Identification and Experimental Validation of NOP10 as a Biomarker for Diffuse Large B-Cell Lymphoma.

INTRODUCTION

Diffuse large B-cell lymphoma (DLBCL) is the most common non- Hodgkin lymphoma. Despite its high prevalence, treatment options remain limited, and molecular mechanisms underlying its pathogenesis remain poorly understood. This study aimed to identify potential biomarkers for DLBCL by integrating microarray analysis, Mendelian randomization (MR), and experimental validation.

MATERIALS AND

METHODS

DLBCL-related microarray datasets were downloaded from the Gene Expression Omnibus database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify hub genes in DLBCL. Moreover, we integrated genome-wide association studies data with expression quantitative trait loci to identify genes with potential causal relationships to DLBCL. The predictive power of the identified biomarker gene was evaluated using receiver operating characteristic (ROC) curves. Functional experiments were conducted to elucidate the biological roles of this gene.

RESULTS

Differential expression analysis and WGCNA identified 119 differentially expressed genes and 156 central genes, resulting in the identification of 16 hub genes in DLBCL. MR analysis revealed 188 genes with significant causal associations with DLBCL, ultimately identifying nucleolar protein 10 (NOP10) as a key biomarker. NOP10 demonstrated strong predictive performance, with the area under the curve values consistently above 0.852 across all datasets. Experimental validation in DLBCL cell lines showed that NOP10 knockdown significantly inhibited cell proliferation, induced apoptosis, and reduced the migratory capacity.

DISCUSSION: Although recent studies have made progress in identifying biomarkers associated with lymphoma onset and progression, establishing causality remains challenging due to reverse causation and confounding factors. The integration of MR in this study addressed these limitations by inferring the causality between exposure and outcome. Our findings were experimentally validated. The combination of microarray analysis and MR in our analytical approach provides a robust framework for identifying biomarker genes in various diseases and guiding the development of future therapeutic strategies.

CONCLUSION

Our study has identified NOP10 as a promising biomarker of DLBCL, providing potential insights into the molecular mechanisms underlying this disease. Moreover, a multi-method approach integrating microarray analysis, MR, and experimental validation has established a robust framework for advancing biomarker discovery and identifying therapeutic targets for various diseases.