Non-Coding RNA最新文献

Introduction: Lung cancer remains one of the most prevalent and deadly cancers globally, with high mortality rates largely due to late-stage diagnosis, aggressive progression, and frequent recurrence. Despite advancements in diagnostic techniques and therapeutic interventions, the overall prognosis for lung cancer patients continues to be dismal.

Method: Emerging research has identified non-coding RNAs (ncRNAs), including microRNAs, long non-coding RNAs, and circular RNAs, as critical regulators of gene expression, significantly influencing cancer biology. These ncRNAs play pivotal roles in various aspects of lung cancer pathogenesis, including tumor initiation, progression, metastasis, and resistance to therapy.

Results: We provide a comprehensive analysis of the current understanding of ncRNAs in lung cancer, emphasizing their potential as biomarkers for early diagnosis, prognostication, and the prediction of the therapeutic response. We explore the biological functions of ncRNAs, their involvement in key oncogenic pathways, and the molecular mechanisms by which they modulate gene expression and cellular processes in lung cancer. Furthermore, this review highlights recent advances in ncRNA-based diagnostic tools and therapeutic strategies, such as miRNA mimics and inhibitors, lncRNA-targeted therapies, and circRNA-modulating approaches, offering promising avenues for personalized medicine.

Conclusion: Finally, we discuss the challenges and future directions in ncRNA research, including the need for large-scale validation studies and the development of efficient delivery systems for ncRNA-based therapies. This review underscores the potential of ncRNAs to revolutionize lung cancer management by providing novel diagnostic and therapeutic options that could improve patient outcomes.

Circular RNAs (circRNAs) have been shown to be pivotal regulators in various human diseases by participating in gene splicing, acting as microRNA (miRNA) sponges, interacting with RNA-binding proteins (RBPs), and translating into short peptides. As the back-splicing products of pre-mRNAs, many circRNAs can modulate the expression of their host genes through transcriptional, post-transcriptional, translational, and post-translational control via interaction with other molecules. This review provides a detailed summary of these regulatory mechanisms based on the class of molecules that they interact with, which encompass DNA, mRNA, miRNA, and RBPs. The co-expression of circRNAs with their parental gene productions (including linear counterparts and proteins) provides potential diagnostic biomarkers for multiple diseases. Meanwhile, the different regulatory mechanisms by which circRNAs act on their host genes via interaction with other molecules constitute complex regulatory networks, which also provide noticeable clues for therapeutic strategies against diseases. Future research should explore whether these proven mechanisms can play a similar role in other types of disease and clarify further details about the cross-talk between circRNAs and host genes. In addition, the regulatory relationship between circRNAs and their host genes in circRNA circularization, degradation, and cellular localization should receive further attention.

Following the acute phase of SARS-CoV-2 infection, certain individuals experience persistent symptoms referred to as long COVID. This study analyzed the patients categorized into three distinct groups: (1) individuals presenting rheumatological symptoms associated with long COVID, (2) patients who have successfully recovered from COVID-19, and (3) donors who have never contracted COVID-19. A notable decline in the expression of miR-200c-3p, miR-766-3p, and miR-142-3p was identified among patients exhibiting rheumatological symptoms of long COVID. The highest concentration of miR-142-3p was found in healthy donors. One potential way to reduce miRNA concentrations is through antibody-mediated hydrolysis. Not only can antibodies possessing RNA-hydrolyzing activity recognize the miRNA substrate specifically, but they also catalyze its hydrolysis. The analysis of the catalytic activity of plasma antibodies revealed that antibodies from patients with long COVID demonstrated lower hydrolysis activity against five fluorescently labeled oligonucleotide sequences corresponding to the Flu-miR-146b-5p, Flu-miR-766-3p, Flu-miR-4742-3p, and Flu-miR-142-3p miRNAs and increased activity against the Flu-miR-378a-3p miRNA compared to other patient groups. The changes in miRNA concentrations and antibody-mediated hydrolysis of miRNAs are assumed to have a complex regulatory mechanism that is linked to gene pathways associated with the immune system. We demonstrate that all six miRNAs under analysis are associated with a large number of signaling pathways associated with immune response-associated pathways.

Prostate cancer (PCa) is a prevalent malignancy in men globally. Current diagnostic methods like PSA testing have limitations, leading to overdiagnosis and unnecessary treatment. Castration-resistant prostate cancer (CRPC) emerges in some patients receiving androgen deprivation therapy (ADT). This study explores the potential of circulating microRNA-107 (miR-107) in liquid biopsies as a prognosis tool to differentiate CRPC from non-castration-resistant PCa (NCRPC). We designed a case-control study to evaluate circulating miR-107 in serum as a potential prognosis biomarker. We analyzed miR-107 expression in liquid biopsies and found significantly higher levels (p < 0.005) in CRPC patients, compared to NCRPC. Notably, miR-107 expression was statistically higher in the advanced stage (clinical stage IV), compared to stages I-III. Furthermore, CRPC patients exhibited significantly higher miR-107 levels (p < 0.05), compared to NCRPC. These findings suggest that miR-107 holds promise as a non-invasive diagnostic biomarker for identifying potential CRPC patients.

A "watch and wait" strategy, delaying treatment until active disease manifests, is adopted for most CLL cases; however, prognostic models incorporating biomarkers have shown to be useful to predict treatment requirement. In our prospective O-CLL1 study including 224 patients, we investigated the predictive role of 513 microRNAs (miRNAs) on time to first treatment (TTFT). In the context of this study, six well-established variables (i.e., Rai stage, beta-2-microglobulin levels, IGVH mutational status, del11q, del17p, and NOTCH1 mutations) maintained significant associations with TTFT in a basic multivariable model, collectively yielding a Harrell's C-index of 75% and explaining 45.4% of the variance in the prediction of TTFT. Concerning miRNAs, 73 out of 513 were significantly associated with TTFT in a univariable model; of these, 16 retained an independent relationship with the outcome in a multivariable analysis. For 8 of these (i.e., miR-582-3p, miR-33a-3p, miR-516a-5p, miR-99a-5p, and miR-296-3p, miR-502-5p, miR-625-5p, and miR-29c-3p), a lower expression correlated with a shorter TTFT, whereas in the remaining eight (i.e., miR-150-5p, miR-148a-3p, miR-28-5p, miR-144-5p, miR-671-5p, miR-1-3p, miR-193a-3p, and miR-124-3p), the higher expression was associated with shorter TTFT. Integrating these miRNAs into the basic model significantly enhanced predictive accuracy, raising the Harrell's C-index to 81.1% and the explained variation in TTFT to 63.3%. Moreover, the inclusion of the miRNA scores enhanced the integrated discrimination improvement (IDI) and the net reclassification index (NRI), underscoring the potential of miRNAs to refine CLL prognostic models and providing insights for clinical decision-making. In silico analyses on the differently expressed miRNAs revealed their potential regulatory functions of several pathways, including those involved in the therapeutic responses. To add a biological context to the clinical evidence, an miRNA-mRNA correlation analysis revealed at least one significant negative correlation between 15 of the identified miRNAs and a set of 50 artificial intelligence (AI)-selected genes, previously identified by us as relevant for TTFT prediction in the same cohort of CLL patients. In conclusion, the identification of specific miRNAs as predictors of TTFT holds promise for enhancing risk stratification in CLL to predict therapeutic needs. However, further validation studies and in-depth functional analyses are required to confirm the robustness of these observations and to facilitate their translation into meaningful clinical utility.

Gene regulation is crucial for cellular function and homeostasis. It involves diverse mechanisms controlling the production of specific gene products and contributing to tissue-specific variations in gene expression. The dysregulation of genes leads to disease, emphasizing the need to understand these mechanisms. Computational methods have jointly studied transcription factors (TFs), microRNA (miRNA), and messenger RNA (mRNA) to investigate gene regulatory networks. However, there remains a knowledge gap in comprehending gene regulatory networks. On the other hand, super-enhancers (SEs) have been implicated in miRNA biogenesis and function in recent experimental studies, in addition to their pivotal roles in cell identity and disease progression. However, statistical/computational methodologies harnessing the potential of SEs in deciphering gene regulation networks remain notably absent. However, to understand the effect of miRNA on mRNA, existing statistical/computational methods could be updated, or novel methods could be developed by accounting for SEs in the model. In this review, we categorize existing computational methods that utilize TF and miRNA data to understand gene regulatory networks into three broad areas and explore the challenges of integrating enhancers/SEs. The three areas include unraveling indirect regulatory networks, identifying network motifs, and enriching pathway identification by dissecting gene regulators. We hypothesize that addressing these challenges will enhance our understanding of gene regulation, aiding in the identification of therapeutic targets and disease biomarkers. We believe that constructing statistical/computational models that dissect the role of SEs in predicting the effect of miRNA on gene regulation is crucial for tackling these challenges.

Liver fibrosis is a significant contributor to liver-related disease mortality on a global scale. Despite this, there remains a dearth of effective therapeutic interventions capable of reversing this condition. Consequently, it is imperative that we gain a comprehensive understanding of the underlying mechanisms driving liver fibrosis. In this regard, the activation of hepatic stellate cells (HSCs) is recognized as a pivotal factor in the development and progression of liver fibrosis. The role of noncoding RNAs (ncRNAs) in epigenetic regulation of HSCs transdifferentiation into myofibroblasts has been established, providing new insights into gene expression changes during HSCs activation. NcRNAs play a crucial role in mediating the epigenetics of HSCs, serving as novel regulators in the pathogenesis of liver fibrosis. As research on epigenetics expands, the connection between ncRNAs involved in HSCs activation and epigenetic mechanisms becomes more evident. These changes in gene regulation have attracted considerable attention from researchers in the field. Furthermore, epigenetics has contributed valuable insights to drug discovery and the identification of therapeutic targets for individuals suffering from liver fibrosis and cirrhosis. As such, this review offers a thorough discussion on the role of ncRNAs in the HSCs activation of liver fibrosis.

This is a mini-review capturing the views and opinions of selected participants at the 2021 IEEE BIBM 3rd Annual LncRNA Workshop, held in Dubai, UAE. The views and opinions are expressed on five broad themes related to problems in lncRNA, namely, challenges in the computational analysis of lncRNAs, lncRNAs and cancer, lncRNAs in sports, lncRNAs and COVID-19, and lncRNAs in human brain activity.

We developed a technology for detecting and quantifying trace nucleic acids using a bracketing protocol designed to yield a copy number with approximately ± 20% accuracy across all concentrations. The microRNAs (miRNAs) let-7b, miR-15b, miR-21, miR-375 and miR-141 were measured in serum and urine samples from healthy subjects and patients with breast, prostate or pancreatic cancer. Detection and quantification were amplification-free and enabled using osmium-tagged probes and MinION, a nanopore array detection device. Combined serum from healthy men (Sigma-Aldrich, St. Louis, MO, USA #H6914) was used as a reference. Total RNA isolated from biospecimens using commercial kits was used as the miRNA source. The unprecedented ± 20% accuracy led to the conclusion that miRNA copy numbers must be normalized to the same RNA content, which in turn illustrates (i) independence from age, sex and ethnicity, as well as (ii) equivalence between serum and urine. miR-21, miR-375 and miR-141 copies in cancers were 1.8-fold overexpressed, exhibited zero overlap with healthy samples and had a p-value of 1.6 × 10^-22, tentatively validating each miRNA as a multi-cancer biomarker. miR-15b was confirmed to be cancer-independent, whereas let-7b appeared to be a cancer biomarker for prostate and breast cancer, but not for pancreatic cancer.

The high incidence of idiopathic recurrent pregnancy loss (iRPL) may stem from the limited research on male contributory factors. Many studies suggest that sperm DNA fragmentation and oxidative stress contribute to iRPL, but their roles are still debated. MicroRNAs (miRNAs) are short non-coding RNAs that regulate various biological processes by modulating gene expression. While differential expression of specific miRNAs has been observed in women suffering from recurrent miscarriages, paternal miRNAs remain unexplored. We hypothesize that analyzing sperm miRNAs can provide crucial insights into the pathophysiology of iRPL. Therefore, this study aims to identify dysregulated miRNAs in the spermatozoa of male partners of iRPL patients. Total mRNA was extracted from sperm samples of iRPL and control groups, followed by miRNA library preparation and high-output miRNA sequencing. Subsequently, raw sequence reads were processed for differential expression analysis, target prediction, and bioinformatics analysis. Twelve differentially expressed miRNAs were identified in the iRPL group, with eight miRNAs upregulated (hsa-miR-4454, hsa-miR-142-3p, hsa-miR-145-5p, hsa-miR-1290, hsa-miR-1246, hsa-miR-7977, hsa-miR-449c-5p, and hsa-miR-92b-3p) and four downregulated (hsa-miR-29c-3p, hsa-miR-30b-5p, hsa-miR-519a-2-5p, and hsa-miR-520b-5p). Functional enrichment analysis revealed that gene targets of the upregulated miRNAs are involved in various biological processes closely associated with sperm quality and embryonic development.