Aims: To evaluate the expression and methylation status of the gene in early and advanced American Joint Committee on Cancer (AJCC) stages of ocular surface squamous neoplasia (OSSN) and to correlate its association with clinicopathological features and survival.
Methods: Sixty-four (35 early and 29 advanced AJCC stage) patients with OSSN formed part of this study and were followed up for 36-58 (mean 48±3.6) months. Immunohistochemical expression of the p16 protein and methylation status of the gene were determined by methylation-specific PCR.
Results: Overexpression of p16 was observed in 18/64 (28%) and hypermethylation in 35/64 (54.7%) OSSN cases. A gradual significant increase in the expression of p16 (0%-48%, P=0.03) and decrease in its methylation (75%-16%, P=0.001) was observed with disease progression from early to advanced tumour stage. Overexpression of p16 was significantly associated with palpebral location and diffuse growth pattern in both early and advanced T stage. Hypermethylation of p16 was significantly associated with history of longer sunlight exposure in both early and advanced T stage of OSSN cases. In advanced T stage, p16 overexpression was associated with reduced disease-free survival (P=0.02) and poor prognosis (HR, 0.2; P=0.03).
Conclusions: OSSN patients presenting at an advanced AJCC stage with p16 overexpression may require more aggressive treatment. Epigenetic inactivation of the gene due to sunlight exposure could be responsible for pathogenesis of OSSN.
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http://dx.doi.org/10.1136/bjophthalmol-2017-311276 | DOI Listing |
Updates Surg
January 2025
Public Health Department, Naples "Federico II" University, AOU "Federico II" - Via S. Pansini 5, 80131, Naples, Italy.
Robotic approach is slowly rising in metabolic surgery, and laparoscopy is still considered the gold standard for Sleeve Gastrectomy. Aim of our study was to assess and compare outcomes of RSG through a matched comparison with LSG. Retrospective search of prospectively maintained database of our surgical department was carried out find all consecutive patients who underwent RSG from April 2023 to August 2024.
View Article and Find Full Text PDFJ Assist Reprod Genet
January 2025
Department of Gynaecology, Cancer Hospital of Dalian University of Technology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Liaoning Province, Shenyang, 110001, The People's Republic of China.
Background: The "Healthy China" initiative, along with advancements in technology for cancer diagnosis and treatment, has significantly enhanced outcomes for patients with gynecologic tumors. The trends of late marriage and delayed childbirth have led to an increasing number of women diagnosed with gynecologic cancers who are seeking fertility preservation in China. This issue is critical yet often overlooked in clinical practice.
View Article and Find Full Text PDFSports Med
January 2025
IRMES-UPR 7329, Institut de Recherche Médicale et d'Épidémiologie du Sport, Université Paris Cité, 11 Avenue du Tremblay, 75012, Paris, France.
The scientific literature on talent identification is extensive, with significant advancements made over the past 30 years. However, as with any field, the translation of research into practice and its impact on the field have been slower than anticipated. Indeed, recent findings highlight a pervasive relative age effect, the effects of maturation being often overlooked, disparate populations between young and senior performers, and a necessity to embrace a holistic approach.
View Article and Find Full Text PDFJ Neurol
January 2025
Department of Medical and Surgical Sciences, University of Foggia, 71122, Foggia, Italy.
Background: Multiple sclerosis (MS) involves a complex interplay between immune-mediated inflammation and neurodegeneration. Recent advances in biomarker research have provided new insights into the molecular underpinnings of MS, including ferritin, neurogranin, Triggering Receptor Expressed on Myeloid cells 2 (TREM2), and neurofilaments light chain.
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Handb Exp Pharmacol
January 2025
Genentech Inc, South San Francisco, CA, USA.
In this chapter, we envision the future of Quantitative Systems Pharmacology (QSP) which integrates closely with emerging data and technologies including advanced analytics, novel experimental technologies, and diverse and larger datasets. Machine learning (ML) and Artificial Intelligence (AI) will increasingly help QSP modelers to find, prepare, integrate, and exploit larger and diverse datasets, as well as build, parameterize, and simulate models. We picture QSP models being applied during all stages of drug discovery and development: During the discovery stages, QSP models predict the early human experience of in silico compounds created by generative AI.
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