Iron Metabolism-Related Gene Signatures for Predicting the Prognosis of Patients With Diffuse Large B-cell Lymphoma

doi:10.21203/rs.3.rs-3308769/v1

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Iron Metabolism-Related Gene Signatures for Predicting the Prognosis of Patients With Diffuse Large B-cell Lymphoma

https://doi.org/10.21203/rs.3.rs-3308769/v1

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Background

Diffuse large B-cell lymphoma (DLBCL) is a common and highly aggressive type of lymphoma. Iron metabolism plays a critical role in various 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 to establish the gene co-expression network. Enrichment analysis was used to screen the key gene and analyze its expression and possible mechanism of action in patients with DLBCL.

Results

GATA1 was a key gene of iron metabolism in patients with DLBCL. It was related to the myeloid cell differentiation and granulocyte differentiation pathways to affect CD4 + T cells, B cells, and monocytes. GATA1 was also strongly positively correlated with sensitivity to multiple targeted drugs, like imatinib, nilotinib, and crizotinib, but negatively correlated with the PI3K inhibitor, and CDK9 inhibitor. The group with high GATA1 expression had higher overall survival and better prognosis than the group with low expression.

Conclusions

GATA1 is the first time to reveal the molecular mechanisms underlying the relationship between iron metabolism and DLBCL, suggesting that GATA1 is a significant biological target and immune-related biomarker of DLBCL.

Bioinformatics

Diffuse large B-cell lymphoma

GATA1 transcription factor

Gene expression

Iron metabolism disorders

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 cases^[1].In recent years, the incidence of DLBCL in China has been gradually increasing ^[1]. 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 ^[2].

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 ^{[13–15] [3–6]}. 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 ^[7]. Conversely, an increase in iron concentration leads to cell death through the accumulation of reactive oxygen species and lipid peroxidation products, known as ferroptosis ^{[8, 9]}. Ferroptosis is a specific mode of programmed cell death that is dependent on iron metabolism and distinct from apoptosis, necroptosis, and autophagy ^[10]. With research on emerging anticancer pathways, various ferroptosis inducers have been developed for cancer ^{[11, 12]}.

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 ^[16]. 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 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.

2.1 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 ^[17]. 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 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 < 0.05 and |logFold Change|>2 as the criteria for statistical significance.

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. 1A, 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. 2A). Using the interactions between these 123 genes, we constructed a PPI network (Fig. 2B) to understand their interactions (Fig. 2C). 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. 2D). The results of "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. 2E).

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. 3A) were screened from 24,527 genes. Similarly, we applied differential analysis to analyze the differences of genes in DLBCL samples and normal tissue samples from the GS83632 dataset, in which 986 differential genes were screened from 11,023 genes (Fig. 3B). By cross-analyzing the shared genes with the two differential genes, we obtained three shared genes with high reliability: GATA1, KLF1, and ACSL6 (Fig. 3C). 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. 3D).

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. 4A) 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. 4B, C), as well as-alanine metabolism, galactose metabolism, tricarboxylic acid cycle, and bladder carcinogenesis pathways (Fig. 4D).

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. 5A). TIMER, CIBERSORT, and CIBERSORT abs algorithms were used to verify the relationship between GATA1 expression and B cells, monocytes, NK cells, and neutrophils (Fig. 5B, C). We then calculated the EstimateScore, ImmuneScore, and StromalScore of the two groups with high and low GATA1 expression (Fig. 6A, 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. 7A), and significantly negatively correlated with PI3K inhibitors such as buparlisib, Src-Abl inhibitors, and the CDK9 inhibitor palbociclib (Fig. 7B).

Increasing evidence suggests that ferroptosis is involved in the development of various tumors; therefore, targeting iron-related cell death has substantial potential for tumor therapy ^[19]. 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. 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.

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.

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).

Conflict of Interest Statement

The authors have no conflict of interest.

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.

Approval of the research protocol by an Institutional Reviewer Board: N/A.

Informed Consent: N/A.

Registry and the Registration No. of the study/trial: N/A.

Animal Studies: N/A.

Author Contributions

Yuxin Zhang and Zheng Wei designed the study. Yuxin Zhang and Yue Wang performed the data analysis. Yuxin Zhang drafted the manuscript. 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.

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No competing interests reported.

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Version 1

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Iron Metabolism-Related Gene Signatures for Predicting the Prognosis of Patients With Diffuse Large B-cell Lymphoma

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

1. Introduction

2. Materials and Methods

2.1 Data download and processing

2.2 Weighted gene co-expression network analysis

2.3 Identification and features of shared genes in DLBCL and iron metabolism

2.4 Functional enrichment analysis and gene set enrichment analysis

2.5 Association between core genes and DLBCL

2.6 Assessment of the immune landscape

2.7 Statistical analysis

3. Results

3.1 Selection of datasets

3.2 Screening of co-expression modules

3.3 Screening of shared genes

3.4 Validation of GATA1 core genes

3.5 GATA1-related biological processes and their associated signaling pathways

3.6 Correlation between GATA1 and the immune microenvironment

4. Discussion

5. Conclusion

Declarations

References

Additional Declarations

Status:

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