NK 细胞相关预后模型刻画弥漫大 B 细胞淋巴瘤的免疫景观与治疗疗效

NK cells are essential for the detection, identification and prediction of cancer. However, so far, there is no prognostic risk model based on NK cell-related genes to predict the prognosis and treatment outcome of DLBCL patients. This study aimed to explore a risk assessment model that could accurately predict the prognosis and treatment efficacy of DLBCL.

Bioinformatics analysis of the expression profiles of DLBCL samples in the GEO database was performed. Cox regression and LASSO regression analysis were used to determine NK cell-related genes associated with patient's prognosis. Based on these genes, a risk assessment model was constructed to predict the prognosis of patients and the effectiveness of treatment. Finally, qRT-PCR was used to verify the expression of gene tags in clinical samples.

We identified seven prognosis-related NK cell-related genes (MAP2K1, PRKCB, TNFRSF10B, IL18, LAMP1, RASGRP1, and SP110), and DLBCL patients were divided into low- and high-risk groups based on these genes. Survival analysis showed that the prognosis of patients with low-risk group was better. Pathway enrichment analysis showed that the differentially expressed genes between the two risk groups were related to immune response pathways. Compared with the high-risk group, the low-risk group had higher infiltration of immune cells in tumor tissues. Besides, compared with high-risk group, low-risk patients by immunotherapy or other commonly used anti-tumor drugs might have better efficacy after treatment. In addition, qRT-PCR showed that the expression of risk genes including TNFRSF10B, IL18 and LAMP1 were significantly increased in most DLBCL samples compared to control samples, while the expression of protective genes including MAP2K1, PRKCB, RASGRP1 and SP110 were significantly decreased.

The NK cell-related gene signatures were proved to be a reliable indicator of the success of immunotherapy in patients with DLBCL, thus providing a unique evaluation method.