Machine learning-based prognostic model and immune landscape analysis in multiple myeloma.
TL;DR
Novel molecular markers and immune regulatory pathways are uncovered, providing insight into MM prognosis and possible therapeutic strategies, using integrated bioinformatics, machine learning, and experimental approaches.
Abstract
Objective
Multiple myeloma (MM) is a plasma cell malignancy characterized by complex cytogenetic abnormalities and a supportive bone marrow microenvironment that promotes tumor growth and drug resistance. Despite the lack of curative options, novel targeted therapies show promise in overcoming resistance and improving patient outcome. Therefore, this study aimed to identify and validate prognostic biomarkers and underlying mechanisms in MM using integrated bioinformatics, machine learning, and experimental approaches.
Methods
This study used bioinformatics analyses to identify key candidate genes and molecular pathways in MM. Differentially expressed genes (DEGs) were identified from datasets (GSE5900, GSE7116, GSE146649), followed by functional enrichment, protein-protein interaction network, and survival analyses.
Results
GAS2L3 was identified as a key gene in MM, strongly associated with prognosis. A GAS2L3-based risk model predicted patient survival. Functional assays, including Cell Counting Kit-8, Transwell, and terminal deoxynucleotidyl transferase dUTP nick end labeling, showed that GAS2L3 silencing suppressed cell proliferation, migration, and invasion, while promoting apoptosis in RPMI-8226 cells. Immune infiltration analysis revealed significant associations between GAS2L3 and immune cell populations, including memory B cells and natural killer cells. Single-cell transcriptomic analysis identified the HLA-E-KLRK1 signaling axis as a key regulator of MM progression.
Conclusions
This study uncovered novel molecular markers and immune regulatory pathways, providing insight into MM prognosis and possible therapeutic strategies.