Mohadese Safabakhsh - Department of Biology, Faculty of Basic Sciences, Gonbad Kavous University, Gonbad Kavous, Golestan, Iran
Sohrab Boozarpour - Department of Biology, Faculty of Basic Sciences, Gonbad Kavous University, Gonbad Kavous, Golestan, Iran
Kamran Ghaedi - Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
Mina Lashkarboloki - Department of Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
Shaaban Ghalandarayeshi - Department of Mathematics and Statistics, Faculty of Basic Sciences, Gonbad Kavous University, Gonbad Kavous, Golestan, Iran

Breast cancer is the most lethal form of cancer in women, and patients face serious health risks. Long non-coding RNAs are involved in a variety of regulatory processes and can influence cancers development at different levels. This study aimed to introduce a central regulatory lncRNA based on differentially expressed proteins in breast cancer and evaluate its expression level in breast tissues. In this study, proteomic data was obtained from ProteomeXchange and then differentially expressed proteins were detected. The Enrichr database was used to identify the regulatory factors of differentially expressed proteins, and functional enrichment analysis was used to demonstrate key signaling pathways and biological processes. Eventually, a lncRNA with the highest rank in the central hub was chosen, and its expression level was measured by RT-qPCR in ۱۵ breast cancer tissues and their adjacent nontumor tissues. Proteomic analysis recognized ۱۱۴۹ differential expressed proteins in breast cancer with regulatory agents consisting of ۷۶ TFs, ۶۱ kinases, ۳۶۶ miRNAs, and ۱۶۲ lncRNAs. A multi-regulatory network with ۱۸۱۱ nodes and ۴۰۲۲ edges was constructed based on differentially expressed proteins and their associated elements. In addition, these regulatory elements were related to three biological functions and ۱۱ pathways. Finally, bioinformatic analysis identified lncRNA WWTR۱-AS۱ as having the highest node score involved in different mechanisms. Functional experiments confirmed that the expression level of the lncRNA WWTR۱-AS۱ was significantly increased in breast cancer patients.  ROC analysis suggested that this lncRNA can be used as a reliable biomarker. Our data provide evidence that the lncRNA WWTR۱-AS۱ is an effective factor in the regulation of DEPs, is associated with malignant features in breast cancer, and might be useful as a prognostic marker in Breast cancer.

Breast cancer, Computational biology, Gene regulatory networks, Long noncoding RNA, Proteomics