Identification Of Biomarkers For Early Diagnosis Of Multiple Myeloma By Weighted Gene Coexpression Network Analysis And Their Clinical Relevance

Backgrounds and objective:Multiple myeloma（MM）is a malignant tumor with clonal dysplasia of myeloma cells,and it is also one of the most common hematological malignancies.The disease is characterized by unlimited proliferation of plasma cells in bone marrow like tumor cells,and most cases will secrete monoclonal immunoglobulin or its fragment（M protein）,resulting in organ or tissue damage.Bone pain,anemia,renal insufficiency,infection and hypercalcemia are common clinical manifestations.The incidence rate incidence rate of multiple myeloma in western developed countries is around 4/100,000,which is higher than that of Chinese multiple myeloma（0.6-1/100,000）.The average onset age of MM in China is 58 years old,and patients under 30 years old are relatively rare.In recent years,with the continuous optimization and improvement of the treatment scheme of multiple myeloma,the survival rate of patients with multiple myeloma has been rapidly improved.However,the unclear pathogenesis of this kind of disease makes its diagnosis difficult.In addition,multiple myeloma will eventually enter the stage of relapsing and refractory multiple myeloma after multiple chemotherapy treatments,and the efficacy of this stage is not optimistic..Therefore,we strive to conduct a detailed study on the early diagnosis of multiple myeloma,so as to achieve early detection and early treatment However,there are currently limited biomarkers for the early diagnosis of Multiple myeloma.Therefore,new strategies are urgently needed to improve our understanding of the molecular mechanisms of multiple myeloma,especially in terms of tumor evolution,and to provide targets for the treatment and early detection of multiple myeloma.In recent years,through weighted gene Coexpression network analysis,a number of studies have applied WGCNA to provide functional explanations of systems biology and provide some implications for cancer pathophysiology.But we found the WGCNA through the literature on the application of multiple myeloma is few,therefore,this study aimed to explore by WGCNA is easy to detect new biomarkers in multiple myeloma,and using q RT-PCR in clinical patients with multiple myeloma,targeted for the diagnosis of multiple myeloma treatment and medication designed to provide new ideas and leads.Meterials and methods:Differentially expressed genes（DEGs）were screened using GSE47552 from NCBI‐GEO database.The potential relationship between co-expression modules and multiple myeloma was studied by weighted gene co-expression network analysis（WGCNA）and the hub genes were selected.Subsequently,overlapping DEGs and hub genes were used as the key genes of multiple myeloma.We evaluated the diagnostic value of key genes by receiver Operating characteristic（ROC）curves.Then,t test was used to screen the expression levels of key genes with diagnostic value for multiple myeloma in the GSE47552 dataset.Finally,the expression levels of some key genes in multiple myeloma were tested and proved by q RT-PCR,and the correlation between hub genes and multiple myeloma was analyzed.Results:41 multiple myeloma and 20 monoclonal immunoglobulinemia of unknown significance（MGUS）samples were selected for DEGs identification.278 differentially expressed genes were screened by Limma package of R language.Darkgreen module,greenyellow module,turquoise module containing 1963 genes were most significantly correlated with multiple myeloma.238 key genes were screened after the intersection of important modules with differentially expressed genes.In addition,we identified a gene,named SNORNA,is rarely studied in multiple myeloma,and ROC curve analysis showed that SNORA71 A in our prediction model had a good prediction effect（p=0.07）.The area under ROC curve of it was 0.75.The expression of SNORA71 A was increased in the bone marrow samples of multiple myeloma（P=0.05）.QRT-PCR results showed that SNORA71 A was up-regulated in 51 patients specimens compared to the health people group.（P<0.05）.The expression level of SNORA71 A in stage I group A group II group A group III group B was higher than that in healthy subjects（P=0.0453,P=0.0262,P<0.0001,respectively）;The linear correlation analysis showed that creatinine was positively correlated with SNORA71A（r=0.49P=0.0002）.Subsequently,51 patients were grouped according to whether there was kidney injury,and the difference analysis was conducted.It was found that the expression level of SNORA71 A in peripheral blood and bone marrow samples of multiple myeloma was significantly higher than that of normal people,and the expression level of SNORA71 A in the group with kidney injury was lower than that in the group without kidney injury（P = 0.0548,P = 0.0496,P = 0.0015,P =0.0098,respectively）Conclusions:This study found that SNORA71 A was upregulated and associated with clinical stage in multiple myeloma,it suggests that SNORA71 A be used as a novel biomarker for early diagnosis and a potential therapeutic target in multiple myeloma.