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. 2026 Jan 31;17:375. doi: 10.1007/s12672-026-04510-6

A manganese metabolism-related gene signature for prognosis prediction and immune microenvironment description of glioblastoma

Yi Man 1,#, Wanyue Chen 3,#, Guoan Shen 2, Xuanjie Zhao 1, Junlin Lu 1, Xuxin Zhang 1,✉
PMCID: PMC12949155  PMID: 41619094

Abstract

Background

Glioblastoma (GBM), as a high-grade glioma, has high invasiveness and poor clinical prognosis. Manganese is an important trace element, has been proven to be closely related to tumor treatment and tumor immunity. It is necessary to explore the correlation between manganese metabolism-related genes and GBM.

Methods

We downloaded RNA gene expression data and clinical data of GBM patients from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas database (CGGA) databases. Build the signature using data from TCGA-GBM and independently validate it using data from CGGA-GBM. Next, we will use a nomogram to predict the clinical prognosis of GBM patients. Finally, we analyzed the relationship between the prognostic model and immune microenvironment through CIBERSORT.

Results

A total of 495 manganese metabolism-related differentially expressed genes were obtained for the establishment of a subsequent signature in the TCGA-GBM cohort. The following seven genes (PLAT, TIMP1, FN1, CTSB, SCG5, GALNT6 and AMPH) were used to establish the signature and independently validated using the CGGA-GBM dataset. Research has confirmed that the predictive ability of this signature exceeds other clinical features, and the receiver operating characteristic curve has a high area under the curve.

Conclusions

We constructed and validated a novel gene signature related to manganese metabolism in GBM patients. This gene signature not only reliably predicts the clinical outcomes of GBM patients but also has the potential to guide the provision of new treatment options for these patients.

Keywords: Glioblastoma, Manganese metabolism, Risk score, Nomogram, Immune microenvironment

Introduction

Gliomas are common primary tumors of the central nervous system, originating from glial progenitor cells. According to the World Health Organization’s standards, gliomas can be classified into four grades: I, II, III, and IV. The higher the tumor grade, the worse the prognosis [1, 2]. Glioblastoma (GBM) belongs to grade IV glioma, which is the most invasive and highly malignant. The current mainstay of treatment for GBM includes maximum safety surgical tumor resection followed by radiotherapy combined with concomitant and adjuvant temozolomide chemotherapy [3–6]. Despite numerous efforts by researchers in the treatment of GBM. Despite numerous efforts by researchers in the treatment of GBM, the median survival of patients is only 12–18 months due to its rapid progression, treatment resistance, and poor prognosis [7–9]. Therefore, it is necessary to explore new metabolic pathways targeting glioblastoma that may provide new treatment options for its treatment.

Manganese (Mn) is an important trace element that primarily functions as a coenzyme and actively participates in various physiological processes, including the regulation and defence of reactive oxygen species (ROS), as well as neurotransmitter synthesis and ammonia clearance in the brain [10]. Manganese, as a key component of numerous proteins and enzymes, exists in all tissues. As mitochondria are the primary site of manganese metabolism and energy conversion in cells, they have higher concentrations of manganese in tissues rich in mitochondria [10, 11]. Research has shown that the superoxide dismutase activity of manganese can clear ROS, thereby reducing mitochondrial oxidative stress and inhibiting tumor development [12, 13]. In addition, the peroxidase activity of manganese can also mediate the production of ROS for the treatment of various tumors, including GBM [14–17]. Multiple studies have demonstrated that manganese plays a crucial role in activating innate immune responses and tumour immune surveillance, thereby activating the intracellular cGAS-STING pathway to promote anti-tumour immunity [18, 19]. Therefore, based on the multiple roles of manganese in tumor treatment, it is necessary to explore the relationship between manganese metabolism and GBM.

This study is based on genes related to manganese metabolism, and intersects them with differentially expressed genes (DEGs) to obtain the intersection genes for the construction of subsequent models. Seven risk genes were determined: Tissue-Type Plasminogen Activator (PLAT), TIMP Metallopeptidase Inhibitor 1(TIMP1), Fibronectin 1(FN1), Cathepsin B (CTSB), Secretogranin V(SCG5), Polypeptide N-Acetylgalactosaminyltransferase 6(GALNT6) and Amphiphysin(AMPH). GBM patients from the Cancer Genome Atlas (TCGA) database were used for manganese-related prognostic signature construction, and GBM patients from the Chinese Glioma Genome Atlas (CGGA) database were used for independent validation. In addition, the study also investigated its model and analysis of immune cell infiltration, tumor mutation burden, and drug sensitivity, which is expected to provide a deeper understanding of the correlation between manganese metabolism-related genes and GBM. It is expected to provide new directions and perspectives for understanding the metabolic characteristics and treatment of GBM.

Methods

Data download and process

RNA gene expression data, tumor mutational burden (TMB), and corresponding clinical information for GBM patients were retrieved from the TCGA data portal (https://portal.gdc.cancer.gov/). After removing duplicate and low-quality samples,391 TCGA-GBM patients were enrolled in the study and served as the training cohort. In addition, we downloaded, organized, and batch corrected two datasets, mRNAseq_325 and mRNAseq_693, from the CGGA database (https://www.cgga.org.cn/, accession number: mRNAseq_693 and mRNAseq_325). This includes various types of gliomas, and we have screened glioblastoma classified as WHO grade IV for further research. Extract information from GBM patients with complete gene expression and clinical data. 374 CCGA-GBM patients were enrolled in the study, serving as an independent validation cohort. We used “manganese metabolism” as the search keyword in the GeneCards database (https://www.genecards.org/), and selected protein coding genes as conditions to obtain 1917 manganese metabolism-related genes (MMRGs, Supplementary Table S1).

Establishment of a manganese-related prognostic signature for GBM

Differential expression analysis was conducted using the ‘limma’ R package, comparing GBM tissues (n = 386) and normal tissues (n = 5). Genes with log2 fold change (|log2FC|) > 1 and adjusted P-value < 0.05 were considered significantly differentially expressed. In addition, we intersected the DEGs with MMRGs to obtain manganese metabolism-related DEGs for subsequent analysis.

We conducted univariate Cox regression analysis on the intersection genes obtained in the previous step in the TCGA-GBM sample to identify genes with prognostic significance (P < 0.05). Next, LASSO regression analysis was employed to identify the manganese-related prognostic signature, aiming to simplify the model and mitigate collinearity and overfitting. Ten-fold cross-validation method was used to perform LASSO regression to further screen genes. On the basis of linear regression, the penalty term (lambda × absolute value of slope) was added to reduce the overfitting of the manganese-related prognostic signature and improve the generalization ability of the manganese-related prognostic signature.

TCGA-GBM is used as the training set, and GBM patients in the CGGA database are used as independent validation sets. The risk score is the sum of the expression levels of each gene multiplied by the correlation coefficient, and is divided into high-risk and low-risk groups by the median. Finally, we conducted Kaplan-Meier survival analysis on patients in the high-risk and low-risk groups to evaluate the differences in survival among different groups of GBM patients by using the ' survival ' R package. Simultaneously, analyze the progression-free survival time of GBM patients in the TCGA database.

Construction and evaluation of the nomogram

We used multivariate Cox regression analysis to evaluate the ability of the risk score as an independent predictor of survival. We included age and gender factors for independent prognostic analysis. We generated a systematic nomogram using the R package ‘rms’ to predict 1-year, 3-year, and 5-year survival probabilities based on clinical parameters and risk scores. In addition, we will combine risk scores with clinical features. Use calibration charts to evaluate the discriminative ability of the nomogram. It is used to evaluate the difference between predicted and actual results. In addition, we also plotted receiver operating characteristic (ROC) curves to determine the accuracy of the prognostic model and obtain the area under the curve (AUC).

Gene set enrichment analysis (GSEA)

GSEA is a computational method that reveals the biological significance of genomic data through statistical correlation analysis between gene expression profile data and predefined functional gene sets. We used the GSEA method to evaluate the differences in enriched pathways between high-risk and low-risk groups using GBM patients in the TCGA database.

Analysis of immune cell infiltration levels between high-risk and low-risk patients

CIBERSORT(Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts)is an important method for estimating the abundance of immune cells. CIBERSORT provides 22 common immune infiltrating cell expression data LM22 files, including immune cells of different cell types and functional states. Next, we evaluated the differences in immune cell infiltration levels among GBM patients in different risk groups. In addition, we also applied the xCell analysis method to analyze immune cell infiltration in patients of high and low-risk groups.

Tumor mutation burden analysis

Obtain TMB data of GBM patients from the TCGA database and analyse and compare the differences in TMB between high-risk and low-risk groups using the ' maftools ' R package. P < 0.05 was deemed statistically significant.

Drug sensitivity analysis

OncoPredict is an R package for predicting drug response and cell line screening data in vivo or cancer patients using biomarkers. It is necessary to adopt personalised treatments for patients with different subtypes or risk groups; therefore, we conducted a drug sensitivity analysis on GBM patients in high- and low-risk groups.

Results

Establishment of a manganese-related prognostic signature for GBM

We performed differential gene expression on normal tissues and GBM tissues in the TCGA database, obtaining 3677 DEGs (Fig. 1A). After intersecting them with 1422 MMRGs, we obtained 495 intersecting genes for subsequent analysis (Fig. 1B).

Fig. 1.

Fig. 1

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Screening of manganese-related DEGs. A Volcano plot of differential gene expression between normal and GBM tissues in the TCGA database. B Venn diagram of DEGs and manganese-related genes

A total of 47 genes were significantly correlated with overall survival (P < 0.01) after the filtration using univariate Cox hazard analysis (Fig. 2A). Next, we employed LASSO regression to construct a manganese-related prognostic signature, aiming to obtain a more accurate prognostic signature (Fig. 2B-C). Finally, A manganese-related prognostic signature was established to evaluate the survival risk for each GBM patient in the TCGA dataset as follows: Risk score = 0.02851 × expression of PLAT + 0.06212 × expression of TIMP1 + 0.00042 × expression of FN1 + 0.02738 × expression of CTSB + 0.06242 × expression of SCG5 + 0.02310 × expression of GALNT6 + 0.07403 × expression of AMPH. According to the median risk score, patients were divided into high-risk and low-risk groups. The results of survival analysis showed that the survival rate of GBM patients in the low-risk group was higher than that in the high-risk group in the TCGA database (Fig. 2D, P < 0.001). At the same time, independent validation was conducted on GBM patients in the CGGA database, and patients in the low-risk group had a better prognosis (Fig. 2E, P < 0.05). Comparing the progression-free survival of two groups of patients, the same results were obtained (Fig. 3A, P < 0.001).

Fig. 2.

Fig. 2

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Establishment of a manganese-related prognostic signature for GBM. A Univariate Cox regression analysis of the 47 manganese-related risk signatures. B, C The LASSO Cox regression was used to identify the most robust manganese-related signatures. D Kaplan-Meier survival curve in TCGA training cohort. E Kaplan-Meier survival curve in the CGGA validation cohort

Fig. 3.

Fig. 3

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Manganese-related prognostic signature is an independent prognostic factor for GBM patients. A Progression-free survival time of high and low-risk GBM patients in the TCGA database. B The forest plot of the univariate Cox regression analysis in the TCGA dataset. C The forest plot of the multivariate Cox regression analysis in the TCGA dataset. D ROC analysis for overall survival prediction according to the survival time. E ROC analysis for overall survival prediction according to the other clinical features. F The distribution of risk score levels and survival status of GBM patients in the TCGA database, as well as the expression heatmap of genes in high-risk/low-risk groups in the manganese-related prognostic signature. G, H Correlation analysis between risk scores and other clinical features

Manganese-related risk score is an independent prognostic factor

Next, we investigated the independence of the manganese-related prognostic signature in GBM patients in the TCGA dataset. Univariate Cox regression analysis showed that risk score was a prognostic factor for overall survival (Fig. 3B, HR = 5.466, P < 0.001). In addition, multivariate Cox regression analysis confirmed the independent prognostic value of risk score (Fig. 3C, HR = 4.162, P < 0.001). The AUC values for 1-, 3-, and 5-year overall survival, were 0.639, 0.746, and 0.766, respectively (Fig. 3D). In addition, the area under the AUC value for 1-year overall survival based on risk score is higher than other clinical features (Fig. 3E). Figure 3F shows the distribution of risk score levels and survival status of GBM patients in the TCGA database, as well as the expression heatmap of genes in high-risk/low-risk groups in the manganese-related prognostic signature. In addition, we also conducted a correlation analysis between risk scores and other clinical features, and the results showed that risk scores were significantly correlated with age and gender (Fig. 3G-H, P < 0.001). In addition, we have added a study on the correlation between clinical parameters and models of GBM patients in the CGGA dataset (Fig. S1). The results are consistent with the TCGA database. A nomogram was constructed by integrating both seven manganese-related genes and clinical characteristics to predict the overall survival of GBM patients (Fig. 4A). Figure 4B shows the calibration plot of the nomogram, indicating its high prediction accuracy.

Fig. 4.

Fig. 4

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Nomogram and GSEA analysis results. A Nomogram to predict the 1-, 3-, and 5-year overall survival of GBM patients in the TCGA set. B Calibration curve for the nomogram. C GSEA analysis results

GSEA analysis

Next, we also conducted GSEA functional enrichment analysis on GBM patients in the TCGA database. The results showed that there were 133 significantly enriched signaling pathways in high-risk GBM patients. The top five enrichment pathways are chemokine signaling pathway, complement and coagulation cascades, cytokine-cytokine receptor interaction, hematopoietic cell lineage, and leishmania infection (Fig. 4C; Table S2).

Analysis of immune cell infiltration levels between high-risk and low-risk patients

As shown in Fig. 5A, compared with GBM patients in the low-risk group, patients in the high-risk group had significantly higher proportions of B cells naive and undifferentiated macrophages (M0). In addition, we conducted a correlation analysis between manganese-related prognostic genes and immune cells, and the results showed that TIMP1 and FN1 were correlated with several immune cells (Fig. 5B). In addition, the application of xCell analysis method showed that there were differences in various immune cell infiltrations among GBM patients in different risk groups (Fig. S2).

Fig. 5.

Fig. 5

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CIBERSORT and tumor mutation burden analysis. A Differential analysis of immune cells in different risk groups. B Correlation analysis between genes in the manganese-related prognostic signature and immune cell infiltration. C, D Genes with the highest frequency of somatic mutations in the group with high- and low-risk scores are listed below. E Joint analysis of tumor mutation burden and high/low risk

Tumor mutation burden analysis

In the subsequent analysis, we studied the tumor mutation burden of GBM patients in the TCGA dataset and found that the top 15 genes were all PTEN, TP53, EGFR, TTN, MUC16, FLG, RYR2, NF1, PIK3CA, ATRX, LRP2, PIK3R1, RB1, SPTA1 and LRP1. However, the proportion of mutations in different genes varies between high and low-risk groups (Fig. 5C-D). In addition, we also analyzed the prognosis of patients with high tumor mutation burden (H-TMB), low tumor mutation burden (L-TMB), as well as high-risk and low-risk groups. The results showed that the differences were statistically significant (Fig. 5E, P < 0.001).

Drug sensitivity analysis

It is crucial to assign different risk levels to patients in order to guide personalized treatment. Further analysis of drug sensitivity was conducted on patients in different risk groups. The results showed that GBM patients in different risk groups had varying sensitivities to the following drugs (Table S3).

Discussion

GBM is the most common and worst-prognosis malignant tumor in the central nervous system, and its high invasiveness and heterogeneity make treatment extremely challenging [20]. Many researchers are dedicated to the study of GBM metabolomics, diagnosis, and treatment, proposing many innovative research and treatment methods that differ from traditional methods. However, the unique biological characteristics of GBM determine that its treatment is difficult [21–26]. It is necessary to develop new treatment methods to improve the prognosis of GBM patients. Manganese, as an important trace element, plays a crucial role in physiological and pathological processes in the body [27]. It is necessary to explore its metabolic processes and pathways in the body.

Researchers have developed various manganese-based nanomaterials for the treatment of tumors, which also have broad application prospects in the field of nanomedicine [28]. Mainly manifested in the following aspects: (1) as a biocompatible nanocarrier, providing immunotherapy drugs for tumor immunotherapy; (2) As an adjuvant to regulate the tumor immune microenvironment and enhance immunotherapy; (3) Activate the host immune system through the cGAS-STING pathway, triggering tumor immunotherapy; (4) Real time monitoring of tumor immunotherapy efficacy through magnetic resonance imaging shows that the binding of Mn ions to proteins can significantly enhance T1 weighted magnetic resonance imaging signals [28–30]. The above research confirms the important role of manganese in tumor imaging, treatment, and tumor immunity.

Based on the important role of manganese mentioned above, this study utilized genes related to manganese metabolism to train a manganese-related prognostic signature using GBM patients from the TCGA database. Simultaneously integrated GBM patients from the CGGA database for independent validation. Finally, the following seven genes (PLAT, TIMP1, FN1, CTSB, SCG5, GALNT6, and AMPH) were included in the prognostic signature. Furthermore, it has been confirmed that the prognostic signature can serve as an independent predictor of survival for GBM patients.

GSEA enrichment analysis is a computational method that uses statistical correlation analysis between gene expression profile data and predefined functional gene sets to reveal the biological significance of genomic data [31]. It can discover the association between gene expression patterns and biological events. The results of GSEA enrichment analysis showed that the manganese-related prognostic signature may be related to tumor immunity in GBM patients, so the study compared the differences in immune cell infiltration between high-risk and low-risk groups. We utilized both the CIBERSORT and xCell analytical methods to confirm the disparities in M0 macrophages between high-risk and low-risk patient cohorts. M0 macrophages, in their unactivated, initial state, exhibit fundamental phagocytic and antigen-presentation capabilities, serving as precursors to M1 (pro-inflammatory) and M2 (anti-inflammatory/reparative) macrophages. The polarization equilibrium of M0 macrophages plays a pivotal role in sustaining immune homeostasis, and the mechanisms governing this balance are a focal point of research in inflammatory diseases, tumorigenesis, and tissue repair [32].

In addition, we also downloaded and organized the TMB data of GBM patients from the TCGA database and conducted an analysis. TMB refers to the total number of somatic gene coding errors, base substitutions, gene insertions or deletions detected per million bases [33–35]. This indicator is calculated using high-throughput sequencing technology to detect 425 genes and serves as a key biomarker for evaluating the efficacy of immune checkpoint inhibitor therapy [33–35]. This study found differences in TMB between high-risk and low-risk GBM patients. The main manifestations in GBM patients are mutations in PTEN and TP53. PTEN gene mutation refers to an abnormality in the PTEN gene located on human chromosome 10, resulting in impaired protein function encoded by it [36, 37]. This gene is a key tumor suppressor gene that maintains genomic stability by regulating cell proliferation, apoptosis, and other processes [36, 37]. Mutations may lead to increased cancer risk (such as breast cancer and thyroid cancer), dysplasia (such as PTEN hamartoma syndrome) and other diseases [36, 37]. The TP53 gene mutation is one of the most common genetic variations in cancer, mainly affecting the most important tumor suppressor gene in the human body, leading to abnormal cell proliferation and cancer occurrence [38, 39]. This type of mutation is associated with various malignant tumors and may affect treatment efficacy and prognosis [41, 42].

Finally, a drug sensitivity analysis was conducted based on the above research content, which is expected to provide a new perspective for the personalized and precise treatment of GBM. Given the substantial number of drugs identified through drug sensitivity analysis, it is anticipated that future research will involve experimental studies further validated by multi-center, large-cohort models, aiming to screen out drugs with higher potential efficacy for GBM treatment and improve the prognosis of GBM patients.

In this study, we developed a prognostic signature for GBM patients based on genes associated with manganese metabolism. Compared with other metabolic or immune-related signatures constructed by other researchers, we observed that our training and validation sets comprised a relatively large number of samples. The model demonstrated favorable results, with the AUC outperforming other clinical features [40–42]. More importantly, we innovatively focused on the role of manganese-related metabolic genes in GBM, aiming to attract more researchers to this research area and provide additional experimental evidence.

Limitations

A limitation of this study is that the manganese-related prognostic signature is based on GBM patients from a public database and requires a larger, multicenter cohort to validate its predictive performance. In addition, more basic experiments (qPCR, IHC, cellular/animal functional assays) and omics data should be conducted to reveal the mechanism of manganese in the occurrence, development, and treatment of GBM. Therefore, large-scale, multicenter studies are necessary to validate and expand upon our findings. Despite its limitations, this study is expected to provide more attention and understanding of the manganese metabolism pathway in GBM.

Conclusion

We constructed and validated a novel gene signature related to manganese metabolism in GBM patients. This gene signature not only reliably predicts the clinical outcomes of GBM patients but also has the potential to guide the provision of new treatment options for these patients.

Acknowledgements

All contributors to the present work have been listed as co-authors.

Author contributions

Yi Man (First Author) # : Writing-original draft, Software, Data curation.WanYue Chen # : Validation, Investigation.Guoan Shen: Investigation.Xuanjie Zhao: Conceptualization, Investigation.Junlin Lu: Investigation.Xuxin Zhang: Project administration, Writing-review & editing, Funding acquisition.

Data availability

The TCGA-GBM dataset presented in current study can be found in the TCGA (https://portal.gdc.cancer.gov/). On this website, GBM is selected as the disease type for downloading and organizing data on TCGA-GBM. In addition, the CCGA-GBM dataset presented in current study can be found in the CCGA (https://portal.gdc.cancer.gov/, accession number: mRNAseq_693 and mRNAseq_325).

Declarations

Ethics approval and consent to participate

This study does not involve animal or clinical experiments. All data were obtained from public databases, and therefore, it does not require submission for ethical review. Not applicable. This study did not include any direct involvement of human participants, and all analyzed data were derived from publicly accessible repositories.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Yi Man and Wanyue Chen have contributed equally to this manuscript.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The TCGA-GBM dataset presented in current study can be found in the TCGA (https://portal.gdc.cancer.gov/). On this website, GBM is selected as the disease type for downloading and organizing data on TCGA-GBM. In addition, the CCGA-GBM dataset presented in current study can be found in the CCGA (https://portal.gdc.cancer.gov/, accession number: mRNAseq_693 and mRNAseq_325).

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