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Research Article
Kumaravel Somasundaram
Indian Institute of Science Bangalore
Corresponding Author
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Background
Glioblastoma (GBM) is the most common primary malignant brain tumor in adults exhibiting infiltration into surrounding tissues, recurrence, and resistance to therapy. GBM infiltration 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.
Methods
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 identified 127 CAMs to be significantly 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.
Results
Among the differentially regulated CAMs (n = 181, 34.9%), major proportion of them were found to be regulated by miRNAs (n = 95, 49.7%) followed by DNA methylation (n = 32, 16.7%), and gene copy number variation (n = 12, 6.2%). We found that PTGFRN (Prostaglandin F2 receptor inhibitor) to be upregulated in GBM tumor samples and cell lines with a significant poor prognostic correlation with patient survival. Silencing PTGFRN diminished cell growth, colony formation, anchorage-independent growth, migration, and invasion and also led to cell cycle arrest and induction of apoptosis. At mechanistic level, silencing of PTGFRN reduced pro-proliferative and promigratory signaling pathways such as ERK, AKT, and mTOR. PTGFRN upregulation was found to be due to loss of its promoter methylation and downregulation of miR-137 in GBM. PTGFRN was also found to be higher in glioma stem-like cells (GSCs) and is required for GSC growth and survival.
Conclusion
In this study, we provide a comprehensive overview of the differential regulation of CAMs and causes for their deregulation. We also establish an oncogenic role of PTGFRN in GBM thus signifying it as a potential therapeutic target.
Keywords
Glioblastoma, CAM, PTGFRN, GSC, Growth, Migration
Figure 1
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Figure 4
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
- AdditionalFileRawblotsCAMsandPTGFRNinGliomaBMCCancer.pdf
- Malaetal.SupplementaryTables16.xlsx
- Supple.Fig.1.pdf
Supple. Fig. 1. Flow chart describing the use of various datasets to identify the importance of CAMs for glioma development and progression CAMs (n=518) derived from various sources that included CAMs family genes based on protein domain structures such as cadherin, integrin, and immunoglobulin and Gene Ontology (GO) terms related to cell adhesion and ‘CAMs and Homo sapiens’ key word query against NCBI Entrez annotations and various literature, manually curated and compiled and were used in this study. The first and second branch depicts the use of TCGA Agilent dataset to identify differentially expressed CAMs in GBM followed by the possible causes of their differential regulation and survival analysis, respectively. The last branch identifies the GSC specific CAMs over both NSCs and DGCs. Functional studies were carried out for PTGFRN which is deregulated in GBM and GSCs and it indicates poor prognosis. The number in brackets shows the number of samples used for analysis. GSCs: Glioma-like stem cells, NSCs: Normal neural stem cells, DGCs: Differentiated glioma stem cells, CNV: Copy number variation.
- Supple.Fig.2.pdf
Supple. Fig. 2. Differentially regulated CAMs in GBM (A, B, and C) Heatmaps indicating differentially regulated CAMs in REMBRANDT, GSE22866, and GSE7696, respectively.
- Supple.Fig.3.pdf
Supple. Fig. 3. Role of CNV, methylation, and miRNA in the regulation of CAMs in GBM (A) Waterfall plot depicting the copy number variation in CAMs altered in more than 1.5% samples of GBM. Each vertical line represents one sample. Red denotes amplification and blue denotes deletion of the CAMs. The number represents the proportion of the samples in which the CAM is amplified or deleted. (B) Heatmaps representing the differentially regulated CAMs which are also differentially methylated. The yellow colour indicates the hypermethylated CpGs (n=18) which corresponds to downregulated genes (n=20) shown in green, right. The blue colour depicts the hypomethylated CpGs (n=28) which corresponds to upregulated genes (n=12) shown in red, right. (C) Tabular illustration represents the CAMs and the putative targeting miRNAs. Differentially expressed miRNAs predicted to target the CAMs were identified using miRwalk. Only those miRNAs which were predicted to target the CAMs in seven or more than seven algorithms in miRwalk and having reciprocal regulation as compared to targeted CAMs are shown. The green or red box indicates the predicted miRNA-CAM targeting pair, whereas the empty box indicates non-targeting miRNA-CAM pair. Left: upregulated CAMs predicted to be targeted by downregulated miRNAs; right: downregulated CAMs predicted to be targeted by upregulated miRNAs. (D) Venn diagrams depicting the summary of CAMs regulation, top: the number of upregulated CAMs regulated by the 3 factors individually and in combination, bottom: the number of downregulated genes regulated by the 3 factors individually and in combination.
- Supple.Fig.4.pdf
Supple. Fig. 4. Knockdown of PTGFRN reduces cell growth, migration and invasion in GBM (A) Western blot represents the protein levels of PTGFRN after silencing PTGFRN with either shPTGFRN or shNT in U343, T98G, U251, and U87 and β-Actin was used as loading control (required portion of the blot is shown after cropping from the whole blot). (B) Line graphs show the relative cell viability in U87, U343, T98G, and U251, (C) representative images of colonies in U343 and T98G, (D) images show the relative soft agar colonies in U343 and U251, (E) representative images of migration, and (F) invasion in U251 after silencing PTGFRN and quantification showed as bar graphs. (G) Histograms represent the DNA content by PI staining to assess Cell cycle in T98G after silencing of PTGFRN and bar graph represents the percent of cells in different phases of the cell cycle. (H) Flow cytometry dot plots represent the annexin-V positive cell population in U251 after silencing PTGFRN and quantification showed as bar diagrams. Student’s t-test was performed to test the statistical significance and the symbols are shown. (ns) not significant; (*) p≤0.05; (**) p≤0.01 and (***) p≤0.001.
- Supple.Fig.5.pdf
Supple. Fig. 5. PTGFRN is upregulated in GSCs and required for its survival (A) Bar graph show the transcript levels of PTGFRN after silencing of PTGFRN either with shPTGFRN or shNT in U343 and (B) representative images of neurospheres and their quantification. Student’s t-test was performed to test the statistical significance and the symbols are shown. (ns) not significant; (*) p≤0.05; (**) p≤0.01 and (***) p≤0.001.
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This preprint is under consideration at BMC Cancer. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Kumaravel Somasundaram
Indian Institute of Science Bangalore
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 1
Posted 11 Mar, 2021
No community comments so far
Editorial decision: Major revision
On 16 Apr, 2021
Review #5 received
Received 10 Apr, 2021
Review #4 received
Received 10 Apr, 2021
Review #3 received
Received 10 Apr, 2021
Reviewer #8 agreed
On 06 Apr, 2021
Reviewer #7 agreed
On 06 Apr, 2021
Reviewer #6 agreed
On 06 Apr, 2021
Reviewer #5 agreed
On 06 Apr, 2021
Reviewer #4 agreed
On 06 Apr, 2021
Review #18 received
Received 30 Mar, 2021
Review #17 received
Received 30 Mar, 2021
Review #11 received
Received 30 Mar, 2021
Review #12 received
Received 30 Mar, 2021
Review #13 received
Received 30 Mar, 2021
Review #2 received
Received 30 Mar, 2021
Review #10 received
Received 30 Mar, 2021
Review #16 received
Received 30 Mar, 2021
Review #15 received
Received 30 Mar, 2021
Review #14 received
Received 30 Mar, 2021
Review #1 received
Received 30 Mar, 2021
Reviewer #2 agreed
On 10 Mar, 2021
Reviewer #13 agreed
On 10 Mar, 2021
Reviewer #37 agreed
On 10 Mar, 2021
Reviewer #3 agreed
On 10 Mar, 2021
Reviewer #36 agreed
On 10 Mar, 2021
Reviewer #19 agreed
On 10 Mar, 2021
Reviewer #30 agreed
On 10 Mar, 2021
Reviewer #31 agreed
On 10 Mar, 2021
Reviewer #32 agreed
On 10 Mar, 2021
Reviewer #1 agreed
On 10 Mar, 2021
Reviewer #12 agreed
On 10 Mar, 2021
Reviewer #18 agreed
On 10 Mar, 2021
Reviewer #21 agreed
On 10 Mar, 2021
Reviewer #29 agreed
On 10 Mar, 2021
Reviewer #35 agreed
On 10 Mar, 2021
Reviewer #34 agreed
On 10 Mar, 2021
Reviewer #33 agreed
On 10 Mar, 2021
Reviewer #11 agreed
On 10 Mar, 2021
Reviewer #20 agreed
On 10 Mar, 2021
Reviewers invited
Invitations sent on 10 Mar, 2021
Editor assigned
On 10 Mar, 2021
Editor invited
On 10 Mar, 2021
Submission checks complete
On 10 Mar, 2021
First submitted
On 20 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Cancer Biology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Glioblastoma (GBM) is the most common primary malignant brain tumor in adults exhibiting infiltration into surrounding tissues, recurrence, and resistance to therapy. GBM infiltration 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.
Methods
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 identified 127 CAMs to be significantly 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.
Results
Among the differentially regulated CAMs (n = 181, 34.9%), major proportion of them were found to be regulated by miRNAs (n = 95, 49.7%) followed by DNA methylation (n = 32, 16.7%), and gene copy number variation (n = 12, 6.2%). We found that PTGFRN (Prostaglandin F2 receptor inhibitor) to be upregulated in GBM tumor samples and cell lines with a significant poor prognostic correlation with patient survival. Silencing PTGFRN diminished cell growth, colony formation, anchorage-independent growth, migration, and invasion and also led to cell cycle arrest and induction of apoptosis. At mechanistic level, silencing of PTGFRN reduced pro-proliferative and promigratory signaling pathways such as ERK, AKT, and mTOR. PTGFRN upregulation was found to be due to loss of its promoter methylation and downregulation of miR-137 in GBM. PTGFRN was also found to be higher in glioma stem-like cells (GSCs) and is required for GSC growth and survival.
Conclusion
In this study, we provide a comprehensive overview of the differential regulation of CAMs and causes for their deregulation. We also establish an oncogenic role of PTGFRN in GBM thus signifying it as a potential therapeutic target.
Figure 1
Figure 2
Figure 3
Figure 4
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