Analysis and Validation of TMED3 correlates with poor prognosis and tumor immune infiltration of glioma

Glioma is the most common primary intracranial tumor. It is notorious for its high degree of malignancy, strong invasion, and poor prognosis. The transmembrane emp24 trafficking protein 3 (TMED3) belongs to the TMED family, which is responsible for intracellular protein transport and innate immune signal transmission. More and more evidence shows that TMED3 plays a key role in the tumor progression of human cancer. However, the role and potential molecular mechanism of TMED3 in glioma have not been clarified. TMED3 expression levels, clinical data, survival prognosis, prediction of upstream miRNA, and immune-related analyses were all analyzed utilizing relevant databases. Finally, a molecular cell experiment confirmed TMED3 expression in glioma. We discovered that TMED3 is overexpressed in most tumors, including gliomas, and is associated with tumor staging and prognosis. Subsequently, a combination of a series of bioinformatics analyses, including correlation and survival analyses, identified miR-1296-5p as the most potent upstream miRNA of TMED3 in gliomas.Additionally, we analyzed the relationship between TMED3 level and tumor immune cell infiltration and immune checkpoint expression. TMED3 is highly expressed in gliomas and is associated with tumor staging and affects the prognosis of patients. Therefore, the TMED3 gene may be a potential immunotherapy target and prognostic marker for gliomas.


Introduction
Glioma is the most common tumor in the central nervous system (80%), with the highest degree of malignancy and the strongest invasion. At present, there are various methods to treat glioma, which are generally combined with surgery, radiotherapy, or chemotherapy. However, the 5 year relative survival rate is about 5%. In addition, the recurrence rate of glioma is high, so it is necessary to explore a new method to treat glioma (Ostrom et al. 2014).
Transmembrane emp24 domain containing (TMED) proteins constitute a highly conserved family of proteins that exist as monomers or dimers of various compositions (Jenne et al. 2002). TMED family consists of type I single-pass transmembrane proteins that are found in all eukaryotes. TMED family members have emerged as key protein transport regulators (Aber et al. 2019). TMED3 has been definitively linked to the occurrence and progression of several human cancers, including osteosarcoma (Xu et al. 2021), squamous cell carcinoma of the lung (Xie et al. 2021), breast cancer , and colorectal cancer (Wang et al. 2022). However, there is currently a paucity of thorough studies on TMED3 expression, prognosis, and mechanism in glioblastoma. Furthermore, no link between TMED3 and tumor immune infiltration in glioma has been established.
In this study, we first analyzed the expression and survival of TMED3 in various types of human cancers. Next, we also explored the regulation of non-coding RNA (ncRNA) related to TMED3 in glioma. Finally, we determined the relationship between TMED3 expression and immune cell infiltration, immune cell biomarkers, or immune checkpoints. Finally, we verified the expression level of TMED3 in glioma by cell experiment.

UCSC database analysis
We downloaded the unified and standardized pan-cancer data set: TCGA Target GTEX (PANCAN,n = 19,131,g = 60,499) from the UCSC (https:// xenab urows er. net/) database, and further extracted the expression data of ENSG00000166557 (TMED3) gene in each sample. Further, we screened the sample sources as follows: Solid Tissue Normal, Primary Solid Tumor, Primary Tumor, Normal Tissue, primary blood derived cancer-bone marrow, The samples of primary blood derived cancer-peripheral blood, in addition, we filtered the samples with expression level of 0, and further carried out log2(x + 0.001) transformation on each expression value. Finally, we eliminated the cancer species with less than 3 samples in a single cancer species, and finally obtained the expression data of 34 cancer species.We used R software (version 4.1.3) to calculate the expression difference between normal samples and tumor samples in each tumor, and used unpaired Wilcoxon rank sum and signed rank tests to analyze the significance of the difference. Next, the authenticity of the data is verified using the TIMER database (https:// cistr ome. shiny apps. io/ timer/) and the UCSC database. Survival and clinical phenotype data were extracted from each sample downloaded from the USCS and the R-package was used for correlation analysis of clinical phenotype and prognosis.The selection criteria for classifying as statistically significant were set at p value < 0.05.

Immunoinfiltration analysis
The correlation between TMED3 and immune infiltration degree and immune checkpoint of CD4 T lymphocytes, CD8 T lymphocytes, B cells, macrophages, and dendritic cells was carried out in the sangerbox (http:// vip. sange rbox. com/ home. html).

RNA extraction and quantitative real-time polymerase chain reaction (qRT-PCR)
The Total RNA Small Amount Extraction Kit (Axygen, Corning, USA) was used to extract total RNA from cell lines according to the manufacturer's instructions. Sangon Biotech developed primers for the amplification of TMED3 and the endogenous control actin (Shanghai, China). The SYBR Green master mix kit (Tiangen Biotech, Beijing, China) was used for qRT-PCR according to the manufacturer's instructions. The 2-ΔΔCt method was used to calculate fold changes in target gene expression. SDS-PAGE (Solar Bio, Beijing, China) was used to separate the protein, which was then transferred to a polyvinylidene fluoride membrane (Millipore, Billerica, MA, USA) and mixed with TMED3(1: 2000), GAPDH (1: 10,000; Proteintech, USA) was incubated at 4℃for 12 h. The HRP-bound anti-mouse or rabbit IgG antibody (1:10,000, affinity) was incubated for 2 h at 25 °C. ECL chemiluminescence reagent (UE,Suzhou,China) was used to detect the target protein band. The strip density was measured and compared to the internal control using a gel imaging technique.

Statistical analysis
The statistical analysis in this study was automatically calculated by the online database mentioned above. Statistical significance was defined as p value 0.05 or log rank p value 0.05.  (Fig. 1A). We further verified the expression of TMED3 in these cancer types by using the TIMER database. Except for those cancers whose normal tissue samples are less than 3 instances, substantial changes in TMED3 expression between tumors and normal tissues were observed in 19 malignancies, as shown in Fig. 1B, when compared to the matching normal control.Among them, TMED3 is in BLCA, BRCA, Chol, COAD, ESCA, HNSC-HPVneg, KIRC, KIRP, LIHC, LUAD, LUSC, PRAD, Rectum adenocarcinoma (READ), SKCM, STAD, THCA, UCEC. On the contrary, compared with the normal tissues in HNSC and Kidney Chromophobe (KICH), the level of TREM2 in tumors is down-regulated. In addition, the results from the Xena database showed that the expression of TMED3 was significantly increased in some cancers, including ACC, BLCA, BRCA, CHOL, COAD, DLBC, ESCA, GBM, KIRC, KIRP, LIHC, LUAD, LUSC, OV, PAAD, PCPG, PRAD, READ, SKCM, STAD, TGCT, THCA, THYM, UCEC, UCS, but low TMED3 expression in HNSC compared with non-tumor tissue (Fig. 1C). The results from three different databases are basically consistent, which indicates that TMED3 may play a key regulatory role in the carcinogenesis of cancer.

Clinical correlation analysis of TMED3 in Pan-Cancer
We downloaded a unified and standardized pan-cancer data set, TCGA PAN-Cancer (PANCAN, n = 10,535, g = 60,499) from the UCSC database, and extracted the expression data of the ENSG0000166557 (TMED3) gene in each sample. Further, the samples from primary blood-derived cancer (peripheral blood and primary tumor) were selected. In addition, we also filtered the samples with an expression level of 0, and further carried out a log2 (x + 0.001) transformation on each expression value. Finally, we eliminated cancer species with fewer than 3 samples in a single cancer species. Finally, the expression data of 37 cancer species was obtained. We used R software (version 4.1.3) to calculate the gene expression difference of each tumor in different clinical stage samples, used an unpaired Student's t-Test to analyze the difference significance between pairs, and used ANOVA to test the difference between multiple groups of samples. The results are shown in the following Figs. 2A-F.

The prognostic values of TMED3 in Pan-Cancer
In order to study the relationship between TMED3 expression level and prognosis, we analyzed the survival associations of each cancer, including Overall Survival (OS), Disease-specific Survival (DSS), Disease-specific Survival (DFI), and Progression-free Interval (PFI). We established the Cox proportional hazards regression model by using the coxph function of the R software package survival (version 4.1.3) to analyze the relationship between gene expression and the prognosis of each tumor, and we statistically tested it with the Logrank test to obtain the significance of prognosis. With regard to OS (Fig. 3A), we observed that the high expression of TMED3 had poor prognosis in these 15 tumor types (GBMLGG, LGG, LAML, BRCA, LUAD, KIRP, KIPAN, KIRC, LIHC, SKCM, BLCA, UVM, LAML, ALL, and KICH). Furthermore, we analyzed the relationship  (Fig. 3B). The increased expression of TMED3 indicates that the DFI rate of KIPAN is worse. However, it shows very poor clinical results in THCA patients with low expression of TMED3 (Fig. 3C). Meanwhile, the results of COX regression analysis showed that TMED3 expression was associated with PFI in GBMLGG, LGG, CESC, KIRP, KIPAN, HNSC, GBM, KIRC, SKCM, SKCM-M, MESO, UVM, PCPG PRAD, and OV (Fig. 3D). Combining OS, DSS and PFI, we can clearly find that the prognosis of glioma patients with high expression of TMED3 is worse.

Prediction and analysis of upstream miRNAs of TMED3
MicroRNA (miRNA) is a kind of endogenous small RNA with a length of about 20-24 nucleotides that is responsible for regulating gene expression in cells. To determine whether TMED3 is regulated by some mirnas, we used the intersection method of five databases (ENCORI, miRDB, mirDIP, miRWalk, and TarBase) to predict the upstream mirnas that might bind to TMED3. In order to visualize the results, we made a venn diagram using a website. In order to visualize the results, we made a venn diagram using a website (http:// bioin forma tics. psb. ugent. be). Under the premise that the disease is glioma, we analyzed the correlation between TMED3 expression and miRNA using the Fig. 2 Clinic correlation analysis of TMED 3 in Pan-Cancer (A-F), Pan-cancer differential expression of TMED 3 in WHO stages in indicated tumor types from UCSC database. *p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001. ns not significant starbase database (https:// starb ase. sysu. edu. cn/ panCa ncer. php). Two miRNAs not recorded in the database were discarded. We found a significant negative correlation between TMED3 and five predicted miRNAs and a positive correlation with seven predicted miRNAs. There were no statistical expression relationships between TMED3 and the other 29 predicted miRNAs. (Fig. 4 A-C) Based on the mechanism of miRNA controlling target gene expression, there should be a negative association between miRNA and SEMA3F. Finally, we focused on miRNAs that were adversely associated with TMED3 and their prognostic value in glioma. (Fig. 4 D-H)The up-regulation of hsa-miR-1296-5p was

Relationship between TMED3 and immune cell infiltration in glioma
Immune cell infiltration has been identified as a prognostic biomarker in several cancers (McGuigan et al. 2021). We observed significant changes in immunocyte infiltration levels in gliomas at different TMED3 copies through the SCNA module of the TIMER database, comparing the infiltration levels of each SCNA category to normal using a two-sided Wilcoxon rank sum test. Following that, we investigate the relationship between TMED3 expression and different amounts of immune cell infiltration in glioma. As shown in Figs. 5, in glioblastoma, the expression level of TMED3 is significantly positively correlated with CD4 + T cells and dendritic cells. Furthermore, the expression level of TMED3 and the levels of CD8 + T cells was positively correlated in low-grade glioma. Moreover, tumor purity is linked to clinical characteristics, genomic expression, and biological properties of tumor patients. We discovered that the purity values of LGG and GBM are diametrically opposed, which might explain why their biological properties are so dissimilar.

Relationship between TMED3 and immune checkpoints in glioma
Checkpoint therapy (ICT) is a novel treatment for malignant tumors that improves the anti-tumor immune response of T cells and hence has a high curative efficacy (Xu et al. 2020). To further understand the potential carcinogenic effects of TMED3 in gliomas, we evaluated the relationship of TMED3 to immune checkpoints. As shown in Fig. 6, TMED3 was found to be positively correlated with most immune checkpoints, with CD276, VEGFB, ARG1, HMGB1, CX3CL1, and ICOSLG all showing significant

Molecular experimental verification
For protein blotting and PCR investigations, we utilized this kit to extract protein and RNA from normal brain tissue cells (HEB) and glioma cell lines (T98G, U118, and U251). After that, PS software was used to process the western blot data, and Graphpad prism8 was used to analyze the RT-PCR results. As shown in Fig. 7, TMED3 is overexpressed in gliomas as compared to normal brain tissue.

Discussion
Glioma is an internal brain tumor that originates from glial cell progenitor cells, accounting for 81% of malignant brain tumors. Conventional therapies, including surgery, chemotherapy, and radiotherapy, have limited improvement in the prognosis of patients with glioma (Ostrom et al. 2014). Secondly, immunotherapy is a cancer treatment revolution that has emerged as a viable technique for penetrating the blood-brain barrier. As a result, improved indicators for immunotherapy and glioma diagnosis are required from a biological standpoint (Xu et al. 2020).
TMED3 is involved in the onset and progression of a variety of human malignancies. However, there is currently inadequate data on TMED3 in glioma, which requires additional research. This research is unique because it focuses on p24/transmembrane emp24 domain family proteins, which are rarely studied in immunotherapy.
In this study, the expression of TMED3 in glioma was explored for the first time. First, we downloaded the unified and standardized pan-cancer data set from the UCSC database to conduct pan-cancer analysis on the expression of TMED3, and then further used the TIMER database and Xena Shiny database to verify the expression of TMED3. Secondly, the staging and prognosis of TMED3 and glioma were analyzed. Finally, the results were verified again by cell experiments. We found that TMED3 was highly expressed in gliomas, and it was closely related to tumor stage and prognosis.
MicroRNA is a kind of short non-coding RNA that plays a function in the post-transcriptional control of gene expression and is a potent regulator of numerous cellular processes (Saliminejad et al. 2019). To explore the upstream regulatory miRNA of TMED3, we introduced five prediction programs, including Encori, Mirdb, Mirdip, Mirwalk, and Tarbase, to predict miRNA that may bind to TMED3. The relationship between miRNA and TMED3 on the prognosis of glioma was also analyzed, and finally a statistically significant miRNA was obtained. The up-regulation of miR-1296-5p was positively correlated with the prognosis of patients and negatively correlated with the expression of TMED3. MiR-1296-5p has been found to act as a tumor suppressor miRNA in gastric cancer, breast cancer, and osteosarcoma. At present, studies have found that miR-1296-5p can inhibit the proliferation, migration, and invasion of tumors by reducing the expression of target genes (Jia et al. 2019;Zang et al. 2020;Chen et al. 2017).
Immune cell infiltration can affect the efficacy of chemotherapy, radiotherapy, or immunotherapy as well as the prognosis of cancer patients. (Zhang et al. 2018) Immunotherapy has changed the traditional cancer treatment and revitalized the field of tumor immunology. We hope to explore the mechanism of immune escape from cancer through the analysis of glioma-infiltrating immune cells, thereby providing an opportunity to develop new therapeutic strategies. The results showed that the expression of TMED3 was positively correlated with CD4 T lymphocytes, CD8 T lymphocytes, macrophages, and other different immune cells, indicating that the expression level of TMED3 could reflect the On the other hand, we searched into the association between TMED3 and immunological checkpoints, and found that high TMED3 expression was linked to CD276, VEGFB, ARG1, HMGB1, CX3CL1, and ICOSLG in glioma, demonstrating that targeting TMED3 might improve immunotherapy's therapeutic impact in glioblastoma.
Author contributions Chunliang-Wang conceived the study. Gang-Liao and Meimei-Zhang performed database search and analysis, experimental verification, and wrote the manuscript. All authors participated in the biological analysis, interpretation of the results, reading and approval of the final version of the manuscript.

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Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.  RT-qPCR, (B) cell lines detected by western blot. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001. Experiments were repeated three times