Prediction and Analysis of Key Genes in Glioblastoma Based on Bioinformatics

SV2B/miR-34a/miR-128 axis as prognostic biomarker in glioblastoma multiforme

Glioblastoma (GBM) is a heterogenous primary brain tumour that is characterised with unfavourable patient prognosis. The identification of biomarkers for managing brain malignancies is of utmost importance. MicroRNAs (miRNAs) are small, non-coding RNAs implicated in cancer development. This study aimed to assess the prognostic significance of miRNAs and their gene targets in GBM. An in silico approach was employed to investigate the differentially expressed miRNAs in GBM. The most dysregulated miRNAs were identified and analysed via Sfold in association with their gene target. The candidate gene was studied via multi-omics approaches, followed by in vitro and in vivo experiments. The in silico analyses revealed that miR-128a and miR-34a were significantly downregulated within GBM. Both miRNAs displayed high binding affinity to the synaptic vesicle glycoprotein 2B (SV2B) 3' untranslated region (3'UTR). SV2B exhibited upregulation within brain regions with high synaptic activity. Significantly higher SV2B levels were observed in high grade brain malignancies in comparison to their normal counterparts. SV2B expression was observed across the cytoplasm of GBM cells. Our findings underscored the downregulated expression patterns of miR-128a and miR-34a, alongside the upregulation of SV2B in GBM suggesting the importance of the SV2B/miR-34a/miR-128 axis as a potential prognostic approach in GBM management.

Identification of LOX as a candidate prognostic biomarker in Glioblastoma multiforme

BACKGROUND

Glioblastoma multiforme (GBM) is the most malignant type of glioma. GBM tumors grow rapidly, have a high degree of malignancy, and are characterized by a fast disease progression. Unfortunately, there is a lack of effective treatments. An effective strategy for the treatment of GBM would be to identify key biomarkers correlating with the occurrence and progression of GBM and developing these biomarkers into therapeutic targets.

METHOD AND RESULTS

In this study, using integrated bioinformatics analysis, we identified differentially expressed genes (DEGs), including 130 genes that were upregulated in GBM compared to normal brain tissue, and 128 genes that were downregulated in GBM. Based on Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis, these genes were associated with regulation of tumor cell adhesion, differentiation, morphology in GBM and were mainly enriched in Complement and coagulation cascades pathway. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to construct a Protein-Protein Interaction network. Ten hub genes were identified, including FN1, CD44, MYC, CDK1, SERPINE1, COL3A1, COL1A2, LOX, POSTN and EZH2, all of which were significantly upregulated in GBM, these results were confirmed by oncomine database exploration. Alteration analysis of hub genes found that patients with alteration in at least one of the hub genes showed shorter median survival times (p = 0.013) and shorter median disease-free survival times (p = 2.488E-3) than patients without alterations in any of the hub genes. Multiple tests for survival analysis showed that among individual hub genes only expression of LOX was correlated with patient survival (P < 0.05).GDS4467 data set was used to analyze the expression of LOX in gliomas with different degrees of malignancy, and it was found that the expression level of LOX was positively correlated with the malignant degree of gliomas.By analyzing GDS 4535 data set showed that the expression level of LOX was positively correlated with the differentiation degree of GBM cells CONCLUSION: This research suggests that FN1, CD44, MYC, CDK1, SERPINE1, COL3A1, COL1A2, LOX, POSTN and EZH2 are key genes in GBM. However, only LOX is correlated with patient survival and promotes glioblastoma cell differentiation and tumor recurrence. LOX may be a candidate prognostic biomarker and potential therapeutic target for GBM.

MultiSimNeNc: A network representation learning-based module identification method by network embedding and clustering

Accurate identification of gene modules based on biological networks is an effective approach to understanding gene patterns of cancer from a module-level perspective. However, most graph clustering algorithms just consider low-order topological connectivity, which limits their accuracy in gene module identification. In this study, we propose a novel network-based method, MultiSimNeNc, to identify modules in various types of networks by integrating network representation learning (NRL) and clustering algorithms. In this method, we first obtain the multi-order similarity of the network using graph convolution (GC). Then, we aggregate the multi-order similarity to characterize the network structure and use non-negative matrix factorization (NMF) to achieve low-dimensional node characterization. Finally, we predict the number of modules based on the bayesian information criterion (BIC) and use the gaussian mixture model (GMM) to identify modules. To testify to the efficacy of MultiSimeNc in module identification, we apply this method to two types of biological networks and six benchmark networks, where the biological networks are constructed based on the fusion of multi-omics data from glioblastoma (GBM). The analysis shows that MultiSimNeNc outperforms several state-of-the-art module identification algorithms in identification accuracy, which is an effective method for understanding biomolecular mechanisms of pathogenesis from a module-level perspective.

FZD1/KLF10-hsa-miR-4762-5p/miR-224-3p-circular RNAs axis as prognostic biomarkers and therapeutic targets for glioblastoma: a comprehensive report

BACKGROUND

The circular RNA (circRNA) plays a vital role in the pathogenesis of tumors as a competitive endogenous RNA (ceRNA). Given the high aggressiveness and fatality rate of glioblastoma (GBM) as well as poor prognosis, it is necessary to construct a circRNA-related ceRNA network for further studies on the mechanism of GBM and identify possible biomarkers as well as therapeutic drugs.

METHODS

Three datasets from the gene expression omnibus (GEO) database were downloaded to distinguish differential circRNAs, microRNAs, and messenger RNAs respectively in GBM. With the help of GEPIA2, circBank, CSCD, TargetScan, miRDB, and miRTarBase databases, we established a circRNAs-related ceRNA network in GBM. Functional enrichments were employed to profile the most relevant mRNAs to indirectly clarify the mechanisms of the ceRNA network. Based on the expression profile data and survival information of GBM patients from the GEO and the cancer genome atlas (TCGA) databases, we performed survival analysis to select prognostic mRNAs and constructed a novel circRNA-miRNA-mRNA central regulatory subnetwork. The DGIdb database was used to find potential drug-gene interactions.

RESULTS

The datasets obtained from the GEO and TCGA databases were analyzed, and 504 differentially expressed mRNAs (DEmRNAs), 71 differentially expressed microRNAs (DEmiRNAs), and 270 differentially expressed circRNAs (DEcircRNAs) were screened out. The novel ceRNA regulatory network included 22 circRNAs, 11 miRNAs, and 15 mRNAs. FZD1 and KLF10 were significantly correlated with the overall survival rate of patients with GBM (P < 0.05). The final survival subnetwork contained six circRNAs, two miRNAs, and two mRNAs. Two small-molecule compounds and one antibody could be used as therapeutic drugs for GBM. Interestingly, the Wnt signaling pathway appeared in both KEGG and GO functional terms.

CONCLUSIONS

Results of this study demonstrate that FZD1 and KLF10 may exert regulatory functions in GBM, and the ceRNA-mediated network could be a therapeutic strategy for GBM.

Non-negative matrix factorization and differential expression analyses identify hub genes linked to progression and prognosis of glioblastoma multiforme

One of the most prevailing primary brain tumors in adult human male is glioblastoma multiforme (GBM), which is categorized by rapid cellular growth. Even though the combination therapy comprises surgery, chemotherapy, and adjuvant therapies, the survival rate, on average, is 14.6 months. Glioma stem cells (GSCs) have key roles in tumorigenesis, progression, and defiance against chemotherapy and radiotherapy. In our study, firstly, the gene expression dataset GSE124145 was retrieved; the non-negative matrix factorization (NMF) method was applied on GBM dataset, and differentially expressed genes analysis (DEGs) was performed. After which, overlapping genes between metagenes and DEGs were detected to examine the Gene Ontology (GO) categories in the biological process (BP) in the stemness of GBM. The common hub genes were used to construct protein-protein interaction (PPI) network and further GO, while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was utilized to pinpoint the real hub genes. The analysis of hub genes particular for the same GO categories demonstrated that specific hub genes triggered distinct features of the same biological processes. After utilizing GSE124145 and The Cancer Genome Atlas (TCGA) dataset for survival analysis, we screened five real hub genes: GUCA1A, RFC2, GNG11, MMP19, and NRG1, which are strongly associated with the progression and prognosis of GBM. The DEGs analysis revealed that all real hub genes were overexpressed in GBM and TCGA datasets, which further validates our results. The constructed study of PPI, GO, and KEGG pathway on common hub genes was performed. Finally, the KEGG pathways performed on the top 15 candidate hub genes (including six real hub genes) of the PPI network in the GBM gene expression dataset study found mitogen-activated protein kinase (Mapk) signaling pathway to be the most significant pathway. The rest of the hub genes reviewed throughout the analysis might be favorable targets for diagnosing and treating GBM and lower-grade gliomas.

Identification of Prognostic Biomarkers for Glioblastoma Based on Transcriptome and Proteome Association Analysis

Objective:

Glioblastoma multiforme (GBM) is the most malignant primary brain tumor in adults. This study aimed to identify significant prognostic biomarkers related to GBM.

Methods:

We collected 3 GBM and 3 healthy human brain samples for transcriptome and proteomic sequencing analysis. Differentially expressed genes (DEGs) between GBM and control samples were identified using the edge R package in R. Functional enrichment analyses, prediction of long noncoding RNA target genes, and protein-protein interaction network analyses were performed. Subsequently, transcriptomic and proteomic association analyses, validation using The Cancer Genome Atlas (TCGA) database, and survival and prognostic analyses were conducted. Then the hub genes directly related to GBM were screened. Finally, the expression of key genes was verified by quantitative polymerase chain reaction (qPCR).

Results:

Totally, 1140 transcripts and 503 proteins were significantly up- or down-regulated. A total of 25 genes were upregulated and 62 were downregulated at both the transcriptome and proteome levels. Results from TCGA database showed that 84 of these 87 genes matched with transcriptome sequencing results. A Cox regression analysis suggested that Fibronectin 1(FN1) was a prognostic risk factor. The qPCR results showed that FN1 was significantly upregulated in GBM samples.

Conclusions:

FN1 may play a role in GBM progression through ECM-receptor interaction and PI3K-Akt signaling pathways. FN1 may be considered as a prognostic biomarkers related to GBM.

PTPRM methylation induced by FN1 promotes the development of glioblastoma by activating STAT3 signalling

CONTEXT

The phosphorylation of signal transducer and activator of transcription protein 3 (STAT3) is up-regulated in glioblastoma (GBM) cells and is regulated by protein tyrosine phosphatase receptor type M (PTPRM). Fibronectin-1 (FN1) is also reported to be up-regulated in GBM.

OBJECTIVE

We explored the role of FN1-induced PTPRM methylation in GBM.

MATERIALS AND METHODS

The lentivirus particles of oe-PTPRM, sh-PTPRM, oe-FN1, sh-FN1, or their negative controls (NSCs) were transfected into GBM cells with or without stattic (0.5 μM, 24 h) or 5-aza (1 μM, 0, 2, 4 h) treatments. Methylation-specific PCR was performed to detect PTPRM methylation levels.

RESULTS

PTPRM was down-regulated (0.373 ± 0.124- and 0.455 ± 0.109-fold), FN1 and p-STAT3 were up-regulated (p < 0.001) in A172 and U87 MG cells as compared to NSCs. Overexpressing PTPRM inhibited STAT3 phosphorylation. Interfering with PTPRM increased colony numbers in A172 and U-87 MG cells (2.253 ± 0.111- and 2.043 ± 0.19-fold), and stattic reduced them. Cell viability was reduced after treatment with 5-aza in A172 and U-87 MG cells (p < 0.05). P-STAT3 was down-regulated after 5-aza treatment. Overexpressing FN1 decreased PTPRM levels (p < 0.001), knockdown of FN1 decreased PTPRM methylation and inhibited STAT3 phosphorylation. Overexpressing FN1 increased cell viability (1.497 ± 0.114- and 1.460 ± 0.151-fold), and stattic or 5-aza reversed such effects (p < 0.05).

DISCUSSION AND CONCLUSIONS

The up-regulation of FN1 reduced PTPRM by increasing its methylation, resulting in an increase of STAT3 phosphorylation and promoting GBM cell proliferation. Interfering with FN1 may be a potential therapeutic target for GBM.

Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning

Precision medicine is a medical approach to administer patients with a tailored dose of treatment by taking into consideration a person's variability in genes, environment, and lifestyles. The accumulation of omics big sequence data led to the development of various genetic databases on which clinical stratification of high-risk populations may be conducted. In addition, because cancers are generally caused by tumor-specific mutations, large-scale systematic identification of single nucleotide polymorphisms (SNPs) in various tumors has propelled significant progress of tailored treatments of tumors (i.e., precision oncology). Machine learning (ML), a subfield of artificial intelligence in which computers learn through experience, has a great potential to be used in precision oncology chiefly to help physicians make diagnostic decisions based on tumor images. A promising venue of ML in precision oncology is the integration of all available data from images to multi-omics big data for the holistic care of patients and high-risk healthy subjects. In this review, we provide a focused overview of precision oncology and ML with attention to breast cancer and glioma as well as the Bayesian networks that have the flexibility and the ability to work with incomplete information. We also introduce some state-of-the-art attempts to use and incorporate ML and genetic information in precision oncology.

Identification of robust diagnostic and prognostic gene signatures in different grades of gliomas: a retrospective study

Background

Gliomas are the most common primary tumors of the central nervous system. The complexity and heterogeneity of the tumor makes it difficult to obtain good biomarkers for drug development. In this study, through The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA), we analyze the common diagnostic and prognostic moleculer markers in Caucasian and Asian populations, which can be used as drug targets in the future.

Methods

The RNA-seq data from Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) were analyzed to identify signatures. Based on the signatures, the prognosis index (PI) of every patient was constructed to predict the prognostic risk. Also, gene ontology (GO) functional enrichment analysis and KEGG analysis were conducted to investigate the biological functions of these mRNAs. Glioma patients’ data in the CGGA database were introduced to validate the effectiveness of the signatures among Chinese populations. Excluding the previously reported prognostic markers of gliomas from this study, the expression of HSPA5 and MTPN were examined by qRT-PCR and immunohistochemical assay.

Results

In total, 20 mRNAs were finally selected to build PI for patients from TCGA, including 16 high-risk genes and four low-risk genes. For Chinese patients, the log-rank test p values of PI were both less than 0.0001 in two independent datasets. And the AUCs were 0.831 and 0.907 for 3 years of two datasets, respectively. Moreover, among these 20 mRNAs, 10 and 15 mRNAs also had a significant predictive effect via univariate COX analysis in CGGA_693 and CGGA_325, respectively. qRT-PCR and Immunohistochemistry assay indicated that HSPA5 and MTPN over-expressed in Glioma samples compared to normal samples.

Conclusion

The 20-gene signature can forecast the risk of Glioma in TCGA effectively, moreover it can also predict the risks of Chinese patients through validation in the CGGA database. HSPA5 and MTPN are possible biomarkers of gliomas suitable for all populations to improve the prognosis of these patients.

Bioinformatics analysis of potential core genes for glioblastoma

Background: Glioblastoma (GBM) has a high degree of malignancy, aggressiveness and recurrence rate. However, there are limited options available for the treatment of GBM, and they often result in poor prognosis and unsatisfactory outcomes.

Materials and methods: In order to identify potential core genes in GBM that may provide new therapeutic insights, we analyzed three gene chips (GSE2223, GSE4290 and GSE50161) screened from the GEO database. Differentially expressed genes (DEG) from the tissues of GBM and normal brain were screened using GEO2R. To determine the functional annotation and pathway of DEG, Gene Ontology (GO) and KEGG pathway enrichment analysis were conducted using DAVID database. Protein interactions of DEG were visualized using PPI network on Cytoscape software. Next, 10 Hub nodes were screened from the differentially expressed network using MCC algorithm on CytoHubba software and subsequently identified as Hub genes. Finally, the relationship between Hub genes and the prognosis of GBM patients was described using GEPIA2 survival analysis web tool.

Results: A total of 37 up-regulated and 187 down-regulated genes were identified through microarray analysis. Amongst the 10 Hub genes selected, SV2B appeared to be the only gene associated with poor prognosis in glioblastoma based on the survival analysis.

Conclusion: Our study suggests that high expression of SV2B is associated with poor prognosis in GBM patients. Whether SV2B can be used as a new therapeutic target for GBM requires further validation.

AGAP2-AS1 May Promote the Occurrence and Development of Glioblastoma by Sponging miR-9-5p: Evidence From a ceRNA Network

Glioblastoma (GBM), the primary malignant brain tumor, is typically associated with a poor prognosis and poor quality of life, mainly due to the lack of early diagnostic biomarkers and therapeutic targets. However, gene sequencing technologies and bioinformatics analysis are currently being actively utilized to explore potential targets for the diagnosis and management of malignancy. Herein, based on a variety of bioinformatics tools for the reverse prediction of target genes associated with the prognosis of GBM, a ceRNA network of AGAP2-AS1-miR-9-5p-MMP2/MMP9 was constructed, and a potential therapeutic target for GBM was identified. Enrichment analysis predicted that the ceRNA regulatory network participates in the processes of cell proliferation, differentiation, and migration.

Identification of hub genes related to prognosis in glioma

Glioma, a common malignant tumor of the central nervous system, has high invasiveness. The objective of the present study was to identify genes playing an important role in the development of glioma and to reveal their potential research value. Conjoint analysis on the GSE16011 dataset in the Gene Expression Omnibus (GEO) database and the 'Messenger RNA Expression Microarray of Diffuse Gliomas and Controls' dataset and 'RNA sequencing of Diffuse Gliomas' dataset in the Chinese Glioma Genome Atlas (CGGA) database is carried out in the study. The weighted correlation network analysis (WGCNA) was used to carry out co-expression analysis on the GSE16011. Finally, 24 genes significantly related to grade and prognosis were obtained. In addition, there is no report about CACNG2, JPH3, TUBB6 (tubulin β 6 class V), NRSN1, FAM19A2, NALCN, CDH18, GNAL on glioma.

ceRNA Network Analysis Shows That lncRNA CRNDE Promotes Progression of Glioblastoma Through Sponge mir-9-5p

Glioblastoma accounts for 45.2% of central nervous system tumors. Despite the availability of multiple treatments (e.g., surgery, radiotherapy, chemotherapy, biological therapy, immunotherapy, and electric field therapy), glioblastoma has a poor prognosis, with a 5-year survival rate of approximately 5%. The pathogenesis and prognostic markers of this cancer are currently unclear. To this end, this study aimed to explore the pathogenesis of glioblastoma and identify potential prognostic markers. We used data from the GEO and TCGA databases and identified five genes (ITGA5, MMP9, PTPRN, PTX3, and STX1A) that could affect the survival rate of glioblastoma patients and that were differentially expressed between glioblastoma patients and non-tumors groups. Based on a variety of bioinformatics tools for reverse prediction of target genes associated with the prognosis of GBM, a ceRNA network of messenger RNA (STX1A, PTX3, MMP9)-microRNA (miR-9-5p)-long non-coding RNA (CRNDE) was constructed. Finally, we identified five potential therapeutic drugs (bacitracin, hecogenin, clemizole, chrysin, and gibberellic acid) that may be effective treatments for glioblastoma.

MicroRNA-640 promotes cell proliferation and adhesion in glioblastoma by targeting Slit guidance ligand 1

The effects of microRNAs (miRNAs/miRs) on glioblastoma have attracted the attention of researchers in the last 7 years. However, the role of miR-640 and its targeted gene, Slit guidance ligand 1 (SLIT1), in the development of glioblastoma are not yet fully understood. The present study aimed to investigate the role of miR-640 in the proliferation and adhesion of glioblastoma. Reverse transcription-quantitative PCR analysis was performed to detect miR-640 and SLIT1 expression in glioblastoma tissues and cells. In addition, the Dual-luciferase reporter and RNA-pull down assays were performed to assess the association between miR-640 and SLIT1. The Cell Counting Kit-8, BrdU ELISA, cell adhesion and caspase-3 activity assays were also performed to assess cell viability, proliferation, adhesion and apoptosis of glioblastoma cells, respectively. The results demonstrated that miR-640 expression was upregulated in glioblastoma tissues and cells. In addition, miR-640 promoted the cell viability, proliferation and adhesion of glioblastoma cells, while inhibiting cell apoptosis. SLIT1, a direct downstream target of miR-640, was demonstrated to be downregulated in glioblastoma tissues and cells. Furthermore, overexpression of SLIT1 attenuated the promotive effect of miR-640 on glioblastoma cells. Taken together, these results suggest that miR-640 accelerates the proliferation and adhesion of glioblastoma cell lines by targeting and suppressing SLIT1.

Inference of Subpathway Activity Profiles Reveals Metabolism Abnormal Subpathway Regions in Glioblastoma Multiforme

Glioblastoma, also known as glioblastoma multiforme (GBM), is the most malignant form of glioma and represents 81% of malignant brain and central nervous system (CNS) tumors. Like most cancers, GBM causes metabolic recombination to promote cell survival, proliferation, and invasion of cancer cells. In this study, we propose a method for constructing the metabolic subpathway activity score matrix to accurately identify abnormal targets of GBM metabolism. By integrating gene expression data from different sequencing methods, our method identified 25 metabolic subpathways that were significantly abnormal in the GBM patient population, and most of these subpathways have been reported to have an effect on GBM. Through the analysis of 25 GBM-related metabolic subpathways, we found that (S)-2,3-Epoxysqualene, which was at the central region of the sterol biosynthesis subpathway, may have a greater impact on the entire pathway, suggesting a potential high association with GBM. Analysis of CCK8 cell activity indicated that (S)-2,3-Epoxysqualene can indeed inhibit the activity of U87-MG cells. By flow cytometry, we demonstrated that (S)-2,3-Epoxysqualene not only arrested the U87-MG cell cycle in the G0/G1 phase but also induced cell apoptosis. These results confirm the reliability of our proposed metabolic subpathway identification method and suggest that (S)-2,3-Epoxysqualene has potential therapeutic value for GBM. In order to make the method more broadly applicable, we have developed an R system package crmSubpathway to perform disease-related metabolic subpathway identification and it is freely available on the GitHub (https://github.com/hanjunwei-lab/crmSubpathway).

Serine Incorporator 2 (SERINC2) Expression Predicts an Unfavorable Prognosis of Low-Grade Glioma (LGG): Evidence from Bioinformatics Analysis

Serine Incorporator 2 (SERINC2) is a transmembrane protein that incorporates serine into membrane lipids. The function of SERINC2 in tumors has been reported, but the role of SERINC2 in gliomas is not fully understood. RNA-sequencing data from The Cancer Genome Atlas (TCGA) (530 cases of low-grade glioma (LGG) and 173 cases of glioblastoma multiforme (GBM)) and microarray data from Gene Expression Omnibus (GEO) (Accession No. GSE16011, 284 cases gliomas were included) were acquired. Bioinformatics analysis was performed as the primary method to examine the function of SERINC2 and its correlated genes in glioma. SERINC2 was highly expressed in GBM compared with LGG and normal brain tissues. Elevated SERINC2 expression predicted shorter 5-, 10-, and 15-year overall survival (OS) of LGG patients and isocitrate dehydrogenase-1 (IDH-1) mutation-type LGG patients but had no effect on the OS of GBM patients. Cox regression analysis showed that SERINC2 was an independent factor in LGG OS. Methylation analysis found that 13 CpG methylation sites (methylation450k) correlated with SERINC2 expression in LGG. The mRNA expression level of SERINC2 was significant lower in the DNA deletion group than in the intact and amplification groups. A total of 390 copositive and 244 conegative correlation genes with SERINC2 were obtained from LGG in TCGA-LGG and GSE16011. Gene ontology (GO) category and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed that the copositive correlation genes were primarily enriched in the mitotic process and cell cycle. Combining the results from the protein-protein interaction (PPI) network of SERINC2 correlation genes and CytoHubba led to the selection of 10 hub genes (CDC20, FN1, AURKB, AURKA, KIF2C, BIRC5, CCNB2, UBE2C, CCNA2, and CENPE). OncoLnc analysis confirmed that high expression levels of these hub genes were associated with poor OS in LGG. Our results suggested that aberrant SERINC2 expression existed in glioma and that its expression might be a potential prognostic marker in LGG patients. CDC20, FN1, AURKB, AURKA, KIF2C, BIRC5, CCNB2, UBE2C, CCNA2, and CENPE may be potential biomarkers and therapeutic targets for LGG.

Computational identification and characterization of glioma candidate biomarkers through multi-omics integrative profiling

BACKGROUND

Glioma is one of the most common malignant brain tumors and exhibits low resection rate and high recurrence risk. Although a large number of glioma studies powered by high-throughput sequencing technologies have led to massive multi-omics datasets, there lacks of comprehensive integration of glioma datasets for uncovering candidate biomarker genes.

RESULTS

In this study, we collected a large-scale assemble of multi-omics multi-cohort datasets from worldwide public resources, involving a total of 16,939 samples across 19 independent studies. Through comprehensive molecular profiling across different datasets, we revealed that PRKCG (Protein Kinase C Gamma), a brain-specific gene detectable in cerebrospinal fluid, is closely associated with glioma. Specifically, it presents lower expression and higher methylation in glioma samples compared with normal samples. PRKCG expression/methylation change from high to low is indicative of glioma progression from low-grade to high-grade and high RNA expression is suggestive of good survival. Importantly, PRKCG in combination with MGMT is effective to predict survival outcomes in a more precise manner.

CONCLUSIONS

PRKCG bears the great potential for glioma diagnosis, prognosis and therapy, and PRKCG-like genes may represent a set of important genes associated with different molecular mechanisms in glioma tumorigenesis. Our study indicates the importance of computational integrative multi-omics data analysis and represents a data-driven scheme toward precision tumor subtyping and accurate personalized healthcare.

High gene expression levels of VEGFA and CXCL8 in the peritumoral brain zone are associated with the recurrence of glioblastoma: A bioinformatics analysis

The present study aimed to identify differentially regulated genes between the peritumoral brain zone (PBZ) and tumor core (TC) of glioblastoma (GBM), to elucidate the underlying molecular mechanisms and provide a target for the treatment of tumors. The GSE13276 and GSE116520 datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) for the PBZ and TC were obtained using the GEO2R tool. The bioinformatics and evolutionary genomics online tool Venn was used to identify common DEGs between the two datasets. The Database for Annotation, Visualization, and Integrated Discovery online tool was used to analyze enriched pathways of the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The Search Tool for the Retrieval of Interacting Genes/Proteins online tool was used to construct a protein-protein interaction (PPI) network of DEGs. Hub genes were identified using Cytohubba, a plug-in for Cytoscape. The Gene Expression Profiling Interactive Analysis (GEPIA) database was utilized to perform survival analysis. In total, 75 DEGs, including 12 upregulated and 63 downregulated genes, were identified. In the GO term analysis, these DEGs were mainly enriched in ‘regulation of angiogenesis’ and ‘central nervous system development’. Furthermore, in the KEGG pathway analysis, the DEGs were mainly enriched in ‘bladder cancer’ and ‘endocytosis’. When filtering the results of the PPI network analysis using Cytohubba, a total of 10 hub genes, including proteolipid protein 1, myelin associated oligodendrocyte basic protein, contactin 2, myelin oligodendrocyte glycoprotein, myelin basic protein, myelin associated glycoprotein, SRY-box transcription factor 10, C-X-C motif chemokine ligand 8 (CXCL8), vascular endothelial growth factor A (VEGFA) and plasmolipin, were identified. These hub genes were further subjected to GO term and KEGG pathway analysis, and were revealed to be enriched in ‘central nervous system development’, ‘bladder cancer’ and ‘rheumatoid arthritis’. These hub genes were used to perform survival analysis using the GEPIA database, and it was determined that VEGFA and CXCL8 were significantly associated with a reduction in the overall survival of patients with GBM. In conclusion, the results suggest that the recurrence of GBM is associated with high gene expression levels VEGFA and CXCL8, and the development of the central nervous system.

Defining Protein Pattern Differences Among Molecular Subtypes of Diffuse Gliomas Using Mass Spectrometry

Molecular characterization of diffuse gliomas has thus far largely focused on genomic and transcriptomic interrogations. Here, we utilized mass spectrometry and overlay protein-level information onto genomically defined cohorts of diffuse gliomas to improve our downstream molecular understanding of these lethal malignancies. Bulk and macrodissected tissues were utilized to quantitate 5,496 unique proteins over three glioma cohorts subclassified largely based on their IDH and 1p19q codeletion status (IDH wild type (IDHwt), n = 7; IDH mutated (IDHmt), 1p19q non-codeleted, n = 7; IDH mutated, 1p19q-codeleted, n = 10). Clustering analysis highlighted proteome and systems-level pathway differences in gliomas according to IDH and 1p19q-codeletion status, including 287 differentially abundant proteins in macrodissection-enriched tumor specimens. IDHwt tumors were enriched for proteins involved in invasiveness and epithelial to mesenchymal transition (EMT), while IDHmt gliomas had increased abundances of proteins involved in mRNA splicing. Finally, these abundance changes were compared with IDH-matched GBM stem-like cells (GSCs) to better pinpoint protein patterns enriched in putative cellular drivers of gliomas. Using this integrative approach, we outline specific proteins involved in chloride transport (e.g. chloride intracellular channel 1, CLIC1) and EMT (e.g. procollagen-lysine, 2-oxoglutarate 5-dioxygenase 3, PLOD3, and serpin peptidase inhibitor clade H member 1, SERPINH1) that showed concordant IDH-status-dependent abundance differences in both primary tissue and purified GSC cultures. Given the downstream position proteins occupy in driving biology and phenotype, understanding the proteomic patterns operational in distinct glioma subtypes could help propose more specific, personalized, and effective targets for the management of patients with these aggressive malignancies.

Identification of key candidate genes and pathways in glioblastoma by integrated bioinformatical analysis

Glioblastoma (GBM), characterized by high morbidity and mortality, is one of the most common lethal diseases worldwide. To identify the molecular mechanisms that contribute to the development of GBM, three cohort profile datasets (GSE50161, GSE90598 and GSE104291) were integrated and thoroughly analyzed; these datasets included 57 GBM cases and 22 cases of normal brain tissue. The current study identified differentially expressed genes (DEGs), and analyzed potential candidate genes and pathways. Additionally, a DEGs-associated protein-protein interaction (PPI) network was established for further investigation. Then, the hub genes associated with prognosis were identified using a Kaplan-Meier analysis based on The Cancer Genome Atlas database. Firstly, the current study identified 378 consistent DEGs (240 upregulated and 138 downregulated). Secondly, a cluster analysis of the DEGs was performed based on functions of the DEGs and signaling pathways were analyzed using the enrichment analysis tool on DAVID. Thirdly, 245 DEGs were identified using PPI network analysis. Among them, two co-expression modules comprising of 30 and 27 genes, respectively, and 35 hub genes were identified using Cytoscape MCODE. Finally, Kaplan-Meier analysis of the hub genes revealed that the increased expression of calcium-binding protein 1 (CABP1) was negatively associated with relapse-free survival. To summarize, all enriched Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways may participate in mechanisms underlying GBM occurrence and progression, however further studies are required. CABP1 may be a key gene associated with the biological process of GBM development and may be involved in a crucial mechanism of GBM progression.

Pathological changes are associated with shifts in the employment of synonymous codons at the transcriptome level

Background

The usage of different synonymous codons reflects the genome organization and has been connected to parameters such as mRNA abundance and protein folding. It is also been established that mutations targeting specific synonymous codons can trigger disease.

Results

We performed a systematic meta-analysis of transcriptome results from 75 datasets representing 40 pathologies. We found that a subset of codons was preferentially employed in abundant transcripts, while other codons were preferentially found in low-abundance transcripts. By comparing control and pathological transcriptomes, we observed a shift in the employment of synonymous codons for every analyzed disease. For example, cancerous tissue employed preferentially A- or U-ending codons, shifting from G- or C-ending codons, which were preferred by control tissues. This analysis was able to discriminate patients and controls with high specificity and sensitivity.

Conclusions

Here we show that the employment of specific synonymous codons, quantified at the whole transcriptome level, changes profoundly in many diseases. We propose that the changes in codon employment offer a novel perspective for disease studies, and could be used to design new diagnostic tools.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5921-9) contains supplementary material, which is available to authorized users.

Identification of Key Genes and Circular RNAs in Human Gastric Cancer

Background

Globally, gastric cancer (GC) is the third most common source of cancer-associated mortality. The aim of this study was to identify key genes and circular RNAs (circRNAs) in GC diagnosis, prognosis, and therapy and to further explore the potential molecular mechanisms of GC.

Material/Methods

Differentially expressed genes (DEGs) and circRNAs (DE circRNAs) between GC tissues and adjacent non-tumor tissues were identified from 3 mRNA and 3 circRNA expression profiles. Functional analyses were performed, and protein–protein interaction (PPI) networks were constructed. The significant modules and key genes in the PPI networks were identified. Kaplan-Meier analysis was performed to evaluate the prognostic value of these key genes. Potential miRNA-binding sites of the DE circRNAs and target genes of these miRNAs were predicted and used to construct DE circRNA–miRNA–mRNA networks.

Results

A total of 196 upregulated and 311 downregulated genes were identified in GC. The results of functional analysis showed that these DEGs were significantly enriched in a variety of functions and pathways, including extracellular matrix-related pathways. Ten hub genes (COL1A1, COL3A1, COL1A2, COL5A2, FN1, THBS1, COL5A1, SPARC, COL18A1, and COL11A1) were identified via PPI network analysis. Kaplan-Meier analysis revealed that 7 of these were associated with a poor overall survival in GC patients. Furthermore, we identified 2 DE circRNAs, hsa_circ_0000332 and hsa_circ_0021087. To reveal the potential molecular mechanisms of circRNAs in GC, DE circRNA–microRNA–mRNA networks were constructed.

Conclusions

Key candidate genes and circRNAs were identified, and novel PPI and circRNA–microRNA–mRNA networks in GC were constructed. These may provide useful information for the exploration of potential biomarkers and targets for the diagnosis, prognosis, and therapy of GC.

Molecular mechanisms underlying gliomas and glioblastoma pathogenesis revealed by bioinformatics analysis of microarray data

The aim of this study was to identify key genes associated with gliomas and glioblastoma and to explore the related signaling pathways. Gene expression profiles of three glioma stem cell line samples, three normal astrocyte samples, three astrocyte overexpressing 4 iPSC-inducing and oncogenic factors (myc(T58A), OCT-4, p53DD, and H-Ras(G12V)) samples, three astrocyte overexpressing 7 iPSC-inducing and oncogenic factors (OCT4, H-Ras(G12V), myc(T58A), p53DD, cyclin D1, CDK4(RC24) and hTERT) samples and three glioblastoma cell line samples were downloaded from the ArrayExpress database (accession: E-MTAB-4771). The differentially expressed genes (DEGs) in gliomas and glioblastoma were identified using FDR and t tests, and protein-protein interaction (PPI) networks for these DEGs were constructed using the protein interaction network analysis. The GeneTrail2 1.5 tool was used to identify potentially enriched biological processes among the DEGs using gene ontology (GO) terms and to identify the related pathways using the Kyoto Encyclopedia of Genes and Genomes, Reactome and WikiPathways pathway database. In addition, crucial modules of the constructed PPI networks were identified using the PEWCC1 plug-in, and their topological properties were analyzed using NetworkAnalyzer, both available from Cytoscape. We also constructed microRNA-target gene regulatory network and transcription factor-target gene regulatory network for these DEGs were constructed using the miRNet and binding and expression target analysis. We identified 200 genes that could potentially be involved in the gliomas and glioblastoma. Among them, bioinformatics analysis identified 137 up-regulated and 63 down-regulated DEGs in gliomas and glioblastoma. The significant enriched pathway (PI3K-Akt) for up-regulated genes such as COL4A1, COL4A2, EGFR, FGFR1, LAPR6, MYC, PDGFA, SPP1 were selected as well as significant GO term (ear development) for up-regulated genes such as CELSR1, CHRNA9, DDR1, FGFR1, GLI2, LGR5, SOX2, TSHR were selected, while the significant enriched pathway (amebiasis) for down-regulated gene such as COL3A1, COL5A2, LAMA2 were selected as well as significant GO term (RNA polymerase II core promoter proximal region sequence-specific binding (5) such as MEIS2, MEOX2, NR2E1, PITX2, TFAP2B, ZFPM2 were selected. Importantly, MYC and SOX2 were hub proteins in the up-regulated PPI network, while MET and CDKN2A were hub proteins in the down-regulated PPI network. After network module analysis, MYC, FGFR1 and HOXA10 were selected as the up-regulated coexpressed genes in the gliomas and glioblastoma, while SH3GL3 and SNRPN were selected as the down-regulated coexpressed genes in the gliomas and glioblastoma. MicroRNA hsa-mir-22-3p had a regulatory effect on the most up DEGs, including VSNL1, while hsa-mir-103a-3p had a regulatory effect on the most down DEGs, including DAPK1. Transcription factor EZH2 had a regulatory effect on the both up and down DEGs, including CD9, CHI3L1, MEIS2 and NR2E1. The DEGs, such as MYC, FGFR1, CDKN2A, HOXA10 and MET, may be used for targeted diagnosis and treatment of gliomas and glioblastoma.

Gene expression meta-analysis in diffuse low-grade glioma and the corresponding histological subtypes

Diffuse low-grade glioma (DLGG) is a well-differentiated, slow-growing tumour with an inherent tendency to progress to high-grade glioma. The potential roles of genetic alterations in DLGG development have not yet been fully delineated. Therefore, the current study performed an integrated gene expression meta-analysis of eight independent, publicly available microarray datasets including 291 DLGGs and 83 non-glioma (NG) samples to identify gene expression signatures associated with DLGG. Using INMEX, 708 differentially expressed genes (DEGs) (385 upregulated and 323 downregulated genes) were identified in DLGG compared to NG. Furthermore, 497 DEGs (222 upregulated and 275 downregulated genes) corresponding to two histological types were identified. Of these, high expression of HIP1R significantly correlated with increased overall survival, whereas high expression of TBXAS1 significantly correlated with decreased overall survival. Additionally, network-based meta-analysis identified FN1 and APP as the key hub genes in DLGG compared with NG. PTPN6 and CUL3 were the key hub genes identified in the astrocytoma relative to the oligodendroglioma. Further immunohistochemical validation revealed that MTHFD2 and SPARC were positively expressed in DLGG, whereas RBP4 was positively expressed in NG. These findings reveal potential molecular biomarkers for diagnosis and therapy in patients with DLGG and provide a rich and novel candidate reservoir for future studies.

Systemic analysis of genome-wide expression profiles identified potential therapeutic targets of demethylation drugs for glioblastoma

Glioblastoma represents one of the most aggressive malignant brain tumors with high morbidity and motility. Demethylation drugs have been developed for its treatment with little efficacy has been observed. The purpose of this study was to screen therapeutic targets of demethylation drugs or bioactive molecules for glioblastoma through systemic bioinformatics analysis. We firstly downloaded genome-wide expression profiles from the Gene Expression Omnibus (GEO) and conducted the primary analysis through R software, mainly including preprocessing of raw microarray data, transformation between probe ID and gene symbol and identification of differential expression genes (DEGs). Secondly, functional enrichment analysis was conducted via the Database for Annotation, Visualization and Integrated Discovery (DAVID) to explore biological processes involved in the development of glioblastoma. Thirdly, we constructed protein-protein interaction (PPI) network of interested genes and conducted cross analysis for multi datasets to obtain potential therapeutic targets for glioblastoma. Finally, we further confirmed the therapeutic targets through real-time RT-PCR. As a result, biological processes that related to cancer development, amino metabolism, immune response and etc. were found to be significantly enriched in genes that differential expression in glioblastoma and regulated by 5'aza-dC. Besides, network and cross analysis identified ACAT2, UFC1 and CYB5R1 as novel therapeutic targets of demethylation drugs which also confirmed by real time RT-PCR. In conclusions, our study identified several biological processes and genes that involved in the development of glioblastoma and regulated by 5'aza-dC, which would be helpful for the treatment of glioblastoma.