Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, 250061, China.
J Transl Med. 2021 Dec 20;19(1):514. doi: 10.1186/s12967-021-03180-y.
The successful identification of breast cancer (BRCA) prognostic biomarkers is essential for the strategic interference of BRCA patients. Recently, various methods have been proposed for exploring a small prognostic gene set that can distinguish the high-risk group from the low-risk group.
Regularized Cox proportional hazards (RCPH) models were proposed to discover prognostic biomarkers of BRCA from gene expression data. Firstly, the maximum connected network with 1142 genes by mapping 956 differentially expressed genes (DEGs) and 677 previously BRCA-related genes into the gene regulatory network (GRN) was constructed. Then, the 72 union genes of the four feature gene sets identified by Lasso-RCPH, Enet-RCPH, [Formula: see text]-RCPH and SCAD-RCPH models were recognized as the robust prognostic biomarkers. These biomarkers were validated by literature checks, BRCA-specific GRN and functional enrichment analysis. Finally, an index of prognostic risk score (PRS) for BRCA was established based on univariate and multivariate Cox regression analysis. Survival analysis was performed to investigate the PRS on 1080 BRCA patients from the internal validation. Particularly, the nomogram was constructed to express the relationship between PRS and other clinical information on the discovery dataset. The PRS was also verified on 1848 BRCA patients of ten external validation datasets or collected cohorts.
The nomogram highlighted that the importance of PRS in guiding significance for the prognosis of BRCA patients. In addition, the PRS of 301 normal samples and 306 tumor samples from five independent datasets showed that it is significantly higher in tumors than in normal tissues ([Formula: see text]). The protein expression profiles of the three genes, i.e., ADRB1, SAV1 and TSPAN14, involved in the PRS model demonstrated that the latter two genes are more strongly stained in tumor specimens. More importantly, external validation illustrated that the high-risk group has worse survival than the low-risk group ([Formula: see text]) in both internal and external validations.
The proposed pipelines of detecting and validating prognostic biomarker genes for BRCA are effective and efficient. Moreover, the proposed PRS is very promising as an important indicator for judging the prognosis of BRCA patients.
成功鉴定乳腺癌（BRCA）预后生物标志物对于对 BRCA 患者进行策略性干预至关重要。最近，已经提出了各种方法来探索能够区分高危组和低危组的小预后基因集。
提出了正则化 Cox 比例风险（RCPH）模型，用于从基因表达数据中发现 BRCA 的预后生物标志物。首先，通过将 956 个差异表达基因（DEG）和 677 个先前与 BRCA 相关的基因映射到基因调控网络（GRN）中，构建了一个包含 1142 个基因的最大连通网络。然后，通过 Lasso-RCPH、Enet-RCPH、[Formula: see text]-RCPH 和 SCAD-RCPH 模型确定的四个特征基因集的 72 个联合基因被识别为稳健的预后生物标志物。通过文献检查、BRCA 特异性 GRN 和功能富集分析验证了这些生物标志物。最后，基于单变量和多变量 Cox 回归分析，建立了用于 BRCA 的预后风险评分（PRS）指数。对来自内部验证的 1080 名 BRCA 患者进行生存分析。特别是，在发现数据集上构建了列线图以表达 PRS 与其他临床信息之间的关系。还在 10 个外部验证数据集或收集的队列中的 1848 名 BRCA 患者上验证了 PRS。
列线图突出显示了 PRS 在指导 BRCA 患者预后意义方面的重要性。此外，来自五个独立数据集的 301 个正常样本和 306 个肿瘤样本的 PRS 表明，它在肿瘤中明显高于正常组织（[Formula: see text]）。PRS 模型中涉及的三个基因（ADRB1、SAV1 和 TSPAN14）的蛋白质表达谱表明，后两个基因在肿瘤标本中染色更强。更重要的是，外部验证表明，高危组在内部和外部验证中的生存都比低危组差（[Formula: see text]）。
用于检测和验证 BRCA 预后生物标志物的建议管道是有效且高效的。此外，所提出的 PRS 作为判断 BRCA 患者预后的重要指标非常有前途。
