Identification of a prognostic metabolic gene signature in diffuse large B‐cell lymphoma

Abstract

Diffuse large B-cell lymphoma (DLBCL) is a highly prominent class of non-Hodgkin lymphoma (NHL), accounting for 30%-40% of all NHL cases. It has been reported to be highly heterogeneous in epidemiological studies. 1,2 Though anti-CD20 (rituximab) immunotherapy has been the standard treatment regimen with satisfactory survival rates for decades, 3 it was only effective on a small portion of patients. Approximately 40% of patients with DLBCL still had poor prognoses and often suffered from an early relapse after the treatment, 4 emphasizing the need for risk stratification to provide accurate and effective therapy. The International Prognostic Index (IPI), 5 involving conventional clinical and pathological parameters, is widely used for prognosis of patients with DLBCL. However, IPI

Enhancer plasticity sustains oncogenic transformation and progression of B-Cell Acute Lymphoblastic leukemia

“…6c). Interestingly, DCTD is overexpressed in malignant gliomas (59), and DLBC (60) and marked as an adverse prognostic factor. In addition, DCTD has long been associated with chemoresistance to gemcitabine (61,62).…”

Construction and validation of a risk scoring model for diffuse large B-cell lymphoma based on ferroptosis-related genes and its association with immune infiltration

“…The discriminatory accuracy was obviously improved compared with the model that only included risk factors like the clinical stage, LDH ratio, age and gender. Beside, the metabolic prognostic model has been generated to assign patients with DLBCL into high-or low-risk clusters, which shows a superiority in aspect of a short-term [51] . Other prognostic scoring models for DLBCL have been reported based on infiltrating immune cells [52] and tumor microenvironment [53] .…”

Identification of three small nucleolar RNAs (snoRNAs) as potential prognostic markers in diffuse large B‐cell lymphoma

“… 23 , 24 Hu et al combined clinical variables and pharmacogenomic genes to construct a risk model to improve drug resistance in DLBCL patients. 25 Risk score models based on prognostic genes related to metabolism, 26 pyroptosis, 27 and ferroptosis 28 have been established for DLBCL, providing novel ideas for individualized therapeutic strategies in DLBCL patients. The past few years have witnessed the potential of several snoRNAs as prognostic molecular biomarkers in various cancers, and predictive risk signatures based on certain snoRNAs have been found in carcinomas of the lung, 29 , 30 liver, 31 stomach, 32 colon, 33 kidney, 34 , 35 bladder, 36 head and neck, 37 and eyes, 38 as well as sarcoma, 39 and T‐cell lymphoma.…”

“…To date, predictive risk models based on mRNAs, 26 , 41 miRNAs, 42 , 43 lncRNAs, 44 , 45 , 46 and circRNAs 47 have been studied in DLBCL. However, no report exists on survival‐related snoRNA signatures in DLBCL patients.…”