Computer methods and programs in biomedicine · 2025
Machine learning-assisted prognosis of multiple myeloma side population cells via SRGs and OCLR stemness index.
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Xiang Xufei, Yang Ruiyi, Li Jicong, Su Brian, Xu Zefang, An Yang
Аннотация
BACKGROUND AND OBJECTIVE: Relapse in Multiple Myeloma, driven by therapy-resistant cancer stem cells, necessitates the development of more specific and accurate prognostic models. Existing stemness indices often lack specificity for the unique biology of Multiple Myeloma. This study aimed to develop, validate, and optimize a novel prognostic gene signature derived from Side Population cells, a well-defined cancer stem cell-enriched subpopulation in Multiple Myeloma.
METHODS: A core stemness gene module was identified from Side Population cell transcriptomes (GSE109651) using Weighted Gene Co-expression Network Analysis, guided by a One-Class Logistic Regression-based stemness index. Stemness-Related Gene scores were computed from this module's key pathways via single-sample Gene Set Enrichment Analysis. A nonlinear programming algorithm was then employed to create an optimally weighted prognostic model. The model's performance was validated in independent cohorts (The Cancer Genome Atlas - Multiple Myeloma Research Foundation, GSE24080, GSE57317) using Cox proportional hazards modeling, and its clinical relevance was assessed via drug sensitivity (OncoPredict) and immunotherapy response (Tumor Immune Dysfunction and Exclusion) prediction.
RESULTS: The resulting Stemness-Related Gene score strongly correlated with the established mRNA stemness index (r=0.62, p<1×10-82). The hsa05222 pathway was identified as the dominant prognostic component (HR=12.765, p<0.0001) and was found to specifically modulate chemoresistance. In contrast, the composite Stemness-Related Gene score better predicted immune evasion potential. The final optimally weighted model, integrating these distinct facets, demonstrated superior prognostic accuracy, consistently outperforming existing benchmarks and simpler models across all validation cohorts.
CONCLUSIONS: This Side Population cell-derived, optimally weighted signature is a robust and multifaceted independent prognostic biomarker for Multiple Myeloma. By distinguishing between chemoresistance and immune evasion profiles, this framework provides a valuable tool to guide personalized, cancer stem cell-targeted therapeutic strategies.
METHODS: A core stemness gene module was identified from Side Population cell transcriptomes (GSE109651) using Weighted Gene Co-expression Network Analysis, guided by a One-Class Logistic Regression-based stemness index. Stemness-Related Gene scores were computed from this module's key pathways via single-sample Gene Set Enrichment Analysis. A nonlinear programming algorithm was then employed to create an optimally weighted prognostic model. The model's performance was validated in independent cohorts (The Cancer Genome Atlas - Multiple Myeloma Research Foundation, GSE24080, GSE57317) using Cox proportional hazards modeling, and its clinical relevance was assessed via drug sensitivity (OncoPredict) and immunotherapy response (Tumor Immune Dysfunction and Exclusion) prediction.
RESULTS: The resulting Stemness-Related Gene score strongly correlated with the established mRNA stemness index (r=0.62, p<1×10-82). The hsa05222 pathway was identified as the dominant prognostic component (HR=12.765, p<0.0001) and was found to specifically modulate chemoresistance. In contrast, the composite Stemness-Related Gene score better predicted immune evasion potential. The final optimally weighted model, integrating these distinct facets, demonstrated superior prognostic accuracy, consistently outperforming existing benchmarks and simpler models across all validation cohorts.
CONCLUSIONS: This Side Population cell-derived, optimally weighted signature is a robust and multifaceted independent prognostic biomarker for Multiple Myeloma. By distinguishing between chemoresistance and immune evasion profiles, this framework provides a valuable tool to guide personalized, cancer stem cell-targeted therapeutic strategies.