Abstract
Breast cancer is the most common cancer among women worldwide. MicroRNAs (miRNAs or miRs) play an important role in tumorigenesis, and thus, they have been identified as potential targets for translational research with diagnostic, prognostic, and therapeutic markers. This study aimed to identify differentially expressed (DE) miRNAs in breast cancer using the Cancer Genome Atlas. The miRNA profiles of 755 breast cancer tissues and 86 adjacent non-cancerous breast tissues were analyzed using Multi Experiment Viewer; miRNA–mRNA network analyses and constructed KEGG pathways with the predicted target genes were performed. The clinical relevance of miRNAs was investigated using area under the receiver operating characteristic curve (AUC) analysis, sensitivity, and specificity. The analysis identified 28 DE miRNAs in breast cancer tissues, including nine upregulated and 19 downregulated miRNAs, compared to non-cancerous breast tissues (p < 0.001). The AUC for each DE miRNA, miR-10b, miR-21, miR-96, miR-99a, miR-100, miR-125b-1, miR-125b-2, miR-139, miR-141, miR-145, miR-182, miR-183, miR-195, miR-200a, miR-337, miR-429, and let-7c, exceeded 0.9, indicating excellent diagnostic performance in breast cancer. Moreover, 1381 potential target genes were predicted using the prediction database tool, miRNet. These genes are related to PD-L1 expression and PD-1 checkpoint in cancer, MAPK signaling, apoptosis, and TNF pathways; hence, they regulate the development, progression, and immune escape of cancer. Thus, these 28 miRNAs can serve as prospective biomarkers for the diagnosis of breast cancer. Taken together, these results provide insight into the pathogenic mechanisms and potential therapies for breast cancer.
Highlights
Breast cancer is the third most common malignancy among women, with annual morbidity increasing worldwide [1]
The miRNA sequencing dataset comprising a total of 755 breast cancer and 86 adjacent non-cancerous breast tissues was obtained from the the Cancer Genome Atlas (TCGA) breast cancer project
208 patients presented with luminal A (27.55%), 74 with luminal B (9.80%), 116 with HER2-positive (15.36%), and 357 with triple-negative breast cancer (TNBC) (47.28%) subtypes
Summary
Breast cancer is the third most common malignancy among women, with annual morbidity increasing worldwide [1]. According to the World Health Organization, 2.1 million new cases and 627,000 deaths were estimated for breast cancer in 2018. Breast cancer accounts for approximately 15% of all cancer-related deaths in women [2]. Breast cancer is a heterogeneous disease classified into four subtypes by gene expression profiling, including luminal A (ER/PR+, HER2−, Ki67+ < 20%), luminal B (ER/PR+ < 20%, HER2−, Ki67+ ≥ 20%), HER2 (ER/PR−, HER2 overexpression), and basal-like (ER−, PR−, HER2−) [3,4]. Detection and improved treatment can aid in better survival and outcomes in patients with breast cancer. Mammography for breast cancer is a widely used screening tool. Alternative methods, such as ultrasound screening, are highly operator-dependent. Tumor serum markers, such as carbohydrate antigen 15–3 (CA-15–3) and carcinoembryonic antigen (CEA), are nonspecific and have limited sensitivity and specificity [5,6]
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