GATA1: A Key Biomarker for Predicting the Prognosis of Patients With Diffuse Large B- cell Lymphoma

GATA1: A Key Biomarker for Predicting the Prognosis of Patients With Diffuse Large B- cell Lymphoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article GATA1: A Key Biomarker for Predicting the Prognosis of Patients With Diffuse Large B- cell Lymphoma Yuxin Zhang, Yue Wang, Shifen Wang, Dawei Cui, Zheng Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4302921/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Diffuse large B-cell lymphoma (DLBCL) is a common and highly aggressive type of lymphoma. Iron metabolism plays a critical role in human diseases, however, which remains completely unclear in patients with DLBCL. The aim is to explore the genetic characteristics and molecular mechanisms underlying iron metabolism in patients with DLBCL. Methods Based on the Gene Expression Omnibus (GEO) and the GeneCards database, weighted gene co-expression network analysis (WGCNA) was performed on the DLBCL sample (GSE83632) and Iron metabolism-related datasets. Enrichment analysis(GO/KEGG enrichment analysis and GSEA enrichment analysis) was used to screen the key gene and analyze its expression and possible mechanism of action in patients with DLBCL. The quantitative real-time PCR (qRT-PCR) was used to verify the expression of GATA1 gene. Results GATA-binding factor 1 (GATA1), as a key gene of iron metabolism in DLBCL patients, was related to the myeloid cell differentiation and granulocyte differentiation pathways to affect CD4 + T cells, B cells, and monocytes. GATA1 was strongly positively correlated with sensitivity to multiple targeted drugs including imatinib, nilotinib, and crizotinib, but negatively correlated with the PI3K and CDK9 inhibitors. The patients with high GATA1 expression had higher overall survival and better prognosis than the patients with low expression. Additionally, high expression of GATA1 gene was confirmed in DLBCL patients by qRT-PCR analysis. Conclusions GATA1 as one of the important genes of iron metabolism suggested a significant biomarker for predicting the prognosis of DLBCL patients. Bioinformatics Diffuse large B-cell lymphoma GATA1 Iron metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Diffuse large B-cell lymphoma (DLBCL) is a common and highly aggressive type of lymphoma, accounting for approximately 1/3 of all non-Hodgkin’s lymphoma case [ 1 ] .In recent years, the incidence of DLBCL in China has been gradually increasing [ 2 ] . Because of the substantial heterogeneity of DLBCL, 30–40% of patients treated with the first-line immunochemotherapy regimen R-CHOP are either insensitive or develop relapsed/refractory lymphoma [ 3 ] . Iron is essential for maintaining normal function and homeostasis in cells. Therefore, an imbalance in iron metabolism is closely related to the occurrence, development, metastasis, and recurrence of many malignant tumors, such as lung cancer, renal cancer, breast cancer, prostate cancer, and liver cancer [ 4 – 10 ] . However, iron metabolism plays a dual role in tumor cells. On the one hand, the proliferation of tumor cells is more dependent on iron than that of normal cells, a phenomenon known as iron addiction [ 11 ] . Conversely, an increase in iron concentration leads to cell death through the accumulation of reactive oxygen species and lipid peroxidation products, known as ferroptosis [ 12 , 13 ] . Ferroptosis is a specific mode of programmed cell death that is dependent on iron metabolism and distinct from apoptosis, necroptosis, and autophagy [ 14 ] . With research on emerging anticancer pathways, various ferroptosis inducers have been developed for cancer [ 15 , 16 ] . Following the rapid development of gene microarray technology, researchers can measure the expression levels of thousands of genes in a short period, which helps gain a deeper understanding of the pathogenesis of diseases at the gene level. Currently, the International Prognostic Index is the main tool used for the clinical evaluation of DLBCL prognosis. Although such evaluations can guide clinical treatment, they merely provide a combination of clinical prognostic parameters that do not represent the heterogeneity of molecular biology in the occurrence and development of the disease. Therefore, it is critical to actively identify molecular biological indicators that affect DLBCL [ 17 ] . However, no previous studies have identified the genetic characteristics and molecular mechanisms underlying iron metabolism in patients with DLBCL. To address this gap in the literature, we conduct bioinformatic analysis to identify the action pathway, core shared genes, and core genes of iron metabolism in the pathogenesis of DLBCL, thereby revealing the mechanism of iron metabolism and therapeutic targets in DLBCL. The results showed that GATA-binding factor 1 (GATA1) is a key gene in the role of iron metabolism in the pathogenesis of DLBCL. It affects the differentiation and maturation of immune cells through the myeloid cell differentiation pathway, especially memory B cells and CD4 + T cells, thus affecting the progression of DLBCL. The survival analysis found that the GATA1 high expression group had better OS. Its expression had a positive correlation effect with BTK inhibitor-related drugs and a negative correlation with PI3K inhibitor-related small molecule drugs, which is meaningful for clinical drug selection. This research contributes to improving the early diagnosis, treatment, drug resistance, and prognosis of DLBCL. 2. Materials and Methods 2.1 Data download and processing We screened transcriptome sequencing datasets related to DLBCL using the Gene Expression Omnibus database. Iron metabolism-related datasets were downloaded from the GeneCards database ( https://www.genecards.com/ ). Mutation information, clinical information, and genome-wide transcript levels of patients with DLBCL were obtained from The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/ ). In addition, we obtained a list of all genes associated with immune response from the InnateDB database ( https://www.innatedb.com/ ). Throughout the study, we background-corrected and normalized all raw data and matched all probe names with their corresponding gene symbols for subsequent analyses. 2.2 Weighted gene co-expression network analysis Following scientific and technological advances and the rapid development of systems biology and bioinformatics, weighted gene co-expression network analysis (WGCNA) has become one of the most popular algorithms for analyzing large amounts of data. WGCNA addresses the correlation between gene sets and sample phenotypes, allowing for the mapping of inter-gene regulatory networks in gene collections and screening of essential regulatory genes closely related to disease [ 18 ] . In this study, we used WGCNA to establish a gene co-expression network for DLBCL and iron metabolism. 2.3 Identification and features of shared genes in DLBCL and iron metabolism We counted the intersection of genes in clinically relevant modules to calculate the shared genes. TCGA data were used to verify the relationship between these shared genes and DLBCL and to observe their location on chromosomes and the frequency of copy number variations. GO enrichment analysis of shared genes was performed using the "ClueGO" and "MCODE" plug-ins in Cytoscape to establish protein–protein interaction (PPI) networks, understand their functions, identify the relationships between proteins, and ultimately screen out the core genes with the most critical roles. In this study, PPI construction was performed primarily using a STRING database ( https://string-db.org/ ), whereby all available and predicted connections between proteins were integrated. 2.4 Functional enrichment analysis and gene set enrichment analysis Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, which is the most common functional enrichment method currently employed in medical research, was used to interpret molecular-level information regarding the higher functions and functioning of biological systems. We applied the "clusterProfiler" R package to perform GO/KEGG enrichment analysis of shared genes and elucidate the underlying mechanisms of disease onset and progression. Gene set enrichment analysis (GSEA) was used to analyze the distribution trend of genes in the predefined gene sets included in the gene list ranked by phenotypic correlation to determine their contribution to the phenotype. The median expression level of GATA1 was employed as a classification criterion to first categorize patients with DLBCL into low- and high-expression groups, before performing GSEA on the subgroups using GSEA software. 2.5 Association between core genes and DLBCL We compiled gene expression profiles of DLBCL and normal tissues from TCGA and GTEx databases to compare the differential expression of core genes in DLBCL. The relationship between core genes and patient prognosis was determined by analyzing the clinical data of DLBCL in TCGA and calculating the overall survival in high and low GATA1 expression groups. 2.6 Assessment of the immune landscape Tumor infiltration by immune cells can profoundly influence tumor progression and the success of anticancer therapies by exerting pro-tumorigenic and anti-tumorigenic effects. As a first step, we used immune cell infiltration and gene expression data from the TIMER database to calculate the relationship between core gene expression and immune cell abundance in DLBCL, and presented these results in heatmaps and bubble plots. Secondly, we used single-sample genomic enrichment analysis, an emerging gene enrichment method, to compare the EstimateScore, ImmuneScore, and StromalScore between high- and low-expression groups of the core genes in each sample. In this calculation, we used gene set variation analysis to transform the expression matrix of individual genes into that of a specific set of genes. Finally, Spearman’s correlation analysis was used to analyze the relationship between core genes and a range of immune-related genes, such as immune checkpoint-related and immune cell subpopulation-related genes. 2.7 Quantitative Real-time (qPCR) analysis From February to April 2024, a total of 9 adult inpatients with DLBCL were included in our study. The DLBCL diagnoses were confirmed through a tissue biopsy. As control subjects, 9 healthy participants undergoing routine health examinations were included as health controls (HC). For each subject, 2 mL fresh blood was taken, and peripheral blood mononuclear cells (PBMCs) were were isolated by FicollPaque density gradient centrifugation. Our study was carried out in compliance with the Declaration of Helsinki and received approval from the Ethical Committee of our hospital. Additionally, all participants gave informed consent before the start of the study. Total RNA was extracted from each sample with TransZol Up Plus RNA Kit (TransGen Biotech, Beijing) and reverse transcribed (TransScript All-in-One Kit, TransGen Biotech, Beijing). The PCR analysis was done on an ABI StepOnePlus™ system using TransStart Top Green qPCR SuperMix (TransGen Biotech, Beijing) in triplicate. The 2 –△△Ct method was used to determine the relative expressions between HC and DLBCL, with GAPDH as the housekeeping gene. The primer sequences used in this study are listed in Table 1 . Table 1 Primer sequences of human GATA1 used in qPCR assay Primers Sequence (5’ to 3’) human GATA1 forward CACGACACTGTGGCGGAGAAAT human GATA1 reverse TTCCAGATGCCTTGCGGTTTCG 2.7 Statistical analysis All analyses and visualizations were performed using R software (version.4.0.5). Kaplan–Meier survival curves were compared using the log-rank test. Unless otherwise stated, all t-tests in this study used P 2 as the criteria for statistical significance. 3. Results 3.1 Selection of Datasets We selected two datasets of DLBCL from the Gene Expression Omnibus database, GSE83632 and GSE32916. GSE83632 was used for clinical trait analysis. This dataset contained a total of 163 whole blood samples; 76 specimens were blood samples from patients with DLBCL, which formed the experimental group, and 87 were from healthy individuals, which formed the control group. GSE32916 data were used to validate the results. Six datasets related to iron metabolism were screened from GenBank. 3.2 Screening of co-expression modules WGCNA was used to analyze the GSE83632 dataset, with normal blood samples as the controls. We identified 16 gene modules that were closely associated with DLBCL, each of which is marked with a different color in Fig. 1 . Eleven gene modules were positively associated with DLBCL, of which the "dark green" and "magenta" modules were the most closely associated (dark green module: r = 0.5, P = 1e-11; magenta module: r = 0.44, P = 5e- 09; Fig. 1 A, B). These two modules were chosen as targets for subsequent analyses. 3.3 Screening of shared genes A total of 2,068 genes contained in the target module screened in the previous step were intersected with 200 genes related to iron metabolism to obtain 123 shared genes (Fig. 2 A). Using the interactions between these 123 genes, we constructed a PPI network (Fig. 2 B) to understand their interactions (Fig. 2 C). The network was imported into Cytoscape and the "MCODE" plug-in was used to filter genes with more than 20 chains, resulting in 21 shared genes (Fig. 2 D). The results of the "ClueGo" analysis were used to identify the potential mechanisms of these 21 shared genes in DLBCL, which are rich in biological activities, such as the apoptosis signaling pathway, oxidative stress pathway, and heme catabolic pathway (Fig. 2 E). 3.4 Validation of GATA1 core genes To ensure the accuracy of the screened shared genes, we applied differential analysis to analyze DLBCL samples and normal tissue samples from the GSE32918 dataset, in which 5,675 differential genes (Fig. 3 A) were screened from 24,527 genes. Similarly, we applied differential analysis to analyze the differences between genes in DLBCL samples and normal tissue samples from the GS83632 dataset, in which 986 differential genes were screened from 11,023 genes (Fig. 3 B). By cross-analyzing the shared genes with the two differential genes, we obtained three shared genes with high reliability: GATA1, KLF1, and ACSL6 (Fig. 3 C). Considering DLBCL gene expression and prognosis, we identified GATA1 as the core shared gene involved in ferroptosis in DLBCL. In addition, the relationship between GATA1 expression and survival was analyzed using lymphoma-related data from TCGA. Patients with DLBCL and high GATA1 expression exhibited longer overall survival than those with low GATA1 expression (Fig. 3 D). Similarly, we analyzed the relationship between the expression levels of KLF1 and ACSL6 and survival in patients with diffuse large B-cell lymphoma. There was no significance between them(Fig. 3 E、Figure 3 F). 3.5 GATA1-related biological processes and their associated signaling pathways After identifying GATA1 as a core gene, we explored the potential mechanisms of GATA1 function (Fig. 4 A) using the GeneMANIA database ( http://genemania.org/ ), which included analyzing the relationship between GATA1 and its genome-associated-proteins, physical relationships, co-expression networks, and pathways. Subsequently, we performed GO/KEGG functional enrichment analysis and GSEA enrichment analysis of these genes, which showed that GATA1 is associated with the myeloid differentiation pathway, granulocyte differentiation pathway, embryonic organ development, hematopoietic stem cell differentiation, and immune system regulatory processes (Fig. 4 B, C), as well as-alanine metabolism, galactose metabolism, tricarboxylic acid cycle, and bladder carcinogenesis pathways (Fig. 4 D). 3.6 Correlation between GATA1 and the immune microenvironment We employed the ssGSEA algorithm to determine the relationship between GATA1 expression and immune cell infiltration in DLBCL and showed that GATA1 expression was positively correlated with memory B cells, dormant CD4 + T cells, monocytes, and NK cells. In contrast, GATA1 expression was negatively correlated with dormant NK and CD8 + T cells (Fig. 5 A). TIMER, CIBERSORT, and CIBERSORT abs algorithms were used to verify the relationship between GATA1 expression and B cells, monocytes, NK cells, and neutrophils (Fig. 5 B, C). We then calculated the EstimateScore, ImmuneScore, and StromalScore of the two groups with high and low GATA1 expression (Fig. 6 A, B). The results showed that the immune core of the high-GATA1-expression group was significantly higher than that of the low-GATA1-expression group. Finally, we further explored the potential link between drug sensitivity and GATA1 expression using the CellMinerTM database ( https://discover.nci.nih.gov/cellminer/home . do). Notably, GATA1 expression was positively correlated with sensitivity to tyrosine kinase inhibitors such as imatinib and nilotinib, and the ALK inhibitor crizotinib(Fig. 7 A), and significantly negatively correlated with PI3K inhibitors such as buparlisib, Src-Abl inhibitors, and the CDK9 inhibitor palbociclib (Fig. 7 B). 3.7 Verification of hub gene GATA1 expression by qPCR Quantification of the mRNA abundance of GATA1 revealed that it was actively transcribed in the whole blood of DLBCL patients. Further expression levels of the GATA1 in PBMCs from HC and DLBCL patients were measured. Compared with HC, the relative expression levels of GATA1 mRNA were significantly increased in DLBCL patients ( P < 0.05) (Fig. 8 ). 4. Discussion Increasing evidence suggests that ferroptosis is involved in the development of various tumors [ 19 ] ; therefore, targeting iron-related cell death has substantial potential for tumor therapy [ 20 ] . To the best of our knowledge, this is the first study to apply a bioinformatics approach to explore the potential mechanisms underlying the association between iron metabolism and DLBCL, a highly aggressive and common subtype of non-Hodgkin lymphoma. First, we used the WGCNA algorithm, which is currently the most reliable algorithm for co-expression cluster analysis of iron metabolism and DLBCL (GSE83632) datasets. We then counted the intersections of genes in clinically relevant modules to calculate the shared genes. Simultaneously, we observed the biological processes and signaling pathways involved in these shared genes. Interestingly, the results of the enrichment analysis included multiple biological processes related to oxidative stress and apoptosis, which are closely related to DLBCL progression. These results suggest that the development of DLBCL may be related to transcriptional and apoptotic changes mediated by abnormal mitochondrial function [ 21 ] . To verify the authenticity of our data, the new DLBCL dataset GSE32918 and peripheral blood samples from the normal control group were screened again for limma analysis to detect differential genes, as well as their interaction with previously screened shared genes, to finally identify three core genes, GATA1, KLF1, and ACSL6 [ 22 , 23 ] . Considering the expression and prognosis of DLBCL, we identified GATA1 as a key gene involved in iron metabolism that affects DLBCL progression. The GATA family consists of six transcription factors, GATA1–GATA6, known for their ability to bind to the DNA consensus sequence (A/T)GATA(A/G) through their characteristic zinc finger structure [ 24 ] . In our study, through the collation and analysis of DLBCL datasets from the TCGA and GTEx databases, we found that the group with high GATA1 expression had longer overall survival than the group with low GATA1 expression; high GATA1 expression also predicted a better prognosis. This finding was similar to that reported by Chen et al., who found that the expression level of GATA1 in acute promyelocytic leukemia was highest in the low-risk group and lowest in the high-risk group [ 25 ] . Interestingly, GATA1 promotes cell invasion, metastasis, and drug resistance [ 26 – 28 ] . Therefore, the mechanism of action of GATA1 in DLBCL requires further exploration, as does the consistency of GATA1 expression and its effect in peripheral blood and lymphoma tissues. GATA1, the first transcription factor identified in the GATA family, plays a crucial role in regulating the maturation of erythroid and megakaryocytic lineages, expressed and acting on mast cells and eosinophils [ 29 ] . In this study, we analyzed the gene of GATA1 by immune infiltration and observed that GATA1 expression had a positive effect on CD4 + T cells, B cells, and monocytes, but a negative effect on NK cells and neutrophils. GSEA enrichment analysis revealed that GATA1 was associated with myeloid cell differentiation and granulocyte differentiation pathways. Thus, we hypothesize that the regulatory effects of GATA1 in various diseases could be mediated by regulating the activity of CD4 + T cells. Chimeric antigen receptor T cells are a recent hotspot in therapeutic research for DLBCL and are particularly effective in treating refractory or recurrent DLBCL [ 30 , 31 ] . However, limitations still exist, which may be addressed through further study of GATA1 modulation in T cells and DLBCL. Currently, the treatment of choice for DLBCL is chemotherapy; although most patients are sensitive to first-line chemotherapy, 30–40% of patients with DLBCL still relapse after treatment [ 32 ] . Some of these patients who are not transplantable may be treated with small-molecule targeted agents. In our study, GATA1 was closely associated with sensitivity to small-molecule targeted drugs. In particular, the expression of GATA1 was positively correlated with imatinib, nilotinib, and crizotinib sensitivity, but negatively correlated with the PI3K inhibitor copanlisib [ 33 ] , the Src-Abl inhibitor, and the CDK9 inhibitor. Thus, we speculate that GATA1 causes relapsed refractory DLBCL to become resistant to copanlisib by affecting the PI3K/Akt/mTOR pathway; however, the exact mechanism requires further investigation. Our study has some limitations. That is, the results are still at the level of data analysis, and insufficient experimental data exist to confirm our results. Therefore, a reasonable analysis must be performed to verify our conjectures in a stepwise manner. 5. Conclusion In conclusion, through WGCNA, modules related to iron metabolism and DLBCL were identified. After enrichment analysis of the modules, the key gene GATA1 was finally identified. The pathway of GATA1 in DLBCL was investigated and the relationship between GATA1 expression and immune cell infiltration in DLBCL was deduced. Moreover, the relationship between the expression of GATA1 and the sensitivity of DLBCL treatment was analyzed. This study provides novel insights into the molecular mechanism between iron metabolism and DLBCL. Specifically, we present GATA1 as a meaningful biological target and immune-related biomarker of DLBCL. Declarations Acknowledgments We would like to thank Editage (www.editage.cn) for English language editing. Funding Information Our study was supported by the Clinical Research Plan of SHDC (No. SHDC2020CR6005), and National Natural Science Foundation of China (No. 81871709). Conflict of Interest Statement The authors have no conflict of interest. Consent for publication Not applicable. Ethics Statement The clinical data of patients involved in this study were derived from an open-access database; therefore, consent was not required. All information regarding the ethical approval of these open-access databases can be obtained from two published studies. This study was approved by the Ethics Committee of the Xiamen Branch, Zhongshan hospital, Fudan University, and all study protocols complied with the Declaration of Helsinki, all participants gave informed consent before the start of the study. Author Contributions Yuxin Zhang, Zheng Wei and Dawei Cui designed the study. Yuxin Zhang and Yue Wang performed the data analysis. Yuxin Zhang drafted the manuscript. Shifen Wang, Zheng Wei and Dawei Cui revised the manuscript. All authors contributed to the manuscript and approved the submitted version. Code Availability Data analysis and graphical representation were performed using custom R scripts and publicly available packages, as indicated in the text. All scripts are available on request. D ata availability statement All data generated or analyzed during this study are included in this article. References Siegel RL,Miller KD,Fuchs HE,Jemal A. Cancer Statistics, 2021. CA: A Cancer Journal for Clinicians 2021, 71(1):7-33. Crombie JL,Armand P. Diffuse Large B-Cell Lymphoma and High-Grade B-Cell Lymphoma: Genetic Classification and Its Implications for Prognosis and Treatment. Surg Oncol Clin N Am 2020, 29(1):115-125. Liu Y,Barta SK. Diffuse large B-cell lymphoma: 2019 update on diagnosis, risk stratification, and treatment. Am J Hematol 2019, 94(5):604-616. Liao D,Yang G,Yang Y,Tang X,Huang H,Shao J et al. Identification of Pannexin 2 as a Novel Marker Correlating with Ferroptosis and Malignant Phenotypes of Prostate Cancer Cells. Onco Targets Ther 2020, 13:4411-4421. Shen Y,Li X,Zhao B,Xue Y,Wang S,Chen X et al. Iron metabolism gene expression and prognostic features of hepatocellular carcinoma. J Cell Biochem 2018, 119(11):9178-9204. Zhang S,Chang W,Wu H,Wang YH,Gong YW,Zhao YL et al. Pan-cancer analysis of iron metabolic landscape across the Cancer Genome Atlas. J Cell Physiol 2020, 235(2):1013-1024. Ren F,Yang Y,Wu K,Zhao T,Shi Y,Song M et al. The Effects of Dandelion Polysaccharides on Iron Metabolism by Regulating Hepcidin via JAK/STAT Signaling Pathway. Oxid Med Cell Longev 2021, 2021:7184760. Leftin A,Ben-Chetrit N,Klemm F,Joyce JA,Koutcher JA. Iron imaging reveals tumor and metastasis macrophage hemosiderin deposits in breast cancer. PLoS One 2017, 12(9):e0184765. Fischer-Fodor E,Miklasova N,Berindan-Neagoe I,Saha B. Iron, inflammation and invasion of cancer cells. Clujul Med 2015, 88(3):272-277. Steegmann-Olmedillas JL. The role of iron in tumour cell proliferation. Clin Transl Oncol 2011, 13(2):71-76. Dev S,Babitt JL. Overview of iron metabolism in health and disease. Hemodial Int 2017, 21 Suppl 1(Suppl 1):S6-s20. Mou Y,Wang J,Wu J,He D,Zhang C,Duan C et al. Ferroptosis, a new form of cell death: opportunities and challenges in cancer. J Hematol Oncol 2019, 12(1):34. Lu B,Chen XB,Ying MD,He QJ,Cao J,Yang B. The Role of Ferroptosis in Cancer Development and Treatment Response. Front Pharmacol 2017, 8:992. Dixon SJ,Lemberg KM,Lamprecht MR,Skouta R,Zaitsev EM,Gleason CE et al. Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell 2012, 149(5):1060-1072. Wang M,Li S,Wang Y,Cheng H,Su J,Li Q. Gambogenic acid induces ferroptosis in melanoma cells undergoing epithelial-to-mesenchymal transition. Toxicol Appl Pharmacol 2020, 401:115110. Lippmann J,Petri K,Fulda S,Liese J. Redox Modulation and Induction of Ferroptosis as a New Therapeutic Strategy in Hepatocellular Carcinoma. Transl Oncol 2020, 13(8):100785. Zelenetz AD,Gordon LI,Chang JE,Christian B,Abramson JS,Advani RH et al. NCCN Guidelines® Insights: B-Cell Lymphomas, Version 5.2021. J Natl Compr Canc Netw 2021, 19(11):1218-1230. Langfelder P,Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559. Torti SV,Manz DH,Paul BT,Blanchette-Farra N,Torti FM. Iron and Cancer. Annual Review of Nutrition 2018, 38(1):97-125. Jiang X,Stockwell BR,Conrad M. Ferroptosis: mechanisms, biology and role in disease. Nat Rev Mol Cell Biol 2021, 22(4):266-282. Matthews JM,Bhatt S,Patricelli MP,Nomanbhoy TK,Jiang X,Natkunam Y et al. Pathophysiological significance and therapeutic targeting of germinal center kinase in diffuse large B-cell lymphoma. Blood 2016, 128(2):239-248. Bonnet D,Dick JE. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nature Medicine 1997, 3(7):730-737. Lapidot T,Sirard C,Vormoor J,Murdoch B,Hoang T,Caceres-Cortes J et al. A cell initiating human acute myeloid leukaemia after transplantation into SCID mice. Nature 1994, 367(6464):645-648. Ferreira R,Ohneda K,Yamamoto M,Philipsen S. GATA1 function, a paradigm for transcription factors in hematopoiesis. Molecular and cellular biology 2005, 25(4):1215-1227. Lin X,Qiao N,Shen Y,Fang H,Xue Q,Cui B et al. Integration of Genomic and Transcriptomic Markers Improves the Prognosis Prediction of Acute Promyelocytic Leukemia. Clinical cancer research : an official journal of the American Association for Cancer Research 2021, 27(13):3683-3694. Gong S,Li Q,Jeter CR,Fan Q,Tang DG,Liu B. Regulation of NANOG in cancer cells. Mol Carcinog 2015, 54(9):679-687. Li Y,Ke Q,Shao Y,Zhu G,Li Y,Geng N et al. GATA1 induces epithelial-mesenchymal transition in breast cancer cells through PAK5 oncogenic signaling. Oncotarget 2015, 6(6):4345-4356. Chang Z,Zhang Y,Liu J,Guan C,Gu X,Yang Z et al. GATA1 Promotes Gemcitabine Resistance in Pancreatic Cancer through Antiapoptotic Pathway. Journal of oncology 2019, 2019:9474273. Lee W,Weinberg O,Pinkus G. GATA1 Is a Sensitive and Specific Nuclear Marker for Erythroid and Megakaryocytic Lineages. American journal of clinical pathology 2017, 147(4):420-426. Depil S,Duchateau P,Grupp SA,Mufti G,Poirot L. 'Off-the-shelf' allogeneic CAR T cells: development and challenges. Nat Rev Drug Discov 2020, 19(3):185-199. Modi D,Potugari B,Uberti J. Immunotherapy for Diffuse Large B-Cell Lymphoma: Current Landscape and Future Directions. Cancers (Basel) 2021, 13(22). Miao Y,Medeiros LJ,Li Y,Li J,Young KH. Genetic alterations and their clinical implications in DLBCL. Nat Rev Clin Oncol 2019, 16(10):634-652. Yin H,Zhong F,Ouyang Y,Wang Q,Ding L,He S. Upregulation of ADAM12 contributes to accelerated cell proliferation and cell adhesion-mediated drug resistance (CAM-DR) in Non-Hodgkin's Lymphoma. Hematology 2017, 22(9):527-535. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4302921","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":299210618,"identity":"9ede6ae3-f8f4-40b8-9f69-bdcfb8292e1d","order_by":0,"name":"Yuxin Zhang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yuxin","middleName":"","lastName":"Zhang","suffix":""},{"id":299210620,"identity":"4531b828-c861-45d2-a54a-ef4f9447216e","order_by":1,"name":"Yue Wang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Wang","suffix":""},{"id":299210622,"identity":"d84d15cd-0440-47ea-8cb6-5233c90f458e","order_by":2,"name":"Shifen Wang","email":"","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shifen","middleName":"","lastName":"Wang","suffix":""},{"id":299210624,"identity":"b889e689-0bea-440b-a6e9-33a79b915a55","order_by":3,"name":"Dawei Cui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIie3RMQuCQBTA8ReBLoeuJ9R3uBAicOir3BHoks0NDgeBq9/G1gvBlgtXR6e2qGhpik5bmg7dgu4Pj3vD+00HYDL9YkINBUBu98B4APH4INJGPksP4hzlrGmSYuJX4oxhGzBun4SWeHLtE1oWaC5EiEFGjKMN1RIi4hwzrsiBh3iUFoxjRPSkuuyfLfF3oMirD6njHFpCrJbwHsSrrw9MywhhCauFWvwUrfXEqUJ2fybB0s0kq29JMM1sqSdfIdp9ptX3XmWLAccmk8n0T70BamlG7XgYD3UAAAAASUVORK5CYII=","orcid":"","institution":"Zhejiang University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Dawei","middleName":"","lastName":"Cui","suffix":""},{"id":299210626,"identity":"7fc4e1ae-2c1c-4348-ae39-f930c17fbf31","order_by":4,"name":"Zheng Wei","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2024-04-22 03:56:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4302921/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4302921/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56410249,"identity":"088ecfa7-ce64-4f2d-a445-56060c3ffc57","added_by":"auto","created_at":"2024-05-13 20:18:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":216204,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of modules linked to clinical features of DLBCL.\u003c/strong\u003e (A) Cluster dendrogram of co-expressed genes in DLBCL. (B) Heatmap of module–trait relationships in DLBCL.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4302921/v1/3a874f5c2205710dabcb0cd8.png"},{"id":56410247,"identity":"45813b53-0cc8-4365-bc39-b08802a2ba54","added_by":"auto","created_at":"2024-05-13 20:18:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":595782,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCharacterization of shared genes between DLBCL and iron metabolism.\u003c/strong\u003e (A) Venn diagram of shared genes between the two DLBCL modules and one iron metabolism. (B) PPI network of 123 shared genes. (C) Network of GO terms in ClueGO. (D) Network of MCODE. (E) Network of screened gene GO terms in ClueGO.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4302921/v1/5a2483dcac43c4a59c40d8b5.png"},{"id":56410251,"identity":"c93abc2c-fdb5-467f-ac98-9f80422e96d3","added_by":"auto","created_at":"2024-05-13 20:18:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":251657,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGATA1 expression and prognostic value in DLBCL patients.\u003c/strong\u003e(A) Volcano plot of GSE32916 (log2 fold change|\u0026gt; 1 and \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05) and (B) GSE83632 (log2 fold change\u0026gt; 1 and \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05). Significantly upregulated and downregulated genes are depicted as red and blue dots, respectively. (C) Venn diagram of hub genes from the two DLBCL datasets and shared genes. (D) Kaplan–Meier curve of the association between GATA1 and overall survival in patients with DLBCL in TCGA datasets of DLBCL (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05). (E) Kaplan–Meier curve of the association between KLF1 and overall survival in patients with DLBCL in TCGA datasets of DLBCL (\u003cem\u003eP\u003c/em\u003e\u0026gt; 0.05). (F) Kaplan–Meier curve of the association between ACSL6and overall survival in patients with DLBCL in TCGA datasets of DLBCL (\u003cem\u003eP\u003c/em\u003e\u0026gt; 0.05).\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4302921/v1/e245042d509650890d0c0579.png"},{"id":56410248,"identity":"b157b912-0698-4c48-b31a-86e66122be2a","added_by":"auto","created_at":"2024-05-13 20:18:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":466134,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePPI network and functional enrichment analysis of GATA1.\u003c/strong\u003e (A) PPI network of GATA1 and its interacting proteins. (B) GO/KEGG enrichment analysis of GATA1 and its interacting proteins. (C) GO enrichment analysis of GATA1 and its interacting proteins. (D) Gene set enrichment analysis of the top 10 enriched pathways in patients with DLBCL and high GATA1 expression.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4302921/v1/a9ce2a41417942ddf2f74b8d.png"},{"id":56410246,"identity":"15c6e4de-7d75-4624-9b27-92ea449ba179","added_by":"auto","created_at":"2024-05-13 20:18:38","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":393786,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of immune cell infiltration in DLBCL. \u003c/strong\u003e(A) Relationship between GATA1 expression and immune cell subtypes in patients with DLBCL. (B) Immune cells between high-GATA1 and low-GATA1 groups. ssGSEA, single-sample gene set enrichment analysis. ns, no significance, *\u003cem\u003e P\u003c/em\u003e\u0026lt; 0.05. (C) TIMER, CIBERSORT, and CIBERSORT ab algorithms were used to verify the relationship between GATA1 expression and B cells, monocytes, NK cells, and neutrophils.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4302921/v1/1ed5eefab06bbb8bb4234b19.png"},{"id":56410252,"identity":"d86859b6-32aa-4b6c-bc9d-fffcf243a783","added_by":"auto","created_at":"2024-05-13 20:18:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":130457,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune microenvironment analysis in DLBCL patients with high/low LGALS2 expression.\u003c/strong\u003e (A) Heatmap of immune cells between high- and low-expression groups. (B) Comparison of EstimateScore, ImmuneScore, and StromalScore between high-GATA1 and low-GATA1 groups using the ssGSEA algorithm. ns, no significance, *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4302921/v1/5c7224596e23347a935f527c.png"},{"id":56410253,"identity":"436dd849-b150-47ed-93eb-08663b986509","added_by":"auto","created_at":"2024-05-13 20:18:39","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":218388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDrug sensitivity analysis of GATA1. \u003c/strong\u003e(A) Drug sensitivity was positively correlated with GATA1 expression（B）Drug sensitivity was negatively correlated with GATA1 expression.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4302921/v1/bc2847c6abe1f8d573e6a30f.png"},{"id":56410250,"identity":"001fd0a0-9a9f-4642-8a6f-fc28f935c95d","added_by":"auto","created_at":"2024-05-13 20:18:39","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":37304,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eqPCR results about the expression levels of GATA1.\u003c/strong\u003e（A）A nonparametric Student’s \u003cem\u003et\u003c/em\u003e-test was calculated when comparing the two groups of HC and DLBCL. *\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4302921/v1/059d4a20bf69f10f1331d042.png"},{"id":64225791,"identity":"6f9490ba-32ba-4718-a95a-b8b95211b457","added_by":"auto","created_at":"2024-09-10 13:38:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2885169,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4302921/v1/6031ab38-18d2-4f6c-a5f7-bd11c9fc7bcb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"GATA1: A Key Biomarker for Predicting the Prognosis of Patients With Diffuse Large B- cell Lymphoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDiffuse large B-cell lymphoma (DLBCL) is a common and highly aggressive type of lymphoma, accounting for approximately 1/3 of all non-Hodgkin\u0026rsquo;s lymphoma case\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.In recent years, the incidence of DLBCL in China has been gradually increasing \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Because of the substantial heterogeneity of DLBCL, 30\u0026ndash;40% of patients treated with the first-line immunochemotherapy regimen R-CHOP are either insensitive or develop relapsed/refractory lymphoma \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIron is essential for maintaining normal function and homeostasis in cells. Therefore, an imbalance in iron metabolism is closely related to the occurrence, development, metastasis, and recurrence of many malignant tumors, such as lung cancer, renal cancer, breast cancer, prostate cancer, and liver cancer \u003csup\u003e[\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8 CR9\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. However, iron metabolism plays a dual role in tumor cells. On the one hand, the proliferation of tumor cells is more dependent on iron than that of normal cells, a phenomenon known as iron addiction \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Conversely, an increase in iron concentration leads to cell death through the accumulation of reactive oxygen species and lipid peroxidation products, known as ferroptosis \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Ferroptosis is a specific mode of programmed cell death that is dependent on iron metabolism and distinct from apoptosis, necroptosis, and autophagy \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. With research on emerging anticancer pathways, various ferroptosis inducers have been developed for cancer \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFollowing the rapid development of gene microarray technology, researchers can measure the expression levels of thousands of genes in a short period, which helps gain a deeper understanding of the pathogenesis of diseases at the gene level. Currently, the International Prognostic Index is the main tool used for the clinical evaluation of DLBCL prognosis. Although such evaluations can guide clinical treatment, they merely provide a combination of clinical prognostic parameters that do not represent the heterogeneity of molecular biology in the occurrence and development of the disease. Therefore, it is critical to actively identify molecular biological indicators that affect DLBCL \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. However, no previous studies have identified the genetic characteristics and molecular mechanisms underlying iron metabolism in patients with DLBCL.\u003c/p\u003e \u003cp\u003eTo address this gap in the literature, we conduct bioinformatic analysis to identify the action pathway, core shared genes, and core genes of iron metabolism in the pathogenesis of DLBCL, thereby revealing the mechanism of iron metabolism and therapeutic targets in DLBCL. The results showed that GATA-binding factor 1 (GATA1) is a key gene in the role of iron metabolism in the pathogenesis of DLBCL. It affects the differentiation and maturation of immune cells through the myeloid cell differentiation pathway, especially memory B cells and CD4\u003csup\u003e+\u003c/sup\u003e T cells, thus affecting the progression of DLBCL. The survival analysis found that the GATA1 high expression group had better OS. Its expression had a positive correlation effect with BTK inhibitor-related drugs and a negative correlation with PI3K inhibitor-related small molecule drugs, which is meaningful for clinical drug selection. This research contributes to improving the early diagnosis, treatment, drug resistance, and prognosis of DLBCL.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data download and processing\u003c/h2\u003e \u003cp\u003eWe screened transcriptome sequencing datasets related to DLBCL using the Gene Expression Omnibus database. Iron metabolism-related datasets were downloaded from the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.com/\u003c/span\u003e\u003cspan address=\"https://www.genecards.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Mutation information, clinical information, and genome-wide transcript levels of patients with DLBCL were obtained from The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In addition, we obtained a list of all genes associated with immune response from the InnateDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.innatedb.com/\u003c/span\u003e\u003cspan address=\"https://www.innatedb.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Throughout the study, we background-corrected and normalized all raw data and matched all probe names with their corresponding gene symbols for subsequent analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Weighted gene co-expression network analysis\u003c/h2\u003e \u003cp\u003eFollowing scientific and technological advances and the rapid development of systems biology and bioinformatics, weighted gene co-expression network analysis (WGCNA) has become one of the most popular algorithms for analyzing large amounts of data. WGCNA addresses the correlation between gene sets and sample phenotypes, allowing for the mapping of inter-gene regulatory networks in gene collections and screening of essential regulatory genes closely related to disease \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. In this study, we used WGCNA to establish a gene co-expression network for DLBCL and iron metabolism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Identification and features of shared genes in DLBCL and iron metabolism\u003c/h2\u003e \u003cp\u003eWe counted the intersection of genes in clinically relevant modules to calculate the shared genes. TCGA data were used to verify the relationship between these shared genes and DLBCL and to observe their location on chromosomes and the frequency of copy number variations. GO enrichment analysis of shared genes was performed using the \"ClueGO\" and \"MCODE\" plug-ins in Cytoscape to establish protein\u0026ndash;protein interaction (PPI) networks, understand their functions, identify the relationships between proteins, and ultimately screen out the core genes with the most critical roles. In this study, PPI construction was performed primarily using a STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), whereby all available and predicted connections between proteins were integrated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Functional enrichment analysis and gene set enrichment analysis\u003c/h2\u003e \u003cp\u003eGene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, which is the most common functional enrichment method currently employed in medical research, was used to interpret molecular-level information regarding the higher functions and functioning of biological systems. We applied the \"clusterProfiler\" R package to perform GO/KEGG enrichment analysis of shared genes and elucidate the underlying mechanisms of disease onset and progression. Gene set enrichment analysis (GSEA) was used to analyze the distribution trend of genes in the predefined gene sets included in the gene list ranked by phenotypic correlation to determine their contribution to the phenotype. The median expression level of GATA1 was employed as a classification criterion to first categorize patients with DLBCL into low- and high-expression groups, before performing GSEA on the subgroups using GSEA software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Association between core genes and DLBCL\u003c/h2\u003e \u003cp\u003eWe compiled gene expression profiles of DLBCL and normal tissues from TCGA and GTEx databases to compare the differential expression of core genes in DLBCL. The relationship between core genes and patient prognosis was determined by analyzing the clinical data of DLBCL in TCGA and calculating the overall survival in high and low GATA1 expression groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Assessment of the immune landscape\u003c/h2\u003e \u003cp\u003eTumor infiltration by immune cells can profoundly influence tumor progression and the success of anticancer therapies by exerting pro-tumorigenic and anti-tumorigenic effects. As a first step, we used immune cell infiltration and gene expression data from the TIMER database to calculate the relationship between core gene expression and immune cell abundance in DLBCL, and presented these results in heatmaps and bubble plots. Secondly, we used single-sample genomic enrichment analysis, an emerging gene enrichment method, to compare the EstimateScore, ImmuneScore, and StromalScore between high- and low-expression groups of the core genes in each sample. In this calculation, we used gene set variation analysis to transform the expression matrix of individual genes into that of a specific set of genes. Finally, Spearman\u0026rsquo;s correlation analysis was used to analyze the relationship between core genes and a range of immune-related genes, such as immune checkpoint-related and immune cell subpopulation-related genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Quantitative Real-time (qPCR) analysis\u003c/h2\u003e \u003cp\u003eFrom February to April 2024, a total of 9 adult inpatients with DLBCL were included in our study. The DLBCL diagnoses were confirmed through a tissue biopsy. As control subjects, 9 healthy participants undergoing routine health examinations were included as health controls (HC). For each subject, 2 mL fresh blood was taken, and peripheral blood mononuclear cells (PBMCs) were were isolated by FicollPaque density gradient centrifugation. Our study was carried out in compliance with the Declaration of Helsinki and received approval from the Ethical Committee of our hospital. Additionally, all participants gave informed consent before the start of the study. Total RNA was extracted from each sample with TransZol Up Plus RNA Kit (TransGen Biotech, Beijing) and reverse transcribed (TransScript All-in-One Kit, TransGen Biotech, Beijing). The PCR analysis was done on an ABI StepOnePlus\u0026trade; system using TransStart Top Green qPCR SuperMix (TransGen Biotech, Beijing) in triplicate. The 2\u003csup\u003e\u0026ndash;△△Ct\u003c/sup\u003e method was used to determine the relative expressions between HC and DLBCL, with GAPDH as the housekeeping gene. The primer sequences used in this study are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer sequences of human GATA1 used in qPCR assay\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSequence (5\u0026rsquo; to 3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehuman \u003cem\u003eGATA1\u003c/em\u003e forward\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCACGACACTGTGGCGGAGAAAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehuman \u003cem\u003eGATA1\u003c/em\u003e reverse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTTCCAGATGCCTTGCGGTTTCG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses and visualizations were performed using R software (version.4.0.5). Kaplan\u0026ndash;Meier survival curves were compared using the log-rank test. Unless otherwise stated, all t-tests in this study used P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and logFold change|\u0026gt;2 as the criteria for statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Selection of Datasets\u003c/h2\u003e\n\u003cp\u003eWe selected two datasets of DLBCL from the Gene Expression Omnibus database, GSE83632 and GSE32916. GSE83632 was used for clinical trait analysis. This dataset contained a total of 163 whole blood samples; 76 specimens were blood samples from patients with DLBCL, which formed the experimental group, and 87 were from healthy individuals, which formed the control group. GSE32916 data were used to validate the results. Six datasets related to iron metabolism were screened from GenBank.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Screening of co-expression modules\u003c/h2\u003e\n\u003cp\u003eWGCNA was used to analyze the GSE83632 dataset, with normal blood samples as the controls. We identified 16 gene modules that were closely associated with DLBCL, each of which is marked with a different color in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Eleven gene modules were positively associated with DLBCL, of which the \"dark green\" and \"magenta\" modules were the most closely associated (dark green module: r\u0026thinsp;=\u0026thinsp;0.5, P\u0026thinsp;=\u0026thinsp;1e-11; magenta module: r\u0026thinsp;=\u0026thinsp;0.44, P\u0026thinsp;=\u0026thinsp;5e- 09; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). These two modules were chosen as targets for subsequent analyses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Screening of shared genes\u003c/h2\u003e\n\u003cp\u003eA total of 2,068 genes contained in the target module screened in the previous step were intersected with 200 genes related to iron metabolism to obtain 123 shared genes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Using the interactions between these 123 genes, we constructed a PPI network (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB) to understand their interactions (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). The network was imported into Cytoscape and the \"MCODE\" plug-in was used to filter genes with more than 20 chains, resulting in 21 shared genes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). The results of the \"ClueGo\" analysis were used to identify the potential mechanisms of these 21 shared genes in DLBCL, which are rich in biological activities, such as the apoptosis signaling pathway, oxidative stress pathway, and heme catabolic pathway (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003ch2\u003e3.4 Validation of GATA1 core genes\u003c/h2\u003e\n\u003cp\u003eTo ensure the accuracy of the screened shared genes, we applied differential analysis to analyze DLBCL samples and normal tissue samples from the GSE32918 dataset, in which 5,675 differential genes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA) were screened from 24,527 genes. Similarly, we applied differential analysis to analyze the differences between genes in DLBCL samples and normal tissue samples from the GS83632 dataset, in which 986 differential genes were screened from 11,023 genes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). By cross-analyzing the shared genes with the two differential genes, we obtained three shared genes with high reliability: GATA1, KLF1, and ACSL6 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). Considering DLBCL gene expression and prognosis, we identified GATA1 as the core shared gene involved in ferroptosis in DLBCL. In addition, the relationship between GATA1 expression and survival was analyzed using lymphoma-related data from TCGA. Patients with DLBCL and high GATA1 expression exhibited longer overall survival than those with low GATA1 expression (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD). Similarly, we analyzed the relationship between the expression levels of KLF1 and ACSL6 and survival in patients with diffuse large B-cell lymphoma. There was no significance between them(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE、Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003e3.5 GATA1-related biological processes and their associated signaling pathways\u003c/h2\u003e\n\u003cp\u003eAfter identifying GATA1 as a core gene, we explored the potential mechanisms of GATA1 function (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA) using the GeneMANIA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://genemania.org/\u003c/span\u003e\u003c/span\u003e), which included analyzing the relationship between GATA1 and its genome-associated-proteins, physical relationships, co-expression networks, and pathways. Subsequently, we performed GO/KEGG functional enrichment analysis and GSEA enrichment analysis of these genes, which showed that GATA1 is associated with the myeloid differentiation pathway, granulocyte differentiation pathway, embryonic organ development, hematopoietic stem cell differentiation, and immune system regulatory processes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB, C), as well as-alanine metabolism, galactose metabolism, tricarboxylic acid cycle, and bladder carcinogenesis pathways (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003ch2\u003e3.6 Correlation between GATA1 and the immune microenvironment\u003c/h2\u003e\n\u003cp\u003eWe employed the ssGSEA algorithm to determine the relationship between GATA1 expression and immune cell infiltration in DLBCL and showed that GATA1 expression was positively correlated with memory B cells, dormant CD4\u003csup\u003e+\u003c/sup\u003e T cells, monocytes, and NK cells. In contrast, GATA1 expression was negatively correlated with dormant NK and CD8\u003csup\u003e+\u003c/sup\u003e T cells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). TIMER, CIBERSORT, and CIBERSORT abs algorithms were used to verify the relationship between GATA1 expression and B cells, monocytes, NK cells, and neutrophils (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB, C). We then calculated the EstimateScore, ImmuneScore, and StromalScore of the two groups with high and low GATA1 expression (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA, B). The results showed that the immune core of the high-GATA1-expression group was significantly higher than that of the low-GATA1-expression group. Finally, we further explored the potential link between drug sensitivity and GATA1 expression using the CellMinerTM database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://discover.nci.nih.gov/cellminer/home\u003c/span\u003e\u003c/span\u003e. do). Notably, GATA1 expression was positively correlated with sensitivity to tyrosine kinase inhibitors such as imatinib and nilotinib, and the ALK inhibitor crizotinib(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA), and significantly negatively correlated with PI3K inhibitors such as buparlisib, Src-Abl inhibitors, and the CDK9 inhibitor palbociclib (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003e3.7 Verification of hub gene GATA1 expression by qPCR\u003c/h2\u003e\n\u003cp\u003eQuantification of the mRNA abundance of GATA1 revealed that it was actively transcribed in the whole blood of DLBCL patients. Further expression levels of the GATA1 in PBMCs from HC and DLBCL patients were measured. Compared with HC, the relative expression levels of GATA1 mRNA were significantly increased in DLBCL patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIncreasing evidence suggests that ferroptosis is involved in the development of various tumors\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e; therefore, targeting iron-related cell death has substantial potential for tumor therapy \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. To the best of our knowledge, this is the first study to apply a bioinformatics approach to explore the potential mechanisms underlying the association between iron metabolism and DLBCL, a highly aggressive and common subtype of non-Hodgkin lymphoma. First, we used the WGCNA algorithm, which is currently the most reliable algorithm for co-expression cluster analysis of iron metabolism and DLBCL (GSE83632) datasets. We then counted the intersections of genes in clinically relevant modules to calculate the shared genes. Simultaneously, we observed the biological processes and signaling pathways involved in these shared genes. Interestingly, the results of the enrichment analysis included multiple biological processes related to oxidative stress and apoptosis, which are closely related to DLBCL progression. These results suggest that the development of DLBCL may be related to transcriptional and apoptotic changes mediated by abnormal mitochondrial function\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. To verify the authenticity of our data, the new DLBCL dataset GSE32918 and peripheral blood samples from the normal control group were screened again for limma analysis to detect differential genes, as well as their interaction with previously screened shared genes, to finally identify three core genes, GATA1, KLF1, and ACSL6 \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Considering the expression and prognosis of DLBCL, we identified GATA1 as a key gene involved in iron metabolism that affects DLBCL progression.\u003c/p\u003e \u003cp\u003eThe GATA family consists of six transcription factors, GATA1\u0026ndash;GATA6, known for their ability to bind to the DNA consensus sequence (A/T)GATA(A/G) through their characteristic zinc finger structure \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. In our study, through the collation and analysis of DLBCL datasets from the TCGA and GTEx databases, we found that the group with high GATA1 expression had longer overall survival than the group with low GATA1 expression; high GATA1 expression also predicted a better prognosis. This finding was similar to that reported by Chen et al., who found that the expression level of GATA1 in acute promyelocytic leukemia was highest in the low-risk group and lowest in the high-risk group \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Interestingly, GATA1 promotes cell invasion, metastasis, and drug resistance \u003csup\u003e[\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Therefore, the mechanism of action of GATA1 in DLBCL requires further exploration, as does the consistency of GATA1 expression and its effect in peripheral blood and lymphoma tissues.\u003c/p\u003e \u003cp\u003eGATA1, the first transcription factor identified in the GATA family, plays a crucial role in regulating the maturation of erythroid and megakaryocytic lineages, expressed and acting on mast cells and eosinophils \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. In this study, we analyzed the gene of GATA1 by immune infiltration and observed that GATA1 expression had a positive effect on CD4\u0026thinsp;+\u0026thinsp;T cells, B cells, and monocytes, but a negative effect on NK cells and neutrophils. GSEA enrichment analysis revealed that GATA1 was associated with myeloid cell differentiation and granulocyte differentiation pathways. Thus, we hypothesize that the regulatory effects of GATA1 in various diseases could be mediated by regulating the activity of CD4\u0026thinsp;+\u0026thinsp;T cells. Chimeric antigen receptor T cells are a recent hotspot in therapeutic research for DLBCL and are particularly effective in treating refractory or recurrent DLBCL \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. However, limitations still exist, which may be addressed through further study of GATA1 modulation in T cells and DLBCL.\u003c/p\u003e \u003cp\u003eCurrently, the treatment of choice for DLBCL is chemotherapy; although most patients are sensitive to first-line chemotherapy, 30\u0026ndash;40% of patients with DLBCL still relapse after treatment \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Some of these patients who are not transplantable may be treated with small-molecule targeted agents. In our study, GATA1 was closely associated with sensitivity to small-molecule targeted drugs. In particular, the expression of GATA1 was positively correlated with imatinib, nilotinib, and crizotinib sensitivity, but negatively correlated with the PI3K inhibitor copanlisib \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e, the Src-Abl inhibitor, and the CDK9 inhibitor. Thus, we speculate that GATA1 causes relapsed refractory DLBCL to become resistant to copanlisib by affecting the PI3K/Akt/mTOR pathway; however, the exact mechanism requires further investigation.\u003c/p\u003e \u003cp\u003eOur study has some limitations. That is, the results are still at the level of data analysis, and insufficient experimental data exist to confirm our results. Therefore, a reasonable analysis must be performed to verify our conjectures in a stepwise manner.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, through WGCNA, modules related to iron metabolism and DLBCL were identified. After enrichment analysis of the modules, the key gene GATA1 was finally identified. The pathway of GATA1 in DLBCL was investigated and the relationship between GATA1 expression and immune cell infiltration in DLBCL was deduced. Moreover, the relationship between the expression of GATA1 and the sensitivity of DLBCL treatment was analyzed. This study provides novel insights into the molecular mechanism between iron metabolism and DLBCL. Specifically, we present GATA1 as a meaningful biological target and immune-related biomarker of DLBCL.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Editage (www.editage.cn) for English language editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study was supported by the Clinical Research Plan of SHDC (No. SHDC2020CR6005), and National Natural Science Foundation of China (No. 81871709).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical data of patients involved in this study were derived from an open-access database; therefore, consent was not required. All information regarding the ethical approval of these open-access databases can be obtained from two published studies.\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the Xiamen Branch, Zhongshan hospital, Fudan University, and all study protocols complied with the Declaration of Helsinki, all participants gave informed consent before the start of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYuxin Zhang, Zheng Wei and Dawei Cui designed the study. Yuxin Zhang and Yue Wang performed the data analysis. Yuxin Zhang drafted the manuscript. Shifen Wang, Zheng Wei and Dawei Cui revised the manuscript. All authors contributed to the manuscript and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData analysis and graphical representation were performed using custom R scripts and publicly available packages, as indicated in the text. All scripts are available on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003cstrong\u003eata availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL,Miller KD,Fuchs HE,Jemal A. Cancer Statistics, 2021. CA: A Cancer Journal for Clinicians 2021, 71(1):7-33.\u003c/li\u003e\n\u003cli\u003eCrombie JL,Armand P. Diffuse Large B-Cell Lymphoma and High-Grade B-Cell Lymphoma: Genetic Classification and Its Implications for Prognosis and Treatment. Surg Oncol Clin N Am 2020, 29(1):115-125.\u003c/li\u003e\n\u003cli\u003eLiu Y,Barta SK. Diffuse large B-cell lymphoma: 2019 update on diagnosis, risk stratification, and treatment. Am J Hematol 2019, 94(5):604-616.\u003c/li\u003e\n\u003cli\u003eLiao D,Yang G,Yang Y,Tang X,Huang H,Shao J et al. Identification of Pannexin 2 as a Novel Marker Correlating with Ferroptosis and Malignant Phenotypes of Prostate Cancer Cells. Onco Targets Ther 2020, 13:4411-4421.\u003c/li\u003e\n\u003cli\u003eShen Y,Li X,Zhao B,Xue Y,Wang S,Chen X et al. Iron metabolism gene expression and prognostic features of hepatocellular carcinoma. J Cell Biochem 2018, 119(11):9178-9204.\u003c/li\u003e\n\u003cli\u003eZhang S,Chang W,Wu H,Wang YH,Gong YW,Zhao YL et al. Pan-cancer analysis of iron metabolic landscape across the Cancer Genome Atlas. J Cell Physiol 2020, 235(2):1013-1024.\u003c/li\u003e\n\u003cli\u003eRen F,Yang Y,Wu K,Zhao T,Shi Y,Song M et al. The Effects of Dandelion Polysaccharides on Iron Metabolism by Regulating Hepcidin via JAK/STAT Signaling Pathway. Oxid Med Cell Longev 2021, 2021:7184760.\u003c/li\u003e\n\u003cli\u003eLeftin A,Ben-Chetrit N,Klemm F,Joyce JA,Koutcher JA. Iron imaging reveals tumor and metastasis macrophage hemosiderin deposits in breast cancer. PLoS One 2017, 12(9):e0184765.\u003c/li\u003e\n\u003cli\u003eFischer-Fodor E,Miklasova N,Berindan-Neagoe I,Saha B. Iron, inflammation and invasion of cancer cells. Clujul Med 2015, 88(3):272-277.\u003c/li\u003e\n\u003cli\u003eSteegmann-Olmedillas JL. The role of iron in tumour cell proliferation. Clin Transl Oncol 2011, 13(2):71-76.\u003c/li\u003e\n\u003cli\u003eDev S,Babitt JL. Overview of iron metabolism in health and disease. Hemodial Int 2017, 21 Suppl 1(Suppl 1):S6-s20.\u003c/li\u003e\n\u003cli\u003eMou Y,Wang J,Wu J,He D,Zhang C,Duan C et al. Ferroptosis, a new form of cell death: opportunities and challenges in cancer. J Hematol Oncol 2019, 12(1):34.\u003c/li\u003e\n\u003cli\u003eLu B,Chen XB,Ying MD,He QJ,Cao J,Yang B. The Role of Ferroptosis in Cancer Development and Treatment Response. Front Pharmacol 2017, 8:992.\u003c/li\u003e\n\u003cli\u003eDixon SJ,Lemberg KM,Lamprecht MR,Skouta R,Zaitsev EM,Gleason CE et al. Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell 2012, 149(5):1060-1072.\u003c/li\u003e\n\u003cli\u003eWang M,Li S,Wang Y,Cheng H,Su J,Li Q. Gambogenic acid induces ferroptosis in melanoma cells undergoing epithelial-to-mesenchymal transition. Toxicol Appl Pharmacol 2020, 401:115110.\u003c/li\u003e\n\u003cli\u003eLippmann J,Petri K,Fulda S,Liese J. Redox Modulation and Induction of Ferroptosis as a New Therapeutic Strategy in Hepatocellular Carcinoma. Transl Oncol 2020, 13(8):100785.\u003c/li\u003e\n\u003cli\u003eZelenetz AD,Gordon LI,Chang JE,Christian B,Abramson JS,Advani RH et al. NCCN Guidelines\u0026reg; Insights: B-Cell Lymphomas, Version 5.2021. J Natl Compr Canc Netw 2021, 19(11):1218-1230.\u003c/li\u003e\n\u003cli\u003eLangfelder P,Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559.\u003c/li\u003e\n\u003cli\u003eTorti SV,Manz DH,Paul BT,Blanchette-Farra N,Torti FM. Iron and Cancer. Annual Review of Nutrition 2018, 38(1):97-125.\u003c/li\u003e\n\u003cli\u003eJiang X,Stockwell BR,Conrad M. Ferroptosis: mechanisms, biology and role in disease. Nat Rev Mol Cell Biol 2021, 22(4):266-282.\u003c/li\u003e\n\u003cli\u003eMatthews JM,Bhatt S,Patricelli MP,Nomanbhoy TK,Jiang X,Natkunam Y et al. Pathophysiological significance and therapeutic targeting of germinal center kinase in diffuse large B-cell lymphoma. Blood 2016, 128(2):239-248.\u003c/li\u003e\n\u003cli\u003eBonnet D,Dick JE. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nature Medicine 1997, 3(7):730-737.\u003c/li\u003e\n\u003cli\u003eLapidot T,Sirard C,Vormoor J,Murdoch B,Hoang T,Caceres-Cortes J et al. A cell initiating human acute myeloid leukaemia after transplantation into SCID mice. Nature 1994, 367(6464):645-648.\u003c/li\u003e\n\u003cli\u003eFerreira R,Ohneda K,Yamamoto M,Philipsen S. GATA1 function, a paradigm for transcription factors in hematopoiesis. Molecular and cellular biology 2005, 25(4):1215-1227.\u003c/li\u003e\n\u003cli\u003eLin X,Qiao N,Shen Y,Fang H,Xue Q,Cui B et al. Integration of Genomic and Transcriptomic Markers Improves the Prognosis Prediction of Acute Promyelocytic Leukemia. Clinical cancer research : an official journal of the American Association for Cancer Research 2021, 27(13):3683-3694.\u003c/li\u003e\n\u003cli\u003eGong S,Li Q,Jeter CR,Fan Q,Tang DG,Liu B. Regulation of NANOG in cancer cells. Mol Carcinog 2015, 54(9):679-687.\u003c/li\u003e\n\u003cli\u003eLi Y,Ke Q,Shao Y,Zhu G,Li Y,Geng N et al. GATA1 induces epithelial-mesenchymal transition in breast cancer cells through PAK5 oncogenic signaling. Oncotarget 2015, 6(6):4345-4356.\u003c/li\u003e\n\u003cli\u003eChang Z,Zhang Y,Liu J,Guan C,Gu X,Yang Z et al. GATA1 Promotes Gemcitabine Resistance in Pancreatic Cancer through Antiapoptotic Pathway. Journal of oncology 2019, 2019:9474273.\u003c/li\u003e\n\u003cli\u003eLee W,Weinberg O,Pinkus G. GATA1 Is a Sensitive and Specific Nuclear Marker for Erythroid and Megakaryocytic Lineages. American journal of clinical pathology 2017, 147(4):420-426.\u003c/li\u003e\n\u003cli\u003eDepil S,Duchateau P,Grupp SA,Mufti G,Poirot L. \u0026apos;Off-the-shelf\u0026apos; allogeneic CAR T cells: development and challenges. Nat Rev Drug Discov 2020, 19(3):185-199.\u003c/li\u003e\n\u003cli\u003eModi D,Potugari B,Uberti J. Immunotherapy for Diffuse Large B-Cell Lymphoma: Current Landscape and Future Directions. Cancers (Basel) 2021, 13(22).\u003c/li\u003e\n\u003cli\u003eMiao Y,Medeiros LJ,Li Y,Li J,Young KH. Genetic alterations and their clinical implications in DLBCL. Nat Rev Clin Oncol 2019, 16(10):634-652.\u003c/li\u003e\n\u003cli\u003eYin H,Zhong F,Ouyang Y,Wang Q,Ding L,He S. Upregulation of ADAM12 contributes to accelerated cell proliferation and cell adhesion-mediated drug resistance (CAM-DR) in Non-Hodgkin\u0026apos;s Lymphoma. Hematology 2017, 22(9):527-535.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bioinformatics, Diffuse large B-cell lymphoma, GATA1, Iron metabolism","lastPublishedDoi":"10.21203/rs.3.rs-4302921/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4302921/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDiffuse large B-cell lymphoma (DLBCL) is a common and highly aggressive type of lymphoma. Iron metabolism plays a critical role in human diseases, however, which remains completely unclear in patients with DLBCL. The aim is to explore the genetic characteristics and molecular mechanisms underlying iron metabolism in patients with DLBCL.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBased on the Gene Expression Omnibus (GEO) and the GeneCards database, weighted gene co-expression network analysis (WGCNA) was performed on the DLBCL sample (GSE83632) and Iron metabolism-related datasets. Enrichment analysis(GO/KEGG enrichment analysis and GSEA enrichment analysis) was used to screen the key gene and analyze its expression and possible mechanism of action in patients with DLBCL. The quantitative real-time PCR (qRT-PCR) was used to verify the expression of \u003cem\u003eGATA1\u003c/em\u003e gene.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eGATA-binding factor 1 (GATA1), as a key gene of iron metabolism in DLBCL patients, was related to the myeloid cell differentiation and granulocyte differentiation pathways to affect CD4\u003csup\u003e+\u003c/sup\u003e T cells, B cells, and monocytes. GATA1 was strongly positively correlated with sensitivity to multiple targeted drugs including imatinib, nilotinib, and crizotinib, but negatively correlated with the PI3K and CDK9 inhibitors. The patients with high GATA1 expression had higher overall survival and better prognosis than the patients with low expression. Additionally, high expression of \u003cem\u003eGATA1\u003c/em\u003e gene was confirmed in DLBCL patients by qRT-PCR analysis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eGATA1 as one of the important genes of iron metabolism suggested a significant biomarker for predicting the prognosis of DLBCL patients.\u003c/p\u003e","manuscriptTitle":"GATA1: A Key Biomarker for Predicting the Prognosis of Patients With Diffuse Large B- cell Lymphoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-13 20:18:31","doi":"10.21203/rs.3.rs-4302921/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"467f0512-2f4c-44a9-b2fc-5a4c0b4f1091","owner":[],"postedDate":"May 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-10T13:30:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-13 20:18:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4302921","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4302921","identity":"rs-4302921","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}