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

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

Background

Pyroptosis 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.

Methods

Based 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.

Results

A 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.

Conclusion

The PRL risk signature could independently predict overall survival and guide treatment in patients with OC.

Integrative Analysis of Deregulated miRNAs Reveals Candidate Molecular Mechanisms Linking H. pylori Infected Peptic Ulcer Disease with Periodontitis

Objective

Periodontitis is a highly prevalent oral infectious disease and has been increasingly associated with H. pylori infection, gastric inflammation, and gastric cancer but little is known about epigenetic machinery underlying this potentially bidirectional association. The present study is aimed at identifying key deregulated miRNA, their associated genes, signaling pathways, and compounds linking periodontitis with H. pylori-associated peptic ulcer disease.

Methods

miRNA expression datasets for periodontitis-affected and H. pylori-associated peptic ulcer disease-affected tissues were sought from the GEO database. Differentially expressed miRNA (DEmiRNAs) were identified and the overlapping, shared-DEmiRNA between both datasets were determined. Shared-DEmiRNA-target networks construction and functional analyses were constructed using miRNet 2.0, including shared-DEmiRNA-gene, shared-DEmiRNA-transcription factor (TF), and shared-DEmiRNA-compound networks. Functional enrichment analysis for shared DEmiRNA-gene and shared DEmiRNA-TF networks was performed using the KEGG, Reactome, and Geno Ontology (GO) pathways.

Results

11 shared-DEmiRNAs were identified, among which 9 showed similar expression patterns in both diseases, and 7 were overexpressed. miRNA hsa-hsa-mir-155-5p and hsa-mir-29a-3p were top miRNA nodes in both gene and TF networks. The topmost candidate miRNA-deregulated genes were PTEN, CCND1, MDM2, TNRC6A, and SCD while topmost deregulated TFs included STAT3, HIF1A, EZH2, CEBPA, and RUNX1. Curcumin, 5-fluorouracil, and the gallotanin 1,2,6-Tri-O-galloyl-beta-D-glucopyranose emerged as the most relevant linkage compound targets. Functional analyses revealed multiple cancer-associated pathways, PI3K pathways, kinase binding, and transcription factor binding among as enriched by the network-associated genes and TFs.

Conclusion

Integrative analysis of deregulated miRNAs revealed candidate molecular mechanisms comprising of top miRNA, their gene, and TF targets linking H. pylori-infected peptic ulcer disease with periodontitis and highlighted compounds targeting both diseases. These findings provide basis for directing future experimental research.

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

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.

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

Background

As 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.

Methods

By 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.

Results

A 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.

Conclusion

The risk model of 11 inflammation-related lncRNAs can serve as a promising prognostic biomarker for low-grade gliomas patients.