LncRNAs2Pathways: Identifying the pathways influenced by a set of lncRNAs of interest based on a global network propagation method

Longitudinal dysregulation of long non-coding RNAs in Parkinson's disease

Long non-coding RNAs (lncRNAs) have been suggested as potential biomarkers for Parkinson's disease (PD). This study aimed to identify blood-based lncRNA transcripts that are dysregulated in PD over time and could serve as peripheral biomarkers. Using RNA-sequencing data from the Parkinson's Progression Markers Initiative, differential expression between case and control groups at five different time points was detected, and pathway analysis was conducted. Seven transcripts, not previously linked to PD, were consistently dysregulated across all time points, while PD-linked lncRNAs were dysregulated at some but not all time points. Pathway analysis highlighted pathways, known to be affected in PD. This suggested that dysregulated lncRNA transcripts could play a role in PD pathogenesis by affecting well-known PD pathways and highlighted their potential as longitudinal biomarkers for PD. Further studies are needed to validate these findings and explore the potential use of identified lncRNAs as diagnostic and therapeutic targets.

Redefining normal breast cell populations using long noncoding RNAs

Single-cell RNAseq has allowed unprecedented insight into gene expression across different cell populations in normal tissue and disease states. However, almost all studies rely on annotated gene sets to capture gene expression levels and sequencing reads that do not align to known genes are discarded. Here, we discover thousands of long noncoding RNAs (lncRNAs) expressed in human mammary epithelial cells and analyze their expression in individual cells of the normal breast. We show that lncRNA expression alone can discriminate between luminal and basal cell types and define subpopulations of both compartments. Clustering cells based on lncRNA expression identified additional basal subpopulations, compared to clustering based on annotated gene expression, suggesting that lncRNAs can provide an additional layer of information to better distinguish breast cell subpopulations. In contrast, these breast-specific lncRNAs poorly distinguish brain cell populations, highlighting the need to annotate tissue-specific lncRNAs prior to expression analyses. We also identified a panel of 100 breast lncRNAs that could discern breast cancer subtypes better than protein-coding markers. Overall, our results suggest that lncRNAs are an unexplored resource for new biomarker and therapeutic target discovery in the normal breast and breast cancer subtypes.

lncHUB2: aggregated and inferred knowledge about human and mouse lncRNAs

Long non-coding ribonucleic acids (lncRNAs) account for the largest group of non-coding RNAs. However, knowledge about their function and regulation is limited. lncHUB2 is a web server database that provides known and inferred knowledge about the function of 18 705 human and 11 274 mouse lncRNAs. lncHUB2 produces reports that contain the secondary structure fold of the lncRNA, related publications, the most correlated coding genes, the most correlated lncRNAs, a network that visualizes the most correlated genes, predicted mouse phenotypes, predicted membership in biological processes and pathways, predicted upstream transcription factor regulators, and predicted disease associations. In addition, the reports include subcellular localization information; expression across tissues, cell types, and cell lines, and predicted small molecules and CRISPR knockout (CRISPR-KO) genes prioritized based on their likelihood to up- or downregulate the expression of the lncRNA. Overall, lncHUB2 is a database with rich information about human and mouse lncRNAs and as such it can facilitate hypothesis generation for many future studies. The lncHUB2 database is available at https://maayanlab.cloud/lncHUB2. Database URL: https://maayanlab.cloud/lncHUB2.

Meta-Analysis of RNA-Seq Datasets Identifies Novel Players in Glioblastoma

Glioblastoma is a devastating grade IV glioma with poor prognosis. Identification of predictive molecular biomarkers of disease progression would substantially contribute to better disease management. In the current study, we performed a meta-analysis of different RNA-seq datasets to identify differentially expressed protein-coding genes (PCGs) and long non-coding RNAs (lncRNAs). This meta-analysis aimed to improve power and reproducibility of the individual studies while identifying overlapping disease-relevant pathways. We supplemented the meta-analysis with small RNA-seq on glioblastoma tissue samples to provide an overall transcriptomic view of glioblastoma. Co-expression correlation of filtered differentially expressed PCGs and lncRNAs identified a functionally relevant sub-cluster containing DANCR and SNHG6, with two novel lncRNAs and two novel PCGs. Small RNA-seq of glioblastoma tissues identified five differentially expressed microRNAs of which three interacted with the functionally relevant sub-cluster. Pathway analysis of this sub-cluster identified several glioblastoma-linked pathways, which were also previously associated with the novel cell death pathway, ferroptosis. In conclusion, the current meta-analysis strengthens evidence of an overarching involvement of ferroptosis in glioblastoma pathogenesis and also suggests some candidates for further analyses.

Immune-related 3-lncRNA signature with prognostic connotation in a multi-cancer setting

Background

Advances in our understanding of the tumor microenvironment have radically changed the cancer field, highlighting the emerging need for biomarkers of an active, favorable tumor immune phenotype to aid treatment stratification and clinical prognostication. Numerous immune-related gene signatures have been defined; however, their prognostic value is often limited to one or few cancer types. Moreover, the area of non-coding RNA as biomarkers remains largely unexplored although their number and biological roles are rapidly expanding.

Methods

We developed a multi-step process to identify immune-related long non-coding RNA signatures with prognostic connotation in multiple TCGA solid cancer datasets.

Results

Using the breast cancer dataset as a discovery cohort we found 2988 differentially expressed lncRNAs between immune favorable and unfavorable tumors, as defined by the immunologic constant of rejection (ICR) gene signature. Mapping of the lncRNAs to a coding-non-coding network identified 127 proxy protein-coding genes that are enriched in immune-related diseases and functions. Next, we defined two distinct 20-lncRNA prognostic signatures that show a stronger effect on overall survival than the ICR signature in multiple solid cancers. Furthermore, we found a 3 lncRNA signature that demonstrated prognostic significance across 5 solid cancer types with a stronger association with clinical outcome than ICR. Moreover, this 3 lncRNA signature showed additional prognostic significance in uterine corpus endometrial carcinoma and cervical squamous cell carcinoma and endocervical adenocarcinoma as compared to ICR.

Conclusion

We identified an immune-related 3-lncRNA signature with prognostic connotation in multiple solid cancer types which performed equally well and in some cases better than the 20-gene ICR signature, indicating that it could be used as a minimal informative signature for clinical implementation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03654-7.

Zooming in on Long Non-Coding RNAs in Ewing Sarcoma Pathogenesis

Ewing sarcoma (ES) is a rare aggressive cancer of bone and soft tissue that is mainly characterized by a reciprocal chromosomal translocation. As a result, about 90% of cases express the EWS-FLI1 fusion protein that has been shown to function as an aberrant transcription factor driving sarcomagenesis. ES is the second most common malignant bone tumor in children and young adults. Current treatment modalities include dose-intensified chemo- and radiotherapy, as well as surgery. Despite these strategies, patients who present with metastasis or relapse still have dismal prognosis, warranting a better understanding of treatment resistant-disease biology in order to generate better prognostic and therapeutic tools. Since the genomes of ES tumors are relatively quiet and stable, exploring the contributions of epigenetic mechanisms in the initiation and progression of the disease becomes inevitable. The search for novel biomarkers and potential therapeutic targets of cancer metastasis and chemotherapeutic drug resistance is increasingly focusing on long non-coding RNAs (lncRNAs). Recent advances in genome analysis by high throughput sequencing have immensely expanded and advanced our knowledge of lncRNAs. They are non-protein coding RNA species with multiple biological functions that have been shown to be dysregulated in many diseases and are emerging as crucial players in cancer development. Understanding the various roles of lncRNAs in tumorigenesis and metastasis would determine eclectic avenues to establish therapeutic and diagnostic targets. In ES, some lncRNAs have been implicated in cell proliferation, migration and invasion, features that make them suitable as relevant biomarkers and therapeutic targets. In this review, we comprehensively discuss known lncRNAs implicated in ES that could serve as potential biomarkers and therapeutic targets of the disease. Though some current reviews have discussed non-coding RNAs in ES, to our knowledge, this is the first review focusing exclusively on ES-associated lncRNAs.

Long non-coding RNAs: Biogenesis, functions, and clinical significance in gastric cancer

Gastric cancer (GC) is one of the most prevalent malignant tumor types and the third leading cause of cancer-related death worldwide. Its morbidity and mortality are very high due to a lack of understanding about its pathogenesis and the slow development of novel therapeutic strategies. Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs with a length of more than 200 nt. They play crucial roles in a wide spectrum of physiological and pathological processes by regulating the expression of genes involved in proliferation, differentiation, apoptosis, cell cycle, invasion, metastasis, DNA damage, and carcinogenesis. The aberrant expression of lncRNAs has been found in various cancer types. A growing amount of evidence demonstrates that lncRNAs are involved in many aspects of GC pathogenesis, including its occurrence, metastasis, and recurrence, indicating their potential role as novel biomarkers in the diagnosis, prognosis, and therapeutic targets of GC. This review systematically summarizes the biogenesis, biological properties, and functions of lncRNAs and highlights their critical role and clinical significance in GC. This information may contribute to the development of better diagnostics and treatments for GC.

Long non-coding RNAs associated with infection and vaccine-induced immunity

The immune system responds to infection or vaccination through a dynamic and complex process that involves several molecular and cellular factors. Among these factors, long non-coding RNAs (lncRNAs) have emerged as significant players in all areas of biology, particularly in immunology. Most of the mammalian genome is transcribed in a highly regulated manner, generating a diversity of lncRNAs that impact the differentiation and activation of immune cells and affect innate and adaptive immunity. Here, we have reviewed the range of functions and mechanisms of lncRNAs in response to infectious disease, including pathogen recognition, interferon (IFN) response, and inflammation. We describe examples of lncRNAs exploited by pathogenic agents during infection, which indicate that lncRNAs are a fundamental part of the arms race between hosts and pathogens. We also discuss lncRNAs potentially implicated in vaccine-induced immunity and present examples of lncRNAs associated with the antibody response of subjects receiving Influenza or Yellow Fever vaccines. Elucidating the widespread involvement of lncRNAs in the immune system will improve our understanding of the factors affecting immune response to different pathogenic agents, to better prevent and treat disease.

Transcriptome Analysis Identifies GATA3-AS1 as a Long Noncoding RNA Associated with Resistance to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer Patients

Breast cancer is one of the leading causes of mortality in women worldwide, and neoadjuvant chemotherapy has emerged as an option for the management of locally advanced breast cancer. Extensive efforts have been made to identify new molecular markers to predict the response to neoadjuvant chemotherapy. Transcripts that do not encode proteins, termed long noncoding RNAs (lncRNAs), have been shown to display abnormal expression profiles in different types of cancer, but their role as biomarkers in response to neoadjuvant chemotherapy has not been extensively studied. Herein, lncRNA expression was profiled using RNA sequencing in biopsies from patients who subsequently showed either response or no response to treatment. The GATA3-AS1 transcript was overexpressed in the nonresponder group and was the most stable feature when performing selection in multiple random forest models. GATA3-AS1 was experimentally validated by RT-qPCR in an extended group of 68 patients. Expression analysis confirmed that GATA3-AS1 is overexpressed primarily in patients who were nonresponsive to neoadjuvant chemotherapy, with a sensitivity of 92.9%, a specificity of 75.0%, and an area under the curve of approximately 0.90, as measured by receiver operating characteristic curve analysis. The statistical model was based on luminal B-like patients and adjusted by menopausal status and phenotype (odds ratio, 37.49; 95% CI, 6.74-208.42; P = 0.001); GATA3-AS1 was established as an independent predictor of response. Thus, lncRNA GATA3-AS1 is proposed as a potential predictive biomarker of nonresponse to neoadjuvant chemotherapy.

Comprehensive analysis of LncRNAs expression profiles in an in vitro model of steatosis treated with Exendin-4

Background and aims

The hallmark of non-alcoholic fatty liver disease (NAFLD) is the excessive hepatic lipid accumulation. Currently, no pharmacotherapy exists for NAFLD. However, the glucagon-like peptide-1 receptor agonists have recently emerged as potential therapeutics. Here, we sought to identify the long non-coding RNAs (LncRNAs) associated with the steatosis improvement induced by the GLP-1R agonist Exendin-4 (Ex-4) in vitro.

Methods

Steatosis was induced in HepG2 cells with oleic acid. The transcriptomic profiling was performed using total RNA extracted from untreated, steatotic, and Ex-4-treated steatotic cells. We validated a subset of differentially expressed LncRNAs with qRT-PCR and identified the most significantly enriched cellular functions associated with the relevant LncRNAs.

Results

We confirm that Ex-4 improves steatosis in HepG2 cells. We found 379 and 180 differentially expressed LncRNAs between untreated and steatotic cells and between steatotic and Ex-4-treated steatotic cells, respectively. Interestingly, 22 upregulated LncRNAs in steatotic cells became downregulated with Ex-4 exposure, while 50 downregulated LncRNAs in steatotic cells became upregulated in the presence of Ex-4. Although some LncRNAs, such as MALAT1, H19, and NEAT1, were previously associated with NAFLD, the association of others with steatosis and the positive effect of Ex-4 is being reported for the first time. Functional enrichment analysis identified many critical pathways, including fatty acid and pyruvate metabolism, and insulin, PPAR, Wnt, TGF-β, mTOR, VEGF, NOD-like, and Toll-like receptors signaling pathways.

Conclusion

Our results suggest that LncRNAs may play essential roles in the mechanisms underlying steatosis improvement in response to GLP-1R agonists and warrant further functional studies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02885-4.

LncSEA: a platform for long non-coding RNA related sets and enrichment analysis

Long non-coding RNAs (lncRNAs) have been proven to play important roles in transcriptional processes and various biological functions. Establishing a comprehensive collection of human lncRNA sets is urgent work at present. Using reference lncRNA sets, enrichment analyses will be useful for analyzing lncRNA lists of interest submitted by users. Therefore, we developed a human lncRNA sets database, called LncSEA, which aimed to document a large number of available resources for human lncRNA sets and provide annotation and enrichment analyses for lncRNAs. LncSEA supports >40 000 lncRNA reference sets across 18 categories and 66 sub-categories, and covers over 50 000 lncRNAs. We not only collected lncRNA sets based on downstream regulatory data sources, but also identified a large number of lncRNA sets regulated by upstream transcription factors (TFs) and DNA regulatory elements by integrating TF ChIP-seq, DNase-seq, ATAC-seq and H3K27ac ChIP-seq data. Importantly, LncSEA provides annotation and enrichment analyses of lncRNA sets associated with upstream regulators and downstream targets. In summary, LncSEA is a powerful platform that provides a variety of types of lncRNA sets for users, and supports lncRNA annotations and enrichment analyses. The LncSEA database is freely accessible at http://bio.liclab.net/LncSEA/index.php.

LncRNA VINAS regulates atherosclerosis by modulating NF-κB and MAPK signaling

Long noncoding RNAs (lncRNAs) play important roles in regulating diverse cellular processes in the vessel wall, including atherosclerosis. RNA-Seq profiling of intimal lesions revealed a lncRNA, VINAS (Vascular INflammation and Atherosclerosis lncRNA Sequence), that is enriched in the aortic intima and regulates vascular inflammation. Aortic intimal expression of VINAS fell with atherosclerotic progression and rose with regression. VINAS knockdown reduced atherosclerotic lesion formation by 55% in LDL receptor-deficient (LDLR-/-) mice, independent of effects on circulating lipids, by decreasing inflammation in the vessel wall. Loss- and gain-of-function studies in vitro demonstrated that VINAS serves as a critical regulator of inflammation by modulating NF-κB and MAPK signaling pathways. VINAS knockdown decreased the expression of key inflammatory markers, such as MCP-1, TNF-α, IL-1β, and COX-2, in endothelial cells (ECs), vascular smooth muscle cells, and bone marrow-derived macrophages. Moreover, VINAS silencing decreased expression of leukocyte adhesion molecules VCAM-1, E-selectin, and ICAM-1 and reduced monocyte adhesion to ECs. DEP domain containing 4 (DEPDC4), an evolutionary conserved human ortholog of VINAS with approximately 74% homology, showed similar regulation in human and pig atherosclerotic specimens. DEPDC4 knockdown replicated antiinflammatory effects of VINAS in human ECs. These findings reveal a potentially novel lncRNA that regulates vascular inflammation, with broad implications for vascular diseases.

lncFunTK: a toolkit for functional annotation of long noncoding RNAs

Motivation

Thousands of long noncoding RNAs (lncRNAs) were newly identified from high throughput RNA-seq data. Functional annotation and prioritization of these lncRNAs for further experimental validation as well as the functional investigation is the bottleneck step for many noncoding RNA studies.

Results

Here we describe lncFunTK that can run either as standard application or webserver for this purpose. It integrates high throughput sequencing data (i.e. ChIP-seq, CLIP-seq and RNA-seq) to construct the regulatory network associated with lncRNAs. Through the network, it calculates the Functional Information Score (FIS) of each individual lncRNA for prioritizing and inferring its functions through Gene Ontology (GO) terms of neighboring genes. In addition, it also provides utility scripts to support the input data preprocessing and the parameter optimizing. We further demonstrate that lncFunTK can be widely used in various biological systems for lncRNA prioritization and functional annotation.

Availability and implementation

The lncFunTK standalone version is an open source package and freely available at http://sunlab.cpy.cuhk.edu.hk/lncfuntk under the MIT license. A webserver implementation is also available at http://sunlab.cpy.cuhk.edu.hk/lncfuntk/runlncfuntk.html.

Supplementary information

Supplementary data are available at Bioinformatics online.

Inferring disease and pathway associations of long non-coding RNAs using heterogeneous information network model

Recent findings from biological experiments demonstrate that long non-coding RNAs (lncRNAs) are actively involved in critical cellular processes and are associated with innumerable diseases. Computational prediction of lncRNA-disease association draws tremendous research attention nowadays. This paper proposes a machine learning model that predicts lncRNA-disease associations using Heterogeneous Information Network (HIN) of lncRNAs and diseases. A Support Vector Machine classifier is developed using the feature set extracted from a meta-path-based parameter, Association Index derived from the HIN. Performance of the model is validated using standard statistical metrics and it generated an AUC value of 0.87, which is better than the existing methods in the literature. Results are further validated using the recent literature and many of the predicted lncRNA-disease associations are identified as actually existing. This paper also proposes an HIN-based methodology to associate lncRNAs with pathways in which they may have biological influence. A case study on the pathway associations of four well-known lncRNAs (HOTAIR, TUG1, NEAT1, and MALAT1) has been conducted. It has been observed that many times the same lncRNA is associated with more than one biologically related pathways. Further exploration is needed to substantiate whether such lncRNAs have any role in determining the pathway interplay. The script and sample data for the model construction is freely available at http://bdbl.nitc.ac.in/LncDisPath/index.html.

Gene set analysis methods for the functional interpretation of non-mRNA data—Genomic range and ncRNA data

Gene set analysis (GSA) is one of the methods of choice for analyzing the results of current omics studies; however, it has been mainly developed to analyze mRNA (microarray, RNA-Seq) data. The following review includes an update regarding general methods and resources for GSA and then emphasizes GSA methods and tools for non-mRNA omics datasets, specifically genomic range data (ChIP-Seq, SNP and methylation) and ncRNA data (miRNAs, lncRNAs and others). In the end, the state of the GSA field for non-mRNA datasets is discussed, and some current challenges and trends are highlighted, especially the use of network approaches to face complexity issues.

Identification of key pathways regulated by a set of competitive long non-coding RNAs in oral squamous cell carcinoma

Objective

The aim of this study was to identify important pathways regulated by a set of long non-coding RNAs (lncRNAs) in oral squamous cell carcinoma (OSCC).

Methods

A lncRNA-mediated competitive endogenous RNA network (LMCN) was constructed using information on microRNA (miRNA)–mRNA interactions and lncRNA–miRNA intersections from the E-GEOD-37991 transcription profiling data in the ArrayExpress database. A random walk with restart ranking algorithm was then applied to evaluate the influences of protein-coding genes regulated by competitive lncRNAs. Pathway enrichment scores were calculated based on the propagation scores of protein-coding genes. Finally, permutation tests were used to estimate the significance of the pathways.

Results

We obtained lncRNA–mRNA interactions based on miRNAs common to both miRNA–mRNA interactions and lncRNA–miRNA intersections, and used interactions with a z-score > 0.7 to construct a LMCN. Ten lncRNAs were identified as source nodes in the LMCN, and nine pathways with enrichment scores >0.8, including ‘Cell cycle’, ‘Endocytosis’, and ‘Pathways in cancer’, were significantly enriched by these source nodes.

Conclusions

Nine significant pathways regulated by a set of competitive lncRNAs were identified in OSCC, which may play important roles in the development of OSCC via the cell cycle and endocytosis.

Risk assessment model constructed by differentially expressed lncRNAs for the prognosis of glioma

A risk assessment model was constructed using differentially expressed long non‑coding (lnc)RNAs for the prognosis of glioma. Transcriptome sequencing of the lncRNAs and mRNAs from glioma samples were obtained from the TCGA database. The samples were divided into bad and good prognosis groups based on survival time, then differently expressed lncRNAs between these two groups were screened using DEseq and edgeR packages. Multivariate Cox regression analysis was performed to establish a risk assessment system according to the weighted regression coefficient of lncRNA expression. Survival analysis and receiver operating characteristic curve were conducted for the risk assessment model. Furthermore, the co‑expression network of the screened lncRNAs was constructed, followed by the functional enrichment analysis for associated genes. A total of 117 lncRNAs were screened using edgeR and DEseq packages. Among all differently expressed lncRNAs, five lncRNAs (RP3‑503A6, LINC00940, RP11‑453M23, AC009411 and CDRT7) were identified to establish the risk assessment model. The risk assessment model demonstrated a good prognostic function with high area under the curve values in the training, validation and entire sets. The risk score was certified as an independent prognostic factor for gliomas. Multiple genes were screened to be co‑expressed with these five lncRNAs. Functional enrichment analysis demonstrated that they were involved in cytoskeleton, adhesion and Janus kinase/signal transducer and activator of transcription signaling pathway‑associated processes. The present study established a risk assessment model integrating five significantly different expressed lncRNAs, which may help to assess the prognosis of patients with glioma with increased accuracy.

Long noncoding RNAs regulate Wnt signaling during feather regeneration

Long noncoding RNAs (lncRNAs) are non-protein coding transcripts that are involved in a broad range of biological processes. Here, we examined the functional roles of lncRNAs in feather regeneration. RNA-seq profiling of the regenerating feather blastema revealed that the Wnt signaling is among the most active pathways during feather regeneration, with the Wnt ligands and their inhibitors showing distinct expression patterns. Co-expression analysis identified hundreds of lncRNAs with similar expression patterns to either the Wnt ligands (the Lwnt group) or their downstream target genes (the Twnt group). Among these, we randomly picked two lncRNAs in the Lwnt group, and three lncRNAs in the Twnt group to validate their expression and function. Members in the Twnt group regulated feather regeneration and axis formation, whereas members in the Lwnt group showed no obvious phenotype. Further analysis confirmed that the three Twnt group members inhibit Wnt signal transduction and at the same time are down-stream target genes of this pathway. Our results suggested that the feather regeneration model can be utilized to systematically annotate the functions of lncRNAs in the chicken genome.