LncRNA2Target: a database for differentially expressed genes after lncRNA knockdown or overexpression

Jiang, Qinghua; Wang, Jixuan; Wu, Xiaoliang; Ma, Rui; Zhang, Tianjiao; Jin, Shuilin; Han, Zhijie; Tan, Renjie; Peng, Jiajie; Liu, Guiyou; Yu, Li; Wang, Yadong

doi:10.1093/nar/gku1173

Cited by 121 publications

(80 citation statements)

References 12 publications

(11 reference statements)

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“…The NAFLD interaction network was modeled using the resource http://visant.bu.edu/. LncRNA2Target40 (http://bio-annotation.cn/lncrna2target/) and LncRNA2Function40 (http://bio-annotation.cn/lncrna2target/) were used to explore and prioritize lncRNA−mRNA interactions. LncRNA2Function identifies protein‐coding genes that are significantly co‐expressed with one or more lncRNAs across 19 normal human tissues; target genes of an lncRNA are defined as the differentially expressed genes after knocking down or overexpressing the lncRNA.…”

Section: Methodsmentioning

confidence: 99%

Metastasis‐associated lung adenocarcinoma transcript 1 as a common molecular driver in the pathogenesis of nonalcoholic steatohepatitis and chronic immune‐mediated liver damage

Sookoian

Flichman

Garaycoechea

et al. 2018

Hepatology Communications

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Long noncoding RNAs (lncRNAs) are functional molecules that orchestrate gene expression. To identify lncRNAs involved in nonalcoholic fatty liver disease (NAFLD) severity, we performed a multiscale study that included: (a) systems biology modeling that indicated metastasis‐associated lung adenocarcinoma transcript 1 (MALAT1) as a candidate lncRNA for exploring disease‐related associations, (b) translational exploration in the clinical setting, and (c) mechanistic modeling. MALAT1 liver profiling was performed in three consecutive phases, including an exploratory stage (liver samples from patients with NAFLD who were morbidly obese [n = 47] and from 13 individuals with normal liver histology); a replication stage (patients with NAFLD and metabolic syndrome [n =49]); and a hypothesis‐driven stage (patients with chronic hepatitis C and autoimmune liver diseases, [n = 65]). Liver abundance of MALAT1 was associated with NAFLD severity (P = 1 × 10–6); MALAT1 expression levels were up‐regulated 1.75‐fold (P = 0.029) and 3.6‐fold (P = 0.012) in patients with nonalcoholic steatohepatitis compared to those diagnosed with simple steatosis (discovery and replication set, respectively; analysis of covariance adjusted by age, homeostasis model assessment, and body mass index). Quantification of liver vascular endothelial growth factor A messenger RNA, a target of MALAT1, revealed a significant correlation between the two RNAs (R, 0.58; P = 5 × 10–8). Increased levels of MALAT1 were also associated with autoimmune liver diseases. Interactome assessment uncovered significant biological pathways, including Janus kinase‐signal transducers and activators of transcription and response to interferon‐γ. Conclusion: Deregulated expression of MALAT1 stratifies patients into the histologic phenotypes associated with NAFLD severity. MALAT1 up‐regulation seems to be a common molecular mechanism in immune‐mediated chronic inflammatory liver damage. This suggests that convergent pathophenotypes (inflammation and fibrosis) share similar molecular mediators. (Hepatology Communications 2018;2:654‐665)

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Section: Methodsmentioning

confidence: 99%

Metastasis‐associated lung adenocarcinoma transcript 1 as a common molecular driver in the pathogenesis of nonalcoholic steatohepatitis and chronic immune‐mediated liver damage

Sookoian

Flichman

Garaycoechea

et al. 2018

Hepatology Communications

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“…The relevant hits were downloaded and prepared systematically for further manual data curation. Second, RAID v2.0 integrated diverse RNA-associated interactions from other 18 databases, including ChIPBase (10), LncRNA2Target (11), LncRNAdisease (7), miR2Disease (12), miRTarBase (13), MNDR (14), ncRDeathDB (8), NPInter (15), OncomiRDB (16), sRNATarBase (17), StarBase (6), TransmiR (18) and ViRBase (19) as well as five computational prediction databases (DroID (20), EIMMo (21), miRanda (22), miRDB (23) and TargetScan (9)).…”

Section: Data Collectionmentioning

confidence: 99%

RAID v2.0: an updated resource of RNA-associated interactions across organisms

et al. 2016

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With the development of biotechnologies and computational prediction algorithms, the number of experimental and computational prediction RNA-associated interactions has grown rapidly in recent years. However, diverse RNA-associated interactions are scattered over a wide variety of resources and organisms, whereas a fully comprehensive view of diverse RNA-associated interactions is still not available for any species. Hence, we have updated the RAID database to version 2.0 (RAID v2.0, www.rna-society.org/raid/) by integrating experimental and computational prediction interactions from manually reading literature and other database resources under one common framework. The new developments in RAID v2.0 include (i) over 850-fold RNA-associated interactions, an enhancement compared to the previous version; (ii) numerous resources integrated with experimental or computational prediction evidence for each RNA-associated interaction; (iii) a reliability assessment for each RNA-associated interaction based on an integrative confidence score; and (iv) an increase of species coverage to 60. Consequently, RAID v2.0 recruits more than 5.27 million RNA-associated interactions, including more than 4 million RNA–RNA interactions and more than 1.2 million RNA–protein interactions, referring to nearly 130 000 RNA/protein symbols across 60 species.

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“…More importantly, it can reveal the cellular or tissue localization of the lncRNA of interest. Knocking down the expression of a target gene has been a gold standard assay to elucidate its endogenous function [94].…”

Section: Available Assays For Linc-ror Measurementmentioning

confidence: 99%

The Emerging Roles of Long Noncoding RNA ROR (lincRNA-ROR) and its Possible Mechanisms in Human Cancers

Pan

Li²,

Chen³

et al. 2016

Cell Physiol Biochem

119

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To date, there is only up to 2% of protein-coding genes that are stably transcribed, whereas the vast majority are non-coding RNAs (ncRNAs). These ncRNAs, also known as non-messenger RNAs (nmRNAs) or functional RNAs (fRNAs), include transfer RNAs, ribosomal RNAs, microRNAs and long non-coding RNAs (lncRNAs). With the advance of high-resolution microarrays and massively parallel sequencing technology, lncRNAs have gained extended attentions nowadays and are found to play important roles in tumorigenesis and progression of human cancers. Long intergenic non-protein coding RNA, regulator of reprogramming (linc-ROR), was first discovered in induced pluripotent stem cells (iPSCs), where it was controlled by the key pluripotency factors Oct4, Sox2 and Nanog. Linc-ROR has been shown to be dysregulated in many types of cancers, including breast cancer (BC), pancreatic cancer (PC), hepatocellular cancer (HCC), endometrial cancer (EC), and nasopharyngeal carcinoma (NPC). Also, linc-ROR functions as regulatory molecule in a large amount of biological processes. However, the underlying mechanisms of its contribution to carcinogenesis remain to be elucidated. In this review, we will emphasize on the characteristics of linc-ROR and their roles in different types of human cancers.

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LncRNA2Target: a database for differentially expressed genes after lncRNA knockdown or overexpression

Cited by 121 publications

References 12 publications

Metastasis‐associated lung adenocarcinoma transcript 1 as a common molecular driver in the pathogenesis of nonalcoholic steatohepatitis and chronic immune‐mediated liver damage

Metastasis‐associated lung adenocarcinoma transcript 1 as a common molecular driver in the pathogenesis of nonalcoholic steatohepatitis and chronic immune‐mediated liver damage

RAID v2.0: an updated resource of RNA-associated interactions across organisms

The Emerging Roles of Long Noncoding RNA ROR (lincRNA-ROR) and its Possible Mechanisms in Human Cancers

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