Lnc2Cancer 3.0: an updated resource for experimentally supported lncRNA/circRNA cancer associations and web tools based on RNA-seq and scRNA-seq data

HCDT 2.0: A Highly Confident Drug-Target Database for Experimentally Validated Genes, RNAs, and Pathways

Drug-target interactions constitute the fundamental basis for understanding drug action mechanisms and advancing therapeutic discovery. While existing drug-target databases have contributed valuable resources, they exhibit structural and functional fragmentation due to heterogeneous data sources and annotation standards. Building upon the high-confidence drug-gene interactions curated in HCDT 1.0, we present HCDT 2.0, a comprehensive and standardized resource that expands the scope through multiomics data integration. This update incorporates three-dimensional interactions including drug-gene, drug-RNA and drug-pathway interactions. The current version contains 1,284,353 curated interactions: 1,224,774 drug-gene pairs (678,564 drugs × 5,692 genes), 11,770 drug-RNA mappings (316 drugs × 6,430 RNAs), and 47,809 drug-pathway links (6,290 drugs × 3,143 pathways), alongside 16,317 drug-disease associations. To enhance biological interpretability, we further integrated pathway-gene and RNA-gene regulatory relationships. In addition, we integrated 38,653 negative DTIs covering 26,989 drugs and 1,575 genes. This integrative framework not only addresses critical gaps in cross-scale data representation but also establishes a robust foundation for systems pharmacology applications, including drug repurposing, adverse event prediction, and precision oncology strategies.

A Pyroptosis-Related LncRNA Signature for Predicting Prognosis, Immune Features and Drug Sensitivity in Ovarian Cancer

Background

Multiple studies have suggested that lncRNAs and pyroptosis play important roles in ovarian cancer (OC). However, the function of pyroptosis-related lncRNAs (PRLs) in OC is not fully understood.

Methods

Clinical information and RNA-seq data of OC patients (n = 379) were collected from TCGA database. Pearson correlation analysis and univariate Cox analysis were performed to identify prognostic PRLs, respectively. LASSO-COX regression was utilized to construct a prognostic PRLs signature. Kaplan-Meier (K-M) curve analyses and receiver operating characteristics (ROC) were used to evaluate the prognostic prediction of the signature. The association between risk score and tumor microenvironment infiltration, immunotherapy response and chemotherapy sensitivity were also analyzed. In addition, the function of TYMSOS on OC and pyroptosis was experimentally confirmed in cell lines.

Results

Firstly, 32 prognostic PRLs were identified, and a novel prognostic PRLs signature was constructed and validated. Surprisingly, the prognostic PRLs signature could solidly predict the clinical outcome of patients with OC and patients with high-risk score shown a short overall survival. GSEA results suggested that the RPLs were mainly enriched in the inflammatory response pathway, p53 pathway, TGF-β signaling and TNFα signaling. Besides, our results demonstrated that the risk score was significantly associated with patients with immune infiltration, immunotherapy response and the sensitivity of veliparib and metformin. Furthermore, the oncogene effect of TYMSOS on OC by inhibiting pyroptosis was verified by experiments.

Conclusion

This study found that the prognostic PRLs signature may serve as an efficient biomarker in predicting the prognosis, tumor microenvironment infiltration, and sensitivity of chemotherapeutic agents. TYMSOS is a potential biomarker in OC, and it might promote tumor progression by inhibiting pyroptosis.

MFF-nDA: A Computational Model for ncRNA-Disease Association Prediction Based on Multimodule Fusion

Noncoding RNAs(ncRNAs), including piwi-interacting RNA(piRNA), long noncoding RNA(lncRNA), microRNA(miRNA), small nucleolar RNA(snoRNA), and circular RNA(circRNA), contribute significantly to gene expression regulation and serve as key factors in disease association studies and health-related exploration. Accurate prediction of ncRNA-disease associations is crucial for elucidating disease mechanisms and advancing therapeutic development. Recently, computational models based on a graph neural network have extensively emerged for identifying associations among various ncRNAs and diseases. However, existing computational models have not fully utilized integrative information on ncRNs and diseases, and reliance on GNN-based models alone may be limited in performance due to oversmoothing issues. On the other hand, existing models are mainly targeted at a specific type of ncRNA and may not be applicable to most ncRNAs. Therefore, to overcome these limitations, we propound a computational model MFF-nDA based on multimodule fusion. Specifically, we first introduce five types of similarity network information, including three types of ncRNA and two types of disease similarity information, in order to fully explore and optimize the multisource feature information on these entities. Subsequently, we establish three modules: heterogeneous network representation module based on Transformer, association network representation module based on graph convolutional network (GCN), and topological structure representation module based on graph attention network (GAT), which capture diverse features of nodes in heterogeneous networks and topological structure information reflected in association networks. The complementary effects of the three modules also help relieve the oversmoothing issue to some extent. By leveraging the multimodule fusion learning to comprehensively capture the diverse features of these entities, our model outperforms the available state-of-the-art methods, achieving an AUC greater than 0.9000 for each dataset. This demonstrates the highest predictive performance, making it a valuable tool for identifying potential ncRNA associated with diseases. The code of MFF-nDA can be accessed at https://github.com/Jack-Cxy/MFF-nDA.

Targeting Regulatory Noncoding RNAs in Human Cancer: The State of the Art in Clinical Trials

Noncoding RNAs (ncRNAs) are a heterogeneous group of RNA molecules whose classification is mainly based on arbitrary criteria such as the molecule length, secondary structures, and cellular functions. A large fraction of these ncRNAs play a regulatory role regarding messenger RNAs (mRNAs) or other ncRNAs, creating an intracellular network of cross-interactions that allow the fine and complex regulation of gene expression. Altering the balance between these interactions may be sufficient to cause a transition from health to disease and vice versa. This leads to the possibility of intervening in these mechanisms to re-establish health in patients. The regulatory role of ncRNAs is associated with all cancer hallmarks, such as proliferation, apoptosis, invasion, metastasis, and genomic instability. Based on the function performed in carcinogenesis, ncRNAs may behave either as oncogenes or tumor suppressors. However, this distinction is not rigid; some ncRNAs can fall into both classes depending on the tissue considered or the target molecule. Furthermore, some of them are also involved in regulating the response to traditional cancer-therapeutic approaches. In general, the regulation of molecular mechanisms by ncRNAs is very complex and still largely unclear, but it has enormous potential both for the development of new therapies, especially in cases where traditional methods fail, and for their use as novel and more efficient biomarkers. Overall, this review will provide a brief overview of ncRNAs in human cancer biology, with a specific focus on describing the most recent ongoing clinical trials (CT) in which ncRNAs have been tested for their potential as therapeutic agents or evaluated as biomarkers.

Circular RNA in liver cancer research: biogenesis, functions, and roles

Liver cancer, characterized by its insidious nature, aggressive invasiveness, and propensity for metastasis, has witnessed a sustained increase in both incidence and mortality rates in recent years, underscoring the urgent need for innovative diagnostic and therapeutic approaches. Emerging research indicates that CircRNAs (circular RNAs) are abundantly and stably present within cells, with their expression levels closely associated with the progression of various malignancies, including hepatocellular carcinoma. In the context of liver cancer progression, circRNAs exhibit promising potential as highly sensitive diagnostic biomarkers, offering novel avenues for early detection, and also function as pivotal regulatory factors within the carcinogenic process. This study endeavors to elucidate the biogenesis, functional roles, and underlying mechanisms of circRNAs in hepatocellular carcinoma, thereby providing a fresh perspective on the pathogenesis of liver cancer and laying a robust foundation for the development of more precise and effective early diagnostic tools and therapeutic strategies.

Similarity-guided graph contrastive learning for lncRNA-disease association prediction

The increasing research evidence indicates that long non-coding RNAs (lncRNAs) play important roles in regulating biological processes and are closely associated with many human diseases. Computational methods have emerged as indispensable tools for identifying associations between long non-coding RNA (lncRNA) and diseases, primarily due to the time-consuming and costly nature of traditional biological experiments. Given the scarcity of verified lncRNA-disease associations, the intensifying focus on deep learning is playing a crucial role in refining the accuracy of predictive models. Moreover, the contrastive learning method exhibits a clear advantage in situations where data is scarce or annotation costs are high. In this paper, we leverage the advantages of graph neural networks and contrastive learning to innovatively propose a similarity-guided graph contrastive learning (SGGCL) model for predicting lncRNA-disease associations. In the SGGCL model, we employ a novel similarity-guided graph data augmentation method to generate high-quality positive and negative sample pairs, addressing the scarcity of verified data. Additionally, we utilize the RWR algorithm and a graph convolutional neural network for contrastive learning, facilitating the capture of global topology and high-level node embeddings. The experimental results on several datasets demonstrate the superior predictive performance and scalability of our method in lncRNA-disease association prediction compared to state-of-the-art methods.

Single-Cell Transcriptomic Approaches for Decoding Non-Coding RNA Mechanisms in Colorectal Cancer

Non-coding RNAs (ncRNAs) play crucial roles in colorectal cancer (CRC) development and progression. Recent developments in single-cell transcriptome profiling methods have revealed surprising levels of expression variability among seemingly homogeneous cells, suggesting the existence of many more cell types than previously estimated. This review synthesizes recent advances in ncRNA research in CRC, emphasizing single-cell bioinformatics approaches for their analysis. We explore computational methods and tools used for ncRNA identification, characterization, and functional prediction in CRC, with a focus on single-cell RNA sequencing (scRNA-seq) data. The review highlights key bioinformatics strategies, including sequence-based and structure-based approaches, machine learning applications, and multi-omics data integration. We discuss how these computational techniques can be applied to analyze differential expression, perform functional enrichment, and construct regulatory networks involving ncRNAs in CRC. Additionally, we examine the role of bioinformatics in leveraging ncRNAs as diagnostic and prognostic biomarkers for CRC. We also discuss recent scRNA-seq studies revealing ncRNA heterogeneity in CRC. This review aims to provide a comprehensive overview of the current state of single-cell bioinformatics in ncRNA CRC research and outline future directions in this rapidly evolving field, emphasizing the integration of computational approaches with experimental validation to advance our understanding of ncRNA biology in CRC.

Identification and multi-omics analysis of essential coding and long non-coding genes in colorectal cancer

Essential genes are indispensable for the survival of cancer cell. CRISPR/Cas9-based pooled genetic screens have distinguished the essential genes and their functions in distinct cellular processes. Nevertheless, the landscape of essential genes at the single cell levels and the effect on the tumor microenvironment (TME) remains limited. Here, we identified 396 essential protein-coding genes (ESPs) by integration of 8 genome-wide CRISPR loss-of-function screen datasets of colorectal cancer (CRC) cell lines and single-cell RNA sequencing (scRNA-seq) data of CRC tissues. Then, 29 essential long non-coding genes (ESLs) were predicted using Hypergeometric Test (HT) and Personalized PageRank (PPR) algorithms based on ESPs and co-expressed network constructed from scRNA-seq. CRISPR/Cas9 knockout experiment verified the effect of several ESPs and ESLs on the survival of CRC cell line. Furthermore, multi-omics features of ESPs and ESLs were illustrated by examining their expression patterns and transcription factor (TF) regulatory network at the single cell level, as well as DNA mutation and DNA methylation events at bulk level. Finally, through integrating multiple intracellular regulatory networks with cell-cell communication network (CCN), we elucidated that CD47 and MIF are regulated by multiple CRC essential genes, and the anti-cancer drugs sunitinib can interfere the expression of them potentially. Our findings provide a comprehensive asset of CRC ESPs and ESLs, sheding light on the mining of potential therapy targets for CRC.

Role of cancer stem cell heterogeneity in intrahepatic cholangiocarcinoma

Background

Intrahepatic cholangiocarcinoma (ICC) is a highly invasive bile duct cancer with poor prognosis due to frequent recurrence and limited effective treatments. Cancer stem cells (CSCs) contribute to ICC's therapeutic resistance and recurrence, driven by distinct cellular subpopulations with variable tumorigenic properties. Recent advances in single-cell RNA sequencing (scRNA-seq) have enabled a deeper exploration of cellular heterogeneity in tumors, offering insights into unique CSC subgroups that impact ICC progression and patient outcomes. This study aimed to investigate the effect of CSC heterogeneity on the prognosis of ICC.

Methods

The scRNA-seq dataset GSE142784 was retrieved from the Gene Expression Omnibus (GEO) database, and Bulk RNA-seq data were obtained from The Cancer Genome Atlas (TCGA) databases. Hallmarks and AUCell R package were adopted for analyzing the signaling pathway activity, CellChat for observing cell communication between subgroups, and SCENIC for analyzing transcription factors expression. The immune cell infiltration and drug sensitivity of the model were analyzed using the CIBERSORT algorithm and the "pRRophetic" R packages, respectively. And immunohistochemistry (IHC) tests were used to evaluate expression of transcription factors in ICC patients.

Results

Based on scRNA-seq data, five clusters (DLK+, CD13+, CD90+, CD133+, and other cholangiocarcinoma cells) were observed in ICC, which presented different signaling pathway activities, such as HSF1 and STAT1 were highly expressed in the CD133 cluster, and consistent with the results of IHC tests. Pathways like Notch and Wnt/β-catenin signaling transferred among above subgroups. Further, subgroups favored varied immune response and drug sensitivity, and CD133+ subgroup patients showed significantly shortened recurrence-free survival (RFS).

Conclusions

Configuring the subgroup of ICC is helpful for predicting the prognosis and drug resistance in ICC and can provide new strategies for cancer treatment.

Unravelling the Regulatory Roles of lncRNAs in Melanoma: From Mechanistic Insights to Target Selection

Melanoma is the deadliest form of skin cancer, and its treatment poses significant challenges due to its aggressive nature and resistance to conventional therapies. Long non-coding RNAs (lncRNAs) represent a new frontier in the search for suitable targets to control melanoma progression and invasiveness. Indeed, lncRNAs exploit a wide range of regulatory functions along chromatin remodeling, gene transcription, post-transcription, transduction, and post-transduction to ultimately tune multiple cellular processes. The understanding of this intricate and flexible regulatory network orchestrated by lncRNAs in pathological conditions can strategically support the rational identification of promising targets, ultimately speeding up the setup of new therapeutics to integrate the currently available approaches. Here, the most recent findings on lncRNAs involved in melanoma will be analyzed. In particular, the functional links between their mechanisms of action and some frequently underestimated features, like their different subcellular localizations, will be highlighted.

Prediction of circRNA-Disease Associations via Graph Isomorphism Transformer and Dual-Stream Neural Predictor

Circular RNAs (circRNAs) have attracted increasing attention for their roles in human diseases, making the prediction of circRNA-disease associations (CDAs) a critical research area for advancing disease diagnosis and treatment. However, traditional experimental methods for exploring CDAs are time-consuming and resource-intensive, while existing computational models often struggle with the sparsity of CDA data and fail to uncover potential associations effectively. To address these challenges, we propose a novel CDA prediction method named the Graph Isomorphism Transformer with Dual-Stream Neural Predictor (GIT-DSP), which leverages knowledge graph technology to address data sparsity and predict CDAs more effectively. Specifically, the model incorporates multiple associations between circRNAs, diseases, and other non-coding RNAs (e.g., lncRNAs, and miRNAs) to construct a multi-source heterogeneous knowledge graph, thereby expanding the scope of CDA exploration. Subsequently, a Graph Isomorphism Transformer model is proposed to fully exploit both local and global association information within the knowledge graph, enabling deeper insights into potential CDAs. Furthermore, a Dual-Stream Neural Predictor is introduced to accurately predict complex circRNA-disease associations in the knowledge graph by integrating dual-stream predictive features. Experimental results demonstrate that GIT-DSP outperforms existing state-of-the-art models, offering valuable insights for precision medicine and disease-related research.

Constructing methylation-driven ceRNA networks unveil tumor heterogeneity and predict patient prognosis

Cancer development involves a complex interplay between genetic and epigenetic factors, with emerging evidence highlighting the pivotal role of competitive endogenous RNA (ceRNA) networks in regulating gene expression. However, the influence of ceRNA networks by aberrant DNA methylation remains incompletely understood. In our study, we proposed DMceNet, a computational method to characterize the effects of DNA methylation on ceRNA regulatory mechanisms and apply it across eight prevalent cancers. By integrating methylation and transcriptomic data, we constructed methylation-driven ceRNA networks and identified a dominant role of lncRNAs within these networks in two key ways: (i) 17 cancer-shared differential methylation lncRNAs (DMlncs), including PVT1 and CASC2, form a Common Cancer Network (CCN) affecting key pathways such as the G2/M checkpoint, and (ii) 24 cancer-specific DMlncs construct unique ceRNA networks for each cancer type. For instance, in LUAD and STAD, hypomethylation drives DMlncs like PCAT6 and MINCR, disrupting the Wnt signaling pathway and apoptosis. We further investigated the characteristics of these methylation-driven ceRNA networks at the cellular level, revealing how methylation-driven dysregulation varies across distinct cell populations within the tumor microenvironment. Our findings also demonstrate the prognostic potential of cancer-specific ceRNA relationships, highlighting their relevance in predicting patient survival outcomes. This integrated transcriptomic and epigenomic analysis provides new insights into cancer biology and regulatory mechanisms.

RNA sequence analysis landscape: A comprehensive review of task types, databases, datasets, word embedding methods, and language models

Deciphering information of RNA sequences reveals their diverse roles in living organisms, including gene regulation and protein synthesis. Aberrations in RNA sequence such as dysregulation and mutations can drive a diverse spectrum of diseases including cancers, genetic disorders, and neurodegenerative conditions. Furthermore, researchers are harnessing RNA's therapeutic potential for transforming traditional treatment paradigms into personalized therapies through the development of RNA-based drugs and gene therapies. To gain insights of biological functions and to detect diseases at early stages and develop potent therapeutics, researchers are performing diverse types RNA sequence analysis tasks. RNA sequence analysis through conventional wet-lab methods is expensive, time-consuming and error prone. To enable large-scale RNA sequence analysis, empowerment of wet-lab experimental methods with Artificial Intelligence (AI) applications necessitates scientists to have a comprehensive knowledge of both DNA and AI fields. While molecular biologists encounter challenges in understanding AI methods, computer scientists often lack basic foundations of RNA sequence analysis tasks. Considering the absence of a comprehensive literature that bridges this research gap and promotes the development of AI-driven RNA sequence analysis applications, the contributions of this manuscript are manifold: It equips AI researchers with biological foundations of 47 distinct RNA sequence analysis tasks. It sets a stage for development of benchmark datasets related to 47 distinct RNA sequence analysis tasks by facilitating cruxes of 64 different biological databases. It presents word embeddings and language models applications across 47 distinct RNA sequence analysis tasks. It streamlines the development of new predictors by providing a comprehensive survey of 58 word embeddings and 70 language models based predictive pipelines performance values as well as top performing traditional sequence encoding based predictors and their performances across 47 RNA sequence analysis tasks.

mTOR-related linc-PMB promotes mitochondrial biogenesis via stabilizing SIRT1 mRNA through binding to the HuR protein

Mitochondrial dysfunction is implicated in numerous disorders, including type 2 diabetes, Alzheimer's disease and cancer. Long non-coding RNAs (lncRNAs) are emerging as pivotal regulators of cellular energy metabolism, yet their roles remain largely unclear. In this study, we identify an lncRNA named linc-PMB, which is associated with mTOR and promotes mitochondrial biogenesis, through microarray analysis. We demonstrate that the knockdown of linc-PMB results in significantly impaired mitochondrial respiration and biogenesis, along with altered expressions of related genes. Conversely, overexpression of linc-PMB markedly increases mitochondrial function. We further reveal that linc-PMB interacts with the RNA-binding protein HuR, promoting the stabilization of SIRT1 mRNA and a substantial increase in SIRT1 expression, which in turn activates the PGC-1α/mtTFA pathway and mitochondrial biogenesis. Collectively, our findings reveal a novel regulatory pathway in which linc-PMB, through its interaction with HuR, modulates the SIRT1/PGC-1α/mtTFA axis to maintain mitochondrial biogenesis and function.

DisGeNet: a disease-centric interaction database among diseases and various associated genes

The pathogenesis of complex diseases is intricately linked to various genes and network medicine has enhanced understanding of diseases. However, most network-based approaches ignore interactions mediated by noncoding RNAs (ncRNAs) and most databases only focus on the association between genes and diseases. Based on the mentioned questions, we have developed DisGeNet, a database focuses not only on the disease-associated genes but also on the interactions among genes. Here, the associations between diseases and various genes, as well as the interactions among these genes are integrated into a disease-centric network. As a result, there are a total of 502 688 interactions/associations involving 6697 diseases, 5780 lncRNAs (long noncoding RNAs), 16 135 protein-coding genes, and 2610 microRNAs stored in DisGeNet. These interactions/associations can be categorized as protein-protein, lncRNA-disease, microRNA-gene, microRNA-disease, gene-disease, and microRNA-lncRNA. Furthermore, as users input name/ID of diseases/genes for search, the interactions/associations about the search content can be browsed as a list or viewed in a local network-view. Database URL: https://disgenet.cn/.

MarkerDB 2.0: a comprehensive molecular biomarker database for 2025

MarkerDB (https://markerdb.ca) has become a leading resource for comprehensive information on molecular biomarkers. Over the past 3 years, the database has evolved significantly, reflecting the dynamic landscape of biomarker research and increasing demands from its user community. This year's update, which is called MarkerDB 2.0, introduces key improvements to enhance the database's usability, consistency and the range of biomarkers covered. These improvements include (i) the addition of thousands of new biomarkers and associated health conditions, (ii) the inclusion of many new biomarker types and categories, (iii) upgraded searches and data filtering functionalities, (iv) new features for exploring and understanding biomarker panels and (v) significantly expanded and improved descriptions. These upgrades, along with numerous minor improvements in content, interface, layout and overall website performance, have greatly enhanced MarkerDB's usability and capacity to facilitate biomarker interpretation across various research domains. MarkerDB remains committed to providing a free, publicly accessible platform for consolidated information on a wide range of molecular (protein, genetic, chromosomal and chemical/small molecule) biomarkers, covering diagnostic, prognostic, risk, monitoring, safety and response-related biomarkers. We are confident that these upgrades and updates will improve MarkerDB's user friendliness, increase its utility and greatly expand its potential applications to many other areas of clinical medicine and biomedical research.

LncPepAtlas: a comprehensive resource for exploring the translational landscape of long non-coding RNAs

Long non-coding RNAs were commonly viewed as non-coding elements. However, they are increasingly recognized for their ability to be translated into proteins, thereby playing a significant role in various cellular processes and diseases. With developments in biotechnology and computational algorithms, a range of novel approaches are being applied to investigate the translation of long non-coding RNA (lncRNAs). Herein, we developed the LncPepAtlas database (http://www.cnitbiotool.net/LncPepAtlas/), which aims to compile multiple evidences for the translation of lncRNAs and annotations for the upstream regulation of lncRNAs across various species. LncPepAtlas integrated compelling evidence from nine distinct sources for the translation of lncRNAs. These include a dataset comprising 2631 publicly available Ribo-seq samples from nine species, which has been collected and analysed. LncPepAtlas offers extensive annotation for lncRNA upstream regulation and expression profiles across various cancers, tissues or cell lines at transcriptional and translational levels. Importantly, it enables novel antigen predictions for lncRNA-encoded peptides. By identifying numerous peptide candidates that could potentially bind to major histocompatibility complex class I and II molecules, this work may provide new insights into cancer immunotherapy. The function of peptides were inferred by aligning them with experimentally detected proteins. LncPepAtlas aims to become a convenient resource for exploring translatable lncRNAs.

LnCeCell 2.0: an updated resource for lncRNA-associated ceRNA networks and web tools based on single-cell and spatial transcriptomics sequencing data

We describe LnCeCell 2.0 (http://bio-bigdata.hrbmu.edu.cn/LnCeCell), an updated resource for lncRNA-associated competing endogenous RNA (ceRNA) networks and web tools based on single-cell and spatial transcriptomics sequencing (stRNA-seq) data. We have updated the LnCeCell 2.0 database with significantly expanded data and improved features, including (i) 257 single-cell RNA sequencing and stRNA-seq datasets across 86 diseases/phenotypes and 80 human normal tissues, (ii) 836 581 cell-specific and spatial spot-specific ceRNA interactions and functional networks for 1 002 988 cells and 367 971 spatial spots, (iii) 15 489 experimentally supported lncRNA biomarkers related to disease pathology, diagnosis and treatment, (iv) detailed annotation of cell type, cell state, subcellular and extracellular locations of ceRNAs through manual curation and (v) ceRNA expression profiles and follow-up clinical information of 20 326 cancer patients. Further, a panel of 24 flexible tools (including 8 comprehensive and 16 mini-analysis tools) was developed to investigate ceRNA-regulated mechanisms at single-cell/spot resolution. The CeCellTraject tool, for example, illustrates the detailed ceRNA distribution of different cell populations and explores the dynamic change of the ceRNA network along the developmental trajectory. LnCeCell 2.0 will facilitate the study of fine-tuned lncRNA-ceRNA networks with single-cell and spatial spot resolution, helping us to understand the regulatory mechanisms behind complex microbial ecosystems.

Self-Supervised Contrastive Learning on Attribute and Topology Graphs for Predicting Relationships Among lncRNAs, miRNAs and Diseases

Exploring associations between long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and diseases is crucial for disease prevention, diagnosis and treatment. While determining these relationships experimentally is resource-intensive and time-consuming, computational methods have emerged as an attractive way. However, existing computational methods tend to focus on single tasks, neglecting the benefits of leveraging multiple biomolecular interactions and domain-specific knowledge for multi-task prediction. Furthermore, the scarcity of labeled data for lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs) and lncRNA-miRNA interactions (LMIs) poses challenges for comprehensive node embedding learning. This paper proposes a multi-task prediction model (called SSCLMD) that employs self-supervised contrastive learning on attribute and topology graphs to identify potential LDAs, MDAs and LMIs. Firstly, domain knowledge of lncRNAs, miRNAs and diseases as well as their interactions are exploited to construct attribute graph and topology graph, respectively. Then, the nodes are encoded in the attribute and topology spaces to extract the specific and common feature. Meanwhile, the attention mechanism is performed to adaptively fuse the embedding from different views. SSCLMD incorporates contrastive self-supervised learning as a regularize to guide node embedding learning in both attribute and topology space without relying on labels. Severing as a regularize in multi-task learning paradigm, it to improves the model.s generalization capabilities. Extensive experiments on 2 manually curated datasets demonstrate that SSCLMD significantly outperforms baseline methods in LDA, MDA and LMI prediction tasks. Case studies on both old and new datasets further supported SSCLMD's ability to uncover novel disease-related lncRNAs and miRNAs.

Modeling ncRNA Synergistic Regulation in Cancer

Cancer seriously threatens human life and health, and the structure and function of genes within cancer cells have changed relative to normal cells. Essentially, cancer is a polygenic disorder, and the core of its occurrence and development is caused by polygenic synergy. Non-coding RNAs (ncRNAs) act as regulators to modulate gene expression levels, and they provide theoretical basis and new technology for the diagnosis and preventive treatment of cancer. However, the study of ncRNA regulation and its role as biomarkers in cancer remain largely unearthed. Driven by multi-omics data, an abundance of computational methods, tools, and databases have been developed for predicting ncRNA-cancer association/causality, inferring ncRNA regulation, and modeling ncRNA synergistic regulation. This chapter aims to provide a comprehensive perspective of modeling ncRNA synergistic regulation. Since the ncRNAs involved in cancer contribute to modeling cancer-associated ncRNA synergistic regulation, we first review the databases and tools of cancer-related ncRNAs. Then we investigate the existing tools or methods for modeling ncRNA-directed and ncRNA-mediated regulation. In addition, we survey the available computational tools or methods for modeling ncRNA synergistic regulation, including synergistic interaction and synergistic competition. Finally, we discuss the future directions and challenges in modeling ncRNA synergistic regulation.

Computational Resources for lncRNA Functions and Targetome

Long non-coding RNAs (lncRNAs) are a type of non-coding RNA molecules exceeding 200 nucleotides in length and that do not encode proteins. The dysregulated expression of lncRNAs has been identified in various diseases, holding therapeutic significance. Over the past decade, numerous computational resources have been published in the field of lncRNA. In this chapter, we have provided a comprehensive review of the databases as well as predictive tools, that is, lncRNA databases, machine learning based algorithms, and tools predicting lncRNAs utilizing different techniques. The chapter will focus on the importance of lncRNA resources developed for different organisms specifically for humans, mouse, plants, and other model organisms. We have enlisted important databases, primarily focusing on comprehensive information related to lncRNA registries, associations with diseases, differential expression, lncRNA transcriptome, target regulations, and all-in-one resources. Further, we have also included the updated version of lncRNA resources. Additionally, computational identification of lncRNAs using algorithms like Deep learning, Support Vector Machine (SVM), and Random Forest (RF) was also discussed. In conclusion, this comprehensive overview concludes by summarizing vital in silico resources, empowering biologists to choose the most suitable tools for their lncRNA research endeavors. This chapter serves as a valuable guide, emphasizing the significance of computational approaches in understanding lncRNAs and their implications in various biological contexts.

Localization is the key to action: regulatory peculiarities of lncRNAs

To understand the transcriptomic profile of an individual cell in a multicellular organism, we must comprehend its surrounding environment and the cellular space where distinct molecular stimuli responses are located. Contradicting the initial perception that RNAs were nonfunctional and that only a few could act in chromatin remodeling, over the last few decades, research has revealed that they are multifaceted, versatile regulators of most cellular processes. Among the various RNAs, long non-coding RNAs (LncRNAs) regulate multiple biological processes and can even impact cell fate. In this sense, the subcellular localization of lncRNAs is the primary determinant of their functions. It affects their behavior by limiting their potential molecular partner and which process it can affect. The fine-tuned activity of lncRNAs is also tissue-specific and modulated by their cis and trans regulation. Hence, the spatial context of lncRNAs is crucial for understanding the regulatory networks by which they influence and are influenced. Therefore, predicting a lncRNA's correct location is not just a technical challenge but a critical step in understanding the biological meaning of its activity. Hence, examining these peculiarities is crucial to researching and discussing lncRNAs. In this review, we debate the spatial regulation of lncRNAs and their tissue-specific roles and regulatory mechanisms. We also briefly highlight how bioinformatic tools can aid research in the area.

Pivotal Role of miRNA-lncRNA Interactions in Human Diseases

New technologies have shown that most of the genome comprises transcripts that cannot code for proteins and are referred to as non-coding RNAs (ncRNAs). Some ncRNAs, like long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), are of substantial interest because of their critical function in controlling genes and numerous biological activities. The expression levels and function of miRNAs and lncRNAs are rigorously monitored throughout developmental processes and the maintenance of physiological homeostasis. Due to their critical roles, any dysregulation or changes in their expression can significantly influence the pathogenesis of various human diseases. The interactions between miRNAs and lncRNAs have been found to influence gene expression in various ways. These interactions significantly influence the understanding of disease etiology, cellular processes, and potential therapeutic targets. Different experimental and in silico methods can be used to investigate miRNA-lncRNA interactions. By aiding the elucidation of miRNA-lncRNA interactions and deepening the understanding of post-transcriptional gene regulation, researchers can open a new window for designing hypotheses, conducting experiments, and discovering methods for diagnosing and treating complex human diseases. This review briefly summarizes miRNA and lncRNA functions, discusses their interaction mechanisms, and examines the experimental and computational methods used to study these interactions. Additionally, we highlight significant studies on lncRNA and miRNA interactions in various diseases from 2000 to 2024, using the academic research databases such as PubMed, Google Scholar, ScienceDirect, and Scopus.

Human lncRNAs harbor conserved modules embedded in different sequence contexts

We analyzed the structure of human long non-coding RNA (lncRNAs) genes to investigate whether the non-coding transcriptome is organized in modular domains, as is the case for protein-coding genes. To this aim, we compared all known human lncRNA exons and identified 340 pairs of exons with high sequence and/or secondary structure similarity but embedded in a dissimilar sequence context. We grouped these pairs in 106 clusters based on their reciprocal similarities. These shared modules are highly conserved between humans and the four great ape species, display evidence of purifying selection and likely arose as a result of recent segmental duplications. Our analysis contributes to the understanding of the mechanisms driving the evolution of the non-coding genome and suggests additional strategies towards deciphering the functional complexity of this class of molecules.

LncRNA AFAP1-AS1 Promotes Oral Squamous Cell Carcinoma Development by Ubiquitin-Mediated Proteolysis

BACKGROUND AND PURPOSE

Long noncoding RNA (lncRNA) dysregulation has been reported to play a pivotal role in the development of cancers. In this study, we aimed to screen the key lncRNA in oral squamous cell carcinoma (OSCC) via bioinformatics analysis and further validate the function of lncRNA in vitro and in vivo.

METHODS

Bioinformatics analysis was conducted to identify differentially expressed lncRNAs between control and OSCC samples. Quantitative real-time-polymerase chain reaction was employed to detect the expression of differentially expressed lncRNAs in human tongue squamous cell carcinoma and human oral keratinocytes cell lines. The biological function of lncRNA and its mechanism were examined via the experimental assessment of the cell lines with the lncRNA overexpressed and silenced. Additionally, to further explore the function of lncRNA in the progression of OSCC, xenograft tumour mouse models were established using 25 mice (5 groups, each with 5 mice). Tumour formation was observed at 2 weeks after the cell injection, and the tumours were resected at 5 weeks post-implantation.

RESULTS

Two lncRNAs, LINC00958 and AFAP1-AS1, were found to be correlated with the prognosis of OSCC. The results of the quantitative real-time-polymerase chain reaction indicated that the 2 lncRNAs were highly expressed in OSCC. In combination with the previous literature, we found AFAP1-AS1 to be a potentially important biomarker for OSCC. Thus, we further investigated its biological function and found that AFAP1-AS1 silencing inhibited cell proliferation, migration, and invasion whereas AFAP1-AS1 overexpression reversed the effect of AFAP1-AS1 silencing (P < .05). Mechanism analysis revealed that AFAP1-AS1 regulated the development of OSCC through the ubiquitin-mediated proteolysis pathway.

CONCLUSIONS

AFAP1-AS1 is an oncogene that aggravates the development of OSCC via the ubiquitin-mediated proteolysis pathway. It also provides a novel potential therapy for OSCC.

LUCAT1-Mediated Competing Endogenous RNA (ceRNA) Network in Triple-Negative Breast Cancer

Breast cancer is a heterogeneous disease comprising multiple molecularly distinct subtypes with varied prevalence, prognostics, and treatment strategies. Among them, triple-negative breast cancer, though the least prevalent, is the most aggressive subtype, with limited therapeutic options. Recent emergence of competing endogenous RNA (ceRNA) networks has highlighted how long noncoding RNAs (lncRNAs), microRNAs (miRs), and mRNA orchestrate a complex interplay meticulously modulating mRNA functionality. Focusing on TNBC, this study aimed to construct a ceRNA network using differentially expressed lncRNAs, miRs, and mRNAs. We queried the differentially expressed lncRNAs (DElncRNAs) between TNBC and luminal samples and found 389 upregulated and 386 downregulated lncRNAs, including novel transcripts in TNBC. DElncRNAs were further evaluated for their clinical, functional, and mechanistic relevance to TNBCs using the lnc2cancer 3.0 database, which presented LUCAT1 (lung cancer-associated transcript 1) as a putative node. Next, the ceRNA network (lncRNA-miRNA-mRNA) of LUCAT1 was established. Several miRNA-mRNA connections of LUCAT1 implicated in regulating stemness (LUCAT1-miR-375-Yap1, LUCAT1-miR181-5p-Wnt, LUCAT1-miR-199a-5p-ZEB1), apoptosis (LUCAT1-miR-181c-5p-Bcl2), drug efflux (LUCAT1-miR-200c-ABCB1, LRP1, MRP5, MDR1), and sheddase activities (LUCAT1-miR-493-5p-ADAM10) were identified, indicating an intricate regulatory mechanism of LUCAT1 in TNBC. Indeed, LUCAT1 silencing led to mitigated cell growth, migration, and stem-like features in TNBC. This work sheds light on the LUCAT1 ceRNA network in TNBC and implies its involvement in TNBC growth and progression.

The crosstalk between alternative splicing and circular RNA in cancer: pathogenic insights and therapeutic implications

RNA splicing is a fundamental step of gene expression. While constitutive splicing removes introns and joins exons unbiasedly, alternative splicing (AS) selectively determines the assembly of exons and introns to generate RNA variants corresponding to the same transcript. The biogenesis of circular RNAs (circRNAs) is inextricably associated with AS. Back-splicing, the biogenic process of circRNA, is a special form of AS. In cancer, both AS and circRNA deviate from the original track. In the present review, we delve into the intricate interplay between AS and circRNAs in the context of cancer. The relationship between AS and circRNAs is intricate, where AS modulates the biogenesis of circRNAs and circRNAs in return regulate AS events. Beyond that, epigenetic and posttranscriptional modifications concurrently regulate AS and circRNAs. On the basis of this modality, we summarize current knowledge on how splicing factors and other RNA binding proteins regulate circRNA biogenesis, and how circRNAs interact with splicing factors to influence AS events. Specifically, the feedback loop regulation between circRNAs and AS events contributes greatly to oncogenesis and cancer progression. In summary, resolving the crosstalk between AS and circRNA will not only provide better insight into cancer biology but also provoke novel strategies to combat cancer.

Circ_0089761 accelerates colorectal cancer metastasis and immune escape via miR-27b-3p/PD-L1 axis

Circular RNAs have been implicated as critical regulators in the initiation and progression of colorectal cancer (CRC). This study was intended to elucidate the functional significance of the circ_0089761/miR-27b-3p/programmed cell death ligand 1 (PD-L1) axis in CRC. Our findings indicated that circ_0089761 expression was significantly elevated in CRC tissues and cell lines. Furthermore, the high expression of circ_0089761 was correlated with TNM stage and tumor size. Silencing circ_0089761 inhibited CRC cell proliferation, migration, and invasion, and increased apoptosis. Mechanistically, circ_0089761 facilitated its biological function by binding to miR-27b-3p to upregulate PD-L1 expression in CRC. Coculture experiments confirmed that low expression of circ_0089761 impeded CD8 + T cell apoptosis and depletion, activated CD8 + T cell function, and increased secretion of the immune effector cytokines IFN-γ, TNF-α, perforin, and granzyme-B. MiR-27b-3p inhibition or PD-L1 overexpression partially impeded CD8 + T cell function. The circ_0089761/miR-27b-3p/PD-L1 axis is postulated to exert pivotal functions in the mechanistic progression of CRC. Furthermore, it holds promising prospects as a feasible biomarker and therapeutic target for CRC.

Emerging Roles of ncRNAs in Type 2 Diabetes Mellitus: From Mechanisms to Drug Discovery

Type 2 diabetes mellitus (T2DM), a high-incidence chronic metabolic disorder, has emerged as a global health issue, where most patients need lifelong medication. Gaining insights into molecular mechanisms involved in T2DM development is expected to provide novel strategies for clinical prevention and treatment. Growing evidence validates that non-coding RNAs (ncRNAs) including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) function as crucial regulators in multiple biological processes of T2DM, inspiring various potential targets and drug candidates. In this review, we summarize the current understanding of ncRNA roles in T2DM and discuss the potential use of ncRNAs as targets and active molecules for drug discovery.

The pan-cancer landscape presented ITGA7 as a prognostic determinant, tumor suppressor, and oncogene in multiple tumor types

Integrin α7 (ITGA7) is an extracellular matrix-binding protein. Integrins are the main type of cell adhesive molecules in mammals, playing a role in many biological pathways. Although various studies have shown correlations between ITGA7 and various types of cancer, a comprehensive study at a pan-cancer level has not yet been conducted. In this study, we investigated the function of ITGA7 in distinct tumor types using the multi-omics relevant information, then two CeRNA regulatory network was drawn to identify the ITGA7 hub regulatory RNAs. The results indicated that the expression of ITGA7 varies in different tumors. Overexpression of ITGA7 was correlated with a worse OS in BLCA, LGG, and UVM, and the downregulation of ITGA7 was related to a worse OS in PAAD. In addition, BLCA, and UVM showed poor PFS in association with ITGA7 overexpression, and PAAD, SARC, and THCA indicated poor PFS in correlation with ITGA7 under expression. Further analyses of ITGA7 gene alteration data showed that ITGA7 amplifications may have an impact on Kidney Chromophobe prognosis. In 20 types of tumors, ITGA7 expression was linked to cancer-associated fibroblast infiltration. ITGA7 expression was linked to cancer-associated fibroblast infiltration. ITGA7-Related Gene Enrichment Analysis indicated that ITGA7 expression-correlated and functional binding genes were enriched in homotypic cell-cell adhesion, focal adhesion, and ECM-receptor interaction. This pan-cancer study found that abnormal expression of ITGA7 was correlated with poor prognosis and metastasis in different types of tumors. Thus, the ITGA7 gene may prove to be a promising biomarker for the prognosis and complication prevention of different cancers.

LDAGM: prediction lncRNA-disease asociations by graph convolutional auto-encoder and multilayer perceptron based on multi-view heterogeneous networks

BACKGROUND

Long non-coding RNAs (lncRNAs) can prevent, diagnose, and treat a variety of complex human diseases, and it is crucial to establish a method to efficiently predict lncRNA-disease associations.

RESULTS

In this paper, we propose a prediction method for the lncRNA-disease association relationship, named LDAGM, which is based on the Graph Convolutional Autoencoder and Multilayer Perceptron model. The method first extracts the functional similarity and Gaussian interaction profile kernel similarity of lncRNAs and miRNAs, as well as the semantic similarity and Gaussian interaction profile kernel similarity of diseases. It then constructs six homogeneous networks and deeply fuses them using a deep topology feature extraction method. The fused networks facilitate feature complementation and deep mining of the original association relationships, capturing the deep connections between nodes. Next, by combining the obtained deep topological features with the similarity network of lncRNA, disease, and miRNA interactions, we construct a multi-view heterogeneous network model. The Graph Convolutional Autoencoder is employed for nonlinear feature extraction. Finally, the extracted nonlinear features are combined with the deep topological features of the multi-view heterogeneous network to obtain the final feature representation of the lncRNA-disease pair. Prediction of the lncRNA-disease association relationship is performed using the Multilayer Perceptron model. To enhance the performance and stability of the Multilayer Perceptron model, we introduce a hidden layer called the aggregation layer in the Multilayer Perceptron model. Through a gate mechanism, it controls the flow of information between each hidden layer in the Multilayer Perceptron model, aiming to achieve optimal feature extraction from each hidden layer.

CONCLUSIONS

Parameter analysis, ablation studies, and comparison experiments verified the effectiveness of this method, and case studies verified the accuracy of this method in predicting lncRNA-disease association relationships.

ACLNDA: an asymmetric graph contrastive learning framework for predicting noncoding RNA-disease associations in heterogeneous graphs

Noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), play crucial roles in gene expression regulation and are significant in disease associations and medical research. Accurate ncRNA-disease association prediction is essential for understanding disease mechanisms and developing treatments. Existing methods often focus on single tasks like lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs), or lncRNA-miRNA interactions (LMIs), and fail to exploit heterogeneous graph characteristics. We propose ACLNDA, an asymmetric graph contrastive learning framework for analyzing heterophilic ncRNA-disease associations. It constructs inter-layer adjacency matrices from the original lncRNA, miRNA, and disease associations, and uses a Top-K intra-layer similarity edges construction approach to form a triple-layer heterogeneous graph. Unlike traditional works, to account for both node attribute features (ncRNA/disease) and node preference features (association), ACLNDA employs an asymmetric yet simple graph contrastive learning framework to maximize one-hop neighborhood context and two-hop similarity, extracting ncRNA-disease features without relying on graph augmentations or homophily assumptions, reducing computational cost while preserving data integrity. Our framework is capable of being applied to a universal range of potential LDA, MDA, and LMI association predictions. Further experimental results demonstrate superior performance to other existing state-of-the-art baseline methods, which shows its potential for providing insights into disease diagnosis and therapeutic target identification. The source code and data of ACLNDA is publicly available at https://github.com/AI4Bread/ACLNDA.

SCancerRNA: Expression at the Single-cell Level and Interaction Resource of Non-coding RNA Biomarkers for Cancers

Non-coding RNAs (ncRNAs) participate in multiple biological processes associated with cancers as tumor suppressors or oncogenic drivers. Due to their high stability in plasma, urine, and many other fluids, ncRNAs have the potential to serve as key biomarkers for early diagnosis and screening of cancers. During cancer progression, tumor heterogeneity plays a crucial role, and it is particularly important to understand the gene expression patterns of individual cells. With the development of single-cell RNA sequencing (scRNA-seq) technologies, uncovering gene expression in different cell types for human cancers has become feasible by profiling transcriptomes at the cellular level. However, a well-organized and comprehensive online resource that provides access to the expression of genes corresponding to ncRNA biomarkers in different cell types at the single-cell level is not available yet. Therefore, we developed the SCancerRNA database to summarize experimentally supported data on long ncRNA, microRNA, PIWI-interacting RNA, small nucleolar RNA, and circular RNA biomarkers, as well as data on their differential expression at the cellular level. Furthermore, we collected biological functions and clinical applications of biomarkers to facilitate the application of ncRNA biomarkers to cancer diagnosis, as well as the monitoring of progression and targeted therapies. SCancerRNA also allows users to explore interaction networks of different types of ncRNAs, and build computational models in the future. SCancerRNA is freely accessible at http://www.scancerrna.com/BioMarker.

Mask-Guided Target Node Feature Learning and Dynamic Detailed Feature Enhancement for lncRNA-Disease Association Prediction

Identifying new relevant long noncoding RNAs (lncRNAs) for various human diseases can facilitate the exploration of the causes and progression of these diseases. Recently, several graph inference methods have been proposed to predict disease-related lncRNAs by exploiting the topological structure and node attributes within graphs. However, these methods did not prioritize the target lncRNA and disease nodes over auxiliary nodes like miRNA nodes, potentially limiting their ability to fully utilize the features of the target nodes. We propose a new method, mask-guided target node feature learning and dynamic detailed feature enhancement for lncRNA-disease association prediction (MDLD), to enhance node feature learning for improved lncRNA-disease association prediction. First, we designed a heterogeneous graph masked transformer autoencoder to guide feature learning, focusing more on the features of target lncRNA (disease) nodes. The target nodes were increasingly masked as training progressed, which helps develop a more robust prediction model. Second, we developed a graph convolutional network with dynamic residuals (GCNDR) to learn and integrate the heterogeneous topology and features of all lncRNA, disease, and miRNA nodes. GCNDR employs an interlayer residual strategy and a residual evolution strategy to mitigate oversmoothing caused by multilayer graph convolution. The interlayer residual strategy estimates the importance of node features learned in the previous GCN encoding layer for nodes in the current encoding layer. Additionally, since there are dependencies in the importance of features of individual lncRNA (disease, miRNA) nodes across multiple encoding layers, a gated recurrent unit-based strategy is proposed to encode these dependencies. Finally, we designed a perspective-level attention mechanism to obtain more informative features of lncRNA and disease node pairs from the perspectives of mask-enhanced and dynamic-enhanced node features. Cross-validation experimental results demonstrated that MDLD outperformed 10 other state-of-the-art prediction methods. Ablation experiments and case studies on candidate lncRNAs for three diseases further proved the technical contributions of MDLD and its capability to discover disease-related lncRNAs.

An ontology-based knowledge graph for representing interactions involving RNA molecules

The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to each patient's biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are constantly produced and available from public repositories, they are scattered across different databases and a centralized, uniform, and semantically consistent representation of the "RNA world" is still lacking. We propose RNA-KG, a knowledge graph (KG) encompassing biological knowledge about RNAs gathered from more than 60 public databases, integrating functional relationships with genes, proteins, and chemicals and ontologically grounded biomedical concepts. To develop RNA-KG, we first identified, pre-processed, and characterized each data source; next, we built a meta-graph that provides an ontological description of the KG by representing all the bio-molecular entities and medical concepts of interest in this domain, as well as the types of interactions connecting them. Finally, we leveraged an instance-based semantically abstracted knowledge model to specify the ontological alignment according to which RNA-KG was generated. RNA-KG can be downloaded in different formats and also queried by a SPARQL endpoint. A thorough topological analysis of the resulting heterogeneous graph provides further insights into the characteristics of the "RNA world". RNA-KG can be both directly explored and visualized, and/or analyzed by applying computational methods to infer bio-medical knowledge from its heterogeneous nodes and edges. The resource can be easily updated with new experimental data, and specific views of the overall KG can be extracted according to the bio-medical problem to be studied.

Long Non-Coding RNAs, Nuclear Receptors and Their Cross-Talks in Cancer-Implications and Perspectives

Long non-coding RNAs (lncRNAs) play key roles in various epigenetic and post-transcriptional events in the cell, thereby significantly influencing cellular processes including gene expression, development and diseases such as cancer. Nuclear receptors (NRs) are a family of ligand-regulated transcription factors that typically regulate transcription of genes involved in a broad spectrum of cellular processes, immune responses and in many diseases including cancer. Owing to their many overlapping roles as modulators of gene expression, the paths traversed by lncRNA and NR-mediated signaling often cross each other; these lncRNA-NR cross-talks are being increasingly recognized as important players in many cellular processes and diseases such as cancer. Here, we review the individual roles of lncRNAs and NRs, especially growth factor modulated receptors such as androgen receptors (ARs), in various types of cancers and how the cross-talks between lncRNAs and NRs are involved in cancer progression and metastasis. We discuss the challenges involved in characterizing lncRNA-NR associations and how to overcome them. Furthering our understanding of the mechanisms of lncRNA-NR associations is crucial to realizing their potential as prognostic features, diagnostic biomarkers and therapeutic targets in cancer biology.

Predicting lncRNA-Disease Associations Based on a Dual-Path Feature Extraction Network with Multiple Sources of Information Integration

Identifying the associations between long noncoding RNAs (lncRNAs) and disease is critical for disease prevention, diagnosis and treatment. However, conducting wet experiments to discover these associations is time-consuming and costly. Therefore, computational modeling for predicting lncRNA-disease associations (LDAs) has become an important alternative. To enhance the accuracy of LDAs prediction and alleviate the issue of node feature oversmoothing when exploring the potential features of nodes using graph neural networks, we introduce DPFELDA, a dual-path feature extraction network that leverages the integration of information from multiple sources to predict LDA. Initially, we establish a dual-view structure of lncRNAs and disease and a heterogeneous network of lncRNA-disease-microRNA (miRNA) interactions. Subsequently, features are extracted using a dual-path feature extraction network. In particular, we employ a combination of a graph convolutional network, a convolutional block attention module, and a node aggregation layer to perform multilayer topology feature extraction for the dual-view structure of lncRNAs and diseases. Additionally, we utilize a Transformer model to construct the node topology feature residual network for obtaining node-specific features in heterogeneous networks. Finally, XGBoost is employed for LDA prediction. The experimental results demonstrate that DPFELDA outperforms the benchmark model on various benchmark data sets. In the course of model exploration, it becomes evident that DPFELDA successfully alleviates the issue of node feature oversmoothing induced by graph-based learning. Ablation experiments confirm the effectiveness of the innovative module, and a case study substantiates the accuracy of DPFELDA model in predicting novel LDAs for characteristic diseases.

ARAP1-AS1: a novel long non-coding RNA with a vital regulatory role in human cancer development

Long non-coding RNAs (lncRNAs) have garnered significant attention in biomedical research due to their pivotal roles in gene expression regulation and their association with various human diseases. Among these lncRNAs, ArfGAP With RhoGAP Domain, Ankyrin Repeat, And PH Domain 1 - Antisense RNA 1 (ARAP1-AS1) has recently emerged as an novel oncogenic player. ARAP1-AS1 is prominently overexpressed in numerous solid tumors and wields influence by modulating gene expression and signaling pathways. This regulatory impact is realized through dual mechanisms, involving both competitive interactions with microRNAs and direct protein binding. ARAP1-AS1 assumes an important role in driving tumorigenesis and malignant tumor progression, affecting biological characteristics such as tumor expansion and metastasis. This paper provides a concise review of the regulatory role of ARAP1-AS1 in malignant tumors and discuss its potential clinical applications as a biomarker and therapeutic target. We also address existing knowledge gaps and suggest avenues for future research. ARAP1-AS1 serves as a prototypical example within the burgeoning field of lncRNA studies, offering insights into the broader landscape of non-coding RNA molecules. This investigation enhances our comprehension of the complex mechanisms that govern the progression of cancer.

Long noncoding RNA profiling unveils LINC00960 as unfavorable prognostic biomarker promoting triple negative breast cancer progression

Long noncoding RNAs (lncRNAs) play a critical role in breast cancer pathogenesis, including Triple-Negative Breast Cancer (TNBC) subtype. Identifying the lncRNA expression patterns across different breast cancer subtypes could provide valuable insights into their potential utilization as disease biomarkers and therapeutic targets. In this study, we profiled lncRNA expression in 96 breast cancer cases, revealing significant differences compared to normal breast tissue. Variations across breast cancer subtypes, including Hormone Receptor-positive (HR + ), HER2-positive (HER2 + ), HER2 + HR + , and TNBC, as well as in relation to tumor grade and patients' age at diagnosis were observed. TNBC and HER2+ subtypes showed distinct clustering, while HER2 + HR+ tumors clustered closer to HR+ tumors based on their lncRNA profiles. Our data identified numerous enriched lncRNAs in TNBC, notably the elevated expression of LINC00960, which was subsequently validated in two additional datasets. Analysis of LINC00960 expression in an independent TNBC cohort (n = 360) revealed elevated expression of LINC00960 to correlate with cell movement, invasion, proliferation, and migration functional categories. Depletion of LINC00960 significantly reduced TNBC cell viability, colony formation, migration, and three-dimensional growth, while increasing cell death. Mechanistically, transcriptomic profiling of LINC00960-depleted cells confirmed its tumor-promoting role, likely through sponging of hsa-miR-34a-5p, hsa-miR-16-5p, and hsa-miR-183-5p, leading to the upregulation of cancer-promoting genes including BMI1, KRAS, and AKT3. Our findings highlight the distinct lncRNA expression patterns in breast cancer subtypes and underscore the crucial role for LINC00960 in promoting TNBC pathogenesis, suggesting its potential utilization as a prognostic marker and therapeutic target.

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.

Pan-cancer analysis of super-enhancer-induced LINC00862 and validation as a SIRT1-promoting factor in cervical cancer and gastric cancer

Immune checkpoints inhibitors are effective but it needs more precise biomarkers for patient selection. We explored the biological significance of LINC00862 in pan-cancer by bioinformatics. And we studied its regulatory mechanisms using chromatin immunoprecipitation and RNA immunoprecipitation assays etc. TCGA and single-cell sequencing data analysis indicated that LINC00862 was overexpressed in the majority of tumor and stromal cells, which was related with poor prognosis. LINC00862 expression was related with immune cell infiltration and immune checkpoints expression, and had a high predictive value for immunotherapy efficacy. Mechanistically, LINC00862 competitively bound to miR-29c-3p to unleash SIRT1's tumor-promoting function. SIRT1 inhibitor-EX527 were screened by virtual screening and verified by in vitro and vivo assays. Notably, acetyltransferase P300-mediated super-enhancer activity stimulated LINC00862 transcription. Collectively, LINC00862 could be a diagnostic and prognostic biomarker. LINC00862 could also be a predictive biomarker for immunotherapy efficacy. Super-enhancer activity is the driver for LINC00862 overexpression in cervical cancer and gastric cancer.

Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns

Cancer, a collection of more than two hundred different diseases, remains a leading cause of morbidity and mortality worldwide. Usually detected at the advanced stages of disease, metastatic cancer accounts for 90% of cancer-associated deaths. Therefore, the early detection of cancer, combined with current therapies, would have a significant impact on survival and treatment of various cancer types. Epigenetic changes such as DNA methylation are some of the early events underlying carcinogenesis. Here, we report on an interpretable machine learning model that can classify 13 cancer types as well as non-cancer tissue samples using only DNA methylome data, with 98.2% accuracy. We utilize the features identified by this model to develop EMethylNET, a robust model consisting of an XGBoost model that provides information to a deep neural network that can generalize to independent data sets. We also demonstrate that the methylation-associated genomic loci detected by the classifier are associated with genes, pathways and networks involved in cancer, providing insights into the epigenomic regulation of carcinogenesis.

Long noncoding RNA TUG1 promotes malignant progression of osteosarcoma by enhancing ZBTB7C expression

BACKGROUND

Dysregulation of long non-coding RNAs (lncRNAs) is an important component of tumorigenesis. Aberrant expression of lncRNA taurine upregulated gene 1 (lncTUG1) has been reported in various tumors; however, its precise role and key targets critically involved in osteosarcoma (OS) progression remain unclear.

METHODS

The expression profiles of lncRNAs and their regulated miRNAs related to OS progression were assessed by bioinformatics analysis and confirmed by qRT-PCR of OS cells. The miRNA targets were identified by transcriptome sequencing and verified by luciferase reporter and RNA pull-down assays. Several in vivo and in vitro approaches, including CCK8 assay, western blot, qRT-PCR, lentiviral transduction and OS cell xenograft mouse model were established to validate the effects of lncTUG1 regulation of miRNA and the downstream target genes on OS cell growth, apoptosis and progression.

RESULTS

We found that lncTUG1 and miR-26a-5p were inversely up or down-regulated in OS cells, and siRNA-mediated lncTUG1 knockdown reversed the miR-26a-5p down-regulation and suppressed proliferation and enhanced apoptosis of OS cells. Further, we identified that an oncoprotein ZBTB7C was also upregulated in OS cells that were subjected to lncTUG1/miR-26a-5p regulation. More importantly, ZBTB7C knockdown reduced the ZBTB7C upregulation and ZBTB7C overexpression diminished the anti-OS effects of lncTUG1 knockdown in the OS xenograft model.

CONCLUSIONS

Our data suggest that lncTUG1 acts as a miR-26a-5p sponge and promotes OS progression via up-regulating ZBTB7C, and targeting lncTUG1 might be an effective strategy to treat OS.

Comprehensive review for non-coding RNAs: From mechanisms to therapeutic applications

Non-coding RNAs (ncRNAs) are an assorted collection of transcripts that are not translated into proteins. Since their discovery, ncRNAs have gained prominence as crucial regulators of various biological functions across diverse cell types and tissues, and their abnormal functioning has been implicated in disease. Notably, extensive research has focused on the relationship between microRNAs (miRNAs) and human cancers, although other types of ncRNAs, such as long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), are also emerging as significant contributors to human disease. In this review, we provide a comprehensive summary of our current knowledge regarding the roles of miRNAs, lncRNAs, and circRNAs in cancer and other major human diseases, particularly cancer, cardiovascular, neurological, and infectious diseases. Moreover, we discuss the potential utilization of ncRNAs as disease biomarkers and as targets for therapeutic interventions.

GEnDDn: An lncRNA-Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network

Accumulating studies have demonstrated close relationships between long non-coding RNAs (lncRNAs) and diseases. Identification of new lncRNA-disease associations (LDAs) enables us to better understand disease mechanisms and further provides promising insights into cancer targeted therapy and anti-cancer drug design. Here, we present an LDA prediction framework called GEnDDn based on deep learning. GEnDDn mainly comprises two steps: First, features of both lncRNAs and diseases are extracted by combining similarity computation, non-negative matrix factorization, and graph attention auto-encoder, respectively. And each lncRNA-disease pair (LDP) is depicted as a vector based on concatenation operation on the extracted features. Subsequently, unknown LDPs are classified by aggregating dual-net neural architecture and deep neural network. Using six different evaluation metrics, we found that GEnDDn surpassed four competing LDA identification methods (SDLDA, LDNFSGB, IPCARF, LDASR) on the lncRNADisease and MNDR databases under fivefold cross-validation experiments on lncRNAs, diseases, LDPs, and independent lncRNAs and independent diseases, respectively. Ablation experiments further validated the powerful LDA prediction performance of GEnDDn. Furthermore, we utilized GEnDDn to find underlying lncRNAs for lung cancer and breast cancer. The results elucidated that there may be dense linkages between IFNG-AS1 and lung cancer as well as between HIF1A-AS1 and breast cancer. The results require further biomedical experimental verification. GEnDDn is publicly available at https://github.com/plhhnu/GEnDDn.

LINC00982-encoded protein PRDM16-DT regulates CHEK2 splicing to suppress colorectal cancer metastasis and chemoresistance

Metastasis is one of the key factors of treatment failure in late-stage colorectal cancer (CRC). Metastatic CRC frequently develops resistance to chemotherapeutic agents. This study aimed to identify the novel regulators from "hidden" proteins encoded by long noncoding RNAs (lncRNAs) involved in tumor metastasis and chemoresistance. Methods: CRISPR/Cas9 library functional screening was employed to identify the critical suppressor of cancer metastasis in highly invasive CRC models. Western blotting, immunofluorescence staining, invasion, migration, wound healing, WST-1, colony formation, gain- and loss-of-function experiments, in vivo experimental metastasis models, multiplex immunohistochemical staining, immunohistochemistry, qRT-PCR, and RT-PCR were used to assess the functional and clinical significance of FOXP3, PRDM16-DT, HNRNPA2B1, and L-CHEK2. RNA-sequencing, co-immunoprecipitation, qRT-PCR, RT-PCR, RNA affinity purification, RNA immunoprecipitation, MeRIP-quantitative PCR, fluorescence in situ hybridization, chromatin immunoprecipitation and luciferase reporter assay were performed to gain mechanistic insights into the role of PRDM16-DT in cancer metastasis and chemoresistance. An oxaliplatin-resistant CRC cell line was established by in vivo selection. WST-1, colony formation, invasion, migration, Biacore technology, gain- and loss-of-function experiments and an in vivo experimental metastasis model were used to determine the function and mechanism of cimicifugoside H-1 in CRC. Results: The novel protein PRDM16-DT, encoded by LINC00982, was identified as a cancer metastasis and chemoresistance suppressor. The down-regulated level of PRDM16-DT was positively associated with malignant phenotypes and poor prognosis of CRC patients. Transcriptionally regulated by FOXP3, PRDM16-DT directly interacted with HNRNPA2B1 and competitively decreased HNRNPA2B1 binding to exon 9 of CHEK2, resulting in the formation of long CHEK2 (L-CHEK2), subsequently promoting E-cadherin secretion. PRDM16-DT-induced E-cadherin secretion inhibited fibroblast activation, which in turn suppressed CRC metastasis by decreasing MMP9 secretion. Cimicifugoside H-1, a natural compound, can bind to LEU89, HIS91, and LEU92 of FOXP3 and significantly upregulated PRDM16-DT expression to repress CRC metastasis and reverse oxaliplatin resistance. Conclusions: lncRNA LINC00982 can express a new protein PRDM16-DT to function as a novel regulator in cancer metastasis and drug resistance of CRC. Cimicifugoside H-1 can act on the upstream of the PRDM16-DT signaling pathway to alleviate cancer chemoresistance.

MSGD: a manually curated database of genomic, transcriptomic, proteomic and drug information for multiple sclerosis

Multiple sclerosis (MS) is the most common inflammatory demyelinating disease of the central nervous system. 'Omics' technologies (genomics, transcriptomics, proteomics) and associated drug information have begun reshaping our understanding of multiple sclerosis. However, these data are scattered across numerous references, making them challenging to fully utilize. We manually mined and compiled these data within the Multiple Sclerosis Gene Database (MSGD) database, intending to continue updating it in the future. We screened 5485 publications and constructed the current version of MSGD. MSGD comprises 6255 entries, including 3274 variant entries, 1175 RNA entries, 418 protein entries, 313 knockout entries, 612 drug entries and 463 high-throughput entries. Each entry contains detailed information, such as species, disease type, detailed gene descriptions (such as official gene symbols), and original references. MSGD is freely accessible and provides a user-friendly web interface. Users can easily search for genes of interest, view their expression patterns and detailed information, manage gene sets and submit new MS-gene associations through the platform. The primary principle behind MSGD's design is to provide an exploratory platform, aiming to minimize filtration and interpretation barriers while ensuring highly accessible presentation of data. This initiative is expected to significantly assist researchers in deciphering gene mechanisms and improving the prevention, diagnosis and treatment of MS. Database URL: http://bio-bigdata.hrbmu.edu.cn/MSGD.

LncRNAs in Immune and Stromal Cells Remodel Phenotype of Cancer Cell and Tumor Microenvironment

Emerging studies suggest that long non-coding RNAs (lncRNAs) participate in the mutual regulation of cells in tumor microenvironment, thereby affecting the anti-tumor immune activity of immune cells. Additionally, the intracellular pathways mediated by lncRNAs can affect the expression of immune checkpoints or change the cell functions, including cytokines secretion, of immune and stromal cells in tumor microenvironment, which further influences cancer patients' prognosis and treatment response. With the in-depth research, lncRNAs have shown great potency as a new immunotherapy target and predict immunotherapy response. The research on lncRNAs provides us with a new insight into developing new immunotherapy drugs and predicting the outcome of immunotherapy. With development of RNA sequencing technology, amounts of lncRNAs were found to be dysregulated in immune and stromal cells rather than tumor cells. These lncRNAs function through ceRNA network or regulating transcript factor activity, thus leading abnormal differentiation and activation of immune and stromal cells. Here, we review the function of lncRNAs in the immune microenvironment and focus on the alteration of lncRNAs in immune and stromal cells, and discuss how these alterations affect tumor growth, metastasis and treatment response.

Neighborhood based computational approaches for the prediction of lncRNA-disease associations

MOTIVATION

Long non-coding RNAs (lncRNAs) are a class of molecules involved in important biological processes. Extensive efforts have been provided to get deeper understanding of disease mechanisms at the lncRNA level, guiding towards the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of disease-lncRNA associations allow to identify the most promising candidates to be verified in laboratory, reducing costs and time consuming.

RESULTS

We propose novel approaches for the prediction of lncRNA-disease associations, all sharing the idea of exploring associations among lncRNAs, other intermediate molecules (e.g., miRNAs) and diseases, suitably represented by tripartite graphs. Indeed, while only a few lncRNA-disease associations are still known, plenty of interactions between lncRNAs and other molecules, as well as associations of the latters with diseases, are available. A first approach presented here, NGH, relies on neighborhood analysis performed on a tripartite graph, built upon lncRNAs, miRNAs and diseases. A second approach (CF) relies on collaborative filtering; a third approach (NGH-CF) is obtained boosting NGH by collaborative filtering. The proposed approaches have been validated on both synthetic and real data, and compared against other methods from the literature. It results that neighborhood analysis allows to outperform competitors, and when it is combined with collaborative filtering the prediction accuracy further improves, scoring a value of AUC equal to 0966.

AVAILABILITY

Source code and sample datasets are available at: https://github.com/marybonomo/LDAsPredictionApproaches.git.

Prognostic analysis of hepatocellular carcinoma based on cuproptosis -associated lncRNAs

OBJECTIVES

Cuproptosis represents an innovative type of cell death, distinct from apoptosis, driven by copper dependency, yet the involvement of copper apoptosis-associated long non-coding RNAs (CRLncRNAs) in hepatocellular carcinoma (HCC) remains unclear. This study is dedicated to unveiling the role and significance of these copper apoptosis-related lncRNAs within the context of HCC, focusing on their impact on both the development of the disease and its prognosis.

METHODS

We conducted an analysis of gene transcriptomic and clinical data for HCC cases by sourcing information from The Cancer Genome Atlas database. By incorporating cuproptosis-related genes, we established prognostic features associated with cuproptosis-related lncRNAs. Furthermore, we elucidated the mechanism of cuproptosis-related lncRNAs in the prognosis and treatment of HCC through comprehensive approaches, including Lasso and Cox regression analyses, survival analyses of samples, as well as examinations of tumor mutation burden and immune function.

RESULTS

We developed a prognostic model featuring six cuproptosis-related lncRNAs: AC026412.3, AC125437.1, AL353572.4, MKLN1-AS, TMCC1-AS1, and SLC6A1-AS1. This model demonstrated exceptional prognostic accuracy in both training and validation cohorts for patients with tumors, showing significantly longer survival times for those categorized in the low-risk group compared to the high-risk group. Additionally, our analyses, including tumor mutation burden, immune function, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway enrichment, and drug sensitivity, further elucidated the potential mechanisms through which cuproptosis-associated lncRNAs may influence disease outcome.

CONCLUSIONS

The model developed using cuproptosis-related long non-coding RNAs (lncRNAs) demonstrates promising predictive capabilities for both the prognosis and immunotherapy outcomes of tumor patients. This could play a crucial role in patient management and the optimization of immunotherapeutic strategies, offering valuable insights for future research.