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Background: Platelet-related exosomes (PREs) are microparticles secreted by platelets into the bloodstream and are implicated in various cancer processes. This study aims to identify critical genes involved in Breast Cancer (BC)-associated PREs and to evaluate their role in cancer prognosis. PLA2G4A was identified as a key gene through the use of machine learning techniques and various genomic analyses, providing a foundation for precision medicine in BC treatment.
Methods: Download cancer-related data from databases such as UCSC Xena and ExMdb, use LASSO Cox regression and various machine learning algorithms to screen genes associated with BC survival, and perform functional and pathway enrichment analysis. The expression, immune relevance, diagnostic efficacy, and drug sensitivity of the PLA2G4A gene in pan-cancer and BC were specifically analyzed. The function of PLA2G4A in BC was validated through experiments, and its drug response and molecular docking were predicted using various databases and software tools.
Results: Machine learning methods and LASSO Cox regression were applied to analyze the relationship between gene expression and BC survival. PLA2G4A was identified as a key gene associated with cancer prognosis, supported by analyses of differential gene expression, survival outcomes, single nucleotide variations (SNVs), and copy number variations (CNVs). Biological pathway analyses through KEGG, GO, and GSEA highlighted PLA2G4A's involvement in key cancer-related processes. In vitro studies, including cell scratch assays, Transwell migration assays, and EdU proliferation tests, demonstrated that overexpression of PLA2G4A inhibited the proliferation and migration of BC cells.
Conclusions: PLA2G4A plays a crucial role in the progression of BC, acting as a potential tumor-suppressor gene. The findings support its potential as a prognostic biomarker and further investigation is needed to explore its therapeutic potential in clinical settings.
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http://dx.doi.org/10.1007/s12672-025-03118-6 | DOI Listing |
J Eval Clin Pract
September 2025
Department of Orthopedics and Traumatology, Medical Faculty, University of Health Sciences, Antalya, Turkey.
Aims And Objective: The field of medical statistics has experienced significant advancements driven by integrating innovative statistical methodologies. This study aims to conduct a comprehensive analysis to explore current trends, influential research areas, and future directions in medical statistics.
Methods: This paper maps the evolution of statistical methods used in medical research based on 4,919 relevant publications retrieved from the Web of Science.
J Magn Reson Imaging
September 2025
Department of Neurology, Dell Medical School, University of Texas at Austin, Austin, Texas, USA.
Background: Cerebrovascular reactivity reflects changes in cerebral blood flow in response to an acute stimulus and is reflective of the brain's ability to match blood flow to demand. Functional MRI with a breath-hold task can be used to elicit this vasoactive response, but data validity hinges on subject compliance. Determining breath-hold compliance often requires external monitoring equipment.
View Article and Find Full Text PDFZhong Nan Da Xue Xue Bao Yi Xue Ban
May 2025
Department of Geriatric Pulmonary and Critical Care Medicine, Xiangya Hospital, Central South University; National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Changsha 410008.
Objectives: Non-small cell lung cancer (NSCLC) is associated with poor prognosis, with 30% of patients diagnosed at an advanced stage. Mutations in the and genes are important prognostic factors for NSCLC, and targeted therapies can significantly improve survival in these patients. Although tissue biopsy remains the gold standard for detecting gene mutations, it has limitations, including invasiveness, sampling errors due to tumor heterogeneity, and poor reproducibility.
View Article and Find Full Text PDFDermatitis
September 2025
From the Department of Dermatology, Venereology and Leprology, All India Institute of Medical Sciences (AIIMS), Bhopal, India.
Contact dermatitis (CD), which includes both allergic CD and irritant CD, is a common inflammatory condition that can pose significant diagnostic challenges. Although patch testing is the gold standard for identifying causative allergens for allergic contact dermatitis (ACD), it is time-consuming, subjective, and requires expert interpretation. Recent advancements in artificial intelligence (AI), particularly in machine learning (ML) and deep learning, have shown promise in improving the accuracy, efficiency, and accessibility of CD diagnosis and management.
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