West Virginia University Cancer Institute/Mary Babb Randolph Cancer Center, West Virginia University, Morgantown, WV 26506, USA.
Health Effects Laboratory Division, National Institute for Occupational and Safety & Health, Morgantown, WV 26505, USA.
Cells. 2023 Jul 23;12(14):1917. doi: 10.3390/cells12141917.
Breast cancer treatment can be improved with biomarkers for early detection and individualized therapy. A set of 86 microRNAs (miRNAs) were identified to separate breast cancer tumors from normal breast tissues ( = 52) with an overall accuracy of 90.4%. Six miRNAs had concordant expression in both tumors and breast cancer patient blood samples compared with the normal control samples. Twelve miRNAs showed concordant expression in tumors vs. normal breast tissues and patient survival ( = 1093), with seven as potential tumor suppressors and five as potential oncomiRs. From experimentally validated target genes of these 86 miRNAs, pan-sensitive and pan-resistant genes with concordant mRNA and protein expression associated with in-vitro drug response to 19 NCCN-recommended breast cancer drugs were selected. Combined with in-vitro proliferation assays using CRISPR-Cas9/RNAi and patient survival analysis, MEK inhibitors PD19830 and BRD-K12244279, pilocarpine, and tremorine were discovered as potential new drug options for treating breast cancer. Multi-omics biomarkers of response to the discovered drugs were identified using human breast cancer cell lines. This study presented an artificial intelligence pipeline of miRNA-based discovery of biomarkers, therapeutic targets, and repositioning drugs that can be applied to many cancer types.
乳腺癌的治疗可以通过生物标志物进行早期检测和个体化治疗得到改善。一组 86 个 microRNAs(miRNAs)被鉴定出来,可以将乳腺癌肿瘤与正常乳腺组织(=52)分开,总体准确率为 90.4%。与正常对照组相比,有 6 个 miRNA 在肿瘤和乳腺癌患者的血液样本中表达一致。12 个 miRNA 在肿瘤与正常乳腺组织和患者生存中表达一致(=1093),其中 7 个是潜在的肿瘤抑制因子,5 个是潜在的癌基因。从这 86 个 miRNA 的实验验证靶基因中,选择了具有一致 mRNA 和蛋白表达的泛敏感和泛耐药基因,这些基因与 19 种 NCCN 推荐的乳腺癌药物的体外药物反应有关。结合使用 CRISPR-Cas9/RNAi 的体外增殖测定和患者生存分析,发现 MEK 抑制剂 PD19830 和 BRD-K12244279、毛果芸香碱和震颤素可作为治疗乳腺癌的潜在新药选择。使用人类乳腺癌细胞系鉴定了对发现药物反应的多组学生物标志物。本研究提出了一种基于 miRNA 的生物标志物、治疗靶点和再定位药物发现的人工智能管道,可应用于多种癌症类型。