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Identification of novel combined biomarkers in the diagnosis of multiple myeloma

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posted on 2021-12-06, 23:00 authored by Yanxia Jin, Yuxing Liang, Yanting Su, Lingyun Hui, Hailing Liu, Lu Ding, Fuling Zhou

Multiple myeloma (MM) is a haematological malignant disease with a clonal proliferation of plasma cells, and timely surveillance is helpful to improve the survival rate of patients with MM. However, there is a lack of simple and effective biomarkers for the diagnosis, prognosis, and residual disease evaluation of MM.

In the detection cohort, we used the samples from six newly diagnosed MM patients and six control subjects. Plasma proteins were labelled with dimethyl reagents and enriched by lectin AANL6, then the deglycosylated peptides were identified by LC-MS/MS. Differentially expressed proteins were used for further exploration. In the validation cohort, we used 90 newly diagnosed patients with MM and 70 cases of unrelated diseases as controls. The diagnosis performance was analysed by ROC analysis using SPSS.

In this study, we show, using lectin blots with AANL6, that glycosylation levels were higher in MM patients than in controls. After AANL6 enrichment, we detected 58 differentially expressed proteins using quantitative proteomics. We further validated one candidate Fibulin-1 (FBLN1). Using an Elisa assay, we showed that FBLN1 expression was increased in plasma of 90 cases of MM, and which was significantly correlated with DKK1 expression. ROC analysis showed that these two markers had a 95.7% specificity for determining the diagnosis of MM.

These data suggest that the MM cases display increased glycosylation after AANL6 enrichment and that the combined expression of FBLN1 and DKK1 can be used as an effective diagnostic biomarker.

Funding

This work was supported by the Science, Technology and Innovation Seed Fund Project [Grant Numbers cxpy20160001; cxpy20160012] at Zhongnan Hospital of Wuhan University; the Natural Science Foundation of China (NSFC) programme [Grant Numbers 81770179; 320000908]; the Natural Science Foundation of Hubei Province programme [Grant Number 2020CFB417].

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