A Pan-cancer Analysis of the Role of the Transmembrane Protein 91(TMEM91) in Human Tumors

doi:10.21203/rs.3.rs-2827180/v1

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A Pan-cancer Analysis of the Role of the Transmembrane Protein 91(TMEM91) in Human Tumors

https://doi.org/10.21203/rs.3.rs-2827180/v1

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Transmembrane protein 91(TMEM91) encodes a protein belonging to the transmembrane protein family which mediates many human physiological processes, such as the regulation of cell migration and invasion, and participates in the immune response. At present, research on the TMEM family members focuses mostly on the field of molecular mechanisms, and the role of TMEM91 in tumor cells is still unrecognized. Using data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, we can analysis the expression of TMEM91 in various tumors. The Kaplan-Meier method was used for the evaluation of the prognostic significance of TMEM91 in patients with pan-cancer. The dif-ferential expression of TMEM91 in diverse cancers with different clinical characteristics was analyzed with the UALCAN database. TIMER was used to explore how TMEM91 correlates with immune infiltration. The correlations between TMEM91 expression immune checkpoint (ICP), tumor mutational burden (TMB), and microsatellite instability (MSI) in human cancers were analyzed via the SangerBox database. Gene Set Cancer Analysis (GSCA) platform was used to investigate the correlation between TMEM91 expression with Copy number variations (CNV) and methylation. Protein-Protein Interaction analysis was performed in the GeneMANIA database. Gene Ontology and Kyoto Encyclopedia of Genes pathway en-richment analyses were further conducted for exploration of TMEM91 function.

According to the finding of this study, downregulated TMEM91 expression was observed in numerous tumor tissues. The low TMEM91 expression group showed poor overall survival (OS) and disease-free survival (DFS). TMEM91 was positively correlated with can-cer-associated fibroblast (CAF), and nature killer T cell (NKT), and negatively correlated with CD4 + T cells, B cells and common lymphoid progenitor (CLP). Here, we show that there is a positive relationship between Contingent Negative Variation (CNV) and expression of TMEM91, whereas the correlation of TMEM91 expression with DNA methylation was nega-tive in all cases. Molecular biology experiments were performed to confirm the tumor pro-moting role of TMEM91 in glioma. Function analysis showed that TMEM91 expres-sion-related genes were mainly enriched in response to type I interferon /regulation of viral genome replication/negative regulation of viral process/movement in host environment. In addition, the association between the expression of TMEM91 and the use of the anticancer drug, sensitive anti-tumor drug based on CellMiner were predicted, such as the anticancer drug AS-703569, Hydroxyurea. Our pan-cancer analysis provides a deep understanding of the functions of TMEM91.TMEM91 may affect the oncogenesis and metastasis in different cancers via mediating the immune infiltrating cells and the degree of methylation. This study sheds new light on the mechanism of TMEM family in cancer.

TMEM91

pan-cancer analysis

immune

methylation

glioma

molecular biology experiments

In recent decades, malignant tumors have exhibited staggering morbidity and mortality worldwide, and remained a major threat to the health of people since the 21st century ^[1]. Although we have discovered lots of oncogenes and tumor suppressor genes, which are all related to signaling pathways for cell growth or death, produced many anti-tumor agents and treatment technologies, cancer treatment still seems to have reached a bottleneck ^[2]. The onset and development of cancer can take hundreds of different forms, either location dependent or a spectrum of changes in the cells of origin and genome that affect the response to treatment. For most cancer lineages, many complex molecular landscapes remain incomplete ^[3]. Pan-cancer analyses can undoubtedly increase our understanding of the molecular landscape of cancer. At present, pan-cancer research has been widely used to identify tumor markers, and key genes and signaling pathways involved in cancer. Furthermore, combining pan-cancer analysis with various omics analysis can help us understand the molecular mechanisms of cancer metastasis and development ^{[4, 5]}.

Membrane proteins refer to a group of proteins that can bind to or integrate into cell membranes or organelle membranes. Membrane proteins represent a large part of all proteins, constituting approximately 30% of the proteome^{[6, 7]}. Membrane proteins can be classified into three categories according to the strength of binding to the membrane and their location, Peripheral membrane proteins, integrative membrane proteins, and lipid-anchored proteins^{[8, 9]}.More studies showing that membrane proteins play a fundamental role during cancer development by transmitting information between the extracellular environment and the cytoplasmic proteins^[10]. Such as receptor protein tyrosine kinase (RPTKs) regulates the occurrence and development of cancer by controlling the biological function of MAPK, PI3K/AKT pathway, et al.Transmembrane proteins (TMEMs) are a type of integrative protein that contains at least one transmembrane segment that passes completely or partially through the biofilms ^{[11, 12]}. Most of the proteins in the TMEM family are grouped into the TMEM family due to poorly described^[11]. It is renamed when further demonstrated, such as G protein-coupled receptor proteins, and ion channels ^[12]. Studies have found that TMEM family proteins are involved in modulating cancer cell metastasis: and appear crucial for some steps of the metastatic cascade in several cancer types^[10]. For example, TMEM45A affects the proliferation and migration of human ovarian cancer cells and glioma cells ^{[13, 14]}, Doolan et al.༟^[15]And it was found through biological information that TMEM25 may be an effective biomarker in the diagnosis and prognosis of breast cancer. TMEM16A, also known as DOG1 as a promising biomarker for gastrointestinal stromal tumors (GIST)^[16]. The ratio of TMEM176A to TMEM176B protein is expected to become a potential useful markers for lymphoma, lung carcinoma^[17]. In lung cancer cells, siRNA-TMEM98 can inhibited the cells invasion and migration^[18] .Hence, TMEM proteins could be considered as significant prognostic markers, and play a crucial role in cancer treatment.

TMEM91 also known as DSCP3, IFITMD6, is located on the human chromosome 19q13.2 and contains two conserved transmembrane domains, and there are currently no relevant studies on this gene. NCBI summary shows that the gene is mainly act on hematopoietic progenitor cell differentiation. Also to be an integral component of membrane and active in intracellular membrane-bounded organelle and membrane. TMEM91are highly expressed in the bone marrow ((RPKM 6.3), testis (RPKM 5.0) in the bone marrow and testes. Based on several databases, we investigated the expression of TMEM91 and its association with tumorigenesis and prognosis, tumor infiltrating-immune cells, and the degree of methylation. Our data imply that TMEM91 may affect tumor development and prognosis by impacting the tumor microenvironment and methylation.

2.1. The Expression Levels of TMEM91 are Aberrant Across Various Normal and Cancer Tissues

A comparative study of normal tissues and cancers revealed the expression of TMEM91 was strongly different. The data extracted from TCGA and GTE_X indicated that the expression difference was significant for 31 of the 33 cancer types^[19]. The expression level of TMEM91 in most tumors were lower when compared to normal tissues except that higher expression lever in head and neck cancer (HNSC), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), pancreatic adenocarcinoma (PAAD), and cholangiocarcinoma (CHOL). (Fig. 1a) The expression of TMEM91 from Cancer Cell Line Encyclopedia (CCLE) is different in diverse cancer lines, and the difference was significant. In most cancers, the expression of TMEM91 is lower than average. It is worth noting that among leukemia tumor cells, the expression is the highest (Fig. 1b)

2.2. Analysis of Link Between TMEM91 Expression Level and Prognosis

Using data from the TCGA database, we examined the relationship between Tmem91 expression levels and OS in various cancer types through single-variate Cox regression analysis. The hazard ratios(HR)for TMEM91 were significant in KIPAN, COADREAD, COAD, STAD, GBMLGG, LAML, BRCA, PAAD, ACC, UVM, LGG, and LIHC, (Fig. 2a). Following that, a low TMEM91 expression level was associated with a poorer OS and DFS in the TCGA cancer types shown in Fig. 2b-c (HR = 1.1, Fig. 2b.HR = 0.9, Fig. 2c). The subsequent survival analysis, which used patients data dichotomized for optimal cut-off value in each cancer type (Fig. 2d), demonstrated that survival differences in OS-related cancer types were all significant, indicating low TMEM91 expression had worse outcomes in BRCA and UVM. And patients with low TMEM91 expression had better outcomes in COAD, STAD, LAML, ACC, LGG, and LIHC. (Fig. 2d).

2.3. Pan-Cancer Analysis of the Correlation between TMEM91 Expression and Clinicopathology

To investigate the association between the TMEM91 expression and clinicopathological features in multiple cancers, we assessed the TMEM91 expression in stage 1,2,3,4, cancer patients. The results from the TCGA database revealed that the expression of TMEM91 was significantly downregulated in BRCA, CESC, KICH, STAD, and UCEC (Fig. 3c, Fig. 3d, Fig. 3g, Fig. 3j, Fig. 3l). And the expression of TMEM91 was significantly upregulated in LIHC, KIRC, and CHOL. (Fig. 3i, Fig. 3h, Fig. 3e). The expression was consistent in several advanced cancers, including BLCA and THCA(Fig. 3b, Fig. 3k)

2.4. Immune Cell Infiltration Analysis of TMEM91 Across Cancers

We explored the correlations between TMEM91 expression and immune cell infiltration levels across cancers according to the above conclusion. We employed the TIMER2.0 database to show the landscape of TMEM91 associated with various immune cell infiltrations, which were conducted in a variety of quantitative immune infiltration platforms in cancer. The results reveal infiltration levels of CD4 + T cells, CD8 + T cells, B cells, cancer-associated fibroblast (CAF), macrophages, neutrophils, common lymphoid progenitor (CLP), and nature killer T cell (NKT) in pan-cancer (Fig. 4A). In most tumors, we observed a moderate negative correlation between TMEM91 and multiple infiltrating immune cells including CD4 + T cells, B cells and CLP. However, TMEM91 was positively correlated with CAF, and NKT cells. Association with the expression of TMEM91 is reduced in most tumors, CAF, and NKT cells express lowly, CD4 + T cells, B cells, and CLP express highly in tumors.

2.5. Correlations Between TMEM91 and Immunomodulators ,TMB

There are now multiple genes that are tightly linked to and recognized as immune response checkpoints. We sought to determine whether there is a relationship between TMEM91 expression and the expression of such checkpoint genes using the Sangerbox website. In diverse cancer types, correlation analysis between TMEM91 and checkpoint genes expression indicated a significant connection(p < 0.05) with genes including ADORA2A, TGFB1, VEGFB, TNFSF14, and TNFSF18. Furthermore, considerable co-expression of TMEM91 with more immune checkpoint genes was found in SKCM, CESC, SARC, ACC, and OV. The findings suggest that TMEM91 is involved in the regulation of the tumor immune response via immune checkpoint activity modulation. Meanwhile, we conducted the correlation analysis of TMEM91 expression with TMB and MSI.As shown in Fig. 5b, the expression with TMB appeared significantly negative correlation with TMB in GBM, BRCA, STAD and PRAD. In contrast, there was a positive correlation with TMB in OV. For MSI, TMEM91 expression was positively related with MSI in LGG, LUAD, PRAD and DLBC, and negatively related with MSI in STAD.

2.6. Pan-Cancer Analysis of the Mutation Profile and the Methylation Level of TMEM91

Furthermore, a Spearman association between TMEM91 CNV and mRNA was performed. In LGG, OV, LIHC, LUAD, UCS, LUSC, BRCA, SARC, GBM, HNSC, CESC, BLCA, and STAD, there is a substantial positive connection between TMEM91 CNV and mRNA expression. TMEM91 methylation landscape in pan-cancer was also investigated, TMEM91 methylation was shown to be strongly linked with TMEM91 mRNA expression in 25 of the 33 cancers. Especially in OV, LIHC, and GBM, the relevance is particularly obvious show the top three with the highest correlation scores(Fig. 6b).

2.7 PPI Network of TMEM91 in Cancers and Enrichment Analysis

Next, we utilized the GeneMANIA online program to create a PPI network for TMEM91, which is displayed in Fig. 6a, to investigate the probable processes by which TMEM91 contributed to carcinogenesis. The results from GeneMANIA showed that TMEM91 had potential interactions with IFITM1, IFITM2, IFITM3. Main functions induced response to type I interferon, cellular response to type I interferon, regulation of viral genome replication, negative regulation of viral process, movement in host environment. To further identify the possible role of TMEM91, the GO and KEGG enrichment analysis was performed on molecules that interacted with TMEM91 obtained from GeneMANIA. The biological processes (BP) enriched in this dataset were primarily those related to negative regulation of viral entry into host cell/response to interferon − alpha/negative regulation of viral life cycle/response to interferon − beta/regulation of viral entry into host cell/modulation by symbiont of entry into host/negative regulation of viral genome replication/regulation of viral genome replication/negative regulation of viral process/type I interferon signaling pathway, while the cellular components(CC) enriched were primarily those related to postsynaptic density membrane/synaptic vesicle membrane/exocytic vesicle membrane/postsynaptic specialization membrane. The major enriched pathway was B cell receptor signaling pathway according to the KEGG analysis.

2.8. TMEM91 and Drug Response

To further investigate the role of TMEM91 as potential target in cancer therapy, we analyzed the relationship between the expression of TMEM91 and the response to anti-cancer drugs. TMEM91 expression was positively connected with drug response in patients treated with the anticancer drug AS-703569, the anticancer drug Hydroxyurea, the anticancer drug Nelarabine, the anticancer drug Ipatasertib, the anticancer drug Asparaginase and Methylprednisolone. AS-703569 is an inhibitor that inhibits tumor growth in breast, lung, colorectal and leukemia. Additionally, there is a negative connection between TMEM91 expression and the anticancer drug Cobimetinib, ARRY-704, and RO-5126766. An illustration of the relationship between TMEM91 expression and expected medication response can be found in Fig. 8.

2.9. Clinical Correlation Analysis of TMEM91 in glioma

First, GBM is one of the top three show the relevance and the correlation scores. So, we explored the role of TMEM91 in glioma. Then, TMEM91 expression levels in LGG and GBM are lower than in normal tissues. And according to the Cox regression analysis, TMEM91 had the highest risk effect in GBM, and LGG. Furthermore, the TMEM91 expression in LGG and GBM is positive with the degree of CAF cells’ immune infiltration. After the data of TCGA-LGG were standardized, TMEM91 was divided into high-expression and low-expression groups according to the median of TMEM91 expression. Then, according to the padj < 0.05, and sorted in descending order according to the absolute value of Log fold change, the top 500 genes were screened out as differential genes. The differential genes were screened for enrichment analysis. Enrichment analysis of biological processes showed that these molecules are mainly involved in the pattern specification process, and regionalization. Cellular Component enrichment results indicated that these molecules are mainly enriched in the motile cilium. The molecular function enrichment analysis manifested that these molecules mostly play a role in DNA-binding transcription activator activity, RNA polymerase II-specific, DNA-binding transcription activator activity, and hormone activity. The major enriched pathway is transcriptional misregulation in cancer according to the KEGG analysis. Then, we explored the role of TMEM91 in U87 by molecular biological methods.

First, by overexpression TMEM91 in U87 cells, we examined the protein levels of AKT, p-AKT, ERK, p-ERK, using western blot analysis. Compared with control cells, overexpression TMEM91 cells showed significantly decreased levels of p-AKT, p-ERK, while no significant difference was observed in the level of AKT, ERK. (Fig. 9b). Using the CCK8 kit, the results showed that 72 hours after TMEM91 overexpression, the proliferation ability of cells was significantly increased (Fig. 9c). And, flow cytometry showed that overexpression of TMEM91 decreased apoptosis of U87 cells (Fig. 9d).

TMEM91 is a member of the TMEM family. At present, it is unclear about the role TMEM91 plays in various cancers. But many members of the TMEM family have been shown to play a role in the onset and development of cancer. Together, our study provides insights into the application of TMEM91 as a potential prognostic biomarker in cancers in the context of immune-oncology and contributes to the development of TMEM91-targeting therapeutic strategies.

According to this study,TMEM91 expression in tumors and normal tissues had significant differences based on the TCGA and GTEx databases. The expression levels of TMEM91 were lower in most cancers when compared to normal tissues. Meanwhile, by using the K-M survival analysis method, it was found that low expression was associated with poor OS and DSS. Furthermore, the lower expression of TMEM91 in BRCA and UVM showed a poor prognosis, and low TMEM91 expression had better outcomes in COAD, STAD, LAML, ACC, and LGG. In addition, we also found that the high expression of PAAD indicated a good prognosis. Thus, TMEM91 was a prognostic biomarker of OS in COAD, STAD, LAML, ACC, LGG, and PAAD. (Fig. 2d). In Fig. 3, TMEM91 expression was closely related to individual stages of cancers. These findings indicate that TMEM91 has a significant role in predicting the prognosis of cancer patients and is expected to become a powerful biomarker of prognosis for cancer patients.

The tumor immune microenvironment plays a significant function in tumor cell clearance and immune escape^[20]. Several reports have suggested that immune cell infiltration level in the tumor immune microenvironment is associated with tumor development and prognosis^[21]. This study reported that the expression of TMEM91 was positively associated with infiltration levels of CAF and NKT, suggesting that TMEM91 was likely to affect tumor development and prognosis by impacting the tumor microenvironment. For example, association with the expression of TMEM91 is reduced in most tumors, CAF and NKT are expressed lowly in most tumors. Thus, the unfavorable prognostic value of TMEM91 in BRCA and UVM could be explained, at least in part by the lowly expressed between TMEM91 and CAF, NKT infiltration.(low expression of TMEM91 and infiltration of CAF and NKT)

In addition, the correlation analysis between TMEM91 and the pan-cancer immunomodulatory factors suggests that the TMEM91 expression was highly correlated to the expression of specific immunomodulatory genes, especially in SKCM, CESC, SARC, ACC, and OV. Therefore, we hypothesized that TMEM91 could be a powerful and promising biomarker in predicting tumor immunotherapy effects.

We also concentrate on intracellular signaling pathways and the regulatory factor activity, which is regarded to be important^[22]. The relationships between CNV, methylation, and TMEM91 expression were all investigated in depth. In our study, TMEM91 methylation was shown to be strongly linked with TMEM91 mRNA expression in 25 of the 33 cancers. DNA methylation is a major form of epigenetic modification of DNA that regulates gene expression without altering the sequence of DNA^[23]. The link between DNA methylation and cancer was gradually discovered in recent decades. Hypermethylation within promoter regions often leads to the silencing or inactivation of tumor suppressor genes in cancerous cells^[24]. The above study showed that DNA methylation of TMEM91 was upregulated in most common cancers, which is consistent with the downregulation of the TMEM91 expression. So TMEM91 may affect patient prognosis by influencing the degree of methylation in cancer, thereby regulating the cancer. The relationship between DNA methylation and TMEM91 expression warrants a more indepth study.

At the same time, by conducting enrichment analysis, we explained the molecules potentially interacting with TMEM91 are mainly involved in response to interferon − beta/regulation of viral entry into host cell/modulation by symbiont of entry into host /regulation of viral genome replication/negative regulation of viral process/type I interferon signaling pathway. These processes also indicated the significant involvement of TMEM91 in the progression of tumors^[25–29], by reading the literature. Then, we identified the relationship between TMEM91 expression and medication response. As Fig. 1c shows, TMEM91 is the most expressive in leukemia cells, and the drug Hydroxyurea, Nelarabine and, Asparaginase are just used for leukemia treatment leukemia. However, the underlying mechanism of how the drug Hydroxyurea, Nelarabine and Asparaginase work against leukemia needs more investigation.

And in Fig. 9, the major enriched pathway is transcriptional misregulation in cancer according to the KEGG analysis. Correlation(Consistent)with the results in Fig. 6b, in LGG, the expression of TMEM91 correlated with the degree of methylation. Studies show that DNA methylation generally functions as a repressive transcriptional signal. The methylated DNA may physically hinder the binding of transcription factors to genes, then structurally inhibit the transcription of genes in chromatin and may also interact with other transcription inhibitors^{[30, 31]}. Gene transcription is suppressed by these possible processes. Therefore, Downregulation of TMEM91 in most tumors might caused by the methylation of DNA.

In addition, we also explored the role of TMEM91 in glioma cells U87. CCK8 and flow cytometry assays showed that the proliferation ability of cells was significantly increased (Fig. 9c) and apoptosis was suppressed by overexpression TMEM91 in U87 cells (Fig. 9d). These results are consistent with the conclusion in Fig. 3, when TMEM91 expressed lowly in LGG, the cancer patients had better outcomes. And, we demonstrated that overexpression of TMEM91 showed significantly decreased levels of p-AKT, p-ERK. It suggested that TMEM91 might promote cancer cell migration and invasion via inhibiting the phosphorylation of AKT/ERK protein. ^[32–36].

In conclusion, we conducted a comprehensive TMEM91 pan-cancer analysis, showing its potential function as a cancer prognosis biomarker and effective immunotherapy response prediction. The abnormal expression of TMEM91 was related to the prognosis, immune cell infiltration, and DNA methylation in many kinds of tumors. we also verified the cancer-promoting effect of TMEM91 in glioma cells. Even though we explored and incorporated information from several databases, there was still some limitations in the present study. Further investigation should do confirm the clinical application of TMEM91. However, the bioinformatic analysis offered us some important insights into TMEM91 in cancers. It's worth thinking about whether TMEM91 has an effect on the AKT/ERK pathway by regulating the activity of methylation modifiers.

Gene expression analysis of TMEM91

Using TCGA combined with Genotype Tissue Expression (GTEx) cohort,TMEM91 expression was analyzed between tumors and corresponding normal tissues in 28 tumors via Sangerbox (http://SangerBox.com/Tool)^[37], including glioblastoma multiforme (GBM), brain lower grade glioma (LGG), uterine corpus endometrial carcinoma (UCEC), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), lung adenocarcinoma (LUAD), esophageal carcinoma (ESCA), kidney renal papillary cell carcinoma (KIRP), colon adenocarcinoma (COAD), prostate adenocarcinoma (PRAD), stomach adenocarcinoma (STAD), head and neck cancer (HNSC), 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) rectum adenocarcinoma (READ), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), testicular germ cell tumors (TGCT), uterine carcinosarcoma (UCS),acute myeloid leukemia (LAML) ,pheochromocytoma and paraganglioma (PCPG) ,adrenocortical carcinoma (ACC) ,kidney chromophobe (KICH) ,cholangiocarcinoma (CHOL).

The gene expression data involved tumor cell line was collected from Cancer Cell Line Encyclopedia (CCLE) database (https://portals.broadinstitute.org/ccle)

Survival Analysis

Cox regression analysis was used to look at the relationship between TMEM91 expression and patients’ overall survival (OS) in each cancer type using the TCGA databases. After separating patients into high and low TMEM91 expression groups through the best separation method, the Kaplan–Meier method was used to create the survival curves of patients in each cancer type. The survival were investigated via Sangerbox (http://SangerBox.com/Tool)^[37]

The overall survival (OS) and disease-free survival (DFS) survival map data for TMEM91 in various tumor types in TCGA database were obtained via GEPIA2 online website (http://gepia.cancer-pku.cn)^[38]. The high-expression and low-expression cohorts of TMEM91 was obtained through the expression threshold of the cutoff-high (50%) and cutoff-low (50%) values. The “Survival Analysis” module of GEPIA2 was used to analyze special survival plots with log-rank P-values.

UALCAN

The UALCAN database (http://ualcan.path.uab.edu/analysis.html) was used to investigate TMEM91 differential expression in diverse cancers with individual cancer groups. The significance of differences was evaluated using Student's t-test, and p < 0.05 was considered statistically significant

Immune Cell Infiltration Enrichment

Tumor Immune Estimation Resource (TIMER) (https://cistrome.shinyapps.io/timer) is a database-driven web application that calculates immune cell infiltration scores for diverse immune cell types^[39], including CD4 + Tcells,CD8 + Tcells,B cells, cancer-associated fibroblast(CAF), macrophage, neutrophil, NK cells, ,cancer myeloid progenitor, endothelial cell(Endo),hematopoietic stem cell(HSC), T cell follicular helper(Tfh),nature killer T cell(NKT),T cell regulatory(Tregs). TIMER has previously computed and saved the immune cell infiltration scores of pan-cancer data from the TCGA database. The infiltration data were retrieved and examined to see if there was a link between TMEM91 expression and infiltration. Sangerbox website (http://SangerBox.com/Tool)^[37], which is a useful online platform for TCGA data analysis, was used to explore relationship between TMEM91 expression and Immune Checkpoint (ICP) Genes, tumor mutation burden (TMB) and microsatellite instability (MSI).

TMEM91 CNV and Methylation Profile in Pan-Cancer Based on GSCA

Gene Set Cancer Analysis (GSCA) platform is a web server that integrates multiomics data based on the TCGA database (http://bioinfo.life.hust.edu.cn/web/GSCA/)^[40]. Copy number variation (CNV), methylation, are among the studies offered by GSCA^[41]. Correlation between mRNA expression quantity of TMEM91 and CNV in different tumors analyzed by GSCA website. Also, the correlation between the amount of mRNA expression and the degree of methylation of TMEM91 in different tumors was analyzed by GSCA website.

Protein-Protein Interaction Network Construction

The GeneMANIA (http://genemania.org/) is an interactive and user-friendly website for building a protein-protein interaction(PPI) network, which provides gene function prediction hypotheses and identifies gene with comparable roles ^{[42, 43]}. Gene oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of molecules with potential roles in TMEM91 were performed using cluster Profiler packages.

TMEM91 and Drug Response

CellMiner (http://discover.nci.nih.gov/cellminer/) suggested a link between TMEM91 expression and drug response. CellMiner is generated by the Genomic and Pharmacology Facilit, DTB, CCR, NCI, NIH. It is a database and query tool designed for the cancer research community to facilitate integration and study of molecular and pharmacological data for the NCI-60 cancerous cell lines. The NCI-60, a panel of 60 diverse human cancer cell lines used by the Developmental Therapeutics Program of the U.S. National Cancer Institute to screen over 100,000 chemical compounds and natural products (since 1990).

Cell Culture

Glioma cell line(U87) were obtained from the Shanghai life Academy of Sciences Cell Library (Shanghai China). U87 cells were grown at 37°C and 5% CO2 in Dulbecco’s modified Eagle’s medium (DMEM; gibco, United States) supplemented with 10% foetal bovine serum. TMZ was obtained from Sigma-Aldrich Corporation.

Plasmid Construction and Cell Transfection

Overexpression plasmid of TMEM91(CMV-GFP-TMEM91) was constructed and stored in our laboratory (Chongqing, China). The CMV-GFP-CTL vector was used as an internal control. CMV-GFP-TMEM91 and CMV-GFP-CTL were transfected in U87 cells by applying LipofectamineTM2000 according to the manufacturer’s protocol.

CCK8 Detection

U87 cells transfected with GFP-TMEM91 were digested when their level reached 90%, and then they were inoculated into 96-well culture plates with 3×103cells per well, and 5 multiple wells were designed for each group. Then, they were cultured in a 37°C, 5% CO2 incubator, and tested at 0h, 24h, 48h and 72h using the CCK-8 kit (WLA074, China).

Flow Cytometry Assay

For cell apoptosis assay, specific operating procedures refer to the manufacturer’ s protocols. Cell apoptosis was then measured by using the flow cytometer (BD biosciences, NJ, USA). In graph, the four quadrants respectively stand for necrotic cells, viable cells, early stage apoptotic cells and late stage apoptotic cells.

Western Blotting Analysis

Glioma cells were lysed in 300 µL SDS sample buffer (Sangon Biotech, China) containing 1 mM phosphatase inhibitor and 1 mM PMSF, and denatured proteins (20 µg) were resolved on 15% SDS PAGE gels and transferred to PVDF membranes. After blocking with 5% milk at room temperature, the membranes were incubated with the primary antibody overnight at 4°C, followed by incubation with a horseradish peroxidase–conjugated secondary antibody for 2 h at room temperature. After washing the membranes thrice with TBST, ECL Reagent (Sangon Biotech, China) was added for chemiluminescent detection.5. Conclusions

This section is not mandatory but can be added to the manuscript if the discussion is unusually long or complex.

This section is not mandatory but may be added if there are patents resulting from the work reported in this manuscript.

Author Contributions: ZJ involved in data collection process of the study and drafting the manuscript. LQ and TS took part in the design and are responsible for the content of the manuscript. All authors read and approved the final manuscript.

Funding: This research was funded by the Natural Science Foundation of Chongqing, grant number No.cstc2021jcyj-msmX0019”and this research was funded by the Science and Technology Research Project of Chongqing Municipal Education Commission of China, grant number No.KJQN202000438”

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Data Availability Statement: The original contributions presented in the study are included in the article Material. Further inquiries can be directed to the corresponding author.

Acknowledgments: Thanks to the Key Laboratory of Clinical Laboratory Diagnostics of Chongqing Medical University for providing equipment for molecular biology experiments

Conflicts of Interest: The authors declare no conflict of interest.

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No competing interests reported.

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A Pan-cancer Analysis of the Role of the Transmembrane Protein 91(TMEM91) in Human Tumors

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Abstract

Figures

1. Introduction

2. Results

2.1. The Expression Levels of TMEM91 are Aberrant Across Various Normal and Cancer Tissues

2.2. Analysis of Link Between TMEM91 Expression Level and Prognosis

2.3. Pan-Cancer Analysis of the Correlation between TMEM91 Expression and Clinicopathology

2.4. Immune Cell Infiltration Analysis of TMEM91 Across Cancers

2.5. Correlations Between TMEM91 and Immunomodulators ,TMB

2.6. Pan-Cancer Analysis of the Mutation Profile and the Methylation Level of TMEM91

2.7 PPI Network of TMEM91 in Cancers and Enrichment Analysis

2.8. TMEM91 and Drug Response

2.9. Clinical Correlation Analysis of TMEM91 in glioma

3. Discussion

4. Materials And Methods

5. Patents

Declarations

References

Additional Declarations

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