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Machine learning-based cuproptosis prognostic signature Identifies LDHA as a critical regulator of progression in multiple myeloma

bioinformatics
molecular-biology
internal-medicine
oncology
data-mining-and-machine-learning

Abstract

Background: Multiple myeloma (MM) is a cancer of plasma cells and is characterized by a poor prognosis. However, the role of cuproptosis in MM remains uncertain.

Methods: Cuproptosis-related genes in MM were initially identified through single-cell analysis of the GSE161801 dataset from the Gene Expression Omnibus (GEO) database and Weighted Correlation Network Analysis (WGCNA) of the GSE24080 dataset. Subsequently, utilizing expression data from five multicenter cohorts comprising 2387 patients, we identified 107 prognostic genes consistently observed across these cohorts. Employing machine learning algorithms, we evaluated 95 unique combinations to select the optimal algorithm for developing a cuproptosis prognosis model (CPPS) based on the average C-index from four test groups.

Results: The reliable outcomes obtained from Kaplan-Meier (KM) analysis, univariate Cox regression, ROC curve analysis, and calibration curve analyses in the training cohort and four testing cohorts validate the efficacy of CPPS in predicting MM outcomes. Our CPPS model holds significant clinical relevance for MM, as individuals with low CPPS levels exhibit a poorer prognosis, heightened immune cell infiltration, increased expression of specific immune checkpoints, and enhanced sensitivity to immunotherapy. Furthermore, CPPS remains predictive of patient prognosis in other tumors. RT-qPCR analysis revealed elevated levels of SNRPE, ILF2, LDHA, and PDHA1 in MM patient samples compared to those in healthy individuals, whereas FLNA, TAGLN2, and ANXA2 expression was lower in the MM group than in the control cohort. The CCK-8 assay and flow cytometry confirmed that LDHA knockdown inhibits MM cell proliferation and promotes apoptosis. Immunofluorescence experiments have indicated that LDHA may promote MM progression and development via the NF-κB pathway.

Conclusion: Our study provides a valuable tool for guiding future clinical and personalized treatment approaches for MM.

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