Loss of 14-3-3σ expression through DNA methylation has been associated with carcinogenesis and the prognosis for various cancer types. Detection of methylation of the gene in serum may be useful for diagnostic utility. The present study aimed to investigate the correlation between 14-3-3σ methylation level in 36 paired tumor tissues of non-small cell lung cancer (NSCLC) and matched serum using methylation-specific polymerase chain reaction. The prognostic significance of 14-3-3σ expression in 167 NSCLC was also evaluated using immunohistochemistry. Methylation of the 14-3-3σ gene was identified in all samples. The methylation level in the serum (mean 87.7%, range 64.6-100%) was higher compared with tumor (mean 46.7%, range 25.3-56.3%). However, no significant correlation between methylation levels in tissues and serums was observed (Spearman's correlation, -0.036; P=0.837). In the 167 tumor tissues, the majority of the cases (83.8%) exhibited negative expression. Adenocarcinoma is more likely to exhibit negative expression (91.4%) compared with squamous cell carcinoma (70.2%). No significant difference was identified in the overall survival according to 14-3-3σ expression status and 14-3-3σ expression did not demonstrated independent prognostic significance. In conclusion, NSCLC harbors certain levels of 14-3-3σ methylation in the tumor and the sera of patients. The clinical value of serum 14-3-3σ methylation should be further elucidated. Immunohistochemical expression 14-3-3σ protein has limited value on prognostic significance.
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http://dx.doi.org/10.3892/ol.2017.6881 | DOI Listing |
Plant Physiol Biochem
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Henan Engineering Research Center of Green Pesticide Creation & Intelligent Pesticide Residue Sensor Detection and School of Resources and Environment, Henan Institute of Science and Technology, Xinxiang, Henan, 453003, China. Electronic address:
Continuous misuse of difenoconazole (DFZ) results in farmland contamination, posing risks to crops and human health. Salicylic acid (SA) has been shown to enhance plant resistance and reduce pesticide phytotoxicity and accumulation. However, whether SA effectively reduces DFZ phytotoxicity and accumulation and its underlying mechanisms remain poorly understood.
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Department of Biology, Faculty of Science, Ege University, Izmir, Türkiye. Electronic address:
Salinization is a significant global issue causes irreversible damage to plants by reducing osmotic potential, inhibiting seed germination, and impeding water uptake. Seed germination, a crucial step towards the seedling stage is regulated by several hormones and genes, with the balance between abscisic acid and gibberellin being the key mechanism that either promotes or inhibits this process. Additionally, mucilage, a gelatinous substance, is known to provide protection against drought, herbivory, soil adhesion, and seed sinking.
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School of Pharmaceutical Sciences, Guizhou University, Guiyang, 550025, China. Electronic address:
Temozolomide, a widely used alkylating agent for glioblastoma treatment, faces significant challenges due to the development of resistance, which severely impacts patient survival. This underscores the urgent need for novel strategies to overcome this barrier. Focal adhesion kinase (FAK), an intracellular non-receptor tyrosine kinase, is highly expressed in glioblastoma cells and has been identified as a promising therapeutic target for anti-glioblastoma drug development.
View Article and Find Full Text PDFBiomed Phys Eng Express
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F. Joseph Halcomb III, MD, Department of Biomedical Engineering, University of Kentucky, 143 Graham Ave., Lexington, Kentucky, 40506, UNITED STATES.
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View Article and Find Full Text PDFBiomed Phys Eng Express
January 2025
Dept. Mechanical Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, Chiba, 263-8522, JAPAN.
Albumin and γ-globulin concentrations in subcutaneous adipose tissues (SAT) have been quantified by multivariate regression based on admittance relaxation time distribution (mraRTD) under the fluctuated background of sodium electrolyte concentration. The mraRTD formulates P = Ac + Ξ (P: peak matrix of distribution function magnitude ɣP and frequency τP, c: concentration matrix of albumin cAlb, γ-globulin Gloc, and sodium electrolyte Nac, A: coefficient matrix of a multivariate regression model, and Ξ: error matrix). The mraRTD is implemented by two processes which are: 1) the training process of A through the maximum likelihood estimation of P and 2) the quantification process of cAlb, Gloc, and Nac through the model prediction.
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