Dataset Information

Role of microRNA-296-3p in the malignant transformation of sinonasal inverted papilloma.

ABSTRACT: Inverted papilloma (IP) is a benign tumor occurring in the nasal cavity and paranasal sinuses. It is reported that 5-15% of IPs undergo malignant transformation into squamous cell carcinoma (SCC), and the role of microRNAs (miRNA/miR) in this process remains to be elucidated. In the present study, whole miRNA profiles using samples of IP and SCC were investigated, in order to detect the function of miRNA in the carcinogenesis of IP. Samples from IPs (n=5) and SCC lesions (n=5), which arose from IPs, were used for miRNA analysis. A total of 200 miRNAs exhibited a >2-fold differential expression between IP and SCC. miR-296-3p was markedly upregulated in SCC with a 23-fold difference. Computational analysis indicated that miR-296-3p targeted PTEN, which regulates the phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt) signaling pathway and PTEN is involved in the carcinogenesis of SCC. miR-296-3p directly regulated PTEN expression in head and neck cancer cells, with PTEN protein levels decreased in 4/19 the SCCs (21.0%), as compared with those in the IPs (76.4%). Positive p21 staining was observed in 64.7% of IPs; this was a significantly increased rate compared with that for SCCs (26.3%, P=0.0086). The results of the present study indicated that there were marked changes in the miRNA expression signature during the malignant transition. miR-296-3p may serve an important role in the malignant transformation of IPs via the regulation of PTEN, combined with the subsequent inhibition of the PI3K/Akt signaling pathway, and may be a novel agent for cancer prevention.

SUBMITTER: Kakizaki T

PROVIDER: S-EPMC5494725 | biostudies-other | 2017 Jul

REPOSITORIES: biostudies-other

ACCESS DATA

Similar Datasets

Project description:BackgroundDNA methylation may be involved in the regulation of malignant transformation from sinonasal inverted papilloma (SNIP) to squamous cell carcinoma (SCC). The study of gene methylation changes and screening of differentially methylated loci (DMLs) are helpful to predict the possible key genes in the malignant transformation of SNIP-SCC.Materials and methodsMicroarray dataset GSE125399 was downloaded from the Gene Expression Omnibus (GEO) database and differentially methylated loci (DMLs) were analyzed using R language (Limma package). ClusterProfiler R package was used to perform Gene Ontology (GO) analysis on up-methylated genes and draw bubble maps. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and its visualization analysis were analyzed to speculate the possible key Genes in SNIP-SCC malignant transformation. Subsequently, SNIP cases archived in our department were collected, tissue microarray was made, and immunohistochemical staining was performed to analyze the expression levels of UCKL1, GSTT1, HLA-G, MAML2 and NRGN in different grades of sinonasal papilloma tissues.ResultsAnalysis of dataset GSE125399 identified 56 DMLs, including 49 upregulated DMLs and 7 downregulated DMLs. Thirty-one genes containing upregulated DNA methylation loci and three genes containing downregulated DNA methylation loci were obtained by methylation microarray annotation analysis. In addition, KEGG pathway visualization analysis of 31 up-methylated genes showed that there were four significantly up-methylated genes including UCKL1, GSTT1, HLA-G and MAML2, and one significantly down-methylated gene NRGN. Subsequently, compared with non-neoplasia nasal epithelial tissues, the expression of HLA-G and NRGN was upregulated in grade I, II, III and IV tissues, while the expression of MAML2 was lost. The protein expression changes of MAML2 and NRGN were significantly negatively correlated with their gene methylation levels.ConclusionsBy analyzing the methylation dataset, we obtained four up-regulated methylation genes UCKL1, GSTT1, HLA-G and MAML2 and one down-regulated gene NRGN. MAML2, a tumor suppressor gene with high methylation modification but loss of protein expression, and NRGN, a tumor gene with low methylation modification but upregulated protein expression, can be used as biological indicators to judge the malignant transformation of SNIP-SCC.

Project description:Objective To explore the best MRI radiomics-based machine learning model for differentiation of sinonasal inverted papilloma (SNIP) and malignant sinonasal tumor (MST), and investigate whether the combination of radiomics features and clinic–radiological features can produce a superior diagnostic performance. Methods The database of 247 patients with SNIP (n=106) or MST (n=141) were analyzed. Dataset from scanner A were randomly divided into training set (n=135) and test set 1 (n=58) in a ratio of 7:3, and dataset from scanner B and C were used as an additional independent test set 2 (n=54). Fourteen clinic-radiological features were analyzed by using univariate analysis, and those with significant differences were applied to construct clinical model. Based on the radiomics features extracted from single sequence (T2WI or CE-T1WI) and combined sequence, four commonly used classifiers (logistic regression (LR), support vector machine (SVM), decision tree (DT) and k-nearest neighbor (KNN)) were employed to constitute twelve different machine learning models, and the best-performing one was confirmed as the optimal radiomics model. Furthermore, a combined model incorporated best radiomics feature subsets and clinic-radiological features was developed. The diagnostic performances of these models were assessed by the area under the receiver operating characteristic (ROC) curve (AUC) and the calibration curves. Results Five clinic-radiological features (age, convoluted cerebriform pattern sign, heterogeneity, adjacent bone involvement and infiltration of surrounding tissue) were considered to be significantly different between the tumor groups (P < 0.05). Among the twelve machine learning models, the T2WI-SVM model exhibited optimal predictive efficacy for classification tasks on the two test sets, with the AUC of 0.878 and 0.914, respectively. For three types of diagnostic models, the combined model achieved highest AUC of 0.912 (95%CI: 0.807-0.970) and 0.927 (95%CI: 0.823-0.980) for differentiation of SNIP and MST in test 1 and test 2 sets, which performed prominently better than clinical model (P=0.011, 0.005), but not significantly different from the optimal radiomics model (P=0.100, 0.452). Conclusion The machine learning model based on T2WI sequence and SVM classifier achieved best performance in differentiation of SNIP and MST, and the combination of radiomics features and clinic-radiological features significantly improved the diagnostic capability of the model.

Dataset Information

Role of microRNA-296-3p in the malignant transformation of sinonasal inverted papilloma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets