Depletion of poly(A)-specific ribonuclease (PARN) inhibits proliferation of human gastric cancer cells by blocking cell cycle progression

Zhang, Li-Na; Yan, Yong-Bin

doi:10.1016/j.bbamcr.2014.12.004

Cited by 30 publications

(47 citation statements)

References 57 publications

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“…Cell cycle analysis of the PARN-deficient skin fibroblasts showed a 7% increase in the number of cells that stayed in G1/G0 and a 7% decrease in the number of cells that progressed to S/G2/M compared with healthy controls (figure 5E). This suggests impairment in transition of cells from the G1 to S phase, similar to the defect reported in PARN -knockdown gastric cancer cells 48. Apoptosis was not statistically different from controls cells (see online supplementary figure S8).…”

Section: Resultssupporting

confidence: 79%

Bone marrow failure and developmental delay caused by mutations in poly(A)-specific ribonuclease (PARN)

et al. 2015

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Section: Resultssupporting

confidence: 79%

Bone marrow failure and developmental delay caused by mutations in poly(A)-specific ribonuclease (PARN)

et al. 2015

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“…In agreement with previous reports showing that PARN depletion increases p53 mRNA levels by~1.7-1.9-fold (Zhang & Yan, 2015;Shukla et al, 2019) p53 transcript levels were increased by about twofold in KO/empty cells (P = 0.001) ( Fig 3J). Up-regulation at the protein level was of about twofold as well ( Fig 3I).…”

Section: Parn Regulates the Expression Levels Of Telomere-related Gensupporting

confidence: 93%

Impaired telomere integrity and rRNA biogenesis in PARN‐deficient patients and knock‐out models

et al. 2019

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PARN, poly(A)‐specific ribonuclease, regulates the turnover of mRNAs and the maturation and stabilization of the h TR RNA component of telomerase. Biallelic PARN mutations were associated with Høyeraal–Hreidarsson (HH) syndrome, a rare telomere biology disorder that, because of its severity, is likely not exclusively due to h TR down‐regulation. Whether PARN deficiency was affecting the expression of telomere‐related genes was still unclear. Using cells from two unrelated HH individuals carrying novel PARN mutations and a human PARN knock‐out (KO) cell line with inducible PARN complementation, we found that PARN deficiency affects both telomere length and stability and down‐regulates the expression of TRF1 , TRF2 , TPP1 , RAP1 , and POT1 shelterin transcripts. Down‐regulation of dyskerin‐encoding DKC1 mRNA was also observed and found to result from p53 activation in PARN‐deficient cells. We further showed that PARN deficiency compromises ribosomal RNA biogenesis in patients' fibroblasts and cells from heterozygous Parn KO mice. Homozygous Parn KO however resulted in early embryonic lethality that was not overcome by p53 KO. Our results refine our knowledge on the pleiotropic cellular consequences of PARN deficiency.

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“…Gastric carcinoma and adenocarcinoma cells often show dysregulated protein kinase C–dependent cell signal transduction compared to normal gastric cells, supporting MARCKS as a potential biomarker in gastric cancer . It has been reported that gene PARN is upregulated in gastric tumor tissues, and PARN depletion significantly inhibits the proliferation of gastric cancer cell lines MKN28 and AGS and promotes cell death . TOMM20 expression has been detected to be specifically localized to gastric cancer cells and strongly associated with reduced survival, and it has been suggested as a promising biomarker for predicting the prognosis of patients with gastric cancer .…”

Section: Discussionmentioning

confidence: 99%

Robust semiparametric gene‐environment interaction analysis using sparse boosting

2019

Statistics in Medicine

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For the pathogenesis of complex diseases, gene‐environment (G‐E) interactions have been shown to have important implications. G‐E interaction analysis can be challenging with the need to jointly analyze a large number of main effects and interactions and to respect the “main effects, interactions” hierarchical constraint. Extensive methodological developments on G‐E interaction analysis have been conducted in recent literature. Despite considerable successes, most of the existing studies are still limited as they cannot accommodate long‐tailed distributions/data contamination, make the restricted assumption of linear effects, and cannot effectively accommodate missingness in E variables. To directly tackle these problems, a semiparametric model is assumed to accommodate nonlinear effects, and the Huber loss function and Qn estimator are adopted to accommodate long‐tailed distributions/data contamination. A regression‐based multiple imputation approach is developed to accommodate missingness in E variables. For model estimation and selection of relevant variables, we adopt an effective sparse boosting approach. The proposed approach is practically well motivated, has intuitive formulations, and can be effectively realized. In extensive simulations, it significantly outperforms multiple direct competitors. The analysis of The Cancer Genome Atlas data on stomach adenocarcinoma and cutaneous melanoma shows that the proposed approach makes sensible discoveries with satisfactory prediction and stability.

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Depletion of poly(A)-specific ribonuclease (PARN) inhibits proliferation of human gastric cancer cells by blocking cell cycle progression

Cited by 30 publications

References 57 publications

Bone marrow failure and developmental delay caused by mutations in poly(A)-specific ribonuclease (PARN)

Bone marrow failure and developmental delay caused by mutations in poly(A)-specific ribonuclease (PARN)

Impaired telomere integrity and rRNA biogenesis in PARN‐deficient patients and knock‐out models

Robust semiparametric gene‐environment interaction analysis using sparse boosting

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