J Cancer 2023; 14(3):403-416. doi:10.7150/jca.80926 This issue Cite
1. Department of Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
2. Department of Medical Oncology, Tianjin First Central Hospital, School of Medicine. Nankai University, Tianjin, 300192, China
3. Department of Senior Ward, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, 300060, China
4. Tianjin Sino-US Diagnostics, Tianjin, 300380, China
Background: The diffuse large B-cell lymphoma (DLBCL) is a heterogeneous lymphoma with a dismal outcome, due to approximately 40% patients will be relapsed or refractory to the standard therapy of rituximab plus cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP). Therefore, we need urgently to explore the approach to classify the risk of DLBCL patients accurately and accurately targeting therapy. The ribosome is a vital cellular organelle that is mainly responsible for translation mRNA into protein, moreover, more and more reports revealed that ribosome was associated with cellular proliferation and tumorigenesis. Therefore, our study aimed to construct a prognostic model of DLBCL patients using ribosome-related genes (RibGs).
Method: We screened differentially expressed RibGs between healthy donors' B cells and DLBCL patients' malignant B cells in GSE56315 dataset. Next, we performed analyses of univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses to establish the prognostic model consisting of 15 RibGs in GSE10846 training set. Then, we validated the model by a range of analyses including Cox regression, Kaplan-Meier survival, ROC curve, and nomogram in training and validation cohorts.
Results: The RibGs model showed a reliably predictive capability. We found the upregulated pathways in high-risk group most associated with innate immune reaction such as interferon response, complement and inflammatory responses. In addition, a nomogram including age, gender, IPI score and risk score was constructed to help explain the prognostic model. We also discovered the high-risk patients were more sensitive to some certain drugs. Finally, knocking out the NLE1 could inhibit the proliferation of DLBCL cell lines.
Conclusion: As far as we know, it is the first time to predict the prognosis of DLBCL using the RibGs and give a new sight for DLBCL treatment. Importantly, the RibGs model could be acted as a supplementary to the IPI in classifying the risk of DLBCL patients.
Keywords: DLBCL, ribosome-related genes, prognostic model, targeting therapy, NLE1