A 9‐lncrna signature for predicting prognosis and immune response in diffuse large b‐cell lymphoma

Background: Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous disease that requires personalized clinical treatment. To assign patients into different risk categories, cytogenetic abnormalities and genetic mutations have been widely applied to the prognostic stratification of DLBCL. Increasing evidence has demonstrated that deregulated epigenetic modifications and long noncoding RNAs (lncRNAs) contribute to the initiation and progression of DLBCL. However, specific lncRNAs that affect epigenetic regulation and their value in predicting prognosis and therapy response remain uncertain. Methods: LncRNA expression profiles and clinical features of DLBCL samples were collected from the GEO database and 188 DLBCL patients in our center was as a validation cohort. A linear regression based on the modified LASSO algorithm was performed to construct a predictive model named lncRNA-regulating epigenetic event signature (ELncSig). Results: A list of 2,025 epigenetic regulatory genes was generated from GeneCards and 9 lncRNAs (PRKCQ-AS1, C22orf34, HCP5, AC007389.3, APTR, SNHG19, ELFN1-AS1, LINC00487, and LINC00877) were tested and validated to establish the ELncSig which could distinguish different survival outcomes. Functional characterization of ELncSig showed that it could be an indicator of the immune microenvironment and is correlated with distinctive mutational characteristics. Univariate and multivariate analyses showed that ELncSig was independent of traditional prognostic factors. Conclusions: We constructed a lncRNA signature based on epigenetic-related genes to predict the prognosis of DLBCL and also proved that this new signature could affect other coding proteins in addition to epigenetic genes. Importantly, ELncSig might be associated with immune infiltration levels and even the efficacy of tumor immunotherapy. Keywords: aggressive B-cell non-Hodgkin lymphoma, diagnostic and prognostic biomarkers, risk models No conflicts of interests pertinent to the abstract.