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

Han, Junwei; Liu, Siyao; Sun, Zeguo; Zhang, Yunpeng; Zhang, Fan; Zhang, Chunlong; Shang, Desi; Yang, Haixiu; Su, Fei; Xu, Yanjun; Li, Chunquan; Ren, Huan; Li, Xia

doi:10.1038/srep46566

Cited by 25 publications

(21 citation statements)

References 76 publications

(124 reference statements)

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“…Identification of the potential signaling pathways that lncRNAs regulate is critical from a therapeutic point of view. In some cases, deciphering the signaling pathway and its upstream or downstream regulators can indicate the mechanisms used by a specific lncRNA (29). In this study, VINAS and DEPDC4 knockdown in cytokine-activated ECs reduced the phosphorylation of IκBα and p38 MAPK while having no significant effects on phosphorylation of the AKT signaling pathway (Figure 4).…”

Section: Discussionmentioning

confidence: 64%

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

Simion¹,

Zhou²,

Pierce³

et al. 2020

JCI Insight

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Section: Discussionmentioning

confidence: 64%

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

Simion¹,

Zhou²,

Pierce³

et al. 2020

JCI Insight

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“…We also compare our results with LncRNAs2Pathways [53]. LncRNAs2Pathways is an R-based tool under a LncPath package name.…”

Section: Comparison For the Pathway Enrichment Resultsmentioning

confidence: 97%

Functional Enrichment Analysis of Deregulated Long Non-Coding RNAs in Cancer Based on their Genomic Neighbors

Olgun

Taştan

2020

Preprint

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The dysregulation of long non-coding RNAs' (lncRNAs) expressions has been implicated in cancer. Since most of the lncRNAs' are not functionally characterized well, investigating the set of perturbed lncRNAs are is challenging. Existing methods that inspect lncRNAs functionally rely on the co-expressed coding genes, which are far better characterized functionally. LncRNAs can be known to act as transcriptional regulators; they may activate or repress the neighborhood's coding genes on the genome. Based on this, in this work, we aim to analyze the deregulated lncRNAs in cancer by taking into account their ability to regulate nearby loci on the genome. We perform functional analysis on differentially expressed lncRNAs for 28 different cancers considering their adjacent coding genes. We identify that some deregulated lncRNAs are cancer-specific, but a substantial number of lncRNAs are shared across cancers. Next, we assess the similarities of the cancer types based on the functional enrichment of the deregulated lncRNA sets. We find some cancers are very similar in the functions and biological processes related to the deregulated lncRNAs. We observe that some of the cancers for which we find similarity can be linked through primary, metastatic site relations. We investigate the similarity of enriched functional terms for the deregulated lncRNAs and the mRNAs. We further assess the enriched functions' similarity to the functions and processes that the known cancer driver genes take place. We believe that our methodology help to understand the impact of the lncRNAs in cancer functionally.

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“…The importance of cytoskeleton-associated proteins and pathways involved in glioma has also been reported in numerous previous studies ( 27 , 28 ). In addition, increasing evidence revealed that lncRNAs are associated with the occurrence and development of glioma by regulating the structure of reorganization of cytoskeleton-associated proteins and signaling pathways ( 29 , 30 ). Additionally, the association between metastasis or cytoskeleton and the prognosis of glioma has also been reported ( 31 , 32 ).…”

Section: Discussionmentioning

confidence: 99%

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

Zhou

Liu

et al. 2018

Oncol Rep

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

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

Cited by 25 publications

References 76 publications

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

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

Functional Enrichment Analysis of Deregulated Long Non-Coding RNAs in Cancer Based on their Genomic Neighbors

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

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