RESUMO
The aim of the present study is to examine the expression level of peripheral mir-21 in multiple myeloma (MM) patients and to determine its clinical significance. MM patients (30), monoclonal gammopathy of undetermined significance (MGUS) patients (14), and normal controls (20) were recruited to determine the serum level of ß2-MG, IgA and IgM, IgG, λ, κ, TP, ALB, Hb, LDH, and Ca(2+). Gene expression of mir-21 was quantified by SYBR green real-time fluorescent quantitative PCR. We found that the expression level of serum mir-21 in the MM group was significantly higher than the MGUS group and the NC group (P < 0.01). According to the ISS installment, the level of mir-21, lgG, κ, and ALB in the MM group in stage I differed from that in stages II and III. The level of IgA, ß2-MG in stage III was higher as compared with stage I and II (P < 0.05 and P < 0.01).The levels of mir-21, κ, (κ+λ), IgG, (IgG + IgA + IgM), and ß2-MG in MM patients were positively correlated with ALB (P < 0.01). Based on the results, miR-21 plays an important role as an oncogene. Mir-21 may be important in the occurrence, development, and disease prognosis of MM.
Assuntos
Regulação Neoplásica da Expressão Gênica , MicroRNAs/genética , Gamopatia Monoclonal de Significância Indeterminada/metabolismo , Mieloma Múltiplo/metabolismo , Adulto , Idoso , Idoso de 80 Anos ou mais , Biomarcadores Tumorais , Feminino , Humanos , Imunoglobulinas/sangue , Masculino , MicroRNAs/sangue , Pessoa de Meia-Idade , Gamopatia Monoclonal de Significância Indeterminada/sangue , Gamopatia Monoclonal de Significância Indeterminada/genética , Mieloma Múltiplo/sangue , Mieloma Múltiplo/genética , Regulação para CimaRESUMO
Studying the network of protein-protein interactions (PPIs) will provide valuable insights into the inner workings of cells. It is vitally important to develop an automated, high-throughput tool that efficiently predicts protein-protein interactions. This study proposes a new model for PPI prediction based on the concept of chaos game representation and the wavelet transform, which means that a considerable amount of sequence-order effects can be incorporated into a set of discrete numbers. The advantage of using chaos game representation and the wavelet transform to formulate the protein sequence is that it can more effectively reflect its overall sequence-order characteristics than the conventional correlation factors. Using such a formulation frame to represent the protein sequences means that the random forest algorithm can be used to conduct the prediction. The results for a large-scale independent test dataset show that the proposed model can achieve an excellent performance with an accuracy value of about 0.86 and a geometry mean value of about 0.85. The model is therefore a useful supplementary tool for PPI predictions. The predictor used in this article is freely available at http://www.jci-bioinfo.cn/PPI.