Breast cancer is among the lethal types of cancer with a high mortality rate, globally. Its high prevalence can be controlled through improved analysis and identification of disease-specific biomarkers. Recently, long non-coding RNAs (lncRNAs) have been reported as key contributors of carcinogenesis and regulate various cellular pathways through post-transcriptional regulatory mechanisms. The specific aim of this study was to identify the novel interactions of aberrantly expressed genetic components in breast cancer by applying integrative analysis of publicly available expression profiles of both lncRNAs and mRNAs. Differential expression patterns were identified by comparing the breast cancer expression profiles of samples with controls. Significant co-expression networks were identified through WGCNA analysis. WGCNA is a systems biology approach used to elucidate the pattern of correlation between genes across microarray samples. It is also used to identify the highly correlated modules. The results obtained from this study revealed significantly differentially expressed and co-expressed lncRNAs and their cis- and trans-regulating mRNA targets which include RP11-108F13.2 targeting TAF5L, RPL23AP2 targeting CYP4F3, CYP4F8 and AL022324.2 targeting LRP5L, AL022324.3, and Z99916.3, respectively. Moreover, pathway analysis revealed the involvement of identified mRNAs and lncRNAs in major cell signalling pathways, and target mRNAs expression is also validated through cohort data. Thus, the identified lncRNAs and their target mRNAs represent novel biomarkers that could serve as potential therapeutics for breast cancer and their roles could also be further validated through wet labs to employ them as potential therapeutic targets in future.

中文翻译：

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长链非编码 RNA 及其靶标作为乳腺癌的潜在生物标志物。

乳腺癌是全球死亡率高的致命癌症之一。通过改进对疾病特异性生物标志物的分析和鉴定，可以控制其高患病率。最近，据报道，长链非编码 RNA (lncRNA) 是致癌的关键因素，并通过转录后调节机制调节各种细胞途径。本研究的具体目的是通过对公开可用的 lncRNA 和 mRNA 表达谱进行综合分析，确定乳腺癌中异常表达的遗传成分的新相互作用。通过比较样品与对照的乳腺癌表达谱来鉴定差异表达模式。通过 WGCNA 分析确定了重要的共表达网络。WGCNA 是一种系统生物学方法，用于阐明微阵列样本中基因之间的相关模式。它还用于识别高度相关的模块。从这项研究中获得的结果揭示了显着差异表达和共表达的 lncRNA 及其顺式和反式调节 mRNA 靶标，包括靶向 TAF5L 的 RP11-108F13.2、靶向 CYP4F3、CYP4F8 的 RPL23AP2 和靶向 LRP5L、AL022324.3 的 AL022324.2 ，和 Z99916.3，分别。此外，通路分析揭示了已鉴定的 mRNA 和 lncRNA 参与了主要的细胞信号通路，并且目标 mRNA 的表达也通过队列数据得到验证。因此，