LncExpDB: an expression database of human long non-coding RNAs

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.

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

Background

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

Scope of review

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

Major conclusions

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

Mitochondrial sequencing identifies long noncoding RNA features that promote binding to PNPase

Extranuclear localization of long noncoding RNAs (lncRNAs) is poorly understood. Based on machine learning evaluations, we propose a lncRNA-mitochondrial interaction pathway where polynucleotide phosphorylase (PNPase), through domains that provide specificity for primary sequence and secondary structure, binds nuclear-encoded lncRNAs to facilitate mitochondrial import. Using FVB/NJ mouse and human cardiac tissues, RNA from isolated subcellular compartments (cytoplasmic and mitochondrial) and cross-linked immunoprecipitate (CLIP) with PNPase within the mitochondrion were sequenced on the Illumina HiSeq and MiSeq, respectively. lncRNA sequence and structure were evaluated through supervised [classification and regression trees (CART) and support vector machines (SVM)] machine learning algorithms. In HL-1 cells, quantitative PCR of PNPase CLIP knockout mutants (KH and S1) was performed. In vitro fluorescence assays assessed PNPase RNA binding capacity and verified with PNPase CLIP. One hundred twelve (mouse) and 1,548 (human) lncRNAs were identified in the mitochondrion with Malat1 being the most abundant. Most noncoding RNAs binding PNPase were lncRNAs, including Malat1. lncRNA fragments bound to PNPase compared against randomly generated sequences of similar length showed stratification with SVM and CART algorithms. The lncRNAs bound to PNPase were used to create a criterion for binding, with experimental validation revealing increased binding affinity of RNA designed to bind PNPase compared to control RNA. The binding of lncRNAs to PNPase was decreased through the knockout of RNA binding domains KH and S1. In conclusion, sequence and secondary structural features identified by machine learning enhance the likelihood of nuclear-encoded lncRNAs binding to PNPase and undergoing import into the mitochondrion.NEW & NOTEWORTHY Long noncoding RNAs (lncRNAs) are relatively novel RNAs with increasingly prominent roles in regulating genetic expression, mainly in the nucleus but more recently in regions such as the mitochondrion. This study explores how lncRNAs interact with polynucleotide phosphorylase (PNPase), a protein that regulates RNA import into the mitochondrion. Machine learning identified several RNA structural features that improved lncRNA binding to PNPase, which may be useful in targeting RNA therapeutics to the mitochondrion.

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.

Cell Type- and Age-Specific Expression of lncRNAs across Kidney Cell Types

Key Points

Background

Accumulated evidence demonstrates that long noncoding RNAs (lncRNAs) regulate cell differentiation and homeostasis, influencing kidney aging and disease. Despite their versatility, the function of lncRNA remains poorly understood because of the lack of a reference map of lncRNA transcriptome in various cell types.

Methods

In this study, we used a targeted single-cell RNA sequencing method to enrich and characterize lncRNAs in individual cells. We applied this method to various mouse tissues, including normal and aged kidneys.

Results

Through tissue-specific clustering analysis, we identified cell type–specific lncRNAs that showed a high correlation with known cell-type marker genes. Furthermore, we constructed gene regulatory networks to explore the functional roles of differentially expressed lncRNAs in each cell type. In the kidney, we observed dynamic expression changes of lncRNAs during aging, with specific changes in glomerular cells. These cell type– and age-specific expression patterns of lncRNAs suggest that lncRNAs may have a potential role in regulating cellular processes, such as immune response and energy metabolism, during kidney aging.

Conclusions

Our study sheds light on the comprehensive landscape of lncRNA expression and function and provides a valuable resource for future analysis of lncRNAs (https://gist-fgl.github.io/sc-lncrna-atlas/).

lncRNA CDKN2B-AS1 is downregulated in patients with ventricular fibrillation in acute myocardial infarction

Ventricular fibrillation (VF) in acute myocardial infarction (AMI) is the main cause of deaths occurring in the acute phase of an ischemic event. Although it is known that genetics may play an important role in this pathology, the possible role of long non-coding RNAs (lncRNA) has never been studied. Therefore, the aim of this work is to study the expression of 10 lncRNAs in patients with and without VF in AMI. For this purpose, the expression of CDKN2B-AS1, KCNQ1OT1, LIPCAR, MALAT1, MIAT, NEAT1, SLC16A1-AS1, lnc-TK2-4:2, TNFRSF14-AS1, and UCA1 were analyzed. After the analysis and Bonferroni correction, the lncRNA CDKN2B-AS showed a statistical significance lower expression (P values of 2.514 x 10-5). In silico analysis revealed that six proteins could be related to the possible effect of lncRNA CDKN2B-AS1: AGO3, PLD4, POU4F1, ZNF26, ZNF326 and ZNF431. These in silico proteins predicted to have a low cardiac expression, although there is no literature indicating a potential relationship with VF in AMI. Thus, the lncRNA CDKN2B-AS1 shows a significant lower expression in patients with VF in AMI vs patients without VF in AMI. Literature data suggest that the role of CDKN2B1-AS is related to the miR-181a/SIRT1 pathway.

Long non-coding RNAs in cardiac hypertrophy and heart failure: functions, mechanisms and clinical prospects

The surge in reports describing non-coding RNAs (ncRNAs) has focused attention on their possible biological roles and effects on development and disease. ncRNAs have been touted as previously uncharacterized regulators of gene expression and cellular processes, possibly working to fine-tune these functions. The sheer number of ncRNAs identified has outpaced the capacity to characterize each molecule thoroughly and to reliably establish its clinical relevance; it has, nonetheless, created excitement about their potential as molecular targets for novel therapeutic approaches to treat human disease. In this Review, we focus on one category of ncRNAs - long non-coding RNAs - and their expression, functions and molecular mechanisms in cardiac hypertrophy and heart failure. We further discuss the prospects for this specific class of ncRNAs as novel targets for the diagnosis and treatment of these conditions.

DNA methylation of imprint control regions associated with Alzheimer's disease in non-Hispanic Blacks and non-Hispanic Whites

Alzheimer's disease (AD) prevalence is twice as high in non-Hispanic Blacks (NHBs) as in non-Hispanic Whites (NHWs). The objective of this study was to determine whether aberrant methylation at imprint control regions (ICRs) is associated with AD. Differentially methylated regions (DMRs) were bioinformatically identified from whole-genome bisulfite sequenced DNA derived from brain tissue of 9 AD (5 NHBs and 4 NHWs) and 8 controls (4 NHBs and 4 NHWs). We identified DMRs located within 120 regions defined as candidate ICRs in the human imprintome ( https://genome.ucsc.edu/s/imprintome/hg38.AD.Brain_track ). Eighty-one ICRs were differentially methylated in NHB-AD, and 27 ICRs were differentially methylated in NHW-AD, with two regions common to both populations that are proximal to the inflammasome gene, NLRP1, and a known imprinted gene, MEST/MESTIT1. These findings indicate that early developmental alterations in DNA methylation of regions regulating genomic imprinting may contribute to AD risk and that this epigenetic risk differs between NHBs and NHWs.

Systematic Analysis to Identify the MIR99AHG-has-miR-21-5p-EHD1 CeRNA Regulatory Network as Potential Biomarkers in Lung Cancer

Lung cancer (LC) remains an extremely lethal disease worldwide, and effective prognostic biomarkers are at top priority. With the rapid development of high-throughput sequencing and bioinformatic analysis methods, the quest to characterize cancer transcriptomes continues to move forward. However, the integrated systematic analysis of lncRNA-miRNA-mRNA regulatory network in LC is lacking. In this study, we collected samples of cancer tissues and adjacent normal tissues from patients with lung cancer and conducted transcriptome and small RNA sequencing to identify differentially expressed genes (DEGs), miRNAs (DEMs), and lncRNAs (DELs). The regulatory roles of miRNAs in LC were explained by functional analysis on DEM-targeted genes. The lncRNA-miRNA pairs, miRNA-mRNA pairs, and lncRNA-mRNA pairs were identified and combined to construct the interplay of lncRNA-miRNA-mRNA. We evaluated the prognostic value of selected lncRNA-miRNA-mRNA by Kaplan-Meier analysis. Finally, we analyzed the expression levels of selected DEM, DELs, and DEGs in lung cancer patients and healthy people to verify our findings. A total of 1492 DEGs, 12 DEMs, and 604 DELs were identified in LC patients. Based on the bioinformatic analysis and the regulatory mechanism of ceRNAs, 3 lncRNAs (GATA2-AS1, LINC00632, MIR99AHG), 1 miRNA (hsa-miR-21-5p) and 5 targeted genes (RECK, TIMP3, EHD1, RASGRP1 and ERG) were figured out first. Through further Kaplan-Meier analysis screening the prognostic value, we finally found the hub subnetwork (MIR99AHG-hsa-miR-21-5p-EHD1) by collating lncRNA-miRNA pairs, miRNA-mRNA pairs and lncRNA-mRNA pairs. As the key of ceRNA regulatory network, the expression of miRNA-21-5p in lung cancer patients was significantly higher than that in healthy people (P < 0.01), and its high expression was significantly associated with poor prognosis (P = 0.0025). Our study successfully constructed a MIR99AHG-hsa-miR-21-5p-EHD1 mutually regulatory network, suggesting the potential efficient biomarkers in LC.

Exosome lncRNA IFNG-AS1 derived from mesenchymal stem cells of human adipose ameliorates neurogenesis and ASD-like behavior in BTBR mice

BACKGROUND

The transplantation of exosomes derived from human adipose-derived mesenchymal stem cells (hADSCs) has emerged as a prospective cellular-free therapeutic intervention for the treatment of neurodevelopmental disorders (NDDs), as well as autism spectrum disorder (ASD). Nevertheless, the efficacy of hADSC exosome transplantation for ASD treatment remains to be verified, and the underlying mechanism of action remains unclear.

RESULTS

The exosomal long non-coding RNAs (lncRNAs) from hADSC and human umbilical cord mesenchymal stem cells (hUCMSC) were sequenced and 13,915 and 729 lncRNAs were obtained, respectively. The lncRNAs present in hADSC-Exos encompass those found in hUCMSC-Exos and are associated with neurogenesis. The biodistribution of hADSC-Exos in mouse brain ventricles and organoids was tracked, and the cellular uptake of hADSC-Exos was evaluated both in vivo and in vitro. hADSC-Exos promote neurogenesis in brain organoid and ameliorate social deficits in ASD mouse model BTBR T + tf/J (BTBR). Fluorescence in situ hybridization (FISH) confirmed lncRNA Ifngas1 significantly increased in the prefrontal cortex (PFC) of adult mice after hADSC-Exos intraventricular injection. The lncRNA Ifngas1 can act as a molecular sponge for miR-21a-3p to play a regulatory role and promote neurogenesis through the miR-21a-3p/PI3K/AKT axis.

CONCLUSION

We demonstrated hADSC-Exos have the ability to confer neuroprotection through functional restoration, attenuation of neuroinflammation, inhibition of neuronal apoptosis, and promotion of neurogenesis both in vitro and in vivo. The hADSC-Exos-derived lncRNA IFNG-AS1 acts as a molecular sponge and facilitates neurogenesis via the miR-21a-3p/PI3K/AKT signaling pathway, thereby exerting a regulatory effect. Our findings suggest a potential therapeutic avenue for individuals with ASD.

Pan-cancer analyses identify MIR210HG overexpression, epigenetic regulation and oncogenic role in human tumors and its interaction with the tumor microenvironment

BACKGROUND

Molecular entities showing dysregulation in multiple cancers may hold great biomarker or therapeutic potential. There is accumulating evidence that highlights the dysregulation of a long non-coding RNA, MIR210HG, in various cancers and its oncogenic role. However, a comprehensive analysis of MIR210HG expression pattern, molecular mechanisms, diagnostic or prognostic significance or evaluation of its interaction with tumor microenvironment across various cancers remains unstudied.

METHODS

A systematic pan-cancer analysis was done using multiple public databases and bioinformatic tools to study the molecular role and clinical significance of MIR210HG. We have analyzed expression patterns, genome alteration, transcriptional and epigenetic regulation, correlation with patient survival, immune infiltrates, co-expressed genes, interacting proteins, and pathways associated with MIR210HG.

RESULTS

The Pan cancer expression analysis of MIR210HG through various tumor datasets demonstrated that MIR210HG is significantly upregulated in most cancers and increased with the tumor stage in a subset of them. Furthermore, prognostic analysis revealed high MIR210HG expression is associated with poor overall and disease-free survival in specific cancer types. Genetic alteration analysis showed minimal alterations in the MIR210HG locus, indicating that overexpression in cancers is not due to gene amplification. The exploration of SNPs on MIR210HG suggested possible structural changes that may affect its interactions with the miRNAs. The correlation of MIR210HG with promoter methylation was found to be significantly negative in nature in majority of cancers depicting the possible epigenetic regulation of expression of MIR210HG. Additionally, MIR210HG showed negative correlations with immune cells and thus may have strong impact on the tumor microenvironment. Functional analysis indicates its association with hypoxia, angiogenesis, metastasis, and DNA damage repair processes. MIR210HG was found to interact with several proteins and potentially regulate chromatin modifications and transcriptional regulation.

CONCLUSIONS

A first pan-can cancer analysis of MIR210HG highlights its transcriptional and epigenetic deregulation and oncogenic role in the majority of cancers, its correlation with tumor microenvironment factors such as hypoxia and immune infiltration, and its potential as a prognostic biomarker and therapeutic target in several cancers.

LncRTPred: Predicting RNA-RNA mode of interaction mediated by lncRNA

Long non-coding RNAs (lncRNAs) play a significant role in various biological processes. Hence, it is utmost important to elucidate their functions in order to understand the molecular mechanism of a complex biological system. This versatile RNA molecule has diverse modes of interaction, one of which constitutes lncRNA-mRNA interaction. Hence, identifying its target mRNA is essential to understand the function of an lncRNA explicitly. Existing lncRNA target prediction tools mainly adopt thermodynamics approach. Large execution time and inability to perform real-time prediction limit their usage. Further, lack of negative training dataset has been a hindrance in the path of developing machine learning (ML) based lncRNA target prediction tools. In this work, we have developed a ML-based lncRNA-mRNA target prediction model- 'LncRTPred'. Here we have addressed the existing problems by generating reliable negative dataset and creating robust ML models. We have identified the non-interacting lncRNA and mRNAs from the unlabelled dataset using BLAT. It is further filtered to get a reliable set of outliers. LncRTPred provides a cumulative_model_score as the final output against each query. In terms of prediction accuracy, LncRTPred outperforms other popular target prediction protocols like LncTar. Further, we have tested its performance against experimentally validated disease-specific lncRNA-mRNA interactions. Overall, performance of LncRTPred is heavily dependent on the size of the training dataset, which is highly reflected by the difference in its performance for human and mouse species. Its performance for human species shows better as compared to that for mouse when applied on an unknown data due to smaller size of the training dataset in case of mouse compared to that of human. Availability of increased number of lncRNA-mRNA interaction data for mouse will improve the performance of LncRTPred in future. Both webserver and standalone versions of LncRTPred are available. Web server link: http://bicresources.jcbose.ac.in/zhumur/lncrtpred/index.html. Github Link: https://github.com/zglabDIB/LncRTPred.

Identification of Important Genes Associated with the Development of Atherosclerosis

Atherosclerosis is one of the most important medical problems due to its prevalence and significant contribution to the structure of temporary and permanent disability and mortality. Atherosclerosis is a complex chain of events occurring in the vascular wall over many years. Disorders of lipid metabolism, inflammation, and impaired hemodynamics are important mechanisms of atherogenesis. A growing body of evidence strengthens the understanding of the role of genetic and epigenetic factors in individual predisposition and development of atherosclerosis and its clinical outcomes. In addition, hemodynamic changes, lipid metabolism abnormalities, and inflammation are closely related and have many overlapping links in regulation. A better study of these mechanisms may improve the quality of diagnosis and management of such patients.

Computational Characterization of Undifferentially Expressed Genes with Altered Transcription Regulation in Lung Cancer

A transcriptome profiles the expression levels of genes in cells and has accumulated a huge amount of public data. Most of the existing biomarker-related studies investigated the differential expression of individual transcriptomic features under the assumption of inter-feature independence. Many transcriptomic features without differential expression were ignored from the biomarker lists. This study proposed a computational analysis protocol (mqTrans) to analyze transcriptomes from the view of high-dimensional inter-feature correlations. The mqTrans protocol trained a regression model to predict the expression of an mRNA feature from those of the transcription factors (TFs). The difference between the predicted and real expression of an mRNA feature in a query sample was defined as the mqTrans feature. The new mqTrans view facilitated the detection of thirteen transcriptomic features with differentially expressed mqTrans features, but without differential expression in the original transcriptomic values in three independent datasets of lung cancer. These features were called dark biomarkers because they would have been ignored in a conventional differential analysis. The detailed discussion of one dark biomarker, GBP5, and additional validation experiments suggested that the overlapping long non-coding RNAs might have contributed to this interesting phenomenon. In summary, this study aimed to find undifferentially expressed genes with significantly changed mqTrans values in lung cancer. These genes were usually ignored in most biomarker detection studies of undifferential expression. However, their differentially expressed mqTrans values in three independent datasets suggested their strong associations with lung cancer.

Long noncoding RNA study: Genome-wide approaches

Long noncoding RNAs (lncRNAs) have been confirmed to play a crucial role in various biological processes across several species. Though many efforts have been devoted to the expansion of the lncRNAs landscape, much about lncRNAs is still unknown due to their great complexity. The development of high-throughput technologies and the constantly improved bioinformatic methods have resulted in a rapid expansion of lncRNA research and relevant databases. In this review, we introduced genome-wide research of lncRNAs in three parts: (i) novel lncRNA identification by high-throughput sequencing and computational pipelines; (ii) functional characterization of lncRNAs by expression atlas profiling, genome-scale screening, and the research of cancer-related lncRNAs; (iii) mechanism research by large-scale experimental technologies and computational analysis. Besides, primary experimental methods and bioinformatic pipelines related to these three parts are summarized. This review aimed to provide a comprehensive and systemic overview of lncRNA genome-wide research strategies and indicate a genome-wide lncRNA research system.

A New Understanding of Long Non-Coding RNA in Hepatocellular Carcinoma-From m6A Modification to Blood Biomarkers

With recent advancements in biological research, long non-coding RNAs (lncRNAs) with lengths exceeding 200 nucleotides have emerged as pivotal regulators of gene expression and cellular phenotypic modulation. Despite initial skepticism due to their low sequence conservation and expression levels, their significance in various biological processes has become increasingly apparent. We provided an overview of lncRNAs and discussed their defining features and modes of operation. We then explored their crucial function in the hepatocarcinogenesis process, elucidating their complex involvement in hepatocellular carcinoma (HCC). The influential role of lncRNAs within the HCC tumor microenvironment is emphasized, illustrating their potential as key modulators of disease dynamics. We also investigated the significant influence of N6-methyladenosine (m6A) modification on lncRNA function in HCC, enhancing our understanding of both their roles and their upstream regulators. Additionally, the potential of lncRNAs as promising biomarkers was discussed in liver cancer diagnosis, suggesting a novel avenue for future research and clinical application. Finally, our work underscored the dual potential of lncRNAs as both contributors to HCC pathogenesis and innovative tools for its diagnosis. Existing challenges and prospective trajectories in lncRNA research are also discussed, emphasizing their potential in advancing liver cancer research.

Functional knockout of long non-coding RNAs with genome editing

An effective loss-of-function study is necessary to investigate the biological function of long non-coding RNA (lncRNA). Various approaches are available, including RNA silencing, antisense oligos, and CRISPR-based genome editing. CRISPR-based genome editing is the most widely used for inactivating lncRNA function at the genomic level. Knocking out the lncRNA function can be achieved by removing the promoter and the first exon (PE1), introducing pre-termination poly(A) signals, or deleting the entire locus, unlike frameshift strategies used for messenger RNA (mRNA). However, the intricate genomic interplay between lncRNA and neighbor genes makes it challenging to interpret lncRNA function accurately. This article discusses the advantages and disadvantages of each lncRNA knockout method and envisions the potential future directions to facilitate lncRNA functional study.

The lncRNA expression profile signature of leukemia stem cells is altered upon PI3K/mTOR inhibition: an in vitro and in silico study

Genetic and/or epigenetic alterations in hematopoietic stem cells (HSCs) contribute to leukemia stem cell (LSC) formation. We aimed to identify alterations in the lncRNA expression profile signature of LSCs upon inhibition of PI3K/Akt/mTOR signaling, which provides selective advantages to LSCs. We also aimed to elucidate the potential interaction networks and functions of differentially expressed lncRNAs (DELs). We suppressed PI3K/Akt/mTOR signaling in LSC and HSC cell-lines by specific PI3K/mTOR dual-inhibitor (VS-5584) and confirmed the inhibition by antibody-array. We defined DELs by qRT-PCR. Increased SRA, ZEB2-AS1, antiPeg11, DLX6-AS, SNHG4, and decreased H19, PCGEM1, CAR-Intergenic-10, L1PA16, IGF2AS, and SNHG5 levels (|log2fold-change|>5) were strictly associated with PI3K/Akt/mTOR pathway inhibition in LSC. We performed in silico analyses for DELs. ZEB2-AS1 was found to be specifically expressed in normal bone marrow and predominantly lower in leukemic cell-lines. Three sub-clusters were identified for DELs and they were associated with "abnormality of multiple cell lineages in the bone marrow." DELs were most highly enriched for "glucuronidation" Reactome pathway and "ascorbate and aldarate metabolism" and "inositol phosphate metabolism" KEGG pathways. Transcription factors, MBD4, NANOG, PAX6, RELA, CEBPB, and CEBPA were predicted to be associated with the DEL profile. SRA was predicted to interact with CREB1, RARA, and PPARA. The possible DELs' targets were predicted to form six ontological groups, be highly enriched for phosphoprotein, and be involved in "PPAR signaling pathway" and "ChREBP regulation by carbohydrates and cAMP." These results will help to elucidate the roles of lncRNAs in the mechanisms that provide selective advantages to leukemia stem cells.

Integrated analysis of copy number variation-associated lncRNAs identifies candidates contributing to the etiologies of congenital kidney anomalies

Congenital anomalies of the kidney and urinary tract (CAKUT) are disorders resulting from defects in the development of the kidneys and their outflow tract. Copy number variations (CNVs) have been identified as important genetic variations leading to CAKUT, whereas most CAKUT-associated CNVs cannot be attributed to a specific pathogenic gene. Here we construct coexpression networks involving long noncoding RNAs (lncRNAs) within these CNVs (CNV-lncRNAs) using human kidney developmental transcriptomic data. The results show that CNV-lncRNAs encompassed in recurrent CAKUT associated CNVs have highly correlated expression with CAKUT genes in the developing kidneys. The regulatory effects of two hub CNV-lncRNAs (HSALNG0134318 in 22q11.2 and HSALNG0115943 in 17q12) in the module most significantly enriched in known CAKUT genes (CAKUT_sig1, P = 1.150 × 10^-6) are validated experimentally. Our results indicate that the reduction of CNV-lncRNAs can downregulate CAKUT genes as predicted by our computational analyses. Furthermore, knockdown of HSALNG0134318 would downregulate HSALNG0115943 and affect kidney development related pathways. The results also indicate that the CAKUT_sig1 module has function significance involving multi-organ development. Overall, our findings suggest that CNV-lncRNAs play roles in regulating CAKUT genes, and the etiologies of CAKUT-associated CNVs should take account of effects on the noncoding genome.

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

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

N6-methyladenosine modified LINC00901 promotes pancreatic cancer progression through IGF2BP2/MYC axis

Accumulating evidence indicates that RNA methylation at N6-methyladenosine (m6A) plays an important regulatory role in gene expression and aberrant mRNA m6A modification is often associated with a variety of cancers. However, little is known whether and how m6A-modification impacts long non-coding RNA (lncRNA) and lncRNA-mediated tumorigenesis, particularly in pancreatic ductal adenocarcinoma (PDAC). In the present study, we report that a previously uncharacterized lncRNA, LINC00901, promotes pancreatic cancer cell growth and invasion and moreover, LINC00901 is subject to m6A modification which regulates its expression. In this regard, YTHDF1 serves as a reader for the m6A modified LINC00901 and downregulates the LINC00901 level. Notably, two conserved m6A sites in LINC00901 are critical to the recognition of LINC00901 by YTHDF1. Finally, RNA sequencing (RNA-seq) and gene function analysis revealed that LINC00901 positively regulates MYC through upregulation of IGF2BP2, a known RNA binding protein that can enhance MYC mRNA stability. Together, our results suggest that there is a LINC00901-IGF2BP2-MYC axis through which LINC00901 promotes PDAC progression in an m6A dependent manner.

Copy number variation-associated lncRNAs may contribute to the etiologies of congenital heart disease

Copy number variations (CNVs) have long been recognized as pathogenic factors for congenital heart disease (CHD). Few CHD associated CNVs could be interpreted as dosage effect due to disruption of coding sequences. Emerging evidences have highlighted the regulatory roles of long noncoding RNAs (lncRNAs) in cardiac development. Whereas it remains unexplored whether lncRNAs within CNVs (CNV-lncRNAs) could contribute to the etiology of CHD associated CNVs. Here we constructed coexpression networks involving CNV-lncRNAs within CHD associated CNVs and protein coding genes using the human organ developmental transcriptomic data, and showed that CNV-lncRNAs within 10 of the non-syndromic CHD associated CNVs clustered in the most significant heart correlated module, and had highly correlated coexpression with multiple key CHD genes. HSALNG0104472 within 15q11.2 region was identified as a hub CNV-lncRNA with heart-biased expression and validated experimentally. Our results indicated that HSALNG0104472 should be a main effector responsible for cardiac defects of 15q11.2 deletion through regulating cardiomyocytes differentiation. Our findings suggested that CNV-lncRNAs could potentially contribute to the pathologies of a maximum proportion of 68.4% (13/19) of non-syndromic CHD associated CNVs. These results indicated that explaining the pathogenesis of CHD associated CNVs should take account of the noncoding regions.

Data resources and computational methods for lncRNA-disease association prediction

Increasing interest has been attracted in deciphering the potential disease pathogenesis through lncRNA-disease association (LDA) prediction, regarding to the diverse functional roles of lncRNAs in genome regulation. Whilst, computational models and algorithms benefit systematic biology research, even facilitate the classical biological experimental procedures. In this review, we introduce representative diseases associated with lncRNAs, such as cancers, cardiovascular diseases, and neurological diseases. Current publicly available resources related to lncRNAs and diseases have also been included. Furthermore, all of the 64 computational methods for LDA prediction have been divided into 5 groups, including machine learning-based methods, network propagation-based methods, matrix factorization- and completion-based methods, deep learning-based methods, and graph neural network-based methods. The common evaluation methods and metrics in LDA prediction have also been discussed. Finally, the challenges and future trends in LDA prediction have been discussed. Recent advances in LDA prediction approaches have been summarized in the GitHub repository at https://github.com/sheng-n/lncRNA-disease-methods.

Seven non-differentially expressed 'dark biomarkers' show transcriptional dysregulation in chronic lymphocytic leukemia

Aim: Transcriptional regulation is actively involved in the onset and progression of various diseases. This study used the feature-engineering approach model-based quantitative transcription regulation to quantitatively measure the correlation between mRNA and transcription factors in a reference dataset of chronic lymphocytic leukemia (CLL) transcriptomes. Methods: A comprehensive investigation of transcriptional regulation changes in CLL was conducted using 973 samples in six independent datasets. Results & conclusion: Seven mRNAs were detected to have significantly differential model-based quantitative transcription regulation values but no differential expression between CLL patients and controls. We called these genes 'dark biomarkers' because their original expression levels did not show differential changes in the CLL patients. The overlapping lncRNAs might have contributed their transcripts to the expression miscalculations of these dark biomarkers.

Single-Cell Transcriptome Analysis Reveals Paraspeckles Expression in Osteosarcoma Tissues

Nuclear paraspeckles are subnuclear bodies contracted by nuclear-enriched abundant transcript 1 (NEAT1) long non-coding RNA, localised in the interchromatin space of mammalian cell nuclei. Paraspeckles have been critically involved in tumour progression, metastasis and chemoresistance. To this date, there are limited findings to suggest that paraspeckles, NEAT1 and heterogeneous nuclear ribonucleoproteins (hnRNPs) directly or indirectly play roles in osteosarcoma progression. Herein, we analysed NEAT1, paraspeckle proteins (SFPQ, PSPC1 and NONO) and hnRNP members (HNRNPK, HNRNPM, HNRNPR and HNRNPD) gene expression in 6 osteosarcoma tumour tissues using the single-cell RNA-sequencing method. The normalised data highlighted that the paraspeckles transcripts were highly abundant in osteoblastic OS cells, except NEAT1, which was highly expressed in myeloid cell 1 and 2 subpopulations.

GAE-LGA: integration of multi-omics data with graph autoencoders to identify lncRNA-PCG associations

Long non-coding RNAs (lncRNAs) can disrupt the biological functions of protein-coding genes (PCGs) to cause cancer. However, the relationship between lncRNAs and PCGs remains unclear and difficult to predict. Machine learning has achieved a satisfactory performance in association prediction, but to our knowledge, it is currently less used in lncRNA-PCG association prediction. Therefore, we introduce GAE-LGA, a powerful deep learning model with graph autoencoders as components, to recognize potential lncRNA-PCG associations. GAE-LGA jointly explored lncRNA-PCG learning and cross-omics correlation learning for effective lncRNA-PCG association identification. The functional similarity and multi-omics similarity of lncRNAs and PCGs were accumulated and encoded by graph autoencoders to extract feature representations of lncRNAs and PCGs, which were subsequently used for decoding to obtain candidate lncRNA-PCG pairs. Comprehensive evaluation demonstrated that GAE-LGA can successfully capture lncRNA-PCG associations with strong robustness and outperformed other machine learning-based identification methods. Furthermore, multi-omics features were shown to improve the performance of lncRNA-PCG association identification. In conclusion, GAE-LGA can act as an efficient application for lncRNA-PCG association prediction with the following advantages: It fuses multi-omics information into the similarity network, making the feature representation more accurate; it can predict lncRNA-PCG associations for new lncRNAs and identify potential lncRNA-PCG associations with high accuracy.

lncRNASNP v3: an updated database for functional variants in long non-coding RNAs

Long non-coding RNAs (lncRNAs) act as versatile regulators of many biological processes and play vital roles in various diseases. lncRNASNP is dedicated to providing a comprehensive repository of single nucleotide polymorphisms (SNPs) and somatic mutations in lncRNAs and their impacts on lncRNA structure and function. Since the last release in 2018, there has been a huge increase in the number of variants and lncRNAs. Thus, we updated the lncRNASNP to version 3 by expanding the species to eight eukaryotic species (human, chimpanzee, pig, mouse, rat, chicken, zebrafish, and fruitfly), updating the data and adding several new features. SNPs in lncRNASNP have increased from 11 181 387 to 67 513 785. The human mutations have increased from 1 174 768 to 2 387 685, including 1 031 639 TCGA mutations and 1 356 046 CosmicNCVs. Compared with the last release, updated and new features in lncRNASNP v3 include (i) SNPs in lncRNAs and their impacts on lncRNAs for eight species, (ii) SNP effects on miRNA-lncRNA interactions for eight species, (iii) lncRNA expression profiles for six species, (iv) disease & GWAS-associated lncRNAs and variants, (v) experimental & predicted lncRNAs and drug target associations and (vi) SNP effects on lncRNA expression (eQTL) across tumor & normal tissues. The lncRNASNP v3 is freely available at http://gong_lab.hzau.edu.cn/lncRNASNP3/.

LncBook 2.0: integrating human long non-coding RNAs with multi-omics annotations

LncBook, a comprehensive resource of human long non-coding RNAs (lncRNAs), has been used in a wide range of lncRNA studies across various biological contexts. Here, we present LncBook 2.0 (https://ngdc.cncb.ac.cn/lncbook), with significant updates and enhancements as follows: (i) incorporation of 119 722 new transcripts, 9632 new genes, and gene structure update of 21 305 lncRNAs; (ii) characterization of conservation features of human lncRNA genes across 40 vertebrates; (iii) integration of lncRNA-encoded small proteins; (iv) enrichment of expression and DNA methylation profiles with more biological contexts and (v) identification of lncRNA-protein interactions and improved prediction of lncRNA-miRNA interactions. Collectively, LncBook 2.0 accommodates a high-quality collection of 95 243 lncRNA genes and 323 950 transcripts and incorporates their abundant annotations at different omics levels, thereby enabling users to decipher functional significance of lncRNAs in different biological contexts.

RiceNCexp: a rice non-coding RNA co-expression atlas based on massive RNA-seq and small-RNA seq data

Non-coding RNAs (ncRNAs) play important roles in regulating expression of protein-coding genes. Although gene expression databases have emerged in a timely manner, a comprehensive expression database for ncRNAs is still lacking. Herein, we constructed a rice ncRNA co-expression atlas (RiceNCexp), based on 491 RNA-seq and 274 small RNA (sRNA)-seq datasets. RiceNCexp hosts four types of ncRNAs, namely lncRNAs, PHAS genes, miRNAs, and phasiRNAs. RiceNCexp provides comprehensive expression information for rice ncRNAs in 22 tissues/organs, an efficient tau-based mining tool for tissue-specific ncRNAs, and the robust co-expression analysis among ncRNAs or between ncRNAs and protein-coding genes, based on 116 pairs of RNA-seq and sRNA-seq libraries from the same experiments. In summary, RiceNCexp is a user-friendly and comprehensive rice ncRNA co-expression atlas and can be freely accessed at https://cbi.njau.edu.cn/RiceNCexp/.

HIV-1 Tat and cocaine impact astrocytic energy reservoirs and epigenetic regulation by influencing the LINC01133-hsa-miR-4726-5p-NDUFA9 axis

Clinical research has proven that HIV-positive (HIV+) individuals with cocaine abuse show behavioral and neurocognitive disorders. Noncoding RNAs (ncRNAs), such as long ncRNAs (lncRNAs) and microRNAs (miRNAs), are known to regulate gene expression in the contexts of HIV infection and drug abuse. However, there are no specific lncRNA or miRNA biomarkers associated with HIV-1 Transactivator of transcription protein (Tat) and cocaine coexposure. In the central nervous system (CNS), astrocytes are the primary regulators of energy metabolism, and impairment of the astrocytic energy supply can trigger neurodegeneration. The aim of this study was to uncover the roles of lncRNAs and miRNAs in the regulation of messenger RNA (mRNA) targets affected by HIV infection and cocaine abuse. Integrative bioinformatics analysis revealed altered expression of 10 lncRNAs, 10 miRNAs, and 4 mRNA/gene targets in human primary astrocytes treated with cocaine and HIV-1 Tat. We assessed the alterations in the expression of two miRNAs, hsa-miR-2355 and hsa-miR-4726-5p; four lncRNAs, LINC01133, H19, HHIP-AS1, and NOP14-AS1; and four genes, NDUFA9, KYNU, HKDC1, and LIPG. The results revealed interactions in the LINC01133-hsa-miR-4726-5p-NDUFA9 axis that may eventually help us understand cocaine- and HIV-1 Tat-induced astrocyte dysfunction that may ultimately result in neurodegeneration.

Targeting HOTAIRM1 Ameliorates Glioblastoma by Disrupting Mitochondrial Oxidative Phosphorylation and Serine Metabolism

Serine hydroxymethyltransferase 2 (SHMT2), which catalyzes the conversion of serine to glycine and one-carbon transfer reactions in mitochondria, is significantly upregulated in glioblastoma (GBM). However, the mechanism by which the stability of SHMT2 gene expression is maintained to drive GBM tumorigenesis has not been clarified. Herein, through microarray screening, we identified that HOXA Transcript Antisense RNA, Myeloid-Specific 1 (HOTAIRM1) modulates the SHMT2 level in various GBM cell lines. Serine catabolism and mitochondrial oxidative phosphorylation activities were decreased by HOTAIRM1 inhibition. Mechanistically, according to our mass spectrometry and eCLIP-seq results, HOTAIRM1 can bind to PTBP1 and IGF2BP2. Furthermore, HOTAIRM1 maintains the stability of SHMT2 by promoting the recognition of an m⁶A site and the interaction of PTBP1/IGF2BP2 with SHMT2 mRNA. The stability of HOTAIRM1 can also be enhanced and results in positive feedback regulation to support the progression of GBM. Thus, targeting HOTAIRM1 could be a promising metabolic therapy for GBM.

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HOTAIRM1 regulates mitochondrial activity in GBM

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The target genes of HOTAIRM1 and the interacting RBPs were screened and identified

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SHMT2 mRNA has an m⁶A site that can be recognized by IGF2BP2

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HOTAIRM1 regulates the stability of SHMT2 by binding to PTBP1 and IGF2BP2

Identification and Validation of lncRNA-SNHG17 in Lung Adenocarcinoma: A Novel Prognostic and Diagnostic Indicator

Background

Lung cancer has the highest death rate among cancers globally. Accumulating evidence has indicated that cancer-related inflammation plays an important role in the initiation and progression of lung cancer. However, the prognosis, immunological role, and associated regulation axis of inflammatory response-related gene (IRRGs) in non-small-cell lung cancer (NSCLC) remains unclear.

Methods

In this study, we perform comprehensive bioinformatics analysis and constructed a prognostic inflammatory response-related gene (IRRGs) and related competing endogenous RNA (ceRNA) network. We also utilized the Pearson’s correlation analysis to determine the correlation between IRRGs expression and tumor mutational burden (TMB), microsatellite instability (MSI), tumor-immune infiltration, and the drug sensitivity in NSCLC. Growth curve and Transwell assay used to verify the function of SNHG17 on NSCLC progression.

Results

First, we found that IRRGs were significantly upregulated in lung cancer, and its high expression was correlated with poor prognosis; high expression of IRRGs was significantly correlated with the tumor stage and poor prognosis in lung cancer patients. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment indicated that these IRRGs are mainly involved in the inflammatory and immune response-related signaling pathway in the progression of NSCLC. We utilized 10 prognostic-related genes to construct a prognostic IRRGs model that could predict the overall survival of lung adenocarcinoma (LUAD) patients possessing high specificity and accuracy. Our evidence demonstrated that IRRGs expression was significantly correlated with the TMB, MSI, immune-cell infiltration, and diverse cancer-related drug sensitivity. Finally, we identified the upstream regulatory axis of IRRGs in NSCLC, namely, lncRNA MIR503HG/SNHG17/miR-330-3p/regulatory axis. Finally, knockdown of SNHG17 expression inhibited lung adenocarcinoma (LUAD) cell proliferation and migration. Our findings confirmed that SNHG17 is a novel oncogenic lncRNA and may be a biomarker for the prognosis and diagnosis of LUAD.

Conclusion

DNA hypomethylation/lncRNA MIR503HG/SNHG17/microRNA-330-3p/regulatory axis may be a valuable biomarker for prognosis and is significantly correlated with immune cell infiltration in lung cancer.

LINC00514 facilitates cell proliferation, migration, invasion, and epithelial-mesenchymal transition in non-small cell lung cancer by acting on the Wnt/β-catenin signaling pathway

The long non-coding RNA (lncRNA) LINC00514 was identified to play an essential oncogenic function in different human cancers, but its effects in non-small cell lung cancer (NSCLC) are yet to be elucidated. In this study, we evaluated the function of LINC00514 in NSCLC. LINC00514 expression and prognosis in NSCLC were analyzed using qRT-PCR and online bioinformatic tools. The bioeffects of LINC0514 in NSCLC cells were examined using cell counting kit-8, colony formation, and transwell assays. Western blotting was used to measure the expression of the target proteins. The LINC00514 regulation of the Wnt/β-catenin signaling pathway was assessed using a specific agonist (LiCl) and luciferase reporter assay. We found that LINC00514 expression was elevated in NSCLC cells and clinical samples and that increased LINC00514 expression predicted poorer patient prognosis. Silencing LINC00514 suppresses proliferation, migration, and invasion of NSCLC cells. Downregulation of LINC00514 inhibited Wnt/β-catenin signaling and epithelial-mesenchymal transition (EMT). Moreover, suppression of the biological phenotypes of NSCLC cells induced by LINC00514 gene silencing was restored after LiCl treatment. Finally, we found that silencing LINC00514 attenuated the growth of xenograft tumors in vivo. Altogether, this study provides the latest convincing evidence that LINC00514 facilitates the malignant biological behavior of NSCLC cells through activation of the Wnt/β-catenin pathway, which might offer a beneficial approach for the treatment of NSCLC.

Precision Oncology: Artificial Intelligence and DNA Methylation Analysis of Circulating Cell-Free DNA for Lung Cancer Detection

Background

Lung cancer (LC) is a leading cause of cancer-deaths globally. Its lethality is due in large part to the paucity of accurate screening markers. Precision Medicine includes the use of omics technology and novel analytic approaches for biomarker development. We combined Artificial Intelligence (AI) and DNA methylation analysis of circulating cell-free tumor DNA (ctDNA), to identify putative biomarkers for and to elucidate the pathogenesis of LC.

Methods

Illumina Infinium MethylationEPIC BeadChip array analysis was used to measure cytosine (CpG) methylation changes across the genome in LC. Six different AI platforms including support vector machine (SVM) and Deep Learning (DL) were used to identify CpG biomarkers and for LC detection. Training set and validation sets were generated, and 10-fold cross validation performed. Gene enrichment analysis using g:profiler and GREAT enrichment was used to elucidate the LC pathogenesis.

Results

Using a stringent GWAS significance threshold, p-value <5x10^-8, we identified 4389 CpGs (cytosine methylation loci) in coding genes and 1812 CpGs in non-protein coding DNA regions that were differentially methylated in LC. SVM and three other AI platforms achieved an AUC=1.00; 95% CI (0.90-1.00) for LC detection. DL achieved an AUC=1.00; 95% CI (0.95-1.00) and 100% sensitivity and specificity. High diagnostic accuracies were achieved with only intragenic or only intergenic CpG loci. Gene enrichment analysis found dysregulation of molecular pathways involved in the development of small cell and non-small cell LC.

Conclusion

Using AI and DNA methylation analysis of ctDNA, high LC detection rates were achieved. Further, many of the genes that were epigenetically altered are known to be involved in the biology of neoplasms in general and lung cancer in particular.

FibroDB: Expression Analysis of Protein-Coding and Long Non-Coding RNA Genes in Fibrosis

Most long non-coding RNAs (lncRNAs) are expressed at lower levels than protein-coding genes and their expression is often restricted to specific cell types, certain time points during development, and various stress and disease conditions, respectively. To revisit this long-held concept, we focused on fibroblasts, a common cell type in various organs and tissues. Using fibroblasts and changes in their expression profiles during fibrosis as a model system, we show that the overall expression level of lncRNA genes is significantly lower than that of protein-coding genes. Furthermore, we identified lncRNA genes whose expression is upregulated during fibrosis. Using dermal fibroblasts as a model, we performed loss-of-function experiments and show that the knockdown of the lncRNAs LINC00622 and LINC01711 result in gene expression changes associated with cellular and inflammatory responses, respectively. Since there are no lncRNA databases focused on fibroblasts and fibrosis, we built a web application, FibroDB, to further promote functional and mechanistic studies of fibrotic lncRNAs.

LncPep: A Resource of Translational Evidences for lncRNAs

Long noncoding RNAs (lncRNAs) are a type of transcript that is >200 nucleotides long with no protein-coding capacity. Accumulating studies have suggested that lncRNAs contain open reading frames (ORFs) that encode peptides. Although several noncoding RNA-encoded peptide-related databases have been developed, most of them display only a small number of experimentally validated peptides, and resources focused on lncRNA-encoded peptides are still lacking. We used six types of evidence, coding potential assessment tool (CPAT), coding potential calculator v2.0 (CPC2), N6-methyladenosine modification of RNA sites (m6A), Pfam, ribosome profiling (Ribo-seq), and translation initiation sites (TISs), to evaluate the coding potential of 883,804 lncRNAs across 39 species. We constructed a comprehensive database of lncRNA-encoded peptides, LncPep (http://www.shenglilabs.com/LncPep/). LncPep provides three major functional modules: 1) user-friendly searching/browsing interface, 2) prediction and BLAST modules for exploring novel lncRNAs and peptides, and 3) annotations for lncRNAs, peptides and supporting evidence. Taken together, LncPep is a user-friendly and convenient platform for discovering and investigating peptides encoded by lncRNAs.

Knockdown of long non-coding RNA SNHG8 suppresses the progression of esophageal cancer by regulating miR-1270/BACH1 axis

The emerging evidence showed that lncRNAs (long non-coding RNAs) could regulate the progression and affect the malignant behaviors of cancers. LncRNA SNHG8 (small nucleolar RNA host gene 8) has been reported to participate in most cancers development. Here in this study, the role of lncRNA SNHG8 in esophageal cancer was uncovered by a series of functional experiments. The expression pattern of SNHG8 in tumor tissues or cells was first detected by qRT-PCR. Using a lentivirus knockdown shRNA is to repress the expression of SNHG8. Subsequently, the in vitro and in vivo experiments were utilized to evaluate whether the malignant behaviors of esophageal cancer were influenced by knockdown SNHG8. The results indicated that lncRNA SNHG8 should be a cancer-promoting factor with a relatively high expression level in esophageal cancer. Moreover, knockdown SNHG8 inhibited the cell viability and induced cell apoptosis in KYSE30 and TE-1 cells. In addition, based on the results of the binding site analysis and the luciferase reporter system, SNHG8 functions by the miR-1270/BACH1 axis. The follow-up experiments verified that lncRNA SNHG8 could down-regulate the expression of miR-1270 to increase the BACH1 expression. Finally, we confirmed that knockdown SNHG8 retarded the progression of esophageal cancer with a xenograft model. To sum up, our findings suggested that lncRNA SNHG8 is a cancer-promoting factor in esophageal cancer. Knockdown SNHG8 could suppress the progression of esophageal cancer, which implies SNHG8 could be used as a therapeutic target in esophageal cancer.

lncRNAfunc: a knowledgebase of lncRNA function in human cancer

The long non-coding RNAs associating with other molecules can coordinate several physiological processes and their dysfunction can impact diverse human diseases. To date, systematic and intensive annotations on diverse interaction regulations of lncRNAs in human cancer were not available. Here, we built lncRNAfunc, a knowledgebase of lncRNA function in human cancer at https://ccsm.uth.edu/lncRNAfunc, aiming to provide a resource and reference for providing therapeutically targetable lncRNAs and intensive interaction regulations. To do this, we collected 15 900 lncRNAs across 33 cancer types from TCGA. For individual lncRNAs, we performed multiple interaction analyses of different biomolecules including DNA, RNA, and protein levels. Our intensive studies of lncRNAs provide diverse potential mechanisms of lncRNAs that regulate gene expression through binding enhancers and 3'-UTRs of genes, competing for miRNA binding sites with mRNAs, recruiting the transcription factors to gene promoters. Furthermore, we investigated lncRNAs that potentially affect the alternative splicing events through interacting with RNA binding Proteins. We also performed multiple functional annotations including cancer stage-associated lncRNAs, RNA A-to-I editing event-associated lncRNAs, and lncRNA expression quantitative trait loci. lncRNAfunc is a unique resource for cancer research communities to help better understand potential lncRNA regulations and therapeutic lncRNA targets.

α-Synuclein antisense transcript SNCA-AS1 regulates synapses- and aging-related genes suggesting its implication in Parkinson's disease

SNCA protein product, α‐synuclein, is widely renowned for its role in synaptogenesis and implication in both aging and Parkinson's disease (PD), but research efforts are still needed to elucidate its physiological functions and mechanisms of regulation. In this work, we aim to characterize SNCA‐AS1, antisense transcript to the SNCA gene, and its implications in cellular processes. The overexpression of SNCA‐AS1 upregulates both SNCA and α‐synuclein and, through RNA‐sequencing analysis, we investigated the transcriptomic changes of which both genes are responsible. We highlight how they impact neurites' extension and synapses' biology, through specific molecular signatures. We report a reduced expression of markers associated with synaptic plasticity, and we specifically focus on GABAergic and dopaminergic synapses, for their relevance in aging processes and PD, respectively. A reduction in SNCA‐AS1 expression leads to the opposite effect. As part of this signature is co‐regulated by the two genes, we discriminate between functions elicited by genes specifically altered by SNCA‐AS1 or SNCA's overexpression, observing a relevant role for SNCA‐AS1 in synaptogenesis through a shared molecular signature with SNCA. We also highlight how numerous deregulated pathways are implicated in aging‐related processes, suggesting that SNCA‐AS1 could be a key player in cellular senescence, with implications for aging‐related diseases. Indeed, the upregulation of SNCA‐AS1 leads to alterations in numerous PD‐specific genes, with an impact highly comparable to that of SNCA's upregulation. Our results show that SNCA‐AS1 elicits its cellular functions through the regulation of SNCA, with a specific modulation of synaptogenesis and senescence, presenting implications in PD.

exoRBase 2.0: an atlas of mRNA, lncRNA and circRNA in extracellular vesicles from human biofluids

Extracellular vesicles (EVs) are small membranous vesicles that contain an abundant cargo of different RNA species with specialized functions and clinical implications. Here, we introduce an updated online database (http://www.exoRBase.org), exoRBase 2.0, which is a repository of EV long RNAs (termed exLRs) derived from RNA-seq data analyses of diverse human body fluids. In exoRBase 2.0, the number of exLRs has increased to 19 643 messenger RNAs (mRNAs), 15 645 long non-coding RNAs (lncRNAs) and 79 084 circular RNAs (circRNAs) obtained from ∼1000 human blood, urine, cerebrospinal fluid (CSF) and bile samples. Importantly, exoRBase 2.0 not only integrates and compares exLR expression profiles but also visualizes the pathway-level functional changes and the heterogeneity of origins of circulating EVs in the context of different physiological and pathological conditions. Our database provides an attractive platform for the identification of novel exLR signatures from human biofluids that will aid in the discovery of new circulating biomarkers to improve disease diagnosis and therapy.

Combination of resveratrol and BIBR1532 inhibits proliferation of colon cancer cells by repressing expression of LncRNAs

Colorectal cancer (CRC) is the third most common cancer worldwide. The development of tumor drug resistance is observed in the treatment of CRC. Combinations of anticancer agents are attracting considerable interest in order to overcome drug resistance in CRC. This study aims to investigate the effect of resveratrol and BIBR1532, either alone or in combination, on the cell viability as well as on expression of long non-coding RNAs (LncRNAs) for HT-29 colon adenocarcinoma cells. The cytotoxic effects of resveratrol and BIBR1532 on HT-29 cells were determined using WST-1 test. Flow cytometry was used to determine apoptotic cell death after treatments. Real-Time PCR was used to identify expression of LncRNAs after treatments. LncExpDB and GEPIA2 were used to evaluate expression profiles of LncRNAs, whose expression levels were decreased in HT-29 cells after treatments, in normal tissues and colon adenocarcinoma tumors. IC₅₀ concentrations of BIBR1532 and resveratrol were found to be 50.81 μM at 48 h and 86.23 μM at 72 h, respectively. Combination index value was 1.07617. BIBR1532, resveratrol, or their combination reduced the cell viability of HT-29 cells. CCAT1, CRNDE, HOTAIR, PCAT1, PVT1, SNHG16 were down-regulated after treatments. In silico analysis revealed that LncRNAs whose expression levels were decreased after treatments were associated with CRC. Resveratrol, BIBR1532, or their combination may have anti-proliferative effect on colorectal cancer cells through repressing expression of LncRNAs that are involved in progression of CRC.

LncRNAWiki 2.0: a knowledgebase of human long non-coding RNAs with enhanced curation model and database system

LncRNAWiki, a knowledgebase of human long non-coding RNAs (lncRNAs), has been rapidly expanded by incorporating more experimentally validated lncRNAs. Since it was built based on MediaWiki as its database system, it fails to manage data in a structured way and is ineffective to support systematic exploration of lncRNAs. Here we present LncRNAWiki 2.0 (https://ngdc.cncb.ac.cn/lncrnawiki), which is significantly improved with enhanced database system and curation model. In LncRNAWiki 2.0, all contents are organized in a structured manner powered by MySQL/Java and curators are able to submit/edit annotations based on the curation model that includes a wider range of annotation items. Moreover, it is equipped with popular online tools to help users identify lncRNAs with potentially important functions, and provides more user-friendly web interfaces to facilitate data curation, retrieval and visualization. Consequently, LncRNAWiki 2.0 incorporates a total of 2512 lncRNAs and 106 242 associations for disease, function, drug, interacting partner, molecular signature, experimental sample, CRISPR design, etc., thus providing a comprehensive and up-to-date resource of functionally annotated lncRNAs in human.

PRNCR1: a long non-coding RNA with a pivotal oncogenic role in cancer

Long non-coding RNAs (lncRNAs) have been gaining importance in the field of cancer research in recent years. PRNCR1 (prostate cancer-associated non-coding RNA1) is a 12.7 kb, intron-less lncRNA found to play an oncogenic role in malignancy of diverse organs including prostate, breast, lung, oral cavity, colon and rectum. Single-nucleotide polymorphisms (SNPs) of PRNCR1 locus have been found to be associated with cancer susceptibility in different populations. In this review, an attempt has been made for the first time to summarize all sorts of available data on PRNCR1 to date from relevant databases (GeneCard, LncExpDB, Ensembl genome browser, and PubMed). As functional roles of PRNCR1, miRNA (microRNA) sponging was mostly highlighted in the pathogenesis of different cancer; in addition, an association of the lncRNA with chromatin-modifying complex to enhance androgen receptor-mediated gene transcription was reported in prostate cancer. Diagnostic and prognostic importance of PRNCR1 was found in some malignancies suggesting potency of the lncRNA to serve as a clinical biomarker. For PRNCR1 SNPs, although cancer susceptibility of the risk alleles/genotypes was reported in different populations, majorities of the findings were not replicated and underlying molecular mechanisms remained unexplored. Therapeutic implication of PRNCR1 was not studied well and future research may come up in this direction for intervening novel strategies to fight against cancer.

Advances in Non-Coding RNA Sequencing

Non-coding RNAs (ncRNAs) comprise a set of abundant and functionally diverse RNA molecules. Since the discovery of the first ncRNA in the 1960s, ncRNAs have been shown to be involved in nearly all steps of the central dogma of molecular biology. In recent years, the pace of discovery of novel ncRNAs and their cellular roles has been greatly accelerated by high-throughput sequencing. Advances in sequencing technology, library preparation protocols as well as computational biology helped to greatly expand our knowledge of which ncRNAs exist throughout the kingdoms of life. Moreover, RNA sequencing revealed crucial roles of many ncRNAs in human health and disease. In this review, we discuss the most recent methodological advancements in the rapidly evolving field of high-throughput sequencing and how it has greatly expanded our understanding of ncRNA biology across a large number of different organisms.

(In)Distinctive Role of Long Non-Coding RNAs in Common and Rare Ovarian Cancers

Rare ovarian cancers (ROCs) are OCs with an annual incidence of fewer than 6 cases per 100,000 women. They affect women of all ages, but due to their low incidence and the potential clinical inexperience in management, there can be a delay in diagnosis, leading to a poor prognosis. The underlying causes for these tumors are varied, but generally, the tumors arise due to alterations in gene/protein expression in cellular processes that regulate normal proliferation and its checkpoints. Dysregulation of the cellular processes that lead to cancer includes gene mutations, epimutations, non-coding RNA (ncRNA) regulation, posttranscriptional and posttranslational modifications. Long non-coding RNA (lncRNA) are defined as transcribed RNA molecules, more than 200 nucleotides in length which are not translated into proteins. They regulate gene expression through several mechanisms and therefore add another level of complexity to the regulatory mechanisms affecting tumor development. Since few studies have been performed on ROCs, in this review we summarize the mechanisms of action of lncRNA in OC, with an emphasis on ROCs.

The Functional Role of Long Non-Coding RNAs in Melanoma

Melanoma is the most lethal skin cancer, with increasing incidence worldwide. The molecular events that drive melanoma development and progression have been extensively studied, resulting in significant improvements in diagnostics and therapeutic approaches. However, a high drug resistance to targeted therapies and adverse effects of immunotherapies are still a major challenge in melanoma treatment. Therefore, the elucidation of molecular mechanisms of melanomagenesis and cancer response to treatment is of great importance. Recently, many studies have revealed the close association of long noncoding RNAs (lncRNAs) with the development of many cancers, including melanoma. These RNA molecules are able to regulate a plethora of crucial cellular processes including proliferation, differentiation, migration, invasion and apoptosis through diverse mechanisms, and even slight dysregulation of their expression may lead to tumorigenesis. lncRNAs are able to bind to protein complexes, DNA and RNAs, affecting their stability, activity, and localization. They can also regulate gene expression in the nucleus. Several functions of lncRNAs are context-dependent. This review summarizes current knowledge regarding the involvement of lncRNAs in melanoma. Their possible role as prognostic markers of melanoma response to treatment and in resistance to therapy is also discussed.

Transcriptional characterization of subcutaneous adipose tissue in obesity affected women highlights metabolic dysfunction and implications for lncRNAs

Obesity is a complex disease with multifactorial causes, and its prevalence is becoming a serious health crisis. For this reason, there is a crucial need to identify novel targets and players. With this aim in mind, we analyzed via RNA-sequencing the subcutaneous adipose tissue of normal weight and obesity-affected women, highlighting the differential expression in the two tissues. We specifically focused on long non-coding RNAs, as 6 of these emerged as dysregulated in the diseased-tissue (COL4A2-AS2, RPS21-AS, PELATON, ITGB2-AS1, ACER2-AS and CTEPHA1). For each of them, we performed both a thorough in silico dissection and in vitro validation, to predict their function during adipogenesis. We report the lncRNAs expression during adipose derived stem cells differentiation to adipocytes as model of adipogenesis and their potential modulation by adipogenesis-related transcription factors (C/EBPs and PPARγ). Moreover, inhibiting CTEPHA1 expression we investigated its impact on adipogenesis-related transcription factors, showing its significative dysregulation of C/EBPα expression. Lastly, we dissected the subcellular localization, pathway involvement and disease-correlation for coding differentially expressed genes. Together, these findings highlight a transcriptional deregulation at the basis of obesity, impacted by both coding and long non-coding RNAs.

Evolutionarily conserved transcriptional landscape of the heart defining the chamber specific physiology

Cardiovascular disease (CVD) remains the leading cause of death worldwide. A deeper characterization of regional transcription patterns within different heart chambers may aid to improve our understanding of the molecular mechanisms involved in myocardial function and further, our ability to develop novel therapeutic strategies. Here, we used RNA sequencing to determine differentially expressed protein coding (PC) and long non-coding (lncRNA) transcripts within the heart chambers across seven vertebrate species and identified evolutionarily conserved chamber specific genes, lncRNAs and pathways. We investigated lncRNA homologs based on sequence, secondary structure, synteny and expressional conservation and found most lncRNAs to be conserved by synteny. Regional co-expression patterns of transcripts are modulated by multiple factors, including genomic overlap, strandedness and transcript biotype. Finally, we provide a community resource designated EvoACTG, which informs researchers on the conserved yet intertwined nature of the coding and non-coding cardiac transcriptome across popular model organisms in CVD research.