A computational approach on studying the regulation of TGF-β1-stimulated Runx2 expression by MicroRNAs in human breast cancer cells

Reverse predictive analysis of Rhizoma Pinelliae and Rhizoma Coptidis on differential miRNA target genes in lung adenocarcinoma

To use bioinformatics and network analysis to reveal the mechanism of "Rhizoma Pinelliae-Rhizoma Coptidis" herb pair in the treatment of lung adenocarcinoma. The target and pathway of "Rhizoma Pinelliae-Rhizoma Coptidis" herb pair in the treatment of lung adenocarcinoma were explored by online databases and network analysis tools, and the potential biomarkers of "Rhizoma Pinelliae-Rhizoma Coptidis" herb pair in the treatment of lung adenocarcinoma were predicted in reverse. A total of 59 traditional Chinese medicine compounds and 510 drug targets were screened in this study. A total of 25 micro-RNAs and 15,323 disease targets were obtained through GEO2R software analysis. In the end, 294 therapeutic targets and 47 core targets were obtained. A total of 186 gene ontology enrichment assays were obtained, and core therapeutic targets play multiple roles in biological processes, molecular functions, and cellular composition. Kyoto encyclopedia of genes and genomes pathway enrichment analysis showed that the core targets were mainly enriched in cancer-related pathways, immune-related pathways, endocrine-related pathways, etc, among which the non-small cell lung cancer pathway was the most significant core pathway. Molecular docking shows that the compound and the target have good binding ability. "Rhizoma Pinelliae-Rhizoma Coptidis" herb pair plays a mechanism of action in the treatment of lung adenocarcinoma through multiple targets and pathways. miR-5703, miR-3125, miR-652-5P, and miR-513c-5p may be new biomarkers for the treatment of lung adenocarcinoma.

Circ_CUX1/miR-130b-5p/p300 axis for parathyroid hormone-stimulation of Runx2 activity in rat osteoblasts: A combined bioinformatic and experimental approach

Parathyroid hormone (PTH) regulates the expression of bone remodeling genes by enhancing the activity of Runx2 in osteoblasts. p300, a histone acetyltransferase, acetylated Runx2 to activate the expression of its target genes. PTH stimulated the expression of p300 in rat osteoblastic cells. Increasing studies suggested the potential of non-coding RNAs (ncRNAs), such as microRNAs (miRNAs) and circular RNAs (circRNAs), in regulating gene expression under both physiological and pathological conditions. In this study, we hypothesized that PTH regulates Runx2 activity via ncRNAs-mediated p300 expression in rat osteoblastic cells. Bioinformatics and experimental approaches identified PTH-upregulation of miR-130b-5p and circ_CUX1 that putatively target p300 and miR-130b-5p, respectively. An antisense-mediated knockdown of circ_CUX1 was performed to determine the sponging activity of circ_CUX1. Knockdown of circ_CUX1 promoted miR-130b-5p activity and reduced p300 expression, resulting in decreased Runx2 acetylation in rat osteoblastic cells. Further, bioinformatics analysis identified the possible signaling pathways that regulate Runx2 activity and osteoblast differentiation via circ_CUX1/miR-130b-5p/p300 axis. The predicted circ_CUX1/miR-130b-5p/p300 axis might pave the way for better diagnostic and therapeutic approaches for bone-related diseases.

Role of Long Non-Coding RNA in Regulating ER Stress Response to the Progression of Diabetic Complications

Chronic hyperglycemia damages the nerves and blood vessels, culminating in other vascular complications. Such complications enhance cytokine, oxidative and endoplasmic reticulum (ER) stress. ER is the primary organelle where proteins are synthesised and attains confirmatory changes before its site of destination. Perturbation of ER homeostasis activates signaling sensors within its lumen, the unfolded protein response (UPR) that orchestrates ER stress and is extensively studied. Increased ER stress markers are reported in diabetic complications in addition to lncRNA that acts as an upstream marker inducing ER stress response. This review focuses on the mechanisms of lncRNA that regulate ER stress markers, especially during the progression of diabetic complications. Through this systemic review, we showcase the dysfunctional lncRNAs that act as a leading cause of ER stress response to the progression of diabetic complications.

The microRNA-cell surface proteoglycan axis in cancer progression

Proteoglycans consist one of the major extracellular matrix class of biomolecules that demonstrate nodal roles in cancer progression. Μodern diagnostic and therapeutic approaches include proteoglycan detection and pharmacological targeting in various cancers. Proteoglycans orchestrate critical signaling pathways for cancer development and progression through dynamic interactions with matrix components. It is well established that the epigenetic signatures of cancer cells play critical role in guiding their functional properties and metastatic potential. Secreted microRNAs (miRNAs) reside in a complex network with matrix proteoglycans, thus affecting cell-cell and cell-matrix communication. This mini-review aims to highlight current knowledge on the proteoglycan-mediated signaling cascades that regulate miRNA biogenesis in cancer. Moreover, the miRNA-mediated proteoglycan regulation during cancer progression and mechanistic aspects on the way that proteoglycans affect miRNA expression are presented. Recent advances on the role of cell surface proteoglycans in exosome biogenesis and miRNA packaging and expression are also discussed.

Bioinformatics Analysis Reveals the Related Role of miR-511-5p in the Progression of Breast Cancer

Breast cancer remains a dangerous disease, and delving the molecular mechanism of breast cancer is still necessary. To illustrate the role of miR-511-5p, TCGA database was used to excavate the abundance of miR-511-5p, and the miR-511-5p level was measured in the pathological tissues and tumor cell lines. Moreover, the targets of miR-511-5p were identified with miRDIP and GEPIA and then were used for functional enrichment analysis. Besides, the targets of miR-511-5p were analyzed with the protein-protein interaction (PPI) network for the hub nodes, and then the expression levels of the hub nodes were visualized with the GEPIA database. The results showed that miR-511-5p was significantly downregulated in multiple types of tumor samples in the online database, and the downregulated miR-511-5p was also found in pathological tissues and tumor cell lines. Moreover, 48 genes were identified as the potential targets of miR-511-5p by miRDIP and GEPIA databases and enriched in cell cycle, PI3K/AKT, and P53 pathways. Besides, seven genes including BRCA1, FN1, CCNE1, CCND1, CHEK1, BUB3, and CDC25A were identified as the hub nodes by the PPI network, and CCNE1 and CHEK1 were confirmed to be related with the prognostic survival of the patients with breast cancer. In conclusion, the proofs in this study suggest that reduced miR-511-5p was a biomarker event for breast cancer, and CCNE1 and CHEK1 served as potential targets of miR-511-5p to involve the progression of breast cancer.

Systems biology and machine learning approaches identify drug targets in diabetic nephropathy

Diabetic nephropathy (DN), the leading cause of end-stage renal disease, has become a massive global health burden. Despite considerable efforts, the underlying mechanisms have not yet been comprehensively understood. In this study, a systematic approach was utilized to identify the microRNA signature in DN and to introduce novel drug targets (DTs) in DN. Using microarray profiling followed by qPCR confirmation, 13 and 6 differentially expressed (DE) microRNAs were identified in the kidney cortex and medulla, respectively. The microRNA-target interaction networks for each anatomical compartment were constructed and central nodes were identified. Moreover, enrichment analysis was performed to identify key signaling pathways. To develop a strategy for DT prediction, the human proteome was annotated with 65 biochemical characteristics and 23 network topology parameters. Furthermore, all proteins targeted by at least one FDA-approved drug were identified. Next, mGMDH-AFS, a high-performance machine learning algorithm capable of tolerating massive imbalanced size of the classes, was developed to classify DT and non-DT proteins. The sensitivity, specificity, accuracy, and precision of the proposed method were 90%, 86%, 88%, and 89%, respectively. Moreover, it significantly outperformed the state-of-the-art (P-value ≤ 0.05) and showed very good diagnostic accuracy and high agreement between predicted and observed class labels. The cortex and medulla networks were then analyzed with this validated machine to identify potential DTs. Among the high-rank DT candidates are Egfr, Prkce, clic5, Kit, and Agtr1a which is a current well-known target in DN. In conclusion, a combination of experimental and computational approaches was exploited to provide a holistic insight into the disorder for introducing novel therapeutic targets.