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Wu B. Ferritin and Iron Levels Inversely Associated With Lymphoma Risk: A Mendelian Randomization Study. J Hematol 2024; 13:179-185. [PMID: 39493607 PMCID: PMC11526578 DOI: 10.14740/jh1335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2024] [Accepted: 10/12/2024] [Indexed: 11/05/2024] Open
Abstract
Background Current knowledge on iron's role in lymphoma development is very limited, with studies yielding inconsistent findings. To address this gap, we conducted a rigorous two-sample mendelian randomization study, aiming to elucidate the potential associations between iron storage and the risk of developing lymphoma. Methods This study leveraged extensive genetic data derived from a comprehensive genome-wide association study (GWAS) comprising 257,953 individuals. The primary objective was to pinpoint single-nucleotide polymorphisms (SNPs) that are significantly associated with iron storage. Subsequently, this genetic information was analyzed in conjunction with summary-level data pertaining to lymphoma cases and controls, sourced from the IEU open GWAS project, which included a sample size of 3,546 lymphoma cases and 487,257 controls. To evaluate the relationship between iron storage and lymphoma risk, an inverse variance-weighted method with random effects was employed, complemented by rigorous sensitivity analyses. Results Genetic predisposition to high ferritin and serum iron status was causally associated with lower odds of lymphoma. Ferritin exhibited an odds ratio (OR) of 0.777 (95% confidence interval (CI): 0.628 - 0.961, P = 0.020), indicating 22.3% reduced odds of lymphoma associated with a one standard deviation increase in ferritin levels. Similarly, serum iron demonstrated an OR of 0.776 (95% CI: 0.609 - 0.989, P = 0.040), corresponding to 22.4% decreased odds of lymphoma for a one standard deviation increase in serum iron. Conclusions This study suggests that individuals with genes linked to higher iron storage levels have a lower risk of developing lymphoma, but further research is necessary before making any clinical recommendations.
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Affiliation(s)
- Boyuan Wu
- Division of Biostatistics, School of Global Public Health, New York University, New York, NY 10003, USA.
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Vaughan J, Patel M, Suchard M, Gededzha M, Ranchod H, Howard W, Snyman T, Wiggill T. Derangements of immunological proteins in HIV-associated diffuse large B-cell lymphoma: the frequency and prognostic impact. Front Cell Infect Microbiol 2024; 14:1340096. [PMID: 38633747 PMCID: PMC11021765 DOI: 10.3389/fcimb.2024.1340096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 03/12/2024] [Indexed: 04/19/2024] Open
Abstract
Introduction Diffuse large B-cell lymphoma (DLBCL) is an aggressive malignancy of B-cells frequently encountered among people living with HIV. Immunological abnormalities are common in immunocompetent individuals with DLBCL, and are often associated with poorer outcomes. Currently, data on derangements of immunological proteins, such as cytokines and acute phase reactants, and their impact on outcomes in HIV-associated DLBCL (HIV-DLBCL) is lacking. This study assessed the levels and prognostic relevance of interleukin (IL)-6, IL-10 and Transforming Growth Factor Beta (TGFβ), the acute phase proteins C-reactive protein (CRP) and ferritin; serum free light chains (SFLC) (elevation of which reflects a prolonged pro-inflammatory state); and the activity of the immunosuppressive enzyme Indoleamine 2,3-dioxygenase (IDO)in South African patients with DLBCL. Methods Seventy-six patients with incident DLBCL were enrolled, and peripheral blood IL-6, IL-10, TGFβ, SFLC and IDO-activity measured in selected patients. Additional clinical and laboratory findings (including ferritin and CRP) were recorded from the hospital records. Results Sixty-one (80.3%) of the included patients were people living with HIV (median CD4-count = 148 cells/ul), and survival rates were poor (12-month survival rate 30.0%). The majority of the immunological proteins, except for TGFβ and ferritin, were significantly higher among the people living with HIV. Elevation of IL-6, SFLC and IDO-activity were not associated with survival in HIV-DLBCL, while raised IL-10, CRP, ferritin and TGFβ were. On multivariate analysis, immunological proteins associated with survival independently from the International Prognostic Index (IPI) included TGFβ, ferritin and IL-10. Conclusion Derangements of immunological proteins are common in HIV-DLBCL, and have a differential association with survival compared to that reported elsewhere. Elevation of TGFβ, IL-10 and ferritin were associated with survival independently from the IPI. In view of the poor survival rates in this cohort, investigation of the directed targeting of these cytokines would be of interest in our setting.
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Affiliation(s)
- Jenifer Vaughan
- Department of Molecular Medicine and Haematology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- National Health Laboratory Services, Johannesburg, South Africa
| | - Moosa Patel
- Department of Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Clinical Haematology Unit, Chris Hani Baragwanath Academic Hospital, Johannesburg, South Africa
| | - Melinda Suchard
- Department of Chemical Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Maemu Gededzha
- National Health Laboratory Services, Johannesburg, South Africa
- Department of Immunology, University of the Witwatersrand, Johannesburg, South Africa
| | - Heena Ranchod
- Department of Chemical Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- National Institute for Communicable Diseases, Centre for Vaccines and Immunology, Johannesburg, South Africa
| | - Wayne Howard
- Department of Chemical Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- National Institute for Communicable Diseases, Centre for Vaccines and Immunology, Johannesburg, South Africa
| | - Tracy Snyman
- National Health Laboratory Services, Johannesburg, South Africa
| | - Tracey Wiggill
- Department of Molecular Medicine and Haematology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- National Health Laboratory Services, Johannesburg, South Africa
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Jelicic J, Larsen TS, Andjelic B, Juul-Jensen K, Bukumiric Z. Should we use nomograms for risk predictions in diffuse large B cell lymphoma patients? A systematic review. Crit Rev Oncol Hematol 2024; 196:104293. [PMID: 38346460 DOI: 10.1016/j.critrevonc.2024.104293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 01/24/2024] [Accepted: 02/07/2024] [Indexed: 02/24/2024] Open
Abstract
Models based on risk stratification are increasingly reported for Diffuse large B cell lymphoma (DLBCL). Due to a rising interest in nomograms for cancer patients, we aimed to review and critically appraise prognostic models based on nomograms in DLBCL patients. A literature search in PubMed/Embase identified 59 articles that proposed prognostic models for DLBCL by combining parameters of interest (e.g., clinical, laboratory, immunohistochemical, and genetic) between January 2000 and 2024. Of them, 40 studies proposed different gene expression signatures and incorporated them into nomogram-based prognostic models. Although most studies assessed discrimination and calibration when developing the model, many lacked external validation. Current nomogram-based models for DLBCL are mainly developed from publicly available databases, lack external validation, and have no applicability in clinical practice. However, they may be helpful in individual patient counseling, although careful considerations should be made regarding model development due to possible limitations when choosing nomograms for prognostication.
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Affiliation(s)
- Jelena Jelicic
- Department of Hematology, Sygehus Lillebaelt, Vejle, Denmark; Department of Hematology, Odense University Hospital, Odense, Denmark.
| | - Thomas Stauffer Larsen
- Department of Hematology, Odense University Hospital, Odense, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Bosko Andjelic
- Department of Haematology, Blackpool Victoria Hospital, Lancashire Haematology Centre, Blackpool, United Kingdom
| | - Karen Juul-Jensen
- Department of Hematology, Odense University Hospital, Odense, Denmark
| | - Zoran Bukumiric
- Department of Statistics, Faculty of Medicine, University of Belgrade, Serbia
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Wan M, Zhang W, Huang H, Fang X, Chen Y, Tian Y, Yao Y, Weng H, Chen Z, Yu L, Tian Y, Huang H, Li X, Hong H, Lin T. Development and validation of a novel prognostic nomogram for advanced diffuse large B cell lymphoma. Clin Exp Med 2024; 24:64. [PMID: 38554186 PMCID: PMC10981611 DOI: 10.1007/s10238-024-01326-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 03/07/2024] [Indexed: 04/01/2024]
Abstract
Advanced diffuse large B cell lymphoma (DLBCL) is a common malignant tumor with aggressive clinical features and poor prognosis. At present, there is lack of effective prognostic tool for patients with advanced (stage III/IV) DLBCL. The aim of this study is to identify prognostic indicators that affect survival and response and establish the first survival prediction nomogram for advanced DLBCL. A total of 402 patients with advanced DLBCL were enrolled in this study. COX multivariate analysis was used to obtain independent prognostic factors. The independent prognostic factors were included in the nomogram, and the nomogram to predict the performance of the model was established by R rms package, C-index (consistency index), AUC curve and calibration curve. The training and validation cohorts included 281 and 121 patients. In the training cohort, multivariate analysis showed that Ki-67 (70% (high expression) vs ≤ 70% (low expression), p < 0.001), LDH (lactate dehydrogenase) (elevated vs normal, p = 0.05), FER (ferritin) (elevated vs normal, p < 0.001), and β2-microglobulin (elevated vs normal, p < 0.001) were independent predictors and the nomogram was constructed. The nomogram showed that there was a significant difference in OS among the low-risk, intermediate-risk and high-risk groups, with 5-year survival rates of 81.6%, 44% and 6%, respectively. The C-index of the nomogram in the training group was 0.76. The internal validation of the training group showed good consistency. In the internal validation cohort of the training group, the AUC was 0.828, and similar results were obtained in the validation group, with a C-index of 0.74 and an AUC of 0.803. The proposed nomogram provided a valuable individualized risk assessment of OS in advanced DLBCL patients.
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Affiliation(s)
- Mengdi Wan
- Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China
| | - Wei Zhang
- Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China
| | - He Huang
- Department of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China
| | - Xiaojie Fang
- Department of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China
| | - Yungchang Chen
- Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China
| | - Ying Tian
- Department of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China
| | - Yuyi Yao
- Department of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China
| | - Huawei Weng
- Department of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China
| | - Zegeng Chen
- Department of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China
| | - Le Yu
- Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China
| | - Yuke Tian
- Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China
| | - Huageng Huang
- Department of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China
| | - Xudong Li
- Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China
| | - Huangming Hong
- Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China.
| | - Tongyu Lin
- Department of Medical Oncology, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan Province, China.
- Department of Medical Oncology, State Key Laboratory of Oncology in Southern China and Collaborative Innovation Center of Cancer Medicine, Sun Yat-Sen University Cancer Center, 651 Dongfeng, Road East, Guangzhou, 510060, Guangdong, China.
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