The Tuberculosis Drug Streptomycin as a Potential Cancer Therapeutic: Inhibition of miR-21 Function by Directly Targeting Its Precursor

Unlocking novel therapeutic avenues in glioblastoma: Harnessing 4-amino cyanine and miRNA synergy for next-gen treatment convergence

Glioblastoma (GBM) poses a formidable challenge in oncology due to its aggressive nature and dismal prognosis, with average survival rates around 15 months despite conventional treatments. This review proposes a novel therapeutic strategy for GBM by integrating microRNA (miRNA) therapy with 4-amino cyanine molecules possessing near-infrared (NIR) properties. miRNA holds promise in regulating gene expression, particularly in GBM, making it an attractive therapeutic target. 4-amino cyanine molecules, especially those with NIR properties, have shown efficacy in targeted tumor cell degradation. The combined approach addresses gene expression regulation and precise tumor cell degradation, offering a breakthrough in GBM treatment. Additionally, the review explores noncoding RNAs classification and characteristics, highlighting their role in GBM pathogenesis. Advanced technologies such as antisense oligonucleotides (ASOs), locked nucleic acids (LNAs), and peptide nucleic acids (PNAs) show potential in targeting noncoding RNAs therapeutically, paving the way for precision medicine in GBM. This synergistic combination presents an innovative approach with the potential to advance cancer therapy in the challenging landscape of GBM.

Unraveling the noncoding RNA landscape in glioblastoma: from pathogenesis to precision therapeutics

Glioblastoma (GBM) is an aggressive type IV brain tumor that originates from astrocytes and has a poor prognosis. Despite intensive research, survival rates have not significantly improved. Noncoding RNAs (ncRNAs) are emerging as critical regulators of carcinogenesis, progression, and increased treatment resistance in GBM cells. They influence angiogenesis, migration, epithelial-to-mesenchymal transition, and invasion in GBM cells. ncRNAs, such as long ncRNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs), are commonly dysregulated in GBM. miRNAs, such as miR-21, miR-133a, and miR-27a-3p, are oncogenes that increase cell proliferation, metastasis, and migration by targeting TGFBR1 and BTG2. In contrast, lncRNAs, such as HOXD-AS2 and LINC00511, are oncogenes that increase the migration, invasion, and proliferation of cells. CircRNAs, such as circ0001730, circENTPD7, and circFOXO3, are oncogenes responsible for cell growth, angiogenesis, and viability. Developing novel therapeutic strategies targeting ncRNAs, cell migration, and angiogenesis is a promising approach for GBM. By targeting these dysregulated ncRNAs, we can potentially restore a healthy balance in gene expression and influence disease progression. ncRNAs abound within GBM, demonstrating significant roles in governing the growth and behavior of these tumors. They may also be useful as biomarkers or targets for therapy. The use of morpholino oligonucleotides (MOs) suppressing the oncogene expression of HOTAIR, BCYRN1, and cyrano, antisense oligonucleotides (ASOs) suppressing the expression of ncRNAs such as MALAT1 and miR-10b, locked nucleic acids (LNAs) suppressing miR-21, and peptide nucleic acids (PNAs) suppressing the expression of miR-155 inhibited the PI3K pathway, tumor growth, angiogenesis, proliferation, migration, and invasion. Targeting oncogenic ncRNAs with RNA-interfering strategies such as MOs, ASOs, LNAs, CRISPR-Cas9 gene editing, and PNA approaches may represent a promising therapeutic strategy for GBM. This review emphasizes the critical role of ncRNAs in GBM pathogenesis, as well as the potential for new therapeutic strategies targeting these pathways to improve the prognosis and quality of life for GBM patients.

Multitask joint learning with graph autoencoders for predicting potential MiRNA-drug associations

The occurrence of many diseases is associated with miRNA abnormalities. Predicting potential drug-miRNA associations is of great importance for both disease treatment and new drug discovery. Most computation-based approaches learn one task at a time, ignoring the information contained in other tasks in the same domain. Multitask learning can effectively enhance the prediction performance of a single task by extending the valid information of related tasks. In this paper, we presented a multitask joint learning framework (MTJL) with a graph autoencoder for predicting the associations between drugs and miRNAs. First, we combined multiple pieces of information to construct a high-quality similarity network of both drugs and miRNAs and then used a graph autoencoder (GAE) to learn their embedding representations separately. Second, to further improve the embedding quality of drugs, we added an auxiliary task to classify drugs using the learned representations. Finally, the embedding representations of drugs and miRNAs were linearly transformed to obtain the predictive association scores between them. A comparison with other state-of-the-art models shows that MTJL has the best prediction performance, and ablation experiments show that the auxiliary task can enhance the embedding quality and improve the robustness of the model. In addition, we show that MTJL has high utility in predicting potential associations between drugs and miRNAs by conducting two case studies.

Predicting potential small molecule-miRNA associations utilizing truncated schatten p-norm

MicroRNAs (miRNAs) have significant implications in diverse human diseases and have proven to be effectively targeted by small molecules (SMs) for therapeutic interventions. However, current SM-miRNA association prediction models do not adequately capture SM/miRNA similarity. Matrix completion is an effective method for association prediction, but existing models use nuclear norm instead of rank function, which has some drawbacks. Therefore, we proposed a new approach for predicting SM-miRNA associations by utilizing the truncated schatten p-norm (TSPN). First, the SM/miRNA similarity was preprocessed by incorporating the Gaussian interaction profile kernel similarity method. This identified more SM/miRNA similarities and significantly improved the SM-miRNA prediction accuracy. Next, we constructed a heterogeneous SM-miRNA network by combining biological information from three matrices and represented the network with its adjacency matrix. Finally, we constructed the prediction model by minimizing the truncated schatten p-norm of this adjacency matrix and we developed an efficient iterative algorithmic framework to solve the model. In this framework, we also used a weighted singular value shrinkage algorithm to avoid the problem of excessive singular value shrinkage. The truncated schatten p-norm approximates the rank function more closely than the nuclear norm, so the predictions are more accurate. We performed four different cross-validation experiments on two separate datasets, and TSPN outperformed various most advanced methods. In addition, public literature confirms a large number of predictive associations of TSPN in four case studies. Therefore, TSPN is a reliable model for SM-miRNA association prediction.

Identification of miRNA-Small Molecule Associations by Continuous Feature Representation Using Auto-Encoders

MicroRNAs (miRNAs) are short non-coding RNAs that play important roles in the body and affect various diseases, including cancers. Controlling miRNAs with small molecules is studied herein to provide new drug repurposing perspectives for miRNA-related diseases. Experimental methods are time- and effort-consuming, so computational techniques have been applied, relying mostly on biological feature similarities and a network-based scheme to infer new miRNA–small molecule associations. Collecting such features is time-consuming and may be impractical. Here we suggest an alternative method of similarity calculation, representing miRNAs and small molecules through continuous feature representation. This representation is learned by the proposed deep learning auto-encoder architecture. Our suggested representation was compared to previous works and achieved comparable results using 5-fold cross validation (92% identified within top 25% predictions), and better predictions for most of the case studies (avg. of 31% vs. 25% identified within the top 25% of predictions). The results proved the effectiveness of our proposed method to replace previous time- and effort-consuming methods.

Dual-Network Collaborative Matrix Factorization for predicting small molecule-miRNA associations

MicroRNAs (miRNAs) play crucial roles in multiple biological processes and human diseases and can be considered as therapeutic targets of small molecules (SMs). Because biological experiments used to verify SM-miRNA associations are time-consuming and expensive, it is urgent to propose new computational models to predict new SM-miRNA associations. Here, we proposed a novel method called Dual-network Collaborative Matrix Factorization (DCMF) for predicting the potential SM-miRNA associations. Firstly, we utilized the Weighted K Nearest Known Neighbors (WKNKN) method to preprocess SM-miRNA association matrix. Then, we constructed matrix factorization model to obtain two feature matrices containing latent features of SM and miRNA, respectively. Finally, the predicted SM-miRNA association score matrix was obtained by calculating the inner product of two feature matrices. The main innovations of this method were that the use of WKNKN method can preprocess the missing values of association matrix and the introduction of dual network can integrate more diverse similarity information into DCMF. For evaluating the validity of DCMF, we implemented four different cross validations (CVs) based on two distinct datasets and two different case studies. Finally, based on dataset 1 (dataset 2), DCMF achieved Area Under receiver operating characteristic Curves (AUC) of 0.9868 (0.8770), 0.9833 (0.8836), 0.8377 (0.7591) and 0.9836 ± 0.0030 (0.8632 ± 0.0042) in global Leave-One-Out Cross Validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV and 5-fold CV, respectively. For case studies, plenty of predicted associations have been confirmed by published experimental literature. Therefore, DCMF is an effective tool to predict potential SM-miRNA associations.

Efficient NMR Screening Approach to Discover Small Molecule Fragments Binding Structured RNA

We describe a scalable nuclear magnetic resonance (NMR) screening approach to identify and prioritize small molecule fragments that bind to structured RNAs. This approach is target agnostic and, therefore, amenable to many RNA structures and libraries, and it provides initial hits for further synthetic elaboration and structure-based drug discovery efforts. We demonstrate the approach on the pre-miR-21 stem-loop, which is of significant interest in oncology and metabolic diseases. We screened the pre-miR-21 hairpin using a small (420 compounds) commercially available fragment library and identified 18 hits in the first round of triage screening. This was further refined to four fragments which passed all screening cascade filters. Among these four hits, a thiadiazole fragment was demonstrated to bind the Dicer cleavage site of pre-miR-21 by target-detected NMR experiments and through the observation of clear intermolecular NOEs.

Monitoring Dicer-Mediated miRNA-21 Maturation and Ago2 Loading by a Dual-Colour FIT PNA Probe Set

The inhibition of micro RNA (miRNA) maturation by Dicer and loading matured miRNAs into the RNA-induced silencing complex (RISC) is envisioned as a modality for treatment of cancer. Existing methods for evaluating maturation either focus on the conversion of modified precursors or detect mature miRNA. Whereas the former is not applicable to native pre-miRNA, the latter approach underestimates maturation when both nonmatured and matured miRNA molecules are subject to cleavage. We present a set of two orthogonally labelled FIT PNA probes that distinguish between cleaved pre-miRNA and the mature miRNA duplex. The probes allow Dicer-mediated miR21 maturation to be monitored and Ago2-mediated unwinding of the miR21 duplex to be assayed. A two-channel fluorescence readout enables measurement in real-time without the need for specialized instrumentation or further enzyme mediated amplification.

Bleomycin Can Cleave an Oncogenic Noncoding RNA

Noncoding RNAs are pervasive in cells and contribute to diseases such as cancer. A question in biomedical research is whether noncoding RNAs are targets of medicines. Bleomycin is a natural product that cleaves DNA; however, it is known to cleave RNA in vitro. Herein, an in-depth analysis of the RNA cleavage preferences of bleomycin A5 is presented. Bleomycin A5 prefers to cleave RNAs with stretches of AU base pairs. Based on these preferences and bioinformatic analysis, the microRNA-10b hairpin precursor was identified as a potential substrate for bleomycin A5. Both in vitro and cellular experiments demonstrated cleavage. Importantly, chemical cleavage by bleomycin A5 in the microRNA-10b hairpin precursors occurred near the Drosha and Dicer enzymatic processing sites and led to destruction of the microRNA. Evidently, oncogenic noncoding RNAs can be considered targets of cancer medicines and might elicit their pharmacological effects by targeting noncoding RNA.

Macro and small over micro: macromolecules and small molecules that regulate microRNAs

Given the correlation between the deregulation of specific miRNAs and disease onset, it is critical to identify miRNA regulators that effectively control miRNAs involved in the pathogenesis of target diseases. This review provides the latest update on oligonucleotide- and small-molecule-based miRNA regulators, and discusses assays developed to screen for small-molecule regulators.