act as oncogenes in maintaining cellular viability and accelerating metastasis of breast cancer cells, implying the possibility of which to be designed as biomarkers and/or therapeutic targets for breast cancer.

背景 迄今为止，乳腺癌仍是导致女性肿瘤相关死亡的首要原因，尽管肿瘤学领域已取得若干突破性进展以降低其死亡率。鉴于肿瘤异质性与个体差异，亟需挖掘更可靠的生物标志物，以助力乳腺癌精准医学的发展。
方法 本研究基于TCGA-BRCA与METABRIC数据库，分析肿瘤组织与正常组织间差异表达的RNA结合蛋白（RNA binding protein, RBP）。本次研究聚焦于四种乳腺癌亚型中共有的差异表达RBP。通过Lasso惩罚Cox分析、逐步多因素Cox分析及Kaplan-Meier生存曲线，筛选出可预测乳腺癌预后的核心RBP编码基因，并构建预后模型。随后，通过计算风险评分、开展生存分析、ROC曲线分析及列线图构建，在独立队列GSE20685、GSE4922与FUSCC-TNBC中验证该模型的效能。此外，本研究通过常规体外实验探讨候选RBP在乳腺癌中的病理功能，并通过检索比较毒理基因组学数据库（Comparative Toxicogenomics Database）预测靶向这些RBP的潜在治疗化合物。
结果 本研究共筛选出62个在肿瘤组织与正常组织间差异表达的RBP。其中，MRPL12、MRPL13与POP1这3个RBP可作为独立风险因素，且其表达水平与患者不良预后显著相关。基于这3个RBP构建的预后模型，可有效预测乳腺癌患者的生存概率，且具有统计学意义。进一步实验验证显示，下调内源性MRPL12、MRPL13或POP1的表达，可显著抑制乳腺癌细胞的体外增殖与迁移能力。此外，本研究预测得到对乙酰氨基酚（Acetaminophen）、氨基甲酸乙酯（Urethane）与衣霉素（Tunicamycin）等化合物，可同时靶向MRPL12、MRPL13与POP1以治疗乳腺癌。
结论 本研究筛选并构建了基于3个RBP的预后特征及列线图，用于预测乳腺癌患者的生存概率。MRPL12、MRPL13与POP1可能作为癌基因，通过维持乳腺癌细胞活力并促进其转移发挥作用，提示这三个分子可作为乳腺癌的生物标志物及/或治疗靶点进行开发。