An integrated machine learning and mendelian randomization approach identifies SERPING1 as a prognostic biomarker associated with CD8 + T-cell infiltration in DLBCL.

backgroundSERPING1, which encodes the C1 inhibitor (C1-INH) of the complement system, and plays a key regulator in regulating inflammatory responses and immune homeostasis. SERPING1 is downregulated in various disease, this downregulation occurs through the body's negative feedback resulting from the overactivation of the complement system in diseases such as infections and acute inflammatory responses. Additionally, SERPING1 is vital for tumor immunomodulation. Diffuse large B-cell lymphoma (DLBCL) is a common and aggressive type of non-Hodgkin lymphoma. More treatment options are becoming available for this disease. However, some patients still experience recurrence or even disease progression during treatment. Consequently, elucidating the molecular underpinnings of DLBCL's malignant behavior and identifying novel prognostic markers and therapeutic targets are paramount for improving patient outcomes.

methodsThis investigation utilized multi-omics integration combined with machine learning algorithms to identify CD8 + T cell-associated hub genes and clarify their roles in DLBCL pathogenesis. Data were obtained from public repositories including the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Utilizing multi-omics and machine learning techniques such as differential analysis, WGCNA, feature selection, scRNA-seq analysis, molecular docking, and Mendelian randomization, we identified four key hub genes (MAFB, TMEM176A, SERPING1, and C1QB) linked to CD8 + T cells, highlighting SERPING1 as central. They have potential value in the diagnosis, prognosis, and immunotherapy of DLBCL.

resultsAnalysis of six GEO datasets (GSE56315, GSE25638, GSE12453, GSE12195, GSE32018, GSE83632) and TCGA-DLBC RNA-sequencing data, following batch correction, revealed 209 differentially expressed genes (DEGs). CIBERSORT revealed significant immune infiltration disparities, with CD8 + T cells notably enriched in DLBCL. WGCNA identified the yellow module (151 genes) as strongly correlated with CD8 + T cell infiltration, yielding 40 core DEGs upon intersection with DEGs. Functional enrichment analysis highlighted their involvement in chemokine signaling and complement cascades. A machine learning framework utilizing 12 algorithms identified eight key genes: ANKRD22, C1QB, C1QC, CD163, MAFB, SERPING1, TMEM176A, and TNFSF13B. The combination of Least Absolute Shrinkage and Selection Operator (LASSO) with Linear Discriminant Analysis (LDA) demonstrated the highest diagnostic efficacy, achieving an area under the curve (AUC) of 0.799. Four hub genes (MAFB, TMEM176A, SERPING1, and C1QB) were identified from the eight key genes through the intersection of eight feature selection methods and validated using protein-protein interaction networks and receiver operating characteristic curve analysis, achieving an AUC greater than 0.9. SHapley Additive exPlanations analysis underscored the predictive dominance of MAFB, while a prognostic nomogram incorporating these genes demonstrated high accuracy (1-/3-/5-year calibration). scRNA-sequencing revealed hub gene enrichment in non-classical monocytes, and CellChat implicated their roles in intercellular communication via the APP/GALECTIN pathways. Molecular docking and dynamics confirmed the stable binding of SERPING1-doxorubicin (ΔG = - 7.6 kcal/mol), supported by the root mean square deviation/Rg stability.

conclusionSummary-data-based Mendelian randomization analysis identified a closed positive genetic association between SERPING1 overexpression and DLBCL incidence risk (P < 0.05), with no statistical evidence of horizontal pleiotropy, suggesting that SERPING1 overexpression may be a potential risk-related factor for DLBCL onset. SERPING1 is significantly upregulated in DLBCL tissues, suggesting that its expression is regulated in a disease-specific manner. SERPING1 exhibits a pattern characterized by "local selective activation and overall inhibition" through the unbalanced regulation of the complement system in the tumor microenvironment, acting as a pro-tumor hub gene in synergistic regulation with C1QB.