LncRNA ZFAS1: Role in tumorigenesis and other diseases

Ghafouri‐Fard, Soudeh; Kamali, Mohammad; Abak, Atefe; Shoorei, Hamed; Taheri, Mohammad

doi:10.1016/j.biopha.2021.111999

Cited by 17 publications

(16 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies have shown that lncRNAs extensively participate in cell biological processes and regulate gene expression at multiple levels, including pre-transcription and post-transcription levels ( Munteanu et al, 2021 ). lncRNAs have various biological functions, and their abnormal expressions are tightly associated with embryonic development and diseases, especially tumors ( Ghafouri-Fard et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Prognostic prediction of clear cell renal cell carcinoma based on lipid metabolism-related lncRNA risk coefficient model

Liu

Lai

et al. 2023

Front. Genet.

View full text Add to dashboard Cite

Objective: In order to predict the prognosis in patients with clear cell renal cell carcinoma (ccRCC) so as to understand cancer lipid metabolism and sensitivity to immune-targeting drugs, model algorithms were used to establish a risk coefficient model of long non-coding RNAs (lncRNAs) associated with lipid metabolism.Methods: The transcriptome data were retrieved from TCGA, and lncRNAs associated with lipid metabolism were obtained through Pearson correlation and differential expression analyses. Differentially expressed lipid metabolism-related lncRNAs and lipid metabolism-related lncRNA pairs were obtained using the R language software. The minimum absolute shrinkage method and the selector operation regression method were used to construct the model and draw the receiver operator characteristic curve. High-risk patients were differentiated from low-risk patients through the cut-off value, and the correlation analyses of the high-risk subgroup and low-risk subgroup were performed.Results: This research discovered that 25 pairs of lncRNAs were associated with the lipid metabolism of ccRCC, and 12 of these pairs were utilized to build the model. In combination with clinical data, the areas under the 1-, 3- and 5-year survival curves of ccRCC patients were 0.809, 0.764 and 0.792, separately. The cut-off value was used to perform subgroup analysis. The results showed that high-risk patients had poor prognosis. The results of Cox multivariate regressive analyses revealed that age and risk score were independent prediction factors of ccRCC prognosis. In addition, immune cell infiltration, the levels of gene expression at immune checkpoints, and high-risk patients more susceptible to sunitinib-targeted treatment were assessed by the risk model.Conclusion: Our team identified new prognostic markers of ccRCC and established risk models that could assess the prognosis of ccRCC patients and help determine which type of patients were more susceptible to sunitinib. These discoveries are vital for the optimization of risk stratification and personalized management.

show abstract

Section: Discussionmentioning

confidence: 99%

Prognostic prediction of clear cell renal cell carcinoma based on lipid metabolism-related lncRNA risk coefficient model

Liu

Lai

et al. 2023

Front. Genet.

View full text Add to dashboard Cite

show abstract

“…Indeed, ZFAS1 was up-regulated in metastasis samples and related to poor RFS [ 38 ]. High expression of ZFAS1 was closely associated with worse disease-free survival for PCa or gliomas patients [ 39 , 40 ].…”

Section: Discussionmentioning

confidence: 99%

LncRNA weighted gene co-expression network analysis reveals novel biomarkers related to prostate cancer metastasis

et al. 2022

View full text Add to dashboard Cite

Background Most prostate cancer patients die from metastasis and lack accurate efficacious biomarkers to monitor the disease behavior, optimize treatment and assess prognosis. Herein, we aimed to identify meaningful lncRNA biomarkers associated with prostate cancer metastatic progression. Methods By repurposing microarray probes, 11,624 lncRNAs in prostate cancer were obtained from Gene Expression Omnibus database (GSE46691, N = 545; GSE29079, N = 235; GSE94767, N = 130). Weighted gene co-expression network analysis was applied to determine the co-expression lncRNA network pertinent to metastasis. Hub lncRNAs were screened. RNA-seq and clinical data from the Cancer Genome Atlas prostate cancer (TCGA-PRAD) cohort (N = 531) were analyzed. Transwell assay and bioinformatic analysis were performed for mechanism research. Results The high expression levels of nine hub lncRNAs (FTX, AC005261.1, NORAD, LINC01578, AC004542.2, ZFAS1, EBLN3P, THUMPD3-AS1, GAS5) were significantly associated with Gleason score and increased probability of metastatic progression. Among these lncRNAs, ZFAS1 had the consistent trends of expression in all of the analysis from different cohorts, and the Kaplan-Meier survival analyses showed higher expression of ZFAS1 was associated with shorter relapse free survival. In-vitro studies confirmed that downregulation of ZFAS1 decreased prostate cancer cell migration. Conclusion We offered some new insights into discovering lncRNA markers correlated with metastatic progression of prostate cancer using the WGCNA. Some may serve as potential prognostic biomarkers and therapeutic targets for advanced metastatic prostate cancer.

show abstract

“…ZFAS1 has been reported to be upregulated in epilepsy which promotes neuronal apoptosis and neuroinflammation ( 179 ). The inhibition of PI3K/AKT pathway can silence ZFAS1 leading the downregulation of epilepticus-induced neural autophagy ( 180 ). TUG1 positively regulates inflammatory processes in many neurological diseases such as epilepsy, cognitive impairment, and ischemic stroke ( 181 , 182 ).…”

Section: Mtor Related Non-coding Rnamentioning

confidence: 99%

The Coordination of mTOR Signaling and Non-Coding RNA in Regulating Epileptic Neuroinflammation

Zeng

Chen

et al. 2022

Front. Immunol.

View full text Add to dashboard Cite

Epilepsy accounts for a significant proportion of the burden of neurological disorders. Neuroinflammation acting as the inflammatory response to epileptic seizures is characterized by aberrant regulation of inflammatory cells and molecules, and has been regarded as a key process in epilepsy where mTOR signaling serves as a pivotal modulator. Meanwhile, accumulating evidence has revealed that non-coding RNAs (ncRNAs) interfering with mTOR signaling are involved in neuroinflammation and therefore articipate in the development and progression of epilepsy. In this review, we highlight recent advances in the regulation of mTOR on neuroinflammatory cells and mediators, and feature the progresses of the interaction between ncRNAs and mTOR in epileptic neuroinflammation.

show abstract

LncRNA ZFAS1: Role in tumorigenesis and other diseases

Cited by 17 publications

References 66 publications

Prognostic prediction of clear cell renal cell carcinoma based on lipid metabolism-related lncRNA risk coefficient model

Prognostic prediction of clear cell renal cell carcinoma based on lipid metabolism-related lncRNA risk coefficient model

LncRNA weighted gene co-expression network analysis reveals novel biomarkers related to prostate cancer metastasis

The Coordination of mTOR Signaling and Non-Coding RNA in Regulating Epileptic Neuroinflammation

Contact Info

Product

Resources

About