Co-LncRNA: investigating the lncRNA combinatorial effects in GO annotations and KEGG pathways based on human RNA-Seq data

Zhao, Zheng; Bai, Jing; Wu, Aiwei; Wang, Yuan; Zhang, Jinwen; Wang, Zishan; Li, Yongsheng; Xu, Juan; Li, Xia

doi:10.1093/database/bav082

Cited by 112 publications

(93 citation statements)

References 31 publications

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“…In addition, the epigenetic context of HIV linked mRNA-lnRNA pairs was determined for H3K4Me1, H3K4Me3, H3K27Ac, H3K36Me3 together with cap analysis gene expression (CAGE) peaks by making use of the Integrative Genomics Viewer34. Lastly, the co-expression database Co-LncRNA (http://www.bio-bigdata.com/Co-LncRNA) was browsed to determine similar co-expression events that correspond with our dataset35.…”

Section: Methodsmentioning

confidence: 99%

Differential expression of lncRNAs during the HIV replication cycle: an underestimated layer in the HIV-host interplay

Trypsteen

Mohammadi

Hecke

et al. 2016

Sci Rep

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Studying the effects of HIV infection on the host transcriptome has typically focused on protein-coding genes. However, recent advances in the field of RNA sequencing revealed that long non-coding RNAs (lncRNAs) add an extensive additional layer to the cell’s molecular network. Here, we performed transcriptome profiling throughout a primary HIV infection in vitro to investigate lncRNA expression at the different HIV replication cycle processes (reverse transcription, integration and particle production). Subsequently, guilt-by-association, transcription factor and co-expression analysis were performed to infer biological roles for the lncRNAs identified in the HIV-host interplay. Many lncRNAs were suggested to play a role in mechanisms relying on proteasomal and ubiquitination pathways, apoptosis, DNA damage responses and cell cycle regulation. Through transcription factor binding analysis, we found that lncRNAs display a distinct transcriptional regulation profile as compared to protein coding mRNAs, suggesting that mRNAs and lncRNAs are independently modulated. In addition, we identified five differentially expressed lncRNA-mRNA pairs with mRNA involvement in HIV pathogenesis with possible cis regulatory lncRNAs that control nearby mRNA expression and function. Altogether, the present study demonstrates that lncRNAs add a new dimension to the HIV-host interplay and should be further investigated as they may represent targets for controlling HIV replication.

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Section: Methodsmentioning

confidence: 99%

Differential expression of lncRNAs during the HIV replication cycle: an underestimated layer in the HIV-host interplay

Trypsteen

Mohammadi

Hecke

et al. 2016

Sci Rep

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“…Sub-organ specifically expressed lncRNAs are also shown in Figure 1A and described in Supplementary table 2. GO enrichment analysis for protein-coding genes that are co-expressed with the deregulated lncRNAs was performed by the Co-LncRNA algorithm [31], which identifies CEGs that are significantly related to gene expression, RNA splicing, protein metabolism, Tie/Notch/Wnt pathways, apoptosis, cell cycle, endocytosis, and trafficking activities, which were summarized and visualized by REViGO approach (Figure 1C and Supplementary table 2). …”

Section: Resultsmentioning

confidence: 99%

“…Briefly, Co-LncRNA is a web-based tool that performs Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses based on co-expressed protein-coding genes (CEGs) of a single or multiple lncRNAs [31]. The REViGO resource was employed to summarize and visualize the enriched GO terms in a scatterplot graph based on the corrected p-values obtained by Co-LncRNA resource [32].…”

Section: Methodsmentioning

confidence: 99%

Unraveling the oral cancer lncRNAome: Identification of novel lncRNAs associated with malignant progression and HPV infection

2016

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Objectives The role of long non-coding RNA (lncRNA) expression in human head and neck squamous cell carcinoma (HNSCC) is still poorly understood. In this study, we aimed at establishing the onco-lncRNAome profiling of HNSCC and to identify lncRNAs correlating with prognosis and patient survival. Materials and Methods The Atlas of Noncoding RNAs in Cancer (TANRIC) database was employed to retrieve the lncRNA expression information generated from The Cancer Genome Atlas (TCGA) HNSCC RNA-sequencing data. RNA-sequencing data from HNSCC cell lines were also considered for this study. Bioinformatics approaches, such as differential gene expression analysis, survival analysis, principal component analysis, and Co-LncRNA enrichment analysis were performed. Results Using TCGA HNSCC RNA-sequencing data from 426 HNSCC and 42 adjacent normal tissues, we found 728 lncRNA transcripts significantly and differentially expressed in HNSCC. Among the 728 lncRNAs, 55 lncRNAs were significantly associated with poor prognosis, such as overall survival and/or disease-free survival. Next, we found 140 lncRNA transcripts significantly and differentially expressed between Human Papilloma Virus (HPV) positive tumors and HPV negative tumors. Thirty lncRNA transcripts were differentially expressed between TP53 mutated and TP53 wild type tumors. Co-LncRNA analysis suggested that protein-coding genes that are co-expressed with these deregulated lncRNAs might be involved in cancer associated molecular events. With consideration of differential expression of lncRNAs in a HNSCC cell lines panel (n=22), we found several lncRNAs that may represent potential targets for diagnosis, therapy and prevention of HNSCC. Conclusion LncRNAs profiling could provide novel insights into the potential mechanisms of HNSCC oncogenesis.

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“…To further investigate the fundamental functions and pathways associated with PVT1 and miRNAs in HCC, bioinformatics analyses, including GO and KEGG analyses, were performed as described [31,32]. Genes with more than three overlapping nucleotide sequences (either those genes listed as overexpressed in the GEO database and TCGA, shown to be downregulated in the gene microarray, or identified as the target genes of miR-424-5p by more than five prediction algorithms) were used for bioinformatics analysis.…”

Section: The Potential Pathways Associated With Pvt1 and Mirnasmentioning

confidence: 99%

A Preliminary Investigation of PVT1 on the Effect and Mechanisms of Hepatocellular Carcinoma: Evidence from Clinical Data, a Meta-Analysis of 840 Cases, and In Vivo Validation

Wen

Zhang

Huang

et al. 2018

Cell Physiol Biochem

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Background/Aims: Hepatocellular carcinoma (HCC) remains a difficult problem that significantly affects the survival of the afflicted patients. Accumulating evidence has demonstrated the functions of long non-coding RNA (lncRNA) in HCC. In the present study, we aimed to explore the potential roles of PVT1 in the tumorigenesis and progression of HCC. Methods: In this study, quantitative reverse transcription-polymerase chain reaction (RT-qPCR) was applied to detect the differences between PVT1 expression in HCC tissues and cell lines. Then, the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were searched to confirm the relationship between PVT1 expression and HCC. Moreover, a meta-analysis comprising TCGA, GEO, and RT-qPCR was applied to estimate the expression of PVT1 in HCC. Then, cell proliferation was evaluated in vitro. A chicken chorioallantoic membrane (CAM) model of HCC was constructed to measure the effect on tumorigenicity in vivo. To further explore the sponge microRNA (miRNA) of PVT1 in HCC, we used TCGA, GEO, a gene microarray, and target prediction algorithms. TCGA and GEO and the gene microarray were used to select the differentially expressed miRNAs, and the different target prediction algorithms were applied to predict the target miRNAs of PVT1. Results: We found that PVT1 was markedly overexpressed in HCC tissue than in normal liver tissues based on both RT-qPCR and data from TCGA, and the overexpression of PVT1 was closely related to the gender and race of the patient as well as to higher HCC tumor grades. Also, a meta-analysis of 840 cases from multiple sources (TCGA, GEO and the results of our in-house RT-qPCR) showed that PVT1 gained moderate value in discriminating HCC patients from normal controls, confirming the results of RT-qPCR. Additionally, the upregulation of PVT1 could promote HCC cell proliferation in vitro and vivo. Based on the competing endogenous RNA (ceRNA) theory, the PVT1/miR-424-5p/INCENP axis was finally selected for further research. The in silico prediction revealed that there were complementary sequences between PVT1 and miR-424-5p as well as between miR-424-5p and INCENP. Furthermore, a negative correlation trend was found between miR-424-5p and PVT1 based on RT-qPCR, whereas a positive correlation trend was found between PVT1 and INCENP based on data from TCGA. Also, INCENP small interfering RNA (siRNA) could significantly inhibit cell proliferation and viability. Conclusions: We hypothesized that PVT1 could affect the biological function of HCC cells via targeting miR-424-5p and regulating INCENP. Focusing on the new insight of the PVT1/miR-424-5p/INCENP axis, this study provides a novel perspective for HCC therapeutic strategies.

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Co-LncRNA: investigating the lncRNA combinatorial effects in GO annotations and KEGG pathways based on human RNA-Seq data

Cited by 112 publications

References 31 publications

Differential expression of lncRNAs during the HIV replication cycle: an underestimated layer in the HIV-host interplay

Differential expression of lncRNAs during the HIV replication cycle: an underestimated layer in the HIV-host interplay

Unraveling the oral cancer lncRNAome: Identification of novel lncRNAs associated with malignant progression and HPV infection

A Preliminary Investigation of PVT1 on the Effect and Mechanisms of Hepatocellular Carcinoma: Evidence from Clinical Data, a Meta-Analysis of 840 Cases, and In Vivo Validation

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