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Pharmacogenetics of Neoadjuvant MAP Chemotherapy in Localized Osteosarcoma: A Study Based on Data from the GEIS-33 Protocol.
Pharmaceutics
December 2024
Medical Oncology Department, Hospital de la Santa Creu i Sant Pau, 08041 Barcelona, Spain.
Osteosarcoma is a rare disease, but it is the most frequent malignant bone tumor. Primary treatment consists of preoperative MAP (methotrexate (MTX), doxorubicin and cisplatin) chemotherapy followed by surgery and adjuvant chemotherapy. Pathological response to preoperative chemotherapy is one of the most important prognostic factors, but molecular biomarkers are lacking.
View Article and Find Full Text PDFInvestigation of and gene polymorphisms and their association with susceptibility to colorectal cancer.
Radiol Oncol
January 2025
1Biochemistry Section, Institute of Chemical Sciences, University of Peshawar, Peshawar, Pakistan.
Background: This study investigates the association of single nucleotide polymorphism in glutathione S transferase P1 (rs1695 and rs1138272) and phosphatase and TENsin homolog (rs701848 and rs2735343) with the risk of colorectal cancer (CRC).
Patients And Methods: In this case-control study, 250 healthy controls and 200 CRC patients were enrolled. All subjects were divided into 3 groups: healthy control, patients, and overall (control + patients).
Polymorphic variants of the genes for enzymes of the antioxidant system, apoptosis and inflammation as potential predictors of myocardial infarction.
Vavilovskii Zhurnal Genet Selektsii
November 2024
Institute of Biochemistry and Genetics - Subdivision of the Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia.
Myocardial infarction (MI) is a multifactorial polygenic disease that develops as a result of a complex interaction of numerous genetic factors and the external environment. Accordingly, the contribution of each of them separately is usually not large and may significantly depend on the state of other accompanying factors. The purpose of the study was to search for informative predictors of MI risk based on polygenic analysis of polymorphic variants of (1) the antioxidant defense enzyme genes PON1 (rs662), PON2 (rs7493), CAT (rs1001179), MSRA (rs10098474) and GSTP1 (rs1695); (2) the apoptosis genes CASP8 (rs3834129), TP53 (rs1042522) and BCL2 (rs12454712); and (3) the inflammation genes CRP (rs1205), CX3CR1 (rs3732378), IL6 (rs1800795) and CCL2 (rs1024611).
View Article and Find Full Text PDFGenetic polymorphisms, methylation, and expression levels in the GSTP1 and MGMT genes in urothelial bladder tumors.
Gene
March 2025
State University of Londrina (UEL), Department of General Biology, Center of Biological Sciences, Londrina, PR 86057-970, Brazil. Electronic address:
Background: Alteration in DNA repair and metabolism genes can affect the maintenance of DNA integrity or xenobiotics metabolism, potentially leading to DNA damage accumulation. The present study investigated the association between polymorphisms in Glutathione S-Transferase Pi 1 (GSTP1, rs1695) and O-6-Methylguanine-DNA Methyltransferase (MGMT, rs2308321) genes with urothelial bladder cancer (UBC) susceptibility and prognosis. Furthermore, the methylation patterns of the promoter region of these genes were analyzed in tumor and non-tumor bladder tissues, besides MGMT gene expression in tumor samples.
View Article and Find Full Text PDFMachine learning approaches and genetic determinants that influence the development of type 2 diabetes mellitus: a genetic association study in Brazilian patients.
Braz J Med Biol Res
December 2024
Laboratório de Patologia Molecular, Instituto de Ciências Biológicas, Universidade Federal de Goiás, Goiânia, GO, Brasil.
This genetic association study including 120 patients with type 2 diabetes mellitus (T2DM) and 166 non-diabetic individuals aimed to investigate the association of polymorphisms in the genes GSTM1 and GSTT1 (gene deletion), GSTP1 (rs1695), ACE (rs4646994), ACE2 (rs2285666), VEGF-A (rs28357093), and MTHFR (rs1801133) with the development of T2DM in the population of Goiás, Brazil. Additionally, the combined effects of these polymorphisms and the possible differences between sexes in susceptibility to the disease were evaluated. Finally, machine learning models were integrated to select the main risk characteristics for the T2DM diagnosis.
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