Identification of tumor immune infiltration‐associated snornas for improving the prognosis of patients with diffuse large b‐cell lymphoma

Background: Diffuse large B-cell lymphoma (DLBCL) is a highly aggressive non-Hodgkin lymphoma. Most studies on reliable biomarkers to better predict the immunity regulation and the tumor microenvironment. This study aims to establish a model of small nucleolar RNAs (snoRNAs) derived from immune infiltration-related cells for risk stratification and improving clinical outcomes of DLBCL. Methods: we established a computational framework to identify an immune cell infiltration-related snoRNA signature (IMMsno) through integrative analysis for snoRNA of immune cell lines and 551 DLBCL patients with gene expression profiles (GSE10846 and GSE87371), validated in GSE181063 containts 1310 patients with the overall survival (OS). IMMsno was developed with Least Absolute Shrinkage and Selector Operation (LASSO) regression and visible by nomogram integrating clinical and pathological information (cell of origin, International Prognostic Index and clinical stage). Then the influence of the IMMsno clusters on the molecular functional enrichment and immunotherapy in DLBCL was comprehensively investigated. Results: Six tumor immune infiltration-associated snoRNAs (SNHG1, SNHG5, SNHG6, SNHG12, SNHG16, SNHG19) and LASSO selected five of them to develop IMMsno. The IMMsno stratified DLBCL patients into the high-score group and low-score group, and a high IMMsno score was associated with poor DLBCL prognosis (HR = 2.049, 95% CI = 1.628–2.578, p-value = 0.001). Cox multivariate analysis conformed that IMMsno score is an independent predictive prognosis factor adjusted by other clinical characters. Further analysis accounting for IMMsno linked to the biological functions of antigen processing and presentation and signaling pathway of Complement and coagulation cascades, implying a better response in low-score IMMsno group to immunotherapy. Conclusions: Our finding suggested the potential biological effects of snoRNAs in evaluating the tumor immune microenvironment, also as a predictive biomarkers of DLBCL prognosis. The research was funded by: This project was supported by the National Natural Science Foundation of China (No. 81773524, No. 81502878, and No. 82273720 Keywords: bioinformatics, cancer health disparities, computational and systems biology, diagnostic and prognostic biomarkers No conflicts of interests pertinent to the abstract.