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Integrated Bioinformatics Analysis of mRNAs and miRNAs Identified Potential Biomarkers of Oral Squamous Cell Carcinoma.

ABSTRACT: Background: Oral cancer is a frequently encountered neoplasm of the head and neck region, being the eighth most common type of human malignancy worldwide. Despite improvement in its control, morbidity and mortality, rates have improved little in the past decades. The present investigations about gene interaction and pathways still could not clear the appearance and development of oral squamous cell carcinoma (OSCC), completely. The aim of this study is to investigate the key genes and microRNAs interaction in OSCC. Materials and Methods: The microarray datasets GSE13601 and GSE98463, including mRNA and miRNA profiles, were extracted from the GEO database and were analyzed using GEO2R. Functional and pathway enrichment analyses were performed by using the DAVID database. The protein-protein interaction (PPI) network was constructed and analyzed using STRING database and Cytoscape software, respectively. Finally, miRDB was applied to predict the targets of the differentially expressed miRNAs (DEMs). Results: Totally, 97 differentially expressed genes (DEGs) were found in OSCC, including 66 up-regulated and 31 down-regulated genes. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses showed that up-regulated genes were significantly enriched in movement of cell or subcellular component, cell adhesion, biological adhesion, cellular localization, apoptotic signaling pathway, while the downregulated genes were enriched in muscle system process and oxidation-reduction process. From the PPI network, the top 10 nodes with the highest degree were detected as hub genes. In addition, 18 DEMs were screened, which included 7 up-regulated and 11 down-regulated miRNAs. STAT1 was potentially targeted by three miRNAs, including has-miR- 6825-5P, has-miR-4495, and has-miR-5580-3P. Conclusion: The roles of DEMs such as hsa-mir-5580-3p in OSCC through interactions with DEGs CD44, ACLY, ACTR3, STAT1, LAMC2 and YWHAZ may offer a suitable candidate biomarker pattern for diagnosis, prognosis and treatment processes in OSCC.

SUBMITTER: Amiri-Dashatan N

PROVIDER: S-EPMC7568896 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Integrated Bioinformatics Analysis of mRNAs and miRNAs Identified Potential Biomarkers of Oral Squamous Cell Carcinoma.

Amiri-Dashatan Nasrin N Koushki Mehdi M Jalilian Ali A Ahmadi Nayeb Ali NA Rezaei-Tavirani Mostafa M

Asian Pacific journal of cancer prevention : APJCP 20200629 6

Background: Oral cancer is a frequently encountered neoplasm of the head and neck region, being the eighth most common type of human malignancy worldwide. Despite improvement in its control, morbidity and mortality, rates have improved little in the past decades. The present investigations about gene interaction and pathways still could not clear the appearance and development of oral squamous cell carcinoma (OSCC), completely. The aim of this study is to investigate the key genes and microRNAs ...[more]

PMID: 32597160

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Project description:Background: Calcific aortic valve disease (CAVD) is the most common valvular heart disease in the aging population, resulting in a significant health and economic burden worldwide, but its underlying diagnostic biomarkers and pathophysiological mechanisms are not fully understood. Methods: Three publicly available gene expression profiles (GSE12644, GSE51472, and GSE77287) from human Calcific aortic valve disease (CAVD) and normal aortic valve samples were downloaded from the Gene Expression Omnibus database for combined analysis. R software was used to identify differentially expressed genes (DEGs) and conduct functional investigations. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to identify key feature genes as potential biomarkers for Calcific aortic valve disease (CAVD). Receiver operating characteristic (ROC) curves were used to evaluate the discriminatory ability of key genes. The CIBERSORT deconvolution algorithm was used to determine differential immune cell infiltration and the relationship between key genes and immune cell types. Finally, the Expression level and diagnostic ability of the identified biomarkers were further validated in an external dataset (GSE83453), a single-cell sequencing dataset (SRP222100), and immunohistochemical staining of human clinical tissue samples, respectively. Results: In total, 34 identified DEGs included 21 upregulated and 13 downregulated genes. DEGs were mainly involved in immune-related pathways such as leukocyte migration, granulocyte chemotaxis, cytokine activity, and IL-17 signaling. The machine learning algorithm identified SCG2 and CCL19 as key feature genes [area under the ROC curve (AUC) = 0.940 and 0.913, respectively; validation AUC = 0.917 and 0.903, respectively]. CIBERSORT analysis indicated that the proportion of immune cells in Calcific aortic valve disease (CAVD) was different from that in normal aortic valve tissues, specifically M2 and M0 macrophages. Key genes SCG2 and CCL19 were significantly positively correlated with M0 macrophages. Single-cell sequencing analysis and immunohistochemical staining of human aortic valve tissue samples showed that SCG2 and CCL19 were increased in Calcific aortic valve disease (CAVD) valves. Conclusion: SCG2 and CCL19 are potential novel biomarkers of Calcific aortic valve disease (CAVD) and may play important roles in the biological process of Calcific aortic valve disease (CAVD). Our findings advance understanding of the underlying mechanisms of Calcific aortic valve disease (CAVD) pathogenesis and provide valuable information for future research into novel diagnostic and immunotherapeutic targets for Calcific aortic valve disease (CAVD).

Dataset Information

Integrated Bioinformatics Analysis of mRNAs and miRNAs Identified Potential Biomarkers of Oral Squamous Cell Carcinoma.

Publications

Integrated Bioinformatics Analysis of mRNAs and miRNAs Identified Potential Biomarkers of Oral Squamous Cell Carcinoma.

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