Small nucleolar RNAs (snoRNAs) are increasingly recognized for their critical role in the pathogenesis and characterization of various human diseases. Consequently, the precise identification of snoRNA-disease associations (SDAs) is essential for the progression of diseases and the advancement of treatment strategies. However, conventional biological experimental approaches are costly, time-consuming, and resource-intensive; therefore, machine learning-based computational methods offer a promising solution to mitigate these limitations. This paper proposes a model called 'GBDTSVM', representing a novel and efficient machine learning approach for predicting snoRNA-disease associations by leveraging a Gradient Boosting Decision Tree (GBDT) and Support Vector Machine (SVM). 'GBDTSVM' effectively extracts integrated snoRNA-disease feature representations utilizing GBDT, and SVM is subsequently utilized to classify and identify potential associations. Furthermore, the method enhances the accuracy of these predictions by incorporating Gaussian integrated profile kernel similarity for both snoRNAs and diseases. Experimental evaluation of the GBDTSVM model demonstrates superior performance compared to state-of-the-art methods in the field, achieving an AUROC of 0.96 and an AUPRC of 0.95 on the 'MDRF' dataset. Moreover, our model shows superior performance on two more datasets named 'LSGT' and 'PsnoD'. Additionally, a case study conducted on the predicted snoRNA-disease associations verified the top-ranked snoRNAs across twelve prevalent diseases, further validating the efficacy of the GBDTSVM approach. These results underscore the model's potential as a robust tool for advancing snoRNA-related disease research. Source codes and datasets for our proposed framework can be obtained from: https://github.com/mariamuna04/gbdtsvm.

Excessive reactive oxygen species (ROS) are closely associated with the initiation and progression of cancers. As the most abundant intracellular antioxidant, glutathione (GSH) plays a critical role in regulating cellular ROS levels, modulating physiological processes, and is intricately linked to tumor progression and drug resistance. However, the underlying mechanisms remain not fully elucidated. Non-coding RNAs (ncRNAs), including long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), are key regulators of GSH levels. Different ncRNAs modulate various pathways involved in GSH metabolism, and these regulatory targets have the potential to serve as therapeutic targets for enhancing cancer treatment. In this review, we summarize the functions of GSH metabolism and highlight the significance of ncRNA-mediated regulation of GSH in cancer progression, drug resistance, and clinical applications.

Hepatitis B virus (HBV) causes liver cancer, which is the third most common cause of cancer-related death worldwide. Chronic inflammation via HBV in the host hepatocytes causes hepatocyte remodeling (hepatocyte transformation and immortalization) and hepatocellular carcinoma (HCC). Recognizing cancer stages accurately to optimize early screening and diagnosis is a primary concern in the outlook of HBV-induced hepatocyte remodeling and liver cancer. Genomic signatures play important roles in addressing this issue. Recently, machine learning (ML) models and bioinformatics analysis have become very important in discovering novel genomic signatures for the early diagnosis, treatment, and prognosis of HBV-induced hepatic cell remodeling and HCC. We discuss the recent literature on the ML approach and bioinformatics analysis revealed novel genomic signatures for diagnosing and forecasting HBV-associated hepatocyte remodeling and HCC. Various genomic signatures, including various microRNAs and their associated genes, long noncoding RNAs (lncRNAs), and small nucleolar RNAs (snoRNAs), have been discovered to be involved in the upregulation and downregulation of HBV-HCC. Moreover, these genetic biomarkers also affect different biological processes, such as proliferation, migration, circulation, assault, dissemination, antiapoptosis, mitogenesis, transformation, and angiogenesis in HBV-infected hepatocytes.

Small nucleolar RNAs are non-coding transcripts that guide chemical modifications of RNA substrates and modulate gene expression at the epigenetic and post-transcriptional levels. However, the extent of their regulatory potential and the underlying molecular mechanisms remain poorly understood. Here, we identify a conserved, previously unannotated intronic C/D-box snoRNA, termed snR107, hosted in the fission yeast long non-coding RNA mamRNA and carrying two independent cellular functions. On the one hand, snR107 guides site-specific 25S rRNA 2'-O-methylation and promotes pre-rRNA processing and 60S subunit biogenesis. On the other hand, snR107 associates with the gametogenic RNA-binding proteins Mmi1 and Mei2, mediating their reciprocal inhibition and restricting meiotic gene expression during sexual differentiation. Both functions require distinct cis-motifs within snR107, including a conserved 2'-O-methylation guiding sequence. Together, our results position snR107 as a dual regulator of rRNA modification and gametogenesis effectors, expanding our vision on the non-canonical functions exerted by snoRNAs in cell fate decisions.

Since the framing of the Central Dogma, it has been speculated that physically distinct ribosomes within cells may influence gene expression and cellular physiology. While heterogeneity in ribosome composition has been reported in bacteria, protozoans, fungi, zebrafish, mice and humans, its functional implications remain actively debated. Here, we review recent evidence demonstrating that expression of conserved variant ribosomal DNA (rDNA) alleles in bacteria, mice and humans renders their actively translating ribosome pool intrinsically heterogeneous at the level of ribosomal RNA (rRNA). In this context, we discuss reports that nutrient limitation-induced stress in Escherichia coli leads to changes in variant rRNA allele expression, programmatically altering transcription and cellular phenotype. We highlight that cells expressing ribosomes from distinct operons exhibit distinct drug sensitivities, which can be recapitulated in vitro and potentially rationalized by subtle perturbations in ribosome structure or in their dynamic properties. Finally, we discuss evidence that differential expression of variant rDNA alleles results in different populations of ribosome subtypes within mammalian tissues. These findings motivate further research into the impacts of rRNA heterogeneities on ribosomal function and predict that strategies targeting distinct ribosome subtypes may hold therapeutic potential.This article is part of the discussion meeting issue 'Ribosome diversity and its impact on protein synthesis, development and disease'.

The small nucleolar RNA (snoRNA) SNORD116 is a small non-coding RNA of interest across multiple biomedical fields of research. Much of the investigation into SNORD116 has been undertaken in the context of the congenital disease Prader-Willi syndrome, wherein SNORD116 expression is lost. However, emerging evidence indicates wider roles in various disease and tissue contexts such as cellular growth, metabolism and signalling. Nevertheless, a conclusive mechanism of action for SNORD116 remains to be established. Here, we review the key findings from these investigations, with the aim of identifying common elements from which to elucidate potential targets and mechanisms of SNORD116. A key recurring element identified is disruption to the insulin/IGF-1 and PI3K/mTOR signalling pathways, contributing to many of the phenotypes associated with SNORD116 modulation explored in this review.

Small nucleolar RNAs (snoRNAs) are one of the most abundant and evolutionary ancient group of functional non-coding RNAs. They were originally described as guides of post-transcriptional rRNA modifications, but emerging evidence suggests that snoRNAs fulfil an impressive variety of cellular functions. To reveal the true complexity of snoRNA-dependent functions, we need to catalogue first the complete repertoire of snoRNAs in a given cellular context. While the systematic mapping and characterization of "snoRNAomes" for some species have been described recently, this has not been done hitherto for the zebrafish (Danio rerio). Using size-fractionated RNA sequencing data from adult zebrafish tissues, we created an interactive "snoRNAome" database for this species. Our custom-designed analysis pipeline allowed us to identify with high-confidence 67 previously unannotated snoRNAs in the zebrafish genome, resulting in the most complete set of snoRNAs to date in this species. Reanalyzing multiple previously published datasets, we also provide evidence for the dynamic expression of some snoRNAs during the early stages of zebrafish development and tissue-specific expression patterns for others in adults. To facilitate further investigations into the functions of snoRNAs in zebrafish, we created a novel interactive database, snoDanio, which can be used to explore small RNA expression from transcriptomic data.

Noncoding RNAs comprise most of the transcriptome and represent an emerging area of research. Among them, small nucleolar RNAs (snoRNAs) have emerged as a promising target because they have been associated with the development and evolution of several diseases, including psychiatric disorders. snoRNAs are expressed in the brain, with some showing brain-specific expression that indicates specific roles in brain development, function, and dysfunction. However, the role of snoRNAs in conditions that affect the brain needs further investigation to be better understood. This scoping review summarizes existing literature on studies that have investigated snoRNAs in psychiatry and offers insight into potential pathophysiological mechanisms to be further investigated in future research.

Coronavirus disease 2019 (COVID-19), caused by infection with the enveloped RNA betacoronavirus, SARS-CoV-2, led to a global pandemic involving over 7 million deaths. Macrophage inflammatory responses impact COVID-19 severity; however, it is unclear whether macrophages are infected by SARS-CoV-2. We sought to identify mechanisms regulating macrophage expression of ACE2, the primary receptor for SARS-CoV-2, and to determine if macrophages are susceptible to productive infection. We developed a humanized ACE2 (hACE2) mouse whereby hACE2 cDNA was cloned into the mouse ACE2 locus under control of the native promoter. We validated the susceptibility of hACE2 mice to SARS-CoV-2 infection relative to wild-type mice and an established K18-hACE2 model of acute fulminating disease. Intranasal exposure to SARS-CoV-2 led to pulmonary consolidations with cellular infiltrate, edema, and hemorrhage, consistent with pneumonia, yet unlike the K18-hACE2 model, hACE2 mice survived and maintained stable weight. Infected hACE2 mice also exhibited a unique plasma chemokine, cytokine, and growth factor inflammatory signature relative to K18-hACE2 mice. Infected hACE2 mice demonstrated evidence of viral replication in infiltrating lung macrophages, and infection of macrophages in vitro revealed a transcriptional profile indicative of altered RNA and ribosomal processing machinery as well as activated cellular antiviral defense. Macrophage IL-1β-driven NF-κB transcription of ACE2 was an important mechanism of dynamic ACE2 upregulation, promoting macrophage susceptibility to infection. Experimental models of COVID-19 that make use of native hACE2 expression will allow for mechanistic insight into factors that can either promote host resilience or increase susceptibility to worsening severity of infection.

Existing studies indicate that dysregulation or abnormal expression of small nucleolar RNA (snoRNA) is closely associated with various diseases, including lung cancer. Furthermore, these diseases often involve multiple targets, making the redevelopment of traditional medicines highly promising. Accurate prediction of potential snoRNA therapeutic targets is essential for early disease intervention and the redevelopment of traditional medicines. Additionally, researchers have developed artificial intelligence (AI)-based methods to screen and predict potential snoRNA therapeutic targets, thereby advancing traditional drug redevelopment. However, existing methods face challenges such as imbalanced datasets and the dominance of high-degree nodes in graph neural networks (GNNs), which compromise the accuracy of node representations. To address these challenges, we propose an AI model based on variational graph autoencoders (VGAEs) that integrates decoupling and Kolmogorov-Arnold Network (KAN) technologies. The model reconstructs snoRNA-disease graphs by learning snoRNA and disease representations, accurately identifying potential snoRNA therapeutic targets. By decoupling similarity from node degree, the model mitigates the dominance of high-degree nodes, enhances prediction accuracy in scenarios like lung cancer, and leverages KAN technology to improve adaptability and flexibility to new data. Case studies revealed that snoRNA SNORA21 and SNORD33 are abnormally expressed in lung cancer patients and are strong candidates for potential therapeutic targets. These findings validate the proposed model's effectiveness in identifying therapeutic targets for diseases like lung cancer, supporting early screening and treatment, and advancing the redevelopment of traditional medicines. Data and experimental findings are archived in: https://github.com/shmildsj/data.

Small nucleolar RNAs (snoRNAs) are non-coding RNAs (ncRNAs) that regulate many cellular processes. Changes in the profiles of cellular ncRNAs and those secreted in exosomes are observed during viral infection. In our study, we analysed differences in expression profiles of snoRNAs isolated from exosomes of influenza (IAV)-infected and non-infected MDCK cells using high-throughput sequencing. The analysis revealed 133 significantly differentially regulated snoRNAs (131 upregulated and 2 downregulated), including 93 SNORD, 38 SNORA, and 2 SCARNA. The most upregulated was SNORD58 (log2FoldChange = 9.61), while the only downregulated snoRNAs were SNORD3 (log2FC = -2.98) and SNORA74 (log2FC = -2.67). Several snoRNAs previously described as involved in viral infections were upregulated, including SNORD27, SNORD28, SNORD29, SNORD58, and SNORD44. In total, 533 interactors of dysregulated snoRNAs were identified using the RNAinter database with an assigned confidence score ≥ 0.25. The main groups of predicted interactors were transcription factors (TFs, 169 interactors) and RNA-binding proteins (RBPs, 130 interactors). Among the most important were pioneer TFs such as POU5F1, SOX2, CEBPB, and MYC, while in the RBP category, notable interactors included Polr2a, TNRC6A, IGF2BP3, and FMRP. Our results suggest that snoRNAs are involved in pro-viral activity, although follow-up studies including experimental validation would be beneficial.

Emerging evidence underscores the critical role of impaired mRNA translation in various neurobiological conditions. Ribosomal RNA (rRNA), essential for protein synthesis, undergoes crucial post-transcriptional modifications such as 2'-O-ribose methylation, pseudouridylation, and base modifications. These modifications, particularly 2'-O-ribose methylation is vital for stabilizing rRNA structures and optimizing translation efficiency by regulating RNA integrity and its interactions with proteins. Concentrated in key regions like decoding sites and the peptidyl transferase center, dysregulation of these modifications can disrupt ribosomal function, contributing to the pathogenesis of diverse neurological conditions, including mental health disorders, developmental abnormalities, and neurodegenerative diseases. Mechanistically, 2'-O-ribose methylation involves interactions between small nucleolar RNAs (snoRNAs), snoRNPs, and fibrillarin, forming a complex regulatory network crucial for maintaining ribosomal integrity and function. Recent research highlights the association of defective ribosome biogenesis with a spectrum of CNS disorders, emphasizing the importance of understanding rRNA mechanisms in disease pathology. This review focuses on the pivotal role of 2'-O-ribose methylation in shaping ribosomal function and its potential implications for unraveling the pathophysiology of CNS disorders. Insights gained from studying these RNA modifications could pave the way for new therapeutic strategies targeting ribosomal dysfunction and associated neuropathological conditions, advancing precision medicine and therapeutic interventions.

Small non-coding RNAs (ncRNAs) have recently emerged as potential biomarkers of sarcopenia. However, previous studies have rarely explored the association of small ncRNAs with sarcopenic components, especially muscle strength and physical performance. We aimed to examine circulating small ncRNA profiles to detect low muscle strength and physical performance in older adults.

Ninety-eight older adults were randomly selected from Korean Frailty and Aging Cohort Study and classified into the "Normal," "Low muscle strength (MS) only," "Low physical performance (PP) only," and "Low MS and PP" groups by Asian Working Group for Sarcopenia 2019 criteria. We used high-throughput sequencing to delineate small ncRNA profiles in plasma. Differentially expressed small ncRNAs were analyzed to reveal distinct patterns based on muscle strength and physical performance status.

In "Low MS and PP" group, 119 miRNAs, 86 piRNAs, 92 snoRNAs, 106 snRNAs, and 15 tRNAs were differentially expressed compared to "Normal" group (p < 0.05). After Benjamini-Hochberg adjustment, 39 miRNAs, 2 piRNAs, 75 snoRNAs, 48 snRNAs, and 15 tRNAs showed differential expression in "Low MS and PP" group compared to than "Normal" group (adjusted p < 0.05). No significant differences were observed in comparisons between the other groups (adjusted p > 0.05).

The expression of circulating small ncRNAs were comprehensively characterized, revealing distinct signatures in older adults with both low muscle strength and physical performance compared to normal individuals. Although preliminary, this characterization can advance small ncRNA research on age-related declines in muscle strength and physical performance by providing foundational data for further investigation.

DNA methylation plays a regulatory role in the oncogenesis and tumor progression and is valuable in the diagnosis and prognosis of cancer. While circulating tumor DNA (ctDNA) is widely used in the detection of oncogenic mutations and the guidance of treatment in advanced non-small cell lung cancer (NSCLC), studies of ctDNA methylation remains insufficient. We aim to investigate the methylation profiles of ctDNA in patients with advanced NSCLC undergoing EGFR-tyrosine kinase inhibitor (EGFR-TKI) therapy and to discover novel biomarkers with predictive or prognostic value.

We recruited 49 patients with EGFR-mutated advanced NSCLC undergoing EGFR-TKI as first-line treatment. Utilizing next-generation sequencing, we examined the somatic mutations and methylation signatures within the tumor-associated genomic regions of ctDNA from pre-treatment blood. Subsequently, we explored the association of these molecular features with the patients' response to therapy and their progression-free survival (PFS).

Genomic mutation profiling revealed no significant association of PFS or best overall response (BOR) and ctDNA status. Evaluation of ctDNA methylation showed a negative correlation between the methylation of small nucleolar RNA (snoRNA) genes and PFS (R=-0.31, P=0.043). Furthermore, high-level methylation of SNORD3F was associated with poorer PFS (mPFS 346d vs 243d, HR 0.49, 95% CI 0.24-0.93, P=0.029).

Our study explored the prognostic value of ctDNA methylation in patients with advanced NSCLC undergoing targeted therapies and first revealed the predictive role of SNORD3F.

Although implicated as deleterious in many organisms, aneuploidy can underlie rapid phenotypic evolution. However, aneuploidy will be maintained only if the benefit outweighs the cost, which remains incompletely understood. To quantify this cost and the molecular determinants behind it, we generated a panel of chromosome duplications in Saccharomyces cerevisiae and applied comparative modeling and molecular validation to understand aneuploidy toxicity. We show that 74%-94% of the variance in aneuploid strains' growth rates is explained by the cumulative cost of genes on each chromosome, measured for single-gene duplications using a genomic library, along with the deleterious contribution of small nucleolar RNAs (snoRNAs) and beneficial effects of tRNAs. Machine learning to identify properties of detrimental gene duplicates provided no support for the balance hypothesis of aneuploidy toxicity and instead identified gene length as the best predictor of toxicity. Our results present a generalized framework for the cost of aneuploidy with implications for disease biology and evolution.

Bisphenols (BPs), including BPA, BPF, BPS, and BPAF, are synthetic phenolic organic compounds and endocrine-disrupting chemicals. These organics have been broadly utilized to produce epoxy resins, polycarbonate plastics, and other products. Mounting evidence has shown that BPs, especially BPA, may enter into the human body and participate in the development of human diseases mediated by nuclear hormone receptors. Moreover, BPA may negatively affect human health at the epigenetic level through processes such as DNA methylation and histone acetylation. Recent studies have demonstrated that, as part of epigenetics, noncoding RNAs (ncRNAs), including microRNAs (miRNAs), long noncoding RNAs (lncRNAs), circular RNAs (circRNAs), and small nucleolar RNAs (snoRNAs), have vital impacts on BP-related diseases, such as reproductive system diseases, nervous system diseases, digestive system diseases, endocrine system diseases, and other diseases. Moreover, based on the bioinformatic analysis, changes in ncRNAs may be relevant to normal activities and functions and BP-induced diseases. Thus, we conducted a meta-analysis to identify more promising ncRNAs as biomarkers and therapeutic targets for BP exposure and relevant human diseases. In this review, we summarize the regulatory functions of ncRNAs induced by BPs in human diseases and latent molecular mechanisms, as well as identify prospective biomarkers and therapeutic targets for BP exposure and upper diseases.

Acute kidney injury (AKI) signifies a sudden and prolonged decline in kidney function characterized by tubular cell death and interstitial inflammation. Small nucleolar RNAs (snoRNAs) play pivotal roles in oxidative stress and inflammation, and may play an important role in the AKI process, which remains elusive. an elevated expression of Snord3a is revealed in renal tubules in response to AKI and demonstrates that Snord3a deficiency alleviates renal injury in AKI mouse models. Notably, the deficiency of Snord3a exhibits a mitigating effect on the stimulator of interferon genes (STING)-associated ferroptosis phenotypes and the progression of tubular injury. Mechanistically, Snord3a is shown to regulate the STING signaling axis via promoting STING gene transcription; administration of Snord3a antisense oligonucleotides establishes a significant therapeutic advantage in AKI mouse models. Together, the findings elucidate the transcription regulation mechanism of STING and the crucial roles of the Snord3a-STING axis in ferroptosis during AKI, underscoring Snord3a as a potential prognostic and therapeutic target for AKI.

Small nucleolar RNAs (snoRNAs) were previously regarded as a class of functionally conserved housekeeping genes, primarily involved in the regulation of ribosome biogenesis by ribosomal RNA (rRNA) modification. However, some of them are involved in several biological processes via complex molecular mechanisms. DNA damage response (DDR) is a conserved mechanism for maintaining genomic stability to prevent the occurrence of various human diseases. It has recently been revealed that snoRNAs are involved in DDR at multiple levels, indicating their relevant theoretical and clinical significance in this field. The present review systematically addresses four main points, including the biosynthesis and classification of snoRNAs, the mechanisms through which snoRNAs regulate target molecules, snoRNAs in the process of DDR, and the significance of snoRNA in disease diagnosis and treatment. It focuses on the potential functions of snoRNAs in DDR to help in the discovery of the roles of snoRNAs in maintaining genome stability and pathological processes.

Hepatocellular carcinoma (HCC) is the most common type of liver cancer with a high death rate in the world. The molecular mechanisms related to the pathogenesis of HCC have not been precisely defined so far. Hence, this review aimed to address the potential cross-talk between noncoding RNAs (ncRNAs) and programmed cell death in HCC. All related papers in the English language up to June 2023 were collected and screened. The searched keywords in scientific databases, including Scopus, PubMed, and Google Scholar, were HCC, ncRNAs, Epigenetic, Programmed cell death, Autophagy, Apoptosis, Ferroptosis, Chemoresistance, Tumor recurrence, Prognosis, and Prediction. According to the reports, ncRNAs, comprising long ncRNAs, microRNAs, circular RNAs, and small nucleolar RNAs can affect cell proliferation, migration, invasion, and metastasis, as well as cell death-related processes, such as autophagy, ferroptosis, necroptosis, and apoptosis in HCC by regulating cancer-associated genes and signaling pathways, for example, phosphoinositide 3-kinase/Akt, extracellular signal-regulated kinase/MAPK, and Wnt/β-catenin signaling pathways. It seems that ncRNAs, as epigenetic regulators, can be utilized as biomarkers in diagnosis, prognosis, survival and recurrence rates prediction, chemoresistance, and evaluation of therapeutic response in HCC patients. However, more scientific evidence is suggested to be accomplished to confirm these results.

Deregulation of small non-coding RNAs (sncRNAs) has been associated with the onset of metastasis. We evaluated the expression of sncRNAs in patients with early-stage breast cancer, performing RNA sequencing in 60 patients for whom tumor and sentinel lymph node (SLN) samples were available, and conducting differential expression, gene ontology, enrichment and survival analyses. Sequencing annotation classified most of the sncRNAs into small nucleolar RNA (snoRNAs, 70%) and small nuclear RNA (snRNA, 13%). Our results showed no significant differences in sncRNA expression between tumor or SLNs obtained from the same patient. Differential expression analysis showed down-regulation (n = 21) sncRNAs and up-regulation (n = 2) sncRNAs in patients with locoregional metastasis. The expression of SNHG5, SNORD90, SCARNA2 and SNORD78 differentiated luminal A from luminal B tumors, whereas SNORD124 up-regulation was associated with luminal B HER2+ tumors. Discriminating analysis and receiver-operating curve analysis revealed a signature of six snoRNAs (SNORD93, SNORA16A, SNORD113-6, SNORA7A, SNORA57 and SNORA18A) that distinguished patients with locoregional metastasis and predicted patient outcome. Gene ontology and Reactome pathway analysis showed an enrichment of biological processes associated with translation initiation, protein targeting to specific cell locations, and positive regulation of Wnt and NOTCH signaling pathways, commonly involved in the promotion of metastases. Our results point to the potential of several sncRNAs as surrogate markers of lymph node metastases and patient outcome in early-stage breast cancer patients. Further preclinical and clinical studies are required to understand the biological significance of the most significant sncRNAs and to validate our results in a larger cohort of patients.

snoRNAs are a class of non-coding RNAs known to guide site specifically RNA modifications such as 2'-O-methylation and pseudouridylation. Recent results regarding snoRNA alterations in cancer has been made available and suggest their potential evaluation as diagnostic and prognostic biomarkers. A large part of these data, however, was not consistently confirmed and failed to provide mechanistic insights on the contribution of altered snoRNA expression to the neoplastic process. Here, we aim to critically review the available literature on snoRNA in cancer focusing on the studies elucidating the functional consequences of their deregulation. Beyond the canonical guide function in RNA processing and modification we also considered additional roles in which snoRNA, in various forms and through different modalities, are involved and that have been recently reported.

Ovarian cancer (OC) ranks as the fifth leading factor for female mortality globally, with a substantial burden of new cases and mortality recorded annually. Survival rates vary significantly based on the stage of diagnosis, with advanced stages posing significant challenges to treatment. OC is primarily categorized as epithelial, constituting approximately 90% of cases, and correct staging is essential for tailored treatment. The debulking followed by chemotherapy is the prevailing treatment, involving platinum-based drugs in combination with taxanes. However, the efficacy of chemotherapy is hindered by the development of chemoresistance, both acquired during treatment (acquired chemoresistance) and intrinsic to the patient (intrinsic chemoresistance). The emergence of chemoresistance leads to increased mortality rates, with many advanced patients experiencing disease relapse shortly after initial treatment. This review delves into the multifactorial nature of chemoresistance in OC, addressing mechanisms involving transport systems, apoptosis, DNA repair, and ovarian cancer stem cells (OCSCs). While previous research has identified genes associated with these mechanisms, the regulatory roles of non-coding RNA (ncRNA) and nuclear receptors in modulating gene expression to confer chemoresistance have remained poorly understood and underexplored. This comprehensive review aims to shed light on the genes linked to different chemoresistance mechanisms in OC and their intricate regulation by ncRNA and nuclear receptors. Specifically, we examine how these molecular players influence the chemoresistance mechanism. By exploring the interplay between these factors and gene expression regulation, this review seeks to provide a comprehensive mechanism driving chemoresistance in OC.

Small nucleolar RNAs (snoRNAs) represent a class of non-coding RNAs that play pivotal roles in post-transcriptional RNA processing and modification, thereby contributing significantly to the maintenance of cellular functions related to protein synthesis. SnoRNAs have been discovered to possess the ability to influence cell fate and alter disease progression, holding immense potential in controlling human diseases. It is suggested that the dysregulation of snoRNAs in cancer exhibits differential expression across various cancer types, stages, metastasis, treatment response and/or prognosis in patients. On the other hand, colorectal cancer (CRC), a prevalent malignancy of the digestive system, is characterized by high incidence and mortality rates, ranking as the third most common cancer type. Recent research indicates that snoRNA dysregulation is associated with CRC, as snoRNA expression significantly differs between normal and cancerous conditions. Consequently, assessing snoRNA expression level and function holds promise for the prognosis and diagnosis of CRC. Nevertheless, current comprehension of the potential roles of snoRNAs in CRC remains limited. This review offers a comprehensive survey of the aberrant regulation of snoRNAs in CRC, providing valuable insights into the discovery of novel biomarkers, therapeutic targets, and potential tools for the diagnosis and treatment of CRC and furnishing critical cues for advancing research into CRC and the judicious selection of therapeutic targets.

Non-coding RNAs (ncRNAs) are abundant single-stranded RNA molecules in human cells, involved in various cellular processes ranging from DNA replication and mRNA translation regulation to genome stability defense. MicroRNAs are multifunctional ncRNA molecules of 18-24 nt in length, involved in gene silencing through base-pair complementary binding to target mRNA transcripts. piwi-interacting RNAs are an animal-specific class of small ncRNAs sized 26-31 nt, responsible for the defense of genome stability via the epigenetic and post-transcriptional silencing of transposable elements. Long non-coding RNAs are ncRNA molecules defined as transcripts of more than 200 nucleotides, their function depending on localization, and varying from the regulation of cell differentiation and development to the regulation of telomere-specific heterochromatin modifications. The current review provides recent data on the several forms of small and long non-coding RNA's potential to act as diagnostic, prognostic or therapeutic target for various human diseases.

Small nucleolar RNAs (snoRNAs) constitute a prevalent class of noncoding RNAs localized within the nucleoli of eukaryotic cells. Their involvement in diverse diseases underscores the significance of forecasting associations between snoRNAs and diseases. However, conventional experimental techniques for such predictions suffer limitations in scalability, protracted timelines, and suboptimal success rates. Consequently, efficient computational methodologies are imperative to realize the accurate predictions of snoRNA-disease associations. Herein, we introduce GCLSDA-graph Convolutional Network and contrastive learning predict snoRNA disease associations. GCLSDA is an innovative framework that combines graph convolution networks and self-supervised learning for snoRNA-disease association prediction. Leveraging the repository of MNDR v4.0 and ncRPheno databases, we construct a robust snoRNA-disease association dataset, which serves as the foundation to create bipartite graphs. The computational prowess of the light graph convolutional network (LightGCN) is harnessed to acquire nuanced embedded representations of both snoRNAs and diseases. With careful consideration, GCLSDA intelligently incorporates contrast learning to address the challenging issues of sparsity and over-smoothing inside correlation matrices. This combination not only ensures the precision of predictions but also amplifies the model's robustness. Moreover, we introduce the augmentation technique of random noise to refine the embedded snoRNA representations, consequently enhancing the precision of predictions. Within the domain of contrast learning, we unite the tasks of contrast and recommendation. This harmonization streamlines the cross-layer contrast process, simplifying the information propagation and concurrently curtailing computational complexity. In the area of snoRNA-disease associations, GCLSDA constantly shows its promising capacity for prediction through extensive research. This success not only contributes valuable insights into the functional roles of snoRNAs in disease etiology, but also plays an instrumental role in identifying potential drug targets and catalyzing innovative treatment modalities.

Over the past decade, extracellular vesicles (EVs) have emerged as a promising source of cancer-derived RNAs for liquid biopsies. However, blood contains a pool of heterogeneous EVs released by a variety of cell types, making the identification of cancer RNA biomarkers challenging. Here, we performed deep sequencing of plasma EV RNA cargo in 32 patients with locally advanced breast cancer (BC) at diagnosis and 7 days after breast surgery and in 30 cancer-free healthy controls (HCs). To identify BC-derived RNA biomarkers, we searched for RNAs that had higher levels in BC EVs at the time of diagnosis compared with HCs and decreased after surgery. Data analysis showed that the fractions of miRNAs, snRNAs, snoRNAs, and tRFs were increased, but the fraction of lncRNAs was decreased in BC EVs as compared to HCs. BC-derived biomarker candidates were identified across various RNA biotypes. Considered individually, they had very high specificity but moderate sensitivity for the detection of BC, whereas a biomarker model composed of eight RNAs: SNORD3H, SNORD1C, SNORA74D, miR-224-5p, piR-32949, lnc-IFT-122-2, lnc-C9orf50-4, and lnc-FAM122C-3 was able to distinguish BC from HC EVs with an AUC of 0.902 (95% CI = 0.872-0.931, p = 3.4 × 10-9) in leave-one-out cross-validation. Furthermore, a number of RNA biomarkers were correlated with the ER and HER2 expression and additional biomarker models were created to predict hormone receptor and HER2 status. Overall, this study demonstrated that the RNA composition of plasma EVs is altered in BC patients and that they contain cancer-derived RNA biomarkers that can be used for BC detection and monitoring using liquid biopsies.