Unveiling the Diagnostic and Prognostic potential of BMP Pathway and Hypoxia-inducible Factors in Glioblastoma Multiforme

Unveiling the Diagnostic and Prognostic potential of BMP Pathway and Hypoxia-inducible Factors in Glioblastoma Multiforme | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unveiling the Diagnostic and Prognostic potential of BMP Pathway and Hypoxia-inducible Factors in Glioblastoma Multiforme Behnaz Yazdani, Adel Rezvani Sichani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4232372/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Glioblastoma Multiforme (GBM) is a devastating neoplastic growth affecting the brain, with a dismal prognosis. The underlying diagnostic and prognostic potential hypoxia-inducible-factors and BMP pathway in this devastating malignancy remains poorly understood, lacking compelling preventive strategies. Methods and materials: A bioinformatic study was conducted using integrative bioinformatics techniques for the analysis of GBM count data, which were obtained from the Cancer Genome Atlas (TCGA) database and underwent normalization and differential expression analysis (DEG). Gene Set Enrichment Analysis (GSEA), Differential gene expression analysis, and correlation analysis using Pearson method were conducted for the genes involved in the BMP pathway. Gene Ontology and Protein-protein interaction analyses were employed. Survival analysis and Receiver Operating test (ROC) were also performed to identify potential prognostic and diagnostic biomarkers. Results The results revealed that the expression levels of EPAS1, HIF3A, CHRDL1, NOG, BMP6, and AHSG genes did not exhibit a statistically significant difference between GBM cancer samples and normal tissue samples. Further DEG analysis indicated that the majority of genes from the BMP pathogenesis were significantly downregulated in GBM cancer samples and a positive correlation was observed between the expression levels of EPAS1, BMPR2, and MAPK1 genes. the Top DEGs were correlated with specific pathways, such as the TGF-beta signaling pathway, pathways in cancer, and the cell cycle. By ROC test we identified the best diagnostic biomarkers for GBM and SMURF1 gene is predicted to have significant prognostic capability. Conclusion These findings highlight the possible utility of these genes as promising diagnostic and prognostic biomarkers for the early detection of GBM. Oncology Epigenetics & Genomics Bioinformatics Glioblastoma Multiforme Bone Morphogenetic Proteins Hypoxia-Inducible Factors TCGA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Glioblastoma Multiforme (GBM), a brain cancer that can originate from brain tissue ( 1 ), has been classified as an aggressive form of cancer with poor survival rates ( 2 ). The causes of glioblastoma cancer are still poorly understood, and preventing glioma accurately remains a challenge ( 3 ). In 2023, the American Cancer Society reported approximately 24810 new cases of glioblastoma cancer, with a staggering 18990 cases resulting in death ( 4 ). This highlights the urgent need for early biomarkers to detect and prevent glioblastoma cancer ( 5 ). Bone Morphogenetic Proteins (BMPs), part of the TGF-β superfamily, play crucial roles in embryonic tissue development, bone and cartilage formation, and tissue regeneration ( 6 ). Studies have shown a significant association between altered expression levels of BMP proteins and cancer cell progression or suppression ( 7 ). Depending on the specific protein member and type of cancer cell, BMP proteins can exhibit dual roles as oncogenes or tumor suppressors ( 8 ). Additionally, the expression levels of genes in the BMP pathway have been implicated in cancer cell progression at more advanced stages ( 9 , 10 ). The expression of BMP proteins can be influenced by various factors, including oxygen levels ( 11 , 12 ). Under hypoxic conditions, cancer cells undergo adaptive changes to survive and proliferate, which involve alterations in the expression levels of specific proteins in various cancer-related pathways ( 13 ). Certain BMP proteins can either be induced or downregulated by hypoxia, depending on the cell type ( 11 ). Several studies have demonstrated the involvement of Hypoxia-Inducible Factors (HIFs) in regulating the BMP signaling pathway under hypoxic conditions. Specifically, HIF-1α Factor has been observed to repress the BMP signaling pathway in high-grade glioma (HGG) cancer cells ( 14 ). Moreover, BMP proteins not only regulate stem cells during brain tissue development but also have the potential to induce apoptosis, mitogenic arrest, and cell differentiation in central nervous system (CNS) stem cells ( 15 ). However, the correlation between the expression patterns of BMP proteins and Hypoxia-Inducible Factors in glioblastoma cancer patients remains poorly understood. To address this gap, our study focuses on analyzing the expression patterns of HIF factors and different members of the BMP pathway in TCGA GBM cancer and normal tissue samples. Various techniques, including GSEA, DEG, and correlation analysis, were employed to identify the most perturbed genes in the BMP pathway. Furthermore, functional annotation and Protein-protein interaction network analyses were conducted to elucidate the molecular function of BMP proteins in GBM cancer. Finally, ROC curve and survival analyses were performed to assess the prognostic and diagnostic potential of these proteins and identify robust biomarkers for GBM cancer detection. Methods and Materials Data Collection and differential gene expression analysis The RNA-seq count data of patients with GBM (Glioblastoma multiforme) cancer was acquired from the TCGA database ( The Cancer Genome Atlas Program (TCGA) - NCI) and subjected to analysis in adherence to the principles outlined in the Declaration of Helsinki and TCGA guidelines for data access and usage. Before identifying the differentially expressed genes (DEGs) in GBM cancer samples compared to normal samples, the normalized count data for all samples was converted into logarithmic form (Log2 ratio). The RNAseq data of the GBM project was obtained from the TCGA dataset using the "TCGAbiolinks" package, a powerful tool in the R programming language specifically designed for the retrieval and analysis of TCGA data. This package provides seamless integration with the TCGA platform, ensuring efficient and accurate data extraction. Following data collection, the GBM cancer and normal tissue samples were analyzed using the R program and the Limma package. The gene expression data underwent preprocessing and normalization using the R programming using the Limma package ( 16 ). The Limma package is widely recognized for its utility in differential gene expression analysis, making it an ideal choice for this study. To ensure reliable and precise normalization of the RNAseq count data, the Voom normalization method was employed. The Voom method employs a statistical framework to transform count data into log2 ratios, providing a robust basis for downstream analysis and comparison of gene expression ratios( 17 , 18 ). The metan package in R programming with the Pearson statistical method was used for the calculation of p -values and correlation coefficient values. GSEA Analysis When it comes to studying biological pathways and their connection to specific diseases, the gene set enrichment analysis (GSEA) technique proves to be highly valuable. It provides insightful information by evaluating the correlation between the expression levels of a specific set of genes and the defined phenotype for each group of samples. Essentially, GSEA allows users to gauge the level of correlation among a set of genes associated with a particular biological pathway in the context of a specific disease of interest ( 19 ). For this particular study, we selected a list of 42 genes that are predicted to play a role in the BMP signaling pathway. These genes were obtained from the MSigDB database ( https://www.gsea-msigdb.org/ ) ( 20 ). To perform the GSEA analysis, we utilized the normalized expression data from GBM TCGA samples as the expression matrix. In order to define the phenotypes for each sample, we relied on the histopathological and clinical data provided in the TCGA database. Specifically, two phenotypes were defined: "cancer" and "normal." To perform the GSEA analysis, we utilized version 4.0.3 of the GSEA software developed by the BOARD Institute. The tTest method was chosen as the ranking method for the gene list, while the other parameters were maintained at the software's default settings. Through an examination of the correlation between the expression level of each gene within the gene set across all samples, the GSEA analysis produced a Ranking metric score. This score serves as an indicator of the enrichment level of individual genes within the given gene set. Functional Enrichment Analysis To identify the most important biological pathways in GBM cancer, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted on the top 200 genes with differential expression in GBM cancer samples ( 21 ). This analysis was performed using the DAVID database (version 6.8, available at https://david-d.ncifcrf.gov/ ) and employed the hypergeometric algorithm ( 22 , 23 ). The selection of functionally enriched biological processes and KEGG pathways was based on a screening criterion of a P-value less than .05 and the count of genes predicted to be involved in each Gene Ontology category. Protein-Protein Interaction Network Analysis To gain a deeper understanding of the protein-protein interactions involving HIF factors, BMP proteins, and the top differentially expressed genes in GBM cancer, we employed the Search Tool for Retrieval of Interacting Genes (STRING) database ( 24 ). This online tool helps with the evaluation of protein-protein interaction networks (PPI). Specifically, we utilized the STRING database (version 10.0, accessible at http://www.string-db.org/ ) and selected the top significant 200 DEGs (differentially expressed genes) in GBM cancer, along with HIF factors and the 17 top-scoring genes enriched in the BMP pathway as our input genes. In this analysis, the selected input genes were defined as DEGs, while the species was specified as human. We set the PPI score threshold to 0.4, allowing us to create subsets of medium-confidence human PPI networks. To visualize and interpret the predicted PPI network, we utilized the Cytoscape tool (version: 3.2.0, available at http://www.cytoscape.org/ ) ( 25 , 26 ). The CytoNCA (version 2.1.6, http://apps.cytoscape.org/apps/cytonca ) tool was employed to analyze the topological characteristics of the node network. The calculation parameter was set to exclude weights. This permitted the extraction of node scores, which in turn facilitated the ranking of nodes based on their importance in the protein-protein interaction (PPI) network. By considering nodes with high scores as "hub proteins," we can identify key contributors to PPI networks. Receiver operating characteristic test One way to estimate the diagnostic capability and effectiveness of specific genes in GBM cancer is by employing the Receiver Operating Characteristic (ROC) test. This test was conducted on 17 genes with high enrichment scores in the BMP pathway, as identified through GSEA analysis using normalized gene expression data from GBM cancer and normal samples. By identifying genes with higher AUC (Area Under Curve) values and smaller P-values, we can anticipate their potential as valuable diagnostic biomarkers. Survival Analysis The OncoLnc repository is a database specifically tailored to streamline the analysis of cancer-related data. It operates as a comprehensive database that collates information from different online data sources, including The Cancer Genome Atlas (TCGA) ( 27 ). OncoLnc integrates an extensive array of data, spanning gene expression profiles, clinical data, and survival statistics. Consequently, it has fostered the identification of potential prognostic markers, biomarkers, and therapeutic targets across diverse cancer types. By examining survival data in conjunction with gene expression profiles, the investigation of significant genes associated with clinical outcomes is made more accessible. The OncoLnc platform ( http://www.oncolnc.org ) offers access to gene expression data for mRNAs and miRNAs from The Cancer Genome Atlas (TCGA), alongside RNA-Seq expression data. All the top 17 enriched scored genes in BMP pathway were analyzed by the OncoLnc repository for survival analysis in GBM cancer. Results GSEA analysis of BMP pathway in GBM cancer In the field of molecular biology and genetics, identifying statistically significant gene signatures holds great potential for understanding various phenotypes and their underlying genetic mechanisms. To better comprehend the relationship between gene sets and phenotypic traits, advanced analytical methods such as Gene Set Enrichment Analysis (GSEA) have proven to be invaluable tools. GSEA analysis allows for the systematic examination of gene sets, helping with the detection of altered biological pathways and providing insights into relevant biological mechanisms. In our study, we utilized an established statistical threshold (FDR < 0.25) to select the statistically significant gene signatures. The significance level is expressed as a normalized enrichment score (NES), which quantifies the degree of association between a phenotype and a gene signature (Fig. 1 .A-C). Upon analyzing the BMP pathway with GSEA technique in GBM cancer and normal count data, a distinct pathway enrichment in the normal phenotype was observed with an enrichment score (ES) of -0.17 and Nominal p-value of 0.89, which is not statistically significant and we can predict that the BMP pathway is not significantly differentially enriched in GBM cancer samples in compare to normal samples. To identify key genes associated with GSEA cluster enrichment, a ranking metric called the 'Rank Metric Score.' was utilized. This score is calculated based on the signal-to-noise ratio of each gene, providing valuable insights into its significance in the enriched list. By help of this analysis, we detected and selected the top 14 core genes from BMP pathway that drive the enrichment score of the GSEA clusters for further analyses. Differential gene expression and Correlation analyses Differential gene expression test was performed on the top enriched genes from the BMP pathway in GBM cancer samples, as well as the three members of hypoxia-inducible factor alpha transcription factors .According to the volcano plots (Fig. 1 .D-F), the expression levels of certain genes, namely EPAS1(Endothelial PAS Domain Protein 1), HIF3A (Hypoxia Inducible Factor 3 Subunit Alpha), CHRDL1 (Chordin Like 1), NOG (Noggin), BMP6 (Bone Morphogenetic Protein 6), and AHSG (Alpha 2-HS Glycoprotein), did not show significant variations in GBM cancer samples when compared to normal tissue samples. The data indicates that the Log2 Foldchange and -Log10 p-value for EPAS1 were 0.24 and 0.24 respectively. Similarly, for HIF3A, these values were − 0.66 and 0.31 respectively. In the case of CHRDL1, the Log2 Foldchange and -Log10 p-value were − 1.82 and 1.54, while for NOG, they were 0.66 and 0.66. Additionally, the Log2 Foldchange and -Log10 p-value for BMP6 were − 0.93 and 0.75, and for AHSG, they were − 0.82 and 0.55 respectively. However, HIF1A (Log2 Foldchange = 1.42, -Log10 p-value = 4.71) gene showed to be significantly upregulated in cancer samples. DEG analysis results demonstrated a notable decrease in the activity of several genes linked to the BMP pathway in cancerous samples when compared to normal samples. The genes with significant decreased expression levels in GBM cancer samples include SOSTDC1 (Sclerostin Domain Containing 1) with a Log2 Foldchange of -3.85 and -Log10 p-value of 5.41, GREM1 (Gremlin 1, DAN Family BMP Antagonist) with Log2 Foldchange of -2.93 and -Log10 p-value = 2.23, CHRD (chordin) with Log2 Foldchange of -1.96 and -Log10 p-value of 5.47, SMAD7 (SMAD Family Member 7) with Log2 Foldchange of -1.75 and -Log10 p-value of 6.05, PPM1A (Protein Phosphatase, Mg2+/Mn2 + Dependent 1A) with Log2 Foldchange of -1.51 and -Log10 p-value of 13.29, BMPR2 (Bone morphogenetic protein receptor type-2) with Log2 Foldchange of -1.30 and -Log10 p-value of 10.16, MAPK1 (Mitogen-activated protein kinase 1) with Log2 Foldchange of -1.47 and -Log10 p-value of 8.10, ZFYVE16 (Zinc Finger FYVE-Type Containing 16) with Log2 Foldchange of -0.80 and -Log10 p-value of 4.75, GSK3B (Glycogen Synthase Kinase 3 Beta) with Log2 Foldchange of -0.87 and -Log10 p-value of 3.19, and SMURF1 (SMAD-specific E3 ubiquitin protein ligase 1) with Log2 Foldchange of -0.72 and -Log10 p-value of 2.42. These findings suggest an anomaly in the BMP pathway during the development of cancer. The decline in SOSTDC1, GREM1, CHRD, SMAD7, PPM1A, BMPR2, MAPK1, ZFYVE16, GSK3B, and SMURF1 genes might contribute to the progression and pathogenesis of cancer. The correlation analysis was performed between hypoxia-inducible transcription alpha members and top enriched genes from BMP pathway. The expression level of HIF1A in GBM cancer samples revealed a moderate positive correlation with the expression levels of EPAS1 (Correlation Coefficient = 0.45, p -value = 5.98E-10), PPM1A(Correlation Coefficient = 0.42, p -value = 9.45E-09) genes along with a weak negative correlation with SOSTDC1 (Correlation Coefficient= -0.24, p -value = 0.0014) gene (Fig. 1 .G). A moderate positive correlation was also detected between the expression levels of EPAS1 and BMPR2(Correlation Coefficient = 0.40, p -value = 3.37E-08) genes. The CHRDL1 expression level negatively correlated with the expression levels of HIF1A (Correlation Coefficient= -0.29, p -value = 0.0001) and EPAS1(Correlation Coefficient= -0.25, p -value = 0.0008) genes. The expression level of SMAD7 gene also positively correlated with GREM1(Correlation Coefficient = 0.46, p -value = 1.61E-9), and SMURF1(Correlation Coefficient = 0.46, p -value = 1.31E-10) expression levels. The BMPR2 expression level also demonstrated a moderate positive correlation with MAPK1(Correlation Coefficient = 0.48, p -value = 3.47E-11) expression ratio among glioblastoma cancer samples. The Log2 expression level graph of all the following genes is also illustrated in Fig. 2 . However, the Log2 Foldchange values are much more statistically reliable compared to Log2 expression levels. To better understand the correlation between the expression levels of HIF alpha transcription factors with the top enriched genes from the BMP pathway, correlation analysis was performed on GBM cancer and normal samples. Gene Ontology and Functional enrichment analyses To gain insights into the biological functions of top enriched genes from BMP pathway in GBM cancer, gene ontology (GO) function enrichment analysis was done. The online DAVID database was utilized for this analysis, which offers a comprehensive platform for functional annotation and enrichment analysis. The GO function enrichment analysis provided valuable information regarding the biological processes (BP), molecular functions (MF), and cellular components (CC) associated with the selected genes. The analysis helped uncover specific functional categories that play a crucial role in GBM development and progression. In addition to the gene ontology (GO analysis, KEGG pathway enrichment analysis was also done to identify the key pathways most likely to be involved in GBM cancer. As depicted in Fig. 3 , The biological processes of the top DEG genes were involved in the positive and negative regulation of transcription from RNA polymerase II promoter (GO:0000122 and GO:0045944), as well as protein phosphorylation (GO:0006468) (Fig. 3 .A). Most of these genes were estimated to be most likely localized in the nucleus (GO:0005634) and cytoplasm (GO:0005737) (Fig. 3 .B). The molecular functions of these genes were also shown to be associated with ATP binding (GO:0005524), DNA binding (GO:0003677), and protein serine/threonine kinase activity (GO:0004674) (Fig. 3 .C). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database is a great resource that provides a complete collection of pathway information and functional annotations. Through the KEGG pathway enrichment analysis, the top 200 significantly differentially expressed genes in GBM cancer samples, were correlated with unique pathways, such as TGF-beta signaling pathway (hsa04350), pathways in cancer (hsa05200), and cell cycle (hsa04110) (Fig. 3 .D). This analysis shed light on the intricate interplay of biological processes involved in GBM development, facilitating a deeper understanding of disease progression. Protein-Protein interaction network analysis The PPI network of the top 200 DEG genes in Glioblastoma cancer samples, top enriched genes from the BMP pathway, and HIF alpha factors in GBM cancer were constructed using the STRING database and CytoNCA application in Cytoscape software. The PPI network demonstrated the interconnectivity between these proteins and the genes that are up and downregulated in GBM cancer samples were colored as blue and orange colors respectively (Fig. 3 .E). While EPAS1 and HIF3A genes did not interact with the rest of the genes significantly, HIF1A was predicted to interact with CDK1 (Cyclin Dependent Kinase 1), CCNA2 (Cyclin A2), MAPK1 (Mitogen-activated protein kinase 1), POTEF (POTE Ankyrin Domain Family Member F), GSK3B (Glycogen Synthase Kinase 3 Beta), and TROAP (Trophinin Associated Protein) genes. In the case of our study, The CytoNCA application allowed us to calculate the node connectivity between the genes from the constructed PPI network. The degree scores of the top 20 genes were determined, with an emphasis on identifying key nodes that play a crucial role in the PPI network. Among these genes, we observed substantial connectivity, with degree values exceeding 20. As shown in the Supplementary Table.1, the cyclin B1 (CCNB1), DNA topoisomerase II alpha (TOP2A), cyclin-dependent Kinase 1 (CDK1), cyclin A2 (CCNA2), and aurora kinase B (AURKB) genes demonstrated highest connectivity and degree scores within the PPI network. Receiver operating characteristic test The detection and distinction of tumor samples from control tissues are crucial in the early diagnosis of GBM cancer. The use of potential biomarkers plays a vital role in differentiation of cancer samples from normal tissue samples. Therefore, we evaluated the diagnostic potential of the HIF alpha factors and the top enriched genes from BMP pathway in GBM cancer samples. By utilizing GraphPad Prism software (version 9.1.0), we conducted the ROC examination to assess the diagnostic potential of these potential biomarkers. As demonstrated in the Fig. 4 , the 7 genes had significant AUC (Area Under Curve) values above 0.90, including SOSTDC1 (AUC = 0.96, p-value = 0.0004), CHRD (AUC = 0.96, p-value = 0.0004), SMAD7 (AUC = 0.97, p-value = 0.0003), PPM1A (AUC = 1, p-value = 0.0001), MAPK1 (AUC = 0.95, p-value = 0.0005), SMURF1 (AUC = 0.91, p-value = 0.001), and HIF1A (AUC = 0.96, p-value = 0.0004) genes. These findings bring us one step closer to the early detection of GBM cancer, which is crucial for effective treatment and improved patient outcomes. Further studies can explore the clinical application of these enzymes in diagnostic processes and pave the way for enhanced precision medicine approaches in GBM management. Survival analysis By examining the relationship between gene expression levels and patient survival data, researchers can identify genes that may play a crucial role in cancer prognosis. Survival analysis provides insights into the impact of these genetic factors on patient survival outcomes, enabling personalized treatment strategies. OncoLnc database incorporates the Logrank p-value calculation, a statistical test used to determine the significance of differences in survival between different patient groups based on gene expression levels. This valuable feature allows researchers to assess the statistical significance of observed differences in survival outcomes and validate the relevance of potential biomarkers in cancer progression. using the OncoLnc online database, A Cox regression analysis was conducted utilizing the normalized count data of HIF alpha factors and the top enriched genes from BMP pathway in GBM cancer samples to evaluate their impact with regard to multiple covariates on the survival of patients with GBM cancer. By considering variables such as age, gender, tumor stage, and gene/miRNA expression levels, Cox regression enables the identification of independent prognostic factors. Based on the survival graphs extracted from OncoLnc database, which are shown in Fig. 5 , only the expression level of SMURF1 significantly correlated (Logrank p-value = 0.002) with the survival of patients with GBM cancer, and those who had lower expression levels of SMURF1 gene survived significantly better compared to group of patients with higher expression level of SMURF1 gene. Therefore, it can be concluded that the SMURF1 gene can be a high-potential prognostic biomarker in GBM cancer. Discussion Multiforme Glioblastoma, abbreviated to GBM, is a highly aggressive tumor of the brain and constitutes an important ongoing challenge in understanding its molecular underpinnings and finding preventive strategies ( 3 ). Epidemiological data from recent years paints an increasingly worrisome picture of increasing GBM incidence rates coupled with low survival rates ( 2 ). Thus, there exists a compelling rationale that calls for identifying early biomarkers for timely detection and possible prevention interventions. An important part in GBM development is the role of Bone Morphogenetic Proteins (BMPs) which are members of the Transforming Growth Factor-β protein family. The proteins perform several roles such as: control CNS stem cell differentiation, apoptotic pathway, mitotic arrest and glioma-derived precursor cell differentiation ( 6 , 37 ). Expression of dysregulated BMP protein has been implicated in oncogenic progression and modulation of tumor-suppressive pathways ( 39 ). However, the functional role of BMP proteins depends on the specific protein variant and tumor phenotype ( 7 ). In addition, oxygen tension has been identified as an influential factor in modulating BMP protein expression ( 12 ). In high-grade glioma cells, hypoxic conditions have been shown to inhibit the BMP pathway through the HIF-1α protein ( 14 ). Understanding the intricate interplay between BMP protein expression patterns and HIF profiles in GBM patients remains a fascinating area for scientific inquiry. To address this, the current study aims to unravel the complex expression patterns of HIF factors and BMP pathway constituents within GBM tumor tissue, comparing them to non-neoplastic counterparts. To analyze the enrichment pattern of genes from the BMP pathway in GBM cancer and normal count data, we employed the GSEA technique in this study. Interestingly, the GSEA results indicated a distinct enrichment pattern in the normal phenotype. Nevertheless, the observed enrichment failed to attain statistical significance, indicating that the differential enrichment of the BMP pathway in GBM cancer samples compared to normal samples is not substantial. Subsequently, we identified the top 14 core genes from the BMP pathway that drive the enrichment score of the GSEA clusters using Rank metric scores. These genes will undergo further analysis. Moreover, a thorough examination was carried out on the variance in gene expression of HIF alpha transcription factors and the top 14 enriched genes from the BMP pathway in samples of GBM cancer. The analysis revealed that EPAS1, HIF3A, CHRDL1, NOG, BMP6, and AHSG genes did not exhibit a statistically significant difference between GBM cancer and normal tissue samples. In our previous research, HIF3A also showed no significant differential expression level in different types of TCGA cancer samples ( 32 ). However, the HIF1A gene showed significant upregulation in cancer samples, consistent with previous studies on the expression level of HIF1A in high-grade glioma cells under hypoxic conditions ( 14 ). Further exploration of differentially expressed genes (DEGs) unveiled noteworthy findings. The majority of genes from the BMP pathway were significantly downregulated in GBM cancer samples compared to normal samples. Notable examples include SOSTDC1, GREM1, CHRD, SMAD7, PPM1A, BMPR2, MAPK1, ZFYVE16, GSK3B, and SMURF1 genes. The correlation analysis by Pearson method has unveiled intriguing relationships between HIF alpha transcription factors and the top enriched genes from the BMP pathway in GBM cancer samples. Notably, the expression levels of HIF1A and EPAS1 correlated with PPM1A and BMPR2 genes while negatively correlated with SOSTDC1 and CHRDL1 genes. This negative correlation suggests a regulatory antagonist relationship between HIF1A and these genes within the BMP pathway, aligning with previous studies ( 14 ). While the investigation of the correlation between HIF1A and BMPR2 receptor expression in cancer cells and different tissue types has been limited in recent years, studies have highlighted that hypoxic conditions can downregulate the expression of the BMPR2 gene in lung cells of rats ( 28 ). The MAPK1 gene also showed a moderate correlation with BMPR2 expression level. Furthermore, additional studies have underscored the participation of the Mitogen-activated protein kinase (MAPK) signaling pathway and MAPK-1 in the control of cellular death in neuronal cells subjected to stressful conditions. ( 29 , 30 , 31 ). The evaluation of HIF alpha factors and enriched genes from the BMP pathway in GBM cancer samples exhibits their potential as biomarkers with AUC values. Detecting GBM cancer at a stage is crucial, for improving treatment effectiveness and patient outcomes. To assess their potential, we conducted ROC analysis to evaluate both sensitivity and specificity of these biomarkers. Among the genes analyzed, seven displayed significant AUC values exceeding 0.90, including SOSTDC1, CHRD, SMAD7, PPM1A, MAPK1, SMURF1, and the HIF1A transcription factor. Previous investigations by other research groups revealed a significant relationship between SMAD7 and the TGF-beta signaling pathway in glioblastoma cells. These insights further support the importance of our findings ( 34 , 35 , 36 , 38 ). In addition, it is worth noting that a remarkable association was observed in survival analysis when examining the expression level of the SMURF1 gene and the survival rate of patients affected by GBM cancer. Interestingly, those individuals with a lower expression level of SMURF1 gene exhibited more favorable survival rates in comparison to those with a higher expression level. These outcomes closely align with previous investigations that have shed light on the oncogenic function of the SMURF1 gene during glioblastoma progression, underscoring its potential significance as a powerful prognostic biomarker for GBM cancer ( 33 ). Furthermore, these findings distinctly emphasize the promise of these genes as both diagnostic and prognostic biomarkers for the timely detection of GBM cancer. The precise identification of these biomarkers has the potential to significantly contribute towards the design of effective therapeutic strategies and the enhancement of patient outcomes. Moreover, the intricately intertwined interaction between HIF alpha and the BMP pathway possesses tremendous potential in terms of identifying novel targets and developing innovative treatment approaches for individuals afflicted with GBM. Identification of new and specific biomarkers with help of bioinformatic techniques can help with the early detection of different types of cancer ( 40 ). However, the identified biomarkers should be evaluated by further experimental investigations in order to enter early-stage clinical phases. Conclusion Glioblastoma Multiforme (GBM) is a severe type of brain tumor with a poor chance of survival. In this investigation, an analysis was conducted on the levels of expression of the BMP pathway and HIF alpha factors in GBM cancer samples compared to normal tissue samples. HIF1A gene demonstrated a significant upregulation in cancer samples and the majority of genes from the BMP pathway were significantly downregulated in cancer. With help of ROC test and survival analysis, we identified potential biomarkers with diagnostic and prognostic potential for GBM cancer. Additionally, functional enrichment The PPI network analyses showed that the majority of the DEGs were related to the TGF-beta signaling pathway, pathways in cancer, and the cell cycle. In our study, we introduced specific genes potential biomarkers for the early detection and prognosis of GBM cancer. Declarations Conflict of interest statement All authors declared no conflict of interest in this study. Authors’ contributions The study design was performed by B. Yazdani. Data analysis was done by B. Yazdani. Interpretations of the data and bioinformatics analysis were performed by B. Yazdani, and A.R. Sichani. Manuscript writing was performed by B. Yazdani, and A.R. Sichani. The final version of the manuscript was approved by all authors. Acknowledgments none References Wu W, Klockow JL, Zhang M, Lafortune F, Chang E, Jin L, Wu Y, Daldrup-Link HE (2021) Glioblastoma multiforme (GBM): An overview of current therapies and mechanisms of resistance. Pharmacol Res 171:105780 Smoll NR, Schaller K, Gautschi OP (2013) Long-term survival of patients with glioblastoma multiforme (GBM). J Clin Neurosci 20(5):670–675 Holland EC (2000) Glioblastoma multiforme: the terminator. Proceedings of the National Academy of Sciences. ;97(12):6242-4 Clancy E (2023) ACS Report Shows Prostate Cancer on the Rise, Cervical Cancer on the Decline. Renal & Urology News. Feb 23:NA- Sasmita AO, Wong YP, Ling AP (2018) Biomarkers and therapeutic advances in glioblastoma multiforme. Asia-Pac J Clin Oncol 14(1):40–51 Wang RN, Green J, Wang Z, Deng Y, Qiao M, Peabody M, Zhang Q, Ye J, Yan Z, Denduluri S, Idowu O (2014) Bone Morphogenetic Protein (BMP) signaling in development and human diseases. Genes Dis 1(1):87–105 Thawani JP, Wang AC, Than KD, Lin CY, La Marca F, Park P (2010) Bone morphogenetic proteins and cancer: review of the literature. Neurosurgery 66(2):233–246 Bach DH, Park HJ, Lee SK (2018) The dual role of bone morphogenetic proteins in cancer. Mol Therapy-Oncolytics 8:1–3 Davis H, Raja E, Miyazono K, Tsubakihara Y, Moustakas A (2016) Mechanisms of action of bone morphogenetic proteins in cancer. Cytokine Growth Factor Rev 27:81–92 Singh A, Morris RJ (2010) The Yin and Yang of bone morphogenetic proteins in cancer. Cytokine Growth Factor Rev 21(4):299–313 Maegdefrau U, Amann T, Winklmeier A, Braig S, Schubert T, Weiss TS, Schardt K, Warnecke C, Hellerbrand C, Bosserhoff AK (2009) Bone morphogenetic protein 4 is induced in hepatocellular carcinoma by hypoxia and promotes tumour progression. J Pathology: J Pathological Soc Great Br Irel 218(4):520–529 Wu X, Chang MS, Mitsialis SA, Kourembanas S (2006) Hypoxia Regulates Bone Morphogenetic Protein Signaling Through C-Terminal–Binding Protein 1. Circul Res 99(3):240–247 Wu X, Chang MS, Mitsialis SA, Kourembanas S (2006) Hypoxia Regulates Bone Morphogenetic Protein Signaling Through C-Terminal–Binding Protein 1. Circul Res 99(3):240–247 Pistollato F, Chen HL, Rood BR, Zhang HZ, D'Avella D, Denaro L, Gardiman M, Te Kronnie G, Schwartz PH, Favaro E, Indraccolo S (2009) Hypoxia and HIF1α repress the differentiative effects of BMPs in high-grade glioma. Stem Cells 27(1):7–17 Chen HL, Panchision DM (2007) Concise review: bone morphogenetic protein pleiotropism in neural stem cells and their derivatives—alternative pathways, convergent signals. Stem Cells 25(1):63–68 Team RD (2010) R: A language and environment for statistical computing. (No Title) Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, Sabedot TS, Malta TM, Pagnotta SM, Castiglioni I, Ceccarelli M (2016) TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res 44(8):e71 Robinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140 Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences. ;102(43):15545-50 Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP (2011) Molecular signatures database (MSigDB) 3.0. Bioinformatics 27(12):1739–1740 Gene Ontology Consortium (2004) The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res 32(suppl1):D258–D261 Huang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, Guo Y, Stephens R, Baseler MW, Lane HC, Lempicki RA (2007) DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res 35(suppl2):W169–W175 Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA (2003) DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 4(9):1–1 Doncheva NT, Morris JH, Gorodkin J, Jensen LJ (2018) Cytoscape StringApp: network analysis and visualization of proteomics data. J Proteome Res 18(2):623–632 Kohl M, Wiese S, Warscheid B (2011) Cytoscape: software for visualization and analysis of biological networks. Data mining in proteomics: from standards to applications. :291–303 Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504 Anaya J (2016) OncoLnc: linking TCGA survival data to mRNAs, miRNAs, and lncRNAs. PeerJ Comput Sci 2:e67 Ehata S, Miyazono K (2022) Bone morphogenetic protein signaling in cancer; some topics in the recent 10 years. Front Cell Dev Biology 10:883523 Takahashi H, Goto N, Kojima Y, Tsuda Y, Morio Y, Muramatsu M, Fukuchi Y (2006) Downregulation of type II bone morphogenetic protein receptor in hypoxic pulmonary hypertension. Am J Physiology-Lung Cell Mol Physiol 290(3):L450–L458 Choi YS, Horning P, Aten S, Karelina K, Alzate-Correa D, Arthur JS, Hoyt KR, Obrietan K (2017) Mitogen-and stress-activated protein kinase 1 regulates status epilepticus-evoked cell death in the hippocampus. ASN neuro 9(5):1759091417726607 Mishra OP, Delivoria-Papadopoulos M (2004) Effect of hypoxia on the expression and activity of mitogen-activated protein (MAP) kinase-phosphatase-1 (MKP-1) and MKP-3 in neuronal nuclei of newborn piglets: the role of nitric oxide. Neuroscience 129(3):665–673 Yazdani B, Sirous H (2022) Expression analysis of HIF-3α as a potent prognostic biomarker in various types of human cancers: a case of meta-analysis. Res Pharm Sci 17(5):508–526 Woringer M, Darzacq X, Izeddin I (2014) Geometry of the nucleus: a perspective on gene expression regulation. Curr Opin Chem Biol 20:112–119 Shivashankar GV (2011) Mechanosignaling to the cell nucleus and gene regulation. Annual Rev Biophys 40:361–378 Kim WS, Weickert CS, Garner B (2008) Role of ATP-binding cassette transporters in brain lipid transport and neurological disease. J Neurochem 104(5):1145–1166 Brunet A, Datta SR, Greenberg ME (2001) Transcription-dependent and-independent control of neuronal survival by the PI3K–Akt signaling pathway. Curr Opin Neurobiol 11(3):297–305 Golestaneh N, Mishra B (2005) TGF-β, neuronal stem cells and glioblastoma. Oncogene 24(37):5722–5730 Wick W, Naumann U, Weller M (2006) Transforming growth factor-β: a molecular target for the future therapy of glioblastoma. Curr Pharm Design 12(3):341–349 Hover LD, Abel TW, Owens P (2015) Genomic analysis of the BMP family in glioblastomas. TranslaTional oncogenomics 7:1 Parsazad E, Esrafili F, Yazdani B, Ghafarzadeh S, Razmavar N, Sirous H (2023) Integrative bioinformatics analysis of ACS enzymes as candidate prognostic and diagnostic biomarkers in colon adenocarcinoma. Res Pharm Sci 18(4):413–429 Additional Declarations The authors declare no competing interests. Supplementary Files Suplementarytable1.docx Supplementary Table 1.Protein–Protein Interaction Network analysis results obtained from by CytoNCA application in the Cytoscape software. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4232372","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288497716,"identity":"d47b4f68-f72f-4610-876f-2182f201e665","order_by":0,"name":"Behnaz Yazdani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYBACAwYeGDMBiCpsgAzGxgMkaDmTBtLSQIIWxpbDYCZeLebsvcce/PhzWF6+PfnZg4cN5+3Wth8G2lJjE41Li2XPuXTDHp7Dho09z8wNEnfcTt52JhGo5VhabgMuh93IMZPgkTjM2CyRYCaReOZ2stkBoBbGhsO4tdx/Yyb5x+CwfZtE+jeJxLZzyWbnHxLQcoPHTJon4XBijwTQusS2A3ZmNwjYYtmTYyYtcyA9eQbPmzKJhDPJCWY3gLYk4PGLOfsZM8k3f6xt57enb5P8UWFnb3Y+/eGDDzU2OLVAQTOclQhWmYBfOQjUwVn2hBWPglEwCkbBSAMAcw5m0vsw9bYAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-4243-1330","institution":"Bioscience Department, Faculty of Science and Technology (FCT), Universitat de Vic - Universitat Central de Catalunya (Uvic-UCC), Vic, Spain","correspondingAuthor":true,"prefix":"","firstName":"Behnaz","middleName":"","lastName":"Yazdani","suffix":""},{"id":288497748,"identity":"e0496981-90fd-4461-9650-d015960285da","order_by":1,"name":"Adel Rezvani Sichani","email":"","orcid":"","institution":"Department of Food Science and Technology, Shahreza Branch, Islamic Azad University, Shahreza, Iran.","correspondingAuthor":false,"prefix":"","firstName":"Adel","middleName":"Rezvani","lastName":"Sichani","suffix":""}],"badges":[],"createdAt":"2024-04-07 17:24:10","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4232372/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4232372/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54319272,"identity":"8c9642d5-e8fb-428a-a686-2ed315d75008","added_by":"auto","created_at":"2024-04-08 18:48:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":773121,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene set enrichment analysis (GSEA), Differential gene expression , and Pearson Correlation analyses of BMP pathway in GBM cancer. (A) \u003c/strong\u003eEnrichment plot of BMP pathway. The green line demonstrates the enrichment score (ES) values, which is based on the sum of the weighted enrichment scores. \u003cstrong\u003e(B) \u003c/strong\u003eHeat map illustrates the top 14 core genes from BMP pathway, which are selected based on Rank Metric Scores. \u003cstrong\u003e(C) \u003c/strong\u003eThe list of Rank Metric Scores of the genes from BMP pathway in GBM cancer samples compared to normal samples.\u003cstrong\u003e (D-F) \u003c/strong\u003eVolcano plots created based on Log2 Fold change (X-axis) and -Log10 adj p-value (Y-axis). Those genes with Log2 Foldchange value less or equal than -2 are shown with red circles, and those with according values above 2 are colored as blue. The genes with Log2 Foldchange values between the range of -2 and 2 are colored as gray, and those with -log10 adj p-value below 2 are colored as black, which is a sign of insignificant statistical difference in the expression level of the according genes across the cancer and normal samples\u003cstrong\u003e. (G)\u003c/strong\u003e shows the Pearson correlation analysis results. The \u003cem\u003ep\u003c/em\u003e-values smaller than 0.05 were considered as statistically significant.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4232372/v1/a5ce9d78cc95b5ef11927c9c.png"},{"id":54319274,"identity":"7a66fd01-94b9-489b-a12a-03732d947b47","added_by":"auto","created_at":"2024-04-08 18:48:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":183320,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLog2 expression level of top enriched genes from BMP pathway in GBM cancer. \u003c/strong\u003eMost of the top enriched genes from BMP pathway demonstrated significant difference in their expression levels in cancer tissues compared with control tissue samples. BMP6, AHSG, ZFYVE16, EPAS1, and HIF3A genes did not show significant differential expression across the cancer and normal samples. P-values smaller than 0.05 were considered as statistically significant.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4232372/v1/5a077ce1f230ea2b05041f8c.png"},{"id":54319578,"identity":"22a6ea21-2f7b-4fbf-b662-868534311131","added_by":"auto","created_at":"2024-04-08 18:56:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":893450,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene Ontology (GO) , Functional enrichment analysis, and Protein-Protein Interaction Network Analysis. (A), \u003c/strong\u003eGO analysis of biological processes shows that most of the DEGs in GBM cancer are involved in positive and negative regulation of transcription from RNA polymerase II promoter \u003cstrong\u003e(B) \u003c/strong\u003eCell Component of most of the genes were the nucleus and cytosol \u003cstrong\u003e(C)\u003c/strong\u003e and the molecular function of most of the selected genes was ATP-binding and protein serine/threonine kinase activities. \u003cstrong\u003e(D)\u003c/strong\u003e KEGG pathway analysis demonstrates that most of the DEGs were mostly associated with TGF-beta signaling pathway and pathways in cancer.\u003cstrong\u003e(E)\u003c/strong\u003e As shown in the PPI network, the Up-regulated and Down-regulated genes in the expression data of the GBM cancer are shown with blue and orange colors respectively. Those genes with higher interconnectivity, are shown with bigger circles.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4232372/v1/dc11125f1a3ba6b1e906dcf8.png"},{"id":54319277,"identity":"99b53156-1d53-45e1-ae15-52eea6c098ad","added_by":"auto","created_at":"2024-04-08 18:48:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":282368,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic (ROC) test. \u003c/strong\u003eThe diagnostic potential of the top enriched genes from the BMP pathway in the detection of GBM cancer samples has been compared from the healthy control tissues. P-values smaller than 0.05 were considered as statistically significant.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4232372/v1/3547a68f9a71e9dd42392e6f.png"},{"id":54319275,"identity":"4cedf71f-b324-46b7-946e-4801b3ecb286","added_by":"auto","created_at":"2024-04-08 18:48:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":192716,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival analysis with Oncolnc Database. \u003c/strong\u003eSurvival analysis was performed with help of the OncoLnc online database on the top enriched genes from the BMP signaling pathway. Logrank p-values smaller than 0.05 were considered as statistically significant. Only SMURF1 gene was predicted to have significant prognostic biomarker potential for GBM cancer.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4232372/v1/f819a845e3d215148f5114ae.png"},{"id":54320067,"identity":"0352b7fd-b2e9-44cb-8125-d9e4952710bc","added_by":"auto","created_at":"2024-04-08 19:04:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2095488,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4232372/v1/bba5b03e-1bbb-4c3f-88bb-4ea12e98b4f6.pdf"},{"id":54319273,"identity":"e37fe35e-f896-45d2-b603-e1badb7c9d7b","added_by":"auto","created_at":"2024-04-08 18:48:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15481,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1.\u003c/strong\u003eProtein–Protein Interaction Network analysis results obtained from by CytoNCA application in the Cytoscape software.\u003c/p\u003e","description":"","filename":"Suplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4232372/v1/35c12c85808e4d7eec557754.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eUnveiling the Diagnostic and Prognostic potential of BMP Pathway and Hypoxia-inducible Factors in Glioblastoma Multiforme\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlioblastoma Multiforme (GBM), a brain cancer that can originate from brain tissue (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), has been classified as an aggressive form of cancer with poor survival rates (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The causes of glioblastoma cancer are still poorly understood, and preventing glioma accurately remains a challenge (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In 2023, the American Cancer Society reported approximately 24810 new cases of glioblastoma cancer, with a staggering 18990 cases resulting in death (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). This highlights the urgent need for early biomarkers to detect and prevent glioblastoma cancer (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBone Morphogenetic Proteins (BMPs), part of the TGF-β superfamily, play crucial roles in embryonic tissue development, bone and cartilage formation, and tissue regeneration (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Studies have shown a significant association between altered expression levels of BMP proteins and cancer cell progression or suppression (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Depending on the specific protein member and type of cancer cell, BMP proteins can exhibit dual roles as oncogenes or tumor suppressors (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Additionally, the expression levels of genes in the BMP pathway have been implicated in cancer cell progression at more advanced stages (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe expression of BMP proteins can be influenced by various factors, including oxygen levels (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Under hypoxic conditions, cancer cells undergo adaptive changes to survive and proliferate, which involve alterations in the expression levels of specific proteins in various cancer-related pathways (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Certain BMP proteins can either be induced or downregulated by hypoxia, depending on the cell type (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral studies have demonstrated the involvement of Hypoxia-Inducible Factors (HIFs) in regulating the BMP signaling pathway under hypoxic conditions. Specifically, HIF-1α Factor has been observed to repress the BMP signaling pathway in high-grade glioma (HGG) cancer cells (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Moreover, BMP proteins not only regulate stem cells during brain tissue development but also have the potential to induce apoptosis, mitogenic arrest, and cell differentiation in central nervous system (CNS) stem cells (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the correlation between the expression patterns of BMP proteins and Hypoxia-Inducible Factors in glioblastoma cancer patients remains poorly understood. To address this gap, our study focuses on analyzing the expression patterns of HIF factors and different members of the BMP pathway in TCGA GBM cancer and normal tissue samples. Various techniques, including GSEA, DEG, and correlation analysis, were employed to identify the most perturbed genes in the BMP pathway. Furthermore, functional annotation and Protein-protein interaction network analyses were conducted to elucidate the molecular function of BMP proteins in GBM cancer. Finally, ROC curve and survival analyses were performed to assess the prognostic and diagnostic potential of these proteins and identify robust biomarkers for GBM cancer detection.\u003c/p\u003e"},{"header":"Methods and Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection and differential gene expression analysis\u003c/h2\u003e \u003cp\u003eThe RNA-seq count data of patients with GBM (Glioblastoma multiforme) cancer was acquired from the TCGA database ( The Cancer Genome Atlas Program (TCGA) - NCI) and subjected to analysis in adherence to the principles outlined in the Declaration of Helsinki and TCGA guidelines for data access and usage. Before identifying the differentially expressed genes (DEGs) in GBM cancer samples compared to normal samples, the normalized count data for all samples was converted into logarithmic form (Log2 ratio).\u003c/p\u003e \u003cp\u003eThe RNAseq data of the GBM project was obtained from the TCGA dataset using the \"TCGAbiolinks\" package, a powerful tool in the R programming language specifically designed for the retrieval and analysis of TCGA data. This package provides seamless integration with the TCGA platform, ensuring efficient and accurate data extraction. Following data collection, the GBM cancer and normal tissue samples were analyzed using the R program and the Limma package. The gene expression data underwent preprocessing and normalization using the R programming using the Limma package (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The Limma package is widely recognized for its utility in differential gene expression analysis, making it an ideal choice for this study.\u003c/p\u003e \u003cp\u003eTo ensure reliable and precise normalization of the RNAseq count data, the Voom normalization method was employed. The Voom method employs a statistical framework to transform count data into log2 ratios, providing a robust basis for downstream analysis and comparison of gene expression ratios(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The metan package in R programming with the Pearson statistical method was used for the calculation of \u003cem\u003ep\u003c/em\u003e-values and correlation coefficient values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGSEA Analysis\u003c/h2\u003e \u003cp\u003eWhen it comes to studying biological pathways and their connection to specific diseases, the gene set enrichment analysis (GSEA) technique proves to be highly valuable. It provides insightful information by evaluating the correlation between the expression levels of a specific set of genes and the defined phenotype for each group of samples. Essentially, GSEA allows users to gauge the level of correlation among a set of genes associated with a particular biological pathway in the context of a specific disease of interest (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor this particular study, we selected a list of 42 genes that are predicted to play a role in the BMP signaling pathway. These genes were obtained from the MSigDB database ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). To perform the GSEA analysis, we utilized the normalized expression data from GBM TCGA samples as the expression matrix. In order to define the phenotypes for each sample, we relied on the histopathological and clinical data provided in the TCGA database. Specifically, two phenotypes were defined: \"cancer\" and \"normal.\"\u003c/p\u003e \u003cp\u003eTo perform the GSEA analysis, we utilized version 4.0.3 of the GSEA software developed by the BOARD Institute. The tTest method was chosen as the ranking method for the gene list, while the other parameters were maintained at the software's default settings. Through an examination of the correlation between the expression level of each gene within the gene set across all samples, the GSEA analysis produced a Ranking metric score. This score serves as an indicator of the enrichment level of individual genes within the given gene set.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eTo identify the most important biological pathways in GBM cancer, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted on the top 200 genes with differential expression in GBM cancer samples (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This analysis was performed using the DAVID database (version 6.8, available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david-d.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david-d.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and employed the hypergeometric algorithm (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The selection of functionally enriched biological processes and KEGG pathways was based on a screening criterion of a P-value less than .05 and the count of genes predicted to be involved in each Gene Ontology category.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eProtein-Protein Interaction Network Analysis\u003c/h2\u003e \u003cp\u003eTo gain a deeper understanding of the protein-protein interactions involving HIF factors, BMP proteins, and the top differentially expressed genes in GBM cancer, we employed the Search Tool for Retrieval of Interacting Genes (STRING) database (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). This online tool helps with the evaluation of protein-protein interaction networks (PPI). Specifically, we utilized the STRING database (version 10.0, accessible at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.string-db.org/\u003c/span\u003e\u003cspan address=\"http://www.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and selected the top significant 200 DEGs (differentially expressed genes) in GBM cancer, along with HIF factors and the 17 top-scoring genes enriched in the BMP pathway as our input genes. In this analysis, the selected input genes were defined as DEGs, while the species was specified as human.\u003c/p\u003e \u003cp\u003eWe set the PPI score threshold to 0.4, allowing us to create subsets of medium-confidence human PPI networks. To visualize and interpret the predicted PPI network, we utilized the Cytoscape tool (version: 3.2.0, available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cytoscape.org/\u003c/span\u003e\u003cspan address=\"http://www.cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The CytoNCA (version 2.1.6, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://apps.cytoscape.org/apps/cytonca\u003c/span\u003e\u003cspan address=\"http://apps.cytoscape.org/apps/cytonca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) tool was employed to analyze the topological characteristics of the node network. The calculation parameter was set to exclude weights. This permitted the extraction of node scores, which in turn facilitated the ranking of nodes based on their importance in the protein-protein interaction (PPI) network. By considering nodes with high scores as \"hub proteins,\" we can identify key contributors to PPI networks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eReceiver operating characteristic test\u003c/h2\u003e \u003cp\u003eOne way to estimate the diagnostic capability and effectiveness of specific genes in GBM cancer is by employing the Receiver Operating Characteristic (ROC) test. This test was conducted on 17 genes with high enrichment scores in the BMP pathway, as identified through GSEA analysis using normalized gene expression data from GBM cancer and normal samples. By identifying genes with higher AUC (Area Under Curve) values and smaller P-values, we can anticipate their potential as valuable diagnostic biomarkers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis\u003c/h2\u003e \u003cp\u003eThe OncoLnc repository is a database specifically tailored to streamline the analysis of cancer-related data. It operates as a comprehensive database that collates information from different online data sources, including The Cancer Genome Atlas (TCGA) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). OncoLnc integrates an extensive array of data, spanning gene expression profiles, clinical data, and survival statistics. Consequently, it has fostered the identification of potential prognostic markers, biomarkers, and therapeutic targets across diverse cancer types. By examining survival data in conjunction with gene expression profiles, the investigation of significant genes associated with clinical outcomes is made more accessible. The OncoLnc platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.oncolnc.org\u003c/span\u003e\u003cspan address=\"http://www.oncolnc.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) offers access to gene expression data for mRNAs and miRNAs from The Cancer Genome Atlas (TCGA), alongside RNA-Seq expression data. All the top 17 enriched scored genes in BMP pathway were analyzed by the OncoLnc repository for survival analysis in GBM cancer.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGSEA analysis of BMP pathway in GBM cancer\u003c/h2\u003e \u003cp\u003eIn the field of molecular biology and genetics, identifying statistically significant gene signatures holds great potential for understanding various phenotypes and their underlying genetic mechanisms. To better comprehend the relationship between gene sets and phenotypic traits, advanced analytical methods such as Gene Set Enrichment Analysis (GSEA) have proven to be invaluable tools. GSEA analysis allows for the systematic examination of gene sets, helping with the detection of altered biological pathways and providing insights into relevant biological mechanisms. In our study, we utilized an established statistical threshold (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.25) to select the statistically significant gene signatures. The significance level is expressed as a normalized enrichment score (NES), which quantifies the degree of association between a phenotype and a gene signature (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.A-C).\u003c/p\u003e \u003cp\u003eUpon analyzing the BMP pathway with GSEA technique in GBM cancer and normal count data, a distinct pathway enrichment in the normal phenotype was observed with an enrichment score (ES) of -0.17 and Nominal p-value of 0.89, which is not statistically significant and we can predict that the BMP pathway is not significantly differentially enriched in GBM cancer samples in compare to normal samples. To identify key genes associated with GSEA cluster enrichment, a ranking metric called the 'Rank Metric Score.' was utilized. This score is calculated based on the signal-to-noise ratio of each gene, providing valuable insights into its significance in the enriched list. By help of this analysis, we detected and selected the top 14 core genes from BMP pathway that drive the enrichment score of the GSEA clusters for further analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDifferential gene expression and Correlation analyses\u003c/h2\u003e \u003cp\u003eDifferential gene expression test was performed on the top enriched genes from the BMP pathway in GBM cancer samples, as well as the three members of hypoxia-inducible factor alpha transcription factors .According to the volcano plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.D-F), the expression levels of certain genes, namely EPAS1(Endothelial PAS Domain Protein 1), HIF3A (Hypoxia Inducible Factor 3 Subunit Alpha), CHRDL1 (Chordin Like 1), NOG (Noggin), BMP6 (Bone Morphogenetic Protein 6), and AHSG (Alpha 2-HS Glycoprotein), did not show significant variations in GBM cancer samples when compared to normal tissue samples.\u003c/p\u003e \u003cp\u003eThe data indicates that the Log2 Foldchange and -Log10 p-value for EPAS1 were 0.24 and 0.24 respectively. Similarly, for HIF3A, these values were \u0026minus;\u0026thinsp;0.66 and 0.31 respectively. In the case of CHRDL1, the Log2 Foldchange and -Log10 p-value were \u0026minus;\u0026thinsp;1.82 and 1.54, while for NOG, they were 0.66 and 0.66. Additionally, the Log2 Foldchange and -Log10 p-value for BMP6 were \u0026minus;\u0026thinsp;0.93 and 0.75, and for AHSG, they were \u0026minus;\u0026thinsp;0.82 and 0.55 respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, HIF1A (Log2 Foldchange\u0026thinsp;=\u0026thinsp;1.42, -Log10 p-value\u0026thinsp;=\u0026thinsp;4.71) gene showed to be significantly upregulated in cancer samples. DEG analysis results demonstrated a notable decrease in the activity of several genes linked to the BMP pathway in cancerous samples when compared to normal samples. The genes with significant decreased expression levels in GBM cancer samples include SOSTDC1 (Sclerostin Domain Containing 1) with a Log2 Foldchange of -3.85 and -Log10 p-value of 5.41, GREM1 (Gremlin 1, DAN Family BMP Antagonist) with Log2 Foldchange of -2.93 and -Log10 p-value\u0026thinsp;=\u0026thinsp;2.23, CHRD (chordin) with Log2 Foldchange of -1.96 and -Log10 p-value of 5.47, SMAD7 (SMAD Family Member 7) with Log2 Foldchange of -1.75 and -Log10 p-value of 6.05, PPM1A (Protein Phosphatase, Mg2+/Mn2\u0026thinsp;+\u0026thinsp;Dependent 1A) with Log2 Foldchange of -1.51 and -Log10 p-value of 13.29, BMPR2 (Bone morphogenetic protein receptor type-2) with Log2 Foldchange of -1.30 and -Log10 p-value of 10.16, MAPK1 (Mitogen-activated protein kinase 1) with Log2 Foldchange of -1.47 and -Log10 p-value of 8.10, ZFYVE16 (Zinc Finger FYVE-Type Containing 16) with Log2 Foldchange of -0.80 and -Log10 p-value of 4.75, GSK3B (Glycogen Synthase Kinase 3 Beta) with Log2 Foldchange of -0.87 and -Log10 p-value of 3.19, and SMURF1 (SMAD-specific E3 ubiquitin protein ligase 1) with Log2 Foldchange of -0.72 and -Log10 p-value of 2.42.\u003c/p\u003e \u003cp\u003eThese findings suggest an anomaly in the BMP pathway during the development of cancer. The decline in SOSTDC1, GREM1, CHRD, SMAD7, PPM1A, BMPR2, MAPK1, ZFYVE16, GSK3B, and SMURF1 genes might contribute to the progression and pathogenesis of cancer. The correlation analysis was performed between hypoxia-inducible transcription alpha members and top enriched genes from BMP pathway. The expression level of HIF1A in GBM cancer samples revealed a moderate positive correlation with the expression levels of EPAS1 (Correlation Coefficient\u0026thinsp;=\u0026thinsp;0.45, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;5.98E-10), PPM1A(Correlation Coefficient\u0026thinsp;=\u0026thinsp;0.42, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;9.45E-09) genes along with a weak negative correlation with SOSTDC1 (Correlation Coefficient= -0.24, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.0014) gene (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.G).\u003c/p\u003e \u003cp\u003eA moderate positive correlation was also detected between the expression levels of EPAS1 and BMPR2(Correlation Coefficient\u0026thinsp;=\u0026thinsp;0.40, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;3.37E-08) genes. The CHRDL1 expression level negatively correlated with the expression levels of HIF1A (Correlation Coefficient= -0.29, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.0001) and EPAS1(Correlation Coefficient= -0.25, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.0008) genes. The expression level of SMAD7 gene also positively correlated with GREM1(Correlation Coefficient\u0026thinsp;=\u0026thinsp;0.46, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;1.61E-9), and SMURF1(Correlation Coefficient\u0026thinsp;=\u0026thinsp;0.46, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;1.31E-10) expression levels. The BMPR2 expression level also demonstrated a moderate positive correlation with MAPK1(Correlation Coefficient\u0026thinsp;=\u0026thinsp;0.48, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;3.47E-11) expression ratio among glioblastoma cancer samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Log2 expression level graph of all the following genes is also illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. However, the Log2 Foldchange values are much more statistically reliable compared to Log2 expression levels. To better understand the correlation between the expression levels of HIF alpha transcription factors with the top enriched genes from the BMP pathway, correlation analysis was performed on GBM cancer and normal samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGene Ontology and Functional enrichment analyses\u003c/h2\u003e \u003cp\u003eTo gain insights into the biological functions of top enriched genes from BMP pathway in GBM cancer, gene ontology (GO) function enrichment analysis was done. The online DAVID database was utilized for this analysis, which offers a comprehensive platform for functional annotation and enrichment analysis. The GO function enrichment analysis provided valuable information regarding the biological processes (BP), molecular functions (MF), and cellular components (CC) associated with the selected genes. The analysis helped uncover specific functional categories that play a crucial role in GBM development and progression. In addition to the gene ontology (GO analysis, KEGG pathway enrichment analysis was also done to identify the key pathways most likely to be involved in GBM cancer.\u003c/p\u003e \u003cp\u003eAs depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, The biological processes of the top DEG genes were involved in the positive and negative regulation of transcription from RNA polymerase II promoter (GO:0000122 and GO:0045944), as well as protein phosphorylation (GO:0006468) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.A). Most of these genes were estimated to be most likely localized in the nucleus (GO:0005634) and cytoplasm (GO:0005737) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.B). The molecular functions of these genes were also shown to be associated with ATP binding (GO:0005524), DNA binding (GO:0003677), and protein serine/threonine kinase activity (GO:0004674) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.C). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database is a great resource that provides a complete collection of pathway information and functional annotations.\u003c/p\u003e \u003cp\u003eThrough the KEGG pathway enrichment analysis, the top 200 significantly differentially expressed genes in GBM cancer samples, were correlated with unique pathways, such as TGF-beta signaling pathway (hsa04350), pathways in cancer (hsa05200), and cell cycle (hsa04110) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.D). This analysis shed light on the intricate interplay of biological processes involved in GBM development, facilitating a deeper understanding of disease progression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eProtein-Protein interaction network analysis\u003c/h2\u003e \u003cp\u003eThe PPI network of the top 200 DEG genes in Glioblastoma cancer samples, top enriched genes from the BMP pathway, and HIF alpha factors in GBM cancer were constructed using the STRING database and CytoNCA application in Cytoscape software. The PPI network demonstrated the interconnectivity between these proteins and the genes that are up and downregulated in GBM cancer samples were colored as blue and orange colors respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhile EPAS1 and HIF3A genes did not interact with the rest of the genes significantly, HIF1A was predicted to interact with CDK1 (Cyclin Dependent Kinase 1), CCNA2 (Cyclin A2), MAPK1 (Mitogen-activated protein kinase 1), POTEF (POTE Ankyrin Domain Family Member F), GSK3B (Glycogen Synthase Kinase 3 Beta), and TROAP (Trophinin Associated Protein) genes. In the case of our study, The CytoNCA application allowed us to calculate the node connectivity between the genes from the constructed PPI network. The degree scores of the top 20 genes were determined, with an emphasis on identifying key nodes that play a crucial role in the PPI network. Among these genes, we observed substantial connectivity, with degree values exceeding 20. As shown in the Supplementary Table.1, the cyclin B1 (CCNB1), DNA topoisomerase II alpha (TOP2A), cyclin-dependent Kinase 1 (CDK1), cyclin A2 (CCNA2), and aurora kinase B (AURKB) genes demonstrated highest connectivity and degree scores within the PPI network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eReceiver operating characteristic test\u003c/h2\u003e \u003cp\u003eThe detection and distinction of tumor samples from control tissues are crucial in the early diagnosis of GBM cancer. The use of potential biomarkers plays a vital role in differentiation of cancer samples from normal tissue samples. Therefore, we evaluated the diagnostic potential of the HIF alpha factors and the top enriched genes from BMP pathway in GBM cancer samples. By utilizing GraphPad Prism software (version 9.1.0), we conducted the ROC examination to assess the diagnostic potential of these potential biomarkers. As demonstrated in the Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the 7 genes had significant AUC (Area Under Curve) values above 0.90, including SOSTDC1 (AUC\u0026thinsp;=\u0026thinsp;0.96, p-value\u0026thinsp;=\u0026thinsp;0.0004), CHRD (AUC\u0026thinsp;=\u0026thinsp;0.96, p-value\u0026thinsp;=\u0026thinsp;0.0004), SMAD7 (AUC\u0026thinsp;=\u0026thinsp;0.97, p-value\u0026thinsp;=\u0026thinsp;0.0003), PPM1A (AUC\u0026thinsp;=\u0026thinsp;1, p-value\u0026thinsp;=\u0026thinsp;0.0001), MAPK1 (AUC\u0026thinsp;=\u0026thinsp;0.95, p-value\u0026thinsp;=\u0026thinsp;0.0005), SMURF1 (AUC\u0026thinsp;=\u0026thinsp;0.91, p-value\u0026thinsp;=\u0026thinsp;0.001), and HIF1A (AUC\u0026thinsp;=\u0026thinsp;0.96, p-value\u0026thinsp;=\u0026thinsp;0.0004) genes.\u003c/p\u003e \u003cp\u003eThese findings bring us one step closer to the early detection of GBM cancer, which is crucial for effective treatment and improved patient outcomes. Further studies can explore the clinical application of these enzymes in diagnostic processes and pave the way for enhanced precision medicine approaches in GBM management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis\u003c/h2\u003e \u003cp\u003eBy examining the relationship between gene expression levels and patient survival data, researchers can identify genes that may play a crucial role in cancer prognosis. Survival analysis provides insights into the impact of these genetic factors on patient survival outcomes, enabling personalized treatment strategies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOncoLnc database incorporates the Logrank p-value calculation, a statistical test used to determine the significance of differences in survival between different patient groups based on gene expression levels. This valuable feature allows researchers to assess the statistical significance of observed differences in survival outcomes and validate the relevance of potential biomarkers in cancer progression. using the OncoLnc online database, A Cox regression analysis was conducted utilizing the normalized count data of HIF alpha factors and the top enriched genes from BMP pathway in GBM cancer samples to evaluate their impact with regard to multiple covariates on the survival of patients with GBM cancer. By considering variables such as age, gender, tumor stage, and gene/miRNA expression levels, Cox regression enables the identification of independent prognostic factors. Based on the survival graphs extracted from OncoLnc database, which are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, only the expression level of SMURF1 significantly correlated (Logrank p-value\u0026thinsp;=\u0026thinsp;0.002) with the survival of patients with GBM cancer, and those who had lower expression levels of SMURF1 gene survived significantly better compared to group of patients with higher expression level of SMURF1 gene. Therefore, it can be concluded that the SMURF1 gene can be a high-potential prognostic biomarker in GBM cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eMultiforme Glioblastoma, abbreviated to GBM, is a highly aggressive tumor of the brain and constitutes an important ongoing challenge in understanding its molecular underpinnings and finding preventive strategies (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Epidemiological data from recent years paints an increasingly worrisome picture of increasing GBM incidence rates coupled with low survival rates (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Thus, there exists a compelling rationale that calls for identifying early biomarkers for timely detection and possible prevention interventions.\u003c/p\u003e \u003cp\u003eAn important part in GBM development is the role of Bone Morphogenetic Proteins (BMPs) which are members of the Transforming Growth Factor-β protein family. The proteins perform several roles such as: control CNS stem cell differentiation, apoptotic pathway, mitotic arrest and glioma-derived precursor cell differentiation (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Expression of dysregulated BMP protein has been implicated in oncogenic progression and modulation of tumor-suppressive pathways (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). However, the functional role of BMP proteins depends on the specific protein variant and tumor phenotype (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In addition, oxygen tension has been identified as an influential factor in modulating BMP protein expression (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn high-grade glioma cells, hypoxic conditions have been shown to inhibit the BMP pathway through the HIF-1α protein (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Understanding the intricate interplay between BMP protein expression patterns and HIF profiles in GBM patients remains a fascinating area for scientific inquiry. To address this, the current study aims to unravel the complex expression patterns of HIF factors and BMP pathway constituents within GBM tumor tissue, comparing them to non-neoplastic counterparts.\u003c/p\u003e \u003cp\u003eTo analyze the enrichment pattern of genes from the BMP pathway in GBM cancer and normal count data, we employed the GSEA technique in this study. Interestingly, the GSEA results indicated a distinct enrichment pattern in the normal phenotype. Nevertheless, the observed enrichment failed to attain statistical significance, indicating that the differential enrichment of the BMP pathway in GBM cancer samples compared to normal samples is not substantial. Subsequently, we identified the top 14 core genes from the BMP pathway that drive the enrichment score of the GSEA clusters using Rank metric scores. These genes will undergo further analysis.\u003c/p\u003e \u003cp\u003eMoreover, a thorough examination was carried out on the variance in gene expression of HIF alpha transcription factors and the top 14 enriched genes from the BMP pathway in samples of GBM cancer. The analysis revealed that EPAS1, HIF3A, CHRDL1, NOG, BMP6, and AHSG genes did not exhibit a statistically significant difference between GBM cancer and normal tissue samples. In our previous research, HIF3A also showed no significant differential expression level in different types of TCGA cancer samples (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). However, the HIF1A gene showed significant upregulation in cancer samples, consistent with previous studies on the expression level of HIF1A in high-grade glioma cells under hypoxic conditions (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurther exploration of differentially expressed genes (DEGs) unveiled noteworthy findings. The majority of genes from the BMP pathway were significantly downregulated in GBM cancer samples compared to normal samples. Notable examples include SOSTDC1, GREM1, CHRD, SMAD7, PPM1A, BMPR2, MAPK1, ZFYVE16, GSK3B, and SMURF1 genes.\u003c/p\u003e \u003cp\u003eThe correlation analysis by Pearson method has unveiled intriguing relationships between HIF alpha transcription factors and the top enriched genes from the BMP pathway in GBM cancer samples. Notably, the expression levels of HIF1A and EPAS1 correlated with PPM1A and BMPR2 genes while negatively correlated with SOSTDC1 and CHRDL1 genes. This negative correlation suggests a regulatory antagonist relationship between HIF1A and these genes within the BMP pathway, aligning with previous studies (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). While the investigation of the correlation between HIF1A and BMPR2 receptor expression in cancer cells and different tissue types has been limited in recent years, studies have highlighted that hypoxic conditions can downregulate the expression of the BMPR2 gene in lung cells of rats (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe MAPK1 gene also showed a moderate correlation with BMPR2 expression level. Furthermore, additional studies have underscored the participation of the Mitogen-activated protein kinase (MAPK) signaling pathway and MAPK-1 in the control of cellular death in neuronal cells subjected to stressful conditions. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The evaluation of HIF alpha factors and enriched genes from the BMP pathway in GBM cancer samples exhibits their potential as biomarkers with AUC values. Detecting GBM cancer at a stage is crucial, for improving treatment effectiveness and patient outcomes. To assess their potential, we conducted ROC analysis to evaluate both sensitivity and specificity of these biomarkers. Among the genes analyzed, seven displayed significant AUC values exceeding 0.90, including SOSTDC1, CHRD, SMAD7, PPM1A, MAPK1, SMURF1, and the HIF1A transcription factor. Previous investigations by other research groups revealed a significant relationship between SMAD7 and the TGF-beta signaling pathway in glioblastoma cells. These insights further support the importance of our findings (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, it is worth noting that a remarkable association was observed in survival analysis when examining the expression level of the SMURF1 gene and the survival rate of patients affected by GBM cancer. Interestingly, those individuals with a lower expression level of SMURF1 gene exhibited more favorable survival rates in comparison to those with a higher expression level. These outcomes closely align with previous investigations that have shed light on the oncogenic function of the SMURF1 gene during glioblastoma progression, underscoring its potential significance as a powerful prognostic biomarker for GBM cancer (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, these findings distinctly emphasize the promise of these genes as both diagnostic and prognostic biomarkers for the timely detection of GBM cancer. The precise identification of these biomarkers has the potential to significantly contribute towards the design of effective therapeutic strategies and the enhancement of patient outcomes. Moreover, the intricately intertwined interaction between HIF alpha and the BMP pathway possesses tremendous potential in terms of identifying novel targets and developing innovative treatment approaches for individuals afflicted with GBM. Identification of new and specific biomarkers with help of bioinformatic techniques can help with the early detection of different types of cancer (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). However, the identified biomarkers should be evaluated by further experimental investigations in order to enter early-stage clinical phases.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eGlioblastoma Multiforme (GBM) is a severe type of brain tumor with a poor chance of survival. In this investigation, an analysis was conducted on the levels of expression of the BMP pathway and HIF alpha factors in GBM cancer samples compared to normal tissue samples. HIF1A gene demonstrated a significant upregulation in cancer samples and the majority of genes from the BMP pathway were significantly downregulated in cancer. With help of ROC test and survival analysis, we identified potential biomarkers with diagnostic and prognostic potential for GBM cancer. Additionally, functional enrichment The PPI network analyses showed that the majority of the DEGs were related to the TGF-beta signaling pathway, pathways in cancer, and the cell cycle. In our study, we introduced specific genes potential biomarkers for the early detection and prognosis of GBM cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest statement\u003c/h2\u003e \u003cp\u003eAll authors declared no conflict of interest in this study.\u003c/p\u003e\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e \u003cp\u003eThe study design was performed by B. Yazdani. Data analysis was done by B. Yazdani. Interpretations of the data and bioinformatics analysis were performed by B. Yazdani, and A.R. Sichani. Manuscript writing was performed by B. Yazdani, and A.R. Sichani. The final version of the manuscript was approved by all authors.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003enone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWu W, Klockow JL, Zhang M, Lafortune F, Chang E, Jin L, Wu Y, Daldrup-Link HE (2021) Glioblastoma multiforme (GBM): An overview of current therapies and mechanisms of resistance. Pharmacol Res 171:105780\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmoll NR, Schaller K, Gautschi OP (2013) Long-term survival of patients with glioblastoma multiforme (GBM). J Clin Neurosci 20(5):670\u0026ndash;675\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolland EC (2000) Glioblastoma multiforme: the terminator. Proceedings of the National Academy of Sciences. ;97(12):6242-4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClancy E (2023) ACS Report Shows Prostate Cancer on the Rise, Cervical Cancer on the Decline. Renal \u0026amp; Urology News. Feb 23:NA-\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSasmita AO, Wong YP, Ling AP (2018) Biomarkers and therapeutic advances in glioblastoma multiforme. Asia-Pac J Clin Oncol 14(1):40\u0026ndash;51\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang RN, Green J, Wang Z, Deng Y, Qiao M, Peabody M, Zhang Q, Ye J, Yan Z, Denduluri S, Idowu O (2014) Bone Morphogenetic Protein (BMP) signaling in development and human diseases. Genes Dis 1(1):87\u0026ndash;105\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThawani JP, Wang AC, Than KD, Lin CY, La Marca F, Park P (2010) Bone morphogenetic proteins and cancer: review of the literature. Neurosurgery 66(2):233\u0026ndash;246\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBach DH, Park HJ, Lee SK (2018) The dual role of bone morphogenetic proteins in cancer. Mol Therapy-Oncolytics 8:1\u0026ndash;3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis H, Raja E, Miyazono K, Tsubakihara Y, Moustakas A (2016) Mechanisms of action of bone morphogenetic proteins in cancer. Cytokine Growth Factor Rev 27:81\u0026ndash;92\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh A, Morris RJ (2010) The Yin and Yang of bone morphogenetic proteins in cancer. Cytokine Growth Factor Rev 21(4):299\u0026ndash;313\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaegdefrau U, Amann T, Winklmeier A, Braig S, Schubert T, Weiss TS, Schardt K, Warnecke C, Hellerbrand C, Bosserhoff AK (2009) Bone morphogenetic protein 4 is induced in hepatocellular carcinoma by hypoxia and promotes tumour progression. J Pathology: J Pathological Soc Great Br Irel 218(4):520\u0026ndash;529\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu X, Chang MS, Mitsialis SA, Kourembanas S (2006) Hypoxia Regulates Bone Morphogenetic Protein Signaling Through C-Terminal\u0026ndash;Binding Protein 1. Circul Res 99(3):240\u0026ndash;247\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu X, Chang MS, Mitsialis SA, Kourembanas S (2006) Hypoxia Regulates Bone Morphogenetic Protein Signaling Through C-Terminal\u0026ndash;Binding Protein 1. Circul Res 99(3):240\u0026ndash;247\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePistollato F, Chen HL, Rood BR, Zhang HZ, D'Avella D, Denaro L, Gardiman M, Te Kronnie G, Schwartz PH, Favaro E, Indraccolo S (2009) Hypoxia and HIF1α repress the differentiative effects of BMPs in high-grade glioma. Stem Cells 27(1):7\u0026ndash;17\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen HL, Panchision DM (2007) Concise review: bone morphogenetic protein pleiotropism in neural stem cells and their derivatives\u0026mdash;alternative pathways, convergent signals. Stem Cells 25(1):63\u0026ndash;68\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeam RD (2010) R: A language and environment for statistical computing. (No Title)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, Sabedot TS, Malta TM, Pagnotta SM, Castiglioni I, Ceccarelli M (2016) TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res 44(8):e71\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobinson MD, McCarthy DJ, Smyth GK (2010) edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139\u0026ndash;140\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences. ;102(43):15545-50\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiberzon A, Subramanian A, Pinchback R, Thorvaldsd\u0026oacute;ttir H, Tamayo P, Mesirov JP (2011) Molecular signatures database (MSigDB) 3.0. Bioinformatics 27(12):1739\u0026ndash;1740\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGene Ontology Consortium (2004) The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res 32(suppl1):D258\u0026ndash;D261\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, Guo Y, Stephens R, Baseler MW, Lane HC, Lempicki RA (2007) DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res 35(suppl2):W169\u0026ndash;W175\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA (2003) DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 4(9):1\u0026ndash;1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoncheva NT, Morris JH, Gorodkin J, Jensen LJ (2018) Cytoscape StringApp: network analysis and visualization of proteomics data. J Proteome Res 18(2):623\u0026ndash;632\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKohl M, Wiese S, Warscheid B (2011) Cytoscape: software for visualization and analysis of biological networks. Data mining in proteomics: from standards to applications. :291\u0026ndash;303\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498\u0026ndash;2504\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnaya J (2016) OncoLnc: linking TCGA survival data to mRNAs, miRNAs, and lncRNAs. PeerJ Comput Sci 2:e67\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEhata S, Miyazono K (2022) Bone morphogenetic protein signaling in cancer; some topics in the recent 10 years. Front Cell Dev Biology 10:883523\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakahashi H, Goto N, Kojima Y, Tsuda Y, Morio Y, Muramatsu M, Fukuchi Y (2006) Downregulation of type II bone morphogenetic protein receptor in hypoxic pulmonary hypertension. Am J Physiology-Lung Cell Mol Physiol 290(3):L450\u0026ndash;L458\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi YS, Horning P, Aten S, Karelina K, Alzate-Correa D, Arthur JS, Hoyt KR, Obrietan K (2017) Mitogen-and stress-activated protein kinase 1 regulates status epilepticus-evoked cell death in the hippocampus. ASN neuro 9(5):1759091417726607\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMishra OP, Delivoria-Papadopoulos M (2004) Effect of hypoxia on the expression and activity of mitogen-activated protein (MAP) kinase-phosphatase-1 (MKP-1) and MKP-3 in neuronal nuclei of newborn piglets: the role of nitric oxide. Neuroscience 129(3):665\u0026ndash;673\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYazdani B, Sirous H (2022) Expression analysis of HIF-3α as a potent prognostic biomarker in various types of human cancers: a case of meta-analysis. Res Pharm Sci 17(5):508\u0026ndash;526\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoringer M, Darzacq X, Izeddin I (2014) Geometry of the nucleus: a perspective on gene expression regulation. Curr Opin Chem Biol 20:112\u0026ndash;119\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShivashankar GV (2011) Mechanosignaling to the cell nucleus and gene regulation. Annual Rev Biophys 40:361\u0026ndash;378\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim WS, Weickert CS, Garner B (2008) Role of ATP-binding cassette transporters in brain lipid transport and neurological disease. J Neurochem 104(5):1145\u0026ndash;1166\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrunet A, Datta SR, Greenberg ME (2001) Transcription-dependent and-independent control of neuronal survival by the PI3K\u0026ndash;Akt signaling pathway. Curr Opin Neurobiol 11(3):297\u0026ndash;305\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGolestaneh N, Mishra B (2005) TGF-β, neuronal stem cells and glioblastoma. Oncogene 24(37):5722\u0026ndash;5730\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWick W, Naumann U, Weller M (2006) Transforming growth factor-β: a molecular target for the future therapy of glioblastoma. Curr Pharm Design 12(3):341\u0026ndash;349\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHover LD, Abel TW, Owens P (2015) Genomic analysis of the BMP family in glioblastomas. TranslaTional oncogenomics 7:1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParsazad E, Esrafili F, Yazdani B, Ghafarzadeh S, Razmavar N, Sirous H (2023) Integrative bioinformatics analysis of ACS enzymes as candidate prognostic and diagnostic biomarkers in colon adenocarcinoma. Res Pharm Sci 18(4):413\u0026ndash;429\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Glioblastoma Multiforme, Bone Morphogenetic Proteins, Hypoxia-Inducible Factors, TCGA","lastPublishedDoi":"10.21203/rs.3.rs-4232372/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4232372/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eGlioblastoma Multiforme (GBM) is a devastating neoplastic growth affecting the brain, with a dismal prognosis. The underlying diagnostic and prognostic potential hypoxia-inducible-factors and BMP pathway in this devastating malignancy remains poorly understood, lacking compelling preventive strategies.\u003c/p\u003e\u003ch2\u003eMethods and materials:\u003c/h2\u003e \u003cp\u003eA bioinformatic study was conducted using integrative bioinformatics techniques for the analysis of GBM count data, which were obtained from the Cancer Genome Atlas (TCGA) database and underwent normalization and differential expression analysis (DEG). Gene Set Enrichment Analysis (GSEA), Differential gene expression analysis, and correlation analysis using Pearson method were conducted for the genes involved in the BMP pathway. Gene Ontology and Protein-protein interaction analyses were employed. Survival analysis and Receiver Operating test (ROC) were also performed to identify potential prognostic and diagnostic biomarkers.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results revealed that the expression levels of EPAS1, HIF3A, CHRDL1, NOG, BMP6, and AHSG genes did not exhibit a statistically significant difference between GBM cancer samples and normal tissue samples. Further DEG analysis indicated that the majority of genes from the BMP pathogenesis were significantly downregulated in GBM cancer samples and a positive correlation was observed between the expression levels of EPAS1, BMPR2, and MAPK1 genes. the Top DEGs were correlated with specific pathways, such as the TGF-beta signaling pathway, pathways in cancer, and the cell cycle. By ROC test we identified the best diagnostic biomarkers for GBM and SMURF1 gene is predicted to have significant prognostic capability.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings highlight the possible utility of these genes as promising diagnostic and prognostic biomarkers for the early detection of GBM.\u003c/p\u003e","manuscriptTitle":"Unveiling the Diagnostic and Prognostic potential of BMP Pathway and Hypoxia-inducible Factors in Glioblastoma Multiforme","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-08 18:48:26","doi":"10.21203/rs.3.rs-4232372/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"29b47aee-a533-47b9-b2e1-772981590a2e","owner":[],"postedDate":"April 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30373060,"name":"Oncology"},{"id":30373061,"name":"Epigenetics \u0026 Genomics"},{"id":30373062,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2024-04-08T18:48:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-08 18:48:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4232372","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4232372","identity":"rs-4232372","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}