Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
Sci Rep. 2023 Sep 22;13(1):15771. doi: 10.1038/s41598-023-42904-6.
Metastasis is a major breast cancer hallmark due to which tumor cells tend to relocate to regional or distant organs from their organ of origin. This study is aimed to decipher the interaction among 113 differentially expressed genes, interacting non-coding RNAs and drugs (614 miRNAs, 220 lncRNAs and 3241 interacting drugs) associated with metastasis in breast cancer. For an extensive understanding of genetic interactions in the diseased state, a backbone gene co-expression network was constructed. Further, the mRNA-miRNA-lncRNA-drug interaction network was constructed to identify the top hub RNAs, significant cliques and topological parameters associated with differentially expressed genes. Then, the mRNAs from the top two subnetworks constructed are considered for transcription factor (TF) analysis. 39 interacting miRNAs and 1641 corresponding TFs for the eight mRNAs from the subnetworks are also utilized to construct an mRNA-miRNA-TF interaction network. TF analysis revealed two TFs (EST1 and SP1) from the cliques to be significant. TCGA expression analysis of miRNAs and lncRNAs as well as subclass-based and promoter methylation-based expression, oncoprint and survival analysis of the mRNAs are also done. Finally, functional enrichment of mRNAs is also performed. Significant cliques identified in the study can be utilized for identification of newer therapeutic interventions for breast cancer. This work will also help to gain a deeper insight into the complicated molecular intricacies to reveal the potential biomarkers involved with breast cancer progression in future.
转移是乳腺癌的主要标志之一，因为肿瘤细胞往往会从其起源器官转移到局部或远处器官。本研究旨在破译与乳腺癌转移相关的 113 个差异表达基因、相互作用的非编码 RNA 和药物（614 个 miRNA、220 个 lncRNA 和 3241 个相互作用的药物）之间的相互作用。为了广泛了解疾病状态下的遗传相互作用，构建了一个骨干基因共表达网络。进一步构建了 mRNA-miRNA-lncRNA-药物相互作用网络，以识别与差异表达基因相关的顶级枢纽 RNA、显著的团簇和拓扑参数。然后，考虑从构建的两个顶级子网中选择的 mRNAs 进行转录因子（TF）分析。从这两个子网中选择的 8 个 mRNAs 的 39 个相互作用的 miRNA 和 1641 个相应的 TF 也用于构建 mRNA-miRNA-TF 相互作用网络。TF 分析表明，来自两个团簇的两个 TF（EST1 和 SP1）是显著的。还对 miRNA 和 lncRNA 的 TCGA 表达分析以及基于子类和启动子甲基化的表达、oncoprint 和 mRNAs 的生存分析进行了研究。最后，还对 mRNAs 进行了功能富集分析。研究中鉴定的显著团簇可用于识别乳腺癌的新治疗干预措施。这项工作还有助于更深入地了解复杂的分子复杂性，以揭示未来与乳腺癌进展相关的潜在生物标志物。
