Identification of a three-gene-based prognostic model in multiple myeloma using bioinformatics analysis

Area of Science:

• Hematology
• Oncology
• Genomics

Background:

• Multiple myeloma (MM) is the second most common hematological malignancy with a poor prognosis.
• The underlying pathogenesis of MM remains incompletely understood.
• There is a critical need for improved prognostic models in MM.

Purpose of the Study:

• To identify novel prognostic biomarkers for multiple myeloma (MM).
• To develop and validate a gene expression-based prognostic model for MM patients.
• To assess the independent predictive value of the developed model.

Main Methods:

• Downloaded gene expression data for MM from the Gene Expression Omnibus (GEO) database.
• Identified differentially expressed genes (DEGs) and constructed a protein-protein interaction (PPI) network.
• Utilized Weighted Correlation Network Analysis (WGCNA) to identify key modules and genes.
• Employed Cox regression analysis to build and validate a prognostic model based on key genes.

Main Results:

• Identified 178 differentially expressed genes (DEGs) between MM cases and normal controls.
• Selected 47 key genes from DEGs and hub genes within relevant WGCNA modules.
• Developed a three-gene prognostic model (LYVE1, RNASE1, RNASE2) with independent predictive capacity.
• Validated the model's accuracy using Kaplan-Meier curves and ROC analysis.

Conclusions:

• A novel three-gene prognostic model for multiple myeloma (MM) has been successfully constructed.
• This model demonstrates independent and accurate prognostic capability for MM patients.
• The identified gene signature holds potential for predicting overall survival in MM.