An overview of work flow has been summarized and consists of the following four sections; a) Patients clinical samples, b) GBM RNA-sequence bioinformatic analysis, c) Insilico studies to identify drug targets in glioblastoma and d) validation by MTT Assay as shown in Fig. 1.
2.1 Clinical Samples Analysis
2.1.1 Ethical Approval and Resource Sharing
The study was conducted in accordance with the Declaration of Helsinki, and study received approval by the ethical review board of the Capital University of Science and Technology (CUST), Islamabad, Pakistan (Ref:BI&BS/ERC/19 − 2 and September 23,2019).” All the patients provided verbal and written consent to the scientific use of their data. The biopsy samples of 33 (23 males, 10 females of median age 50 ± 13 years) glioblastoma patients were collected from various surgery departments, public sector tertiary care hospitals of Pakistan who underwent brain surgery between January 2018 and December 2021. Before sample collection, none of the participants in the study had radio therapeutic or chemotherapeutic treatment.
2.1.2 MRI Imaging
Each patient was subjected to an intraoperative MRI scan for trajectory planning on a 3 Tesla MRI scanner (Siemens AG Healthcare, Erlangen) with the following criteria: FoV = 260mm x 260mm, voxel size = 1.03mm3, and image matrix = 256x256.
2.1.3 Histopathology
Twenty one Biopsy samples, approximately 1mm3 in size were collected and using hematoxylin and eosin staining (HE stain), the purity of the tumour samples was assessed by a histopathologist to validate that each sample contained > 80% malignant cells.
2.1.4 Quantitative RT-qPCR Analysis
During ablation, specimens were snap-frozen in liquid nitrogen and kept at -80°C until RNA extraction. The TriZol reagent was used to extract total RNA. Superscript II reverse transcriptase was used to synthesize cDNA (Invitrogen, Paisley, UK), and qPCR was performed to amplify the specific PCR products of the three genes proposed in this study using the SYBR Green Master Mix kit (Thermoscientific, CA, USA). mRNA expression of each gene was examined using the 2 −ΔΔCt method and β-actin as the reference gene [23].
2.1.5 ELISA
Glioblastoma biopsy samples were sealed in sterile containers, snap-frozen until protein extraction. The concentrations of GCSF, GCSFR, and phosphorylated STAT3 were measured using protein-specific ELISA kits (Abcam ELISA kit USA) according to the manufacturer's procedure. A spectrophotometer immediately measured the specific binding optical density at 450 nm.
2.1.6 Cluster Analysis of Gene Expression Using Hierarchical Heat Maps, Principal Component Analysis (PCA), and Uniform Manifold Approximation and Projection (UMAP)
Qlucore Omics Explorer 3.8.9 was used to perform PCA, Hierarchical Heat Map Cluster and UMAP analysis with a cut of q-value (adjusted p value) of < 0.05 for the expression of GCSF, GCSFR and STAT3 genes. The euclidean distance was taken as the default distance technique, and complete linkage as agglomeration mode. After removing variables with high overall variance to reduce the impact of noise, the remaining variables were scaled and centred to have zero mean and unit variance before being visualised using PCA. The projection score was used to determine the ideal filtering threshold in order to keep N variables. We used Uniform Manifold Approximation and Projection (UMAP) on the normalised count data of three genes, and using the default settings (Benjamini-Hochberg) in Qlucore Omics Explorer 3.8.2 [46] .
2.2 Bioinformatic Analysis of Genome Atlas Databases of Glioblastoma
2.2.1 Transcriptomic Analysis
To investigate the differentially expressed genes (DEGs) in GBM patients, The Cancer Genome Atlas (TCGA), Genome Tissues Expression database (GTEx) and Gene Expression Profiling and Interactive Analyses GEPIA2 were used [9]. We retrieved expression data of 163 glioblastoma cases from TCGA and 207 normal brain tissues from GTEx. We validated differential analysis of GCSF, GCSFR, and STAT3 expression via GEPIA2 and depicted the results using a boxplot with log2 of transcript count per million representing the expression level of DEGs. Log2FC| Cutoff was applied to compute p-values. The log2FC| Cutoff value was adjusted at 1 with q-value cutoff at 0.01.
2.2.2 Survival Analysis
We grouped glioblastoma multiforme (GBM) samples into high and low GBM classes based on the optimal cut-off using the R package surv Misc and the gepia-2 programme. A survival package was used to conduct a Kaplan-Meier analysis employing a log-rank test to investigate the relationship between GBM-associated genes expression under investigation and survival. A p-value of 0.05 or less was taken as statistically significant. [10].
2.2.3 Genomic Landscape Mutation Analysis
Using a human proteo-genomics database ,Active Driver DB, and the cBioportal database, we analyzed DEGs mutations [11]. These are utilized to detect protein post-translational modification (PTM) sites and to visualize and analyze multimodal cancer genetics. Gene alterations, a set of gene types, the association between gene mutations and the prognosis of GBM patients were assessed using cBioPortal based on the TCGA database. The significance level was set at p < 0.05 [12].
2.2.4 Infiltrative Immune cell Analysis
To investigate the infiltration of various immune cells and their clinical impact, the immune cell correlation analysis of GCSF was practised using the immunedeconv package in R and CIBERSORTx method through TIMER2.0 server, integrating samples data from the TCGA and CGGA datasets. After setting batch correction, executing "Bulk mode," and selecting the quantile normalization algorithm, sample results were adjusted for purity where necessary, and correlations with Spearman's p < 0.05 were shown. Using the Wilcoxon rank-sum test, the differences between the two subgroups were determined [13].
2.2.5 Gene Enrichment Ontology and Protein interactions
Gene ontology studies were conducted utilizing the g: Profiler, David, and Funrich servers [14]. Significant signalling pathways and cellular components of differentially expressed genes were identified using GO. Protein-protein interactions (PPIs) were performed to explore differences in biological function. Metascape and STRING.v.10 were used to find the intrinsic interactions of the source gene [15–16]. Additionally, the functions of the target genes in GBM were retrieved from several databases, including PubMed, CTD, and OMIM [17]. Cytoscape software version 3.6 was used to illustrate the network to investigate the significance of source (DEGs) and target proteins in patients with glioblastoma [18–19].
2.2.6 Pathway Enrichment and Integrated Modelling
We investigated the pathway enrichment of DEGs using the Shiny GO tool and FunRich tool version 3.1.3 with p-values < 0.05 that were statistically significant [20]. The curation and mapping of potential biomarkers were performed using the Reactome, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Wiki pathways [21]. PathVisio3 tool was utilized to reassemble the biological and signalling pathways of prospective biomarkers [22].
2.3 Molecular Docking Using Nisin Bacteriocin Peptide Complex
2.3.1 Retrieval of Experimentally Reported Nisin and Visualization
The crystal structure of the target protein, named GCSF (PDB: 5GW9), was obtained from the Protein Data Bank (www.rcsb.com). The sequence was retrieved from the Uniprot database [24]. Using Chimera software, water molecules and heteroatoms were removed from the PDB data.The potential anticancer Nisin bacteriocin peptide (PDB: 1WCO) was also retrieved from the Protein Data Bank (www.rcsb.com) [25]. The protein and drug library was generated using the Molecular Operating Environment (MOE) software. To prepare proteins and ligands for docking, the protonate 3D technique in MOE was employed to add hydrogens, followed by energy minimization. After minimizing energy, the AMBER99 force field was employed to eliminate additional unbounded structures. Seven optimal configurations were selected using the force field refinement method. Protein-protein docking studies of putative bacteriocins and their interacting proteins were validated by ClusPro [26]. UCSF chimera was used to analyze and visualize the docking results (www.cgl.ucsf.edu/chimera/download.html) [27].
2.3.2 Model Evaluation and Validation
The Procheck tool confirmed the stereochemical correctness and overall structural geometry of the protein structure [28]. The Ramachandran plot statistics were used to assess the model's stability and validate the residues. In addition, the Ramachandran plot and Z-score analysis were performed on four high-resolution GCSF structures selected from the PDB database with PDB ID 5GW9 [29]. Normal mode analysis (NMA) was also carried out to comprehend the stability and flexibility of the docked model. The iMod technique was used to calculate the degree of stability. The elastic network model, deformability, eigenvalue, and covariance matrix were computed [30].
2.4 Cytotoxicity Assay
The cell viability percentage was evaluated using MTT assay. In a 96-well culture plate, approximately 1×103 cells of SF-767 glioblastma cell line were seeded and treated with different dosage concentrations of Nisin (1, 5, 10, 30, 60 and 100 µg/mL) for 48 hours at 37°C. The cellular fraction was labelled with MTT solution (5 mg/mL in PBS) for 4 hours after discarding the supernatant, followed by solubilization in 50 µL of dimethyl sulfoxide (DMSO). The plate also contained cells treated with PBS as a negative control. The absorbance was then measured at 570 nm (with 620 nm as a reference).