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Researchers from the Bioinformatics Department at the Cyprus Institute of Neurology and Genetics (CING) have published a new study in Cancers, offering significant insights for the accurate staging and early detection of multiple myeloma (MM), a serious form of blood cancer.
The research, led by Dr. Nestoros Karathanasis, a postdoctoral researcher at CING, and Dr. George Spyrou, the department's director, was supported by the European ELMUMY program. This initiative investigates risk factors for the progression of monoclonal gammopathies of undetermined significance (MGUS) to multiple myeloma.
In their study, the team used machine learning techniques and gene expression data to accurately determine the stage of MM—whether MGUS or MM—and predict the risk of progression from asymptomatic MGUS to symptomatic MM. The use of multiple gene expression data combined with artificial intelligence algorithms led to high accuracy in identifying MM stages and predicting disease progression.
The AI models also pinpointed key molecular features related to biological pathways like PI3K-Akt, MAPK, Wnt, and mTOR, all of which play a role in the development of MM.
This study is among the first to employ gene expression data to classify MM stages and forecast the transition from asymptomatic MGUS to symptomatic multiple myeloma.
Multiple myeloma is a blood cancer that impacts plasma cells in the bone marrow, leading to abnormal protein production, bone damage, anemia, immune system dysfunction, and kidney issues. Common symptoms include fatigue, bone pain, and frequent infections. Diagnosis typically involves blood tests, urine tests, and bone marrow biopsies, with treatment options including medications, immunotherapy, and bone marrow transplants.
