Reciprocal interplays between MicroRNAs and pluripotency transcription factors in dictating stemness features in human cancers

MicroRNAome profiling of breast cancer unveils hsa-miR-5683 as a tumor suppressor microRNA predicting favorable clinical outcome

BACKGROUND

Breast cancer is a heterogeneous disease with diverse molecular subtypes, underscoring a better understanding of its molecular features and underlying regulatory mechanisms. Therefore, identifying novel prognostic biomarkers and therapeutic targets is crucial for advancing the current standard of care for breast cancer patients.

METHODS

Ninety-six formalin-fixed paraffin-embedded (FFPE) breast cancer samples underwent miRNAome profiling using QIAseq microRNA library kit and sequencing on Illumina platform. Mature miRNA quantification was conducted using CLC Genomics Workbench v21.0.5, while Relapse-free survival (RFS) analysis was conducted using RStudio 2023.09.1. Gain-of-function studies were conducted using miRNA mimics, while the effects of miRNA exogenous expression on cancer hallmark were assessed using 2-dimentional (2D) proliferation assay, three-dimensional (3D) organotypic culture, and live-dead staining. TargetScan database and Ingenuity Pathway Analysis (IPA) were used for miRNA target identification.

RESULTS

Hierarchical clustering based on miRNA expression revealed distinct patterns in relation to PAM50 classification and identified miRNAs panels associated with luminal, HER2, and basal subtypes. hsa-miR-5683 emerged as a potential prognostic biomarker, showing a favorable correlation with RFS and suppressing tumorigenicity under 2D and 3D conditions in triple-negative breast cancer (TNBC) models. Findings were further extended to the MCF7 hormone receptor positive (HR+) model. Transcriptomic profiling of hsa-miR-5683 overexpressing TNBC cells revealed its potential role in key oncogenic pathways. Integration of downregulated genes and CRISPR-Cas9 perturbational effects identified ACLY, RACGAP1, AK4, MRPL51, CYB5B, MKRN1, TMEM230, NUP54, ANAPC13, PGAM1, and SOD1 as bona fide gene targets for hsa-miR-5683.

CONCLUSIONS

Our data provides comprehensive miRNA expression atlas in breast cancer subtypes and underscores the prognostic and therapeutic significance of numerous miRNAs, including hsa-miR-5683 in TNBC. The identified gene targets unravel the intricate regulatory network in TNBC progression, suggesting promising avenues for further research and targeted therapeutic interventions.

A Tiny Viral Protein, SARS-CoV-2-ORF7b: Functional Molecular Mechanisms

This study presents the interaction with the human host metabolism of SARS-CoV-2 ORF7b protein (43 aa), using a protein-protein interaction network analysis. After pruning, we selected from BioGRID the 51 most significant proteins among 2753 proven interactions and 1708 interactors specific to ORF7b. We used these proteins as functional seeds, and we obtained a significant network of 551 nodes via STRING. We performed topological analysis and calculated topological distributions by Cytoscape. By following a hub-and-spoke network architectural model, we were able to identify seven proteins that ranked high as hubs and an additional seven as bottlenecks. Through this interaction model, we identified significant GO-processes (5057 terms in 15 categories) induced in human metabolism by ORF7b. We discovered high statistical significance processes of dysregulated molecular cell mechanisms caused by acting ORF7b. We detected disease-related human proteins and their involvement in metabolic roles, how they relate in a distorted way to signaling and/or functional systems, in particular intra- and inter-cellular signaling systems, and the molecular mechanisms that supervise programmed cell death, with mechanisms similar to that of cancer metastasis diffusion. A cluster analysis showed 10 compact and significant functional clusters, where two of them overlap in a Giant Connected Component core of 206 total nodes. These two clusters contain most of the high-rank nodes. ORF7b acts through these two clusters, inducing most of the metabolic dysregulation. We conducted a co-regulation and transcriptional analysis by hub and bottleneck proteins. This analysis allowed us to define the transcription factors and miRNAs that control the high-ranking proteins and the dysregulated processes within the limits of the poor knowledge that these sectors still impose.

In search of the ratio of miRNA expression as robust biomarkers for constructing stable diagnostic models among multi-center data

MicroRNAs (miRNAs) are promising biomarkers for the early detection of disease, and many miRNA-based diagnostic models have been constructed to distinguish patients and healthy individuals. To thoroughly utilize the miRNA-profiling data across different sequencing platforms or multiple centers, the models accounting the batch effects were demanded for the generalization of medical application. We conducted transcription factor (TF)-mediated miRNA-miRNA interaction network analysis and adopted the within-sample expression ratios of miRNA pairs as predictive markers. The ratio of the expression values between each miRNA pair turned out to be stable across multiple data sources. A genetic algorithm-based classifier was constructed to quantify risk scores of the probability of disease and discriminate disease states from normal states in discovery, with a validation dataset for COVID-19, renal cell carcinoma, and lung adenocarcinoma. The predictive models based on the expression ratio of interacting miRNA pairs demonstrated good performances in the discovery and validation datasets, and the classifier may be used accurately for the early detection of disease.

Prevention of Metastasis by Suppression of Stemness Genes Using a Combination of microRNAs

We propose an original strategy for metastasis prevention using a combination of three microRNAs that blocks the dedifferentiation of cancer cells in a metastatic niche owing to the downregulation of stemness genes. Transcriptome microarray analysis was applied to identify the effects of a mixture of microRNAs on the pattern of differentially expressed genes in human breast cancer cell lines. Treatment of differentiated CD44- cancer cells with the microRNA mixture inhibited their ability to form mammospheres in vitro. The combination of these three microRNAs encapsulated into lipid nanoparticles prevented lung metastasis in a mouse model of spontaneous metastasis. The mixture of three microRNAs (miR-195-5p/miR-520a/miR-630) holds promise for the development of an antimetastatic therapeutic that blocks tumor cell dedifferentiation, which occurs at secondary tumor sites and determines the transition of micrometastases to macrometastases.

Transcriptional regulation of cancer stem cell: regulatory factors elucidation and cancer treatment strategies

Cancer stem cells (CSCs) were first discovered in the 1990s, revealing the mysteries of cancer origin, migration, recurrence and drug-resistance from a new perspective. The expression of pluripotent genes and complex signal regulatory networks are significant features of CSC, also act as core factors to affect the characteristics of CSC. Transcription is a necessary link to regulate the phenotype and potential of CSC, involving chromatin environment, nucleosome occupancy, histone modification, transcription factor (TF) availability and cis-regulatory elements, which suffer from ambient pressure. Especially, the expression and activity of pluripotent TFs are deeply affected by both internal and external factors, which is the foundation of CSC transcriptional regulation in the current research framework. Growing evidence indicates that regulating epigenetic modifications to alter cancer stemness is effective, and some special promoters and enhancers can serve as targets to influence the properties of CSC. Clarifying the factors that regulate CSC transcription will assist us directly target key stem genes and TFs, or hinder CSC transcription through environmental and other related factors, in order to achieve the goal of inhibiting CSC and tumors. This paper comprehensively reviews the traditional aspects of transcriptional regulation, and explores the progress and insights of the impact on CSC transcription and status through tumor microenvironment (TME), hypoxia, metabolism and new meaningful regulatory factors in conjunction with the latest research. Finally, we present opinions on omnidirectional targeting CSCs transcription to eliminate CSCs and address tumor resistance.

Pharmacological roles of lncRNAs in diabetic retinopathy with a focus on oxidative stress and inflammation

Diabetic retinopathy (DR) is a complication caused by abnormal glucose metabolism, which affects the vision and quality of life of patients and severely impacts the society at large.DR has a complex pathogenic process. Evidence from multiple studies have shown that oxidative stress and inflammation play pivotal roles in DR.Additionally, with the rapid development of various genetic detection methods, the abnormal expression of long non-coding RNAs (lncRNAs) have been confirmed to promote the development of DR.Research has demonstrated the potential of lncRNAs as ideal biomarkers and theranostic targets in DR. In this narrative review, we will focus on the research results on mechanisms underlying DR, list lncRNAs confirmed to be closely related to these mechanisms, and discuss their potential clinical application value and limitations.

Non-coding RNAs in renal cell carcinoma: Implications for drug resistance

Renal cell carcinoma (RCC) represents a malignant tumor of the urinary system. Individuals with early-stage RCC could be cured by surgical treatment, but a considerable number of cases of advanced RCC progress to drug resistance. Recently, numerous reports have demonstrated that a variety of non-coding RNAs (ncRNAs) contribute to tumor occurrence and development. ncRNAs can act as oncogenic or tumor suppressor genes to regulate proliferation, migration, drug resistance and other processes in RCC cells through a variety of signaling pathways. Considering the lack of treatment options for advanced RCC after drug resistance, ncRNAs may be a good choice as biomarkers of drug resistance in RCC and targets to overcome drug resistance. In this review, we discussed the effects of ncRNAs on drug resistance in RCC and the great potential of ncRNAs as a biomarker of or a new therapeutic method in RCC.