Prostaglandin F2 receptor inhibitor (PTGFRN) a cell adhesion molecule, promotes cell growth and migration in glioblastoma

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults exhibiting inltration into surrounding tissues, recurrence, and resistance to therapy. GBM inltration is accomplished by many deregulated factors such as cell adhesion molecules (CAMs), which are membrane proteins that participate in cell-cell and cell-ECM interactions to regulate survival, proliferation, migration, and stemness. A comprehensive bioinformatics analysis of CAMs (n = 518) in multiple available datasets revealed genetic and epigenetic alterations among CAMs in GBM. Univariate Cox regression analysis using TCGA dataset identied 127 CAMs to be signicantly correlated with survival. The poor prognostic indicator PTGFRN was chosen to study its role in glioma. Silencing of PTGFRN in glioma cell lines was achieved by stable expression of short hairpin RNA (shRNA) against PTGFRN gene. PTGFRN was silenced and performed cell growth, migration, invasion, cell cycle, and apoptosis assays were performed. Neurosphere and limiting delusion assays were also performed after silencing of PTGFRN to know its role in GSCs.


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Background Glioblastoma (GBM) is the most common primary malignant tumor of brain neoplasia in adults and GBM patients confer worst prognosis with the median survival of around one year (1). GBM cells in ltrate into the brain parenchyma and are responsible for the complications encountered in surgery, therapy, and the lethality of the disease (2).
Adhesion of cells to a substratum (ECM) and to one another is primarily accomplished by a family of surface proteins called cell adhesion molecules (CAMs), which are involved in regulation of normal development and pathology of diseases including cancer (3). Initially, CAMs were depicted as tumor suppressors; however, many reports emphasized their oncogenic functions in several cancers including glioma (4,5).
Though, several reports emphasize the importance of individual CAMs in GBM initiation and progression, none of them attempted to overview all the CAMs. In the present study, we analysed 518 CAMs for their transcriptional changes in GBM using multiple datasets and explored the probable causes for the deregulation. Further, we also provide experimental evidence to establish the pro-proliferative, promigratory function, and regulation of PTGFRN, an upregulated CAM, in GBM.

Compilation of CAMs
A list of manually curated 518 CAMs (Supplementary Table 1) was prepared from various sources such as Entrez query 'CAMs and homo sapiens', GO term annotations related to cell adhesion and literature was used for analysis in this study.

Differential expression analysis
The gene expression data for GBM was downloaded from TCGA, REMBRANDT, GSE7696, and GSE22866. The differential expression was calculated by subtracting average value of control samples from average of GBM samples. Statistical signi cance was tested using Wilcox Mann-Whitney test. Genes showing fold change ≤-0.58 or ≥0.58 and signi cant p-value (p-value≤0.05) were considered as differentially expressed.

Survival Analysis
All CAMs were subjected for univariate cox regression analysis using TCGA Agilent dataset and SPSS software. GraphPad Prism software 5.0 was used for the Kaplan Meier survival analysis.

Copy number variation
Copy number variation of CAMs was analysed using data from cBioPortal (http://www.cbioportal.org/) and calculated the percentage of samples in which a particular CAM ampli ed or deleted.

Methylation data analysis
The CpGs corresponding to differentially expressed genes were fetched from TCGA DNA Methylation dataset (Illumina In nium Human DNA methylation 450K array). The data for control samples was taken from GSE79122. For each probe, the differential beta value was calculated by subtracting the average beta value in control from the average beta value in the GBM. In order to identify differentially methylated probes, the difference of >0.3 absolute beta value. Wilcox Mann-Whitney test was used to calculate statistical signi cance.

MicroRNA
The differentially regulated CAMs were used as an input for miRwalk. The miRNAs which were predicted by minimum, 7 or more algorithms to target the CAMs were taken to further analysis. The miRNAs that were reciprocally regulated with respect to their target CAMs were considered as miRNA and CAM pairs. Cell lines, normal brain tissues, and plasmids Glioma cell lines (U373, T98G, U251, U87, LN229, U343, LN18, A172, and U138), immortalized astrocytes (NHA and SVG), and 293T were cultured in DMEM (Sigma-Aldrich, #D5648) supplemented with 10% Fetal Bovine Serum (FBS), Penicillin, and Streptomycin. U251, U87, U373, T98G, and 293T were bought from ECACC. LN229 and NHA were gifted from late Dr. Abhijit Guha (University of Toronto, Canada). Patient tumor derived primary GSC lines MGG8, MGG6, and MGG4 were procured from Dr. Wakimoto H.
(Massachusetts General Hospital, Boston, USA) and 1035 was obtained from Dr. Santosh Kesari (University of California, San Diego, USA) and were cultured as neurospheres.
Non-tumorous control brain tissue samples (N1-N5) procured from patients with intractable epilepsy during surgery at National Institute of Mental Health and Sciences (NIMHANS), Bangalore, India. The tissues were snap-frozen in liquid nitrogen and stored at −80 °C and used for RNA isolation. The tissue samples were obtained after informed and taken written consent from all patients prior to use in the study. The study has been approved by the ethics committee of the NIMHANS and Indian Institute of Science (IISc). In the present study control brain tissue samples used for isolation of RNA and to measure PTGFRN transcript levels.

Lentivirus preparation and transduction
293T cells transfected with shRNA (4 µg) along with the helper plasmids pSPAX and pMD2.G (3:1) using Lipofectamine 2000 (Invitrogen, #11668019) transfection reagent in 60 mm dish. The media was changed after 5 h of transfection and supernatant, which contain virus particles, was collected after 60 h of transfection and the virus was used to infect glioma cells in presence of 8 µg/ml of Polybrene (Sigma, #107689).

cDNA conversion and qPCR
The total RNA was isolated by using Trizol (Sigma-Aldrich, #T9424) method. RNA was converted to cDNA by using cDNA conversion kit (Life Technologies, #4368813). Subsequently, RT-qPCR was performed and fold change was calculated by ΔΔct method.

Proliferation assay
The cell viability was assessed by trypan blue assay. Brie y, the cells expressing control shRNA or shRNA against PTGFRN were plated in 6 well plate, 10 4 cells per well. Cell viability checked on every 3 rd day using Vi-cell counter (Beckman Coulter, #383722) and normalization was done using the reading of day 1 as control for each condition. Statistical analysis was done using Student's t-test.

Colony suppression assay
The cells stably expressing shRNA were counted and plated in a 6 well plate in triplicates at the seeding density of 10 3 cells/well and grown for 2-3 weeks replacing the media every 2-3 days once. Colonies were xed in chilled methanol overnight followed by staining with crystal violet (0.05% w/v) for 30 min. Colonies were quanti ed either by manual counting or counting using ImageJ software. The statistical signi cance was calculated by Student's t-test.

Soft agar colony formation assay
In the soft agar assay, cells were counted and plated in 10 4 cells/well in 1.2 ml of 0.4% of low melting agarose (BD Biosciences, #214230) in 6 well plate containing 0.6% agarose base layer. Each condition was plated in triplicates. After 2-3 weeks, images were taken and quanti cation was performed. Statistical analysis was done using Student's t-test.

Migration and invasion assays
Trans-well Matrigel invasion assay is an in-vitro method to assess the ability of cells to degrade extracellular matrix proteins in response to a stimulus. Migration of cancer cells was assayed in 24 well Boyden chamber with 8 µm pore size polycarbonate membrane (BD Biosciences, San Diego, USA). For invasion assay the membranes, precoated with matrigel (a mix of extracellular matrix proteins) were used (BD bioscience, Sandiego, USA). The matrigel layer serves as a reconstituted basement membrane which occludes the pores. Cells (5x10 4 ) were resuspended in 500 μl serum-free medium and placed in the upper chamber, and the lower chamber was lled with 600 μl medium with 10% FBS (serving as a chemoattractant). Cells were incubated for 22 hours and after incubation, the cells remaining on the upper surface of the membrane were removed by wiping with a wet cotton bud. The cells on the lower surface of the membrane were xed in chilled methanol and stained with 0.05% crystal violet and counted under the light microscope. The statistical signi cance was calculated by student's t-test.

Cell cycle analysis by Flow cytometry
For analysing the distribution of cells in different phases of cell cycle both the oating and adherent cells were used. The adherent cells were washed with PBS and harvested by trypsinization. The single cell suspension was pelleted down and resuspended in 300 μl of PBS and xed with chilled 100% ethanol (700 μl) by adding drop by drop while gentle vertexing. Cells were xed by incubating them in -20°C overnight. Incubation was followed by re-pelleting of cells and complete removal of ethanol by two PBS washes and nally followed by treatment with RNaseA (10 µg/ml) for 2-3 h at 37°C. Cells were stained with propidium iodide (PI), 10 μg/ml and subjected to ow cytometry using FACSVERSE instrument (BD Biosciences) to assay the effects on cell cycle pro le.
Annexin V-FITC (Fluorescein isothiocyanate) and PI staining Annexin V-FITC/PI double staining was utilized to quantify apoptosis in each condition. The apoptotic cells were determined under different conditions by ow cytometry based analyses using Annexin V-FITC Apoptosis kit from Biovision (K-101) following the manufacturer's instructions. The percentage of healthy population, early/late apoptotic population and necrotic population was determined from each condition and percentage of apoptotic cells was plotted.
Neurosphere assay and Limiting dilution assay GSC cells were infected with lentivirus expressing either NTshRNA or shRNA against PTGERN or ASTN1. After 48 h of infection, the sphere aggregates formed were dissociated into single cells, counted and plated 10 4 cells/well in ultra-low attachment 6 well dish and cultured for 7 days. Fresh medium was replenished every 2-3 days. Number of spheres were counted after 7 days of plating and plotted graphs.
For limiting dilution assay, neurospheres were dissociated into single cells and counted. Cells were plated in ultra-low attachment 96 well plates wherein a range of cells (1, 10, 25, 50, 75, and 100 cells/well) were plated into 8 wells for each condition. Fresh medium was replenished every 2-3 days. After 7-10 days, wells forming the spheres were counted in control knockdown and gene knockdown condition. Graph was plotted using ELDA (Extreme Limiting Dilution Analysis) software.

Regulated CAMs in GBM
To elucidate the deregulation of CAMs in GBM, we manually curated a comprehensive list of 518 CAMs (Supplementary Table 1) from various sources such as gene Entrez query 'CAMs and homo sapiens' and other related publications in the literature [20,21]. We performed an integrated bioinformatics analysis to nd out the deregulated CAMs in GBM and the reasons for their deregulation (Supplementary Fig. 1). To identify the transcriptional changes in CAMs in GBM, we analysed the expression of CAMs in TCGA dataset (n=582) and found a total of 181 CAMs to be differentially regulated in GBM as compared to control samples. Among the deregulated CAMs, 88 were signi cantly upregulated and 93 were signi cantly downregulated (Fig. 1A). We found nearly an equal percentage (48% and 52%) of CAMs differentially expressed in GBM as compared to control. We observed similar results in the REMBRANDT dataset ( Supplementary Fig. 2A) and this was also validated in GSE22866 and GSE7696 datasets. We observed that 40-79% of 181 CAMs to be similarly differentially regulated in these datasets ( Supplementary Fig. 2B, 2C, and Supplementary Table 2). Thus, we identi ed differentially regulated CAMs in GBM indicating that CAMs might be playing both oncogenic and tumor suppressor functions in GBM.
We examined the possible mechanisms behind the differential regulation of CAMs in GBM. The copy number variation analysis in TCGA data showed that out of the 88 upregulated CAMs, 5 were ampli ed and among 93 downregulated CAMs, 7 were deleted in more than 1% of tumors ( Supplementary Fig. 3A and Supplementary Table 3). Thus, 5.68% of upregulated CAMs were found to be ampli ed, whereas 7.52% of downregulated CAMs were found to be deleted at their gene loci. Further, we examined the role of epigenetic mechanisms that might lead to the differential regulation of CAMs. Analysis of differential methylation using TCGA methylation data uncovered that out of the 181 deregulated CAMs, 32 CAMs showed differential methylation in GBM as compared to control. We identi ed that 12 genes are upregulated corresponding to the 18 hypomethylated CpGs and 20 genes are downregulated corresponding to 28 hypermethylated CpGs ( Supplementary Fig. 3B and Supplementary Table 4). This was also validated in our patient cohort (GSE79122) and GSE60274 wherein most of the genes were similarly differentially methylated (Supplementary Fig. 3C). Therefore, we identi ed 6.6% of upregulated CAMs to be hypomethylated and 11% of downregulated CAMs to be hypermethylated.
It is well established that the gene transcript levels can be regulated by miRNAs. We investigated to identify the downregulated miRNAs that are predicted to target upregulated CAMs and upregulated miRNA that are predicted to target downregulated CAMs. We used TCGA expression data for miRNA and mRNA and identi ed 9 downregulated miRNAs can putatively target 52 upregulated CAMs, and 5 upregulated miRNAs can putatively target 43 downregulated CAMs in GBM (Supplementary Fig. 3D and Supplementary Table 5). Thus, 59% of the upregulated CAMs and 46.2% of the downregulated CAMs were regulated by miRNAs suggesting that miRNAs play a pivotal role in the regulation of differentially expressed CAMs apart from copy number variation and DNA methylation (Supplementary Fig. 3E).

Prostaglandin F2 receptor inhibitor (PTGFRN) is upregulated in GBM and is a poor prognostic indicator
Towards predicting the prognostic values of CAMs, we carried out univariate cox regression analysis using TCGA dataset. This analysis identi ed 127 CAMs to be signi cantly correlated with survival. Among them, 84 CAMs were found to correlate to poor prognosis and 43 CAMs were found to correlate to good prognosis in GBM (Supplementary Table 6). A transmembrane scaffolding protein PTGFRN, which predicted poor prognosis in GBM, was chosen for in-depth investigation due to various following reasons. First, patients with high PTGFRN transcripts had signi cantly lower median survival compared to those with low PTGFRN transcripts (Fig. 1B). Second, PTGFRN transcript levels were found to be signi cantly upregulated in GBM as compared to control brain samples in multiple datasets (Fig. 1C). Third, it was observed that PTGFRN expression was found to be signi cantly upregulated: in classical and mesenchymal subtypes as compared to neural and pro-neural subtypes (Fig. 1D), in wild type IDH1 tumors as compared to mutant IDH1 tumors (Fig. 1E), and in G-CIMP-tumors as compared to G-CIMP+ tumors of GBM (Fig. 1F). It is reported that wild type IDH1 and G-CIMP-subtypes of GBM are more aggressive as compared to their counterparts (21). However, no signi cant difference in expression of PTGFRN was found between MGMT methylated and unmethylated groups of GBM (Fig. 1G). Further, the transcript level of PTGFRN was found to be upregulated in majority of the glioma cell lines as compared to control brain tissues and SVG (Fig. 1H). The protein levels were found to be varying but distinctly higher in T98G, A172, LN229, U373, and U343 as compared to SVG (Fig. 1I). Thus, the results so far indicate that the PTGFRN is highly upregulated in GBM, associated with wild type IDH1 and G-CIMP-GBMs and is a predictor of poor prognosis in GBM.

PTGFRN expression is regulated by promoter DNA methylation and miR-137 in GBM
The analysis of mechanisms behind the deregulation of CAMs in GBM revealed that PTGFRN might be regulated by methylation and miRNAs. We carried out PTGFRN promoter methylation analysis and found that two CpG probes predicted to be hypomethylated signi cantly in GBM as compared to control in TCGA 450K methylation array, GSE60274 methylation dataset (27K array), and GSE79122 methylation dataset (450K array) (Fig. 3A). We performed correlation analysis between PTGFRN transcript level and promoter CpG methylation in GBM and found a signi cant negative correlation between expression and methylation (Fig. 3B). Further, miRNA predicting algorithms in miRwalk (http://zmf.umm.uniheidelberg.de/apps/zmf/mirwalk2/) predicted that miR-137 can target 3'UTR of PTGFRN and it was found to be signi cantly downregulated in GBM as compared to control (Fig. 3C). We performed correlation analysis between expression of PTGFRN and miR-137 and found a signi cant negative correlation between expression of PTGFRN and miR-137 in GBM (Fig. 3D). It was observed that miR-137 putatively targets two speci c sites in 3'UTR of PTGFRN (Fig. 3E). The overexpression of miR-137 dramatically reduced the protein levels of PTGFRN in U373 (Fig. 3F). These results suggest that the promoter hypomethylation and miR-137 downregulation might be responsible for upregulation of PTGFRN in GBM.

PTGFRN is upregulated in glioma stem-like cells (GSCs) and is essential for GSC growth
In order to dissect the role of CAMs in GSCs, we investigated the expression of CAMs in GSE54791 and GSE46016 datasets, which carry the transcriptome pro le of GSCs and their corresponding differentiated glioma cells (DGCs) and neural stem cells (NSCs). Bioinformatics analysis revealed that 14 CAMs were signi cantly upregulated, and 8 CAMs were signi cantly downregulated speci cally in GSCs when compared to both NSCs and DGCs (Fig. 4A). Since we found PTGFRN to be one of the most upregulated CAMs in GSCs, we analysed its expression in various GSC datasets. We found that PTGFRN transcript levels to be signi cantly upregulated in GSCs as compared to NSCs (GSE46016) and DGCs (GSE54791) (Fig. 4B). Further, we also observed that PTGFRN protein levels found to be more in GSCs such as MGG8, MGG6, MGG4, and 1035 than their corresponding DGCs (Fig. 4C). Silencing PTGFRN in MGG6, MGG8, and U343 signi cantly reduced neurosphere formation as assessed by neurosphere assay and limiting dilution analysis (Fig. 4D, 4E, 4F, Supplementary Fig. 5A, 5B). From these results, we conclude that PTGFRN is upregulated in GSCs and is essential for GSC growth.

Discussion
Alteration in CAMs is one of the main reasons for enhanced migration and invasion in GBM (22). In this study, we identi ed nearly 35% of CAMs analysed were found to be differentially regulated in GBM. Our results corroborate with previous reports that CAMs exhibit both oncogenic and tumor suppressor functions in GBM (24,25). Further, we explored the mechanism behind the differential expression of CAMs in GBM. More than 50% of deregulated CAMs identi ed to be targeted by miRNA indicating their critical role in the deregulation of CAMs. Some of these miRNA-mRNA pairs have already been reported to play a role in glioma biology (25).
We found that PTGFRN expression to be high in glioma tissues, cell lines, and GSCs and its high expression is correlating with patient poor survival and our results corroborated with previous reports (19,27). We observed that PTGFRN is required for cell growth, migration, invasion, progression of cell cycle, and evading apoptosis in glioma cells and these results suggest its oncogenic role in glioma. It was reported that PTGFRN plays an essential role in muscle regeneration (14) and cell migration in HEK293T (11) and leukocytes (10). Hence, its high expression could be protecting cells from apoptosis, and thereby promoting growth and migration in GBM. An abnormal activation of PI3K/AKT/mTOR signaling has been implicated in tumor initiation, progression, and therapeutic resistance in glioma (27). We found that silencing PTGFRN reduced ERK, AKT, and mTOR signaling in glioma cell lines. This suggests that PTGFRN could be functioning through transmembrane receptor signaling that further modulate prosurvival and promigratory signaling pathways in cancer (28). Our results were further strengthened by the recent reports wherein PTGFRN was found to be conferring resistance to radiation via PI3K-AKT signaling in GBM (18) and its expression correlated with metastatic capacity in lung cancer (16). We identi ed PTGFRN expression could be regulated by promoter hypomethylation and downregulation of miR-137 which is predicted to target PTGFRN in GBM. Our results concur with the ndings of previous report with respect to miR-137 (29).

Conclusion
This study offers a comprehensive overview of deregulation of CAMs and probable reasons for their regulation in GBM. This study also uncovers for the rst time about the promigratory role and regulation of PTGFRN in GBM apart from its role in cell growth. Studying the role of CAMs in GBM biology may be useful for targeting tumor in ltration and PTGFRN can be used as a therapeutic target to treat invasive GBM.
KS and UM; conceived the idea, KS; arranged the funding, UM and TB; designed experiments, performed experiments, and analysed data. UM and KS wrote the manuscript and all the authors read and approved the manuscript.