lncRNA SOX2-OT regulates laryngeal cancer cell proliferation, migration and invasion and induces apoptosis by suppressing miR-654

Prediction of lncRNA-Disease Associations via Closest Node Weight Graphs of the Spatial Neighborhood Based on the Edge Attention Graph Convolutional Network

Accumulated evidence of biological clinical trials has shown that long non-coding RNAs (lncRNAs) are closely related to the occurrence and development of various complex human diseases. Research works on lncRNA–disease relations will benefit to further understand the pathogenesis of human complex diseases at the molecular level, but only a small proportion of lncRNA–disease associations has been confirmed. Considering the high cost of biological experiments, exploring potential lncRNA–disease associations with computational approaches has become very urgent. In this study, a model based on closest node weight graph of the spatial neighborhood (CNWGSN) and edge attention graph convolutional network (EAGCN), LDA-EAGCN, was developed to uncover potential lncRNA–disease associations by integrating disease semantic similarity, lncRNA functional similarity, and known lncRNA–disease associations. Inspired by the great success of the EAGCN method on the chemical molecule property recognition problem, the prediction of lncRNA–disease associations could be regarded as a component recognition problem of lncRNA–disease characteristic graphs. The CNWGSN features of lncRNA–disease associations combined with known lncRNA–disease associations were introduced to train EAGCN, and correlation scores of input data were predicted with EAGCN for judging whether the input lncRNAs would be associated with the input diseases. LDA-EAGCN achieved a reliable AUC value of 0.9853 in the ten-fold cross-over experiments, which was the highest among five state-of-the-art models. Furthermore, case studies of renal cancer, laryngeal carcinoma, and liver cancer were implemented, and most of the top-ranking lncRNA–disease associations have been proven by recently published experimental literature works. It can be seen that LDA-EAGCN is an effective model for predicting potential lncRNA–disease associations. Its source code and experimental data are available at https://github.com/HGDKMF/LDA-EAGCN.

High-throughput sequencing profile of laryngeal cancers: analysis of co-expression and competing endogenous RNA networks of circular RNAs, long non-coding RNAs, and messenger RNAs

Background

Circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs) have been recently identified as new classes of non-coding RNAs which participate in carcinogenesis and tumor progression. However, the functions of these non-coding RNAs and gene expression patterns are largely unknown.

Methods

We carried out high-throughput sequencing to analyze the differential expression of RNAs in 5 coupled laryngeal cancer (LC) and corresponding adjacent noncancerous tissues. Bioinformatics analyses were performed to predict the functions of these non-coding RNAs via co-expression, competing endogenous RNA networks and pathway enrichment analysis. The differential expression of the selected RNAs were confirmed using RT-qPCR. The CCK8, EDU, Transwell, and wound healing assays were conducted to validate the biological functions of SNHG29 in LC. Western blot assay was performed to identify the effects of SNHG29 having on the epithelial to mesenchymal transition process. Kaplan-Meier analysis was used to investigate whether the expression level of SNHG29 correlated with survival in LC patients. One-way ANOVA was used to analyze the correlation between the expression of SNHG29 and clinicopathological parameters of the included patients.

Results

Compared to normal laryngeal tissues, 31,763 non-coding RNAs were upregulated and 11,557 non-coding RNAs were downregulated in cancer tissues. SNHG29 expression was low in the LC cell lines and tissues predicting a better clinical prognosis. SNHG29 was also found to inhibit the proliferation, migration, and invasion ability of LC, exerting a suppressive role in the epithelial to mesenchymal transition process as well. SNHG29 downregulation was significantly correlated with differentiation (P=0.026), T-stage (P=0.041), lymphatic metastasis (P=0.044), and clinical stage (P=0.037). We found that the biological functions of differentially expressed transcripts included cell adhesion, biological adhesion, and migration and invasion related to adherens junction pathways.

Conclusions

Our study was the first to describe the non-coding RNA profile of LC, and suggested that dysregulated non-coding RNAs could be involved in LC tumorigenesis. SNHG29 was demonstrated to play crucial roles in inhibiting the pathogenesis and progression of LC. Our findings provide a new approach for further analyses of pathogenetic mechanisms, the detection of novel transcripts, and the identification of valuable biomarkers for this tumor.

Silenced SOX2-OT alleviates ventricular arrhythmia associated with heart failure by inhibiting NLRP3 expression via regulating miR-2355-3p

Background

Nucleotide‐binding oligomerization domain‐like receptor family pyrin domain containing 3 (NLRP3) inflammasomes are the most important factors in ventricular arrhythmia associated with heart failure (VA‐HF). However, how the relationship between lncRNA and NLRP3 inflammasomes is regulated in VA‐HF has not been investigated in detail. Thus, we aimed to determine the effects of SOX2‐overlapping transcripts (SOX2‐OT) by targeting NLRP3 in rats with VA‐HF.

Methods

We established rats (SPF, male, weight: 240 ± 10 g) with VA‐HF by aortic coarctation and constant‐current stimulation, then injected them with small interfering SOX2‐OT and anti‐miR‐2355‐3p. Six weeks later, SOX2‐OT and miR‐2355‐3p expression was measured using the quantitative reverse transcriptase‐polymerase chain reaction and NLRP3, ASC, caspase‐1, IL‐1β, and TGF‐β1 expression were measured by Western blot analysis; the ventricular chambers were histopathologically analyzed by staining with hematoxylin and eosin, Masson trichrome, and Picro Sirius Red and reactive oxygen species (ROS) levels were assessed by flow cytometry. The targeting relationship between miR‐2355‐3p and SOX2‐OT or NLRP3 was verified using dual‐luciferase reporter gene assays.

Results

The expression of SOX2‐OT and levels of NLRP3 inflammasomes gradually increased in normal rats, and in those with heart failure and with VA‐HF. Silencing SOX2‐OT expression inhibited NLRP3, ASC, caspase‐1, IL‐1β, and TGF‐β1 expression and ROS production, reduced the degrees of cardiomyocyte necrosis and fibrosis and alleviated cardiac dysfunction in rats with VA‐HF. MicroR‐2355‐3p can bind the SOX2‐OT and the 3′‐untranslated region of NLRP3. Inhibiting miR‐2355‐3p reversed the effect of SOX2‐OT in rats with VA‐HF.

Conclusions

Silencing SOX2‐OT alleviated cardiac dysfunction in rats by reducing the activation of NLRP3 inflammasomes via regulating miR‐2355‐3p.

Identification of MSC-AS1, a novel lncRNA for the diagnosis of laryngeal cancer

PURPOSE

Our study was aimed to identify potential lncRNAs related to laryngeal cancer (LC) and explore their potential regulatory mechanisms.

METHODS

RNA sequencing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to identify differentially expressed genes (DEGs). Receiver operating characteristic (ROC) curve analysis was performed to analyze the sensitivity and specificity of differentially expressed lncRNAs (DElncRNAs) as biomarkers. Weighted gene co-expression network analysis (WGCNA) was applied to identify co-expressed DElncRNAs and differentially expressed mRNAs (DEmRNAs) associated with clinical indicators. We performed functional enrichment analysis on target genes and constructed a lncRNA-miRNA-mRNA ceRNA network. The expression of lncRNA and mRNAs in ceRNA network were validated via RT-qPCR.

RESULTS

By differential expression analyzing TCGA and GEO data, 6 up-regulated DElncRNAs were consistently identified, and their predictive performance were suggested to be considerable via ROC curve. 1998 DEmRNAs and 6 lncRNAs were involved in the construction of WGCNA network, in which the MEblue module was positively correlated with clinical stage. Functional enrichment analysis of this module suggested that the functions of DEmRNAs were closely involved in PI3K/Akt pathway. A ceRNA network composed of MSC-AS1, miR-429, COL4A1 and ITGAV was constructed. It was verified by RT-qPCR that the lncRNA and mRNAs in the ceRNA network were highly expressed in multiple LC tissues.

CONCLUSIONS

This study identified lncRNA MSC-AS1 as a potential biomarker of LC. Besides, we constructed a ceRNA network, which provides a basis for the research of ceRNA in LC.