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. 2022 Dec 15;13(1):10. doi: 10.1007/s13205-022-03419-5

Expression analysis and regulation of GLI and its correlation with stemness and metabolic alteration in human brain tumor

Kirti Agrawal 1,2, Saumya Chauhan 3, Dhruv Kumar 1,2,✉
PMCID: PMC9755437  PMID: 36532860

Abstract

GLI gene-mediated hedgehog (Hh) signaling pathway plays a substantial role in brain cancer development and growth including glioblastoma multiforme (GBM), lower-grade glioma (LGG), and medulloblastoma (MB). GLI2 and GLI3 gene expression levels are extremely enhanced in these cancers with poor patient survival. Moreover, GLI genes are correlated with stemness-related factors SOX2, SOX9, POU5F1, and NANOG that work as the driving factors for brain cancer stem cells (CSCs) progression. It's critical to find new ways to combat this deadly malignancy and CSCs. Using in silico approaches, our study explored the role of GLI genes (GLI1, GLI2, and GLI3), the primary transcription factors of the sonic hedgehog (SHH) signaling pathway, in GBM, LGG, MB, and glioblastoma stem-like cells (GSCs). Additionally, we found strong association of angiogenic-related gene VEGFA, metabolic genes ENO1, ENO2, and pluripotency-related genes SOX2, SOX9, NANOG, POU5F1 with GLI genes, suggesting their role in brain tumor initiation and progression. We also studied their transcriptional network and functional category enrichment analysis about brain tumor development to find a better therapeutic strategy against brain cancer and their stem cells.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03419-5.

Keywords: Glioblastoma, Medulloblastoma, GLI1, GLI2, GLI3, SOX2, Brain cancer stem cells

Introduction

According to the Globocan Cancer Statistics data, brain cancer accounts for 1.6% of all cancer with 308,102 new cases worldwide (Sung et al. 2021). Grade IV diffuse glioma, often known as GBM, is the most predominant malignant brain tumor with a terrible prognosis, according to the World Health Organization (Louis et al. 2016). Astrocytomas, ependymomas, and oligodendrogliomas are tumors that occur from supporting glial cells—astrocytes, ependymal cells, and oligodendrocytes. Astrocytomas are the extremely prevalent type of glial tumor. The World Health Organization (WHO) divides gliomas into four categories: grade I (pilocytic astrocytomas), grade II (diffuse astrocytomas), grade III (anaplastic astrocytomas), and grade IV (glioblastomas). GBM is the most malignant and severe type of glioma, has a median survival period of about 12–15 months after diagnosis. It is responsible for 15% of all prime brain tumors, 46% of malignant brain tumors, and 60–75% of astrocytomas (Young et al. 2015; Anjum et al. 2017). GBM, which impacts more males than females, grows more common as people become older, with the tumor becoming more common beyond the age of 45 (Ostrom et al. 2020). GBM is categorized by fast progression and migration and is extremely invasive to the adjoining brain tissues. GBM has a high level of infiltration into the brain parenchyma, making traditional interventions (surgical resection followed by radiotherapy and temozolomide chemotherapy) ineffective in stopping tumor progression (Grossman et al. 2010). When compared to other malignancies like breast and lung cancer, the GBM mortality rate is extraordinarily high, with only 5% of patients surviving for 5 years (Weathers and Gilbert 2014; Henriksson et al. 2011). While GBM is still incurable, recent findings and clinical trials have helped to increase our understanding of how the illness progresses and patient outcomes. Several investigations have identified a sub-population of GBM cells with radiotherapy and chemotherapy-resistant features that may play a part in tumor initiation, development, treatment resistance, and relapse (Chen et al. 2010; Suvà et al. 2014). These cells are known as glioblastoma stem-like cells (GSCs) because of their ability to self-renew, proliferate, and differentiate into multiple lineages and they are thought to be responsible for carcinogenesis (Arceci 2008). One of the key reasons for the ineffectiveness of existing medicines in treating glioblastoma is the failure to eradicate these cancer stem-like cells (Tabatabai and Weller 2011). Medulloblastoma (MB), the most recurrent pediatric malignant brain tumor and a prominent source of cancer-related diseases and mortality in children, has also been found to include cancer stem cells (CSCs) (Northcott et al. 2012; Manoranjan et al. 2013).

Among the four molecular sub-groups of medulloblastoma, sonic-hedgehog-driven medulloblastoma (SHH-MB), is the second most frequent, accounting for 27% of all MB tumors. They are a sub-group with an intermediate prognosis, with total survival rates fluctuating from 35 to 80%. Recurrence is a typical occurrence in SHH-MBs (30%), making its treatment difficult (Kool et al. 2012; Ramaswamy et al. 2013). GLI proteins promote cancer by controlling the transcription of numerous pro-oncogenic factors. Cell proliferation in GLI-related malignancies is improved by GLI-dependent expression of cyclin D1/D2 or N-myc proto-oncogene (Bermudez et al. 2013; Hatton et al. 2006). To mediate CSCs’ self-renewal property, GLI proteins control SOX2 and Nanog expression (Bora-Singhal et al. 2015; Zbinden et al. 2010). Upregulated expression of vascular endothelial growth factor (VEGF) and angiopoietins can enhance GLI-dependent angiogenesis (Carpenter et al. 2015; Pola et al. 2001). Bcl-2 is supposed to be an apoptosis suppressor gene but by activating Bcl2, GLI proteins can shield tumor cells from apoptosis (Regl et al. 2004). Overexpression of Bcl-2 may block or postpone the beginning of apoptosis in tumor cells by choosing and preserving the cancer cells in the G0 phase of the cell cycle (Bostwick and Meiers 2009). Finally, through improving the expression of TGFβ2, GLI proteins may aid malignancies in evading the immune system (Fan et al. 2010). There is evidence of genetic and epigenetic changes of genes encoding GLI proteins in several cancer types. SHH (Hh family ligand), binds to Patched1 (PTCH1) receptor and causes the release of Smoothened (SMO) signal transducer which leads to SHH pathway activation. The absence of PTCH1 is known to free SMO from PTCH-dependent inhibition followed by Gli1 activation. Many studies revealed the upregulated levels of GLI1 and PTCH1 which usually leads to abnormal hedgehog-GLI (HH-GLI) pathway activation in several human tumor types, such as melanoma, basal cell carcinoma (BCC), rhabdomyosarcoma, oral squamous cell carcinoma (OSCC), Glioblastoma, etc. (Kar et al. 2012; Sabol et al. 2018; Tanigawa et al. 2021).

Through our study, we further highlight the overexpression of GLI2 and GLI3 in comparison to GLI1 in GBM, and MB cancer and the role of GLI genes in GBM, and MB tumor development. We observed a strong correlation between GLI genes and stemness-related factors (SOX2, SOX9, NANOG, POU5F1), metabolic genes (ENO1, ENO2), and angiogenesis-related gene (VEGFA). Our findings suggest that inhibiting GLI genes (GLI1, GLI2, GLI3) along with SOX2, ENO1, ENO2, and VEGFA could be a potential therapeutic approach to combat a variety of brain cancers, such as LGG, GBM, and MB.

Methodology

Data collection

In this study, we downloaded gene expression profiles for 153 glioblastoma multiforme (GBM) samples and 518 lower-grade glioma (LGG) samples from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) and 207 normal brain samples from Genotype-Tissue Expression Portal (GTEx, https://gtexportal.org/home/). Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo) database was utilized for the study of medulloblastoma and GSCs gene expression study. For medulloblastoma study, CEL files of Gene expression profiles of medulloblastoma classic samples were taken from GSE10327, and CEL files of Gene expression profiles of normal pediatric samples were extracted from GSE11877. For the study of GSCs, samples of Glioblastoma stem-like cell line were taken from GSE23806, and samples of normal human adult brain were taken from GSE5390. Three GSE datasets (GSE10327, GSE11877, and GSE23806) were based on the Affymetrix GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array), and GSE5390 was based on Affymetrix GPL96 platform (Affymetrix Human Genome U133A Array).

Gene expression analysis

Gene Expression Profiling Interactive Analysis (GEPIA2) webserver tool (http://gepia.cancer-pku.cn) was employed to aggregate the normal brain gene expression from the GTEx database and gene expression profile of GBM and LGG samples from the TCGA database to validate the aberrant expression of the GLI genes (GLI1, GLI2, and GLI3). The expression profile was also validated through using UCSC Xena Functional Genomics Explorer web application tool.

46 samples of medulloblastoma classic and 50 normal pediatric samples were selected from another independent datasets (GSE10327, GSE11877) and utilized to corroborate the abnormal GLI genes expression in medulloblastoma tumor. 20 GSCs and 6 normal brain samples were selected from another independent datasets (GSE23806, GSE5390) to analyze the SOX2 gene expression in GSCs V/S normal brain samples.

Validation of survival analysis

Overall survival (OS) of the GLI genes (GLI1, GLI2, GLI3) in LGG and GBM was plotted by the Kaplan–Meier survival plot and analyzed using the log-rank test in GEPIA2.

Correlational analysis

We plotted correlational analysis of GLI2 using the web application tool LinkedOmics (http://www.linkedomics.org/) having three analytical modules i.e., LinkFinder, LinkInterpreter, and LinkCompare. We executed a Spearman’s Rho static test for the normalized and log2-transformed expression values of several genes in correlation to GLI2 gene expression in volcano plot. A correlational graph of mRNA expression (RNA Seq V2 RSEM) between GLI2 and SOX2 was generated using cBioPortal (https://www.cbioportal.org/). A heatmap was generated of log2 expression values of GLI genes, metabolic genes, and stem cell marker genes using the TCGA data of GBM tumor. The TCGA datasets were processed by Firehose Pipeline, served by FirebrowseR, a R client for Broads Firehose Web API showing the results in the form of expression log2 and z score.

Gene/protein interaction network analysis

GeneMANIA (http://www.genemania.org) was used to examine the gene–gene interaction network of GLI genes with stem cell marker-related genes and metabolic genes. Search Tool for the Retrieval of Interacting Genes (STRING) (https://string-db.org/) database analysis was conducted out to generate the protein–protein interaction (PPI) networks between GLI genes and metabolic genes, stemness-related genes, angiogenesis and metastasis-related genes.

Transcriptional subnetwork and signaling pathway analysis

For analyzing the transcriptional regulatory subnetwork between Transcription Factors (TFs) and their target genes, 9 TFs were mapped (GLI1, GLI2, GLI3, SOX2, POU5F1, NANOG, MYC, SOX9, and CREB1) in Knock TF (http://www.licpathway.net/KnockTF/index.html), a comprehensive human gene expression profile database.

Functional Category Enrichment Analysis

Enrichr (https://maayanlab.cloud/Enrichr/) and LinkInterpreter were employed to investigate and visualize the functional profiles of differentially expressed genes and gene clusters based on the Elsevier pathway collection, DisGeNET, and LinkedOmics Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis.

Results

Differential expression of GLI and SOX2

By plotting the log2 expression values of GLI1 in LGG and GBM tumor tissues as compared to normal tissues, we detected slightly low expression of GLI1 gene in LGG as compared to normal, while the expression was found to increase in IV grade GBM tumor in contrast to normal (Fig. 1a). The log2 values of GLI2 and GLI3 in LGG and GBM show high expression in LGG and GBM in comparison to normal (Fig. 1b, c). Similarly, the log2 expression values of GLI1/2/3 genes are very much higher in pediatric medulloblastoma tumor as compared to normal pediatric brain samples (Fig. 1d, e, f). On plotting the stem cell-related marker SOX2 gene expression value, the expression is found to be significantly high in GSCs of GBM tumor cells in contrast to normal (Fig. 1g).

Fig. 1.

Fig. 1

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GLI1/2/3 and SOX2 differential gene expression: a GLI1 gene expression in normal vs LGG and GBM tumor in the TCGA and GTEx dataset, b GLI2 gene expression in normal vs LGG and GBM tumor in the TCGA and GTEx dataset, c GLI2 gene expression in normal vs LGG and GBM tumor in the TCGA and GTEx dataset, d GLI1 gene expression in pediatric normal vs pediatric medulloblastoma tumor in GEO datasets, e GLI2 gene expression in pediatric normal vs pediatric medulloblastoma tumor in GEO datasets, f GLI2 gene expression in pediatric normal vs pediatric medulloblastoma tumor in GEO datasets, g SOX2 differential expression in GSCs vs normal GEO datasets

To validate the differential gene expression, we used UCSC Xena Browser tool using GTEx and TCGA datasets to verify that the expression levels of GLI2 and GLI3 genes were much higher than GLI1 gene in GBM tumor tissues as compared to normal tissues. In (Fig. 2), we found the log2 (norm_count + 1) scale bracket of GLI1 gene was (1.4–9.3), GLI2 gene scale bracket was (3.6–11), and GLI3 gene scale bracket was (4.2–13) in GBM tumor samples and normal brain samples. Larger and darker red-colored area depicts the higher expression of the gene, white-colored area relates to the neutral expression, and the larger and darker blue-colored area depicts the lower expression of the gene. The expression profile of GLI1 gene is quite similar in tumor vs normal samples, while the GLI2 and GLI3 gene expression chart showed larger and darker red area in tumor samples and larger blue color area in normal samples.

Fig. 2.

Fig. 2

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GLI1, GLI2, GLI3 differential gene expression in normal brain tissue samples and GBM tumor tissue samples in GTEx vs TCGA datasets, indicates high expression of GLI genes in tumor samples compared to the normal

Survival analysis

The prognostic value of GLI genes in LGG and GBM was plotted in the form of Kaplan–Meier plot using GEPIA2 tool. As shown in (Fig. 3), it shows high expression levels of GLI genes were correlated with worse overall survival (OS) time period in IV grade tumor in comparison to lower-grade tumor patients. However, there was no significant difference in the percentage survival of high- and low-expressed GLI genes in GBM patients, but the patients with highly expressed GLI genes show worse survival time in contrast to low GLI expression (Fig. 3d–f).

Fig. 3.

Fig. 3

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Survival analysis of differential GLI genes expression level in LGG and GBM patients. Analysis indicates the lower survival of GBM patients compared to LGG with high GLI genes expression

Correlational analysis

With the help of Firehose Pipeline, we calculated the log2 expression values of various genes, including metabolism, angiogenesis, apoptotic regulator, neuron development, tumor migration and stem cell-related genes as well as oncogenes and used this data to create a heat map of differential expression of these genes in GBM patients relative to GLI genes expression as shown in (Fig. 4a). (Fig. 4b) shows a LinkedOmics-derived Volcano Plot of Spearman's rank correlation of several negatively and positively correlated genes with respect to GLI1 gene expression in GBM tumor. The mRNA expression (RNA Seq V2 RSEM) between GLI1 and SOX2 showed a less positive correlational graph with Spearman value = 0.208 and p value = 0.0149 (Fig. 4c). Likewise, LinkedOmics-derived Volcano Plot of Spearman’s rank correlation of several negatively and positively correlated genes with respect to GLI2, and GLI3 genes in GBM tumor are shown in Fig. 4d, f. Also, (Fig. 4e) showed a positive correlational graph of mRNA expression (RNA Seq V2 RSEM) between GLI1 and SOX2 with Spearman value = 0.349 and p value = 3.170e−5. GLI3 and SOX2 mRNA expression also showed a positive correlation with Spearman value = 0.297 and p value = 4.378e−4 (Fig. 4g). Many oncogenes have been found to be elevated in response to GLI genes in GBM, implying that GLI genes play a substantial role in oncogenic progression shown in (Table 1).

Fig. 4.

Fig. 4

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a Heat map of differential gene expression of several genes with respect to GLI1/2/3 genes, b A volcano plot showing several positively and negatively correlated genes with respect to GLI1 gene, c a positive correlational graph of mRNA expression between GLI1 and SOX2, d a volcano plot showing several positively and negatively correlated genes with respect to GLI2 gene, e a positive correlational graph of mRNA expression between GLI2 and SOX2, f a volcano plot showing several positively and negatively correlated genes with respect to GLI3 gene, g a positive correlational graph of mRNA expression between GLI3 and SOX2

Table 1.

Positive and negative correlation of GLI genes with a list of oncogenes

Correlation of GLI1 gene with oncogenes
S. no. Genes Function Spearman correlation p value FDR (BH) Event_SD
Positively correlated genes with GLI1
 1 AKT1 Cell growth, division, and invasion 1.977e−01 1.429e−02 1.214e−01 1.53e+02
 2 BCL2 Block apoptosis (Programmed cell death) 9.964e−02 2.204e−01 5.059e−01 1.53e+02
 3 BCL6 Transcription repressor 1.012e−01 2.131e−01 4.982e−01 1.53e+02
 4 CD44 Cell adhesion and migration 1.079e−01 1.842e−01 4.643e−01 1.53e+02
 5 CDC6 Regulator of DNA replication 1.145e−01 1.587e−01 4.298e−01 1.53e+02
 6 CREB1 Proto-oncogene 9.631e−02 2.363e−01 5.225e−01 1.53e+02
 7 GLI2 Transcription factor (TF) 5.842e−01 1.000e−11 1.000e−07 1.53e+02
 8 GLI3 Transcription factor 5.028e−01 3.533e−11 2.316e−07 1.53e+02
 9 KLF4 Transcription factor 4.178e−02 6.077e−01 8.112e−01 1.53e+02
 10 MYB TF; Encodes fibroblast growth factor 2.653e−02 7.448e−01 8.906e−01 1.53e+02
 11 MYC TF; Cell signaling molecule 2.903e−02 7.217e−01 8.787e−01 1.53e+02
 12 TP53 Tumor suppressor 4.383e−01 1.454e−08 2.858e−05 1.53e+02
 13 NES Cytoskeleton remodeling neurofilaments 2.340e−01 3.680e−03 6.022e−02 1.53e+02
 14 SMO Proto-oncogene of neuroectodermal tumors 4.627e−01 2.625e−09 1.070e−05 1.53e+02
 15 PTCH1 Tumor suppressor 2.970e−01 1.932e−04 1.153e−02 1.53e+02
Negatively correlated genes with GLI1
 16 MYCN Cell proliferation and DNA synthesis − 4.837e−02 5.523e−01 7.808e−01 1.53e+02
 17 PFKP Glycolysis regulation − 7.494e−02 3.572e−01 6.374e−01 1.53e+02
 18 SUFU Tumor suppressor − 1.444e−03 9.859e−01 9.949e−01 1.53e+02
Correlation of GLI2 gene with oncogenes
 Positively correlated genes with GLI2
  1 AKT1 Cell growth, division, and invasion 2.288e−01 4.447e−03 1.839e−02 1.53e+02
  2 BCL2 Block apoptosis (Programmed cell death) 2.485e−01 1.951e−03 9.602e−03 1.53e+02
  3 BCL6 Transcription repressor 1.480e−01 6.796e−02 1.526e−01 1.53e+02
  4 CD44 Cell adhesion and migration 1.655e−01 4.095e−02 1.035e−01 1.53e+02
  5 CDC6 Regulator of DNA replication 1.912e−01 1.805e−02 5.531e−02 1.53e+02
  6 CREB1 Proto-oncogene 2.118e−01 8.577e−03 3.075e−02 1.53e+02
  7 GLI1 Transcription factor (TF) 5.842e−01 1.000e−19 1.000e−17 1.53e+02
  8 GLI3 Transcription factor 6.457e−01 1.715e−19 7.024e−17 1.53e+02
  9 TP53 Tumor suppressor 4.579e−01 2.668e−09 2.150e−07 1.53e+02
  10 SUFU Tumor suppressor 3.643e−02 6.545e−01 7.680e−01 1.53e+02
  11 SMO Proto-oncogene of neuroectodermal tumors 6.654e−01 1.000e−19 1.000e−17 1.53e+02
  12 MYCN Cell proliferation and DNA synthesis 1.123e−02 8.903e−01 9.325e−01 1.53e+02
  13 NES Cytoskeleton remodeling neurofilaments 3.798e−01 1.552e−06 3.239e−05 1.53e+02
  14 PTCH1 Tumor suppressor 2.230e−01 5.599e−03 2.206e−02 1.53e+02
 Negatively correlated genes with GLI2
  15 MYC TF; Cell signaling molecule − 1.497e−01 6.478e−02 1.470e−01 1.53e+02
  16 PFKP Glycolysis regulation − 1.190e−01 1.430e−01 2.651e−01 1.53e+02
  17 MYB TF; Encodes fibroblast growth factor − 1.554e−02 8.488e−01 9.063e−01 1.53e+02
  18 KLF4 Transcription factor − 5.971e−02 4.630e−01 6.145e−01 1.53e+02
Correlation of GLI3 gene with oncogenes
 Positively correlated genes with GLI3
  1 AKT1 Cell growth, division, and invasion 2.161e−01 7.310e−03 2.650e−02 1.53e+02
  2 BCL2 Block apoptosis (Programmed cell death) 1.545e−01 5.661e−02 1.337e−01 1.53e+02
  3 BCL6 Transcription repressor 1.506e−01 6.321e−02 1.460e−01 1.53e+02
  4 CD44 Cell adhesion and migration 8.976e−02 2.698e−01 4.289e−01 1.53e+02
  5 CDC6 Regulator of DNA replication 2.208e−01 6.089e−03 2.289e−02 1.53e+02
  6 CREB1 Proto-oncogene 1.834e−01 2.326e−02 6.683e−02 1.53e+02
  7 GLI1 Transcription factor (TF) 5.028e−01 3.533e−11 2.316e−07 1.53e+02
  8 GLI2 Transcription factor 6.457e−01 1.715e−19 7.024e−17 1.53e+02
  9 SUFU Tumor suppressor 2.293e−01 4.355e−03 1.752e−02 1.53e+02
  10 MYB TF; Encodes fibroblast growth factor 1.465e−01 7.067e−02 1.593e−01 1.53e+02
  11 MYC TF; Cell signaling molecule 5.469e−02 5.020e−01 6.580e−01 1.53e+02
  12 SMO Proto-oncogene of neuroectodermal tumors 4.600e−01 2.204e−09 1.388e−07 1.53e+02
  13 NES Cytoskeleton remodeling neurofilaments 3.228e−01 4.712e−05 4.218e−04 1.53e+02
  14 PTCH1 Tumor suppressor 2.812e−01 4.293e−04 2.621e−03 1.53e+02
  15 TP53 Tumor suppressor 4.046e−01 2.135e−07 4.830e−06 1.53e+02
 Negatively correlated genes with GLI3
  16 MYCN Cell proliferation and DNA synthesis − 5.613e−02 4.907e−01 6.481e−01 1.53e+02
  17 PFKP Glycolysis regulation − 3.087e−02 7.049e−01 8.164e−01 1.53e+02
  18 KLF4 Transcription factor − 1.120e−01 1.682e−01 3.052e−01 1.53e+02
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Protein–protein interaction network

Gene–gene interaction network was constructed using the online database GeneMania to explore GLI genes and SOX2 gene-related networks and functions (Fig. 5a). Colorful lines represent the types of networks, and the color of nodes represents the associated functions in which the genes are involved. After scrutinizing 52 genes, we found 15 neighboring genes co-expressed with GLI1 (Fig. 5b), 16 neighboring genes co-expressed with GLI2 including SOX2, SMO, NANOG, CREB1, KLF4, POU5F1, AKT1, etc. (Fig. 5c), 20 neighboring genes co-expressed with GLI3 (Fig. 5d), and 13 neighboring genes co-expressed with SOX2 (Fig. 5e). We also found that AKT1 is directly associated with GLI2 and SOX2. Also, it has a direct interaction with metabolic genes and angiogenesis-related gene including VEGFA, ENO1, ENO2, PFKP, etc. (Fig. 5f).

Fig. 5.

Fig. 5

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Gene–gene interaction: a gene interaction network of 52 genes with functions, b GLI1 gene interaction network, c GLI2 gene interaction network, d GLI3 gene interaction network, e SOX2 gene interaction network, f AKT1 gene interaction network

PPI network was constructed using the online tool STRING to explore metabolic proteins association with GLI proteins with respect to cellular metabolic process, brain cancer, etc. (Fig. 6). It represents the association of metabolic proteins with GLI proteins with respect to cellular metabolic process, brain cancer, etc. Shh signaling pathway members, such as GLI1, GLI2, GLI3, SUFU, SMO and GLIS1/2/3 (GLI-related Kruppel-like zinc finger proteins) work as activator or repressor of transcription; metabolic genes like GAPDH, PKM, PFKP, HK2, ENO1/2/3; cancer-related MYC gene, etc., were mapped to shown their interaction and participation in brain cancer. In the following network (Fig. 6), the nodes and lines represent the proteins and the protein–protein associations, respectively.

Fig. 6.

Fig. 6

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Protein–protein interaction (PPI) between GLI proteins and metabolic proteins and their involvement in brain cancer activity. Interaction shows strong correlation of GLI with metabolic-related genes

Transcriptional subnetwork and signaling pathway analysis

The selected nine TFs were input into the Knock TF online tool to construct a transcriptional regulatory subnetwork: GLI1, GLI2, GLI3, SOX2, POU5F1, NANOG, MYC, SOX9, and CREB1. The resulted network is shown in (Fig. 7) displaying the interaction between TFs and their targeted downstream genes. The blue dots represent the genes, red dots represent the TFs, and the blue hollow circle represents the input genes. The TF-target relationships supported by the ChIP-seq data are represented by the thick lines.

Fig. 7.

Fig. 7

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Transcriptional regulatory subnetwork. Functional correlation of GLI2, SOX2, POU5F1, NANOG, MYC, SOX9, and CREB1 in transcriptional network

Functional category enrichment analysis

Figure 8 shows the results of gene set enrichment analysis (GSEA) using the Elsevier pathway collection and DisGeNET. Figure 8a, c provides a table of the top 10 enriched terms, together with their significant p values and q values, as well as volcano plots demonstrating the significance of each gene set from the selected library vs its odds ratio Figure 8b, d. The q value is an adjusted p value for multiple hypothesis testing determined using the Benjamini–Hochberg technique. Each point on the volcano plot represents a single gene set; larger blue points indicate significant terms (p value < 0.05), whereas smaller gray points indicate non-significant terms. The more significant a point is, the darker its blue color.

Fig. 8.

Fig. 8

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Functional category enrichment analysis: a Top 10 significant terms along with their p values and q values for Elsevier pathway collection, b a volcano plot demonstrating the significance of each gene set from the Elsevier pathway collection library versus its odds ratio, c top 10 significant terms along with their p values and q values for DisGeNET, d a volcano plot demonstrating the significance of each gene set from the DisGeNET library versus its odds ratio

Gene set enriched terms for GLI1, GLI2, GLI3 genes from KEGG pathway have been displayed in Fig. 9 along with its corresponding table of the gene sets with its description, enrichment score (ES), normalized enrichment score (NES), p value and FDR value (Table 2). 25 positive-related categories and 25 negative-related categories were identified as enriched categories for GLI1/2/3 genes, respectively, out of which 20 most significant positive-related categories and representatives in the reduced sets for each gene are shown in the table. According to GSEA results, GLI genes actively participate in the following gene sets:

Fig. 9.

Fig. 9

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KEGG pathway gene set enriched terms: a a volcano plot demonstrating the positive- and negative-related enrichment categories of KEGG pathway with respect to GLI1 gene, b a volcano plot demonstrating the positive- and negative-related enrichment categories of KEGG pathway with respect to GLI2 gene, c a volcano plot demonstrating the positive- and negative-related enrichment categories of KEGG pathway with respect to GLI3 gene

Table 2.

KEGG pathway gene set enrichment analysis of GLI genes

KEGG pathway enriched terms for GLI1 gene
S. no. Gene set Description Size Leading edge number ES NES p value FDR
1 hsa04340 Hedgehog signaling pathway 44 15 0.76588 2.3562 0 0
2 hsa05217 Basal cell carcinoma 63 19 0.72193 2.3328 0 0
3 hsa04392 Hippo signaling pathway 25 11 0.76573 2.0433 0 0
4 hsa04330 Notch signaling pathway 48 18 0.61991 1.9108 0 0.0029577
5 hsa04350 TGF-beta signaling pathway 83 21 0.55111 1.8568 0 0.0074364
6 hsa05200 Pathways in cancer 513 112 0.43313 1.7604 0 0.021488
7 hsa05224 Breast cancer 145 41 0.48757 1.7515 0 0.021549
8 hsa05206 MicroRNAs in cancer 149 53 0.43962 1.5859 0 0.057002
9 hsa04520 Adherens junction 66 21 0.53756 1.7326 0.0029326 0.025727
10 hsa05205 Proteoglycans in cancer 196 57 0.44600 1.6662 0.0025316 0.040692
11 hsa05226 Gastric cancer 147 37 0.45157 1.6389 0 0.042948
12 hsa03030 DNA replication 36 16 0.56410 1.6319 0.0032680 0.043004
13 hsa04064 NF-kappa B signaling pathway 90 27 0.48123 1.6420 0.0027778 0.043309
14 hsa04310 Wnt signaling pathway 143 27 0.44741 1.6105 0.0027701 0.050169
15 hsa05219 Bladder cancer 40 6 0.52904 1.5848 0.012987 0.055332
16 hsa05212 Pancreatic cancer 75 13 0.48239 1.5762 0.0058480 0.055773
17 hsa05168 Herpes simplex infection 167 52 0.43152 1.5922 0 0.056176
18 hsa05222 Small cell lung cancer 92 26 0.49563 1.7174 0 0.026427
19 hsa03440 Homologous recombination 34 19 0.60408 1.7233 0 0.026703
20 hsa04512 ECM–receptor interaction 80 38 0.50174 1.7029 0.0031056 0.029154
KEGG pathway enriched terms for GLI2 gene
 1 hsa04340 Hedgehog signaling pathway 44 11 0.73938 2.132 0 0
 2 hsa05217 Basal cell carcinoma 63 14 0.66646 2.115 0 0
 3 hsa03440 Homologous recombination 34 21 0.71827 2.0073 0 0.00052273
 4 hsa04330 Notch signaling pathway 48 21 0.66048 1.9594 0 0.0019602
 5 hsa03030 DNA replication 36 12 0.66881 1.9075 0 0.00345
 6 hsa04550 Signaling pathways regulating pluripotency of stem cells 137 34 0.48551 1.7009 0 0.027287
 7 hsa05224 Breast cancer 145 35 0.47901 1.6679 0.0032787 0.034762
 8 hsa05206 MicroRNAs in cancer 149 36 0.46984 1.6751 0 0.035783
 9 hsa05200 Pathways in cancer 513 94 0.39602 1.568 0 0.083604
 10 hsa05213 Endometrial cancer 58 16 0.49222 1.5323 0.014706 0.11429
 11 hsa04512 ECM–receptor interaction 80 25 0.46807 1.5184 0.020134 0.11506
 12 hsa04064 NF-kappa B signaling pathway 90 26 0.46032 1.5239 0.010033 0.11518
 13 hsa05168 Herpes simplex infection 167 55 0.42315 1.5002 0 0.12277
 14 hsa00514 Other types of O-glycan biosynthesis 17 5 0.60163 1.4613 0.064 0.15566
 15 hsa05222 Small cell lung cancer 92 27 0.44044 1.4515 0.013333 0.15789
 16 hsa04390 Hippo signaling pathway 148 30 0.4473 1.5822 0 0.076528
 17 hsa04919 Thyroid hormone signaling pathway 116 41 0.46405 1.5904 0 0.076282
 18 hsa04520 Adherens junction 66 17 0.51313 1.6179 0 0.060918
 19 hsa03430 Mismatch repair 23 13 0.72021 1.8379 0 0.0058807
 20 hsa00310 Lysine degradation 44 18 0.61666 1.8299 0.0037453 0.0054016
KEGG pathway enriched terms for GLI3 gene
 1 hsa04340 Hedgehog signaling pathway 44 14 0.71731 2.0981 0 0
 2 hsa05217 Basal cell carcinoma 63 21 0.64393 1.9710 0 0
 3 hsa04330 Notch signaling pathway 48 18 0.67135 1.9548 0 0.00064215
 4 hsa04392 Hippo signaling pathway 25 15 0.72990 1.9029 0 0.0028897
 5 hsa05206 MicroRNAs in cancer 149 51 0.53318 1.8553 0 0.0050088
 6 hsa05224 Breast cancer 145 38 0.52266 1.8394 0 0.0054583
 7 hsa04919 Thyroid hormone signaling pathway 116 27 0.54149 1.8153 0 0.0074306
 8 hsa00310 Lysine degradation 44 16 0.61774 1.7906 0.0038023 0.0098730
 9 hsa03440 Homologous recombination 34 19 0.64132 1.7623 0.0042553 0.013057
 10 hsa05205 Proteoglycans in cancer 196 61 0.46161 1.6748 0 0.029193
 11 hsa03430 Mismatch repair 23 12 0.65758 1.6798 0 0.029539
 12 hsa04512 ECM–receptor interaction 80 31 0.52651 1.6809 0 0.031874
 13 hsa03460 Fanconi anemia pathway 44 19 0.56253 1.6403 0 0.035832
 14 hsa05215 Prostate cancer 96 29 0.48765 1.6216 0 0.038416
 15 hsa01522 Endocrine resistance 94 19 0.49222 1.6267 0 0.038649
 16 hsa00562 Inositol phosphate metabolism 74 32 0.50385 1.6104 0.0040816 0.039239
 17 hsa05200 Pathways in cancer 513 104 0.40837 1.6123 0 0.040134
 18 hsa05226 Gastric cancer 147 52 0.45184 1.5986 0 0.040547
 19 hsa04550 Signaling pathways regulating pluripotency of stem cells 137 62 0.46230 1.5875 0 0.045184
 20 hsa04360 Axon guidance 173 55 0.42350 1.5115 0 0.089901
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Supplementary data analysis

On plotting the cumulative log2 expression values of GLI1 and GLI2 in pediatric medulloblastoma and normal pediatric brain samples, we detected very high expression in tumor samples as compared to the normal (Supplementary Fig. 1).

Similarly, on plotting the Kaplan–Meier plot of the prognostic value of GLI1 and GLI2 genes cumulatively expressed in LGG tumor shows that high expression levels of GLI1/2 genes were correlated with worse overall survival in LGG tumor patients in comparison to patients with low GLI1/2 expression levels (Supplementary Fig. 2).

Discussion

In 1987, GLI1 was discovered as an amplified gene in D-259 MG, a human GBM cell line (Kinzler et al. 1987). Kinzler et al. first localized it to chromosome (12q13–14.3), and then to the chromosomal sub-bands (12q13.3–14.1) (Arheden et al. 1989). The human GLI1 gene’s coding region was discovered in 1998 (Kinzler et al. 1988). In glioma cells, GLI maintain the active chromatin state in the promoters of their target genes, but they are not active in a normal adult brain. It was discovered that GLI promotes the motility of glioma cells. It was discovered that GLI activity is linked to glioma cells that possess stem cell-related markers, and that GLI regulates the expression of the stem cell-related genes SOX2, OCT4 (Takahashi et al. 2007).

In our study, we studied the expression profiles, gene interactions, protein interactions, survival analysis and gene ontology of GLI genes, metabolic genes, stemness-related genes in GBM, MB and GSCs using bioinformatics analysis and observed the noteworthy participation of GLI genes, AKT1, ENO2, VEGFA, and SOX2 in Glioma stem cell program activation, and in a variety of brain cancers. We detected the mRNA expression of GLI genes to be upregulated in the GBM (fourth-stage tumor) patients as compared to LGG tumor patients and normal samples and increased expression of GLI genes in MB tumor suggesting the significant role of GLI in gliomas initiation and progression (Fig. 1). Moreover, SOX2 stem cell marker expression appears to be highly upregulated in glioblastoma stem-like cells, signifying that the gliomas’ cells maintain their cell number throughout (Fig. 1g). Various literature studies support that the GLI1 gene is an activator of Hh signaling pathway and is highly upregulated in malignant gliomas (Ranjan and Srivastava 2017; Wang et al. 2017). Our findings suggest that along with GLI1, the GLI2 and GLI3 genes are highly upregulated in GBM and MB as shown in (Figs. 1, 2) with an elevated expression of GLI2 and GLI3 genes in gliomas, including LGG, GBM, and MB. The expression of GLI1 is found to be slightly low in LGG than normal samples but high in GBM, which may suggest that GLI1 may act as a prognostic marker for high-grade tumors. Also, in an experimental study, the results were found to be similar in which many LGG patients had low GLI1 protein expressions, and also there was no significant difference in patient survival with low GLI1 expression and high GLI1 expression in LGG cancer (Gricius and Kazlauskas 2014).

Change in expression of GLI genes from low to high is suggesting the progression from normal to GBM and LGG to GBM, indicating that GLI genes’ expression is substantially associated with GBM development and progression. We discovered that the GLI genes’ expression is substantially linked to survival rate, as evidenced by poor patient survival rate in LGG and GBM (Fig. 3). Moreover, the patients with high GLI2 and GLI3 gene expression are found to have less survival time than the patients with high GLI1 expression in GBM, suggesting the high activity of GLI2 and GLI3 genes in GBM cancer progression. However, all the three GLI genes are found to show almost similar survival time of less than 150 months with their high expression in LGG patients (Fig. 3a–c).

Along with the overexpression of GLI genes, we found that the expression of metabolism-related genes like ENO1, ENO2, GAPDH, PFKP, etc. was also upregulated (Fig. 4a). Enolase is a key enzyme in the glycolytic process found in from bacteria to mammals. ENO1 has been linked to a variety of malignancies, like bladder and colorectal cancers (Ji et al. 2019; Zhan et al. 2015). In the patient’s serum with small cell lung cancer, ENO2 or NSE is overexpressed (Xu et al. 2016). ENO2’s main function in cancer is to speed up glycolysis, which helps tumor cells meet their increased metabolic demands and allows them to proliferate (Vizin and Kos 2015). Although, we found the expression of ENO3 to be downregulated (Fig. 4a). Similarly, PFK1 (phosphofructokinase 1) plays an important role in glycolytic pathway by altering fructose-6-phosphate to fructose-1,6-bisphosphate, and ATP to ADP. Lee et al. (2017) reported PFKP is the most abundant PFK1 isoform in human glioblastoma cells, and its expression corresponds with overall PFK activity, which is also corroborated in our study with the help of heatmap. VEGFA (vascular endothelial growth factor A), a crucial angiogenic factor that changes the endothelial cell niche to encourage the development of new vessels (Bergers and Benjamin 2003), also induces growth, survival, and metastasis in various malignant tumors (Wu et al. 2006; Pàez-Ribes et al. 2009). We have also observed the high expression of VEGFA in GBM patient samples (Fig. 4a).

AKT is a serine/threonine kinase, which is found to be activated in astrocytoma and is closely associated with poor patient outcomes (Wang et al. 2004). Increased activity of AKT boosts the malignancy of invasive GBM cells, while it has been found that AKT downregulation prevents GBM cell invasion (Zhang et al. 2009). Remarkably, upregulated expression of AKT as well as enhanced phospho-AKT expression levels provides the progression to astrocytoma (Sonoda et al. 2001). In our study, we observed upregulated expression of AKT1 in GBM tumor samples in comparison to normal (Fig. 4a). In our results, we also found the active participation of AKT1 in Glioma stem cell program activation, brain cancer, cancer metastasis, Warburg effect, etc. (Fig. 8a, c). AKT1 is found to be directly correlated with GLI2 as well as SOX2, and it has a direct interaction with metabolic genes and angiogenesis-related gene including VEGFA, ENO1, ENO2, PFKP, etc. (Fig. 5), suggesting that the inhibition of AKT1 along with GLI genes and with metabolic gene can be a better strategy to combat brain cancer. As shown in results, (Fig. 6) depicts that the GLI protein takes part in brain development, regulation of cellular metabolic processes, transcription regulator activity, and in cancer pathways. Also, GLI1, GLI2 and ENO2 (enzyme involved in glycolytic energy metabolism in brain) are found to take part in brain cancer activity. GLI1 and GLI2 proteins are found to be involved in medulloblastoma, inferring their significant role in brain cancer progression.

CSCs show a proclivity for dividing asymmetrically and producing more differentiated progeny, which makes up the majority of the cancer cell population. Undifferentiated CSCs are also resistant to standard chemotherapies that destroy differentiated tumor cells, and they are accountable for tumor growth or recurrence. Because CSCs are a driving factor behind carcinogenesis, metastasis, and therapy failure, eliminating CSCs is critical for long-term treatment efficiency (Beck and Blanpain 2013). CSCs hold significant traits of embryonic stem cells (ESCs) through the conventional molecular signaling pathways, like hedgehog (Hh)-GLI pathway and stemness-related factors like SOX2 (Justilien and Fields 2015). In a research study, PAX6 increases GLI transcription, which causes SOX2 to be upregulated by GLI binding to the SOX2 gene's proximal promoter region. With the overexpression of SOX2, the expression of essential pluripotent factors also increases (Ooki et al. 2018). With our bioinformatic study, we have also found the significant correlation of GLI genes with stemness-related factors like SOX2, SOX9, POU5F1, NANOG, etc. (Figs. 4, 5, 7).

In GSE analysis (Fig. 8), we observed the significant involvement of GLI genes (GLI1, GLI2, GLI3), and SOX2 in Glioma stem cell program activation, medulloblastoma tumor development, GBM, glioma, neuroblastoma, etc. Metabolic gene ENO2, metastasis-related gene AKT1, and angiogenesis-related gene VEGFA are also found to be associated with GBM and other prominent cancers, suggesting that the inhibition of these important genes along with GLI genes could be a ground-breaking approach to target brain cancer. Gene set enriched terms for GLI1/2/3 genes from KEGG pathway suggest that GLI genes actively participate in the important cancer-related gene sets like hsa04340 which describes Hh signaling pathway, hsa04550 that relates to signaling pathways regulating pluripotency of stem cells, hsa05200 which depicts pathways in cancer, etc.

Conclusion

In this study, the analysis of human brain tumor data from TCGA, GTEx, and GEO database suggests that GLI mRNA expression upregulates in GBM, LGG, and medulloblastoma patients than normal. Also, the expression of SOX2 rises in GSCs. We have notably found that the expression of GLI2 and GLI3 genes is more than the GLI1 expression in LGG and GBM. However, the expressions of all three GLI genes in MB are approximately similar with no significant difference. In the network analysis, we observed the strong correlation of GLI with genes associated with metabolic dysregulations, angiogenesis, metastasis and stemness in brain tumors. With our knowledge, we suggest that in terms of future perspective, inhibition of GLI genes along with stemness-related factor SOX2, metabolic gene ENO2, metastasis-related gene AKT1, and angiogenesis-related gene VEGFA could be a pioneering strategy to combat a variety of brain cancers, such as LGG, GBM, and MB. It can also be used for personalized therapeutic treatment with the cancer patients having upregulated GLI genes expression levels. GLI genes can be targeted with drug molecule and can be modeled in combination to other drugs to be better able to induce tumor cell death. Though, more research is required to validate the significance of GLI genes in brain cancer cells. Additionally, targeted drug therapy needs to be validated through in vitro and in vivo investigations. A clear understanding of the mechanism in which GLI genes affect the various GBM, MB signaling pathways requires to be done in future findings.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PPTX 220 KB) (219.8KB, pptx)

Acknowledgements

The authors are thankful to the laboratory colleagues and School of Health Sciences and Technology (SoHST), UPES University, Dehradun, Uttarakhand, 248007, India for partial financial support for this study. We thank our lab members and collaborators for carefully reading the manuscript and contributing valuable inputs for improving the manuscript.

Data availability

All the data related to this study will be made available on request for further study and analysis.

Declarations

Conflict of interest

Authors declare that there are no conflicts of interests among the authors about the publication of the manuscript.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. Ji M, Wang Z, Chen J, Gu L, Chen M, Ding Y, Liu T. 2019. Up-regulated ENO1 promotes the bladder cancer cell growth and proliferation via regulating β-catenin. Biosci Rep. [DOI] [PMC free article] [PubMed]

Supplementary Materials

Supplementary file1 (PPTX 220 KB) (219.8KB, pptx)

Data Availability Statement

All the data related to this study will be made available on request for further study and analysis.

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