Identification of a 14-Gene Prognostic Signature for Diffuse Large B Cell Lymphoma (DLBCL)

Although immunotherapy is a potential strategy to resist cancers, due to the inadequate acknowledge, this treatment is not always effective for diffuse large B cell lymphoma (DLBCL) patients. Based on the current situation, it is critical to systematically investigate the immune pattern. According to the result of univariate and multivariate cox proportional hazards, LASSO regression and Kaplan-Meier survival analysis on immune-related genes (IRGs), a prognostic signature, containing 14 IRGs (AQP9, LMBR1L, FGF20, TANK, CRP, ORM1, JAK1, BACH2, MTCP1, IFITM1, TNFSF10, FGF12, RFX5, and LAP3), was built. This model was validated by external data, and performed well. DLBCL patients were divided into low- and high-risk groups, according to risk scores from risk formula. The results of CIBERSORT showed that different immune status and infiltration pattern were observed in these two groups. Gene set enrichment analysis (GSEA) indicated 12 signaling pathways were significantly enriched in the high-risk group, such as natural killer cell-mediated cytotoxicity, toll-like receptor signaling pathway, and so on. In summary, 14 clinically significant IRGs were screened to build a risk score formula. This formula was an accurate tool to provide a certain basis for the treatment of DLBCL patients.

Hematological factors associated with immunity, inflammation, and metabolism in patients with systemic lupus erythematosus: Data from a Zhuang cohort in Southwest China

Introduction

Hematological parameters play important role in multiple diseases. This study was to investigate the possible association of the routine hematological parameters involved in immunity, inflammation, and metabolism with systemic lupus erythematosus (SLE) in patients of Zhuang ethnicity in Guangxi, southwest China.

Methods

The medical records of 195 Zhuang SLE patients between January 2013 and November 2018 were retrospectively reviewed. Random forest algorithm and multivariate logistic regression were used to identify the feature hematological parameters in patients with SLE. Association rules were explored between each parameter and immunity‐ (IgG, IgA, IgM, C3, and C4), inflammation‐ (ESR, hs‐CRP, and CAR), and metabolism‐ (TG, TC, HDL‐C, LDL‐C, TP, PA, ALB, and UA) related indexes.

Results

Random forest algorithm and logistic regression analysis showed that neutrophil‐to‐lymphocyte ratio (NLR), red blood cell distribution width (RDW), and platelet‐to‐lymphocyte ratio (PLR) were the feature parameters for distinguishing SLE patients from healthy controls. According to the ROC curves, the optimal cutoff values to predict SLE were 1.98 for NLR, 13.35 for RDW, and 145.64 for PLR. Association rule analysis showed that NLR was strongly associated with C3, hs‐CRP, TG, ALB, and UA; RDW was strongly associated with C3, C4, hs‐CRP, TG, and ALB; PLR was strongly associated with IgG, hs‐CRP, HDL‐C, and UA.

Conclusions

Neutrophil‐to‐lymphocyte ratio, RDW, and PLR may serve as effective predictors of dysregulation in immunity, inflammation, and metabolism. These three indicators may be potential for cardiovascular risk assessment in Zhuang SLE patients in southwest China.