کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10909027 | 1087822 | 2014 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Polyclonal serum IgM level identifies a subgroup of multiple myeloma patients with low-risk clinicobiological features and superior survival
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
بیوشیمی، ژنتیک و زیست شناسی مولکولی
تحقیقات سرطان
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
Normal plasma cells (PCs) are either undetectable or outnumbered by the myelomatous PC compartment in bone marrow of multiple myeloma (MM). However, residual normal PCs have been detected in a minority of symptomatic MM patients with superior survival. The number of normal PCs is also an important factor to identify monoclonal gammopathy of undetermined significance (MGUS)-like MM. We speculate that the polyclonal serum IgM level in non-IgM myelomas may reflect the number of residual normal PCs. Here we investigated the prognostic relevance of polyclonal serum IgM level in a series of 485 newly diagnosed symptomatic MM (NDMM) patients. Our results showed that symptomatic MM patients with polyclonal IgM more than 0.5Â g/L displayed a favorable baseline clinical feature, together with a significantly lower frequency of high-risk cytogenetic abnormalities. This group of patients had a significantly prolonged progression-free survival (PFS) and overall survival (OS) regardless of thalidomide or bortezomib therapy. Furthermore, the superior outcome was independent of the depth of response. Our findings suggest that polyclonal IgM level is capable of identifying a group of symptomatic MM patients with distinct clinicobiological characteristics and favorable survival, similar with MGUS-like MM.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Leukemia Research - Volume 38, Issue 6, June 2014, Pages 666-672
Journal: Leukemia Research - Volume 38, Issue 6, June 2014, Pages 666-672
نویسندگان
Gang An, Huijun Wang, Xiaoqi Qin, Lihui Shi, Yan Xu, Shuhui Deng, Weiwei Sui, Guoqing Zhu, Hongjing Yao, Shuhua Yi, Yu Qin, Fei Li, Mu Hao, Kun Ru, Junyuan Qi, Tao Cheng, Jianxiang Wang, Hong Chang, Lugui Qiu,