AUTHOR=Alam Md Shahin , Sultana Adiba , Sun Hongyang , Wu Jin , Guo Fanfan , Li Qing , Ren Haigang , Hao Zongbing , Zhang Yi , Wang Guanghui 

TITLE=Bioinformatics and network-based screening and discovery of potential molecular targets and small molecular drugs for breast cancer

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2022.942126

DOI=10.3389/fphar.2022.942126

ISSN=1663-9812

ABSTRACT=Accurate identification of molecular biomarkers of disease plays an important role in diagnosis, prognosis and therapies. However, Breast cancer (BC) is one of the most common malignant cancers in women worldwide. Thus, the objective of this study is to identify accurately a set of molecular biomarker and small molecular drug that might be effective for BC diagnosis, prognosis and therapies, by using existing bioinformatics and network-based approaches. Nine gene expression profiles (GSE54002, GSE29431, GSE124646, GSE42568, GSE45827, GSE10810, GSE65216, GSE36295, and GSE109169) collected from the Gene Expression Omnibus (GEO) database were used for bioinformatics analysis in this study. Two packages LIMMA & clusterProfiler in R were used to identify Differential Expressed Gene (DEGs) and significant GO and KEGG enrichment terms. We constructed PPI (protein-protein interaction) network through STRING database and identified 8 key genes (KGs) EGFR, FN1, EZH2, MET, CDK1, AURKA, TOP2A, and BIRC5 by using 6 topological measurer Betweenness, Closeness, EcCentricity, Degree, MCC, and MNC in  AnalyzeNetwork tool in Cytoscape. Three online databases GSCALite, NetworkAnalyst, and GEPIA were used to analyze drug enrichment, regulatory interaction networks and gene expression levels of KGs. We checked the prognostic power of KGs through the prediction model using the popular machine learning algorithm SVM (Support Vector Machine). We suggested four TFs (TP63, MYC, SOX2, and KDM5B) and four miRNAs (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, and hsa-mir-23b-3p) as key transcriptional and post-transcriptional regulators of KGs. Finally, we proposed 16 candidate repositioning drugs YM201636, masitinib, SB590885, GSK1070916, GSK2126458, ZSTK474, Dasatinib, Fedratinib, Dabrafenib, Methotrexate, Trametinib, Tubastatin A, BIX02189, CP466722, Afatinib, and Belinostat for BC through molecular docking analysis. Therefore, the proposed results might be playing an effective role in the treatment against BC patients.