Gene expression data and clinical information for pan-cancer analysis were obtained from The Cancer Genome Atlas (TCGA) database. First, the TCGA database (https://portal.gdc.cancer.gov/) was accessed to select RNA sequencing (RNA-Seq) gene expression data across multiple cancer types and the corresponding clinical information, including patient demographics, tumor staging, survival data, and treatment details.

Combining clinical data from The Cancer Genome Atlas with gene expression data, Kaplan–Meier analysis of patients’ overall survival (OS), disease-free survival (DSS), and progression-free interval (PFI) was performed. Furthermore, the prognostic significance of TMEM17 on OS was evaluated by univariate Cox regression analysis. we performed univariate and multivariate Cox regression analyses and utilized the “forestplot” package (version 3.1.3) to generate forest plots displaying the p-values, hazard ratios (HR), and 95% confidence intervals (CI) for each variable. Based on the results of the multivariate Cox proportional hazards model, we then constructed a nomogram using the “rms” package (version 6.8-1) to predict the overall recurrence rate over 5 years. The nomogram graphically represents these factors, allowing the calculation of individual patient prognosis risk based on the points associated with each risk factor.

To examine the differences in TMEM17 expression between GBM and adjacent normal tissues, Thirty GBM samples and thirty adjacent normal tissues were collected from the First Affiliated Hospital of Guangxi Medical University (Nanning, China). This study received ethical approval from The First Affiliated Hospital of Guangxi Medical University Ethics Committee (Approval No. 2023-E108-01).

LN229 and U87MG GBM cell lines were acquired from Procell Life Science & Technology Co., Ltd. (Wuhan, China). Cells were cultured in Dulbecco’s Modified Eagle Medium (Keygen Biotech, Nanjing, China) supplemented with 10% fetal bovine serum (Gibco, Thermo Fisher Scientific, Waltham, MA, USA) and 1% penicillin/streptomycin (100 U/mL) at 37 °C and 5% CO₂. The TMEM17 antibody (sc-514433) was from Santa Cruz Biotechnology Inc. (Dallas, TX, USA). We employed lentivirus for transfection, and constructed TMEM17 knockdown (sh-RILP) and an empty vector control (sh-NC) (OBiO Technology Corp. Ltd., Shanghai, China). Validation of all cell lines was performed by short tandem repeat profiling, and cells with mycoplasma contamination was excluded.

The protein concentration was quantified using a Bicinchoninic Acid (BCA) protein assay kit (Beyotime, Beijing, China) after total protein extraction from the cells. A 10% sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE) gel was used to separate the protein samples, followed by electrotransfer to polyvinylidene difluoride (PVDF) membranes. After being blocked in 5% defatted milk in Tris-buffered saline with Tween 20 for 1 h, the membranes were incubated overnight at 4 °C with primary antibodies against TMEM17 (sc-94962; Santa Cruz Biotechnology), GAPDH (sc-47724; Santa Cruz Biotechnology), and rabbit IgG (Proteintech, Wuhan, China). Then membranes were incubated for 2 h at room temperature with horseradish peroxidase-conjugated secondary antibodies. Proteins were detected with an ECL kit (Beyotime) and quantified using ImageJ (version 1.47V, https://imagej.net/ij/).

For the Cell Counting Kit-8 (CCK-8) assay, LN229 and U87MG cells were seeded into 96-well plates at a density of 5 $\times$ 10⁴ cells per well and incubated with sh-NC or sh-TMEM17 for 0, 24, 48, and 72 h. After incubation with CCK-8 reagent for 2 hour, absorbance values at 450 nm were measured using a microplate reader (Varioskan LUX, Thermo Fisher Scientific, Waltham, MA, USA).

To measure cell proliferation, the EdU assay was performed. Briefly, cells transfected with sh-NC or sh-TMEM17 were plated in 24-well plates and incubated overnight. According to the instructions of the EdU detection kit (RiboBio, Guangzhou, China), cells were incubated with EdU for 2 hours to allow incorporation into newly synthesized DNA. After incubation, cells were fixed with 4% paraformaldehyde for 15 minutes at room temperature, and then permeabilized with 0.5% Triton X-100 for 20 minutes. Subsequently, cells were stained with Apollo reaction cocktail for 30 minutes in the dark to visualize the incorporated EdU. Finally, cell nuclei were counterstained with Hoechst 33342 for 10 minutes. Proliferating cells were detected using fluorescence microscopy (Model BX50F, Olympus, Tokyo, Japan).

Transwell chambers (Corning, Corning, NY, USA) with diluted Matrigel (BD Biosciences, Franklin Lakes, NJ, USA) in each upper chamber were used for the invasion assays. Upper chambers were filled with serum-free medium, while lower chambers were filled with complete medium. In the lower chamber, 4% paraformaldehyde was used for cell fixation and 0.1% crystal violet was used to stain the cells after 24 h of incubation. Observations and counts were made under the microscope (Model CX23, Olympus, Tokyo, Japan) of the chamber.

Flow cytometry was used to measure apoptosis of LN229 and U87MG cells treated for 48 h with sh-NC and sh-TMEM17. Following trypsinization, the cells were collected, resuspended in Binding Buffer, and analyzed according to the instructions of the annexin V-FITC apoptosis detection kit (Thermo Fisher Scientific, Waltham, MA, USA). Next, the cells were analyzed by flow cytometry (BD FACSCanto II, BD Biosciences, San Jose, CA, USA) and quantified using FlowJo software (version 10.7, https://www.flowjo.com/solutions/flowjo).

The lentivirus was designed and constructed by OBiO Technology (Shanghai, China) for the overexpression of Ying Yang 1 (YY1) with GFP. Following the manufacturer’s instructions, lentiviral vectors were transfected into the cells. The control cells were transfected with an empty vector expressing GFP. YY1-overexpressing cells were named OE cells.

The dual luciferase assay was conducted using Promega Corporation’s Dual Luciferase Reporter Assay Kit (Madison, WI, USA). Wild type (WT) or mutant TMEM17 sequences were inserted into pGL3 luciferase reporter vectors. Transient transfection was carried out using Lipofectamine 3000 (Thermo Fisher Scientific, Carlsbad, CA, USA), according to the manufacturer’s instructions. The Dual-Glo Luciferase Assay System from Promega was used to measure firefly luciferase activity 1–2 days after transfection. Renilla luciferase activity was used as an internal control.

SPSS (Version 23, IBM-SPSS Statistics, IBM Corporation, Armonk, NY, USA) and GraphPad Prism 7 (Version 7, GraphPad Software, San Diego, CA, USA) were used for the statistical analyses. Continuous variables are expressed as the mean $\pm$ standard deviation, whereas categorical variables are expressed as counts. Statistical significance was defined as p 0.05 for normally distributed continuous data and k 0.05 for categorical data.

A pan-cancer RNA sequencing study was conducted using TCGA and Genotype-Tissue Expression (GTEx) databases to examine TMEM17 expression. Our results indicated significant differences in TMEM17 expression across 33 cancer types, with 24 of them displaying distinct expression patterns. Specifically, in cancers such as cholangiocarcinoma (CHOL), colon adenocarcinoma, diffuse large B cell lymphoma (DBLCL), esophageal carcinoma, GBM, head and neck squamous cell carcinoma, kidney renal papillary cell carcinoma, acute myeloid leukemia, low-grade glioma, liver hepatocellular carcinoma (LIHC), lung adenocarcinoma, lung squamous cell carcinoma, pancreatic adenocarcinoma, pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma, skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and thymoma (THYM), TMEM17 was upregulated in tumor tissues compared to normal tissues. However, in adenoid cystic carcinoma (ACC), kidney chromophobe, kidney renal clear cell carcinoma (KIRC), ovarian (OV), and tenosynovial giant cell tumor (TGCT), TMEM17 expression was lower (Fig. 1A,B). To further elucidate the expression patterns of TMEM17, we conducted separate analyses using data from TCGA, GTEx, and Cancer Cell Line Encyclopedia (CCLE) datasets. In TCGA tumor tissues, TMEM17 was relatively highly expressed in PCPG, GBM, and THCA, while it had relatively low expression in DLBCL, LIHC, and STAD. In GTEx normal tissues, TMEM17 showed relatively high expression in TGCT, ACC, and GBM, but relatively low expression in THYM, DLBC, and LIHC. In a number of cancer cell lines from the CCLE dataset, TMEM17 exhibited relatively high expression in SKCM, small cell lung cancer, and GBM, but showed relatively low expression in DLBCL, chronic lymphocytic leukemia, and multiple myeloma (Fig. 1C). Additionally, our analyses of TMEM17 protein levels using the Human Protein Atlas (HPA) database revealed that in tumor tissues of breast cancer, GBM, uterine corpus endometrial carcinoma, and LIHC, TMEM17 levels were higher than in normal tissues (Fig. 1D). Subcellular localization of TMEM17 was evaluated in various cell lines, including A-431 epidermoid carcinoma, U-251MG GBM, and U-2OS osteosarcoma cell lines (Fig. 1E).

Fig. 1.

TMEM17 expression levels and localization. (A) Based on TCGA database, the expression of TMEM17 was analyzed in paired samples. (B) TMEM17 showed higher expression in paired tumor samples compared to adjacent tissues. (C) Expression levels of TMEM17 were analyzed across TCGA, GTEx, and CCLE databases. (D) Protein expression of phosphofructokinase, platelet in tumor samples was compared with normal samples across various cancers according to the HPA database. Image credit: Human Protein Atlas, available at v20.proteinatlas.org. The direct URL: https://www.proteinatlas.org/ENSG00000186889-TMEM17/tissue and https://www.proteinatlas.org/ENSG00000186889-TMEM17/pathology. (E) Subcellular localization of TMEM17 was investigated in A-431, U-251MG, and U-2 OS cell lines, also sourced from the HPA dataset. Image credit: Human Protein Atlas, available at v20.proteinatlas.org. The direct URL: https://www.proteinatlas.org/ENSG00000186889-TMEM17/subcellular#human. Statistical significance levels are indicated by ns, no significant, *p 0.05, **p 0.01, ***p 0.001. TMEM17, Transmembrane protein 17; TCGA, The Cancer Genome Atlas; GTEx, Genotype-Tissue Expression; CCLE, Cancer Cell Line Encyclopedia; HPA, Human Protein Atlas; TPM4, Tropomyosin 4; ACC, Adrenocortical Carcinoma; BLCA, Bladder Urothelial Carcinoma; BRCA, Breast Invasive Carcinoma CESC, Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma; CHOL, Cholangiocarcinoma; COAD, Colon Adenocarcinoma; DLBC, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma; ESCA, Esophageal Carcinoma; GBM, Glioblastoma Multiforme; HNSC, Head and Neck Squamous Cell Carcinoma; KICH, Kidney Chromophobe; KIRC, Kidney Renal Clear Cell Carcinoma; KIRP, Kidney Renal Papillary Cell Carcinoma; LAML, Acute Myeloid Leukemia; LGG, Brain Lower Grade Glioma; LIHC, Liver Hepatocellular Carcinoma; LUAD, Lung Adenocarcinoma; LUSC, Lung Squamous Cell Carcinoma; MESO, Mesothelioma; OV, Ovarian Serous Cystadenocarcinoma; PAAD, Pancreatic Adenocarcinoma; PCPG, Pheochromocytoma and Paraganglioma; PRAD, Prostate Adenocarcinoma; READ, Rectum Adenocarcinoma; SARC, Sarcoma; SKCM, Skin Cutaneous Melanoma; STAD, Stomach Adenocarcinoma; TGCT, Testicular Germ Cell Tumors; THCA, Thyroid Carcinoma; THYM, Thymoma; UCEC, Uterine Corpus Endometrial Carcinoma; UCS, Uterine Carcinosarcoma; UVM, Uveal Melanoma.

By analyzing the clinical data of various tumors in TCGA, we investigated the prognostic role of TMEM17. The results of univariate Cox analysis of OS showed that high expression of TMEM17 was correlated with poor OS in ACC, bladder cancer (BLCA), CHOL, GBM, LIHC, and STAD, while it had the opposite effect in KIRC, mesothelioma (MESO), and SKCM, indicating that high expression of TMEM17 was associated with good OS (Fig. 2A). Kaplan–Meier analysis of OS also supported these results (Fig. 2B). It is worth noting that the random forest plot showed that TMEM17 is a risk factor for the prognosis of GBM (1.8008, 95% CI [1.2373, 2.62093]).

Fig. 2.

Prognostic value of TMEM17. (A) OS and expression of TMEM17 were correlated using random forest plots. (B) Kaplan–Meier curve shows the OS rates for different types of tumors including ACC, BLCA, CHOL, GBM, KIRC, LIHC, MESO, SKCM, and STAD. OS, overall survival; ACC, adenoid cystic carcinoma; BLCA, bladder cancer; CHOL, cholangiocarcinoma; GBM, Glioblastoma; KIRC, kidney renal clear cell carcinoma; LIHC, liver hepatocellular carcinoma; MESO, mesothelioma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; CI, confidence interval.

After conducting the aforementioned analyses, we found that TMEM17 exhibited a poor prognosis across all three types of GBM. As a result, we conducted a more comprehensive investigation into the prognostic impact of TMEM17 in GBM. Therefore, the baseline data of GBM patients from the database are shown in Table 1. To this end, we classified patients into two groups based on OV/PFI/DSS and analyzed the expression of TMEM17 within each group. According to the results, TMEM17 expression was not significantly different in OS, but there were noticeable differences in PFI and DSS between the two groups. Additionally, we assessed whether gene expression was correlated with patient survival status. Our findings indicated that as gene expression levels increased, the number of deceased patients increased, whereas the number of surviving patients decreased (Fig. 3A). The results of multivariate Cox proportional hazard regression analysis indicated that GBM patients who expressed high levels of TMEM17 were more likely to die sooner than those who did not. TMEM17 has been identified as an independent risk factor associated with decreased survival in patients with GBM (Fig. 3B). We used forest plots to display the scores of independent risk factor variables (Fig. 3C), and calibration curves to predict the accuracy of the model (Fig. 3D).

Fig. 3.

Survival prognosis analysis of TMEM17 in GBM. (A) Expression of TMEM17 in surviving patients with different OS, Progression-Free Interval (PFI), and Disease-Specific Survival (DSS), and its relationship with patient survival prognosis. (B) Univariate and multivariate Cox analyses of TMEM17 in GBM. (C) Univariate and multivariate forest plots. (D) Prognostic forest plot. Statistical significance levels are indicated by *p 0.05. HR, Hazard Ratio.

Our results confirmed the crucial role of TMEM17 in the progression of GBM. To further explore this finding, we randomly selected tumor tissues and adjacent normal tissues from 30 GBM patients from the sample bank of the First Affiliated Hospital of Guangxi Medical University. We analyzed the mRNA and protein levels of TMEM17 in these samples using Western blotting and quantitative PCR (qPCR) techniques. The study results showed that the mRNA and protein levels of TMEM17 were significantly higher in GBM tissues compared to normal tissues (Fig. 4A,B). Additionally, we analyzed the expression of TMEM17 in different GBM cell lines using the CCLE database (Fig. 4C), and further validated the protein levels of TMEM17 in different cell lines through Western blotting. LN229 and U87MG cell lines exhibited the highest TMEM17 protein expression compared to normal glial cells (Fig. 4D). qPCR results further confirmed higher transcription levels of TMEM17 in LN229 and U87MG cells (Fig. 4E). To further explore the function of TMEM17 in GBM, we designed small interfering RNAs targeting TMEM17 and conducted gene knockdown experiments on LN229 and U87MG cells, validating knockdown efficiency through Western blot analysis and qPCR (Fig. 4F,G). To further investigate the role of TMEM17 in GBM, we measured cell viability using CCK-8 and EdU assays. The results showed that inhibition of TMEM17 significantly reduced the proliferation of GBM cells (Fig. 5A,B). To further explore the regulatory role of TMEM17 in GBM cells, we analyzed apoptotic cells using flow cytometry. The results showed that knocking down TMEM17 significantly increased the apoptosis rate of LN229 and U87MG cells (Fig. 5C). Additionally, results from the transwell assay showed that knockdown of TMEM17 inhibited the migratory function of GBM cells (Fig. 5D).

Fig. 4.

Validation of TMEM17 expression in GBM. (A) Western blot analysis of TMEM17 protein levels in normal and tumor tissues. (B) Quantitative polymerase chain reaction (qPCR) validation of TMEM17 mRNA levels in normal and tumor tissues. (C) Expression of TMEM17 in GBM cell lines from the Cancer Cell Line Encyclopedia (CCLE) database. (D) Western blot analysis of TMEM17 protein levels in different GBM cell lines. (E) qPCR validation of TMEM17 mRNA levels in different GBM cell lines. (F) Western blot verification of TMEM17 knockdown efficiency in LN229 and U87MG cells. (G) qPCR verification of TMEM17 knockdown efficiency in LN229 and U87MG cells. Statistical significance levels are indicated by ns, no significant, *p 0.05, **p 0.01, ***p 0.001. The experiment was repeated three times for each group. The results are expressed as the mean $\pm$ standard error of the mean (SEM).

Fig. 5.

TMEM17 promotes proliferation in GBM cells. (A,B) Cell proliferation was evaluated using the Cell Counting Kit-8 (CCK-8) assay (A) and 5-ethynyl-2${}^{\prime }$-deoxyuridine (EdU) assay (B), with scale bars indicating 100 µm. (C) Cell apoptosis was assessed by flow cytometry. n = 3 independent experiments. (D) Transwell assay, with scale bars indicating 100 µm. Statistical significance levels are indicated by ns, no significant, *p 0.05, **p 0.01, ***p 0.001. The results are expressed as the mean $\pm$ SEM. NC, Negative Control; DAPI, 4′,6-diamidino-2-phenylindole.

To further explore the mechanisms responsible for the upregulation of TMEM17 expression, we conducted computational analysis using the hTFtarget, Cistrome, Jaspar, and ENCODE databases to identify potential upstream transcription factors implicated in TMEM17 upregulation in GBM (Fig. 6A). This analysis showed that GATA-binding factor 2 (GATA2) and YY1 were the likely transcription factors influencing TMEM17. Notably, YY1 was found to be highly expressed in GBM, according to TCGA database (Fig. 6B). Experimental manipulation including knockdown and overexpression of YY1 influenced TMEM17 mRNA levels, whereas knockdown of GATA2 did not have significant effects (Supplementary Fig. 1A). Consequently, our study focused primarily on YY1. We obtained the DNA binding sequence of the YY1 transcription factor from the Jaspar database (Fig. 6C,D) and constructed a series of truncated TMEM17 promoter-luciferase constructs to determine how YY1 regulates TMEM17 transcription. The luciferase reporter assay revealed that the promoter region of the TMEM17 gene, specifically between base pairs 600 to 1200, contains a YY1 response site (Fig. 6E). Consequently, we mutated the predicted promoter sequence of TMEM17 between bases 757 to 764 and constructed both TMEM17-WT and TMEM17-Mut luciferase reporter vectors. The results demonstrated that overexpression of YY1 significantly enhanced the luciferase activity of TMEM17-WT, but had minimal impact on the activity of TMEM17-Mut (Fig. 6F,G).

Fig. 6.

YY1 promotes the transcription of TMEM17. (A) The Venn diagram illustrates the transcription factors identified as binding to the promoter region of TMEM17 in the hTFtarget, Cistrome, Jaspar, and ENCODE databases. (B) Differential expression of YY1 and GATA2 in GBM was analyzed using TCGA database. (C,D) Based on JASPAR scores, a schematic depicts the putative CREB1 binding motif. (E) Following transfection of cells with the TMEM17 promoter or three truncation mutants, the activity of the luciferase was measured. (F,G) Transfection of cells with TMEM17-WT or TMEM17-MUT promoter constructs and measurement of luciferase activity post-transfection were performed. Statistical significance levels are indicated by ns, no significant, **p 0.01, ***p 0.001. The results are expressed as the mean $\pm$ SEM. YY1, Yin Yang 1; hTFtarget, human Transcription Factor target database; ENCODE, Encyclopedia of DNA Elements; GATA2, GATA binding protein 2; MUT, mutant.

Our findings indicate that TMEM17 significantly enhances the proliferation of GBM cells. The phosphoinositide 3-kinase (PI3K)/AKT signaling pathway is closely related to transcriptional regulation, cell proliferation, growth, and apoptosis. Thus, we investigated whether TMEM17 affects the growth of GBM cells by modulating the PI3K/AKT pathway. Western blot analysis showed that knocking down TMEM17 substantially decreased the protein expression of phosphorylated PI3K (p-PI3K) and p-AKT (Fig. 7A). Additionally, after treating the cells with the PI3K activator 740Y-P, we found that the growth inhibitory effect of TMEM17 knockdown on GBM cells was partially reversed (Fig. 7B,C). In addition, 740Y-P also reduced apoptosis caused by TMEM17 knockdown (Fig. 8A) and increased the invasiveness of GBM cells (Fig. 8B). In summary, these research findings demonstrate that TMEM17 actively regulates the PI3K/AKT signaling pathway in GBM cells.

Fig. 7.

TMEM17 regulates the PI3K/AKT pathway affecting the proliferation of GBM. (A) After stable silencing of TMEM17, Western blot analysis was used to assess the protein expression levels of PI3K, p-PI3K, AKT, and p-AKT in GBM cells. (B,C) The effects of the PI3K pathway activator 740Y-P on GBM cell proliferation were evaluated using the CCK-8 (B) and EdU (C) assays, with scale bars indicating 100 µm. n = 3 independent experiments. Statistical significance levels are indicated by ns, no significant, *p 0.05, **p 0.01, ***p 0.001. The results are expressed as the mean $\pm$ SEM. PI3K/AKT, Phosphatidylinositol 3-Kinase/ serine/threonine protein kinase B.

Fig. 8.

TMEM17 regulates the PI3K/AKT pathway affecting apoptosis and migration of GBM. (A) Flow cytometry was used to assess the apoptosis of GBM cells treated with the PI3K pathway activator 740Y-P. (B) 740Y-P restored the decreased invasiveness of GBM cells caused by TMEM17 downregulation, with scale bars indicating 100 µm. n = 3 independent experiments. Statistical significance levels are indicated by ns, no significant, *p 0.05, **p 0.01, ***p 0.001. The results are expressed as the mean $\pm$ SEM.