DICER-AS1 lncRNA: A putative culprit in the pathogenesis of gastric cancer

Afrough, Hanieh; Ghafouri‐Fard, Soudeh; Yousefi, Hassan; Pakzad, Parviz; Oskooei, Vahid Kholghi; Taheri, Mohammad

doi:10.1016/j.yexmp.2020.104490

Cited by 8 publications

(10 citation statements)

References 21 publications

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“…Among these lncRNAs, the function of ZBTB20–AS4, CTD-2201I18.1, AC131097.4, and PAXIP1-AS2 is unclear. However, SNAI3–AS1 has been reported to promote the proliferation and metastasis of HCC cells ( Li et al, 2019 , 2020 ); DGCR9 can enhance the proliferation, migration, and glucose uptake of gastric cancer cells ( Ni et al, 2018 ); DICER1–AS1 promotes malignant behavior of osteosarcoma, colorectal cancer, and other tumors ( Gu et al, 2018 ; Afrough et al, 2020 ; Ma et al, 2020 ); and LncRNA C1RL-AS1 silencing inhibited the malignant phenotype of gastric cancer cells through the Akt/β-catenin/c-myc pathway ( Zhen-Hua et al, 2020 ). The function of these four lncRNAs in gliomas is unknown.…”

Section: Discussionmentioning

confidence: 99%

A Novel Inflammatory lncRNAs Prognostic Signature for Predicting the Prognosis of Low-Grade Glioma Patients

Xiang

Chen

et al. 2021

Front. Genet.

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BackgroundAs immunotherapy has received attention as new treatments for brain cancer, the role of inflammation in the process of glioma is of particular importance. Increasing studies have further shown that long non-coding RNAs (lncRNAs) are important factors that promote the development of glioma. However, the relationship between inflammation-related lncRNAs and the prognosis of glioma patients remains unclear. The purpose of this study is to construct and validate an inflammation-related lncRNA prognostic signature to predict the prognosis of low-grade glioma patients.MethodsBy downloading and analyzing the gene expression data and clinical information of the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) patients with low-grade gliomas, we could screen for inflammatory gene-related lncRNAs. Furthermore, through Cox and the Least Absolute Shrinkage and Selection Operator regression analyses, we established a risk model and divided patients into high- and low-risk groups based on the median value of the risk score to analyze the prognosis. In addition, we analyzed the tumor mutation burden (TMB) between the two groups based on somatic mutation data, and explored the difference in copy number variations (CNVs) based on the GISTIC algorithm. Finally, we used the MCPCounter algorithm to study the relationship between the risk model and immune cell infiltration, and used gene set enrichment analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to explore the enrichment pathways and biological processes of differentially expressed genes between the high- and low-risk groups.ResultsA novel prognostic signature was constructed including 11 inflammatory lncRNAs. This risk model could be an independent prognostic predictor. The patients in the high-risk group had a poor prognosis. There were significant differences in TMB and CNVs for patients in the high- and low-risk groups. In the high-risk group, the immune system was activated more significantly, and the expression of immune checkpoint-related genes was also higher. The GSEA, GO, and KEGG analyses showed that highly expressed genes in the high-risk group were enriched in immune-related processes, while lowly expressed genes were enriched in neuromodulation processes.ConclusionThe risk model of 11 inflammation-related lncRNAs can serve as a promising prognostic biomarker for low-grade gliomas patients.

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Section: Discussionmentioning

confidence: 99%

A Novel Inflammatory lncRNAs Prognostic Signature for Predicting the Prognosis of Low-Grade Glioma Patients

Xiang

Chen

et al. 2021

Front. Genet.

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“…In addition, the DICER-AS1 primers were as follows: DICER-AS1-F , 5′-CGAAGAAATGGAATAACTTCCAAC-3′; DICER-AS1-R : 5′-TTGGTCCAAACACAGAAGATC-3′. The details of these methods are provided in ( 32 , 33 ). The experimental results related to this study were provided by Qingquan Zhang, Nantong University.…”

Section: Methodsmentioning

confidence: 99%

“…The details of these methods are provided in (32,33). The experimental results related to this study were provided by Qingquan Zhang, Nantong University.…”

Section: In-vitro Assaysmentioning

confidence: 99%

Derivation, Comprehensive Analysis, and Assay Validation of a Pyroptosis-Related lncRNA Prognostic Signature in Patients With Ovarian Cancer

et al. 2022

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BackgroundPyroptosis is regulated by long non-coding RNAs (lncRNAs) in ovarian cancer (OC). Therefore, a comprehensive analysis of pyroptosis-related lncRNAs (PRLs) in OC is crucial for developing therapeutic strategies and survival prediction.MethodsBased on public database raw data, mutations in the landscape of pyroptosis-related genes (PRGs) in patients with OC were investigated thoroughly. PRLs were identified by calculating Pearson correlation coefficients. Cox and LASSO regression analyses were performed on PRLs to screen for lncRNAs participating in the risk signature. Furthermore, receiver operating characteristic (ROC) curves, Kaplan–Meier survival analyses, decision curve analysis (DCA) curves, and calibration curves were used to confirm the clinical benefits. To assess the ability of the risk signature to independently predict prognosis, it was included in a Cox regression analysis with clinicopathological parameters. Two nomograms were constructed to facilitate clinical application. In addition, potential biological functions of the risk signature were investigated using gene function annotation. Subsequently, immune-related landscapes and BRCA1/2 mutations were compared in different risk groups using diverse bioinformatics algorithms. Finally, we conducted a meta-analysis and in-vitro assays on alternative lncRNAs.ResultsA total of 374 patients with OC were randomized into training and validation cohorts (7:3). A total of 250 PRLs were selected from all the lncRNAs. Subsequently, a risk signature (DICER1-AS1, MIR600HG, AC083880.1, AC109322.1, AC007991.4, IL6R-AS1, AL365361.1, and AC022098.2) was constructed to distinguish the risk of patient survival. The ROC curve, K-M analysis, DCA curve, and calibration curve indicated excellent predictive performance for determining overall survival (OS) based on the risk signature in each cohort (p < 0.05). The Cox regression analysis indicated that the risk signature was an independent prognostic factor for OS (p < 0.05). Moreover, significant differences in the immune response and BRCA1 mutations were identified in different groups distinguished by the risk signature (p < 0.05). Interestingly, in-vitro assays showed that an alternative lncRNA (DICER1-AS1) could promote OC cell proliferation.ConclusionThe PRL risk signature could independently predict overall survival and guide treatment in patients with OC.

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“…DICER-AS1, which is among the NF-jB-associated lncRNAs, was significantly downregulated in gastric cancer tissues compared with noncancerous tissues (Figure 1a). 7 We also measured DICER-AS1 mRNA levels in 93-paired human gastric cancer and adjacent noncancerous tissues by qPCR. This revealed that DICER-AS1 expression level was significantly decreased in gastric cancer tissues (Figure 1b).…”

Section: Dicer-as1 Was Downregulated In Gastric Cancermentioning

confidence: 99%

“…6 A previous study reported that DICER-AS1, which is a NF-κB-associated lncRNA, was significantly downregulated in gastric cancer tissues compared with non-cancerous tissues, and that its expression level was correlated with disease stage. 7 Therefore, modulating DICER-AS1 expression could affect gastric cancer growth.…”

Section: Introductionmentioning

confidence: 99%

DICER-AS1 functions as competing endogenous RNA that targets CSR1 by sponging microRNA-650 and suppresses gastric cancer progression

Guo

Yan

2021

J Int Med Res

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Objective This study explored the functional interactions between the long non-coding RNA DICER-AS1 and the cellular stress response 1 ( CSR1) gene in gastric cancer. Methods Quantitative polymerase chain reaction (qPCR) and western blotting were used to measure DICER-AS1, CSR1, and miR-650 expression levels. Gastric cancer cell line proliferation and migration abilities were analyzed using the MTT and transwell migration and invasion assays, respectively. Bioinformatic analysis and dual luciferase reporter assays were employed to study the functional interactions among miR-650, DICER-AS1, and CSR1. Results DICER-AS1 and CSR1 expression levels were significantly decreased in gastric cancer tissues compared with normal tissues, and qPCR analysis showed that miR-650 was upregulated in gastric cancer tissues. Bioinformatic analysis and dual luciferase reporter assays revealed that DICER-AS1 functioned as a competing endogenous RNA that sponged miR-650, which in turn regulated CSR1 expression. Importantly, ectopic DICER-AS1 and CSR1 expression inhibited cell proliferation and migration in vitro and suppressed xenograft tumorgenicity in vivo. Conclusions These results suggest that DICER-AS1 functions as a competing endogenous RNA that regulates miR-650 to suppress proliferation and migration of gastric cancer cells by targeting CSR1. These findings indicate that targeting DICER-AS1 and miR-650 could be a novel treatment for gastric cancer.

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DICER-AS1 lncRNA: A putative culprit in the pathogenesis of gastric cancer

Cited by 8 publications

References 21 publications

A Novel Inflammatory lncRNAs Prognostic Signature for Predicting the Prognosis of Low-Grade Glioma Patients

A Novel Inflammatory lncRNAs Prognostic Signature for Predicting the Prognosis of Low-Grade Glioma Patients

Derivation, Comprehensive Analysis, and Assay Validation of a Pyroptosis-Related lncRNA Prognostic Signature in Patients With Ovarian Cancer

DICER-AS1 functions as competing endogenous RNA that targets CSR1 by sponging microRNA-650 and suppresses gastric cancer progression

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