Downregulation of miR‑193a‑3p via targeting cyclin D1 in thyroid cancer

Exploration of the Shared Gene Signatures and Molecular Mechanisms Between Ischemic Stroke and Atherosclerosis

Purpose

Atherosclerosis (AS) is a chronic inflammatory vascular disease and the predominant cause of ischemic stroke (IS). AS is a potential pathogenetic factor in IS. However, the processes by which they interact remain unknown. The purpose of this paper was to investigate the shared gene signatures and putative molecular processes in AS and IS.

Methods

Gene Expression Omnibus (GEO) data for AS and IS microarrays were retrieved. The co-expression modules associated with AS and IS were identified using the Weighted Gene Co-Expression Network Analysis (WGCNA). We constructed an interaction network of shared differentially expressed genes in AS and IS and conducted an enrichment analysis using ClueGO software. We validated the results in a separate cohort through differential gene analysis. Additionally, we retrieved AS and IS-related miRNAs from the Human microRNA Disease Database (HMDD) and predicted their target genes using miRWalk. We then built a network of miRNAs-mRNAs-KEGG pathways using the shared genes.

Results

Through WGCNA, we identified five modules and six modules as significant in AS and IS, respectively. A ClueGO enrichment analysis of common genes showed that highly active CCR1 chemokine receptor binding is critical to AS and IS pathogenesis. The differential analysis expression results in another cohort closely matched these findings. The miRNA-mRNA network suggested that hsa-miR-330-5p, hsa-miR-143-3p, hsa-miR-16-5p, hsa-miR-152-3p might regulate the shared gene KRAS, which could be a key player in AS and IS.

Conclusion

We integrated ischemic stroke and carotid atherosclerosis public database data and found that ATF3, CCL3, CCL4, JUNB, KRAS, and ZC3H12A may affect both, making them novel biomarkers or therapeutic target genes. Clinical samples and expression trends supported our analyses of pivotal genes.

Identification of shared gene signatures in major depressive disorder and triple-negative breast cancer

BACKGROUND

Patients with major depressive disorder (MDD) have an increased risk of breast cancer (BC), implying that these two diseases share similar pathological mechanisms. This study aimed to identify the key pathogenic genes that lead to the occurrence of both triple-negative breast cancer (TNBC) and MDD.

METHODS

Public datasets GSE65194 and GSE98793 were analyzed to identify differentially expressed genes (DEGs) shared by both datasets. A protein-protein interaction (PPI) network was constructed using STRING and Cytoscape to identify key PPI genes using cytoHubba. Hub DEGs were obtained from the intersection of hub genes from a PPI network with genes in the disease associated modules of the Weighed Gene Co-expression Network Analysis (WGCNA). Independent datasets (TCGA and GSE76826) and RT-qPCR validated hub gene expression.

RESULTS

A total of 113 overlapping DEGs were identified between TNBC and MDD. The PPI network was constructed, and 35 hub DEGs were identified. Through WGCNA, the blue, brown, and turquoise modules were recognized as highly correlated with TNBC, while the brown, turquoise, and yellow modules were similarly correlated with MDD. Notably, G3BP1, MAF, NCEH1, and TMEM45A emerged as hub DEGs as they appeared both in modules and PPI hub DEGs. Within the GSE65194 and GSE98793 datasets, G3BP1 and MAF exhibited a significant downregulation in TNBC and MDD groups compared to the control, whereas NCEH1 and TMEM45A demonstrated a significant upregulation. These findings were further substantiated by TCGA and GSE76826, as well as through RT-qPCR validation.

CONCLUSIONS

This study identified G3BP1, MAF, NCEH1 and TMEM45A as key pathological genes in both TNBC and MDD.

Tumor suppressor miR-193a-3p enhances efficacy of BRAF/MEK inhibitors in BRAF-mutated colorectal cancer

Patients with BRAF‐mutated colorectal cancer (CRC) have a poor prognosis despite recent therapeutic advances such as combination therapy with BRAF, MEK, and epidermal growth factor receptor (EGFR) inhibitors. To identify microRNAs (miRNAs) that can improve the efficacy of BRAF inhibitor dabrafenib (DAB) and MEK inhibitor trametinib (TRA), we screened 240 miRNAs in BRAF‐mutated CRC cells and identified five candidate miRNAs. Overexpression of miR‐193a‐3p, one of the five screened miRNAs, in CRC cells inhibited cell proliferation by inducing apoptosis. Reverse‐phase protein array analysis revealed that proteins with altered phosphorylation induced by miR‐193a‐3p were involved in several oncogenic pathways including MAPK‐related pathways. Furthermore, overexpression of miR‐193a‐3p in BRAF‐mutated cells enhanced the efficacy of DAB and TRA through inhibiting reactivation of MAPK signaling and inducing inhibition of Mcl1. Inhibition of Mcl1 by siRNA or by Mcl1 inhibitor increased the antiproliferative effect of combination therapy with DAB, TRA, and anti‐EGFR antibody cetuximab. Collectively, our study demonstrated the possibility that miR‐193a‐3p acts as a tumor suppressor through regulating multiple proteins involved in oncogenesis and affects cellular sensitivity to MAPK‐related pathway inhibitors such as BRAF inhibitors, MEK inhibitors, and/or anti‐EGFR antibodies. Addition of miR‐193a‐3p and/or modulation of proteins involved in the miR‐193a‐3p–mediated pathway, such as Mcl1, to EGFR/BRAF/MEK inhibition may be a potential therapeutic strategy against BRAF‐mutated CRC.

The Involvement of the hsa_circ_0088494-miR-876-3p-CTNNB1/CCND1 Axis in Carcinogenesis and Progression of Papillary Thyroid Carcinoma

Recently, growing studies have demonstrated that circular RNAs (circRNAs) function as critical players in multiple human tumors, including papillary thyroid carcinoma (PTC). However, the expression and underlying potential mechanism of circRNAs in PTC are still not fully elucidated. In this study, 14 candidate differentially expressed circRNAs (DECs) between normal thyroid tissues and benign thyroid tissues or PTC were first screened using the GSE93522 dataset by the GEO2R online tool. Then, the structural loop graphs of these 14 circRNAs were obtained through the CSCD database. After performing miRNA co-prediction by combination of CSCD and CRI databases, a potential circRNA-miRNA sub-network, consisting of 9 circRNAs and 21 miRNAs, was successfully constructed. Subsequently, the expression and prognostic values of these miRNAs were further determined by starBase, and two miRNAs, namely, miR-605-5p and miR-876-3p, were identified as key miRNAs in PTC. Then, their downstream target genes were predicted by the miRNet database. CTNNB1 and CCND1 were found to be two most potential targets of miR-876-3p by combination of multiple in silico analyses, including protein–protein interaction (PPI), hub gene screening, correlation analysis, and expression analysis. Conclusively, we established a key hsa_circ_0088494-miR-876-3p-CTNNB1/CCND1 axis linked to carcinogenesis and progression of PTC, which may provide promising therapeutic targets in treating PTC in the future.