Identification of biomarkers in multiple myeloma: A comprehensive study combining microarray analysis and Mendelian...

Area of Science:

• Oncology
• Genetics
• Immunology

Background:

• Multiple myeloma (MM) treatment advancements have been made, but relapse remains a significant clinical challenge.
• The underlying mechanisms driving MM progression and relapse are not fully understood.
• Identifying novel biomarkers is crucial for developing new therapeutic strategies for MM.

Purpose of the Study:

• To identify potential diagnostic and prognostic biomarkers for multiple myeloma (MM).
• To elucidate the role of identified biomarkers in MM pathogenesis and progression.
• To investigate the causal relationship between key genes and MM risk.

Main Methods:

• Utilized Gene Expression Omnibus (GEO) database for microarray data and clinical information of MM patients.
• Performed differential expression analysis and constructed protein-protein interaction (PPI) networks to identify hub genes.
• Employed receiver operating characteristic (ROC) curves, nomogram construction, functional enrichment analysis, and Mendelian randomization (MR) for validation and mechanistic insights.

Main Results:

• Topological analysis of the PPI network identified five hub genes, with myeloperoxidase (MPO) emerging as the most significant.
• MPO expression was significantly different between MM patients and healthy controls across multiple datasets.
• Functional enrichment analysis indicated MPO's strong association with immune-related pathways in MM.
• Mendelian randomization analysis confirmed a causal relationship between MPO and the risk of developing MM.

Conclusions:

• Myeloperoxidase (MPO) is validated as a promising biomarker for multiple myeloma (MM).
• MPO is potentially involved in the pathogenesis and progression of MM, particularly through immune-related mechanisms.
• The integration of gene expression analysis and MR provides a robust approach for biomarker discovery in complex diseases like MM.