| Sumario: | Background: Breast cancer (BC) is the leading cause of cancer-associated mortality in women worldwide. However, the molecular mechanism underlying the process is still unclear. In this regard, bioinformatics studies play a decisive role in facilitating the path of biological investigations and can ultimately lead to the identification of better molecular candidates for further study. Objectives: Due to the abnormal expression of many coding and non-coding genes in all types of cancers and their relationship with various mechanisms of carcinogenesis, this study aimed at evaluating the expression levels of certain coding and non-coding genes involved in BC based on bioinformatics findings and laboratory investigations. Methods: Gene expression dataset, module extraction, functional enrichment analysis, protein-protein interaction network construction, and RT-qPCR were performed based on bioinformatics methods and laboratory investigations. Additionally, the promoter region mutations of these genes were investigated, using sequencing of extracted DNAs from formalin-fixed paraffin-embedded (FFPE) tumor tissues. Results: A module was selected as a candidate for further investigation. Estrogen receptor 1 (ESR1) and forkhead box Al (FOXA1) showed the highest degrees in the PPI network with 9 and 7 links, respectively. Furthermore, the expression levels of the FOXA1 gene, RNA component of mitochondrial RNA processing endoribonuclease (RMRP), and nuclear enriched abundant transcript 1 (NEAT1) were significantly upregulated in the tumor group compared to the control group (in order, P = 0.044, P = 0.014, and P = 0.0004). The tumors of patients with positive metastasis displayed significantly higher levels of NEAT1 and RMRP expression compared to those of negative metastasis samples (P < 0.05). Moreover, the expression level of RMRP dramatically decreased in HER2-positive patients compared to negative samples (P = 0.011). Finally, no mutations were observed in the promoter sequencing of positive metastasis samples compared to normal samples. Conclusions: The upregulation levels of all three examined genes may correlate with BC progression. Therefore, they could potentially be used as biomarkers for detecting BC development.
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