Dataset Information

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.

ABSTRACT:

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.

SUBMITTER: Wang Z

PROVIDER: S-EPMC8039704 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The aim of the present study was to identify novel microRNA (miRNA) or long noncoding RNA (lncRNA) signatures of laryngeal cancer recurrence and to investigate the regulatory mechanisms associated with this malignancy. Datasets of recurrent and nonrecurrent laryngeal cancer samples were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus database (GSE27020 and GSE25727) to examine differentially expressed miRNAs (DE‑miRs), lncRNAs (DE‑lncRs) and mRNAs (DEGs). miRNA‑mRNA and lncRNA‑miRNA networks were constructed by investigating the associations among these RNAs in various databases. Subsequently, the interactions identified were combined into a competing endogenous RNA (ceRNA) regulatory network. Feature genes in the miRNA‑mRNA network were identified via topological analysis and a recursive feature elimination algorithm. A support vector machine (SVM) classifier was established using the betweenness centrality values in the miRNA‑mRNA network, consisting of 32 optimal feature‑coding genes. The classification effect was tested using two validation datasets. Furthermore, coding genes in the ceRNA network were examined via pathway enrichment analyses. In total, 21 DE‑lncRs, 507 DEGs and 55 DE‑miRs were selected. The SVM classifier exhibited an accuracy of 94.05% (79/84) for sample classification prediction in the TCGA dataset, and 92.66 and 91.07% in the two validation datasets. The ceRNA regulatory network comprised 203 nodes, corresponding to mRNAs, miRNAs and lncRNAs, and 346 lines, corresponding to the interactions among RNAs. In particular, the interactions with the highest scores were HLA complex group 4 (HCG4)‑miR‑33b, HOX transcript antisense RNA (HOTAIR)‑miR‑1‑MAGE family member A2 (MAGEA2), EMX2 opposite strand/antisense RNA (EMX2OS)‑miR‑124‑calcitonin related polypeptide α (CALCA) and EMX2OS‑miR‑124‑γ‑aminobutyric acid type A receptor γ2 subunit (GABRG2). Gene enrichment analysis of the genes in the ceRNA network identified that 11 pathway terms and 16 molecular function terms were significantly enriched. The SVM classifier based on 32 feature coding genes exhibited high accuracy in the classification of laryngeal cancer samples. miR‑1, miR‑33b, miR‑124, HOTAIR, HCG4 and EMX2OS may be novel biomarkers of recurrent laryngeal cancer, and HCG4‑miR‑33b, HOTAIR‑miR‑1‑MAGEA2 and EMX2OS‑miR‑124‑CALCA/GABRG2 may be associated with the molecular mechanisms regulating recurrent laryngeal cancer.