Transcriptome analyses reveal molecular mechanisms underlying phenotypic differences among transcriptional subtypes of glioblastoma

Using molecular signatures, previous studies have defined glioblastoma (GBM) subtypes with different phenotypes, such as the proneural (PN), neural (NL), mesenchymal (MES) and classical (CL) subtypes. However, the gene programmes underlying the phenotypes of these subtypes were less known. We applied weighted gene co-expression network analysis to establish gene modules corresponding to various subtypes. RNA-seq and immunohistochemical data were used to validate the expression of identified genes. We identified seven molecular subtype-specific modules and several candidate signature genes for different subtypes. Next, we revealed, for the first time, that radioresistant/chemoresistant gene signatures exist only in the PN subtype, as described by Verhaak et al, but do not exist in the PN subtype described by Phillips et al PN subtype. Moreover, we revealed that the tumour cells in the MES subtype GBMs are under ER stress and that angiogenesis and the immune inflammatory response are both significantly elevated in this subtype. The molecular basis of these biological processes was also uncovered. Genes associated with alternative RNA splicing are up-regulated in the CL subtype GBMs, and genes pertaining to energy synthesis are elevated in the NL subtype GBMs. In addition, we identified several survival-associated genes that positively correlated with glioma grades. The identified intrinsic characteristics of different GBM subtypes can offer a potential clue to the pathogenesis and possible therapeutic targets for various subtypes.     

A Systems-Genetics Approach and Data Mining Tool to Assist in the Discovery of Genes Underlying Complex Traits in Oryza sativa

Many traits of biological and agronomic significance in plants are controlled in a complex manner where multiple genes and environmental signals affect the expression of the phenotype. In Oryza sativa (rice), thousands of quantitative genetic signals have been mapped to the rice genome. In parallel, thousands of gene expression profiles have been generated across many experimental conditions. Through the discovery of networks with real gene co-expression relationships, it is possible to identify co-localized genetic and gene expression signals that implicate complex genotype-phenotype relationships. In this work, we used a knowledge-independent, systems genetics approach, to discover a high-quality set of co-expression networks, termed Gene Interaction Layers (GILs). Twenty-two GILs were constructed from 1,306 Affymetrix microarray rice expression profiles that were pre-clustered to allow for improved capture of gene co-expression relationships. Functional genomic and genetic data, including over 8,000 QTLs and 766 phenotype-tagged SNPs (p-value < = 0.001) from genome-wide association studies, both covering over 230 different rice traits were integrated with the GILs. An online systems genetics data-mining resource, the GeneNet Engine, was constructed to enable dynamic discovery of gene sets (i.e. network modules) that overlap with genetic traits. GeneNet Engine does not provide the exact set of genes underlying a given complex trait, but through the evidence of gene-marker correspondence, co-expression, and functional enrichment, site visitors can identify genes with potential shared causality for a trait which could then be used for experimental validation. A set of 2 million SNPs was incorporated into the database and serve as a potential set of testable biomarkers for genes in modules that overlap with genetic traits. Herein, we describe two modules found using GeneNet Engine, one with significant overlap with the trait amylose content and another with significant overlap with blast disease resistance.     

PAICS is related to glioma grade and can promote glioma growth and migration

Glioma is a common malignant tumour of the brain. In this study, we aimed to investigate diagnostic biomarkers and its role in glioma. Weighted gene co-expression network analysis (WGCNA) and Cytoscape software were used to screen the marker genes in glioma. RT-qPCR and Western blotting methods were performed to determine the expression of PAICS, ERCC1 and XPA genes in glioma tissues. Expression level of PAICS in different grades of glioma was examined by immunohistochemistry. CCK8 and Colony formation assays were used to detect cell proliferation. Cell adhesion assay was used to detect adhesion ability. Wound healing and transwell tests were used to detect cell migration ability. Flow cytometry was used to detect cell cycle and apoptosis. According to the predicted co-expression network, we identified the hub gene PAICS. Furthermore, we observed that PAICS expression level was up-regulated in glioma tissues compared with normal tissues, and the expression level was correlated with the grade of glioma. Moreover, we found PAICS can promote glioma cells proliferation and migration in vitro. Flow cytometry results showed that si-PAICS cells were stalled at the G1 phase compared with the si-NC cells and knocking down PAICS expression can increase apoptotic rate. PAICS can regulate the mRNA and protein levels of nucleotide excision repair pathway core genes ERCC1 and XPA. l -aspartic acid can affect the expression of PAICS and then inhibit glioma cell proliferation. Our results indicated that PAICS can promote glioma proliferation and migration. PAICS may act as a potential diagnostic marker and a therapeutic target for glioma.     

Characterization of Chemically Induced Liver Injuries Using Gene Co-Expression Modules

Liver injuries due to ingestion or exposure to chemicals and industrial toxicants pose a serious health risk that may be hard to assess due to a lack of non-invasive diagnostic tests. Mapping chemical injuries to organ-specific damage and clinical outcomes via biomarkers or biomarker panels will provide the foundation for highly specific and robust diagnostic tests. Here, we have used DrugMatrix, a toxicogenomics database containing organ-specific gene expression data matched to dose-dependent chemical exposures and adverse clinical pathology assessments in Sprague Dawley rats, to identify groups of co-expressed genes (modules) specific to injury endpoints in the liver. We identified 78 such gene co-expression modules associated with 25 diverse injury endpoints categorized from clinical pathology, organ weight changes, and histopathology. Using gene expression data associated with an injury condition, we showed that these modules exhibited different patterns of activation characteristic of each injury. We further showed that specific module genes mapped to 1) known biochemical pathways associated with liver injuries and 2) clinically used diagnostic tests for liver fibrosis. As such, the gene modules have characteristics of both generalized and specific toxic response pathways. Using these results, we proposed three gene signature sets characteristic of liver fibrosis, steatosis, and general liver injury based on genes from the co-expression modules. Out of all 92 identified genes, 18 (20%) genes have well-documented relationships with liver disease, whereas the rest are novel and have not previously been associated with liver disease. In conclusion, identifying gene co-expression modules associated with chemically induced liver injuries aids in generating testable hypotheses and has the potential to identify putative biomarkers of adverse health effects.     

AtGGM2014, an Arabidopsis gene co-expression network for functional studies

Gene co-expression networks provide an important tool for systems biology studies. Using microarray data from the Array Express database, we constructed an Arabidopsis gene co-expression network, termed At GGM2014, based on the graphical Gaussian model, which contains 102,644 co-expression gene pairs among 18,068 genes. The network was grouped into 622 gene co-expression modules. These modules function in diverse house-keeping, cell cycle, development, hormone response, metabolism, and stress response pathways. We developed a tool to facilitate easy visualization of the expression patterns of these modules either in a tissue context or their regulation under different treatment conditions. The results indicate that at least six modules with tissue-specific expression pattern failed to record modular regulation under various stress conditions. This discrepancy could be best explained by the fact that experiments to study plant stress responses focused mainly on leaves and less on roots, and thus failed to recover specific regulation pattern in other tissues. Overall, the modular structures revealed by our network provide extensive information to generate testable hypotheses about diverse plant signaling pathways. At GGM2014 offers a constructive tool for plant systems biology studies.     

基于SNP共表达网络肝癌分子分型及预后分析

基于SNP突变数据与mRNA表达谱关联分析,构建一种肝癌分子分型方法并对比不同分型预后的差异,并对不同分型肝癌的发生发展机制进一步研究。首先通过TCGA数据库收集359例肝细胞癌患者的SNP突变数据和mRNA表达数据,采用Wilcoxon秩和检验,筛选突变后差异表达基因,并通过生物信息学工具String和Cytoscape构建差异表达基因的蛋白互作网络,筛选连接度最高的10个Hub基因。利用Consensus Cluster Plus软件包,基于Hub基因mRNA表达水平构建NMF分子分型模型,再结合生存数据评估各分型患者的预后。最后利用加权基因共表达网络分析(WGCNA),识别与肝癌分子分型相关的模块,并针对关键模块的基因进行通路富集,从而对不同分型肝癌的基因表达谱进行比较。结果：NMF模型将肝癌分为高危、低危2个分型,其中CDKN2A和FOXO1基因对分型贡献度高。生存分析显示低危组患者的生存情况显著优于高危组,高危组富集多个与肿瘤细胞侵蚀、转移、复发过程相关的信号通路,低危组则与细胞周期和胰液分泌相关。本研究在无先验性信息的前提下,基于突变后显著差异表达的Hub基因表达水平构建的...     

Mechanisms of ferroptosis with immune infiltration and inflammatory response in rotator cuff injury

The processes driving ferroptosis and rotator cuff (RC) inflammation are yet unknown. The mechanism of ferroptosis and inflammation involved in the development of RC tears was investigated. The Gene Expression Omnibus database was used to obtain the microarray data relevant to the RC tears for further investigation. In this study, we created an RC tears rat model for in vivo experimental validation. For the additional function enrichment analysis, 10 hub ferroptosis-related genes were chosen to construct the correlation regulation network. In RC tears, it was discovered that genes related to hub ferroptosis and hub inflammatory response were strongly correlated. The outcomes of in vivo tests showed that RC tears were related to Cd68-Cxcl13, Acsl4-Sat1, Acsl3-Eno3, Acsl3-Ccr7, and Ccr7-Eno3 pairings in regulating ferroptosis and inflammatory response. Thus, our results show an association between ferroptosis and inflammation, providing a new avenue to explore the clinical treatment of RC tears.