The Second Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300600, China.
Key Laboratory of Cancer Prevention and Therapy, Tianjin 300600, China.
Dis Markers. 2021 Oct 4;2021:4731349. doi: 10.1155/2021/4731349. eCollection 2021.
Basal breast cancer subtype is the worst prognosis subtypes among all breast cancer subtypes. Recently, a new tumor stemness index-mRNAsi is found to be able to measure the degree of oncogenic differentiation of tissues. The mRNAsi involved in a variety of cancer processes is derived from the innovative application of one-class logistic regression (OCLR) machine learning algorithm to the whole genome expression of various stem cells and tumor cells. However, it is largely unknown about mRNAsi in basal breast cancer. Here, we find that basal breast cancer carries the highest mRNAsi among all four subtypes of breast cancer, especially 385 mRNAsi-related genes are positively related to the high mRNAsi value in basal breast cancer. This high mRNAsi is also closely related to active cell cycle, DNA replication, and metabolic reprogramming in basal breast cancer. Intriguingly, in the 385 genes, , , , and can act as independent protective prognostic factors, but and can serve as independent bad prognostic factors in patients with basal breast cancer. Remarkably, we establish a robust prognostic model containing the 6 mRNAsi-related genes that can effectively predict the survival rate of patients with the basal breast cancer subtype. Finally, the drug sensitivity analysis reveals that some drug combinations may be effectively against basal breast cancer via targeting the mRNAsi-related genes. Taken together, our study not only identifies novel prognostic biomarkers for basal breast cancers but also provides the drug sensitivity data by establishing an mRNAsi-related prognostic model.
基底样乳腺癌亚型是所有乳腺癌亚型中预后最差的亚型之一。最近，一种新的肿瘤干性指数-mRNAsi 被发现能够衡量组织的致癌分化程度。mRNAsi 涉及多种癌症过程，是从单类逻辑回归 (OCLR) 机器学习算法在各种干细胞和肿瘤细胞的全基因组表达中的创新应用中得出的。然而，基底样乳腺癌中的 mRNAsi 很大程度上是未知的。在这里，我们发现基底样乳腺癌在所有四种乳腺癌亚型中携带最高的 mRNAsi，尤其是 385 个与 mRNAsi 相关的基因与基底样乳腺癌中高 mRNAsi 值呈正相关。这种高 mRNAsi 也与基底样乳腺癌中活跃的细胞周期、DNA 复制和代谢重编程密切相关。有趣的是，在这 385 个基因中，、、、和 可以作为独立的保护性预后因素，而 和 可以作为基底样乳腺癌患者的独立不良预后因素。值得注意的是，我们建立了一个包含 6 个 mRNAsi 相关基因的稳健预后模型，可以有效地预测基底样乳腺癌患者的生存率。最后，药物敏感性分析表明，通过靶向 mRNAsi 相关基因，一些药物组合可能有效地针对基底样乳腺癌。总之，我们的研究不仅确定了基底样乳腺癌的新型预后生物标志物，还通过建立 mRNAsi 相关预后模型提供了药物敏感性数据。
