A predictive model based on immune related genes for diffuse large B cell lymphoma (DLBCL)

Anti-tumor immunity is the front line of human response to malignancy, which may shed light on early diagnosis of diffuse large B cell lymphoma (DLBCL). We aim at the introduction of immune-related genes to bring new insight in the establishment of a predictive model to facilitate the diagnosis of DLBCL and guide its therapy.

First, we identified immune-related genes in DLBCL via GeneCards. With these genes, we conducted least absolute shrinkage and selection operator (LASSO) regression to select the genes with significant contribution to DLBCL and established a validated risk model to generate risk score. Later, a nomogram combining risk score with other common clinical index (age, gender, stage) was established to comprehensively evaluate the survival probability of patients with DLBCL. To guide the treatment, we implemented drug sensitivity analysis. To further understand the modulation and explore potential biomarkers, we constructed a competing endogenous RNA (ceRNA) network.

Hence, we established an immune-related genes-based risk model to predict the survival and progression of DLBCL. Validation of this risk model in internal test dataset and additional external validation datasets confirmed the robust performance of this model. The risk score was also found to be correlated with advanced stages and age over 60 years. We also found four novel second-line chemotherapies that can be used to treat patients with different risk scores.

Overall, we established a predictive risk model based on immune-related genes from transcription level. This risk model can be utilized in clinical practice to facilitate physicians in diagnosing patients with DLBCL at an early stage and guide the treatment of DLBCL.