|
 
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| ORIGINAL ARTICLE |
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| Year : 2023 | Volume
: 7
| Issue : 1 | Page : 37-47 |
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Gene enrichment analysis and protein–protein interaction network topology delineates S-Phase kinase-associated protein 1 and catenin beta-1 as potential signature genes linked to glioblastoma prognosis
K Ashwini1, Pavan Gollapalli2, Shilpa S Shetty1, Ananthan Raghotham3, Praveenkumar Shetty4, Jayaprakash Shetty5, N Suchetha Kumari4
1 Central Research Laboratory, KS Hegde Medical Academy (KSHEMA), Nitte (Deemed to be University), Mangalore, Karnataka, India
2 Division of Bioinformatics and Computational Genomics, Nitte University Center for Science Education and Research (NUCSER), Nitte (Deemed to be University), Deralakatte, Mangalore, Karnataka, India
3 Department of Neurosurgery, KS Hegde Medical Academy (KSHEMA), Nitte (Deemed to be University), Mangalore, Karnataka, India
4 Central Research Laboratory; Department of Biochemistry, KS Hegde Medical Academy (KSHEMA), Nitte (Deemed to be University), Mangalore, Karnataka, India
5 Department of Pathology, KS Hegde Medical Academy (KSHEMA), Nitte (Deemed to be University), Mangalore, Karnataka, India
| Date of Submission | 29-Nov-2022 |
| Date of Decision | 15-Dec-2022 |
| Date of Acceptance | 25-Jan-2023 |
| Date of Web Publication | 14-Mar-2023 |
Correspondence Address:
N Suchetha Kumari
Department of Biochemistry, KS Hegde Medical Academy, Nitte (Deemed to be University), Deralakatte, Mangaluru - 575 018, Karnataka
India
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/bbrj.bbrj_344_22
Background: Glioblastoma (GBM) is the most malignant and accounts for 60% of brain tumors in adults. Current therapy for GBM involves surgical removal of the tumor followed by radiotherapy with concomitant adjuvant therapy temozolomide. Despite improvements in therapy, patient survival remains low. The exact etiology of a brain tumor is uncertain, and numerous unknown genes are involved in the progression of GBM. The aim of the present study was to evaluate various genes involved in GBM through bioinformatic approach. Methods: In the present study, gene expression profile of GSE68424 was retrieved from the GEO database to explore the genes in GBM. Results: Analysis of expression profile data revealed that 33 genes were upregulated and 1189 genes were downregulated based on the log2 fold change cut-off criteria. The genes S-Phase kinase-associated protein 1 (SKP1) and Catenin beta-1 (CTNNB1) have been linked to GBM prognosis. Conclusion: SKP1 and CTNNB1 were identified as a candidate gene for GBM study as a result of these findings. Catenin beta-1 was the protein with the highest closeness centrality value and is the key component of canonical Wnt signaling downstream pathway. More study is needed to establish the molecular function of SKP1 and CTNNB1 in GBM development, as well as the biomarker's specificity and sensitivity.
Keywords: Catenin beta-1, gene ontology, glioblastoma, protein–protein interaction, S-Phase kinase associated protein 1
How to cite this article:
Ashwini K, Gollapalli P, Shetty SS, Raghotham A, Shetty P, Shetty J, Kumari N S. Gene enrichment analysis and protein–protein interaction network topology delineates S-Phase kinase-associated protein 1 and catenin beta-1 as potential signature genes linked to glioblastoma prognosis. Biomed Biotechnol Res J 2023;7:37-47 |
How to cite this URL:
Ashwini K, Gollapalli P, Shetty SS, Raghotham A, Shetty P, Shetty J, Kumari N S. Gene enrichment analysis and protein–protein interaction network topology delineates S-Phase kinase-associated protein 1 and catenin beta-1 as potential signature genes linked to glioblastoma prognosis. Biomed Biotechnol Res J [serial online] 2023 [cited 2023 Apr 1];7:37-47. Available from: https://www.bmbtrj.org/text.asp?2023/7/1/37/371696 |
| Introduction | |  |
Glioblastoma (GBM), the most malignant brain tumor, accounts for 60% of brain tumors in adults.[1] They are most common in individuals between the ages of 45 and 65 years, where males are more likely to develop than females. The current treatment includes surgical removal of the tumor, followed by radiotherapy with concomitant adjuvant therapy temozolomide (TMZ),[2] and then another 6 cycles of TMZ treatment.
In general, the prognostic biomarkers in GBM are age, neurological status, Isocitrate dehydrogenase (IDH) mutation, and MGMT promoter methylation.[3],[4] Primary GBM affects more than 80% of people over the age of 65 years, with endothelial growth factor receptor (EGFR) overexpression, PTEN (Phosphatase and Tensin Homolog) mutation, CDKN2A (p16) deletion, and MDM2 amplification being the most common features (less frequent). Secondary GBM develops from a lower-grade glioma or oligodendroglioma that develops at a young age (mean age 45) and has a TP53 mutation.[5] IDH mutation (IDH 1 and IDH 2) observed 70%–80% in low-grade glioma and secondary GBM and is about 5%–10% in primary GBM.[6] Apart from these genes, there are many unknown genes whose functions need to be flourished in identifying therapy for GBM. Due to the enormous threat of GBM to human health, the treatment remains a significant challenge.
A large amount of genomics and proteomics investigations have been done in recent years to investigate the molecular pathways underlying the formation and progression of GBM. GBM characterization has offered essential information about this molecularly diverse disease. Recent advancements in high-throughput microarrays have received attention and have helped medical biology to reassemble the gene regulatory network. Gene expression variations between normal and disease tissues were discovered using microarray analysis. Unveiling the disease process has remained a key issue in GBM research due to the underlying drawbacks of microarray technology, such as small sample size, measurement inaccuracy, and information insufficiency. As a result, the mechanisms behind this disease have been identified using gene ontology (GO), route information, network-based techniques, and machine learning algorithms.[7]
| Methods | | |
Microarray data
GEO is a public database of high-throughput gene expression data, chips, and microarrays. The gene expression profile of GSE68424 was obtained from the GEO database. This study aims to understand the inhibitory effect of microRNA-10b on gene expression in neurosphere cultures of GBM stem-like cells. GSE68424 contains total of 18 samples: GBM cells transfected with control oligonucleotide biological replicate 1 and 2, GBM cells treated with Lipofectamine 2000 biological replicate 1 and 2, GBM cells transfected with anti-2'-O-MOE-PO oligonucleotide to micro RNA 10b biological replicate 1 and 2 based on GPL570 platform (HG_U133_ Plus_2) Affymetrix Human Genome U133 Plus 2.0 Array.
Extraction of differentially expressed genes
GEO2R, an online tool for GEO databases, was used to conduct this study. The analysis was used to divide the samples into three categories. The differentially expressed genes (DEGs) were then characterized as differentially expressed with log FC against log Exp, with a P < 0.05 threshold was set as statistically significant.
Protein-protein interaction network construction
The STRING database (http://string-db.org) was used to create the protein–protein interaction (PPI) network, which included all genes/proteins and all neighbor interactions.[8] To begin, network interactions connected with human proteins were created based on the seed proteins. With a high confidence level of 0.7, interactions were calculated using text mining, experiments, databases, co-expression, neighborhood, gene fusion, and co-occurrence. The network was then visualized using Cytoscape 3.3.0, which was used to determine node characteristics and perform measurements with default parameters.[9]
Calculating topology parameters for giant network
A network of differentially connected nodes can be used to represent the molecular organization.
The edges reflect dynamic interactions and each node represents a protein. As a result, nodes receive input and output values in the form of mathematical functions.[10] Network analyzer v3.3.1 was used to evaluate the confidence of the interacting network with power law fit of the form y = axb. The betweenness centrality (BC), closeness centrality (CC), and topology correlation coefficient scores are considered to be the network topology parameter. Furthermore, the neighborhood connectivity and shortest path length distributions are considered. A greater BC value suggests that a node has more control over the network's information flow. As a result, BC values are often effective indications for detecting network bottlenecks. The inverse of the average length of the shortest paths to/from all other nodes in the network, the CC value indicates how close a node is to other nodes.
Creation of backbone network of the protein–protein interaction
Proteins with high BC values are commonly regarded to constitute bottlenecks in the transportation network's information flow, according to graph theory.[4] We set the critical node with the highest BC value at 5% of the network's total nodes. A backbone network will be formed by proteins with a higher BC value and the links that connect them. A backbone network will be formed by proteins with a higher BC value and the links that connect them. To establish a backbone network, we extracted the proteins with the top 5% of BC values and the linkages between them from the PPI network for GBM.[11]
Identification of densely connected regions (module analysis) in the protein–protein interaction network
Using the Molecular Detection Complex (MCODE) 4.1, a Cytoscape plug-in, the global network was subjected to cluster analysis to discover densely connected regions of the giant network.[12] By weighting nodes depending on their local neighborhood density, this method discovers dense and connected regions. All network parameters were fixed at 2, 0.2, 4, and 100, including the degree threshold, node score threshold, k-score threshold, and maximum depth of the network networks are most likely composed of multiple subnetworks or functional modules that contribute to a variety of biological activities.
Functional enrichment analysis
GO annotates gene and gene products with molecular functions, biological processes, and cellular components. Database for annotation, visualization, and integrated discovery (DAVID) was used to discover the significantly dysregulated pathways to gain further insight into the functions of DEGs. The DAVID v6.8 is a full Knowledgebase update to the original web-accessible programs' sixth version. DAVID currently provides a comprehensive range of functional annotation tools for investigators to discover the biological meaning behind a large set of genes (https://david.ncifcrf.gov/).
Ethical consideration
This study does not involve human subjects. Hence, ethical clearance is not required for the study.
Statistical analysis
This complete work is done using bioinformatics tool. Statistical analysis is not done.
| Results | | |
Screening of the gene signatures
In the current investigation, gene sets from GSE68424 were used. Low passage GBM stem-like cells (GBM4, GBM6, GBM8, and BT74) were employed in the current reference publication. Nontargeting control oligonucleotides, lipofectamine 2000, and miR-10b inhibitors were used to transfect GBM4, GBM6, and GBM8 cells. Gene sets from GBM4 versus GBM8 and GBM4 versus GBM 6 were considered for analysis. There were 1221 DEGs in total. According to the log2 fold change cut-off criteria, 33 genes were upregulated and 1189 genes were downregulated [Figure 1]. | Figure 1: (a) Volcano map of all DEGs, screening criteria: P < 0.05 and ׀logFC׀>2; (b) Venn intersection map of nontargeting control oligonucleotides, lipofectamine 2000, and miR-10b inhibitors were used to transfect GBM4, GBM6, and GBM8 cells and gene sets from GBM4 versus GBM8 and GBM4 versus GBM 6 were considered for present analysis. DEG: Differentially expressed gene, GBM: Glioblastoma
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Protein–protein interaction network construction and topology analysis of differentially expressed genes
PPI network was constructed by taking set of DEGs associated with GBM in human using STRING database. The number of edges connected to the assigned node is high degree, indicating the significance of the protein in biological interactions. The average node degree of the network showed 10.8 with an expected number of edges 1903. A characteristic feature of PPI network is that it is characterized by small number of nodes which are highly connected and remaining node with few interactions. The Giant network or the core network consists of 491 nodes connected through 1706 edges [Figure 2]. The shortest paths of the network will connect the two randomly selected nodes, BC, CC, and average clustering coefficient of network nodes were plotted and their distributions are represented in [Figure 3]a, [Figure 3]b, [Figure 3]c, [Figure 3]d. The results of the topological parameter analysis of each node from core network are listed in [Table 1], which includes degree (D), BC, and CC. | Figure 2: Giant network or the core network constructed using differentially expressed genes in glioblastoma consisting of 491 nodes connected via 1706 edges
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 | Table 1: The general network measurements for giant and subnetworks of differentially expressed genes s from GSE data set of glioblastoma
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Identifying key genes in the protein–protein interaction | Figure 3: Topological analysis of a protein-protein interaction network with 491 nodes and 1, 706 edges. (a) The histogram represents the shortest path length distribution that demonstrate the small-world property of the network; (b) betweenness centrality distribution fitted by a power law; (c) closeness centrality of the nodes in the network; (d) Represents the clustering coefficient of the nodes in the network
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We selected the nodes with BC and degree based on the values larger than mean ± standard deviation. There were 31 nodes with a high BC value [Table 2], 66 nodes with a large degree [Table 3], and 21 nodes with both a high BC value and a large degree among the 491 total nodes [Table 4]. S-Phase kinase-associated protein 1 (SKP1) is a protein with the highest degree (k = 46), while histone acetyltransferase (HAT) p300 also known as E1A-associated protein p300 (EP300) was the highest BC value of 0.165. Catenin beta-1 (CTNNB1) was the protein with the highest CC value indicating that CTNNB1 is at the network's core. | Table 2: List of proteins (31) with the highest betweenness centrality value and their closeness centrality values
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 | Table 3: List of 66 proteins with a large degree value and their Closeness Centrality values
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 | Table 4: List of proteins with both a large betweenness centrality and degree and their functions
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Backbone network analysis
The backbone network consisted totally of 25 genes with the highest BC value [Figure 4]. A critical point of high BC was set at 5% of the total nodes of the network. CTNNB1 was the protein located at the center of the network with highest CC value and it also has a high degree, thus it controls the information flow in the network. Cell division control (CDC) 5 L is the protein with high BC. CTNNB1 has 14 neighbors chromodomain-helicase-DNA binding protein 8, SMARCA4, phosphatase and tensin homolog, glyceraldehyde-3-phosphate dehydrogenase, neuroblastoma RAS viral oncogene homolog, EP300, Cullin 1 (CUL1), CDC42 homolog, ataxia telangiectasia, protein phosphatase 2 scaffold subunit A beta, protein phosphatase 2 regulatory subunit B'gamma, SKP1, vascular endothelial growth factor A (VEGFA), Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform. | Figure 4: Topology of the backbone network consisting of 25 nodes with a high BC value and 86 edges. The size of the nodes corresponds to their BC values. BC: Betweenness centrality. NRAS: neuroblastoma RAS, CDC42: Cell division control, GAPDH: Glyceraldehyde-3-phosphate dehydrogenase, SKP1: S-Phase kinase-associated protein 1, CTNNB1: Catenin beta-1, VEGFA: Vascular endothelial growth factor A, PPP2R5C: Protein phosphatase 2 regulatory subunit B'gamma, PTEN: Phosphatase and tensin, PPP2R1B: Protein phosphatase 2 scaffold subunit A beta, EP300: E1A-associated protein p300, CHD8: Chromodomain-helicase-DNA binding protein 8
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Identification of densely connected regions (module analysis) in the protein–protein interaction network
From a PPI network, the MCODE method was utilized to identify strongly linked proteins. Module or clustering analysis was carried out using the MCODE approach. To ensure the efficiency of functional partners toward the core network of critical genes in GBM, the clusters were filtered using the parameters specified in the technique. As a result of the clustering analysis of the genes in the interaction network, nine densely interconnected groups appeared [Figure 5] and [Table 5]. | Figure 5: All the interconnected clusters among the 491 genes and their interactions with neighboring genes. The node highlighted with green color represents the seed gene in the cluster. (C1-C9) cluster 1, cluster 2, cluster 3, cluster 4, cluster 5, cluster 6, cluster 7, cluster 8, and cluster 9, respectively
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 | Table 5: Genes belonging to each cluster with respective molecular detection complex scores and clustering coefficients: Modules were ranked based on the molecular detection complex scores which implied that C1 had the highest total density around each node in the module
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Functional enrichment analysis
Functional analysis was performed using DAVID online plug-in. Genes from a list can be displayed on pathway maps to aid biological interpretation in a network setting. Thirty-three upregulated genes were found to be involved in biological processes such as multicellular organism growth, protein ubiquitination involved in ubiquitin-dependent protein catabolic process, negative regulation of epidermal growth factor receptor signaling pathway, and molecular functions such as protein binding, methylated histone binding, cellular components including microtubule organizing center, intracellular membrane-bounded organelle, neuronal cell body, membrane, and KEGG pathway analysis showed the genes are involved in bacterial invasion of epithelial cells. Among the 1188 downregulated genes, top five proteins with lowest P value were found to be involved in biological processes such as regulation of vesicle fusion, positive regulation of transcription, DNA-templated, regulation of transcription from RNA polymerase II promoter, regulation of transcription, DNA-templated, transcription, DNA-templated [Figure 6]a. Cellular components of cytosol, nuclear membrane, cytoplasm, nucleoplasm, and nucleus are shown in [Figure 6]b. Enriched molecular functions include such as DNA-dependent ATPase activity, transcription factor activity, sequence-specific DNA binding, poly (A) RNA binding, DNA binding, and protein binding [Figure 6]c. The enriched KEGG pathways such as phosphatidylinositol signaling system, nonalcoholic fatty liver disease (NAFLD), neurotrophic signaling pathway, endometrial cancer, and Alzheimer's disease (AD) [Figure 6]d. | Figure 6: Gene Ontology (GO) and KEGG pathway analysis of downregulated differentially expressed genes in glioblastoma. (a) Genes involved in Biological process, (b) Genes involved in cellular components, (c) Genes involved in Molecular process, (d) KEGG Pathway analysis
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| Discussion | | |
The present study aimed at identifying the hub proteins involved in the pathogenesis of GBM using PPI network. A total of 1221 DEGs were obtained for construction and analysis of PPI network. The Core network was constructed obtained from STRING database consists of 491 nodes and 1706 edges. The proteins with high degree, BC values, and CC values were identified using a variety of network topological analyses. SKP1 is a protein with the highest degree. The SKP1-CUL1-F-box protein (SCF) ubiquitin ligase complex mediates the ubiquitination of proteins involved in cell cycle progression, signal transduction, and transcription.[13] It is also a component of the Skp1-Pam-Fbxo45 ubiquitin E3 ligase complex, which regulates the epithelial-to-mesenchymal transition.[14] HAT p300 also known as EP300 was the protein with the highest BC value. Histone acetylation serves as a transcriptional activation epigenetic marker oncogenesis and tumor progression have been linked to deletions or inactivating mutations in multiple genes encoding HATs; these changes alter the transcription of genes that regulate important processes such as proliferation, cell cycle progression, and apoptosis[15] CTNNB1 was the protein with highest CC value. It is the key component of canonical Wnt signaling downstream pathway. Glioma growth, malignant progression,[16] and invasion are all aided by abnormal activation of the canonical WNT/-catenin signaling found pathway.[17] To better understand protein functioning in cellular processes, the top 15 genes with the greatest number of enriched functional interactors were obtained. Important proteins were found by the overlap of top genes derived from eight different methods using the CytoHubba tool EP300, cell division cycle 5-like (CDC5 L), SKP1, and FBXO32, CTNNB1, respectively.
In the backbone network, CTNNB1 was the protein with high CC and degree, CDC5 L was the protein with high BC. Both in the giant network and backbone network CTNNB1 was the protein with a high CC value. CTNNB 1 is also called as catenin, involved in the regulation and coordination of cell-cell adhesion and gene transcription. According to the studies, WNT signaling is abnormally active in GBM, and it promotes GBM development and invasion by maintaining stem cell characteristics.[18],[19] The CDC5 L protein is a G2/M transition cell cycle regulator that has been linked to the catalytic steps of premessenger RNA (mRNA) splicing and DNA damage repair. Reports of Chen et al. show the higher expression of CDC5 L in the paraffin-embedded samples of the glioma tissue.[20]
Functional enrichment analysis was carried using DAVID online software. Thirty-three upregulated genes were found to be involved in biological processes such as multicellular organism growth, protein ubiquitination involved in ubiquitin-dependent protein catabolic process, negative regulation of epidermal growth factor receptor signaling pathway, Molecular functions such as protein binding, methylated histone binding, Cellular components including microtubule organizing center, intracellular membrane-bounded organelle, neuronal cell body, membrane, and KEGG pathway analysis showed the genes are involved in Bacterial invasion of epithelial cells. Mycoplasma infections have been found in different cancer tissues including glioma and such infections are associated with cytokine-mediated damage and inflammatory lesions. The role of infectious agents in carcinogenesis requires special attention from clinician. Based on the studies of Alibek et al., combating the pathogens could be a treatment to reduce or prevent these cancers.[21]
Out of 1188 downregulated genes, top five proteins with lowest P value were found to be involved in biological processes such as regulation of vesicle fusion, positive regulation of transcription, DNA-templated, regulation of transcription from RNA polymerase II promoter, regulation of transcription, DNA-templated, transcription, DNA-templated, cellular components of cytosol, nuclear membrane, cytoplasm, nucleoplasm, nucleus, involved in molecular functions such as DNA-dependent ATPase activity, transcription factor activity, sequence-specific DNA binding, poly (A) RNA binding, DNA binding, protein binding, and pathways such as phosphatidylinositol signaling system, NAFLD, neurotrophin signaling pathway, endometrial cancer, and AD. Few of the risk genes associated with AD are also involved in gliomas.[22] A detailed experimental study is required to analyze the genes associated with AD and glioma. Endometrial cancer is the most common cancer in females but brain metastasis from this cancer is rare. From the studies of Gien et al.,[23] women in the study group developed brain metastasis after completion of treatment for endometrial cancer and survival after diagnosis is also limited. Neurotrophins are the proteins involved in neurogenesis including neuronal growth, survival, and differentiation. Studies have shown the involvement of p75NTR (one of the receptors) in glioma cell invasion[24],[25] and proliferation.
After subjecting the genes for MCODE cluster analysis, 9 clusters were obtained [Figure 5]. DAVID functional enrichment analysis of cluster 1 showed that the proteins involved in biological processes such as proteasome-mediated ubiquitin-dependent protein catabolic process, protein ubiquitination involved in ubiquitin-dependent protein catabolic process, protein polyubiquitination, ubiquitin-dependent protein catabolic process, protein ubiquitination, cellular components of SCF ubiquitin ligase complex, cytoplasm, ubiquitin ligase complex, Cul7-RING ubiquitin ligase complex, cytosol, nucleoplasm, nucleus, involved in molecular functions such as ubiquitin-protein transferase activity, ubiquitin protein ligase activity, ligase activity, ubiquitin protein ligase binding, protein binding, ubiquitin-ubiquitin ligase activity, ubiquitin-conjugating enzyme activity, zinc ion binding, KEGG pathways ubiquitin-mediated proteolysis, protein processing in endoplasmic reticulum, and cell cycle.
Cluster 2 proteins are involved in biological processes such as centrosome organization, snRNA processing, G2/M transition of mitotic cell cycle, sister chromatid cohesion, snRNA transcription from RNA polymerase II promoter, cellular components of nucleoplasm, integrator complex, cytosol, kinetochore, centrosome, molecular functions protein binding, microtubule plus-end binding, protein phosphatase type 2A regulator activity, protein homodimerization activity, RNA polymerase II repressing transcription factor binding, microtubule binding, KEGG pathways such as Salmonella infection, oocyte meiosis, vasopressin-regulated water reabsorption. Cluster 3 involved in biological processes such as viral transcription, nuclear-transcribed mRNA catabolic process, nonsense-mediated decay, mRNA export from nucleus, signal-recognition particle-dependent cotranslational protein targeting to membrane, translational initiation, cellular components such as ribosome, cytosolic small ribosomal subunit, nuclear membrane, nucleoplasm, cytosolic large ribosomal subunit, molecular functions such as poly (A) RNA binding, structural constituent of ribosome, protein binding, nucleotide binding, structural constituent of nuclear pore, pathways such as ribosome, RNA transport, and mRNA surveillance pathway. Previous research has shown that tumor cells produce abnormally high quantities of rRNA.[26]
Proteins of cluster 4 are involved in biological processes such as mitochondrial electron transport, NADH to ubiquinone, mitochondrial respiratory chain complex I assembly, cellular components such as mitochondrial respiratory chain complex I, mitochondrial inner membrane, myelin sheath, mitochondrion, molecular functions such as NADH dehydrogenase (ubiquinone) activity, electron carrier activity, KEGG pathways such as oxidative phosphorylation, Parkinson's disease, NAFLD, AD, Huntington's disease, metabolic pathways, and cardiac muscle contraction. Proteins of cluster 5 are involved in biological processes such as mRNA splicing, through spliceosome, Arp2/3 complex-mediated actin nucleation, regulation of RNA splicing, negative regulation of epidermal growth factor receptor signaling pathway, ephrin receptor signaling pathway, cytoplasmic components such as nuclear speck, membrane, clathrin coat of coated pit, Arp2/3 protein complex, U2-type prespliceosome, molecular functions mRNA binding, protein binding, KEGG pathways regulation of the actin cytoskeleton, and endometrial cancer. Cell division CDC5 L is found to be the core protein involved in human Prp19/CDC5 L complex and is required for catalytic step in the pre-mRNA splicing.[27],[28],[29] Studies have shown that CDC5 L upregulated in glioma specimens and associated with clinicopathological parameters and also poor survival in glioma and expression of CDC5 L was associated with glioma grade.[20] CDC5 L overexpression is also observed in cervical cancer.
Cluster 6 are involved in biological process such as carbohydrate metabolic process, cellular components of the extracellular exosome, lysosome, in the pathway enrichment such as other glycan degradation, lysosome. Cluster 7 involved in biological processes such as the vascular endothelial growth factor receptor (VEGFR) signaling pathway, epidermal growth factor receptor signaling pathway, regulation of phosphatidylinositol 3-kinase signaling, positive regulation of cell migration, cellular components such as phosphatidylinositol 3-kinase complex, focal adhesion, cytosol, plasma membrane, molecular functions such as phosphatidylinositol-4,5-bisphosphate 3-kinase activity, signal transducer activity, pathway enrichment analysis including VEGF signaling pathway, PI3K-Akt signaling pathway, pathways in cancer platelet-derived growth factor, and VEGF are two growth factors that are overexpressed in GBM.[30],[31] VEGF and its receptors are involved in GBM angiogenesis. Until now, VEGFR-targeted treatment has yielded very modest outcomes. In clinical trials, drugs such as vatalanib, cediranib, sorafenib, pazopanib, sunitinib, or thalidomide were used alone or in combination with radiation or other chemotherapeutic treatments 60, but the effects of these inhibitors are limited. In GBM, PI3K/Akt/mTOR is usually deregulated. This pathway is involved in GBM cell proliferation, differentiation, growth, survival, and angiogenesis.[32] As a result, specialists are interested in inhibiting the numerous proteins implicated in this pathway. Several small molecule inhibitors of PI3K, Akt, or mTOR have been explored in human studies, but the outcomes are limited.[33],[34] Dual inhibition of PI3K/Akt/mTOR intracellular signaling has been suggested as a preferable option to the inactivation of a single growth factor receptor on the cell surface. Some of these dual inhibitors are currently under investigations.[35],[36]
Cluster 8 proteins are involved in the biological process such as intracellular protein transport, vesicle organization, retrograde transport, endosome to Golgi, cell-cell adhesion, cellular components retromer complex, cytosol, cell-cell adherens junction, cytoplasmic vesicle membrane, molecular functions such as phosphatidylinositol binding, cadherin binding involved in cell-cell adhesion, protein binding, KEGG analysis involved in Endocytosis. impaired endocytosis makes a specific benefit in glioma cancer movement because of delayed receptor tyrosine kinase signaling from the cell surface. Cluster 9 involved in biological processes such as transcription, DNA-templated, negative regulation of transcription from RNA polymerase II promoter, cellular components such as MLL1 complex, PTW/PP1 phosphatase complex, nucleoplasm, chromatin, nuclear chromosome, telomeric region, transcription factor complex, molecular functions such as DNA binding, protein serine/threonine phosphatase activity, transcription factor activity, sequence-specific DNA binding, protein dimerization activity. These aforementioned data support the hypothesis that SKP1 and CTNNB1 were key components of canonical Wnt signaling downstream pathways and can act as promising biomarkers associated with GBM.
| Conclusion | | |
We used a well-known systems biology method to decipher the gene signature in GBM in the current work. EP300, SKP1, and CTNNB1 were identified as candidate hub proteins with high degree, BC, and CC values. These hub proteins can be utilized as possible indicators in GBM, according to network analysis. The enriched GO keywords and KEGG pathways might be linked to the incidence of GBM. SKP1 and CTNNB1 is a gene that has been linked to GBM prognosis. Overall, these findings established SKP1 and CTNNB1 as a promising candidates for GBM research. More research is needed to determine the molecular function of SKP1 and CTNNB1 in GBM progression, as well as its specificity and sensitivity as a GBM biomarker. As a result, future research should focus on the molecular processes and clinical uses of these genes and pathways.
Limitations of the study
This study is done using bioinformatics software to explore the importance of some of the genes involved in GBM by taking GSE68424 obtained from the GEO database. In vitro and in vivo studies have to be done to validate the role of identified genes, SKP1 and CTNNB1 in GBM.
Acknowledgment
The authors are grateful to Nitte (Deemed to be University) for providing an opportunity to carry out this work.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]
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