Enhancing staging in multiple myeloma using an m6A regulatory gene-pairing model

Area of Science:

• Oncology
• Genetics
• Molecular Biology

Background:

• Multiple myeloma (MM) is a cancer of plasma cells, making accurate prognosis prediction difficult.
• Current prognostic models require enhancement for personalized treatment strategies.

Purpose of the Study:

• To develop and validate a novel gene-pairing prognostic risk model for multiple myeloma (MM).
• To assess the model's ability to predict treatment response and differentiate between MM subtypes.

Main Methods:

• Utilized a nested LASSO method to build a prognostic model based on m6A regulatory genes.
• Validated the model on a cohort of 2088 newly diagnosed MM patients.
• Performed single-cell analysis and integrated data from MM cell lines and patient samples.

Main Results:

• The m6A gene-pairing model effectively stratified MM patients into high-risk and low-risk groups (cutoff: -0.133).
• Demonstrated strong predictive performance for response to combination therapy (AUC=0.9) in relapsed/refractory patients.
• Successfully distinguished between smoldering MM, MM, and plasma cell leukemia using specific cutoffs.
• Identified ADAT2 and NUP153 as potential therapeutic targets for high-risk MM.
• Integration with the International Staging System (ISS) improved prognostic accuracy.

Conclusions:

• The developed m6A gene-pairing risk model offers a robust tool for MM prognosis and treatment selection.
• This model supports precision medicine approaches by enabling better patient stratification and identification of therapeutic vulnerabilities.