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.
Bing Xu (Mon,) studied this question.