An integrated bioinformatical analysis of miR‑19a target genes in multiple myeloma

Abstract

MicroRNA (miR)-19a, as an oncomiR, has been studied in several types of cancer; however, its role in the development and progression of multiple myeloma (MM) remains unclear. The present study used a bioinformatics approach to investigate the involvement of miR-19a in MM. miR-19a targets were predicted using target prediction programs, followed by screening for differentially expressed genes in MM. The function of these genes was then annotated using gene ontology term enrichment, signaling pathway enrichment and protein-protein interaction (PPI) analysis. In addition, natural language processing (NLP) was performed to identify genes associated with MM. A total of 715 putative targets of miR-19a were identified in the present study, of which 40 were experimentally validated. A total of 121 genes were identified to be differentially expressed in MM, including 80 upregulated genes and 41 downregulated genes. Among the differentially expressed genes, ras homolog family member B, clathrin heavy chain, prosaposin and protein phosphatase 6 regulatory subunit 2 were predicted target genes of miR-19a. The results of NLP revealed that 2 of the differentially expressed genes, Y-box binding protein 1 and TP53 regulated inhibitor of apoptosis 1, were reported to be associated with MM. In addition, 41 target genes of miR-19a were identified to be associated with the development and progression of MM. These results may aid in understanding the molecular mechanisms of miR-19a in the development and progression of MM. In addition, the results of the present study indicate that targets genes of miR-19a are potential candidate biomarkers for MM.

<p>CELSR1 Acts as an Oncogene Regulated by miR-199a-5p in Glioma</p>

“…miRNAs regulate gene expression at a posttranscriptional level by degrading or repressing target mRNAs, resulting in translational repression or mRNA degradation. 16,17 Our previous study reported that miR199a-5p suppresses glioma cell proliferation, migration, and invasion by inhibiting MAGT1. 18 However, the correlation between CELSR1 and miR-199a-5p has not been reported.…”

Exploration of Artificial Intelligence Use with ARIES in Multiple Myeloma Research

“…In oncology NLP is used for identifying disease stages [16], and evaluating outcomes (such as adverse drug reactions [17] or tumor recurrences [18]). Other NLP projects targeting MM take into account performance to identify genes associated with MM [19], “a standardized hierarchic ontology of cancer treatments” [20] or an “ontology-driven semiological rules base and a consultation form to aid in the diagnosis of plasma cells diseases” [21].…”

LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities

“…But the difference between AAAAAA and UUUUUU is significantly larger than between AAAAAA and AAAAAC . Therefore, we introduce natural language processing technology to solve this problem [35–38]. It can not only transform the original high-dimensional data into low-dimensional continuous real-valued vector, but also learn its effective representation from miRNA sequences in an unsupervised manner.…”