Screening critical genes associated with malignant glioma using bioinformatics analysis

ANXA1: An Important Independent Prognostic Factor and Molecular Target in Glioma

Objective: The expression, prognosis, and related mechanisms of ANXA1 are investigated in glioma, with the objective to find potential therapeutic molecular targets for glioma. Methods: We analyzed the gene expression of ANXA1 using glioma-related databases, including the Chinese Glioma Genome Atlas (CGGA) database, The Cancer Genome Atlas (TCGA) database, and the Gene Expression Omnibus (GEO) database. Moreover, we collected the sample tissues and corresponding paracancerous tissues of 23 glioma patients and then conducted a Western blot experiment to verify the expression and correlate survival of ANXA1. Moreover, we generated survival ROC curves, performing univariate and multivariate Cox analyses and the construction of the nomogram. Differential expression analysis was conducted by high and low grouping based on the median of the ANXA1 gene expression values. We conducted Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Set Enrichment Analysis (GSEA) to explore possible mechanisms, and gene co-expression analysis was also performed. Results: The results showed that the ANXA1 expression level was higher in gliomas than in normal tissues, and a high expression level of ANXA1 in gliomas was associated with poorer prognosis. The independent prognosis analysis showed that the ANXA1 gene was an independent prognostic factor of glioma. In the analysis of KEGG and Gene Set Enrichment Analysis (GSEA), it is shown that ANXA1 may play an important role in glioma patients by affecting extracellular matrix (ECM)-receptor interaction and the focal adhesion signal pathway. The core genes, including COL1A1, COL1A2, FN1, ITGA1, and ITGB1, were screened for gene correlation and prognosis analysis. The expression level of the five genes was verified by qPCR in glioma. We concluded that these five core genes and ANXA1 could play a synergistic role in gliomas. Conclusion: The results indicated that a high expression level of ANXA1 leads to worse prognosis and ANXA1 is an independent prognostic factor and a potentially important target for the treatment of gliomas.

Multivariate analysis reveals differentially expressed genes among distinct subtypes of diffuse astrocytic gliomas: diagnostic implications

Diagnosis and classification of gliomas mostly relies on histopathology and a few genetic markers. Here we interrogated microarray gene expression profiles (GEP) of 268 diffuse astrocytic gliomas-33 diffuse astrocytomas (DA), 52 anaplastic astrocytomas (AA) and 183 primary glioblastoma (GBM)-based on multivariate analysis, to identify discriminatory GEP that might support precise histopathological tumor stratification, particularly among inconclusive cases with II-III grade diagnosed, which have different prognosis and treatment strategies. Microarrays based GEP was analyzed on 155 diffuse astrocytic gliomas (discovery cohort) and validated in another 113 tumors (validation set) via sequential univariate analysis (pairwise comparison) for discriminatory gene selection, followed by nonnegative matrix factorization and canonical biplot for identification of discriminatory GEP among the distinct histological tumor subtypes. GEP data analysis identified a set of 27 genes capable of differentiating among distinct subtypes of gliomas that might support current histological classification. DA + AA showed similar molecular profiles with only a few discriminatory genes overexpressed (FSTL5 and SFRP2) and underexpressed (XIST, TOP2A and SHOX2) in DA vs AA and GBM. Compared to DA + AA, GBM displayed underexpression of ETNPPL, SH3GL2, GABRG2, SPX, DPP10, GABRB2 and CNTN3 and overexpression of CHI3L1, IGFBP3, COL1A1 and VEGFA, among other differentially expressed genes.

Biomarkers of tumor invasiveness in proteomics (Review)

Over the past two decades, quantitative proteomics has emerged as an important tool for deciphering the complex molecular events involved in cancers. The number of references involving studies on the cancer metastatic process has doubled since 2010, while the last 5 years have seen the development of novel technologies combining deep proteome coverage capabilities with quantitative consistency and accuracy. To highlight key findings within this huge amount of information, the present review identified a list of tumor invasive biomarkers based on both the literature and data collected on a biocollection of experimental cell lines, tumor models of increasing invasiveness and tumor samples from patients with colorectal or breast cancer. Crossing these different data sources led to 76 proteins of interest out of 1,245 mentioned in the literature. Information on these proteins can potentially be translated into clinical prospects, since they represent potential targets for the development and evaluation of innovative therapies, alone or in combination. Herein, a systematical review of the biology of each of these proteins, including their specific subcellular/extracellular or multiple localizations is presented. Finally, as an important advantage of quantitative proteomics is the ability to provide data on all these molecules simultaneously in cell pellets, body fluids or paraffin‑embedded sections of tumors/invaded tissues, the significance of some of their interconnections is discussed.

Abuhamad A, Syahir A. Understanding Glioblastoma Biomarkers: Knocking a Mountain with a Hammer

Gliomas are the most frequent and deadly form of human primary brain tumors. Among them, the most common and aggressive type is the high-grade glioblastoma multiforme (GBM), which rapidly grows and renders patients a very poor prognosis. Meanwhile, cancer stem cells (CSCs) have been determined in gliomas and play vital roles in driving tumor growth due to their competency in self-renewal and proliferation. Studies of gliomas have recognized CSCs via specific markers. This review comprehensively examines the current knowledge of the most significant CSCs markers in gliomas in general and in glioblastoma in particular and specifically focuses on their outlook and importance in gliomas CSCs research. We suggest that CSCs should be the superior therapeutic approach by directly targeting the markers. In addition, we highlight the association of these markers with each other in relation to their cascading pathways, and interactions with functional miRNAs, providing the role of the networks axes in glioblastoma signaling pathways.

Differential Expression of miRNAs in Hypoxia ("HypoxamiRs") in Three Canine High-Grade Glioma Cell Lines

Dogs with spontaneous high-grade gliomas increasingly are being proposed as useful large animal pre-clinical models for the human disease. Hypoxia is a critical microenvironmental condition that is common in both canine and human high-grade gliomas and drives increased angiogenesis, chemo- and radioresistance, and acquisition of a stem-like phenotype. Some of this effect is mediated by the hypoxia-induced expression of microRNAs, small (~22 nucleotides long), non-coding RNAs that can modulate gene expression through interference with mRNA translation. Using an in vitro model with three canine high-grade glioma cell lines (J3T, SDT3G, and G06A) exposed to 72 h of 1.5% oxygen vs. standard 20% oxygen, we examined the global “hypoxamiR” profile using small RNA-Seq and performed pathway analysis for targeted genes using both Panther and NetworkAnalyst. Important pathways include many that are well-established as being important in glioma biology, general cancer biology, hypoxia, angiogenesis, immunology, and stem-ness, among others. This work provides the first examination of the effect of hypoxia on miRNA expression in the context of canine glioma, and highlights important similarities with the human disease.

Open Data for Differential Network Analysis in Glioma

The complexity of cancer diseases demands bioinformatic techniques and translational research based on big data and personalized medicine. Open data enables researchers to accelerate cancer studies, save resources and foster collaboration. Several tools and programming approaches are available for analyzing data, including annotation, clustering, comparison and extrapolation, merging, enrichment, functional association and statistics. We exploit openly available data via cancer gene expression analysis, we apply refinement as well as enrichment analysis via gene ontology and conclude with graph-based visualization of involved protein interaction networks as a basis for signaling. The different databases allowed for the construction of huge networks or specified ones consisting of high-confidence interactions only. Several genes associated to glioma were isolated via a network analysis from top hub nodes as well as from an outlier analysis. The latter approach highlights a mitogen-activated protein kinase next to a member of histondeacetylases and a protein phosphatase as genes uncommonly associated with glioma. Cluster analysis from top hub nodes lists several identified glioma-associated gene products to function within protein complexes, including epidermal growth factors as well as cell cycle proteins or RAS proto-oncogenes. By using selected exemplary tools and open-access resources for cancer research and differential network analysis, we highlight disturbed signaling components in brain cancer subtypes of glioma.

Identification of differentially expressed genes and fusion genes associated with malignant progression of spinal cord gliomas by transcriptome analysis

Glioma, the most common histological subtype of primary spinal cord tumors, is considered as a rare central nervous system neoplasm. In this study, 9 glioma samples (4 of grade II and 5 of grade IV with H3K27M positive) were analyzed to examine the molecular mechanisms underlying the malignant progression of gliomas, transcriptome sequencing. Differentially expressed genes (DEGs) in grade IV vs. grade II were analyzed by using the Limma package in R. Enrichment analysis was performed for the individual DEGs through VennPlex software and the Database for Annotation. Gene mutations and fusions were analyzed using the Genome Analysis Toolkit and STAR-Fusion. A total of 416 DEGs were identified in grade IV vs. grade II. Functional analysis of the DEGs showed that GALR1 and GRM5 of neuroactive ligand-receptor interactions signaling pathways may be relaed to malignant progression of gliomas. Further systematic transcriptional profiling identified 11 in-frame/frameshift gene fusions in the tumors. Notably, one novel gene fusions, GATSL2-GTF2I was detected in all of the grade II samples. In summary, the molecular alterations observed in glioma progression may improve the characterization of different human spinal cord glioma grades. The transcriptome analysis of intramedullary spinal cord glioma will provide a new candidate gene list for further mechanism research.