GEP analyses have been successful to classify MM, define risk, identify therapeutic targets, predict treatment response, and understand drug resistance.This generated large amounts of publicly available data that could benefit from easy-to-use bioinformatics resources to analyze them.
Gene expression levels of FUT 8, ST6GAL1, B4GALT1, RECK, and BACH2 identified from publicly available GEP data supported the glycomic changes seen in MM compared to control.
The MM Survival Index14 (MMSI14) was developed from GEP data sets of 22 normal plasma cells (NPC), 5 MM cell lines (MMCL), 44 monoclonal gammopathy of undetermined significance (MGUS), and 351 newly diagnosed MM patients.
Serum levels of sIL-6r can be used as an independent prognostic indicator and further stratify the GEP 70-gene low-risk group to identify an intermediate-risk group in multiple myeloma (MM).