TMEM201 regulates tumor malignancy and immune microenvironment in lower-grade glioma

Abstract

Background

TMEM201, a nuclear membrane protein, is thought to play an important role in tumor progression, but its involvement in lower-grade glioma (LGG) remains unclear. This study aims to investigate the impact of TMEM201 expression on the prognosis, clinical characteristics, and biological behaviors of LGG.

Methods

RNA expression data from the TCGA, CGGA, and GEO databases were analyzed to explore the correlation between TMEM201 expression and clinical features of LGG. In vitro and in vivo experiments were conducted to assess the effects of TMEM201 knockdown on cell proliferation, migration, and tumorigenic potential. DNA methylation sites associated with TMEM201 were analyzed, and KEGG pathway enrichment was performed to identify the molecular mechanisms underlying TMEM201’s role in LGG.

Results

The analysis showed that high TMEM201 expression was associated with poor prognosis, higher WHO grade, and tumor recurrence in LGG patients. Survival analysis confirmed that elevated TMEM201 levels correlated with shorter survival. Knockdown of TMEM201 in SHG44 cells led to significant inhibition of cell proliferation, migration, and tumor formation in vivo. DNA methylation analysis identified key sites regulating TMEM201 expression, and KEGG analysis revealed pathways such as HIF-1, VEGF, and Notch that contribute to tumor progression.

Conclusion

TMEM201 acts as a potential oncogene in LGG by influencing key cellular signaling pathways and tumor behaviors. It is an independent risk factor for poor prognosis and may serve as a promising biomarker for diagnosis and personalized treatment. This study provides valuable insights into the molecular mechanisms of LGG progression and highlights TMEM201 as a therapeutic target.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-026-02313-w.

Introduction

With the continuous advancement of molecular biology, tumor classification has become increasingly refined [1–3]. The TCGA database categorizes gliomas into two types, LGG (lower-grade glioma) and GBM (glioblastoma), for sample collection and analysis, which facilitates more precise research into the mechanisms of tumor pathogenesis. Significant heterogeneity exists between LGG and GBM at both the cellular and molecular levels as well as in clinical prognosis [4]. GBM tumors often exhibit high invasiveness, leading to a high mortality rate [5], whereas LGG cells are relatively less invasive and more sensitive to surgical and chemoradiotherapy treatments, resulting in a better prognosis [6]. Unfortunately, some LGG cells may evolve into GBM, rendering tumor cells resistant to chemoradiotherapy and leading to poor patient outcomes [7]. Therefore, uncovering the mechanisms underlying LGG development and identifying potential key pathogenic genes to prevent its further malignant progression are pressing scientific challenges that need to be addressed.

Under normal circumstances, cells regulate their basic physiological processes, such as growth, division, apoptosis, and migration, through a series of precise signal transduction pathways [8]. When these signaling pathways become dysregulated, they can lead to uncontrolled cell proliferation, evasion of apoptosis, and increased invasiveness, all of which contribute to tumor formation and progression [9]. In many cancers, mutations or regulatory abnormalities occur in these pathways, leading to abnormal cell functions and promoting tumorigenesis. For example, abnormal activation of the Notch signaling pathway facilitates the maintenance of cancer stem cells, enhances tumor cell proliferation and migration, and drives the initiation, progression, and metastasis of various cancers [10, 11]. Beyond promoting tumor growth and metastasis, abnormal signal transduction can also enable tumors to evade the host immune system. Tumor cells alter immune-related signaling pathways (such as the PD-1/PD-L1 and CTLA-4 pathways) to suppress immune cell activity and evade immune attacks [12, 13]. This process plays a crucial role in immune evasion by tumors and is associated with tumor resistance to therapies.

Nuclear membrane proteins play a crucial role in abnormal cellular signal transduction. The nuclear membrane, which serves as the external barrier of the cell nucleus, consists of the outer nuclear membrane (ONM) and the inner nuclear membrane (INM), separated by the perinuclear space. The nuclear membrane not only plays a fundamental role in maintaining nuclear morphology, protecting genome stability, and regulating gene expression but also participates in various critical biological processes such as signal transduction, cell cycle regulation and DNA repair. Dysfunctional nuclear membrane proteins can disrupt cellular signaling pathways, leading to a variety of diseases, particularly cancer. For example, the TMEM protein family plays a regulatory role in the progression of cancer [14]. High expression of TMEM74 and TMEM106C can lead to the development of hepatocellular carcinoma [15, 16], while high expression of TMEM25 significantly reduces the survival rate of breast cancer patients [17]. TMEM201, a type III inner nuclear membrane protein and an important member of the nuclear membrane protein family, has been relatively understudied regarding its role in tumor pathogenesis [18, 19]. This study aims to investigate whether the regulatory mechanisms of TMEM201 are associated with cell proliferation and invasion in LGG. Considering the role of other TMEM family proteins in cancer, TMEM201 may be closely related to signal transduction and the initiation and progression of cancer. Elucidating the relationship between TMEM201 and the prognosis of LGG patients will help uncover the pathogenesis of LGG and provide a potential therapeutic target for personalized treatment, thereby hindering the malignant progression of LGG.

Materials and methods

Data collection

As the understanding of genomics in the field of oncology continues to deepen, there is an increasing demand among researchers for comprehensive genomic analysis tools. Against this background, the importance of the TCGA (https://cancergenome.nih.gov/) database has become increasingly significant. As a core component of The Cancer Genome Atlas project, it offers a publicly accessible database that is invaluable for advancing cancer research. In this study, we focus on glioma, an aggressively invasive brain tumor. We have downloaded RNA expression data from 503 glioma patients and DNA methylation data from 501 cases (Supplementary Table S1) from the TCGA database. Our aim is to analyze in depth how the upregulation of the TMEM201 expression affects the clinical phenotype, survival prognosis, and cellular infiltration in the tumor immune microenvironment of glioma patients.

The advancement of genomic sequencing technology has given rise to the CGGA (http://www.cgga.org.cn/), which has collected sample data from more than 2000 Chinese patients, aiming to reveal the molecular characteristics of gliomas and explore their relationship with clinical phenotypes and prognosis. In this study, 403 RNA sequencing and 142 microarray samples from the CGGA database were extracted to investigate the relationship between TMEM201 expression and the prognosis of glioma patients. Data analysis from the CGGA database not only reveals the potential role of TMEM201, but also verifies the universality of the findings through comparative analysis with the TCGA database. In this study, the clinical information of the extracted LGG patients is shown in Supplementary Tables S2 and S3.

The GEO (https://www.ncbi.nlm.nih.gov/geo/) database provides a wealth of high-throughput gene expression data, which can identify gene expression changes related to the biological mechanisms of tumor occurrence and development. Utilizing the GEO database in conjunction with databases such as TCGA and CGGA allows for the exploration of the impact of individual genes on tumor prognosis, enhancing the depth and breadth of research. In this study, 18 cases from the GSE43378 dataset and 34 cases from the GSE50025 dataset of LGG data were extracted and applied to a meta-analysis, enhancing the reliability of the research conclusions presented in this paper. For the collection of the above three datasets from distinct sources and their corresponding patient information, we first excluded tissue samples from patients with missing clinical data and those with an overall survival time of less than 30 days. The remaining samples were finally included in this study.

Co-expression analysis and KEGG enrichment analysis of TMEM201

In the field of genomics and transcriptomics, co-expression analysis is a key technique that facilitates the identification of gene sets that exhibit coordinated changes in expression with the gene of interest. In this study, we employ co-expression analysis to uncover potential functional associations of TMEM201 within cells. Through this approach, we can further explore the role of TMEM201 in specific biological pathways and how it influences tumor development and clinical prognosis. Co-expression analysis provides a more comprehensive perspective for studying tumors, facilitating an understanding of the mechanisms by which TMEM201 functions within complex biological activities. Additionally, KEGG analysis as an advanced analytical framework, provides a method to identify changes in gene sets within key signaling pathways by analyzing the expression patterns of specific gene sets across different biological contexts. Patients were divided into high-expression and low-expression groups based on the median expression level of TMEM201 in TCGA RNA-seq data. Subsequently, differential expression analysis was performed, and the identified differentially expressed genes were subjected to KEGG pathway enrichment analysis using the “clusterProfiler” package in R (version 4.4.1) software.

Meta-analysis of TMEM201

In the field of oncology, meta-analysis enhances the reliability of statistical results by pooling data from multiple studies. Variations between various databases can affect the consistency of research findings. Therefore, our study adopted a comprehensive strategy, integrating datasets from TCGA (503 cases), CGGA RNA-seq (403 cases), CGGA microarray (142 cases), GSE43378 (18 cases) and GSE50025(34 cases) which cover samples of LGG, for a more extensive data analysis. Through meta-analysis, we aimed to improve the accuracy of the assessment of how the TMEM201 gene affects patient prognosis, thereby facilitating a deeper understanding of the role of TMEM201 in oncology. Furthermore, this study assessed the heterogeneity among various datasets using the Q test (I² statistics) and employed a random-effects model.

Multivariate Cox proportional hazards regression analysis

Extract gene expression data and clinical follow-up information (overall survival [futime], survival status [fustat]) from three distinct datasets, with strict quality control to ensure data completeness. Prior to multivariate analysis, perform univariate Cox regression on candidate variables using corresponding functions. Variables with p < 0.1 are selected for multivariate model construction, so as to balance statistical power and the risk of overfitting. Construct the multivariate Cox model with the formula Surv(futime, fustat) ~ ., then extract hazard ratios (HR), 95% confidence intervals (95% CI) and p-values for tabular summarization. A global p-value > 0.05 indicates that the PH assumption is satisfied. If violations exist, apply stratified Cox regression or time-dependent covariates for correction. Incorporate interaction terms to evaluate the interaction effects between key covariates. Compare the goodness-of-fit of models via likelihood ratio tests, with a significance threshold of p < 0.05 for statistically meaningful interaction effects. In addition, the above procedures were independently performed for each of the three datasets(CGGA RNA-seq, TCGA RNA-seq, CGGA microarray) derived from different sources.

Immune infiltration analysis of TMEM201

The presence of immune cells in the tumor microenvironment is crucial, and their interactions with tumor cells have a significant impact on the initiation, progression, and survival prediction of cancer. By accessing the TIMER database—an online tool for analyzing gene expression data in the tumor immune microenvironment—we successfully extracted expression data for various immune cells and further analyzed this data to identify whether TMEM201 is a key biomarker for immune cells and a potential target for immunotherapy.

Cell culture and transfection

The glioma cell line SHG44 was cultured in DMEM medium (Cat. No. SH30022.01, Cytiva) containing 10% fetal bovine serum (Cat. No. 10091148, Gibco) and supplemented with 1% penicillin-streptomycin (Cat. No. P1400, Solarbio). The cells were maintained in a humidified incubator at 37 °C with 5% CO2. For transfection, cells in good growth condition were seeded into six-well plates. The shRNA targeting TMEM201 (Target seq: 5’-ACCAGACCACCAAGATCAA-3’) and the negative control sh-NC (Target seq: 5’-TTCTCCGAACGTGTCACGT-3’) were synthesized by Shanghai GenePharma (Shanghai, China). The sh-RNA and sh-NC solutions were prepared according to the manufacturer’s instructions and added to the six-well plates. The cells were cultured in an incubator at 37 °C with 5% CO2 for 6–8 h. The medium was then replaced with fresh medium for subsequent experiments.

RT-qPCR

First, the transfected cells were prepared into a single-cell suspension, and total RNA was extracted according to the instructions of the Total RNA Kit I (Cat. No. R6834, Omega Bio-tek). Next, the reverse transcription reaction system was prepared, and the Plus All-in-one 1st Strand cDNA Synthesis SuperMix (gDNA Purge) kit (Cat. No. E047-01B, Novoprotein) was used to reverse transcribe mRNA into cDNA. The RT-PCR reaction was performed using the SYBR qPCR SuperMix Plus system (Cat. No. E096-01B, Novoprotein). The specific primer sequences for TMEM201 were: Forward 5’-AGGATACGCTGGTGCCCTATG-3’ and Reverse 5’-TCTCCTGGAAGCCGTTGTACTG-3’. GAPDH (Forward 5’-AAGGTGAAGGTCGGAGTCAAC-3’; Reverse 5’-GGGGTCATTGATGGCAACAATA-3’) was used as the internal control. The obtained data were analyzed using the 2 − ΔΔCt method in GraphPad Prism (version 9.5) software.

Wound-healing assay

The transfected LGG cells were prepared as a single-cell suspension, and the cell count was determined using a hemocytometer (Invitrogen, C10228). The cell concentration was then adjusted to 1 × 10^6 cells/mL. The adjusted cell suspension was evenly seeded into a six-well plate and placed in an incubator at 37℃ with 5% CO2 for culture. When the cells reached confluence in the six-well plate, vertical scratches were generated using a 200 µL pipette tip on the bottom surface of the plates within the laminar flow hood. The back of the plates was marked with a marker pen in a horizontal direction. The cells were returned to the incubator, and cell migration was monitored under a microscope at 0 h, 24 h, and 48 h, with images captured to record the observations. Finally, ImageJ software (version 1.54 m) was used to quantify the migration area, and the data were statistically analyzed using GraphPad Prism (version 9.5) software.

Transwell migration assay

Transfected SHG44 cells were harvested and resuspended as a single-cell suspension. Cells were counted using a hemocytometer, and the concentration was adjusted to 1 × 10^6 cells/mL. Medium supplemented with 20% fetal bovine serum and 1% penicillin-streptomycin was added to the lower chamber of the Transwell to create a chemoattractant gradient. Subsequently, 100 µL of the cell suspension was added to the upper chamber. The cells were incubated for 48 h. Following incubation, the cells were washed with PBS and fixed with 4% paraformaldehyde (Biosharp, BL539A) for 30 min. The cells were then washed and stained with 0.1% crystal violet (Solarbio, C8470) for 30 min. After washing with PBS, the migrated cells were observed and imaged under a microscope. ImageJ software (version 1.54 m) and GraphPad Prism were used to quantify the stained cells and statistically analyze migration capabilities.

CCK-8 assay

Transfected SHG44 cells were harvested and resuspended as a single-cell suspension. The cell concentration was adjusted to 1 × 10^5 cells/mL, and 100 µL of the cell suspension was seeded into 96-well plates. The plates were incubated at 37 °C with 5% CO2. Following cell attachment, cell viability was assessed at 0, 24, 48, and 72 h. At each time point, 10 µL of CCK-8 reagent (Saint, ST1006.05) was added to each well, and the plates were incubated for an additional 1–2 h. Finally, the absorbance at 450 nm was measured using a microplate reader.

Clone formation assay

Transfected cells were harvested and resuspended as a single-cell suspension. Cells were seeded at a density of 1,000 cells per well and incubated for approximately 2 weeks or until colonies containing > 50 cells formed. The medium was refreshed every 3 days. At the end of the experiment, colonies were washed with PBS and fixed with 4% paraformaldehyde for 30 min. Subsequently, the colonies were stained with 0.1% crystal violet for 30 min at room temperature. After washing with PBS, the plates were air-dried and photographed. Finally, colony numbers were quantified for statistical analysis.

Nude mouse xenograft tumor model assay

SHG44 cells were prepared as a single-cell suspension, and the concentration was adjusted to 1 × 10^6 cells/mL. Nude mice were housed under Specific Pathogen Free (SPF) conditions and allowed to acclimate for one week. The cell suspension was injected subcutaneously into the left flank of the nude mice. The injection site was disinfected with alcohol before and after injection. Following inoculation, the mice were maintained in the SPF environment, and tumor growth was monitored daily. Approximately 30 days post-inoculation, the mice were euthanized. The tumors were dissected, and tumor volume was calculated.

Statistical analyses

All data analyses were performed using R software (version 4.4.1) and GraphPad Prism software (version 9.5). For experimental studies, all in vitro assays (including RT-qPCR, CCK-8, and migration/invasion assays) were performed independently with at least three biological replicates n = 3. For in vivo xenograft studies, mice were randomly assigned to groups (n = 5 per group). Quantitative data are presented as the mean ± standard deviation (SD). Differences between two groups were analyzed using Student’s t-test, while comparisons among multiple groups were performed using one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test. For clinical and bioinformatics data, the chi-square test was employed to assess the correlation between TMEM201 expression/methylation sites and clinical characteristics. Kaplan-Meier (KM) survival analysis with the log-rank test was used to evaluate the influence of TMEM201 on overall survival. Univariate and multivariate Cox regression analyses were conducted to identify independent prognostic factors. Spearman’s correlation analysis was utilized to explore co-expression patterns. A p-value of < 0.05 was considered statistically significant.

Result

TMEM201 involved in a variety of tumor-related clinical characteristics in LGG

In order to gain a deeper understanding of the biological regulatory processes involved in the occurrence and development of tumors, the abnormal expression of genes has gradually become an indispensable part of cancer research. In order to explore whether TMEM201 affects the clinical progression of patients with LGG, we extracted data from TCGA RNA-seq, CGGA RNA-seq, and CGGA microarray for LGG patients and performed chisq.test (Fig. 1A-C). High expression of TMEM201 is predominantly observed in WHO Grade III and recurrent tumors, which are concurrently characterized by IDH-wildtype status, absence of 1p/19q codeletion, and histological enrichment in anaplastic oligoastrocytomas (AO). Notably, patients with high TMEM201 expression are more likely to receive radiochemotherapy. In contrast, low TMEM201 expression is typically associated with WHO Grade II and primary tumors, accompanied by IDH-mutant status, presence of 1p/19q codeletion, and histological prevalence in anaplastic astrocytomas (AA). Consistent findings across multiple cohorts demonstrate that TMEM201 expression exhibits a robust correlation with glioma grade, recurrence status, patient age, treatment history, histological subtype, and core molecular biomarkers. Based on the above results, we found that TMEM201 exhibits differential expression in various tumor-related clinical characteristics, suggesting that it may play the role of an oncogene and participate in the pathological process of LGG patients.

Fig. 1.

The heatmaps illustrate the associations between TMEM201 expression levels (low/high expression) and the core characteristics of gliomas across three cohorts (the TCGA RNA-seq cohort (A), CGGA RNA-seq cohort (B), and CGGA microarray cohort (C)). On the horizontal axis are glioma-related characteristics, including clinical features, pathological features, and molecular features. The vertical axis represents the stratification of TMEM201 expression (low/high expression). The color gradient of the heatmaps indicates the enrichment level of TMEM201 expression in each subgroup, with deeper colors corresponding to higher enrichment levels. Chi-square tests were performed for statistical analysis, and the significance levels are marked in the figures as follows: * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001

High expression of TMEM201 often predicts a shorter overall survival and has good diagnostic power in LGG

Survival analysis and ROC curve analysis can identify potential prognostic factors for cancer patients, thereby guiding clinical decision-making. Therefore, we utilized the selected three databases to extract survival data from LGG patients for analysis. The comprehensive analysis results from multiple sources and large samples have found that the abnormal overexpression of TMEM201 is strongly correlated with a shorter survival period in patients with LGG (Fig. 2A-C). Additionally, we conducted ROC curve analysis on the aforementioned data, and the results of the analysis found that multiple databases have confirmed that the high expression of TMEM201 has good diagnostic efficacy (Fig. 2D-F). Especially in the computational results from the CGGA microarray data, the diagnoses at one, three, and five years all showed excellent performance (AUC > 0.7). Based on the above analysis results, we speculate that the high expression of TMEM201 in LGG patients is an unfavorable oncogenic factor and is closely related to the patients’ unfavorable prognosis. Moreover, the abnormal overexpression of TMEM201 is a potential biomarker that could be used to interrupt the pathological progression of LGG patients.

Fig. 2.

Prognostic and diagnostic value of TMEM201 expression in LGG. A–C Kaplan-Meier survival curves comparing overall survival (OS) between high and low TMEM201 expression groups in the (A) TCGA RNA-seq, (B) CGGA RNA-seq, and (C) CGGA microarray datasets. P-values were calculated using the log-rank test. D–F Receiver operating characteristic (ROC) curves evaluating the diagnostic performance of TMEM201 expression for predicting 1-, 3-, and 5-year survival in the (D) TCGA RNA-seq, (E) CGGA RNA-seq, and (F) CGGA microarray datasets. AUC, area under the curve

TMEM201 is considered to independently increase the risk of developing LGG and the same results have been obtained in meta-analyses

To ensure the quality of the analysis results in this paper, further reliable exploration and analysis are indispensable. For this reason, we conducted univariate and multivariate analyses to assess the impact of TMEM201 on the prognosis of LGG patients. Using the sample data we have obtained from the databases, the results of the univariate analysis found that all three databases support the high expression of TMEM201 and a higher WHO Grade as risk factors for LGG (Fig S1A-C). TCGA RNA-seq and CGGA microarray demonstrate that age is also a risk factor (Fig S1A, B). The recurrence of LGG as a risk factor is supported by CGGA RNA-seq and CGGA microarray (Fig S1B, C). The standalone analysis results from CGGA RNA-seq have proven that different types of Histology are risk factors (Fig S1B). Due to the potential impact of interactions between factors on the results of univariate analysis, we further conducted a multivariate analysis to obtain more convincing results. The analysis results from all three databases have proven that the high expression of TMEM201 and a higher grade of WHO Grade are independent risk factors for LGG, further validating the results of the aforementioned univariate analysis (Fig S1D-F). Additionally, the data from TCGA RNA-seq and CGGA RNA-seq respectively support Age and LGG recurrence as independent risk factors (Fig S1D, E). To expand the study sample size and enhance the reliability and accuracy of the analysis results, we integrated data from CGGA mRNA-array, CGGA RNA-seq, GSE43378, GSE50025, and TCGA RNA-seq.Due to I² = 93%, we selected a random-effects model to conduct the meta-analysis. The comprehensive analysis results once again confirm that high expression of TMEM201 is a risk factor for LGG (Fig S1G). Based on the above research results, we can infer that the abnormal overexpression of TMEM201 in LGG acts as an independent risk factor and has the potential value of predicting patients’ clinical prognosis.

Knocking out the expression of TMEM201 can significantly reduce the proliferation, invasion, and tumorigenic ability of LGG cells

To validate the expression status of TMEM201 in LGG cells, we conducted RT-qPCR analysis to compare TMEM201 levels in normal human astrocytes (HA) and the LGG cell line (SHG44). The results showed that TMEM201 expression in SHG44 cells was significantly higher than that in HA cells (Fig. 3A). For subsequent functional assays, we knocked down TMEM201 in SHG44 cells and assessed the knockdown efficiency. RT-qPCR analysis confirmed a significant reduction in TMEM201 expression in the transfected group (Fig. 3B). To investigate the effects of TMEM201 overexpression on the biological behavior of SHG44 cells in vitro, we first performed CCK-8 and colony formation assays to assess cell proliferation. The results demonstrated that when TMEM201 expression was reduced, the proliferative capacity of SHG44 cells was significantly inhibited (Fig. 3C, D). Next, to verify the effect of TMEM201 on cell migration, we conducted Transwell and wound healing assays. The results showed that TMEM201 knockdown significantly decreased the migratory activity of SHG44 cells (Fig. 3E, F). Additionally, in vivo xenograft assays yielded consistent results; after the same growth period, tumors in the TMEM201 knockdown group were significantly smaller in volume compared to the control group (Fig. 3G). Based on these in vitro and in vivo findings, we conclude that TMEM201 overexpression plays a pivotal oncogenic role in the development of LGG and is closely linked to its malignant biological behaviors.

Fig. 3.

TMEM201 knockdown inhibits proliferation, migration, and tumorigenicity of LGG cells. A RT-qPCR analysis of TMEM201 mRNA expression levels in normal human astrocytes (HA) and glioma cell line (SHG44). B Verification of TMEM201 knockdown efficiency in SHG44 cells transfected with sh-NC or sh-TMEM201 by RT-qPCR. C Cell viability assessed by CCK-8 assay at 0, 24, 48, and 72 h. D Colony formation assay detecting the proliferation ability of SHG44 cells. E Transwell migration assay evaluating cell migration capability. F Wound-healing assay monitoring cell migration at 0 h, 24 h, and 48 h. G Xenograft tumor model in nude mice (n=5 per group). Tumor volumes were measured to assess in vivo tumorigenesis. Data are presented as mean ± SD from at least three independent experiments. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001 vs. control group

Multiple methylation sites associated with TMEM201 can extend patient survival time and have good diagnostic value

DNA methylation plays an increasingly pivotal role in tumor research, can have an impact on epigenetics, and is recognized as one of the most promising regulatory mechanisms. In our study, we collected samples from multiple sources and extracted methylation data related to TMEM201 from LGG patients for analysis, hoping to provide a new perspective on the molecular regulation of LGG. Through data analysis, we have identified 12 methylation sites (cg03890628, cg07232095, cg07899389, cg10877147, cg13148776, cg21003981, cg22334684, cg26070379, cg26114570, cg18176336, cg08886482, cg25408237) that have regulatory effects on TMEM201. (Fig. 4A). We performed a correlation analysis between these 12 methylation sites and the expression levels of TMEM201, the comprehensive correlation analysis of these sites shows a negative correlation with the expression of TMEM201 (Fig. 4B), and the results revealed that there was 1 positively correlated methylation site (cg18176336) and 9 negatively correlated methylation sites(cg03890628, cg07232095, cg07899389, cg10877147, cg13148776, cg21003981, cg22334684, cg26070379, cg26114570) (Fig. 4C-H, Fig S2A-D). To further explore whether the methylation sites with statistical differences can also affect the prognosis of LGG patients, similar to the relationship between TMEM201 expression and clinical prognosis, we conducted a survival curve analysis. The study results revealed that 5 methylation sites (cg07232095 (promoter region), cg26114570 (promoter region), cg07899389 (gene body region), cg13148776 (gene body region), cg22334684 (gene body region)) with a negative correlation to TEAD2 expression levels yielded convincing outcomes, and high expression of these sites was associated with prolonged clinical survival times for LGG patients (Fig S3A-E). Additionally, these five methylation sites (cg07232095, cg26114570, cg07899389, cg13148776, cg22334684) also show good diagnostic potential in clinical practice (Fig S4A-E). Based on the research findings, it is known that the high expression of methylation sites (cg07232095, cg26114570, cg07899389, cg13148776, cg22334684) associated with TMEM201 can prolong the survival time of LGG patients and exhibit potential clinical diagnostic value.

Fig. 4.

Correlation between TMEM201 expression and DNA methylation sites. A Distribution of methylation levels (beta values) for 12 identified CpG sites associated with TMEM201 in LGG. B Overall correlation analysis between TMEM201 expression and its methylation status. C–H Scatter plots showing the specific correlation between TMEM201 expression and individual methylation sites: (C) cg18176336, (D) cg26114570, (E) cg26070379, (F) cg22334684, (G) cg21003981, and (H) cg13148776. The linear regression line and 95% confidence interval (shaded area) are shown. R represents the correlation coefficient

TMEM201 is highly correlated with multiple oncogenes and May be involved in various tumor-related signaling pathways

In cancer research, with the development of sequencing technologies, an increasing amount of genetic information is being collected, which provides a foundation for the conduct of correlation studies. We conducted a correlation analysis with thousands of genes as support, taking TMEM201 as the central gene. The analysis results revealed that among the genes with a correlation coefficient greater than 0.59, there were 5 genes (SDF4, MRPL37, ICMT, UBE2J2, TXLNA) with significant oncogenic effects that ranked at the top. Meanwhile, the 5 most negatively correlated genes (WDFY3-AS2, CRBN, FAM133A, CNRIP1, DNAJC12) all exhibited tumor-suppressing biological functions (Fig. 5A, B). To reveal the potential biological activities through which TMEM201 may be abnormally expressed, we further conducted KEGG enrichment analysis. The results show that genes related to TMEM201 are highly enriched in signaling pathways associated with tumor development and progression, such as Focal adhesion, Cell cycle and Oxidative phosphorylation(Fig. 5C). Therefore, we have reason to believe that TMEM201 may participate in various signaling pathways contributing to the occurrence and development of LGG.

Fig. 5.

Co-expression network and functional enrichment analysis of TMEM201. A Chord diagram displaying the top 5 genes positively correlated (SDF4, MRPL37, ICMT, UBE2J2, TXLNA) and top 5 genes negatively correlated (WDFY3-AS2, CRBN, FAM133A, CNRIP1, DNAJC12) with TMEM201. Red ribbons indicate positive correlation; green ribbons indicate negative correlation. B Detailed list of correlation coefficients (Cor) and P-values for the top co-expressed genes. C KEGG pathway enrichment analysis of TMEM201-related genes

TMEM201 upregulates the expression of multiple immune cells in the immune microenvironment

With the deepening of tumor research, we have found that the tumor immune microenvironment plays an important role in the malignant progression of tumors and immune therapy, which can help researchers and clinicians deepen their understanding of the occurrence and development process of tumors. Firstly, the levels of immune infiltration were scored under different expression levels of TMEM201, and it was found that the scores of StromalScore, ImmuneScore, and ESTIMATEScore all showed a statistically significant positive correlation with the expression of TMEM201 (Fig. 6A). Secondly, to understand the changes in the expression levels of immune cell infiltration behind the scoring differences, we utilized the TIMER database (TIMER2.0) for a more in-depth study. Consistent with the results of the previous step, data analysis revealed that six important immune cell types (tumor purity) were all upregulated with the expression of TMEM201 (Fig. 6B). Thirdly, we conducted survival curve analysis on the immune cell types that showed a statistically significant correlation with the expression trend of TMEM201. The findings indicated that high levels of immune cell infiltration were associated with a shorter clinical survival period in LGG patients (Fig. 6C). Fourthly, to enhance the reliability of the research findings, we further examined the expression patterns of various immune cell markers related to TMEM201 based on the TCGA RNA-seq data. The results were consistent with the previous findings, all showing a positive correlation (Fig S5A). Ultimately, several well-known immune checkpoints also showed a positive correlation with the expression of TMEM201 (Fig S5B). Based on the aforementioned research findings, we have reason to speculate that high expression of TMEM201 is correlated with the upregulation of multiple immune cell infiltration levels, and it may promote the shortening of survival time in LGG patients by potentially participating in the modulation of the tumor immune microenvironment.

Fig. 6.

Relationship between TMEM201 expression and immune infiltration. A Comparison of immune scores (StromalScore, ImmuneScore, ESTIMATEScore) and tumor purity between high and low TMEM201 expression groups. B Correlation analysis between TMEM201 expression and infiltration levels of six immune cell types (B cells, CD8+ T cells, CD4+ T cells, Macrophages, Neutrophils, and Dendritic cells) using the TIMER database. C Kaplan-Meier survival analysis based on the infiltration levels of immune cells significantly correlated with TMEM201. High infiltration levels of B cells, CD4+ T cells, Macrophages, Neutrophils, and Dendritic cells correlate with poor prognosis

Discussion

The nuclear envelope of the cell is an essential barrier that isolates the genetic material within the nucleus from the cytoplasm [20]. The nuclear envelope interacts with nucleoplasmic proteins and chromatin, playing a significant role in gene regulation, genome organization, and signal transduction [21, 22]. These functions are critical in regulating the progression of malignant tumors. TMEM201 is a protein encoded by a gene, primarily localized to the nuclear membrane, and is closely associated with the integrity and function of the nuclear envelope [23]. Therefore, it is hypothesized that TMEM201 may have significant regulatory effects in tumors. However, there are few reports on its regulatory effects in cancer progression. This study aims to explore the role of TMEM201 in the clinical prognosis and molecular regulatory mechanisms of LGG patients, providing a potential target for clinical biotherapeutic strategies for LGG.

In this study, to explore the impact of TMEM201 on the prognosis of LGG patients, a clinical relevance heatmap was first generated, revealing that the expression pattern of TMEM201 tends to align with malignant tumor characteristics. For instance, the expression of TMEM201 was significantly higher in WHO grade III gliomas than in grade II, and it was notably elevated in recurrent tumors compared with primary tumors. Additionally, in terms of molecular characteristics, TMEM201 expression was significantly higher in the IDH wild-type group and the 1p19q non-co-deleted group than in the IDH-mutant group and the 1p19q co-deleted group. These clinical expression features suggest that TMEM201 may play an oncogenic role in the pathological progression of LGG. Subsequently, the Kaplan-Meier method was further employed in three different datasets, all of which indicated that the survival time of patients in the high TMEM201 expression group was significantly shorter compared to those in the low expression group. This finding further supports the hypothesis that TMEM201 acts as an oncogene in LGG. Moreover, this perspective has also been recognized in previous studies. For example: the elevated expression level of TMEM201 in breast cancer can activate the TGFβ signaling pathway, promoting increased tumor cell invasiveness and leading to poor patient prognosis [18].Finally, we found that TMEM201 has high diagnostic value for the prognosis of LGG patients, laying a theoretical foundation for the future use of TMEM201 as a potential biomarker for patient diagnosis and personalized treatment.

To validate the carcinogenic effect of TMEM201 in LGG, the study further employed a Cox analysis and a meta-analysis of TMEM201 to address the statistical limitations of KM survival analysis [24]. The results consistently indicated that high expression of TMEM201 in LGG is an independent risk factor associated with poor patient prognosis.This result enhances our belief that TMEM201 is a potential oncogene in LGG, increasing its credibility. However, the impact of TMEM201 on the biological behavior of LGG cells remains unknown. Therefore, this study utilized multiple experimental methods, including gene interference technology to knock down TMEM201 expression, revealing that the proliferation and invasion abilities of LGG cells were significantly inhibited. This is also consistent with the manifestation observed in the pathological progression of LGG when oncogenes are inhibited. For example the elevated expression level of GRB10 in glioma significantly reduces the overall survival time of glioma patients and knocking down GRB10 expression can significantly inhibit the proliferation, invasion, and tumorigenic abilities of glioma cells [25]. High expression of CNIH4 in LGG is a risk factor for patient prognosis, as it regulates the tumor immune microenvironment and abnormal signaling pathways through various biological mechanisms, while its knockdown can significantly reduce the malignant biological behavior of tumor cells [26]. Based on the above discussion, it is evident that our proposed TMEM201 functions as a potential oncogene in LGG, fully aligning with the fundamental biological characteristics of oncogenes, which strongly supports the credibility of our research perspective.

In the malignant biological progression of tumors, the abnormal regulation of epigenetics is closely linked to the dysregulation of gene expression [27]. To explore the regulatory mechanism behind the elevated expression of TMEM201 in LGG, this study investigated whether DNA methylation in the TMEM201 promoter region influences the expression of TMEM201. The results identified 12 DNA methylation sites that may have regulatory relationships with TMEM201. A noteworthy theory in biology is that hypermethylation of DNA methylation sites often exerts a negative regulatory effect on downstream genes [28]. Therefore, this study, using multiple analytical methods, identified five DNA methylation sites (cg22334684, cg13148776, cg07899389, cg07232095, and cg26114570) among the 12 investigated methylation sites that showed a significant negative correlation with TMEM201 expression. Furthermore, the hypermethylation of these five sites not only significantly prolonged the overall survival of LGG patients but also demonstrated diagnostic value for predicting patient prognosis. Research has indicated that DNA methylation sites can serve as potential therapeutic targets in cancer biological targeted therapy. For instance, SLC22A17 has a significant regulatory relationship with the pathological progression of patients, and its expression is regulated by DNA methylation in cutaneous melanoma. Therefore, both SLC22A17 and DNA methylation have the potential to serve as biomarkers for targeted improvement of patient prognosis [29]. Constructing a DNA methylation-based risk model in hepatocellular carcinoma patients will help assess their potential tumor metastasis risk, and DNA methylation biomarkers have the potential to guide adjuvant chemotherapy decisions and improve prognosis prediction for early-stage CRC patients [30]. Therefore, in this part of the study, we hypothesize that the regulatory mechanism underlying TMEM201 expression patterns may be negatively regulated by the five identified DNA methylation sites (cg22334684, cg13148776, cg07899389, cg07232095, and cg26114570) .

In the pathological process of tumor development, the abnormal regulation of cell signaling pathways by oncogenes is a common mechanism through which they exert their biological functions [31]. Therefore, we employed the KEGG approach to reveal the impact of TMEM201 on cell signaling pathways in this study. The results showed that TMEM201 is associated with several oncogenic signaling pathways, and the activation of these pathways can influence the biological behavior of tumor cells through various mechanisms. For example, the increased activity of the HIF-1 signaling pathway in tumor tissues enhances tumor cell adaptation to hypoxia, thereby promoting tumor cell proliferation and increasing their invasiveness [32]. The VEGF signaling pathway plays a crucial role in tumor biology, primarily by promoting angiogenesis, supporting tumor growth, enhancing metastasis, and modulating the tumor immune microenvironment, thus influencing tumor progression [33, 34]. The Notch signaling pathway plays a key role in processes such as cell fate determination, proliferation, differentiation, apoptosis, angiogenesis, and the maintenance of tumor stem cells. Therefore, the role of the Notch signaling pathway in tumors includes promoting tumor cell proliferation and metastasis, as well as regulating tumor resistance and stem cell characteristics [35, 36]. Based on this, it can be inferred that TMEM201 may exert its biological functions in LGG through the above-mentioned cell signaling pathways. However, we only validated the impact of TMEM201 on cell proliferation and migration, and further exploration of the detailed molecular regulatory mechanisms is needed.

In recent years, with the superior performance of anti-tumor immunotherapy in preclinical experiments, a new focus has emerged in tumor research [37, 38]. However, the premise of anti-tumor immunotherapy is the identification of molecules that play an important regulatory role in the tumor immune microenvironment [39]. Based on the oncogenic biological functions exhibited by TMEM201 in LGG, it may also regulate tumor immunity. Therefore, this study also explored whether TMEM201 has potential value in anti-tumor immunotherapy in LGG. The results showed that TMEM201 only mildly promoted the degree of tumor immune cell infiltration and showed a weak positive expression relationship with known immune checkpoint markers. Thus, it is hypothesized that the role of TMEM201 as a potential target for anti-tumor immunotherapy may be limited.

Conclusion

The study provides the first systematic explanation that TMEM201 plays a potential oncogenic role in LGG by influencing key cellular signaling pathways such as HIF-1, VEGF, and Notch pathways, which are known to regulate tumor cell proliferation, migration, and invasion. Therefore, this study not only broadens the understanding of the regulatory mechanisms of TMEM201 in tumor malignancy progression but also provides a potentially biological target for the formulation of personalized clinical treatment plans for LGG patients.

Abbreviations

LGG

Lower-Grade Glioma

GBM

Glioblastoma

TCGA

The Cancer Genome Atlas

CGGA

Chinese Glioma Genome Atlas

GEO

Gene Expression Omnibus

KEGG

Kyoto Encyclopedia of Genes and Genomes

HR

Hazard Ratio

AUC

Area Under the Curve

IDH

Isocitrate Dehydrogenase

TIMER

Tumor Immune Estimation Resource

Authors’ contributions

SF conceived the study, designed the methodology, performed the investigation, formal analysis, and visualization, and wrote the original draft. GS supervised the project, was responsible for project administration, participated in the study design and methodology, and reviewed and edited the manuscript. All authors read and approved the final manuscript.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Data availability

Publicly available datasets were analyzed in this study. This data can be found here: The Cancer Genome Atlas (TCGA): https://portal.gdc.cancer.gov/ (RNA-seq data, DNA methylation data, and clinical data from 501 LGG patients).Chinese Glioma Genome Atlas (CGGA): http://www.cgga.org.cn/ (142 microarray datasets, 403 RNA-seq datasets, and clinical data from LGG patients).Gene Expression Omnibus (GEO): Accession number GSE43378 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE43378) (18 LGG microarray datasets).

Declarations

Ethics approval and consent to participate

All animal experiments were conducted in accordance with the ethical guidelines of Kaifeng Central Hospital and were approved by the Kaifeng Central Hospital Ethics Committee. The nude mice used in this study were sourced from the institution’s Laboratory Animal Center, and no privately owned animals were involved.All animal procedures were approved by the Kaifeng Central Hospital Ethics Committee. Euthanasia was performed under anesthesia (pentobarbital sodium, 50 mg/kg, i.p.) to minimize suffering, in compliance with institutional and international ethical standards for humane endpoints.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.