Computational Methods and Applications for Identifying Disease-Associated lncRNAs as Potential Biomarkers and Therapeutic Targets

Yan, Congcong; Zhang, Zicheng; Bao, Siqi; Hou, Ping; Zhou, Meng; Xu, Chongyong; Sun, Jie

doi:10.1016/j.omtn.2020.05.018

Cited by 27 publications

(18 citation statements)

References 82 publications

(121 reference statements)

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“…Many computational methods have been developed to understand and characterize the biology of lncRNAs in physiological and pathological conditions [ 28 , 29 ]. In this work, we propose to use the pipeline contained in the RTN package to search for lncRNAs co-expression networks that provide evidence of their biological relevance in BC.…”

Section: Discussionmentioning

confidence: 99%

Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells

Mathias

Groeneveld

Trefflich

et al. 2021

IJMS

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Long non-coding RNAs (lncRNAs) are functional transcripts with more than 200 nucleotides. These molecules exhibit great regulatory capacity and may act at different levels of gene expression regulation. Despite this regulatory versatility, the biology of these molecules is still poorly understood. Computational approaches are being increasingly used to elucidate biological mechanisms in which these lncRNAs may be involved. Co-expression networks can serve as great allies in elucidating the possible regulatory contexts in which these molecules are involved. Herein, we propose the use of the pipeline deposited in the RTN package to build lncRNAs co-expression networks using TCGA breast cancer (BC) cohort data. Worldwide, BC is the most common cancer in women and has great molecular heterogeneity. We identified an enriched co-expression network for the validation of relevant cell processes in the context of BC, including LINC00504. This lncRNA has increased expression in luminal subtype A samples, and is associated with prognosis in basal-like subtype. Silencing this lncRNA in luminal A cell lines resulted in decreased cell viability and colony formation. These results highlight the relevance of the proposed method for the identification of lncRNAs in specific biological contexts.

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Section: Discussionmentioning

confidence: 99%

Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells

Mathias

Groeneveld

Trefflich

et al. 2021

IJMS

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“…In addition to marker gene expression (Yan et al, 2020) and within-sample relative expression orderings (Zhang Z.M. et al, 2020), the gene co-expression pattern also served as a diagnostic signature.…”

Section: Co-expression Network For Lipid Metabolic Genesmentioning

confidence: 99%

Transcriptomic Data Analyses Reveal a Reprogramed Lipid Metabolism in HCV-Derived Hepatocellular Cancer

Liu

Cui

et al. 2020

Front. Cell Dev. Biol.

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Reprograming lipid metabolism, one of the major metabolic alterations in cancer, is believed to play an essential role in cancer development, but the exact molecular mechanism remains elusive. Here, we present a computational study of transcriptomic data of HCC with HCV etiology to investigate how lipid metabolism alters during HCC progression. Our analyses reveal that: (1) cancer tissue cells tend to synthesize fatty acids de novo and its phospholipid derivatives; (2) lipid catabolism and fatty acid oxidation are remarkably down-regulated in HCC; (3) the lipid metabolism in HCC is largely independent of lipids in blood circulation; (4) stage-specific co-expression networks for lipid metabolic genes were identified during HCC progression; and (5) the expression levels of several lipid metabolic genes that are differentially expressed or coexpressed specifically at the HCC stage have a strong correlation with cancer survival. Overall, the results provide detailed information about the reprogramed lipid metabolism in HCV-derived HCC.

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“…Our findings could shed more light on the underlying mechanism of how hyper-methylation negatively regulates gene expression and genomic reprogramming. The findings in this study provided valuable reference for conquering reprogramming obstacle of SCNT and has potential to improve cloning efficiency in practical applications ( Onishi et al, 2000 ; Yan et al, 2020 ; Zhao et al, 2020 ). However, our findings are based on genome wide data analysis and need further experimental validation.…”

Section: Discussionmentioning

confidence: 84%

Characterization of DNA Methylation Patterns and Mining of Epigenetic Markers During Genomic Reprogramming in SCNT Embryos

Cao

Zuo

et al. 2020

Front. Cell Dev. Biol.

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Somatic cell nuclear transfer (SCNT), also known as somatic cell cloning, is a commonly used technique to study epigenetic reprogramming. Although SCNT has the advantages of being safe and able to obtain pluripotent cells, early developmental arrest happens in most SCNT embryos. Overcoming epigenetic barriers is currently the primary strategy for improving reprogramming efficiency and improving developmental rate in SCNT embryos. In this study, we analyzed DNA methylation profiles of in vivo fertilized embryos and SCNT embryos with different developmental fates. Overall DNA methylation level was higher in SCNT embryos during global de-methylation process compared to in vivo fertilized embryos. In addition, promoter region, first intron and 3′UTR were found to be the major genomic regions that were hyper-methylated in SCNT embryos. Surprisingly, we found the length of re-methylated region was directly related to the change of methylation level. Furthermore, a number of genes including Dppa2 and Dppa4 which are important for early zygotic genome activation (ZGA) were not properly activated in SCNT embryos. This study comprehensively analyzed genome-wide DNA methylation patterns in SCNT embryos and provided candidate target genes for improving efficiency of genomic reprogramming in SCNT embryos. Since SCNT technology has been widely used in agricultural and pastoral production, protection of endangered animals, and therapeutic cloning, the findings of this study have significant importance for all these fields.

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Computational Methods and Applications for Identifying Disease-Associated lncRNAs as Potential Biomarkers and Therapeutic Targets

Cited by 27 publications

References 82 publications

Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells

Novel lncRNAs Co-Expression Networks Identifies LINC00504 with Oncogenic Role in Luminal A Breast Cancer Cells

Transcriptomic Data Analyses Reveal a Reprogramed Lipid Metabolism in HCV-Derived Hepatocellular Cancer

Characterization of DNA Methylation Patterns and Mining of Epigenetic Markers During Genomic Reprogramming in SCNT Embryos

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