Role of long non-coding RNA TP73-AS1 in cancer

Chen, Caizhi; Shu, Long; Zou, Wen

doi:10.1042/bsr20192274

Cited by 10 publications

(5 citation statements)

References 41 publications

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“…The lncRNA TP73-AS1 plays a crucial role in many different carcinomas, including hepatocellular carcinoma [ 41 ]. TP73-AS1 regulates proliferation, invasion, migration, apoptosis, and in vivo and in vitro chemoresistance cancer mechanisms through different signaling pathways.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of LncRNAs expression profiles in an in vitro model of steatosis treated with Exendin-4

et al. 2021

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Background and aims The hallmark of non-alcoholic fatty liver disease (NAFLD) is the excessive hepatic lipid accumulation. Currently, no pharmacotherapy exists for NAFLD. However, the glucagon-like peptide-1 receptor agonists have recently emerged as potential therapeutics. Here, we sought to identify the long non-coding RNAs (LncRNAs) associated with the steatosis improvement induced by the GLP-1R agonist Exendin-4 (Ex-4) in vitro. Methods Steatosis was induced in HepG2 cells with oleic acid. The transcriptomic profiling was performed using total RNA extracted from untreated, steatotic, and Ex-4-treated steatotic cells. We validated a subset of differentially expressed LncRNAs with qRT-PCR and identified the most significantly enriched cellular functions associated with the relevant LncRNAs. Results We confirm that Ex-4 improves steatosis in HepG2 cells. We found 379 and 180 differentially expressed LncRNAs between untreated and steatotic cells and between steatotic and Ex-4-treated steatotic cells, respectively. Interestingly, 22 upregulated LncRNAs in steatotic cells became downregulated with Ex-4 exposure, while 50 downregulated LncRNAs in steatotic cells became upregulated in the presence of Ex-4. Although some LncRNAs, such as MALAT1, H19, and NEAT1, were previously associated with NAFLD, the association of others with steatosis and the positive effect of Ex-4 is being reported for the first time. Functional enrichment analysis identified many critical pathways, including fatty acid and pyruvate metabolism, and insulin, PPAR, Wnt, TGF-β, mTOR, VEGF, NOD-like, and Toll-like receptors signaling pathways. Conclusion Our results suggest that LncRNAs may play essential roles in the mechanisms underlying steatosis improvement in response to GLP-1R agonists and warrant further functional studies.

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Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of LncRNAs expression profiles in an in vitro model of steatosis treated with Exendin-4

et al. 2021

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“…Previous works have reported some prognostic lncRNAs in LUAD, such as LOC100132354, CTB-193M12.5, NEAT1, HOTAIR, H19, NNT-AS1, linc01088, TP73-AS1, TINCR, LINC00336, [8][9][22][23][24][25][26][27][28][29], which are not included in TCGA-based prognostic lncRNAs. According to LNCipedia [30], lncRNA RP11-79H23.3…”

Section: Discussionmentioning

confidence: 99%

Six-long Non-coding RNA Signature to Predict the Prognosis of Lung Adenocarcinoma

Wang¹

2020

Preprint

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Background: Long non-coding RNAs (lncRNAs) have been reported to play essential roles in tumorigenesis and cancers prognosis, and they can be a potential cancer prognostic markers. However, in lung adenocarcinoma(LUAD), how lncRNA signatures predict the survival of patients is poorly understood. Our study aims to explore lncRNA signatures and prognostic function in LUAD.Methods: The expression and prognosis data of lncRNAs in LUAD patients was collected from the Cancer Genome Atlas (TCGA) data. All analyses were performed using the R package (version 3.6.2). Metascape, STRING and Cytoscape were used for enrichment analysis and function prediction of the lncRNA co-expressed protein-coding genes.Results: We have collected lncRNA expression data in 466 LUAD tumors, and a six-lncRNA signature(RP11-79H23.3, RP11-309M7.1, CTD-2357A8.3, RP11-108P20.4, U47924.29, LHFPL3-AS2) has been shown to be significantly related to LUAD patients’ overall survival. According to the lncRNA signatures, the high-risk and low-risk groups were divided in LUAD patients with different survival rates. Further multivariable cox regression analysis showed that the prognostic value of this signature was independent of clinical factors. The potential functional roles and hub co-expressed protein-coding genes in the six prognostic lncRNAs are shown in the functional enrichment analysis.Conclusions: These results showed that these six lncRNAs could be independent predicted prognostic biomarkers in LUAD patients.

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“…Epigenetic alterations in this gene result in the silencing of CDKN2A expression, impairing its tumor suppressive function (Psofaki et al, 2010). Similarly, over-expression of Tp73 leads to the loss of its transcriptional activity, disrupting key cellular processes such as apoptosis, cell cycle control, and DNA repair (Chen et al, 2019). Molnár et al (2018) identified TP73 among the top 50 hyper-methylated regions in adenomatous CRC tissues compared to normal samples.…”

Section: Colorectal Cancer Data Analysismentioning

confidence: 99%

Uncovering Alterations in Cancer Epigenetics via Trans-Dimensional Markov Chain Monte Carlo and Hidden Markov Models*

Shokoohi

Khaniki

2023

Preprint

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Epigenetic alterations are key drivers in the development and progression of cancer. Identifying differentially methylated cytosines (DMCs) in cancer samples is a crucial step toward understanding these changes. In this paper, we propose a trans-dimensional Markov chain Monte Carlo (TMCMC) approach that uses hidden Markov models (HMMs) with binomial emission, and bisulfite sequencing (BS-Seq) data, called DMCTHM, to identify DMCs in cancer epigenetic studies. We introduce the Expander-Collider penalty to tackle under and over-estimation in TMCMC-HMMs. We address all known challenges inherent in BS-Seq data by introducing novel approaches for capturing functional patterns and autocorrelation structure of the data, as well as for handling missing values, multiple covariates, multiple comparisons, and family-wise errors. We demonstrate the effectiveness of DMCTHM through comprehensive simulation studies. The results show that our proposed method outperforms other competing methods in identifying DMCs. Notably, with DMCTHM, we uncovered new DMCs and genes in Colorectal cancer that were significantly enriched in the Tp53 pathway.

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Role of long non-coding RNA TP73-AS1 in cancer

Cited by 10 publications

References 41 publications

Comprehensive analysis of LncRNAs expression profiles in an in vitro model of steatosis treated with Exendin-4

Comprehensive analysis of LncRNAs expression profiles in an in vitro model of steatosis treated with Exendin-4

Six-long Non-coding RNA Signature to Predict the Prognosis of Lung Adenocarcinoma

Uncovering Alterations in Cancer Epigenetics via Trans-Dimensional Markov Chain Monte Carlo and Hidden Markov Models*

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