Abstract
Multiple myeloma (MM) is the second most commonly diagnosed hematological malignancy. Understanding the basic mechanisms of the metabolism in MM may lead to new therapies that benefit patients. We collected the gene expression profile data of GSE39754 and performed differential analysis. Furthermore, identify the candidate genes that affect the prognosis of the differentially expressed genes (DEGs) related to the metabolism. Enrichment analysis is used to identify the biological effects of candidate genes. Perform coexpression analysis on the verified DEGs. In addition, the candidate genes are used to cluster MM into different subtypes through consistent clustering. Use LASSO regression analysis to identify key genes, and use Cox regression analysis to evaluate the prognostic effects of key genes. Evaluation of immune cell infiltration in MM is by CIBERSORT. We identified 2821 DEGs, of which 348 genes were metabolic-related prognostic genes and were considered candidate genes. Enrichment analysis revealed that the candidate genes are mainly related to the proteasome, purine metabolism, and cysteine and methionine metabolism signaling pathways. According to the consensus clustering method, we identified the two subtypes of group 1 and group 2 that affect the prognosis of MM patients. Using the LASSO model, we have identified 10 key genes. The prognosis of the high-risk group identified by Cox regression analysis is worse than that of the low-risk group. Among them, PKLR has a greater impact on the prognosis of MM, and the prognosis of MM patients is poor when the expression is high. In addition, the level of immune cell infiltration in the high-risk group is higher than that in the low-risk group. In the summary, metabolism-related genes significantly affect the prognosis of MM patients through the metabolic process of MM patients. PKLR may be a prognostic risk factor for MM patients.
Highlights
Multiple myeloma (MM) is a hematological malignant tumor derived from abnormal monoclonal plasma cells. e main clinical symptoms include anemia, infection, bone destruction, and renal insufficiency [1]
Data Procession. e gene expression profiles of GSE39754 were downloaded from the Gene Expression Omnibus (GEO) database. e GSE39754 included gene expression profiles of CD138 purified myeloma plasma cells from 170 newly diagnosed MM patients and 6 CD138 purified plasma cells from healthy donors. e data were analyzed with the Affymetrix package. e differentially expressed genes (DEGs) were calculated by limma package [13]
To identify gene expression abnormalities of multiple myeloma, we identified differentially expressed genes between MM and controls in GSE39754
Summary
Multiple myeloma (MM) is a hematological malignant tumor derived from abnormal monoclonal plasma cells. e main clinical symptoms include anemia, infection, bone destruction, and renal insufficiency [1]. Multiple myeloma (MM) is a hematological malignant tumor derived from abnormal monoclonal plasma cells. Due to the lack of specificity of these clinical symptoms for the diagnosis of MM, this has led to the current lack of time-sensitive methods for the diagnosis of MM [2]. MM can be alleviated by many clinical drugs and treatment techniques, for example, high-dose chemotherapy, hematopoietic stem cell inhibition, and proteasome inhibitors (bortezomib and immunomodulator lenalidomide) [7]. These treatments cannot make MM patients escape the fate of disease recurrence and aggravation [8]. These treatments cannot make MM patients escape the fate of disease recurrence and aggravation [8]. erefore, exploring new therapeutic targets and disease prognostic indicators for MM is of great significance for the treatment of MM patients
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