Status and prognostic nomogram of patients with Burkitt lymphoma

Lu, Jielun; H, Tan; Li, Bo; Chen, Shuyi; Xu, Lihua; Zou, Yawei

doi:10.3892/ol.2019.11155

Cited by 5 publications

(6 citation statements)

References 35 publications

(25 reference statements)

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“…The overall 3-year OS was lower in our study than in a previous large clinical study (64.5% vs. 70%), which may be due to the fact that the cases in that study were all concentrated in 2009-2018, while the present study was from 2000-2015, suggesting an association with improvement in treatment regime [20]. The introduction of multidrug combination chemotherapy, especially rituximab, has been reported to signi cantly improve the OS rate of BL in adults or children [21][22].…”

Section: Discussioncontrasting

confidence: 90%

Development and validation of nomograms to predict prognosis of Burkitt's lymphoma: a SEER-based study

He,

Weijie,

Yang

et al. 2023

Preprint

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Background: The purpose of this study was to establish two practical and valid nomograms to predict overall survival (OS) and cancer-specific survival (CSS) in patients with Burkitt's lymphoma. Methods: A total of 3972 patients with Burkitt's lymphoma diagnosed in 2000-2015 were screened from the SEER database and randomized into training cohorts (N=2780) and validation cohorts (N=1192). Univariate and multivariate Cox regression analyses were performed to select independent risk factors affecting prognosis, followed by the construction of nomograms for OS and CSS. The reliability of the nomogram was validated with C-index and calibration curve. DCA plots were used to compare the clinical value of the nomogram with Ann Arbor Stage staging. In addition, patients were divided into high-risk and low-risk groups according to the mean of their scores as a cut-off, and survival was compared using the Kaplan-Meier method. Results: According to the results of multivariate Cox regression analysis, the common independent prognostic factors affecting OS and CSS were age, race, marital status, year of diagnosis, primary site, stage, and chemotherapy. Based on these variables, two prediction models were constructed. In the training cohort, the C-index of the nomogram for OS was 0.741. DCA plots indicated that our nomogram had more clinical net benefits than the Ann Arbor staging system. Conclusion: A comprehensive assessment of the incidence and survival prognosis of Burkitt's lymphoma was conducted using a large database, and two nomograms were created to predict patient risk and prognostic factors, thereby guiding clinicians in individualized clinical practice.

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Section: Discussioncontrasting

confidence: 90%

Development and validation of nomograms to predict prognosis of Burkitt's lymphoma: a SEER-based study

He,

Weijie,

Yang

et al. 2023

Preprint

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“…Genes in grey were not included in any module, thus we analyzed the interactive relationships underlying the 7 co-expression modules (Figure 2B ). Given the well-established association between age and prognosis in BL patients, we opted to investigate the module that exhibited the strongest correlation with age for subsequent analysis[ 20 , 21 ]. A significant correlation between the yellow module and age was discovered (Figure 3 ).…”

Section: Resultsmentioning

confidence: 99%

Hub genes and their key effects on prognosis of Burkitt lymphoma

Xu,

Wang,

Zhang

et al. 2023

World J Clin Oncol

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BACKGROUND Burkitt lymphoma (BL) is an exceptionally aggressive malignant neoplasm that arises from either the germinal center or post-germinal center B cells. Patients with BL often present with rapid tumor growth and require high-intensity multi-drug therapy combined with adequate intrathecal chemotherapy prophylaxis, however, a standard treatment program for BL has not yet been established. It is important to identify biomarkers for predicting the prognosis of BLs and discriminating patients who might benefit from the therapy. Microarray data and sequencing information from public databases could offer opportunities for the discovery of new diagnostic or therapeutic targets. AIM To identify hub genes and perform gene ontology (GO) and survival analysis in BL. METHODS Gene expression profiles and clinical traits of BL patients were collected from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was applied to construct gene co-expression modules, and the cytoHubba tool was used to find the hub genes. Then, the hub genes were analyzed using GO and Kyoto Encyclopedia of Genes and Genomes analysis. Additionally, a Protein-Protein Interaction network and a Genetic Interaction network were constructed. Prognostic candidate genes were identified through overall survival analysis. Finally, a nomogram was established to assess the predictive value of hub genes, and drug-gene interactions were also constructed. RESULTS In this study, we obtained 8 modules through WGCNA analysis, and there was a significant correlation between the yellow module and age. Then we identified 10 hub genes ( SRC , TLR4 , CD40 , STAT3 , SELL , CXCL10 , IL2RA , IL10RA , CCR7 and FCGR2B ) by cytoHubba tool. Within these hubs, two genes were found to be associated with OS ( CXCL10 , P = 0.029 and IL2RA , P = 0.0066) by survival analysis. Additionally, we combined these two hub genes and age to build a nomogram. Moreover, the drugs related to IL2RA and CXCL10 might have a potential therapeutic role in relapsed and refractory BL. CONCLUSION From WGCNA and survival analysis, we identified CXCL10 and IL2RA that might be prognostic markers for BL.

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“…3,4 However, most studies on the construction of predictive models have focused on a specific pathological type or site in the gastrointestinal tract. [6][7][8][9][10][11][12][13] Therefore, in this study, PINHL was designated as a separate entity. 14 Today, the Ann Arbor and Lugano stages are used to evaluate the progression of lymphoma.…”

Section: Introductionmentioning

confidence: 99%

“… 3 , 4 However, most studies on the construction of predictive models have focused on a specific pathological type or site in the gastrointestinal tract. 6–13 Therefore, in this study, PINHL was designated as a separate entity. 14 …”

Section: Introductionmentioning

confidence: 99%

Development and External Validation of a Nomogram to Predict Cancer-Specific Survival in Patients with Primary Intestinal Non-Hodgkin Lymphomas

Zhang¹,

Liu²,

Tao³

et al. 2021

CMAR

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Purpose: Primary intestinal non-Hodgkin lymphoma (PINHL) is a biologically and clinically heterogeneous disease. Few individual prediction models are available to establish prognoses for PINHL patients. Herein, a novel nomogram was developed and verified to predict long-term cancer-specific survival (CSS) rates in PINHL patients, and a convenient online risk calculator was created using the nomogram. Materials and Methods: Data on PINHL patients from January 1, 2004, to December 31, 2015, obtained from the Surveillance, Epidemiology, and End Results (SEER) database (n = 2372; training cohort), were analyzed by Cox regression to identify independent prognostic parameters for CSS. The nomogram was internally and externally validated in a SEER cohort (n = 1014) and a First Affiliated Hospital of Guangzhou University of Chinese Medicine (FAHGUCM) cohort (n = 37), respectively. Area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA) were used to evaluate nomogram performance. Results: Five independent predictors were identified, namely, age, marital status, Ann Arbor Stage, B symptoms, and histologic type. The nomogram showed good performance in discrimination and calibration, with C-indices of 0.772 (95% CI: 0.754-0.790), 0.763 (95% CI: 0.734-0.792), and 0.851 (95% CI: 0.755-0.947) in the training, internal validation, and external validation cohorts, respectively. The calibration curve indicated that the nomogram was accurate, and DCA showed that the nomogram had a high clinical application value. AUC values indicated that the prediction accuracy of the nomogram was higher than that of Ann Arbor Stage (training cohort: 0.804 vs 0.630; internal validation cohort: 0.800 vs 0.637; external validation cohort: 0.811 vs 0.598), and Kaplan-Meier curves indicated the same. Conclusion: A nomogram was developed to assist clinicians in predicting the survival of PINHL patients and in making optimal treatment decisions. An online calculator based on the nomogram was made available at https://cuifenzhang.shinyapps.io/DynNomapp/.

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Status and prognostic nomogram of patients with Burkitt lymphoma

Cited by 5 publications

References 35 publications

Development and validation of nomograms to predict prognosis of Burkitt's lymphoma: a SEER-based study

Development and validation of nomograms to predict prognosis of Burkitt's lymphoma: a SEER-based study

Hub genes and their key effects on prognosis of Burkitt lymphoma

Development and External Validation of a Nomogram to Predict Cancer-Specific Survival in Patients with Primary Intestinal Non-Hodgkin Lymphomas

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