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Two miRNA prognostic signatures of head and neck squamous cell carcinoma: A bioinformatic analysis based on the TCGA dataset.

ABSTRACT: MicroRNAs(miRNAs) are maladjusted in multifarious malignant tumor and can be considered as both carcinogens and tumor-inhibiting factor. In the present study, we analyzed the miRNAs expression profiles and clinical information of 481 patients with head and neck squamous cell carcinoma (HNSCC) through the TCGA dataset to identify the prognostic miRNAs signature. A total of 114 significantly differentially expressed miRNAs (SDEMs) were identified, consisting of 60 up-adjusted and 54 down-adjusted miRNAs. The Kaplan-Meier survival method identified the prognostic function of 2 miRNAs (miR-4652-5p and miR-99a-3P). Univariate and multivariate Cox regression analyses indicated that the 2 miRNAs were significant prognostic elements of HNSCC. Furthermore, bioinformatic analysis was conducted by means of 4 online gene predicted toolkits to recognize the target genes, and enrichment analysis was performed on the target genes by DAVID. The outcomes depicted that target genes were correlated with calcium, as well as cell proliferation, circadian entrainment, EGFR, PI3K-Akt-mTOR, and P53 signaling pathways. Finally, the PPI network was conducted in view of STRING database and Cytoscape. Eight hub genes were identified by CytoHubba and MCODE app, respectively, CBL, SKP1, H2AFX, HGF, POLR2F, UBE2I, VAMP2, and GNAI2 genes. As a result, we identified 2 miRNAs signatures, 8 hub genes, and significant signaling pathways for estimating the prognosis of HNSCC. In order to further explore the molecular mechanism of HNSCC occurrence and development, more comprehensive basic and clinical studies are needed.

SUBMITTER: Wu C

PROVIDER: S-EPMC7163094 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Head and neck squamous cell carcinomas (HNSCCs) are highly aggressive tumors with rapid progression and poor prognosis. Human papillomavirus (HPV) infection has been identified as one of the most important carcinogens for HNSCC. As an early event in HNSCC, infection with HPV leads to altered immune profiles in the tumor microenvironment (TME). The TME plays a key role in the progression and transformation of HNSCC. However, the TME in HNSCC is a complex and heterogeneous mix of tumor cells, fibroblasts, different types of infiltrating immune cells, and extracellular matrix. Biomarkers relevant to the TME, and the biological role of these biomarkers, remain poorly understood. To this end, we performed comprehensive analysis of the RNA sequencing (RNA-Seq) data from tumor tissue of 502 patients with HNSCC and healthy tissue of 44 control samples. In total, we identified 4,237 differentially expressed genes, including 2,062 upregulated and 2,175 downregulated genes. Further in-depth bioinformatic analysis suggested 19 HNSCC tumor tissue-specific genes. In the subsequent analysis, we focused on the biomarker candidates shown to be significantly associated with unfavorable patient survival: ITGA5, PLAU, PLAUR, SERPINE1, TGFB1, and VEGFC. We found that the expression of these genes was negatively regulated by DNA methylation. Strikingly, all of these potential biomarkers are profoundly involved in the activation of the epithelial-mesenchymal transition (EMT) pathway in HNSCCs. In addition, these targets were found to be positively correlated with the immune invasion levels of CD4+ T cells, macrophages, neutrophils, and dendritic cells, but negatively correlated with B-cell infiltration and CD8+ T-cell invasion. Notably, our data showed that the expression levels of ITGA5, PLAU, PLAUR, SERPINE1, and TGFB1 were significantly overexpressed in HPV-positive HNSCCs compared to normal controls, indicating the potential role of these biomarkers as transformation and/or malignant progression markers for HNSCCs in patients with HPV infection. Taken together, the results of our study propose ITGA5, PLAU, PLAUR, SERPINE1, and TGFB1 as potential prognostic biomarkers for HNSCCs, which might be involved in the HPV-related TME remodeling of HNSCC. Our findings provide important implications for the development and/or improvement of patient stratification and customized immunotherapies in HNSCC.

Project description:OBJECTIVES:Head and neck squamous cell carcinoma (HNSCC) shows a remarkable heterogeneity between tumors, which may be captured by a variety of quantitative features extracted from diagnostic images, termed radiomics. The aim of this study was to develop and validate MRI-based radiomic prognostic models in oral and oropharyngeal cancer. MATERIALS AND METHODS:Native T1-weighted images of four independent, retrospective (2005-2013), patient cohorts (n = 102, n = 76, n = 89, and n = 56) were used to delineate primary tumors, and to extract 545 quantitative features from. Subsequently, redundancy filtering and factor analysis were performed to handle collinearity in the data. Next, radiomic prognostic models were trained and validated to predict overall survival (OS) and relapse-free survival (RFS). Radiomic features were compared to and combined with prognostic models based on standard clinical parameters. Performance was assessed by integrated area under the curve (iAUC). RESULTS:In oral cancer, the radiomic model showed an iAUC of 0.69 (OS) and 0.70 (RFS) in the validation cohort, whereas the iAUC in the oropharyngeal cancer validation cohort was 0.71 (OS) and 0.74 (RFS). By integration of radiomic and clinical variables, the most accurate models were defined (iAUC oral cavity, 0.72 (OS) and 0.74 (RFS); iAUC oropharynx, 0.81 (OS) and 0.78 (RFS)), and these combined models outperformed prognostic models based on standard clinical variables only (p < 0.001). CONCLUSIONS:MRI radiomics is feasible in HNSCC despite the known variability in MRI vendors and acquisition protocols, and radiomic features added information to prognostic models based on clinical parameters. KEY POINTS:• MRI radiomics can predict overall survival and relapse-free survival in oral and HPV-negative oropharyngeal cancer. • MRI radiomics provides additional prognostic information to known clinical variables, with the best performance of the combined models. • Variation in MRI vendors and acquisition protocols did not influence performance of radiomic prognostic models.

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Two miRNA prognostic signatures of head and neck squamous cell carcinoma: A bioinformatic analysis based on the TCGA dataset.

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