Glioma is one of the most prevalent primary brain tumors, categorized into lower-grade glioma (LGG) and high-grade glioma (HGG), with an annual incidence ranging from 3 to 6 per 100,000 individuals. The genesis and progression of glioma entail complex processes that encompass diverse cellular and molecular mechanisms. By examining the transition of glioma from low grade to high grade, we can unravel its pathophysiological mechanisms and establish a foundation for identifying new therapeutic targets and drugs.Ferroptosis plays a pivotal role in gliomas, linked to critical biological processes including cell proliferation, invasion, and drug resistance.Understanding the role of ferroptosis in glioma onset and progression holds promise for identifying novel targets and strategies in glioma therapy.In this study, we conducted a comprehensive bioinformatics analysis with different grades of glioma, leveraging gene expression data from TCGA and GEO databases. Our integration of differentially expressed genes (DEGs) and ferroptosis drivers revealed a selection of genes, notably DPP4, TFRC, and TIMP1, as significant players in glioma-associated ferroptosis regulation. Validation through real-time quantitative polymerase chain reaction (qPCR) and ferroptosis inhibition confirmed their relevance in glioma pathogenesis. Additionally, our systematic evaluation unveiled the prognostic value of these genes in glioma treatment and their potential impact on immune infiltration dynamics. These findings provide critical insights into ferroptosis-related mechanisms in glioma and offer promising avenues for targeted therapeutic interventions.
Research Article
Identify Key Genes of Ferroptosis and reveals its relationship with immune in Gliomas of different grades: Integrative Analysis from Bioinformatics
https://doi.org/10.21203/rs.3.rs-4363912/v1
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Gliomas
ferroptosis
bioinformatics
TCGA
Immune infiltration
Glioma is one of the most prevalent primary brain tumors, categorized into lower-grade glioma (LGG) and high-grade glioma (HGG) [1], with an annual incidence ranging from 3 to 6 per 100,000 individuals [2]. The genesis and progression of glioma entail complex processes that encompass diverse cellular and molecular mechanisms [3]. By examining the transition of glioma from low grade to high grade, we can unravel its pathophysiological mechanisms and establish a foundation for identifying new therapeutic targets and drugs.
Ferroptosis plays a pivotal role in gliomas, representing a recently uncovered form of regulated cell death driven by iron ion accumulation and lipid peroxide production [4]. In recent years, the pivotal role of ferroptosis in regulating various physiological conditions and pathological processes in humans has been increasingly elucidated [5]. It has been recognized as an adaptive mechanism aimed at rendering cancer cells susceptible to cell death [6].In the context of gliomas, iron-mediated cell demise is intricately linked to critical biological processes including cell proliferation, invasion, and drug resistance [7]. Interventions targeting the ferroptosis pathway have shown promise in curbing glioma cell proliferation and invasion while augmenting treatment sensitivity [8]. Understanding the role of ferroptosis in glioma onset and progression holds promise for identifying novel targets and strategies in glioma therapy.
In this study, we conducted comprehensive bioinformatics analysis, retrieving gene expression data from TCGA to identify differentially expressed genes (DEGs) across different grades of gliomas. Intersection with ferroptosis drivers from the FerrDB database, followed by validation using glioma data from the GEO database, yielded 20 common differentially expressed genes. Subsequently, lasso regression analysis was performed to select relevant molecular regression coefficients, identifying 9 genes with research significance. Furthermore, we utilized the STRING online platform to construct protein-protein interaction (PPI) networks and employed the MCODE plugin in Cytoscape for scoring. Concurrently, Friends analysis was conducted to jointly identify hub genes, leading to the identification of three hub genes: DPP4, TFRC, and TIMP1. Real-time quantitative polymerase chain reaction (qPCR) confirmed the expression of these three genes in tumor cells. Additionally, we employed the ferroptosis inhibitor Fer-1 to validate their crucial roles in ferroptosis and gliomas. Moreover, we systematically evaluated the prognostic value of these three hub genes in glioma treatment. Furthermore, we investigated the relationship between these three hub genes and immune infiltration.
Dataset acquisition :
Initially, we acquired lower-grade glioma (LGG) and high-grade glioma (HGG) samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets [9, 10]. From the TCGA database, we obtained 532 RNA-seq data samples, while two expression profiling datasets (GSE28238 and GSE62802) were retrieved from the GEO database. The GSE28238 dataset comprises 40 lower-grade glioma samples, and the GSE62802 dataset comprises 20 high-grade glioma samples.
Differential analysis:
Differential analyses were conducted using the "limma" package in R (v4.2.2). Differentially Expressed Genes (DEGs) were identified in the TCGA database with criteria of logFC > 0.5 and p < 0.05, resulting in the selection of 5216 DEGs [11, 12]. Subsequently, DEG results were intersected with 264 ferroptosis drivers from a ferroptosis-related database (FerrDB, http://www.zhounan.org/ferrdb/index.html), yielding 29 common DEGs. Following data homogenization, DEGs were validated using datasets GSE28238 and GSE62802. Eventually, 20 DEGs passed the verification. Volcano plots, Venn diagrams, and heatmaps for DEGs were concurrently generated using the R packages ggplot2 and pheatmap.
GO and KEGG Enrichment Analysis:
Functional and pathway enrichment analysis encompassed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. GO analysis spanned three domains: biological process (BP), cellular component (CC), and molecular function (MF). KEGG analysis, containing datasets covering pathways associated with biological functions, diseases, chemicals, and drugs, was utilized. Utilizing the R package clusterProfiler [13], pathway enrichment analysis was conducted for the common DEGs. Subsequently, the results were visualized using the ggplot2 R package.
Construction and Validation Prognostic Model:
To enhance the accuracy of prognostic predictions for these DEGs, we leveraged RNAseq data from the TCGA database to compute regression coefficients through LASSO Cox regression analysis via the "glmnet" package [14, 15]. Subsequently, prognostic-associated DEGs underwent further analysis through multi-factor Cox regression, culminating in the development of a risk model. With a sample size of n = 532, patients were stratified into high-risk or low-risk groups based on the derived formula. Time-varying ROC curves, along with corresponding area under the curve (AUC) values for different DEGs, were calculated for both the training and test cohorts. Subsequently, the risk score for each patient concerning different DEGs was computed. Visualization of the results was achieved using the "ggplot2" package.
Construction of PPI Network and Friends anlysis:
The STRING Online website serves as an online repository and analytical tool for exploring protein interaction networks, consolidating data on protein interactions from various resources. Employing the Friends analysis method, which evaluates functional correlations among genes within a module, aids in identifying pivotal genes by highlighting those more likely to be expressed when interacting with others in the same module. Utilizing the STRING website, we acquired the Protein-Protein Interaction (PPI) network of relevant Differentially Expressed Genes (DEGs), followed by Friends analysis facilitated by the "GOSemSim" package [16]. Subsequently, hub genes within the network were refined based on the obtained results.
Determination of Associations of the DEGs With Immune Cell Infiltration:
In this study, we designated the hub genes acquired earlier as the primary variable and employed the "GSVA" package to compute the correlation between the concentrations of 24 distinct immune cells and the hub genes [17]. Subsequently, visualization was conducted using the "ggplot2" package, employing a lollipop chart to depict the results.
Cell culture, Reagents:
U251 and U87 human glioma cells obtained from Guangzhou Cellcook Biotech Co., Ltd., China, were cultured in Dulbecco's Modified Eagle Medium (DMEM) from Thermo Fisher Scientific, Inc. (Waltham, Massachusetts, cat. no. 220511), supplemented with 10% fetal bovine serum (FBS) from Thermo Fisher Scientific, Inc. (Waltham, Massachusetts, cat. no. 16140071), and maintained at 37°C with 5% CO2. Ferrostatin-1 (Fer-1) was procured from MCE and dissolved in dimethyl sulfoxide (DMSO) under sterile conditions to a storage concentration of 10 mM.
Quantitative real-time PCR:
Total mRNA was isolated from the cells using the Trizol reagent. Subsequently, the total mRNA was reverse transcribed into complementary DNA (cDNA), followed by quantitative polymerase chain reaction (qPCR) analysis using SYBR Green Master Mix. The forward and reverse primers for DPP4 were as follows: Forward Primer: GGGTCACATGGTCACCAGTG, Reverse Primer: TCTGTGTCGTTAAATTGGGCATA. For TFRC, the forward and reverse primers were: Forward Primer: ACCATTGTCATATACCCGGTTCA, Reverse Primer: CAATAGCCCAAGTAGCCAATCAT. For TIMP1, the forward and reverse primers were: Forward Primer: CTTCTGCAATTCCGACCTCGT, Reverse Primer: ACGCTGGTATAAGGTGGTCTG. All samples were analyzed in triplicate with normalization to β-actin in the Sham group. Relative quantification of mRNA expression was measured using the 2 − ΔΔCT method.
Statistical Analysis:
Statistical analyses for the experimental data were performed using R software (version 4.0.2) and GraphPad Prism 8 software. The validation of significant differences was conducted using an independent-sample t-test. Significance levels were represented as follows: **** for P < 0.0001, *** for P < 0.001, ** for P < 0.01, and * for P < 0.05.
Identification and extraction of differentially expressed ferroptosis-related genes in gliomas of different grades:
The RNA-sequencing data and clinical follow-up data of 224 LGG and 247 HGG samples were obtained from the TCGA database, while 264 ferroptosis-related drivers were obtained from the FerrDB database. We conducted an analysis of differential gene expression using the TCGA data, identifying a total of 5216 DEGs (|log2FC| > 0.5 and P < 0.05), comprising 1,838 downregulated DEGs and 3,378 upregulated DEGs. Figure 1(A) depict the Volcano plots illustrating the DEGs.These differential analysis results were then intersected with the ferroptosis drivers, resulting in 29 common DEGs. Further verification using datasets GSE28238 and GSE62802 from the GEO database confirmed 20 common DEGs. Figure 1(B) and 1(C) illustrate the Venn diagram and heatmap.
Enrichment Function Analysis of DEGs:
The outcomes of the GO analysis indicated that the intersecting DEGs were enriched in diverse biological processes, encompassing the regulation of leukocyte cell-cell adhesion, regulation of T cell activation, and oxidoreductase activity (Fig. 2(A), 2(B), 2(C)). Additionally, the KEGG pathway analysis revealed predominant enrichment of the common DEGs in pathways such as HIF-1, Ferroptosis, and JAK-STAT signaling pathways (Fig. 2(A), 2(B), 2(C)).
Construction and Assessment of the Risk-Score System and Identification of Hub Genes:
To investigate the prognostic value of differentially expressed genes (DEGs) in gliomas, we employed the LASSO regression algorithm to refine the gene sets by calculating regression coefficients (Fig. 3(A), 3(B)). Eleven genes with a LASSO coefficient of 0 were excluded, leaving nine genes with prognostic value. The risk coefficient of each gene was calculated, and the distribution of risk scores in the training set is depicted in Fig. 3(C). Notably, WWTR1, TFRC, TIMP1, and other genes are associated with high-risk scores. Subsequently, Friends analysis was conducted on the aforementioned DEGs with prognostic value. The top five genes identified were CDCA3, TFRC, WWTR1, DPP4, and TIMP1 (Fig. 3(D)). To further investigate the involvement of these genes in the occurrence and progression of gliomas across different grades, we constructed a protein-protein interaction (PPI) network map (Fig. 3(E)) using the STRING online database and identified core modules using the MCODE plugin in Cytoscape software. Integrating all of the aforementioned analyses identified DPP4, TFRC, WWTR1, and TIMP1 as hub genes.
Following the identification of DPP4, TFRC, and TIMP1 as hub genes, we analyzed their expression levels in gliomas of various grades using data from the TCGA database(Fig. 4(A), 4(B), 4(C)). Kaplan-Meier survival analysis was performed to illustrate the survival curves and investigate the association between patient survival and these hub genes. Remarkably, the hazard ratio (HR) for all three hub genes exceeded 1, with p-values below 0.01, indicating their role as risk factors for gliomas across different grades(Fig. 4(D), 4(E), 4(F)). Furthermore, the 1-year area under the ROC curve (AUC) was determined as 0.788 for DPP4, 0.808 for TFRC, and 0.873 for TIMP1, underscoring their potential prognostic significance(Fig. 4(G)).
Identification and correlation analysis of immune cell infiltration:
The immune microenvironment in glioma, comprising diverse immune cells and factors, holds significant implications for disease progression and treatment outcomes. Through the utilization of the "GSVA" package with TCGA data, we investigated the correlation between hub genes (DPP4, TFRC, and TIMP1) and immune infiltration across 24 immune cell types(Fig. 5(A), 5(B), 5(C)). Our analysis revealed a positive association between the expression levels of these hub genes and various immune cell populations such as Th2 cells, Eosinophils, T cells, Neutrophils, Th1 cells, Macrophages, and B cells. Conversely, a negative correlation was observed between the expression of hub genes and pDC cells. This comprehensive understanding sheds light on the intricate interplay between hub genes and immune infiltration dynamics in glioma, offering insights for further research and therapeutic strategies (Fig. 5(D), 5(E), 5(F)).
Immunohistochemical analysis and Quantitative real-time PCR validation:
Subsequently, we accessed the expression profiles of the three hub genes in gliomas of varying grades from the TCGA database. Employing COX regression analysis, we examined the survival curves to elucidate the association between patient survival and the expression of hub genes. Notably, DPP4 and TIMP1 were undetectable in low-grade glioma specimens, whereas TFRC exhibited low expression levels. Conversely, in high-grade glioma specimens, both DPP4 and TIMP1 displayed significant expression, while TFRC exhibited elevated expression levels(Fig. 6(A)).
Following this, we chose U251 and U87 human glioma cells as the target cell and utilized Fer-1, an ferroptosis inhibitor, for intervention to measure the qPCR expression of the three hub genes. As depicted in Fig. 6, the mRNA expression levels of DPP4, TFRC, and TIMP1 exhibited a notable decrease following Fer-1 administration compared to the control group. These results suggest that DPP4, TFRC, and TIMP1 may play a key role in ferroptosis in glioma(Fig. 6(B),6(C)).
Gliomas, common neuroepithelial tumors, encompass lower-grade glioma (LGG) and high-grade glioma (HGG). Progression from LGG to HGG signifies increased aggressiveness and resistance to therapies [18]. Ferroptosis, a regulated cell death mechanism, is implicated in glioma progression, with altered iron metabolism and heightened susceptibility to oxidative stress contributing to tumorigenesis. Understanding ferroptosis in gliomas of varying grades offers potential therapeutic targets and prognostic indicators. This study integrates bioinformatics analysis to identify key genes associated with ferroptosis and their relationship with the immune microenvironment in different grades of gliomas, providing insights into targeted interventions and personalized treatment strategies.
In this study, we utilized gene expression data and clinicopathological information from the TCGA database, intersecting it with 264 ferroptosis drivers obtained from the FerrDB ferroptosis database. Subsequently, we validated our findings using glioma datasets of different grades obtained from the GEO database, resulting in the identification of 20 differentially expressed genes (DEGs). We then conducted KEGG and GO analyses on these DEGs. Our findings revealed enrichment in diverse biological processes, including the regulation of leukocyte cell-cell adhesion, regulation of T cell activation, and oxidoreductase activity. Studies have revealed that ferroptosis influences leukocyte adhesion and infiltration into the tumor microenvironment by modulating the expression of adhesion molecules on endothelial cells [19]. Additionally, ferroptosis impacts the activation and function of T cells within the tumor microenvironment through mechanisms such as altering redox balance and lipid metabolism [20, 21]. Moreover, dysregulated oxidoreductase activity, associated with ferroptosis, contributes to oxidative stress and cell death, further promoting glioma progression and therapeutic resistance[22]. Pathway enrichment analysis indicated that these DEGs are primarily enriched in pathways such as HIF-1, Ferroptosis, and JAK-STAT signaling pathways.
Following the LASSO Cox regression analysis, 9 genes with nonzero regression coefficients emerged as potential prognostic markers. Remarkably, CDCA3, TFRC, WWTR1, DPP4, TIMP1, and additional genes surfaced as high-risk candidates for glioma prognosis. Subsequently, we engaged in Friends analysis and construct a protein-protein interaction (PPI) network by STRING website. By employing MCODE, a Cytoscape plugin, we collectively identified three pivotal genes: DPP4, TFRC, and TIMP1. Subsequently, we examined the expression levels of these three hub genes associated with ferroptosis in both low-grade gliomas (LGG) and high-grade gliomas (HGG). Intriguingly, we observed a varying degree of upregulation in the expression of these three hub genes in HGG compared to LGG. Kaplan-Meier survival analysis further underscored the significance of these hub genes in prognostic survival among glioma patients. Additionally, the 1-year area under the ROC curve (AUC) corroborated these findings. Collectively, these outcomes highlight the pivotal role of the three ferroptosis-related hub genes in glioma prognosis and patient survival. Furthermore, they signify promising targets for therapeutic interventions aimed at mitigating the onset and progression of glioma.
Dipeptidyl peptidase-4 (DPP4), a surface serine protease, participates in diverse physiological processes, including glucose homeostasis, immune modulation, and cell adhesion [23]. Recently, DPP4 has drawn considerable attention in cancer research, particularly in gliomas. Research indicates its multifaceted roles in tumor progression, invasion, and immune modulation within the glioma microenvironment [24]. Transferrin receptor (TFRC) crucially mediates cellular uptake of transferrin-bound iron, playing a pivotal role in iron metabolism [25]. In cancer research, especially in gliomas, TFRC has emerged as a pivotal factor influencing tumor growth, invasion, and therapeutic response[26]. Tissue inhibitor of metalloproteinase 1 (TIMP1) acts as a key regulator of extracellular matrix remodeling and is implicated in various physiological and pathological processes, including cancer progression [27]. In gliomas, TIMP1 has garnered attention for its roles in tumor invasion, angiogenesis, and treatment resistance [28]. Nevertheless, the specific involvement of these three hub genes, DPP4, TFRC, and TIMP1, in the onset and progression of ferroptosis across different grades of glioma remains elusive. Further exploration of their roles in this process is warranted, as uncovering their functions could unveil potential therapeutic targets for glioma treatment or strategies for impeding disease progression.
Analyzing the immune microenvironment and infiltration in gliomas is crucial for understanding tumor progression, treatment response, and patient outcomes [29]. Recent evidence suggests that dysregulated iron metabolism affects immune cell function and modulates the tumor immune response in gliomas [30]. Our analysis revealed a positive correlation between the expression levels of three ferroptosis-related hub genes and various immune cell populations, including Th2 cells, Eosinophils, T cells, Neutrophils, Th1 cells, Macrophages, and B cells. Conversely, a negative correlation was observed with pDC cells.These findings suggest a potential link between ferroptosis-related pathways and immune cell infiltration dynamics in gliomas. Additionally, they underscore the complex nature of the immune response in gliomas and imply that iron metabolism-related genes may influence immunotherapeutic effects in glioma patients.
Finally, we queried the expression of three ferroptosis-related in gliomas of different grades in the Human Protein Atlas (HPA) database and conducted qpcr to detect the rna expression levels of three ferroptosis-related hub genes in U251 and U87 human glioma cell. After the re-use of ferroptosis inhibitor Fer-1, the expression of hub gene in the inhibitor group was significantly decreased compared with that in the normal group, further demonstrating the accuracy of our previous analysis and corroborated the notion that these three genes potentially exert a pivotal role in ferroptosis in glioma.However, the precise mechanism underlying their involvement necessitates further investigation.
Overall, this study elucidates the role of ferroptosis in glioma progression and immune modulation, identifying DPP4, TFRC, and TIMP1 as pivotal hub genes and exploring their impact on glioma prognosis. These genes may potentially impede further glioma deterioration by modulating the occurrence and development of ferroptosis within gliomas.
Data availability:
Publicly available datasets were analyzed in this study. These data can be found here: https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga, https://www.ncbi.nlm.nih.gov/gds/
Funding:
This study received financial support from the Chongqing Natural Science Foundation Project (cstc2021jcyj-msxmX0036), the Project of Second Affiliated Hospital of Chongqing Medical University (kryc-yq-2216), and the Chunhui Project Foundation of the Education Department of China (HZKY20220218).
Author information:
Maoxin Zhang, Ning Huang, and Fang Tao has contributed equally to this work.
Authors and Affiliations:
Department of Neurosurgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
Maoxin Zhang, Yuan Cheng, Ping Ma, Jianhe Yue, Ning Huang & Fang Tao
Contributions:
NH and FT designed the research. PM provided experimental guidance. MZ, JY, and YC conducted experiments, performed data analysis, and contributed to manuscript preparation.
Corresponding authors:
Correspondence to Maoxin Zhang and Ning Huang.
Ethics declarations:
Competing interests:
The authors declare no competing interests.
Ethics approval:
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Informed consent:
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Research involving human participants and/or animals:
This study does not contain any studies with human participants or animals performed by the author.
Consent to participant:
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Consent for publication:
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No competing interests reported.