Multiomics Analysis of TMEM200A as a Pan-Cancer Biomarker

Summary

Here, a protocol is presented in which multiple bioinformatic tools are combined to study the biological functions of TMEM200A in cancer. In addition, we also experimentally validate the bioinformatics predictions.

Abstract

The transmembrane protein, TMEM200A, is known to be associated with human cancers and immune infiltration. Here, we assessed the function of TMEM200A in common cancers by multiomics analysis and used in vitro cell cultures of gastric cells to verify the results. The expression of TMEM200A in several human cancer types was assessed using the RNA-seq data from the UCSC Xena database. Bioinformatic analysis revealed a potential role of TMEM200A as a diagnostic and prognostic biomarker.

Cultures of normal gastric and cancer cell lines were grown and TMEM200A was knocked down. The expression levels of TMEM200A were measured by using quantitative real-time polymerase chain reaction and western blotting. In vitro loss-of-function studies were then used to determine the roles of TMEM200A in the malignant behavior and tumor formation of gastric cancer (GC) cells. Western blots were used to assess the effect of the knockdown on epithelial-mesenchymal transition (EMT) and PI3K/AKT signaling pathway in GC. Bioinformatic analysis showed that TMEM200A was expressed at high levels in GC.

The proliferation of GC cells was inhibited by TMEM200A knockdown, which also decreased vimentin, N-cadherin, and Snai proteins, and inhibited AKT phosphorylation. The PI3K/AKT signaling pathway also appeared to be involved in TMEM200A-mediated regulation of GC development. The results presented here suggest that TMEM200A regulates the tumor microenvironment by affecting the EMT. TMEM200A may also affect EMT through PI3K/AKT signaling, thus influencing the tumor microenvironment. Therefore, in pan-cancers, especially GC, TMEM200A may be a potential biomarker and oncogene.

Introduction

Cancer has emerged as a persistent public health issue endangering human health globally¹due to its high morbidity and mortality rates worldwide, posing a heavy financial and medical burden to society². Significant advancements in cancer therapy have been achieved in recent years thanks to the discovery of cancer markers³, and researchers have developed novel diagnostic methods and new drugs to treat cancer. However, some patients with cancer still have poor prognoses because of factors such as medication resistance, side effects of drugs, and chemical sensitivity⁴. Therefore, there is an urgent need for identifying new biomarkers for screening and treating early-stage cancers⁵.

Membrane proteins are proteins that can bind and integrate into cells and organelle membranes⁶. These can be grouped into three categories depending on the strength of binding to the membrane and their location: lipid-anchored proteins, integral proteins, and peripheral membrane proteins^7,8. A transmembrane (TMEM) protein is an integral membrane protein that consists of at least one transmembrane segment⁹, which passes either completely or partially through the biological membrane.

Although the mechanisms of action of proteins belonging to the TMEM family are not well understood, these proteins are known to be involved in several types of cancers¹⁰. Several TMEM proteins are associated with migratory, proliferative, and invasive phenotypes, and their expression is often associated with a patient's prognosis¹¹. Therefore, TMEM family members have become the target of research. A comprehensive review of existing reports on TMEM revealed that they are mostly associated with inter- and intracellular signaling¹², immune-related diseases, and tumorigenesis¹⁰. Many TMEMs also possess important physiological functions, for example, ion channels in the plasma membrane, activation of signal transduction pathways, as well as the mediation of cell chemotaxis, adhesion, apoptosis, and autophagy¹⁰. Therefore, we hypothesized that TMEM proteins may be important prognostic markers in the detection and treatment of tumors.

TMEM200A expression is significantly elevated in gastric cancer (GC). Higher expression of TMEM200A¹³, which has eight exons and a full length of 77.536 kb on chromosome 6q23.1, has been linked to a poor prognosis for overall survival (OS) in cases of GC. Yet the changes in its expression have rarely been reported in oncology studies. This article compares and analyzes the usefulness of TMEM200A as a therapeutic target and tumor diagnostic marker in various cancer studies using different publicly available datasets. We assessed the effectiveness of TMEM200A as a pan-cancer diagnostic and prognostic biomarker as well as its expression levels in various human cancer types using RNA-seq data from the UCSC Xena and TCGA databases, as well as by real-time quantitative polymerase chain reaction (qRT-PCR) and western blotting.

The effect of TMEM200A expression levels on mutation rates, regulatory processes, tumor diagnosis and prognosis, immune infiltration, and immunotherapy was further investigated using a mix of computational tools and dataset websites. CBioPortal and the Catalog of Somatic Mutations in Cancer Cells (COSMIC) databases were used to examine mutations in TMEM200A. Sangerbox and TISIDB websites were utilized to understand how TMEM200A influences immune infiltration. The Tumor Immune Single Cell Center (TISCH) online tool and CancerSEA database were used to investigate the function of TMEM200A. Finally, to assess the impact of TMEM200A on the malignant behavior and tumor development function of GC cells, a loss-of-function experiment was conducted in an in vitro assay. Additionally, western blotting was performed to assess how TMEM200A knockdown affected the PI3K/AKT signaling pathway and the epithelial-mesenchymal transition (EMT) in GC.

Protocol

1. The Cancer Genome Atlas (TCGA) database

NOTE: The Cancer Genome Atlas (TCGA) database contains the sequencing data of genes in different tumor tissues¹⁴. RNA-seq data in TCGA for the study of TMEM200A transcripts per part per million (TPM) formats were extracted from the UCSC Xena website¹⁵ (https://xenabrowser. net/datapages/) and log2 transformed for comparing the expressions between samples.

1. Go to the UCSC Xena website interface.
2. Click on the Launch Xena tab.
3. Click on the DATA SETS tab at the top of the screen.
4. From the 129 cohorts and 1,571 datasets on this page, click on the tab for a total of 33 cancers such as GDC TCGA Acute Myeloid Leukemia (LAML), GDC TCGA Adrenocortical Cancer (ACC), GDC TCGA Bile Duct Cancer (CHOL), and more.
5. From the gene expression RNAseq menu, select and click on the HTSeq - FPKM GDC Hub tab.
   NOTE: HTSeq - FPKM GDC Hub represents data that have been corrected by consent.
6. From the download menu, click on its corresponding link.
   NOTE: By the above method, RNA-Seq data were downloaded for 33 different cancer types.
7. Unpack the zip archive of RNA-seq data for 33 different cancer types in a single file in the desktop folder of the computer.
8. Unify the unzipped txt files in the same folder and place the Perl scripts in this folder.
9. Open the Perl software, copy and paste the path of the folder into the Perl software where the mouse cursor is located, press the Enter key, enter the name of the Perl code and the name of the gene (TMEM200A), and then press the Enter key to get a new txt file(singleGeneExp.txt).
   NOTE: This new txt file (singleGeneExp.txt) contains gene expression of TMEM200A in 33 cancers in different patients.
10. Open the R code (diffR) and copy and paste the path where the singleGeneExp.txt file is located to the line where setwd is located in the R code.
11. Open the R software, run the modified R code (diffR), and draw the picture through the "ggpubr" package.

2. The TIMER2.0 database

1. Go to the TIMER2.0 (http://timer.cistrome.org) database ¹⁶ website interface.
2. Click the Cancer Exploration tab.
3. Enter the gene ID (TMEM200A) in the search box and click the Submit tab to get the differential expression images of TMEM200A in different types of tumors.

3. Human Protein Atlas (HPA)

1. Go to the Human Protein Atlas (HPA) (www.proteinatlas.org) ¹⁷web interface.
2. Type ID (TMEM200A) in the search box and click the Search button. Select TISSUE sub-atlas.
3. Hover over the page and scroll down to find the Protein Expression Overview tab.
   ​NOTE: The Protein Expression Overview section shows the protein expression levels of TMEM200A in 45 organs in the human body.

4. The HumanMethylation450 Illumina Infinium DNA methylation platform array

NOTE: The HumanMethylation450 Illumina Infinium DNA methylation platform array was used to collect data on methylation. We could assess the TMEM200A DNA methylation levels using the SMART (http://www.bioinfo-zs.com/smartapp/) database¹⁸.

1. Go to the SMART website interface.
2. Enter the gene ID (TMEM200A) in the search box to obtain the distribution of TMEM200A in chromosomes and the methylation level of TMEM200A in different types of malignancies.
3. Scroll down the page to the Click to check CpG-aggregated methylation menu and click the Plot button. At the bottom of the page, find and click on the Download Figure button. Download the figure.

5. The UALCAN database

1. Go to the UALCAN website interface.
   NOTE: Box plots of the TMEM200A promoter methylation levels in various malignancies were generated through the UALCAN database (http://ualcan.path.uab.edu/analysisprot.html)¹⁹
2. At the top of the page, click on the TCGA button.
3. Enter TMEM200A in the Gene ID input box on the screen. In the TCGA dataset menu, scroll down and select the type of cancer to be queried.
4. After clicking the Explore button, click on its subgroup analysis Methylation in the Links for analysis menu to get the methylation results of this tumor.
   ​NOTE: By this method, we obtained methylation results for 22 tumors in the UALCAN database.

6. The Catalog of Somatic Mutations in Cancer Cells (COSMIC) database

1. Go to the Catalog of Somatic Mutations in Cancer Cells (COSMIC) website interface.
   NOTE: Data on coding mutations, noncoding mutant genomic rearrangements, and fusion genes in the human genome are available from the Catalog of Somatic Mutations in Cancer Cells (COSMIC) database²⁰ (https://cancer.sanger.ac.uk/cosmic/), which is also used to determine the frequency of various TMEM200A mutations in various cancers.
2. Enter the gene ID (TMEM200A) in the search box and click the SEARCH button to go to a new screen.
3. In the Genes menu, locate and click on the link where the gene ID name of the TMEM200A appears.
4. Find the Mutation distribut grouping column on the new page to get the mutation status of TMEM200A in the tumor.

7. CBioPortal

1. Go to the CBioPortal website interface.
   NOTE: Cancer genomics data may be found, downloaded, analyzed, and visualized using the cBioPortal (www.cioportal.org) database. Somatic mutations, DNA copy number changes (CNAs), mRNA and microRNA (miRNA) expression, DNA methylation, protein abundance, and phosphoprotein abundance are only a few of the genomic data types that are integrated by cBioPortal²¹. With cBioPortal, a broad range of studies may be carried out; however, the major emphasis is on different analyses relating to mutations and their visualization.
2. Select the Pan-cancer analysis of whole genomes dataset from the PanCancer Studies subsection of the Select Studies for Visualization & Analysis section. Then, click the Query By Gene button.
3. Enter the target gene ID (TMEM200A) in the query box of Enter Genes. Click the Submit Query button.
4. Click on the Cancer Type Detailed button at the top of the page to get the mutations of TMEM200A in various types of cancers.

8. Sangerbox 3.0 tool

1. Go to the Sangerbox 3.0 tool interface.
   NOTE: The correlation of TMEM200A expression with immune infiltrating cells, immunosuppressive agents, immunostimulatory factors, and MHC molecules (major histocompatibility complex) in all cancers was analyzed by CIBERSORT immune infiltration using the pan-cancer immune infiltration data in Sangerbox 3.0 tool²² (http://vip.sangerbox.com/).
2. From the toolbar on the left side of the page, select and click on the Immunocellular Analysis (CIBERSORT) tab from the Pan-Cancer Analysis menu.
3. Enter the target gene ID (TMEM200A) in the query box of Enter Genes. Click the Submit button.

9. TISIDB database

1. Go to the TISIDB website interface.
   NOTE: The TISIDB database²³ (http://cis.hku.hk/TISIDB/) was employed to examine the correlation of TMEM200A expression with immune infiltrating cells, immunosuppressive agents, immunostimulatory factors, and MHC molecules in various cancers.
2. Enter the target Gene Symbol (TMEM200A) in the query box of Enter Genes. Click the Submit button.
3. Click on the Lymphocytes, Immunomodulators, Chemokines, and Subtype sections at the top of the page. Download the relevant images at the bottom of the page.

10. The TIDE database

1. Go to the TIDE website interface.
   NOTE: The TIDE database (http://tide.dfci.harvard.edu/)was used to evaluate the potential of TMEM200A as a biomarker for predicting response to tumor immune checkpoint blockade therapy.
2. Enter the email address on the initial screen to register the account and log in.
3. Find the Biomarker Evaluation subsection at the top of the page and click on it.
4. Enter the target gene ID (TMEM200A) in the query box for Enter Genes at the bottom of the page. Then, click the Submit button.
5. In the page, drag the mouse to slide the page down to find the results of the analysis.

11. The CancerSEA database

1. Go to the CancerSEA website interface.
   NOTE: Using single-cell sequencing data, the relationships between TMEM200A expression and the functional state of different tumor cells were investigated. This was done using the CancerSEA database²⁴ (http://biocc.hrbmu.edu.cn/CancerSEA/).
2. At the top of the page, find and click on the Search tab.
3. Enter the target gene ID (TMEM200A) in the query box of Enter Genes. Click the Submit button.

12. The tumor immune single-cell center (TISCH) network tool

1. Go to the tumor immune single-cell center (TISCH) network tool website interface.
   NOTE: The correlation between the expression levels of TMEM200A and cancer cells was also shown by using the tumor immune single-cell center (TISCH) network tool²⁵.
2. Enter the target gene ID (TMEM200A) in the query box of Enter Genes. Click the Explore button.
3. In the Cancer type menu on this page, select and click on the All Cancers dataset. Then, scroll down the page and click the Search button.

13. GeneMANIA

1. Go to the GeneMANIA website interface.
   NOTE: The correlation between TMEM200A and its functionally relevant genes was predicted using the integration strategy for the gene multi-association network Website GeneMANIA (www.genemania.org)²⁶ for constructing gene-gene interaction (GGI) networks.
2. In the search box at the top left of the page, enter the gene ID (TMEM200A) and click Search Tools.
   ​NOTE: The GeneMANIA web tool was used to find 20 genes associated with TMEM200A.

14. Functional enrichment analysis

1. Save the symbol IDs of the genes to be analyzed for enrichment in txt file format.
2. Open the R code (symbolidR) and copy and paste the path where the above txt file is located to the line where setwd is located in the R code.
3. Open the R software and run the modified R code (symbolidR). Convert the symbol ids of these genes to entrezIDs via the "org.Hs.eg.db" package. Get the id.txt file.
4. Open the R code (GOR and KEGGR) and copy and paste the path where the id.txt file is located to the line where setwd is located in the R code.
5. Open the R software and run the modified R code (GOR and KEGGR). To follow this protocol, analyze these genes for GO and KEGG enrichment by the packages "clusterProfiler," "org.Hs.eg.db," and "enrichplot" and plot the enrichment results using the package "gglot2".

15. Analysis of the differences in gene activity

NOTE: TMEM200A scoring in each cancer sample was calculated using ssGSEA (single-sample gene set enrichment analysis)²⁷, and the differential analysis of TMEM200A gene activity in cancerous and healthy tissues was performed for various cancers.

1. Based on the RNA-seq data of 33 tumor types downloaded in step 1.7, unzip all downloaded files and place them in a unified folder.
2. Place the merge.txt file in the above folder.
   NOTE: The data in merge.txt file from BioWolf (www.biowolf.cn) contains gene expression for all patients in all tumors in The Cancer Genome Atlas (TCGA) database.
3. Open the R code (ssGSEAR) and copy and paste the path where the above folder is located to the line where setwd is located in the R code.
4. Take the line where geneName is in the R code (ssGSEAR) and enter the gene ID (TMEM200A).
5. Open the R software and run the modified R code(ssGSEAR). Get the scoring file for gene activity: scores.txt.
   NOTE: With the ssGSEA algorithm, we first construct a gene set file based on the correlation coefficients between genes according to the data provided in the merge.txt file and identify the genes with a higher correlation with the target gene (TMEM200A) from the file. Then, the genes with higher correlation are unified as the active genes of TMEM200A, and finally, we get the scoring of active genes in each sample.
6. Open the R code (scoreDiffR) and copy and paste the path where the scores.txt file is located to the line where setwd is located in the R code.
7. Open the R software, run the modified R code(scoreDiffR), and draw the picture through the "plyr," "reshape2," and "ggpubr" packages.

16. Clinicopathological correlation and survival prognosis analysis

1. Go to the UCSC Xena website interface.
2. Click the Launch Xena tab.
3. Click on the DATA SETS tab at the top of the screen.
4. Hover over the page and scroll down to find the TCGA Pan-Cancer (PANCAN) tab.
5. From the 129 cohorts and 1,571 datasets on this page, click the TCGA Pan-Cancer (PANCAN) tab.
6. From the phenotype menu, select and click on the Curated clinical data tab.
7. From the download menu, click on its corresponding link.
   NOTE: Download clinical data for 33 cancers from the TCGA database at the above link.
8. Unzip the downloaded clinical data file and open the unzipped file in xls file format.
9. Organize and retain the needed clinical data (stage, age, pathological stage, and patient status) in the file, delete the remaining unneeded clinical data, and save the entire file as a txt format file after renaming it clinical.
10. Based on the singleGeneExp.txt obtained in step 1.9, put this file in the same folder as the organized clinical.txt file.
11. Unzip the downloaded clinical data and place the singleGeneExp.txt and clinical.txt files in the same folder as the unzipped clinical files.
12. Open the R code (clinicalDiffR) and copy and paste the path where the above folder is located to the line where setwd is located in the R code.
13. Open the R software, run the modified R code (clinicalDiffR), and draw the picture using the "ggpubr" package.
14. Go to the UCSC Xena website interface.
15. Click the Launch Xena tab.
16. Click on the DATA SETS tab at the top of the screen.
17. From the 129 cohorts and 1,571 datasets on this page, click on the tab for a total of 33 cancers such as GDC TCGA Acute Myeloid Leukemia (LAML), GDC TCGA Adrenocortical Cancer (ACC), GDC TCGA Bile Duct Cancer (CHOL), and more.
18. From the phenotype menu, select and click on the survival data tab.
19. From the download menu, click on its corresponding link.
20. Unpack the zip archive of survival data for 33 different cancer types in a single file in the desktop folder of the computer.
21. Put the unzipped survival data in the same folder as the singleGeneExp.txt file obtained in step 1.9.
22. Open the R code (preOSR) and copy and paste the path where the above folder is located to the line where setwd is located in the R code.
23. Open the R software and run the modified R code (preOSR). Calculate through the "limma" package to get a data file about the survival of the TMEM200A in pan-cancer: expTime.txt.
24. Open the R code (OSR) and copy and paste the path where the expTime.txt file is located to the line where setwd is located in the R code.
25. Open the R software and run the modified R code (OSR). Perform a KM analysis using the "survival" and "survminer" packages based on the data in the expTime.txt file and plot the survival curves for TMEM200A in pan-cancer.

17. Univariate and multivariate Cox regression analyses with forest plot construction

1. Open the R code (COXR) and copy and paste the path where we got the expTime.txt file from 16.23 is located to the line where setwd is located in the R code.
2. Open the R software and run the modified R code (COXR). Based on the data in the expTime.txt file, perform univariate COX regression analyses using the "survival," "survminer," and "forestplot" packages to plot a Univariate -forest plot of TMEM200A in different cancers.
3. Put the expTime.txt file from step 16.23 and clinical.txt from step 16.10 in the same folder.
4. Open the R code (multicoxR) and copy and paste the path where the folder containing the expTime.txt file from step 16.23 and clinical.txt file from step 16.10 is located to the line where setwd is located in the R code.
5. Open the R software and run the modified R code (multicoxR). Perform a multivariate COX regression analysis using the "survival" and "survminer" packages to plot a multivariate-forest plot of TMEM200A in different cancers based on the data in the expTime.txt and clinical.txt files.

18. Prognostic model of gastric cancer based on TMEM200A expression and clinical features

1. Put the clinical.txt file from step 16.10 and the expTime.txt file from step 16.23 in the same folder.
2. Open the R code (NomoR) and copy and paste the path where the above txt file is located to the line where setwd is located in the R code.
3. Open the R software and run the modified R code (NomoR). Use the software packages "survival," "regplot," and "rms" for TMEM200A expression data in GC and clinical data to construct a prognostic model to predict patient survival.

19. Cell culture and siRNA transfection of TMEM200A

NOTE: Human STAD HGC-27 cells, SGC-7901 cells, and human gastric mucosal epithelial GES-1 cells were obtained commercially (see the Table of Materials), revived, inoculated in Roswell Park Memorial Institute (RPMI) 1640 complete medium (containing 10% neonatal fetal bovine serum and 1% penicillin mixture), and cultured at 37 °C in a 5% CO₂ cell culture incubator. The culture medium was changed every 2-3 days. The cells in good growth condition were inoculated in six-well plates according to (2-3) × 10⁵ cells per well.

1. First, take three microcentrifuge tubes and add 8 µL of transfection reagent (see the Table of Materials) and 200 µL of basal medium to each tube. Then, add 4 µg of SiRNA1, SiRNA2, and SiRNA3 to each tube respectively.
   NOTE: For TMEM200A knockdown, siRNA was designed and purchased (see the Table of Materials).
2. Mix the mixture in the three microcentrifuge tubes thoroughly and add to each of the three wells of the 6-well plate. Gently shake the 6-well plate to distribute the mixture evenly. Label the three wells where the mixture was added as SiRNA-1, SiRNA-2, and SiRNA-3.
3. When the cells have been incubated for 4-6 h, replace half of the medium in the three sub-wells in the 6-well plate.
   NOTE: When replacing half of the complete medium, aspirate half of the original complete medium and replenish with half of the fresh complete medium.
4. Twenty-four to 48 h later, use TMEM200A siRNA-transfected cells as the experimental group and untransfected cells as the negative control group. Perform quantitative real-time polymerase chain reaction (qRT-PCR, see section 20) to assess the effect of transfection on the cells.

20. Quantitative real-time polymerase chain reaction

1. Discard the cell culture medium in each subgroup and gently wash the cells twice with 1 mL of phosphate-buffered saline (PBS).
2. Isolate total RNA using an RNA isolation kit (see the Table of Materials).
3. Estimate the RNA concentration and synthesize cDNA with 1 µg of RNA using a cDNA Synthesis Kit, following the manufacturer's instructions.
4. Perform quantitative real-time PCR (qRT-PCR) on 10-fold dilutions of cDNA in 10 µL of reaction mix, including human-specific forward and reverse primers for GAPDH (for standardization), TMEM200A (see the Table of Materials), and qPCR Master Mix, using the Real-Time PCR Assay System.
   NOTE: Perform each experiment in triplicate.
5. Use the following qPCR reaction conditions: initialization, 95 °C for 3 min; denaturation, 95 °C for 10 s; annealing, 60 °C for 30 s; and extension, 80 °C for 10 s; repeat the denaturation, annealing, and extension 40x. Determine the relative expression of each gene using the 2^-ΔΔCt technique²⁸.
6. Repeat the experiments at least three times to obtain biological triplicates.

21. Western blot detection of relevant protein expression

1. Discard the cell culture medium in each subgroup and gently wash the cells with 2 x 1 mL of PBS.
2. Cool the 6-well plates containing the cells on ice and add 150 µL of precooled radio immune precipitation assay (RIPA) lysis buffer (150 mM NaCl, 0.1% Triton X-100, 0.5% sodium deoxycholate, 0.1% SDS, 50 mM Tris-HCl pH 8.0, and freshly added protease inhibitor cocktail). Allow the lysis to proceed on ice for 10 min.
3. Utilizing a plastic cell scraper, remove adhering cells from the dish and delicately transfer the cell solution into a microcentrifuge tube that has been precooled.
4. Centrifuge the cell lysate for 10 min at 1.5 × 10⁴ g at 4 °C. Transfer the supernatant to a new 1.5 mL microcentrifuge tube.
5. Utilize a BCA protein assay Kit to determine the protein content according to the manufacturer's instructions.
6. Load 30 g of protein from each sample on a 10% sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) gel and run at 80 V for 0.5 h followed by 1.5 h at 120 V.
7. Transfer the proteins from the gel to a 45 µm polyvinylidene difluoride (PVDF) membrane at a voltage of 300 mA for 1-1.5 h.
8. After placing the PVDF membrane on a shaker and shaking it for 3 x 5 min, add Tris-buffered saline with Tween 20 (TBST, 20 mM Tris-HCl, pH 7.4, 150 mM NaCl, and 0.1% Tween 20) to the appropriate container with the protein side (gel side) facing up.
9. Place the membrane in the blocking buffer (see the Table of Materials) and incubate it for 0.5 h at room temperature.
10. Wash the membrane with TBST for 3 x 10 min.
11. Incubate the membrane with primary antibodies against phospho-AKT (p-AKT; 1:1,000), total AKT 1:1,000, E-cadherin (E-ca; 1:1,000), N-cadherin (N-ca; 1:1,000), Vimentin 1:1,000, snail 1:1,000, TMEM200A 1:1,000, glyceraldehyde 3-phosphate dehydrogenase (GAPDH; 1:1,000), overnight at 4 °C.
12. Wash the membrane for 3 x 10 min and incubate the membrane with a rabbit or mouse secondary antibody (1:5,000) for 1 h at room temperature.
13. Incubate the PVDF membrane with ECL substrate for 30 s and detect the signal using an imaging system.

22. CCK-8 assay

1. Seed human STAD HGC-27 cells in good growth condition in 96-well plates.
2. When the cell density reaches 60-70%, transfect the cells with TMEM200A siRNA (as in step 19.3).
3. Seed the transfected cells into 96-well plates at a cell density of 5 × 10 ³/well (count viable cells using a cell counter), divided into NC and TMEM200A siRNA groups (with three replicate wells in each group), and incubated at 37 °C.
4. Add CCK-8 reagent after 0, 24, 48, 72, and 96 h and incubate at 37 °C for 2 h. Measure the absorbance (450 nm) using a multifunctional enzyme labeler.

Results

Expression of TMEM200A in various cancers
As illustrated in Figure 1, we first analyzed the differential expression levels of TMEM200A in various cancers through different databases. TMEM200A expression was elevated in cholangiocarcinoma (CHOL), head and neck squamous cell carcinoma (HNSC), renal clear cell carcinoma (KIRC), renal papillary cell carcinoma (KIRP), hepatocellular carcinoma (LIHC), STAD, and thyroid carcinoma (T...

Discussion

TMEM200A belongs to a family of TMEMs that is essential for cancer cells to proliferate³⁸. The variable expression of TMEM200A in different malignancies has received less attention, and a thorough pan-cancer investigation is lacking. However, evidence continues to accumulate, showing that the TMEM transmembrane protein family may be important in keeping cancer cells malignant through interactions with several proteins, for example, activation of TMEM16A Ca²⁺-activated ...

Materials

Name	Company	Catalog Number	Comments
Anti-AKT antibody	Proteintech Group, Inc	60203-2-Ig
Anti-E-cadherin antibody	Proteintech Group, Inc	20874-1-AP
anti-glyceraldehyde 3-phosphate dehydrogenase (GAPDH) antibody	Proteintech Group, Inc	10494-1-AP
Anti-N-cadherin antibody	Proteintech Group, Inc	22018-1-AP
Anti-P-AKT antibody	Proteintech Group, Inc	66444-1-Ig
Anti-snail antibody	Proteintech Group, Inc	13099-1-AP
Anti-Vimentin antibody	Proteintech Group, Inc	10366-1-AP
AxyPrepMultisourceTotalRNAMini- prep Kit	Suzhou Youyi Landi Biotechnology Co., Ltd	UEL-UE-MN-MS-RNA-50G
BCA Protein Assay Kit	Epizyme Biotech	ZJ101L
CCK-8 reagent	MedChemExpress	HY-K0301-500T
Fetal bovine serum (FBS)	CYAGEN BIOSCIENCES (GUANGZHOU) INC	FBSSR-01021
GAPDH primer	Sangon Biotech (Shanghai) Co., Ltd.		Forward primer (5’-3’): TGACATCAAGAAGGTG GTGAAGCAG; Reverse primer (5’-3’): GTGTCGCTGTTGAAG TCAGAGGAG
HighGene plus Transfection reagent	ABclonal	RM09014P
HRP-conjugated Affinipure Goat Anti-Mouse lgG (H+L)	Proteintech Group, Inc	SA00001-1
HRP-conjugated Affinipure Goat Anti-Rabbit lgG (H+L)	Proteintech Group, Inc	SA00001-2
Human gastric mucosal epithelial GES-1 cells	Guangzhou Cellcook Biotech Co.,Ltd.
Human STAD HGC-27 cells	Procell Life Science&Technology Co.,Ltd
Human STAD SGC-7901 cells	Procell Life Science&Technology Co.,Ltd
MonAmp SYBR Green qPCR Mix (None ROX)	Mona (Suzhou) Biotechnology Co., Ltd	MQ10101S
MonScript RTIII All-in-One Mix with dsDNase	Mona (Suzhou) Biotechnology Co., Ltd	MR05101M
Omni-ECL Femto Light Chemiluminescence Kit	Epizyme Biotech	SQ201
PAGE Gel Fast Preparationb Kit	Epizyme Biotech	PG111
Penicillin-streptomycin (Pen-Strep)	Beijing Solarbio Science & Technology Co.,Ltd	P1400-100
Polyvinylidene difluoride (PVDF) membrane	Merck KGaA	IPVH00010-1
Protein Free Rapid Blocking Buffer	Epizyme Biotech	PS108P
RIPA lysis solution	Beijing Solarbio Science & Technology Co., Ltd	R0010
RPMI 1640 complete medium	Thermo Fisher Scientific	C11875500BT
Skimmed milk	Campina: Elk
TBST buffer solution	Beijing Solarbio Science & Technology Co., Ltd	T1082
The protein loading buffer	Epizyme Biotech	LT101S
TMEM200A knockdown plasmid	MiaoLing Plasmid
TMEM200A primer	Sangon Biotech (Shanghai) Co., Ltd.		Forward primer (5’-3’): AAGGCGGTGTGGTGGTTCG; Reverse primer (5’-3’): GATTTTGGTCTCTTTGTCACGGTT
TMEM200A SiRNA1	MiaoLing Plasmid		Forward primer (5’-3’): ACAACTGATGATAAGACCAG; Reverse primer (5’-3’): TGTTGACTACTATTCTGGTC
TMEM200A SiRNA2	MiaoLing Plasmid		Forward primer (5’-3’): CGTGTGAATGTCAATGACTG; Reverse primer (5’-3’): GCACACTTACAGTTACTGAC
TMEM200A SiRNA3	MiaoLing Plasmid		Forward primer (5’-3’): ACAACCACAACATCTGCCCG; Reverse primer (5’-3’): TGTTGGTGTTGTAGACGGGC
Transmembrane protein 200A Antibody	Proteintech Group, Inc	48081-1
Equipment
CO2 cell culture incubator	Haier Group	PYXE-80IR
Electrophoresis instrument	Bio-RAD
Fluorescence quantitative PCR instrument	Bio-RAD
Gel Imaging System (Tanon 5200)	Tanon Science & Technology Co., Ltd	LAB-0002-0007-SHTN
Multifunctional Enzyme Labeler	Berthold