A predictive model based on NCCN-IPI and 2 pirnas for prognostic risk stratification of diffuse large B-cell lymphoma (DLBCL)

M Min Zhang T Tao Guan (State Key Laboratory of Water Pollution Control and Green Resource Recycling State Key Laboratory of Analytical Chemistry for Life Science Frontiers Science Center for Critical Earth Material Cycling School of Environment School of Chemistry and Chemical Engineering Nanjing University Nanjing China) B Bing Xu L Liping Su (2Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Shanxi, China)

Abstract

Abstract Background: This study investigates the prognostic value of abnormally expressed piRNAs in DLBCL and establishes a more accurate prognostic evaluation model for DLBCL by combining with the traditional clinical indicator NCCN-IPI. Methods A total of 107 DLBCL patients treated between 2016 and 2019 were included in this study. After screening based on inclusion and exclusion criteria, 91 DLBCL patients were analyzed with 30 normal human lymph nodes as controls, using high-throughput sequencing and qPCR to detect abnormally expressed piRNAs. The primary endpoint of this study was set as overall survival (OS). Survival analysis was conducted on the 91 DLBCL patients to assess the association between piRNAs and patients' 5-year OS.Cox regression was employed for univariate and multivariate survival analyses to verify the efficacy of piRNAs as independent prognostic factors. Finally, piRNA levels were integrated with the risk stratification of the traditional prognostic indicator (NCCN-IPI) to establish a combined prognostic risk stratification assessment model. A nomogram model was constructed to assist clinical grouping, and its efficacy was evaluated. Results In DLBCL, piR-32260 and piR-28846 can serve as independent prognostic markers. The prognostic model integrating piR-32260, piR-28846, and NCCN-IPI significantly improved the accuracy of DLBCL prognostic stratification, with the C-index of the combined model increasing from 0.6551 to 0.7200, AUC = 0.820. The new prognostic model was divided into four groups: low-risk, intermediate-low-risk, intermediate-high-risk, and high-risk, and it enhanced the ability to identify the high-risk group. The AUC values of the ROC curves for the combined model at 1 year, 3 years, and 5 years were 0.515, 0.755, and 0.773, respectively. The DCA decision curve showed that the net benefit of the model at 3 and 5 years was significantly higher than the “Treat None” line and “Treat All” line. Conclusion The new prognostic model combining piRNAs and NCCN-IPI can better stratify the prognosis of DLBCL to predict OS risk.

Article Details

Journal Blood
Volume / Issue Vol. 146, Issue Supplement 1
Published November 03, 2025
Pages 3667-3667
ISSN 0006-4971
Publisher Elsevier BV

Journal Info

Blood

Elsevier BV

ISSN: 0006-4971 Health Sciences

Authors (4)

M

Min Zhang

T

Tao Guan

State Key Laboratory of Water Pollution Control and Green Resource Recycling State Key Laboratory of Analytical Chemistry for Life Science Frontiers Science Center for Critical Earth Material Cycling School of Environment School of Chemistry and Chemical Engineering Nanjing University Nanjing China

B

Bing Xu

L

Liping Su

2Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Shanxi, China