Identification of Pathogenesis and Prognoses of Relapse in Myeloma by Bioinformatic Method

Background: Multiple myeloma (MM), the second most hematological malignancy, the molecular mechanism and pathogenesis of the relapse of MM is poorly understood. This study aimed to identify novel prognostic model for MM and explore potential mechanism of relapse. Methods: Gene expression data,clinical data(GSE24080) and HTseq-Counts files were downloaded from Gene Expression Omnibus (GEO) and TCGA database. Co-expression modules of genes were built by Weighted Correlation Network Analysis (WGCNA).KEGG and GO enrichment analysis were performed in each module. TATFs (tumor-associated transcription factors) were retrieved from the Cistrome. Twenty-two immune cell compositions was calculated by CIBERSORT algorithm.Univariate and multivariate Cox congression were performed and a predictive model by prognostic genes was constructed,the predictive power of the model was evaluated by Kaplan–Meier curve and time-dependent receiver operating characteristic (ROC) curves. Results: A total of 940 DEGs were identified,and in WGCNA analysis, yellow, brown and sky-blue modules were most associated with clinic traits.The yellow module related with the cell cycle and the brown and sky-blue modules correlated with cytokine and its receptors, where the M2 macrophage fraction is positively correlated with CCL18, CCL2, CCL8, CXCL12 and CCl23 were positively correlated with plasma cells by Cibersort analysis.Prognostic genes were identified and two genes (TPX2,PRAM1) were finally identified to construct a risk model for predicting EFS.