lncFunTK: a toolkit for functional annotation of long noncoding RNAs

Roles of lncRNAs in pancreatic ductal adenocarcinoma: Diagnosis, treatment, and the development of drug resistance

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers, primarily due to its late diagnosis, high propensity to metastasis, and the development of resistance to chemo-/radiotherapy. Accumulating evidence suggests that long non-coding RNAs (lncRNAs) are intimately involved in the treatment resistance of pancreatic cancer cells via interacting with critical signaling pathways and may serve as potential diagnostic/prognostic markers or therapeutic targets in PDAC.

DATA SOURCES

We carried out a systematic review on lncRNAs-based research in the context of pancreatic cancer and presented an overview of the updated information regarding the molecular mechanisms underlying lncRNAs-modulated pancreatic cancer progression and drug resistance, together with their potential value in diagnosis, prognosis, and treatment of PDAC. Literature mining was performed in PubMed with the following keywords: long non-coding RNA, pancreatic ductal adenocarcinoma, pancreatic cancer up to January 2022. Publications relevant to the roles of lncRNAs in diagnosis, prognosis, drug resistance, and therapy of PDAC were collected and systematically reviewed.

RESULTS

LncRNAs, such as HOTAIR, HOTTIP, and PVT1, play essential roles in regulating pancreatic cancer cell proliferation, invasion, migration, and drug resistance, thus may serve as potential diagnostic/prognostic markers or therapeutic targets in PDAC. They participate in tumorigenesis mainly by targeting miRNAs, interacting with signaling molecules, and involving in the epithelial-mesenchymal transition process.

CONCLUSIONS

The functional lncRNAs play essential roles in pancreatic cancer cell proliferation, invasion, migration, and drug resistance and have potential values in diagnosis, prognostic prediction, and treatment of PDAC.

LncGSEA: a versatile tool to infer lncRNA associated pathways from large-scale cancer transcriptome sequencing data

Background

Long non-coding RNAs (lncRNAs) are a growing focus in cancer research. Deciphering pathways influenced by lncRNAs is important to understand their role in cancer. Although knock-down or overexpression of lncRNAs followed by gene expression profiling in cancer cell lines are established approaches to address this problem, these experimental data are not available for a majority of the annotated lncRNAs.

Results

As a surrogate, we present lncGSEA, a convenient tool to predict the lncRNA associated pathways through Gene Set Enrichment Analysis of gene expression profiles from large-scale cancer patient samples. We demonstrate that lncGSEA is able to recapitulate lncRNA associated pathways supported by literature and experimental validations in multiple cancer types.

Conclusions

LncGSEA allows researchers to infer lncRNA regulatory pathways directly from clinical samples in oncology. LncGSEA is written in R, and is freely accessible at https://github.com/ylab-hi/lncGSEA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07900-y.

ncFANs v2.0: an integrative platform for functional annotation of non-coding RNAs

Increasing evidence proves the essential regulatory roles of non-coding RNAs (ncRNAs) in biological processes. However, characterizing the specific functions of ncRNAs remains a challenging task, owing to the intensive consumption of the experimental approaches. Here, we present an online platform ncFANs v2.0 that is a significantly enhanced version of our previous ncFANs to provide multiple computational methods for ncRNA functional annotation. Specifically, ncFANs v2.0 was updated to embed three functional modules, including ncFANs-NET, ncFANs-eLnc and ncFANs-CHIP. ncFANs-NET is a new module designed for data-free functional annotation based on four kinds of pre-built networks, including the co-expression network, co-methylation network, long non-coding RNA (lncRNA)-centric regulatory network and random forest-based network. ncFANs-eLnc enables the one-stop identification of enhancer-derived lncRNAs from the de novo assembled transcriptome based on the user-defined or our pre-annotated enhancers. Moreover, ncFANs-CHIP inherits the original functions for microarray data-based functional annotation and supports more chip types. We believe that our ncFANs v2.0 carries sufficient convenience and practicability for biological researchers and facilitates unraveling the regulatory mechanisms of ncRNAs. The ncFANs v2.0 server is freely available at http://bioinfo.org/ncfans or http://ncfans.gene.ac.

Transcriptome wide analysis of long non-coding RNA-associated ceRNA regulatory circuits in psoriasis

Long non‐coding RNAs (lncRNAs) play critical roles in regulating immune‐associated diseases and chronic inflammatory disorders. Here, we found that lncRNAs involve in the pathogenesis of psoriasis through integrative analysis of RNA‐seq data sets from a psoriasis cohort. Then, lncRNA‐protein‐coding genes (PCGs) co‐expression network analysis demonstrated that lncRNAs extensively interact with IFN‐γ signalling pathway‐associated genes. Further, we validated 3 lncRNAs associate with IFN‐γ signalling pathway activation upon IFN‐γ stimulated in HaCaT cells, and loss of function experiments indicate their functional roles in the activation of inflammatory cytokine genes. Additionally, microRNA target screening analysis showed that lncRNAs may regulate JAK/STAT pathway activity through complete endogenous RNA (ceRNA) mechanism. Further experimental validation of PRKCQ‐AS1/STAT1/miR‐545‐5p regulatory circuitry showed that lncRNAs regulate the expression of JAK/STAT signalling pathway genes through competing for miR‐545‐5p. In summary, our results demonstrated that dysregulation of lncRNA‐JAK/STAT pathway axis promotes the inflammation level in psoriasis and thus provide potential therapeutic targets for psoriasis treatments.

GAPGOM-an R package for gene annotation prediction using GO Metrics

Objective

Properties of gene products can be described or annotated with Gene Ontology (GO) terms. But for many genes we have limited information about their products, for example with respect to function. This is particularly true for long non-coding RNAs (lncRNAs), where the function in most cases is unknown. However, it has been shown that annotation as described by GO terms to some extent can be predicted by enrichment analysis on properties of co-expressed genes.

Results

GAPGOM integrates two relevant algorithms, lncRNA2GOA and TopoICSim, into a user-friendly R package. Here lncRNA2GOA does annotation prediction by co-expression, whereas TopoICSim estimates similarity between GO graphs, which can be used for benchmarking of prediction performance, but also for comparison of GO graphs in general. The package provides an improved implementation of the original tools, with substantial improvements in performance and documentation, unified interfaces, and additional features.

Strategies and technologies for exploring long noncoding RNAs in heart failure

Long non-coding RNA (lncRNA) was once considered to be the "noise" of genome transcription without biological function. However, increasing evidence shows that lncRNA is dynamically expressed in developmental stage or disease status, playing a regulatory role in the process of gene expression and translation. In recent years, lncRNA is considered to be a core node of functional regulatory networks that controls cardiac and also involves in multiple process of heart failure such as myocardial hypertrophy, fibrosis, angiogenesis, etc., which would be a therapeutic target for diseases. In fact, it is the development of technology that has improved our understanding of lncRNAs and broadened our perspective on heart failure. From transcriptional "noise" to star molecule, progress of lncRNAs can't be achieved without the combination of multidisciplinary technologies, especially the emergence of high-throughput approach. Thus, here, we review the strategies and technologies available for the exploration lncRNAs and try to yield insights into the prospect of lncRNAs in clinical diagnosis and treatment in heart failure.

AnnoLnc2: the one-stop portal to systematically annotate novel lncRNAs for human and mouse

With the abundant mammalian lncRNAs identified recently, a comprehensive annotation resource for these novel lncRNAs is an urgent need. Since its first release in November 2016, AnnoLnc has been the only online server for comprehensively annotating novel human lncRNAs on-the-fly. Here, with significant updates to multiple annotation modules, backend datasets and the code base, AnnoLnc2 continues the effort to provide the scientific community with a one-stop online portal for systematically annotating novel human and mouse lncRNAs with a comprehensive functional spectrum covering sequences, structure, expression, regulation, genetic association and evolution. In response to numerous requests from multiple users, a standalone package is also provided for large-scale offline analysis. We believe that updated AnnoLnc2 (http://annolnc.gao-lab.org/) will help both computational and bench biologists identify lncRNA functions and investigate underlying mechanisms.

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.

Multi-Omics Approaches to Study Long Non-coding RNA Function in Atherosclerosis

Atherosclerosis is a complex inflammatory disease of the vessel wall involving the interplay of multiple cell types including vascular smooth muscle cells, endothelial cells, and macrophages. Large-scale genome-wide association studies (GWAS) and the advancement of next generation sequencing technologies have rapidly expanded the number of long non-coding RNA (lncRNA) transcripts predicted to play critical roles in the pathogenesis of the disease. In this review, we highlight several lncRNAs whose functional role in atherosclerosis is well-documented through traditional biochemical approaches as well as those identified through RNA-sequencing and other high-throughput assays. We describe novel genomics approaches to study both evolutionarily conserved and divergent lncRNA functions and interactions with DNA, RNA, and proteins. We also highlight assays to resolve the complex spatial and temporal regulation of lncRNAs. Finally, we summarize the latest suite of computational tools designed to improve genomic and functional annotation of these transcripts in the human genome. Deep characterization of lncRNAs is fundamental to unravel coronary atherosclerosis and other cardiovascular diseases, as these regulatory molecules represent a new class of potential therapeutic targets and/or diagnostic markers to mitigate both genetic and environmental risk factors.

Measures of co-expression for improved function prediction of long non-coding RNAs

BACKGROUND

Almost 16,000 human long non-coding RNA (lncRNA) genes have been identified in the GENCODE project. However, the function of most of them remains to be discovered. The function of lncRNAs and other novel genes can be predicted by identifying significantly enriched annotation terms in already annotated genes that are co-expressed with the lncRNAs. However, such approaches are sensitive to the methods that are used to estimate the level of co-expression.

RESULTS

We have tested and compared two well-known statistical metrics (Pearson and Spearman) and two geometrical metrics (Sobolev and Fisher) for identification of the co-expressed genes, using experimental expression data across 19 normal human tissues. We have also used a benchmarking approach based on semantic similarity to evaluate how well these methods are able to predict annotation terms, using a well-annotated set of protein-coding genes.

CONCLUSION

This work shows that geometrical metrics, in particular in combination with the statistical metrics, will predict annotation terms more efficiently than traditional approaches. Tests on selected lncRNAs confirm that it is possible to predict the function of these genes given a reliable set of expression data. The software used for this investigation is freely available.