Differential Network Analysis Research Articles

Differential Network Analysis Reveals Metabolic Determinants Associated with Mortality in Acute Myocardial Infarction Patients and Suggests Potential Mechanisms Underlying Different Clinical Scores Used To Predict Death.

We present here the differential analysis of metabolite–metabolite association networks constructed from an array of 24 serum metabolites identified and quantified via nuclear magnetic resonance spectroscopy in a cohort of 825 patients of which 123 died within 2 years from acute myocardial infarction (AMI). We investigated differences in metabolite connectivity of patients who survived, at 2 years, the AMI event, and we characterized metabolite–metabolite association networks specific to high and low risks of death according to four different risk parameters, namely, acute coronary syndrome classification, Killip, Global Registry of Acute Coronary Events risk score, and metabolomics NOESY RF risk score. We show significant differences in the connectivity patterns of several low-molecular-weight molecules, implying variations in the regulation of several metabolic pathways regarding branched-chain amino acids, alanine, creatinine, mannose, ketone bodies, and energetic metabolism. Our results demonstrate that the characterization of metabolite–metabolite association networks is a promising and powerful tool to investigate AMI patients according to their outcomes at a molecular level.

ProtExA: A tool for post-processing proteomics data providing differential expression metrics, co-expression networks and functional analytics

ProTExA is a web-tool that provides a post-processing workflow for the analysis of protein and gene expression datasets. Using network-based bioinformatics approaches, ProTExA facilitates differential expression analysis and co-expression network analysis as well as pathway and post-pathway analysis. Specifically, for a given set of protein-gene expression data across samples, ProTExA: (1) performs statistical analysis and filtering to highlight the differentially expressed proteins-genes, (2) performs enrichment analysis to identify top-scored pathways, (3) generates pathway-to-pathway and pathway-to-gene networks (4) generates protein and gene co-expression networks using a variety of methodologies, and (5) applies clustering methodologies to identify sub-networks of co-expressed proteins-genes. The proposed web-tool is a simple yet informative tool, towards understanding and exploitation of protein and gene expression datasets, especially for those that do not have the expertise and local resources to replicate specific analyses in the context of collaborative and scientific data exchanging.

Network analysis uncovers putative genes affecting resistance to tick infestation in Braford cattle skin

BackgroundGenetic resistance in cattle is considered a suitable way to control tick burden and its consequent losses for livestock production. Exploring tick-resistant (R) and tick-susceptible (S) hosts, we investigated the genetic mechanisms underlying the variation of Braford resistance to tick infestation. Skin biopsies from four-times-artificially infested R (n = 20) and S (n = 19) hosts, obtained before the first and 24 h after the fourth tick infestation were submitted to RNA-Sequencing. Differential gene expression, functional enrichment, and network analysis were performed to identify genetic pathways and transcription factors (TFs) affecting host resistance.ResultsIntergroup comparisons of hosts before (Rpre vs. Spre) and after (Rpost vs. Spost) tick infestation found 51 differentially expressed genes (DEGs), of which almost all presented high variation (TopDEGs), and 38 were redundant genes. Gene expression was consistently different between R and S hosts, suggesting the existence of specific anti-tick mechanisms. In the intragroup comparisons, Rpost vs. Rpre and Spost vs. Spre, we found more than two thousand DEGs in response to tick infestation in both resistance groups. Redundant and non-redundant TopDEGs with potential anti-tick functions suggested a role in the development of different levels of resistance within the same breed. Leukocyte chemotaxis was over-represented in both hosts, whereas skin degradation and remodeling were only found in TopDEGs from R hosts. Also, these genes indicated the participation of cytokines, such as IL6 and IL22, and the activation of Wingless (WNT)-signaling pathway. A central gene of this pathway, WNT7A, was consistently modulated when hosts were compared. Moreover, the findings based on a genome-wide association study (GWAS) corroborate the prediction of the WNT-signaling pathway as a candidate mechanism of resistance. The regulation of immune response was the most relevant pathway predicted for S hosts. Members of Ap1 and NF-kB families were the most relevant TFs predicted for R and S, respectively.ConclusionThis work provides indications of genetic mechanisms presented by Braford cattle with different levels of resistance in response to tick infestation, contributing to the search of candidate genes for tick resistance in bovine.

APOE2 orchestrated differences in transcriptomic and lipidomic profiles of postmortem AD brain

BackgroundThe application of advanced sequencing technologies and improved mass-spectrometry platforms revealed significant changes in gene expression and lipids in Alzheimer’s disease (AD) brain. The results so far have prompted further research using “multi-omics” approaches. These approaches become particularly relevant, considering the inheritance of APOEε4 allele as a major genetic risk factor of AD, disease protective effect of APOEε2 allele, and a major role of APOE in brain lipid metabolism.MethodsPostmortem brain samples from inferior parietal lobule genotyped as APOEε2/c (APOEε2/carriers), APOEε3/3, and APOEε4/c (APOEε4/carriers), age- and gender-matched, were used to reveal APOE allele-associated changes in transcriptomes and lipidomes. Differential gene expression and co-expression network analyses were applied to identify up- and downregulated Gene Ontology (GO) terms and pathways for correlation to lipidomics data.ResultsSignificantly affected GO terms and pathways were determined based on the comparisons of APOEε2/c datasets to those of APOEε3/3 and APOEε4/c brain samples. The analysis of lists of genes in highly correlated network modules and of those differentially expressed demonstrated significant enrichment in GO terms associated with genes involved in intracellular proteasomal and lysosomal degradation of proteins, protein aggregates and organelles, ER stress, and response to unfolded protein, as well as mitochondrial function, electron transport, and ATP synthesis. Small nucleolar RNA coding units important for posttranscriptional modification of mRNA and therefore translation and protein synthesis were upregulated in APOEε2/c brain samples compared to both APOEε3/3 and APOEε4/c. The analysis of lipidomics datasets revealed significant changes in ten major lipid classes (exclusively a decrease in APOEε4/c samples), most notably non-bilayer-forming phosphatidylethanolamine and phosphatidic acid, as well as mitochondrial membrane-forming lipids.ConclusionsThe results of this study, despite the advanced stage of AD, point to the significant differences in postmortem brain transcriptomes and lipidomes, suggesting APOE allele associated differences in pathogenic mechanisms. Correlations within and between lipidomes and transcriptomes indicate coordinated effects of changes in the proteasomal system and autophagy—canonical and selective, facilitating intracellular degradation, protein entry into ER, response to ER stress, nucleolar modifications of mRNA, and likely myelination in APOEε2/c brains. Additional research and a better knowledge of the molecular mechanisms of proteostasis in the early stages of AD are required to develop more effective diagnostic approaches and eventually efficient therapeutic strategies.

Efficient computation for differential network analysis with applications to quadratic discriminant analysis

Differential network analysis is an important statistical problem with wide applications. Many statisticians focus on binary problems and propose to perform such analysis by obtaining sparse estimates of the difference between precision matrices. These methods are supported by excellent theoretical properties and practical performance. However, efficient computation for these methods remains a challenging problem. A novel algorithm referred to as the SMORE algorithm is proposed for differential network analysis. The SMORE algorithm has low storage cost and high computation speed, especially in the presence of strong sparsity. In the meantime, the SMORE algorithm provides a unified framework for binary and multiple network problems. In addition, the SMORE algorithm can be applied in high-dimensional quadratic discriminant analysis problems as well, leading to a new approach for multiclass high-dimensional quadratic discriminant analysis. Numerical studies confirm the stability and the efficiency of the proposed SMORE algorithm in both differential network analysis and quadratic discriminant analysis.

Differential network analysis depicts regulatory mechanisms for hepatocellular carcinoma from diverse backgrounds.

Aim: To elucidate the integrative combinational gene regulatory network landscape of hepatocellular carcinoma (HCC) molecular carcinogenesis from diverse background. Materials & methods: Modified gene regulatory network analysis was used to prioritize differentially regulated genes and links. Integrative comparisons using bioinformatics methods were applied to identify potential critical molecules and pathways in HCC with different backgrounds. Results: E2F1 with its surrounding regulatory links were identified to play different key roles in the HCC risk factor dysregulation mechanisms. Hsa-mir-19a was identified as showed different effects in the three HCC differential regulation networks, and showed vital regulatory role in HBV-related HCC. Conclusion: We describe in detail the regulatory networks involved in HCC with different backgrounds. E2F1 may serve as a universal target for HCC treatment.

A Machine Learning Method for Identifying Critical Interactions Between Gene Pairs in Alzheimer's Disease Prediction.

Background: Alzheimer's disease (AD) is the most common type of dementia. Scientists have discovered that the causes of AD may include a combination of genetic, lifestyle, and environmental factors, but the exact cause has not yet been elucidated. Effective strategies to prevent and treat AD therefore remain elusive. The identified genetic causes of AD mainly focus on individual genes, but growing evidence has shown that complex diseases are usually affected by the interaction of genes in a network. Few studies have focused on the interactions and correlations between genes and how they are gradually destroyed or disappear during AD progression. A differential network analysis has been recognized as an essential tool for identifying the underlying pathogenic mechanisms and significant genes for prediction analysis. We therefore aim to conduct a differential network analysis to reveal potential networks involved in the neuropathogenesis of AD and identify genes for AD prediction.Methods: In this paper, we selected 365 samples from the Religious Orders Study and the Rush Memory and Aging Project, including 193 clinically and neuropathologically confirmed AD subjects and 172 no cognitive impairment (NCI) controls. Then, we selected 158 genes belonging to the AD pathway (hsa05010) of the Kyoto Encyclopedia of Genes and Genomes. We employed a machine learning method, namely, joint density-based non-parametric differential interaction network analysis and classification (JDINAC), in the analysis of gene expression data (RNA-seq data). We searched for the differential networks in the RNA-seq data with a pathological diagnosis of AD. Finally, an optimal prediction model was built through cross-validation, which showed good discrimination and calibration for AD prediction.Results: We used JDINAC to derive a gene co-expression network and to explore the relationship between the interaction of gene pairs and AD, and the top 10 differential gene pairs were identified. We then compared the prediction performance between JDINAC and individual genes based on prediction methods. JDINAC provides better accuracy of classification than the latest methods, such as random forest and penalized logistic regression.Conclusions: The interaction between gene pairs is related to AD and can provide more insight than the individual genes in AD prediction.

Time-Varying Differential Network Analysis for Revealing Network Rewiring over Cancer Progression.

To reveal how gene regulatory networks change over cancer development, multiple time-varying differential networks between adjacent cancer stages should be estimated simultaneously. Since the network rewiring may be driven by the perturbation of certain individual genes, there may be some hub nodes shared by these differential networks. Although several methods have been developed to estimate differential networks from gene expression data, most of them are designed for estimating a single differential network, which neglect the similarities between different differential networks. In this article, we propose a new Gaussian graphical model-based method to jointly estimate multiple time-varying differential networks for identifying network rewiring over cancer development. A D-trace loss is used to determine the differential networks. A tree-structured group Lasso penalty is designed to identify the common hub nodes shared by different differential networks and the specific hub nodes unique to individual differential networks. Simulation experiment results demonstrate that our method outperforms other state-of-the-art techniques in most cases. We also apply our method to The Cancer Genome Atlas data to explore gene network rewiring over different breast cancer stages. Hub nodes in the estimated differential networks rediscover well known genes associated with the development and progression of breast cancer.

Differential expression and co-expression gene network analyses reveal molecular mechanisms and candidate biomarkers involved in breast muscle myopathies in chicken

The broiler industry is facing an increasing prevalence of breast myopathies, such as white striping (WS) and wooden breast (WB), and the precise aetiology of these occurrences remains poorly understood. To progress our understanding of the structural changes and molecular pathways involved in these myopathies, a transcriptomic analysis was performed using an 8 × 60 K Agilent chicken microarray and histological study. The study used pectoralis major muscles from three groups: slow-growing animals (n = 8), fast-growing animals visually free from defects (n = 8), or severely affected by both WS and WB (n = 8). In addition, a weighted correlation network analysis was performed to investigate the relationship between modules of co-expressed genes and histological traits. Functional analysis suggested that selection for fast growing and breast meat yield has progressively led to conditions favouring metabolic shifts towards alternative catabolic pathways to produce energy, leading to an adaptive response to oxidative stress and the first signs of inflammatory, regeneration and fibrosis processes. All these processes are intensified in muscles affected by severe myopathies, in which new mechanisms related to cellular defences and remodelling seem also activated. Furthermore, our study opens new perspectives for myopathy diagnosis by highlighting fine histological phenotypes and genes whose expression was strongly correlated with defects.

Reappraisal of FDA approved drugs against Alzheimer’s disease based on differential gene expression and protein interaction network analysis: an in silico approach

Alzheimer’s disease (AD), a most prevailing neurodegenerative disorder with turbulence in cognitive and behavioural abilities, epitomizes one of the highest unmet medical requirements. The current AD treatment focuses merely on symptomatic relief, this explains a dearth in drug research oriented towards unwinding of disease specific druggable targets. On the other hand, toxicity and poor bioavailability hamper the evolution of novel chemical entities (NCE) in clinical trials. Drug repurposing offers a gateway to rejuvenate new therapeutic applications for existing approved drugs. This study concentrates on the identification of potential druggable AD targets and screening of FDA approved drugs with a concept of drug repurposing. differentially expressed genes (DEGs) were identified in frontal cortex, temporal cortex and hippocampus in AD patients from Gene Expression Omnibus (GEO) dataset GSE36980. Protein-protein interaction (PPI) analysis revealed SERPINA3 and BDNF to possess high node degree interaction with literature derived candidate genes (LDGs) in AD males and females, respectively, thus were selected as potential AD targets. Subsequently, FDA approved drugs were screened through the above shortlisted targets and were ranked based on molecular docking and MM-GBSA energy calculations using Glide and Prime tools, respectively. Drugs possessing best docking score and maximum binding energy were further evaluated through molecular dynamics simulation studies, which revealed the affinity of Tiludronic acid and Olsalazine towards SERPINA3 and BDNF, respectively.

Dietary Bioactive Lipid Compounds Rich in Menthol Alter Interactions Among Members of Ruminal Microbiota in Sheep.

This study aimed to investigate the effects of two practically relevant doses of menthol-rich plant bioactive lipid compounds (PBLC) on fermentation, microbial community composition, and their interactions in sheep rumen. Twenty-four growing Suffolk sheep were divided into three treatments and were fed hay ad libitum plus 600 g/d of concentrate containing no PBLC (Control) or PBLC at low dose (80 mg/d; PBLC-L) or high dose (160 mg/d; PBLC-H). After 4 weeks on the diets, samples of ruminal digesta were collected and analyzed for short-chain fatty acid (SCFA), ammonia, and microbiota; microbiota being analyzed in the solid and the liquid digesta fractions separately. Ruminal SCFA and ammonia concentrations were not affected by the PBLC treatments. The microbiota in the solid fraction was more diverse than that in the liquid fraction, and the relative abundance of most taxa differed between these two fractions. In the solid fraction, phylogenetic diversity increased linearly with increased PBLC doses, whereas evenness (lowest in PBLC-L) and Simpson diversity index (greatest in PBLC-H) changed quadratically. In the liquid fraction, however, the PBLC supplementation did not affect any of the microbial diversity measurements. Among phyla, Chloroflexi (highest in PBLC-L) and unclassified_bacteria (lowest in PBLC-L) were altered quadratically by PBLC. Lachnospiraceae, Bacteroidaceae (increased linearly), BS11 (increased in PBLC-L), Christensenellaceae (decreased in PBLC treatments), and Porphyromonadaceae (increased in PBLC treatments) were affected at the family level. Among genera, Butyrivibrio increased linearly in the solid fraction, YRC22 increased linearly in the liquid fraction, whereas Paludibacter increased and BF311 increased linearly with increasing doses of PBLC in both fractions. The PBLC treatments also lowered methanogens within the classes Thermoplasmata and Euryarchaeota. Correlation network analysis revealed positive and negative correlations among many microbial taxa. Differential network analysis showed that PBLC supplementation changed the correlation between some microbial taxa and SCFA. The majority of the predicted functional features were different between the solid and the liquid digesta fractions, whereas the PBLC treatments altered few of the predicted functional gene categories. Overall, dietary PBLC treatments had little influence on the ruminal fermentation and microbiota but affected the associations among some microbial taxa and SCFA.

High-Coverage Targeted Lipidomics Reveals Novel Serum Lipid Predictors and Lipid Pathway Dysregulation Antecedent to Type 2 Diabetes Onset in Normoglycemic Chinese Adults.

Comprehensive assessment of serum lipidomic aberrations before type 2 diabetes mellitus (T2DM) onset has remained lacking in Han Chinese. We evaluated changes in lipid coregulation antecedent to T2DM and identified novel lipid predictors for T2DM in individuals with normal glucose regulation (NGR). In the discovery study, we tested 667 baseline serum lipids in subjects with incident diabetes and propensity score-matched control subjects (n = 200) from a prospective cohort comprising 3,821 Chinese adults with NGR. In the validation study, we tested 250 lipids in subjects with incident diabetes and matched control subjects (n = 724) from a pooled validation cohort of 14,651 individuals with NGR covering five geographical regions across China. Differential correlation network analyses revealed perturbed lipid coregulation antecedent to diabetes. The predictive value of a serum lipid panel independent of serum triglycerides and 2-h postload glucose was also evaluated. At the level of false-discovery rate <0.05, 38 lipids, including triacylglycerols (TAGs), lyso-phosphatidylinositols, phosphatidylcholines, polyunsaturated fatty acid (PUFA)-plasmalogen phosphatidylethanolamines (PUFA-PEps), and cholesteryl esters, were significantly associated with T2DM risk in the discovery and validation cohorts. A preliminary study found most of the lipid predictors were also significantly associated with the risk of prediabetes. Differential correlation network analysis revealed that perturbations in intraclass (i.e., non-PUFA-TAG and PUFA-TAGs) and interclass (i.e., TAGs and PUFA-PEps) lipid coregulation preexisted before diabetes onset. Our lipid panel further improved prediction of incident diabetes over conventional clinical indices. These findings revealed novel changes in lipid coregulation existing before diabetes onset and expanded the current panel of serum lipid predictors for T2DM in normoglycemic Chinese individuals.

A novel analysis method for biomarker identification based on horizontal relationship: identifying potential biomarkers from large-scale hepatocellular carcinoma metabolomics data.

Omics techniques develop quickly and have made a great contribution to disease study. Omics data are usually complex. How to analyze the data and mine important information has been a key part in omics research. To study the nature of disease mechanisms systematically, we propose a new data analysis method to define the network biomarkers based on horizontal comparison (DNB-HC). DNB-HC performs molecule horizontal relationships to characterize the physiological status and differential network analysis to screen the biomarkers. We applied DNB-HC to analyze a large-scale metabolomics data, which contained 550 samples from a nested case-control hepatocellular carcinoma (HCC) study. A network biomarker was defined, and its areas under curves (AUC) in the receiver-operating characteristic (ROC) analysis for HCC discrimination were larger than those defined by six efficient feature selection methods in most cases. The effectiveness was further corroborated by another nested HCC dataset. Besides, the performance of the defined biomarkers was better than that of α-fetoprotein (AFP), a commonly used clinical biomarker for distinguishing HCC from high-risk population of liver cirrhosis in other two independent metabolomics validation sets. All and 90.3% of the AFP false-negative patients with HCC were correctly diagnosed in these two sets, respectively. The experimental results illustrate that DNB-HC can mine more important information reflecting the nature of the research problems by studying the feature horizontal relationship systematically and identifying effective disease biomarkers in clinical practice. Graphical abstract.

Differential network analysis and protein-protein interaction study reveals active protein modules in glucocorticoid resistance for infant acute lymphoblastic leukemia

BackgroundAcute lymphoblastic leukemia (ALL) is the most common type of cancer diagnosed in children and Glucocorticoids (GCs) form an essential component of the standard chemotherapy in most treatment regimens. The category of infant ALL patients carrying a translocation involving the mixed lineage leukemia (MLL) gene (gene KMT2A) is characterized by resistance to GCs and poor clinical outcome. Although some studies examined GC-resistance in infant ALL patients, the understanding of this phenomenon remains limited and impede the efforts to improve prognosis.MethodsThis study integrates differential co-expression (DC) and protein-protein interaction (PPI) networks to find active protein modules associated with GC-resistance in MLL-rearranged infant ALL patients. A network was constructed by linking differentially co-expressed gene pairs between GC-resistance and GC-sensitive samples and later integrated with PPI networks by keeping the links that are also present in the PPI network. The resulting network was decomposed into two sub-networks, specific to each phenotype. Finally, both sub-networks were clustered into modules using weighted gene co-expression network analysis (WGCNA) and further analyzed with functional enrichment analysis.ResultsThrough the integration of DC analysis and PPI network, four protein modules were found active under the GC-resistance phenotype but not under the GC-sensitive. Functional enrichment analysis revealed that these modules are related to proteasome, electron transport chain, tRNA-aminoacyl biosynthesis, and peroxisome signaling pathways. These findings are in accordance with previous findings related to GC-resistance in other hematological malignancies such as pediatric ALL.ConclusionsDifferential co-expression analysis is a promising approach to incorporate the dynamic context of gene expression profiles into the well-documented protein interaction networks. The approach allows the detection of relevant protein modules that are highly enriched with DC gene pairs. Functional enrichment analysis of detected protein modules generates new biological hypotheses and may help in explaining the GC-resistance in MLL-rearranged infant ALL patients.

A Statistical Test for Differential Network Analysis Based on Inference of Gaussian Graphical Model

Differential network analysis investigates how the network of connected genes changes from one condition to another and has become a prevalent tool to provide a deeper and more comprehensive understanding of the molecular etiology of complex diseases. Based on the asymptotically normal estimation of large Gaussian graphical model (GGM) in the high-dimensional setting, we developed a computationally efficient test for differential network analysis through testing the equality of two precision matrices, which summarize the conditional dependence network structures of the genes. Additionally, we applied a multiple testing procedure to infer the differential network structure with false discovery rate (FDR) control. Through extensive simulation studies with different combinations of parameters including sample size, number of vertices, level of heterogeneity and graph structure, we demonstrated that our method performed much better than the current available methods in terms of accuracy and computational time. In real data analysis on lung adenocarcinoma, we revealed a differential network with 3503 nodes and 2550 edges, which consisted of 50 clusters with an FDR threshold at 0.05. Many of the top gene pairs in the differential network have been reported relevant to human cancers. Our method represents a powerful tool of network analysis for high-dimensional biological data.

Transcriptional variation is associated with differences in shoot sodium accumulation in distinct barley varieties

Soil salinity causes large productivity losses for agriculture worldwide. Barley has been identified as one of the more salt tolerant staple crops compared to wheat and rice. Identification of genes and allelic variations underlying various salt tolerance mechanisms in barley will be a practical contribution towards the development of cereal lines with greater salinity tolerance. Here, RNA from six barley varieties with varying leaf blade and sheath Na+ accumulation following salt (NaCl) treatment were sequenced. Differential gene expression analysis, variant calling and gene co-regulatory network analysis was conducted to identify potential molecular components underlying the shoot Na+ phenotypes. We identified novel alleles of HKT1;5 that could be responsible for high Na+ accumulation in blade and sheath. Furthermore, through statistical modelling of gene expression, a Na+/H+ Exchanger (NHX) gene was identified as a candidate for high sheath Na+ accumulation. Through co-expression networks, we discovered expression pattern variation for genes related to terpenoid phenylpropanoid and flavonoid metabolism amongst the six varieties. The gene candidates identified in this study provide us with targets of interest for future characterisation of molecular mechanisms that may contribute to salt stress tolerance in barley.

GAP: A General Framework for Information Pooling in Two-Sample Sparse Inference

This article develops a general framework for exploiting the sparsity information in two-sample multiple testing problems. We propose to first construct a covariate sequence, in addition to the usual primary test statistics, to capture the sparsity structure, and then incorporate the auxiliary covariates in inference via a three-step algorithm consisting of grouping, adjusting and pooling (GAP). The GAP procedure provides a simple and effective framework for information pooling. An important advantage of GAP is its capability of handling various dependence structures such as those arise from high-dimensional linear regression, differential correlation analysis, and differential network analysis. We establish general conditions under which GAP is asymptotically valid for false discovery rate control, and show that these conditions are fulfilled in a range of settings, including testing multivariate normal means, high-dimensional linear regression, differential covariance or correlation matrices, and Gaussian graphical models. Numerical results demonstrate that existing methods can be significantly improved by the proposed framework. The GAP procedure is illustrated using a breast cancer study for identifying gene–gene interactions.

Co-expression Network Analysis Elucidated a Core Module in Association With Prognosis of Non-functioning Non-invasive Human Pituitary Adenoma.

Non-functioning pituitary adenomas (NFPAs) are tumors with clinically challenging features since they have insidious progression. A complex network of gene interactions is thought to have roles in tumor formation and progression. Therefore, revealing the genetic network behind NFPA tumorigenesis is not only essential to attain further knowledge of tumor biology, but also plays a fundamental role in the development of efficacious treatment strategies. Differential co-expression network analysis is an outstanding approach for elucidation of groups of genes which show distinct co-expression patterns among phenotypes. In this study, we carried out a differential co-expression network analysis of NFPA-associated transcriptome dataset (n = 40) considering invasive (n = 22) and non-invasive (n = 18) phenotypes. Furthermore, we identified differentially co-expressed and co-regulated mRNA modules, which might be considered as potential systems biomarkers for NFPA prognosis and invasiveness. As a result, we have identified a novel 13-gene module, including CEACAM6, CYP4B1, EIF2S2, HID1, IFFO1, MYO18A, PDCD2, SGIP1, SWSAP1, and four unknown genes (A_24_P127621, A_24_P255786, A_24_P683553, and A_24_P916979), which was able to categorize the patients into two groups as invasive and non-invasive NFPA with distinct prognosis. The prognostic core module genes were associated with progression and prognosis of brain and glandular based cancers as well. Furthermore, these module genes were also expressed in blood, salivary gland, and spinal cord tissues. These results may provide the evidence on featured gene module which might play a prominent role in NFPA prognosis and sub-typing as effective biomarkers and therapeutic targets in the future.

An Inflammatory Landscape for Preoperative Neurologic Deficits in Glioblastoma.

Introduction: Patients with glioblastoma (GBM), one of the most aggressive forms of primary brain tumors, exhibit a wide range of neurologic signs, ranging from headaches to neurologic deficits and cognitive impairment, at first clinical presentation. While such variability is attributed to inter-individual differences in increased intracranial pressure, tumor infiltration, and vascular compromise, a direct association with disease stage, tumor size and location, edema, and necrotic cell death has yet to be established. The lack of specificity of neurologic symptoms often confounds the diagnosis of GBM. It also limits clinicians’ ability to elect treatment regimens that not only prolong survival but also promote symptom management and high quality of life.Methods: To decipher the heterogeneous presentation of neurologic symptoms in GBM, we investigated differences in the molecular makeup of tumors from patients with and without preoperative neurologic deficits. We used the Ivy GAP (Ivy Glioblastoma Atlas Project) database to integrate RNA sequencing data from histologically defined GBM tumor compartments and neurologic examination records for 41 patients. We investigated the association of neurologic deficits with various tumor and patient attributes. We then performed differential gene expression and co-expression network analysis to identify a transcriptional signature specific to neurologic deficits in GBM. Using functional enrichment analysis, we finally provided a comprehensive and detailed characterization of involved pathways and gene interactions.Results: An exploratory investigation of the association of tumor and patient variables with the early development of neurologic deficits in GBM revealed a lack of robust and consistent clinicopathologic prognostic factors. We detected significant differences in the expression of 728 genes (FDR-adjusted p-value ≤ 0.05 and relative fold-change ≥ 1.5), unique to the cellular tumor (CT) anatomical compartment, between neurologic deficit groups. Upregulated differentially expressed genes in CT were enriched for mesenchymal subtype-predictive genes. Applying a systems approach, we then identified co-expressed gene sets that correlated with neurological deficit manifestation (FDR-adjusted p-value < 0.1). Collectively, these findings uncovered significantly enriched immune activation, oxidative stress response, and cytokine-mediated proinflammatory processes.Conclusion: Our study posits that inflammatory processes, as well as a mesenchymal tumor subtype, are implicated in the pathophysiology of preoperative neurologic deficits in GBM.

Transcriptome analysis reveals lncRNA-mediated complex regulatory network response to DNA damage in the liver tissue of Rattus norvegicus.

DNA is prone to damages, which would result in genetic disorders and enhance risk of tumorigenesis. Hence, understanding the molecular mechanisms of DNA damage and repair will provide deep insights into tumorigenesis, carcinogenesis as well as the corresponding treatments. Aiming at investigating potential long noncoding RNAs (lncRNAs) response against DNA damage, we performed a comprehensive transcriptomic analysis based on RNA sequencing data of the liver tissue from Rattus norvegicus, in which DNA damage was induced using aflatoxin B1, ifosfamide and N-nitrosodimethylamine. Through our analyses, numerous novel lncRNAs are identified for the first time, and differential network analysis discloses lncRNA-mediated regulatory networks related to DNA-damage response. The result shows that these DNA-damage-inducing chemicals might disrupt many lncRNA-mediated interactions involved in diverse biological processes and pathways, for example, immune function and cell adhesion. In contrast, the host might also activate a few RNA interactions in response to DNA damage, involving response to drug and regulation of cell cycle.