The Second Surgical Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
The School of Basic Medicine, Tianjin Medical University, Tianjin, China.
J Transl Med. 2024 Nov 22;22(1):1052. doi: 10.1186/s12967-024-05828-x.
Currently, there is a lack of biomarkers to identify breast cancer (BC) patients who would benefit from CDK4/6 inhibitors. This study combined machine learning (ML) algorithms based on transcriptomic data with both in vivo and in vitro experiments to identify therapeutic efficacy-related biomarkers of the CDK4/6 inhibitor ribociclib from the perspective of long non-coding RNA (lncRNA).
We used the Genomics of Drug Sensitivity in Cancer database along with the "oncoPredict" algorithm to calculate the half maximal inhibitory concentration (IC50) values for ribociclib based on transcriptome data. ML algorithms were utilized to select key lncRNAs related to ribociclib and to establish a model which could be used for selection of potential beneficiaries of ribociclib. Cellular experiments were conducted to validate the ML analysis and explore the potential biological mechanisms by which RERE-AS1 influences ribociclib efficacy and malignant phenotype of BC cells. Correlation analysis with clinical pathological factors, RT-qPCR experiments on tissue specimens, and pan-cancer analysis were carried out to explore the expression pattern, and the prognostic and diagnostic potential of RERE-AS1 in cancers.
We have identified 11 key ribociclib-related lncRNAs and constructed an artificial neural network model (ANNM) based on lncRNA. Cellular experiments demonstrated that overexpression of RERE-AS1 promoted the anti-tumor activity of ribociclib in BC cells. Furthermore, RERE-AS1 is crucial in suppressing the malignant traits of BC cells through the reduction of MEK and ERK phosphorylation levels. Patients with smaller primary tumors and lower pathological stage exhibited higher levels of RERE-AS1 expression. Lastly, a pan-cancer analysis revealed that RERE-AS1 exhibits distinctly abnormal expression patterns, prognostic significance, and clinical diagnostic value in BC, compared to other cancers.
The ANNM established through ML algorithms can serve as predictive indicators for the efficacy of ribociclib in BC patients. LncRNA RERE-AS1, a newly discovered biomarker, holds significant promise for diagnosis, treatment, and enhancing the therapeutic response to ribociclib in BC.
目前，缺乏生物标志物来识别可能从 CDK4/6 抑制剂中获益的乳腺癌（BC）患者。本研究从长链非编码 RNA（lncRNA）的角度，结合基于转录组数据的机器学习（ML）算法以及体内和体外实验，来确定 CDK4/6 抑制剂瑞波西利的治疗疗效相关生物标志物。
我们使用癌症药物基因组学数据库和“oncoPredict”算法，根据转录组数据计算瑞波西利的半最大抑制浓度（IC50）值。ML 算法用于选择与瑞波西利相关的关键 lncRNA，并建立一个可用于选择瑞波西利潜在受益人的模型。进行细胞实验以验证 ML 分析，并探讨 RERE-AS1 影响瑞波西利疗效和 BC 细胞恶性表型的潜在生物学机制。与临床病理因素进行相关性分析，对组织标本进行 RT-qPCR 实验，以及进行泛癌分析，以探索 RERE-AS1 在癌症中的表达模式、预后和诊断潜力。
我们确定了 11 个关键的瑞波西利相关 lncRNA，并基于 lncRNA 构建了人工神经网络模型（ANNM）。细胞实验表明，RERE-AS1 的过表达促进了瑞波西利在 BC 细胞中的抗肿瘤活性。此外，RERE-AS1 通过降低 MEK 和 ERK 磷酸化水平，在抑制 BC 细胞恶性特征方面发挥关键作用。原发肿瘤较小和病理分期较低的患者表现出更高水平的 RERE-AS1 表达。最后，泛癌分析显示，与其他癌症相比，RERE-AS1 在 BC 中表现出明显异常的表达模式、预后意义和临床诊断价值。
通过 ML 算法建立的 ANNM 可作为预测 BC 患者瑞波西利疗效的指标。新发现的生物标志物 lncRNA RERE-AS1 有望用于 BC 的诊断、治疗和增强对瑞波西利的治疗反应。
