Expression Levels of TMED3 in Pan-Cancer
From the UCSC dataset, we observed significant upregulation of TMED3 in 30 tumors, such as Glioblastoma multiforme (GBM), Brain Lower Grade Glioma (LGG), GBMLGG (GBM+LGG), Uterine Corpus Endometrial Carcinoma (UCEC), Breast invasive carcinoma (BRCA), Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), Lung adenocarcinoma (LUAD), Esophageal carcinoma (ESCA), Stomach and Esophageal carcinoma (STES), Kidney renal papillary cell carcinoma (KIRP), Pan-kidney cohort (KIPAN), Colon adenocarcinoma (COAD), Colon adenocarcinoma/Rectum adenocarcinoma Esophageal carcinoma (COADREAD), Prostate adenocarcinoma (PRAD), Prostate adenocarcinoma (STAD), Kidney renal clear cell carcinoma (KIRC), Lung squamous cell carcinoma (LUSC), Liver hepatocellular carcinoma (LIHC), Skin Cutaneous Melanoma (SKCM), Bladder Urothelial Carcinoma (BLCA), Thyroid carcinoma (THCA), Ovarian serous cystadenocarcinoma (OV), Pancreatic adenocarcinoma (PAAD), Testicular Germ Cell Tumors (TGCT), Uterine Carcinosarcoma (UCS), Acute Lymphoblastic Leukemia (ALL), Acute Myeloid Leukemia (LAML), Pheochromocytoma and Paraganglioma (PCPG), Adrenocortical Carcinoma (ACC), Cholangiocarcinoma (CHOL), and significant down-regulation in 2 kinds of tumors, such as Head and Neck squamous cell carcinoma (HNSC), High-Risk Wilms Tumor (WT).(Figure 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 Figure 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 (Figure 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 = 10535, g = 60499) 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 figures 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 (Figure 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 between TMED3 expression and disease-specific survival in pan-cancer. The results suggested that increased expression of TMED3 was associated with poor DSS in GBMLGG, LGG, BRCA, KIRP, KIPAN, KIRC, SKCM, SKCM-M, UVM, and KIRC. (Figure 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.(Figure 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. (Figure 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://bioinformatics.psb.ugent.be). Under the premise that the disease is glioma, we analyzed the correlation between TMED3 expression and miRNA using the starbase database (https://starbase.sysu.edu.cn/panCancer.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. (Figure 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. (Figure 4 D-H)The up-regulation of hsa-miR-1296-5p was positively correlated with the prognosis of the patient. All of these data points to miR-1296-5p as the most likely TMED3 regulating miRNA in glioma.
Relationship between TMED3 and immune cell infiltration in glioma
Immune cell infiltration has been identified as a prognostic biomarker in several cancers. 8We 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 figures 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. 9To further understand the potential carcinogenic effects of TMED3 in gliomas, we evaluated the relationship of TMED3 to immune checkpoints. As shown in Figure 6, TMED3 was found to be positively correlated with most immune checkpoints, with CD276, VEGFB, ARG1, HMGB1, CX3CL1, and ICOSLG all showing significant differences in low-grade gliomas and glioblastomas. All these have provided new ideas for the development of glioma immune checkpoint inhibitors.
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 Fingure 7, TMED3 is overexpressed in gliomas as compared to normal brain tissue.