Dataset Information

Prediction of the mechanism of miRNAs in laryngeal squamous cell carcinoma based on the miRNA-mRNA regulatory network.

ABSTRACT: In this study, a bioinformatics analysis is conducted to screen differentially expressed miRNAs and mRNAs in laryngeal squamous cell carcinoma (LSCC). Based on this information, we explored the possible roles of miRNAs in the pathogenesis of LSCC. The RNA-Seq data from 79 laryngeal cancer samples in the Gene Expression Omnibus (GEO) database were sorted. Differentially expressed miRNAs and mRNAs in LSCC are screened using the PERL programming language, and it was analysed by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The miRNA-mRNA regulatory network of LSCC is constructed using Cytoscape software. Then, quantitative real-time PCR (QRT- PCR), Cell Counting Kit-8 (CCK8) and flow cytometry analysis we are used to further validate key miRNAs. We identified 99 differentially expressed miRNAs and 2,758 differentially expressed mRNAs in LSCC tissues from the GEO database. Four more important miRNAs displaying a high degree of connectivity are selected, these results suggest that they play an important role in the pathogenesis of LSCC. As shown in the present study, we identified specific miRNA-mRNA networks associated with the occurrence and development of LSCC through bioinformatics analysis. We found a miRNA molecule closely related to LSCC based on miRNA-mRNA network: miR-140-3p was down-regulated in LSCC. In addition, the potential antitumor effect of miR-140-3p in LSCC was verified in the experiment, and it was proved that overexpression of miR-140-3p could inhibit the proliferation of LSCC cells and promote cell apoptosis, suggesting that miR-140-3p may be a potential tumor marker in LSCC.

SUBMITTER: Ma J

PROVIDER: S-EPMC8395572 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Background: Laryngeal squamous cell carcinoma (LSCC) is one of the most common malignancy in the respiratory tract and could reduce the quality of life seriously like dyspnea, dysphonia and dysphagia. Moreover, 5-year survival rate has decreased over the past 40 years. This study was designed to identify mRNAs that related to prognosis in LSCC to enable early detection and outcome improvement. Methods: Gene expression profiles from Gene Expression Omnibus (GEO) (GSE59102, GSE84957) and The Cancer Genome Atlas (TCGA) were analyzed to identify differentially expressed genes (DEGs) with the help of bioinformatics tools. Functional enrichment analyses including Gene Ontology (GO) and pathway analysis were carried out to investigate the role of those genes and underlying molecular mechanisms in LSCC. Cox's regression analyses (univariate, LASSO and multivariate in order) were utilized to identify DEGs related with patients' overall survival and a 4-mRNA-based prognostic risk score model was established. Univariate and multivariate Cox's regression analyses were then performed on LSCC data (90 patients left) to identify independent predictors of OS, including the signature and clinicopathologic variables. The prognostic value of the gene signature was further validated and the genes were analyzed by GEPIA to get pan-cancer expression profiles. Results: 444 differentially expressed mRNAs (250 up-regulated, 194 down-regulated) were identified based on the threshold of fold change > 2 and adjusted p value < 0.05. Univariate Cox's regression analysis showed that high risk score (HR: 3.056, 95% confidence interval [CI]: 0.135-0.649, p<0.001) and female (HR: 0.296, 95% CI: 2.020-4.624, p=0.002) were associated with relatively poor prognosis. Further multivariate Cox's regression analysis indicated that risk score and gender were independent prognostic factors (p<0.05). The risk score model could stratify patients into high- and low‑risk groups, which presents significantly differential overall survival (p= 8.252e-04). The AUCs of 1-, 3- and 5-year OS were 0.724, 0.783 and 0.818, respectively. Conclusions: Our study provides evidence that the four-mRNA signature could serve as a biomarker to predict prognosis in LSCC, especially in long-term.

Dataset Information

Prediction of the mechanism of miRNAs in laryngeal squamous cell carcinoma based on the miRNA-mRNA regulatory network.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets