Oral squamous cell carcinoma, the most frequently occurring malignant head and neck tumour, generally exhibits poor prognosis and metastases are the main cause of death. The discovery of reliable prognostic indicators of tumour progression could greatly improve clinical practice. MicroRNAs are involved in the regulation of basic cellular processes such as cell proliferation, differentiation, and apoptosis. Since miRNAs have been shown to be abnormally expressed in different tumours their importance as potential cancer prognostic indicators is increasing. To define the role of miRNA in OSCC tumours we investigated the expression profile of 15 OSCC (8 without metastasis and 7 with lymph node metastasis) using microarray analysis. Thirteen miRNA were significantly overexpressed (miR-489, miR-129, miR-23a, miR-214, miR-23b, miR-92, miR-25, miR-210, miR-212, miR-515, miR-146b, miR-21, miR-338) and 6 miRNA were underexpressed (miR-520h, miR-197, miR-378, miR-135b, miR-224, miR-34a) in oral tumours. Underexpression of mir-155, let-7i, mir-146a was found to characterize progression to metastastatic tumours. Further investigations will elucidate whether differentially expressed miRNAs will help to better classify OSCCs, thus improving diagnoses and patient care.

This report describes a retrospective analysis ofthe month of birth distribution of 2124 children with respiratory allergy in the Rome district between 1964 and 1985, in comparison with the total live births in the same district over the same period. Ofthe 2124 children, 1685 had positive skin testsand or RAST only to mites, and 439 only to grass pollen {P< < 0-001). A significant relationship was found between grass or mite sensitization and the month of birlh. A high proportion of children born in June-September had mite allergy (/'<0005), and even higher was that of those born in March-May with grass sensitivity (P< <0003), compared with the total live birth distribution in the Rome district in the same years as the children examined. These results are consistent with the idea that allergy may be associated with a period of susceptibility to sensitization in early infancy.

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Conventional/targeted chemotherapies and ionizing radiation (IR) are being used both as monotherapies and in combination for the treatment of epithelial ovarian cancer (EOC). Several studies show that these therapies might favor oncogenic signaling and impede anti-tumor responses. MiR-200c is considered a master regulator of EOC-related oncogenes. In this study, we sought to investigate if chemotherapy and IR could influence the expression of miR-200c-3p and its target genes, like the immune checkpoint PD-L1 and other oncogenes in a cohort of EOC patients’ biopsies. Indeed, PD-L1 expression was induced, while miR-200c-3p was significantly reduced in these biopsies post-therapy. The effect of miR-200c-3p target genes was assessed in miR-200c transfected SKOV3 cells untreated and treated with olaparib and IR alone. Under all experimental conditions, miR-200c-3p concomitantly reduced PD-L1, c-Myc and β-catenin expression and sensitized ovarian cancer cells to olaparib and irradiation. In silico analyses further confirmed the anti-correlation between miR-200c-3p with c-Myc and β-catenin in 46 OC cell lines and showed that a higher miR-200c-3p expression associates with a less tumorigenic microenvironment. These findings provide new insights into how miR-200c-3p could be used to hold in check the adverse effects of conventional chemotherapy, targeted therapy and radiation therapy, and offer a novel therapeutic strategy for EOC.

Ovarian cancer is one of the most common gynecological malignancies, ranking third after cervical and uterine cancer. High-grade serous ovarian cancer (HGSOC) is one of the most aggressive subtype, and the late onset of its symptoms leads in most cases to an unfavourable prognosis. Current predictive algorithms used to estimate the risk of having Ovarian Cancer fail to provide sufficient sensitivity and specificity to be used widely in clinical practice. The use of additional biomarkers or parameters such as age or menopausal status to overcome these issues showed only weak improvements. It is necessary to identify novel molecular signatures and the development of new predictive algorithms able to support the diagnosis of HGSOC, and at the same time, deepen the understanding of this elusive disease, with the final goal of improving patient survival. Here, we apply a Machine Learning-based pipeline to an open-source HGSOC Proteomic dataset to develop a decision support system (DSS) that displayed high discerning ability on a dataset of HGSOC biopsies. The proposed DSS consists of a double-step feature selection and a decision tree, with the resulting output consisting of a combination of three highly discriminating proteins: TOP1, PDIA4, and OGN, that could be of interest for further clinical and experimental validation. Furthermore, we took advantage of the ranked list of proteins generated during the feature selection steps to perform a pathway analysis to provide a snapshot of the main deregulated pathways of HGSOC. The datasets used for this study are available in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data portal (https://cptac-data-portal.georgetown.edu/).