LncTarD 2.0: an updated comprehensive database for experimentally-supported functional lncRNA-target regulations in human diseases

Modelling cell type-specific lncRNA regulatory network in autism with Cycle

BACKGROUND

Autism spectrum disorder (ASD) is a class of complex neurodevelopment disorders with high genetic heterogeneity. Long non-coding RNAs (lncRNAs) are vital regulators that perform specific functions within diverse cell types and play pivotal roles in neurological diseases including ASD. Therefore, exploring lncRNA regulation would contribute to deciphering ASD molecular mechanisms. Existing computational methods utilize bulk transcriptomics data to identify lncRNA regulation in all of samples, which could reveal the commonalities of lncRNA regulation in ASD, but ignore the specificity of lncRNA regulation across various cell types.

RESULTS

Here, we present Cycle (Cell type-specific lncRNA regulatory network) to construct the landscape of cell type-specific lncRNA regulation in ASD. We have found that each ASD cell type is unique in lncRNA regulation, and more than one-third and all cell type-specific lncRNA regulatory networks are characterized as scale-free and small-world, respectively. Across 17 ASD cell types, we have discovered 19 rewired and 11 stable modules, along with eight rewired and three stable hubs within the constructed cell type-specific lncRNA regulatory networks. Enrichment analysis reveals that the discovered rewired and stable modules and hubs are closely related to ASD. Furthermore, more similar ASD cell types tend to be connected with higher strength in the constructed cell similarity network. Finally, the comparison results demonstrate that Cycle is a potential method for uncovering cell type-specific lncRNA regulation.

CONCLUSION

Overall, these results illustrate that Cycle is a promising method to model the landscape of cell type-specific lncRNA regulation, and provides insights into understanding the heterogeneity of lncRNA regulation between various ASD cell types.

BioPathNet: Enhancing Link Prediction in Biomedical Knowledge Graphs through Path Representation Learning

Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine, but traditional link prediction (LP) methods are limited in capturing this complexity. Representation-based learning techniques improve prediction accuracy by mapping nodes to low-dimensional embeddings, yet they often struggle with interpretability and scalability. We present BioPathNet, a novel graph neural network framework based on the Neural Bellman-Ford Network (NBFNet), addressing these limitations through path-based reasoning for LP in biomedical knowledge graphs. Unlike node-embedding frameworks, BioPathNet learns representations between node pairs by considering all relations along paths, enhancing prediction accuracy and interpretability. This allows visualization of influential paths and facilitates biological validation. BioPathNet leverages a background regulatory graph (BRG) for enhanced message passing and uses stringent negative sampling to improve precision. In evaluations across various LP tasks, such as gene function annotation, drug-disease indication, synthetic lethality, and lncRNA-mRNA interaction prediction, BioPathNet consistently outperformed shallow node embedding methods, relational graph neural networks and task-specific state-of-the-art methods, demonstrating robust performance and versatility. Our study predicts novel drug indications for diseases like acute lymphoblastic leukemia (ALL) and Alzheimer's, validated by medical experts and clinical trials. We also identified new synthetic lethality gene pairs and regulatory interactions involving lncRNAs and target genes, confirmed through literature reviews. BioPathNet's interpretability will enable researchers to trace prediction paths and gain molecular insights, making it a valuable tool for drug discovery, personalized medicine and biology in general.

Decoding the connection between lncRNA and obesity: Perspective from humans and Drosophila

Background

Obesity is a burgeoning global health problem with an escalating prevalence and severe implications for public health. New evidence indicates that long non-coding RNAs (lncRNAs) may play a pivotal role in regulating adipose tissue function and energy homeostasis across various species. However, the molecular mechanisms underlying obesity remain elusive.

Scope of review

This review discusses obesity and fat metabolism in general, highlighting the emerging importance of lncRNAs in modulating adipogenesis. It describes the regulatory networks, latest tools, techniques, and approaches to enhance our understanding of obesity and its lncRNA-mediated epigenetic regulation in humans and Drosophila.

Major conclusions

This review analyses large datasets of human and Drosophila lncRNAs from published databases and literature with experimental evidence supporting lncRNAs role in fat metabolism. It concludes that lncRNAs play a crucial role in obesity-related metabolism. Cross-species comparisons highlight the relevance of Drosophila findings to human obesity, emphasizing their potential role in adipose tissue biology. Furthermore, it discusses how recent technological advancements and multi-omics data integration enhance our capacity to characterize lncRNAs and their function. Additionally, this review briefly touches upon innovative methodologies like experimental evolution and advanced sequencing technologies for identifying novel genes and lncRNA regulators in Drosophila, which can potentially contribute to obesity research.

Path-based reasoning for biomedical knowledge graphs with BioPathNet

Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine, but traditional link prediction (LP) methods are limited in capturing this complexity. Representation-based learning techniques improve prediction accuracy by mapping nodes to low-dimensional embeddings, yet they often struggle with interpretability and scalability. We present BioPathNet, a novel graph neural network framework based on the Neural Bellman-Ford Network (NBFNet), addressing these limitations through path-based reasoning for LP in biomedical knowledge graphs. Unlike node-embedding frameworks, BioPathNet learns representations between node pairs by considering all relations along paths, enhancing prediction accuracy and interpretability. This allows visualization of influential paths and facilitates biological validation. BioPathNet leverages a background regulatory graph (BRG) for enhanced message passing and uses stringent negative sampling to improve precision. In evaluations across various LP tasks, such as gene function annotation, drug-disease indication, synthetic lethality, and lncRNA-mRNA interaction prediction, BioPathNet consistently outperformed shallow node embedding methods, relational graph neural networks and task-specific state-of-the-art methods, demonstrating robust performance and versatility. Our study predicts novel drug indications for diseases like acute lymphoblastic leukemia (ALL) and Alzheimer's, validated by medical experts and clinical trials. We also identified new synthetic lethality gene pairs and regulatory interactions involving lncRNAs and target genes, confirmed through literature reviews. BioPathNet's interpretability will enable researchers to trace prediction paths and gain molecular insights, making it a valuable tool for drug discovery, personalized medicine and biology in general.

Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration

Non-coding RNAs are pivotal regulators of gene and protein expression, exerting crucial influences on diverse biological processes. Their dysregulation is frequently implicated in the onset and progression of diseases, notably cancer. A profound comprehension of the intricate mechanisms governing ncRNAs is imperative for devising innovative therapeutic interventions against these debilitating conditions. Significantly, nearly 80% of our genome comprises ncRNAs, underscoring their centrality in cellular processes. The elucidation of ncRNA functions is pivotal for grasping the complexities of gene regulation and its implications for human health. Modern genome sequencing techniques yield vast datasets, stored in specialized databases. To harness this wealth of information and to understand the crosstalk of non-coding RNAs, knowledge of available databases is required, and many new sophisticated computational tools have emerged. These tools play a pivotal role in the identification, prediction, and annotation of ncRNAs, thereby facilitating their experimental validation. This Review succinctly outlines the current understanding of ncRNAs, emphasizing their involvement in disease development. It also highlights the databases and tools instrumental in classifying, annotating, and evaluating ncRNAs. By extracting meaningful biological insights from seemingly "junk" data, these tools empower scientists to unravel the intricate roles of ncRNAs in shaping human health.

The Spatio-Temporal Expression Profiles of Silkworm Pseudogenes Provide Valuable Insights into Their Biological Roles

Background

Pseudogenes are sequences that have lost the ability to transcribe RNA molecules or encode truncated but possibly functional proteins. While they were once considered to be meaningless remnants of evolution, recent researches have shown that pseudogenes play important roles in various biological processes. However, the studies of pseudogenes in the silkworm, an important model organism, are limited and have focused on single or only a few specific genes.

Objective

To fill these gaps, we present a systematic genome-wide studies of pseudogenes in the silkworm.

Methods

We identified the pseudogenes in the silkworm using the silkworm genome assemblies, transcriptome, protein sequences from silkworm and its related species. Then we used transcriptome datasets from 832 RNA-seq analyses to construct spatio-temporal expression profiles for these pseudogenes. Additionally, we identified tissue-specifically expressed and differentially expressed pseudogenes to further understand their characteristics. Finally, the functional roles of pseudogenes as lncRNAs were systematically analyzed.

Results

We identified a total of 4410 pseudogenes, which were grouped into 4 groups, including duplications (DUPs), unitary pseudogenes (Unitary), processed pseudogenes (retropseudogenes, RETs), and fragments (FRAGs). The most of pseudogenes in the domestic silkworm were generated before the divergence of wild and domestic silkworm, however, the domestication may also involve in the accumulation of pseudogenes. These pseudogenes were clearly divided into 2 cluster, a highly expressed and a lowly expressed, and the posterior silk gland was the tissue with the most tissue-specific pseudogenes (199), implying these pseudogenes may be involved in the development and function of silkgland. We identified 3299 lncRNAs in these pseudogenes, and the target genes of these lncRNAs in silkworm pseudogenes were enriched in the egg formation and olfactory function.

Conclusions

This study replenishes the genome annotations for silkworm, provide valuable insights into the biological roles of pseudogenes. It will also contribute to our understanding of the complex gene regulatory networks in the silkworm and will potentially have implications for other organisms as well.

Puzzling out the role of MIAT LncRNA in hepatocellular carcinoma

A non-negligible part of our DNA has been proven to be transcribed into non-protein coding RNA and its intricate involvement in several physiological processes has been highly evidenced. The significant biological role of non-coding RNAs (ncRNAs), including long non-coding RNAs (lncRNAs) has been variously reported. In the current review, the authors highlight the multifaceted role of myocardial infarction-associated transcript (MIAT), a well-known lncRNA, in hepatocellular carcinoma (HCC). Since its discovery, MIAT has been described as a regulator of carcinogenesis in several malignant tumors and its overexpression predicts poor prognosis in most of them. At the molecular level, MIAT is closely linked to the initiation of metastasis, invasion, cellular migration, and proliferation, as evidenced by several in-vitro and in-vivo models. Thus, MIAT is considered a possible theranostic agent and therapeutic target in several malignancies. In this review, the authors provide a comprehensive overview of the underlying molecular mechanisms of MIAT in terms of its downstream target genes, interaction with other classes of ncRNAs, and potential clinical implications as a diagnostic and/or prognostic biomarker in HCC.

MIAT LncRNA: A multifunctional key player in non-oncological pathological conditions

The discovery of non-coding RNAs (ncRNAs) has unveiled a wide range of transcripts that do not encode proteins but play key roles in several cellular and molecular processes. Long noncoding RNAs (lncRNAs) are specific class of ncRNAs that are longer than 200 nucleotides and have gained significant attention due to their diverse mechanisms of action and potential involvement in various pathological conditions. In the current review, the authors focus on the role of lncRNAs, specifically highlighting the Myocardial Infarction Associated Transcript (MIAT), in non-oncological context. MIAT is a nuclear lncRNA that has been directly linked to myocardial infarction and is reported to control post-transcriptional processes as a competitive endogenous RNA (ceRNA) molecule. It interacts with microRNAs (miRNAs), thereby limiting the translation and expression of their respective target messenger RNA (mRNA) and regulating protein expression. Yet, MIAT has been implicated in other numerous pathological conditions such as other cardiovascular diseases, autoimmune disease, neurodegenerative diseases, metabolic diseases, and many others. In this review, the authors emphasize that MIAT exhibits distinct expression patterns and functions across different pathological conditions and is emerging as potential diagnostic, prognostic, and therapeutic agent. Additionally, the authors highlight the regulatory role of MIAT and shed light on the involvement of lncRNAs and specifically MIAT in various non-oncological pathological conditions.

IL-2RG as a possible immunotherapeutic target in CRC predicting poor prognosis and regulated by miR-7-5p and miR-26b-5p

Despite advances in treatment strategies, colorectal cancer (CRC) continues to cause significant morbidity and mortality, with mounting evidence a close link between immune system dysfunctions issued. Interleukin-2 receptor gamma (IL-2RG) plays a pivotal role as a common subunit receptor in the IL-2 family cytokines and activates the JAK-STAT pathway. This study delves into the role of Interleukin-2 receptor gamma (IL-2RG) within the tumor microenvironment and investigates potential microRNAs (miRNAs) that directly inhibit IL-2RG, aiming to discern their impact on CRC clinical outcomes. Bioinformatics analysis revealed a significant upregulation of IL-2RG mRNA in TCGA-COAD samples and showed strong correlations with the infiltration of various lymphocytes. Single-cell analysis corroborated these findings, highlighting IL-2RG expression in critical immune cell subsets. To explore miRNA involvement in IL-2RG dysregulation, mRNA was isolated from the tumor tissues and lymphocytes of 258 CRC patients and 30 healthy controls, and IL-2RG was cloned into the pcDNA3.1/CT-GFP-TOPO vector. Human embryonic kidney cell lines (HEK-293T) were transfected with this construct. Our research involved a comprehensive analysis of miRPathDB, miRWalk, and Targetscan databases to identify the miRNAs associated with the 3' UTR of human IL-2RG. The human microRNA (miRNA) molecules, hsa-miR-7-5p and hsa-miR-26b-5p, have been identified as potent suppressors of IL-2RG expression in CRC patients. Specifically, the downregulation of hsa-miR-7-5p and hsa-miR-26b-5p has been shown to result in the upregulation of IL-2RG mRNA expression in these patients. Prognostic evaluation of IL-2RG, hsa-miR-7-5p, and hsa-miR-26b-5p, using TCGA-COAD data and patient samples, established that higher IL-2RG expression and lower expression of both miRNAs were associated with poorer outcomes. Additionally, this study identified several long non-coding RNAs (LncRNAs), such as ZFAS1, SOX21-AS1, SNHG11, SNHG16, SNHG1, DLX6-AS1, GAS5, SNHG6, and MALAT1, which may act as competing endogenous RNA molecules for IL2RG by sequestering shared hsa-miR-7-5p and hsa-miR-26b-5p. In summary, this investigation underscores the potential utility of IL-2RG, hsa-miR-7-5p, and hsa-miR-26b-5p as serum and tissue biomarkers for predicting CRC patient prognosis while also offering promise as targets for immunotherapy in CRC management.

Differential expression of angiogenesis-related genes 'VEGF' and 'angiopoietin-1' in metastatic and EMAST-positive colorectal cancer patients

Abnormal angiogenesis leads to tumor progression and metastasis in colorectal cancer (CRC). This study aimed to elucidate the association between angiogenesis-related genes, including VEGF-A, ANGPT-1, and ANGPT-2 with both metastatic and microsatellite alterations at selected tetranucleotide repeats (EMAST) subtypes of CRC. We conducted a thorough assessment of the ANGPT-1, ANGPT-2, and VEGF-A gene expression utilizing publicly available RNA sequencing and microarray datasets. Then, the experimental validation was performed in 122 CRC patients, considering their disease metastasis and EMAST+/- profile by using reverse transcription polymerase chain reaction (RT-PCR). Subsequently, a competing endogenous RNA (ceRNA) network associated with these angiogenesis-related genes was constructed and analyzed. The expression level of VEGF-A and ANGPT-2 genes were significantly higher in tumor tissues as compared with normal adjacent tissues (P-value < 0.001). Nevertheless, ANGPT-1 had a significantly lower expression in tumor samples than in normal colon tissue (P-value < 0.01). We identified a significantly increased VEGF-A (P-value = 0.002) and decreased ANGPT-1 (P-value = 0.04) expression in EMAST+ colorectal tumors. Regarding metastasis, a significantly increased VEGF-A and ANGPT-2 expression (P-value = 0.001) and decreased ANGPT-1 expression (P-value < 0.05) were established in metastatic CRC patients. Remarkably, co-expression analysis also showed a strong correlation between ANGPT-2 and VEGF-A gene expressions. The ceRNA network was constructed by ANGPT-1, ANGPT-2, VEGF-A, and experimentally validated miRNAs (hsa-miR-190a-3p, hsa-miR-374c-5p, hsa-miR-452-5p, and hsa-miR-889-3p), lncRNAs (AFAP1-AS1, KCNQ1OT1 and MALAT1), and TFs (Sp1, E2F1, and STAT3). Network analysis revealed that colorectal cancer is amongst the 82 significant pathways. We demonstrated a significant differential expression of VEGF-A and ANGPT-1 in colorectal cancer patients exhibiting the EMAST+ phenotype. This finding provides novel insights into the molecular pathogenesis of colorectal cancer, specifically in EMAST subtypes. Yet, the generalization of in silico findings to EMAST+ colorectal cancer warrants future experimental investigations. In the end, this study proposes that the EMAST biomarker could serve as an additional perspective on CMS4 biology which is well-defined by activated angiogenesis and worse overall survival.

Sparassis latifolia and exercise training as complementary medicine mitigated the 5-fluorouracil potent side effects in mice with colorectal cancer: bioinformatics approaches, novel monitoring pathological metrics, screening signatures, and innovative management tactic

BACKGROUND

Prompt identification and assessment of the disease are essential for reducing the death rate associated with colorectal cancer (COL). Identifying specific causal or sensitive components, such as coding RNA (cRNA) and non-coding RNAs (ncRNAs), may greatly aid in the early detection of colorectal cancer.

METHODS

For this purpose, we gave natural chemicals obtained from Sparassis latifolia (SLPs) either alone or in conjunction with chemotherapy (5-Fluorouracil to a mouse colorectal tumor model induced by AOM-DSS. The transcription profile of non-coding RNAs (ncRNAs) and their target hub genes was evaluated using qPCR Real-Time, and ELISA techniques.

RESULTS

MSX2, MMP7, ITIH4, and COL1A2 were identified as factors in inflammation and oxidative stress, leading to the development of COL. The hub genes listed, upstream regulatory factors such as lncRNA PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p have been discovered as biomarkers for prognosis and diagnosis of COL. The SLPs and exercise, effectively decreased the size and quantity of tumors.

CONCLUSIONS

This effect may be attributed to the modulation of gene expression levels, including MSX2, MMP7, ITIH4, COL1A2, PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p. Ultimately, SLPs and exercise have the capacity to be regarded as complementing and enhancing chemotherapy treatments, owing to their efficacious components.

CancerMHL: the database of integrating key DNA methylation, histone modifications and lncRNAs in cancer

The discovery of key epigenetic modifications in cancer is of great significance for the study of disease biomarkers. Through the mining of epigenetic modification data relevant to cancer, some researches on epigenetic modifications are accumulating. In order to make it easier to integrate the effects of key epigenetic modifications on the related cancers, we established CancerMHL (http://www.positionprediction.cn/), which provide key DNA methylation, histone modifications and lncRNAs as well as the effect of these key epigenetic modifications on gene expression in several cancers. To facilitate data retrieval, CancerMHL offers flexible query options and filters, allowing users to access specific key epigenetic modifications according to their own needs. In addition, based on the epigenetic modification data, three online prediction tools had been offered in CancerMHL for users. CancerMHL will be a useful resource platform for further exploring novel and potential biomarkers and therapeutic targets in cancer. Database URL: http://www.positionprediction.cn/.

Applying negative sample denoising and multi-view feature for lncRNA-disease association prediction

Increasing evidence indicates that mutations and dysregulation of long non-coding RNA (lncRNA) play a crucial role in the pathogenesis and prognosis of complex human diseases. Computational methods for predicting the association between lncRNAs and diseases have gained increasing attention. However, these methods face two key challenges: obtaining reliable negative samples and incorporating lncRNA-disease association (LDA) information from multiple perspectives. This paper proposes a method called NDMLDA, which combines multi-view feature extraction, unsupervised negative sample denoising, and stacking ensemble classifier. Firstly, an unsupervised method (K-means) is used to design a negative sample denoising module to alleviate the imbalance of samples and the impact of potential noise in the negative samples on model performance. Secondly, graph attention networks are employed to extract multi-view features of both lncRNAs and diseases, thereby enhancing the learning of association information between them. Finally, lncRNA-disease association prediction is implemented through a stacking ensemble classifier. Existing research datasets are integrated to evaluate performance, and 5-fold cross-validation is conducted on this dataset. Experimental results demonstrate that NDMLDA achieves an AUC of 0.9907and an AUPR of 0.9927, with a 5-fold cross-validation variance of less than 0.1%. These results outperform the baseline methods. Additionally, case studies further illustrate the model's potential in cancer diagnosis and precision medicine implementation.

LncSEA 2.0: an updated platform for long non-coding RNA related sets and enrichment analysis

Long non-coding RNAs (lncRNAs) possess a wide range of biological functions, and research has demonstrated their significance in regulating major biological processes such as development, differentiation, and immune response. The accelerating accumulation of lncRNA research has greatly expanded our understanding of lncRNA functions. Here, we introduce LncSEA 2.0 (http://bio.liclab.net/LncSEA/index.php), aiming to provide a more comprehensive set of functional lncRNAs and enhanced enrichment analysis capabilities. Compared with LncSEA 1.0, we have made the following improvements: (i) We updated the lncRNA sets for 11 categories and extremely expanded the lncRNA scopes for each set. (ii) We newly introduced 15 functional lncRNA categories from multiple resources. This update not only included a significant amount of downstream regulatory data for lncRNAs, but also covered numerous epigenetic regulatory data sets, including lncRNA-related transcription co-factor binding, chromatin regulator binding, and chromatin interaction data. (iii) We incorporated two new lncRNA set enrichment analysis functions based on GSEA and GSVA. (iv) We adopted the snakemake analysis pipeline to track data processing and analysis. In summary, LncSEA 2.0 offers a more comprehensive collection of lncRNA sets and a greater variety of enrichment analysis modules, assisting researchers in a more comprehensive study of the functional mechanisms of lncRNAs.

System biology analysis to develop diagnostic biomarkers, monitoring pathological indexes, and novel therapeutic approaches for immune targeting based on maggot bioactive compounds and polyphenolic cocktails in mice with gastric cancer

Early diagnosis and prognosis are prerequisites for mitigating mortality in gastric cancer (GaCa). Identifying some causative or sensitive elements (coding RNA (cRNA)-non-cRNAs (ncRNAs)) can be very helpful in the early diagnosis of GaCa. Notably, despite significant development in the GaCa treatment, the outcome of patients does not remain satisfactory due to limitations such as multi-drug resistance and tumor relapse. Therefore, more attention has been drawn to complementary therapies and the use of supplements. In this regard, Polyphenol natural compounds (PNC) and maggot larvae (MaLa) alone or in combination were administered along with chemotherapy (paclitaxel) to N-methyl-N-nitrosourea (MNU)- induced murine tumor model. In addition, in order to identify potential diagnostic or prognostic biomarkers, transcriptomics analysis was performed through a bioinformatics approach. Then transcription profile of ncRNAs with their target hub genes was assessed through qPCR Real-Time, Western blot, and ELISA. According to the bioinformatics results, 17 hub genes (e.g., IL-6, CXCL8, MKI67, IL-2, IL-4, IL-10, IL-1β, SPP1, LOX, COL1A1, and IFN-γ) were explored that contribute towards inflammation and oxidative stress and ultimately GaCa development. Upstream of the mentioned hub genes, regulatory factors (lncRNA XIST and NEAT1) were also identified and introduced as prognosis and diagnosis biomarkers for GaCa. Our results showed that PNC alone and in combination with MaLa was able to reduce the size and number of tumors, which is related to the reduction of genes expression levels (including IL-6, CXCL8, MKI67, IL-2, IL-4, IL-10, IL-1β, SPP1, LOX, COL1A1, IFN-γ, NEAT1, and XIST). In conclusion, PNC and MaLa have the potential to be considered as complementary and improving chemotherapy due to their effective compounds. Also, the introduced hub gene and lncRNA in addition to diagnostic and prognostic biomarkers can be used as druggable proteins for novel therapeutic targeting of GaCa.

Compound AC1Q3QWB upregulates CDKN1A and SOX17 by interrupting the HOTAIR-EZH2 interaction and enhances the efficacy of tazemetostat in endometrial cancer

Endometrial cancer (EC) is a common malignancy of the female reproductive system, with an escalating incidence. Recurrent/metastatic EC presents a poor prognosis. The interaction between the long non-coding RNA (lncRNA) HOTAIR and the polycomb repressive complex 2 (PRC2) induces abnormal silencing of tumor suppressor genes, exerting a pivotal role in tumorigenesis. We have previously discovered AC1Q3QWB (AQB), a small-molecule compound targeting HOTAIR-EZH2 interaction. In the present study, we unveil that AQB selectively hampers the interaction between HOTAIR and EZH2 within EC cells, thus reversing the epigenetic suppression of tumor suppressor genes. Furthermore, our findings demonstrate AQB's synergistic effect with tazemetostat (TAZ), an EZH2 inhibitor, significantly boosting the expression of CDKN1A and SOX17. This, in turn, induces cell cycle arrest and impedes EC cell proliferation, migration, and invasion. In vivo experiments further validate AQB's potential by enhancing TAZ's anti-tumor efficacy at lower doses. Our results advocate AQB, a recently discovered small-molecule inhibitor, as a promising agent against EC cells. When combined with TAZ, it offers a novel therapeutic strategy for EC treatment.

The role of lncRNAs and exosomal lncRNAs in cancer metastasis

Tumor metastasis is the main reason for cancer-related death, but there is still a lack of effective therapeutic to inhibit tumor metastasis. Therefore, the discovery and study of new tumor metastasis regulators is a prominent measure for cancer diagnosis and treatment. Long non-coding RNA (lncRNA) is a type of non-coding RNAs over 200 bp in length. It has been shown that the abnormally expressed lncRNAs promote tumor metastasis by participating in the epithelial-to-mesenchymal transition (EMT) process, altering the metastatic tumor microenvironment, or changing the extracellular matrix. It is,thus, critical to explore the regulation of lncRNAs expression in cells and the molecular mechanism of lncRNA-mediated cancer metastasis. Simultaneously, it has been shown that lncRNA is one kind of the main components of exosomes, which protects lncRNAs from being rapidly degraded. Meanwhile, the components of exosomes are parent-specific, making exosomal lncRNAs to be potential tumor metastasis markers and therapeutic targets. In view of this, we also summarized the aberrant enrichment of lncRNAs in exosomes and their role in metastatic cancer. The aberrant lncRNAs and exosomal lncRNAs gradually become biomarkers and therapeutic targets for tumor metastatic, and the potential of lncRNAs in therapeutics are studied here. Besides, the lncRNA-related databases, which could greatly facilitate in the study of lncRNAs and exosomal lncRNAs in metastatic of cancer are included in this review.

Long Non-Coding RNAs in Colorectal Cancer: Navigating the Intersections of Immunity, Intercellular Communication, and Therapeutic Potential

This comprehensive review elucidates the intricate roles of long non-coding RNAs (lncRNAs) within the colorectal cancer (CRC) microenvironment, intersecting the domains of immunity, intercellular communication, and therapeutic potential. lncRNAs, which are significantly involved in the pathogenesis of CRC, immune evasion, and the treatment response to CRC, have crucial implications in inflammation and serve as promising candidates for novel therapeutic strategies and biomarkers. This review scrutinizes the interaction of lncRNAs with the Consensus Molecular Subtypes (CMSs) of CRC, their complex interplay with the tumor stroma affecting immunity and inflammation, and their conveyance via extracellular vesicles, particularly exosomes. Furthermore, we delve into the intricate relationship between lncRNAs and other non-coding RNAs, including microRNAs and circular RNAs, in mediating cell-to-cell communication within the CRC microenvironment. Lastly, we propose potential strategies to manipulate lncRNAs to enhance anti-tumor immunity, thereby underlining the significance of lncRNAs in devising innovative therapeutic interventions in CRC.

Non-coding RNA-mediated modulation of ferroptosis in cardiovascular diseases

Cardiovascular disease (CVD) is a major contributor to increasing morbidity and mortality worldwide and seriously threatens human health and life. Cardiomyocyte death is considered the pathological basis of various CVDs, including myocardial infarction, heart failure, and aortic dissection. Multiple mechanisms, such as ferroptosis, necrosis, and apoptosis, contribute to cardiomyocyte death. Among them, ferroptosis is an iron-dependent form of programmed cell death that plays a vital role in various physiological and pathological processes, from development and aging to immunity and CVD. The dysregulation of ferroptosis has been shown to be closely associated with CVD progression, yet its underlying mechanisms are still not fully understood. In recent years, a growing amount of evidence suggests that non-coding RNAs (ncRNAs), particularly microRNAs, long non-coding RNAs, and circular RNAs, are involved in the regulation of ferroptosis, thus affecting CVD progression. Some ncRNAs also exhibit potential value as biomarker and/or therapeutic target for patients with CVD. In this review, we systematically summarize recent findings on the underlying mechanisms of ncRNAs involved in ferroptosis regulation and their role in CVD progression. We also focus on their clinical applications as diagnostic and prognostic biomarkers as well as therapeutic targets in CVD treatment. DATA AVAILABILITY: No new data were created or analyzed in this study. Data sharing is not applicable to this article.