[Screening of Differential Expression Autophagy Genes Related to the Prognosis of Diffuse Large B-Cell Lymphoma and Establishment of an Autophagy Model].

Abstract

Objective: To screen the differential expression of diffuse large B-cell lymphoma (DLBCL) autophagy-related gene (ARG), explore the mechanism of differential expression of autophagy gene (DEARG) in the occurrence and development of DLBCL and establish a prognostic model.

Methods: Using the NCICCR database containing clinical information and gene expression profile data of 481 patients with DLBCL and the HADb database containing 232 ARGs, the differential expression of ARG in DLBCL was determined by R language, the relationship between ARG and the occurrence and development of DLBCL was analyzed by GO and KEGG, the polygene prognostic model was established by Cox regression algorithm, the survival curve was drawn by Kaplan-Meier method, and the reliability of the prognostic model was evaluated by ROC curve.

Results: A total of 122 DEARGs were extracted from lymph node samples of 481 patients with DLBCL and 5 normal lymph nodes, including 4 up-regulated genes and 118 down-regulated genes. GO enrichment mainly focused on ontological annotations such as mitochondrial autophagy, autophagy regulation, and cell response to external stimuli. KEGG enrichment was mainly concentrated in cell senescence, NOD-like receptor signal pathway, PI3K-Akt signal pathway, and PD-1/PD-L1 signal pathway. Survival analysis was performed on 230 samples with complete clinical information. Univariate Cox analysis showed that 20 ARGs were significantly correlated with overall survival of DLBCL patients. Nine prognostic ARGs (HIF1A, CAPN1, ITPR1, PRKCQ, TRAIL, HDAC1, TSC2, NRG3, and MAPK3) were screened by multivariate Cox regression to establish DLBCL ARG prognostic model. Kaplan-Meier survival curve analysis showed that there was significant difference in survival rate between high risk group and low risk group (P<0.001). Multivariate Cox regression analysis showed that international prognostic index and risk value were independent prognostic indicators of DLBCL patients (P<0.05), the area under ROC curve was 0.762 and 0.747, respectively.

Conclusion: DLBCL ARG prognostic model can be used to predict the prognosis of patients, but it still needs to be confirmed by a large sample of clinical studies.