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题目:: SNPs in PTGS2 and LTA predict pain and quality of life in long term lung cancer survivors.
作者:: Rausch(Sarah M),Gonzalez(Brian D),Clark(Matthew M),Patten(Christi),Felten(Sara),Liu(Heshan),Li(Yafei),Sloan(Jeff),Yang(Ping)
状态:: 发布时间2012-06-05 , 更新时间 2016-10-19
期刊:: Lung Cancer
摘要:: Lung cancer survivors report the lowest quality of life relative to other cancer survivors. Pain is one of the most devastating, persistent, and incapacitating symptoms for lung cancer survivors. Prevalence rates vary with 80-100% of survivors experiencing cancer pain and healthcare costs are five times higher in cancer survivors with uncontrolled pain. Cancer pain often has a considerable impact on quality of life among cancer patients and cancer survivors. Therefore, early identification, and treatment is important. Although recent studies have suggested a relationship between single nucleotide polymorphisms (SNPs) in several cytokine and inflammation genes with cancer prognosis, associations with cancer pain are not clear. Therefore, the primary aim of this study was to identify SNPs related to pain in lung cancer survivors.,Participants were enrolled in the Mayo Clinic Lung Cancer Cohort upon diagnosis of their lung cancer. 1149 Caucasian lung cancer survivors (440 surviving <3 years; 354 surviving 3-5 years; and 355 surviving >5 years) completed study questionnaires and had blood DNA samples available. Ten SNPS from PTGS2 and LTA genes were selected based on the serum-based studies in the literature. Outcomes included pain, and quality of life as measured by the SF-8.,Of the 10 SNPs evaluated in LTA and PTGS2 genes, 3 were associated with pain severity (rs5277; rs1799964), social function (rs5277) and mental health (rs5275). These results suggested both specificity and consistency of these inflammatory gene SNPs in predicting pain severity in lung cancer survivors.,These results provide support for genetic predisposition to pain severity and may aid in identification of lung cancer survivors at high risk for morbidity and poor QOL.
语言:: eng
DOI:

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