MicroRNA - Volume 14, Issue 1, 2025

Estrogen plays a protective role in vascular health due, in part, to its regulation of endothelial inflammation. However, the mechanism(s) by which estrogen negatively regulates inflammatory signaling pathways is not completely understood. MicroRNAs (miRNAs) are recognized as sensitive and selective regulators of cardiovascular function, inflammation, and disease, yet the effects of 17β-estradiol on the endothelial miRNA profile are largely unknown.

The aim of this study was to determine the effect of 17β-estradiol on the expression of inflammation-associated miRNAs in endothelial cells in vitro.

Human Umbilical Vein Endothelial cells (HUVECs) were treated with media in the absence (control) and presence of 17β-estradiol (100 nM) for 24 hr. Thereafter, endothelial cell release of cytokines (IL-6 and IL-8), the intracellular expression of the central protein inflammatory mediator NF-κB, and the levels of inflammatory-associated miRNAs: miR-126, miR-146a, miR-181b, miR-204, and miR-Let-7a, were determined.

17β-estradiol-treated cells released significantly lower levels of IL-6 (47.6±1.5 pg/mL vs. 59.3±4.9 pg/mL) and IL-8 (36.3±2.3 pg/mL vs. 44.0±2.0 pg/mL). Cellular expression of total NF-κB (26.0±2.8 AU vs. 21.2±3.1 AU) was not different between groups; however, activated NF-κB (Ser536) (12.9±1.7 AU vs. 20.2±2.2 AU) was markedly reduced in 17β-estradiol-treated cells as compared to untreated cells. Furthermore, cellular expressions of miR-126 (1.8±0.3 fold), miR-146a (1.7±0.3 fold), miR-181b (2.1±0.4 fold), miR-204 (1.9±0.4 fold), and miR-Let-7a (1.8±0.3 fold) were markedly increased in response to 17β-estradiol treatment.

These data suggest that the anti-inflammatory effect of 17β-estradiol in endothelial cells may be mediated by miRNAs.

Publications reveal different outcomes achieved by genetically knocking out a long non-coding microRNA-host-gene (lncMIRHG) versus the administration of pharmacologic antagomirs specifically targeting the guide strand of such intragenic microRNA. This suggests that lncMIRHGs may perform diverse functions unrelated to their role as intragenic miRNA precursors.

This review synthesizes in silico, in vitro, and in vivo findings from our lab and others to compare the effects of knocking out the long non-coding RNA MIR22HG, which hosts miR-22, versus administering pharmacological antagomirs targeting miR-22-3p.

In silico analyses at the gene, pathway, and network levels reveal both distinct and overlapping targets of hsa-miR-22-3p and its host gene, MIR22HG. While pharmacological antagomirs targeting miR-22-3p consistently improve various metabolic parameters in cell culture and animal models across multiple studies, genetic knockout of MIR22HG yields inconsistent results among different research groups.

Additionally, MIR22HG functions as a circulating endogenous RNA (ceRNA) or “sponge” that simultaneously modulates multiple miRNA-mRNA interactions by competing for binding to several miRNAs.

From a therapeutic viewpoint, genetic inactivation of a lncMIRHG and pharmacologic antagonism of the guide strand of its related intragenic miRNA produce different results. This should be expected as lncMIRHGs play dual roles, both as lncRNA and as a source for primary miRNA transcripts.

Curcumin is known as a bioactive component that is found in the rhizomes of Curcuma longa. Curcumin is well known for its chemo-preventive and anticancer properties. However, its anticancer mechanism in colorectal cancer treatment is unclear, and some studies have shown that many microRNAs (miRs) could be potential targets for curcumin in colorectal cancer (CRC) treatment, so there is a need for their integration and clarification.

We systematically searched international databases, including PubMed, Scopus, and Web of Science, until July 2021 by using some relevant keywords.

The search resulted in 87 papers, among which there were 18 related articles. Curcumin was found to cause the upregulation of miR-497, miR-200c, miR-200b, miR-409-3p, miR‐34, miR‐126, miR-145, miR-206, miR-491, miR-141, miR-429, miR-101, and miR-15a and the downregulation of miR-21, miR-155, miR‐221, miR‐222, miR-17-5p, miR-130a, miR-27, and miR-20a.

The present review study suggests that curcumin may be useful as a novel therapeutic agent for CRC by altering the expression level of miRs.

Myasthenia gravis is an autoimmune disease, and 30% of patients with thymoma often have myasthenia gravis. Patients with thymoma-associated MG (TAMG) have many different clinical presentations compared to non-MG thymoma (NMG), yet their gene expression differences remain unclear.

In this study, we analyzed the Differentially Expressed Genes (DEGs) and analyzed their regulatory microRNAs (miRNAs) in TAMG, which will further clarify the possible pathogenesis of TAMG.

DEGs were calculated using the RNA-sequencing data of TAMG and NMG downloaded from The Cancer Genome Atlas (TCGA) database. R software was then used to analyze the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of DEGs, while STRING was applied to build the protein-protein interaction (PPI) network and Cytoscape to identify and visualize the hub genes. Immune infiltration significances of hub genes were also explored by using the TIMER database and TCGA database. Upstream microRNAs (miRNAs) of the hub genes were predicted by online software.

We comparatively analyzed the gene expression differences between TAMG and NMG groups. A total of 977 DEGs were identified between the two groups (|log fold change (FC)| >2, adjusted P value <0.050), with 555 down-regulated genes and 422 up-regulated genes. Five top hub genes (CTNNB1, EGFR, SOX2, ERBB2, and EGF) were recognized in the PPI network. Analysis based on the TIMER and TCGA databases suggested that 5 hub genes were correlated with multiple immune cell infiltrations and immune checkpoint-related markers, such as PDCD1, CTLA-4, and CD274, in TAMG patients. Lastly, 5 miRNAs were identified to have the potential function of regulating the hub gene expression.

Our study identified 5 hub genes (CTNNB1, EGFR, SOX2, ERBB2, and EGF) and their 5 regulatory miRNAs in TAMG, and the hub genes were correlated with multiple immune cell infiltrations and immune checkpoint-related markers. Our findings could help partially clarify the pathophysiology of TAMG, which could be new potential targets for subsequent clinical immunotherapy.

MicroRNAs (miRNAs), a distinct category of non-coding RNAs, exert multifaceted regulatory functions in a variety of organisms, including humans, animals, and plants. The inventory of identified miRNAs stands at approximately 60,000 among all species and 1,926 in Homo sapiens manifests miRNA expression. Their theranostic role has been explored by researchers over the last few decades, positioning them as prominent therapeutic targets as our understanding of RNA targeting advances. However, limited availability of experimentally determined miRNA structures has constrained drug discovery efforts relying on virtual screening or computational methods, including machine learning and artificial intelligence.

To address this lacuna, miRVim has been developed, providing a repository of human miRNA structures derived from both two-dimensional (MXFold2, CentroidFold, and RNAFold) and three-dimensional (RNAComposer and 3dRNA) structure prediction algorithms, in addition to experimentally available structures from the RCSB PDB repository.

miRVim contains 13,971 predicted secondary structures and 17,045 predicted three-dimensional structures filling the gap of unavailability of miRNA structure data bank. This database aims to facilitate computational data analysis for drug discovery, opening new avenues for advancing technologies such as machine learning-based predictions in the field of RNA biology.

The publicly accessible structures provided by miRVim, available at https://mirna.in/miRVim, offer a valuable resource for the research community, advancing the field of miRNA-related computational analysis and drug discovery.

The differential expression of miRNAs, a key regulator in many cell signaling pathways, has been studied in various malignancies and may have an important role in cancer progression, including colorectal cancer (CRC).

The present study used machine learning and gene interaction study tools to explore the prognostic and diagnostic value of miRNAs in CRC. Integrative analysis of 353 CRC samples and normal tissue data was obtained from the TCGA database and further analyzed by R packages to define the deferentially expressed miRNAs (DEMs). Furthermore, machine learning and Kaplan Meier survival analysis helped better specify the significant prognostic value of miRNAs. A combination of online databases was then used to evaluate the interactions between target genes, their molecular pathways, and the correlation between the DEMs.

The results indicated that miR-19b and miR-20a have a significant prognostic role and are associated with CRC progression. The ROC curve analysis discovered that miR-20a alone and combined with other miRNAs, including hsa-mir-21 and hsa-mir-542, are diagnostic biomarkers in CRC. In addition, 12 genes, including NTRK2, CDC42, EGFR, AGO2, PRKCA, HSP90AA1, TLR4, IGF1, ESR1, SMAD2, SMAD4, and NEDD4L, were found to be the highest score targets for these miRNAs. Pathway analysis identified the two correlated tyrosine kinase and MAPK signaling pathways with the key interaction genes, i.e., EGFR, CDC42, and HSP90AA1.

To better define the role of these miRNAs, the ceRNA network, including lncRNAs, was also prepared. In conclusion, the combination of R data analysis and machine learning provides a robust approach to resolving complicated interactions between miRNAs and their targets.