Construct Gene Co-expression Networks Research Articles

Investigating the Role of Hub Calcification Proteins in Atherosclerosis via Integrated Transcriptomics and Network-Based Approach

Atherosclerosis (AS) is a chronic inflammatory condition of the arteries, characterized by plaque formation that can restrict blood flow and lead to potentially fatal cardiovascular events. Given that AS is responsible for a quarter of global deaths, this study aimed to develop a systematic bioinformatics approach to identify biomarkers and regulatory targets involved in plaque development, with the goal of reducing cardiovascular disease risk. AS-specific mRNA expression profiles were retrieved from a publicly accessible database, followed by differentially expressed genes (DEGs) identification and AS-specific weighted gene co-expression network (WGCN) construction. Thereafter, calcification and atherosclerosis-specific (CASS) DEGs were utilized for protein–protein interaction network (PPIN) formation, followed by gene ontology (GO) term and pathway enrichment analyses. Lastly, AS-specific 3-node miRNA feed-forward loop (FFL) construction and analysis was performed. Microarray datasets GSE43292 and GSE28829 were obtained from gene expression omnibus (GEO). A total of 3785 and 6176 DEGs were obtained in case of GSE28829 and GSE43292; 3256 and 5962 module DEGs corresponding to GSE28829 and GSE43292 were obtained from WGCN. From a total of 54 vascular calcification (VC) genes, 20 and 29 CASS-DEGs corresponding to GSE28829 and GSE43292 were overlapped. As observed from FFL centrality measures, the highest-order subnetwork motif comprised one TF (SOX7), one miRNA (miR-484), and one mRNA (SPARC) in the case of GSE28829. Also, in the case of GSE43292, the highest-order subnetwork motif comprised one TF (ESR2), one miRNA (miR-214-3p), and one mRNA (MEF2C). These findings have important implications for developing new therapeutic strategies for AS. The identified TFs and miRNAs may serve as potential therapeutic targets for treating atherosclerotic plaques, offering insights into the molecular mechanisms underlying the pathogenesis and highlighting new avenues for research and treatment.

Identification of necroptosis genes and characterization of immune infiltration in non-alcoholic steatohepatitis

BackgroundThe most common progressive form of non-alcoholic fatty liver disease (NAFLD) is non-alcoholic steatohepatitis (NASH), which is characterized by the development of cirrhosis, and requires liver transplantation. We screened for the differentially expressed necroptosis-related genes in NASH in this study, and analyzed immune infiltration through microarray and bioinformatics analysis to identify potential biomarkers, and explore the molecular mechanisms involved in NASH.MethodsThe GSE24807 microarray dataset of NASH patients and healthy controls was downloaded, and we identified the differentially expressed genes (DEGs). Necroptosis-related differential genes (NRDEGs) were extracted from these DEGs, and functionally annotated by enrichment analyses. The core genes were obtained by constructing gene co-expression networks using weighted gene co-expression network analysis (WGCNA). Finally, the transcription factor (TF) regulatory network and the mRNA-miRNA network were constructed, and the infiltrating immune cell populations were analyzed with CIBERSORT.ResultsWe identified six necroptosis-related genes (CASP1, GLUL, PYCARD, IL33, SHARPIN, and IRF9), and they are potential diagnostic biomarkers for NASH. In particular, PYCARD is a potential biomarker for NAFLD progression. Analyses of immune infiltration showed that M2 macrophages, γδ T cells, and T follicular helper cells were associated with the immune microenvironment of NASH, which is possibly regulated by CASP1, IL33, and IRF9.ConclusionsWe identified six necroptosis-related genes in NASH, which are also potential diagnostic biomarkers. Our study provides new insights into the molecular mechanisms and immune microenvironment of NASH.

Genetic networks suggest Asperger’s syndrome as a distinct subtype of autism spectrum disorders

BackgroundThe Diagnostic and Statistical Manual of Mental Disorders (DSM-V) issued new diagnostic criteria for autism spectrum disorders (ASD) which resulted in missing the diagnosis of some cases of Asperger’s syndrome (AS). This negatively affected the support received by those affected. In this study, we explored if AS could be biologically stratified from the broader spectrum through a gene co-expression network preservation analysis. MethodsWe analysed the GEO microarray data of 24 individuals with Asperger’s syndrome and 72 individuals with autism. Then, we used a weighted gene co-expression network (WGCNA) pipeline to construct gene co-expression networks. We explored whether these modules share the same co-expression patterns between autism and Asperger’s syndrome using network preservation analysis. ResultsOur results showed that all co-expression modules of autism are preserved into the Asperger’s syndrome. However, three modules of Asperger’s syndrome out of 30 modules were not preserved in autism.Gene enrichment analysis revealed that these modules were involved in chromatin remodelling, immune and neuroinflammatory response, synaptic and neuronal development. Brain enrichment analysis showed significant downregulation of neurodevelopment genes in different brain regions associated with impaired social recognition in Asperger’s syndrome. ConclusionsThe identified genetic and molecular profiles suggest that Asperger’s syndrome, despite sharing numerous similarities with autism, possesses a distinct genetic profile that makes it a distinct subtype of autism. This distinction could have significant implications for the management and treatment strategies tailored to individuals with Asperger’s syndrome.

Comprehensive RNA-Seq Gene Co-Expression Analysis Reveals Consistent Molecular Pathways in Hepatocellular Carcinoma across Diverse Risk Factors.

Hepatocellular carcinoma (HCC) has the highest mortality rate and is the most frequent of liver cancers. The heterogeneity of HCC in its etiology and molecular expression increases the difficulty in identifying possible treatments. To elucidate the molecular mechanisms of HCC across grades, data from The Cancer Genome Atlas (TCGA) were used for gene co-expression analysis, categorizing each sample into its pre-existing risk factors. The R library BioNERO was used for preprocessing and gene co-expression network construction. For those modules most correlated with a grade, functional enrichments from different databases were then tested, which appeared to have relatively consistent patterns when grouped by G1/G2 and G3/G4. G1/G2 exhibited the involvement of pathways related to metabolism and the PI3K/Akt pathway, which regulates cell proliferation and related pathways, whereas G3/G4 showed the activation of cell adhesion genes and the p53 signaling pathway, which regulates apoptosis, cell cycle arrest, and similar processes. Module preservation analysis was then used with the no history dataset as the reference network, which found cell adhesion molecules and cell cycle genes to be preserved across all risk factors, suggesting they are imperative in the development of HCC regardless of potential etiology. Through hierarchical clustering, modules related to the cell cycle, cell adhesion, the immune system, and the ribosome were found to be consistently present across all risk factors, with distinct clusters linked to oxidative phosphorylation in viral HCC and pentose and glucuronate interconversions in non-viral HCC, underscoring their potential roles in cancer progression.

Transcriptome Analysis of Myocardial Ischemic-Hypoxic Injury in Rats and Hypoxic H9C2 Cells.

This study aimed to address inconsistencies in results between the H9C2 myocardial hypoxia (MH) cell line and myocardial infarction (MI) rat models used in MI research. We identified differentially expressed genes (DEGs) and underlying molecular mechanisms using RNA sequencing technology. RNA sequencing was used to analyse DEGs in MI rat tissues and H9C2 cells exposed to hypoxia for 24h. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to identify key biological processes and pathways. Weighted correlation network analysis [weighted gene co-expression network analysis (WGCNA)] was used to construct gene co-expression networks, and hub genes were compared with published MI datasets [Gene Expression Omnibus (GEO)] for target identification. GO analysis revealed enrichment of immune inflammation and mitochondrial respiration processes among 5139 DEGs in MI tissues and 2531 in H9C2 cells. KEGG analysis identified 537 overlapping genes associated with metabolism and oxidative stress pathways. Cross-analyses using the published GSE35088 and GSE47495 datasets identified 40 and 16 overlapping genes, respectively, with nine genes overlapping across all datasets and our models. WGCNA identified a key module in the MI model enriched for mRNA processing and protein binding. GO analysis revealed enrichment of mRNA processing, protein binding and mitochondrial respiratory chain complex I assembly in MI and H9C2 MH models. Five relevant hub genes were identified via a cross-analysis between the 92 hub genes that showed a common expression trend in both models. This study reveals both shared and distinct transcriptomic responses in the MI and H9C2 models, highlighting the importance of model selection for studying myocardial ischaemia and hypoxia.

The protective effects of aqueous extract of Schisandra sphenanthera against alcoholic liver disease partly through the PI3K-AKT-IKK signaling pathway

PurposeThis study aimed to investigated the key chemical components and the effect of the aqueous extract of Schisandra sphenanthera (SSAE) on alcoholic liver disease (ALD) and the related molecular mechanism. MethodsThis study employed UPLC-Q-TOF-MS/MS to identify the chemical compositions in SSAE. ALD rat model was established through oral administration of white spirit. Transcriptome sequencing, weighted gene co-expression network construction analysis (WGCNA), and network pharmacology were used to predict key compositions and pathways targeted by SSAE for the treatment of ALD. Enzyme-linked immunosorbent assay (ELISA), biochemical kits, hematoxylin-eosin (HE) staining, Western blotting (WB) analysis, and immunohistochemical analysis were used to validate the mechanism of action of SSAE in treating ALD. ResultsActive ingredients such as schisandrin A, schisandrol A, and schisandrol B were found to regulate the PI3K/AKT/IKK signaling pathway. Compared to the model group, the SSAE group demonstrated significant improvements in cellular solidification and tissue inflammation in the liver tissues of ALD model rats. Additionally, SSAE regulated the levels of a spartate aminotransferase (AST), alanine aminotransferase (ALT), alcohol dehydrogenase (ADH), and aldehyde Dehydrogenase (ALDH) in serum (P < 0.05); Western blotting and immunohistochemical analyses showed that the expression levels of phosphorylated PI3K, AKT, IKK, NFκB, and FOXO1 proteins were significantly reduced in liver tissues (P < 0.05), whereas the expression level of Bcl-2 proteins was significantly increased (P < 0.05). ConclusionThe active components of SSAE were schisandrin A, schisandrol A, and schisandrol B, which regulated the phosphorylation levels of PI3K, AKT, IKK, and NFκB and the expression of FOXO1 protein and upregulated the expression of Bcl-2 protein in the liver tissues of ALD rats. These findings indicate that SSAE acts against ALD partly through the PI3K-AKT-IKK signaling pathway. This study provided a reference for future research and treatment of ALD and the development of novel natural hepatoprotective drugs.

Weighted gene co-expression network analysis and whole genome sequencing identify potential lung cancer biomarkers

BackgroundTuberculosis (TB) leads to an increased risk of lung cancer (LC). However, the carcinogenetic mechanism of TB remains unclear. We constructed gene co-expression networks and carried out whole-exome sequencing (WES) to identify key modules, hub genes, and the most recurrently mutated genes involved in the pathogenesis of TB-associated LC.MethodsThe data used in this study were obtained from the Gene Expression Omnibus (GEO) and WES. First, we screened LC-related genes in GSE43458 and TB-related genes in GSE83456 by weighted gene co-expression network analysis (WGCNA). Subsequently, we screened differentially expressed genes related to LC and TB in GSE42834. We also performed WES of 15 patients (TB, n = 5; LC, n = 5; TB+LC, n = 5), constructed mutational profiles, and identified differences in the profiles of the three groups for further investigation.ResultsWe identified 278 hub genes associated with tumorigenesis of pulmonary TB. Moreover, WES identified 112 somatic mutations in 25 genes in the 15 patients. Finally, four common genes (EGFR, HSPA2, CECR2, and LAMA3) were confirmed in a Venn diagram of the 278 hub genes and the mutated genes from WES. KEGG analysis revealed various pathway changes. The PI3K–AKT signaling pathway was the most enriched pathway, and all four genes are included in this pathway. Thus, these four genes and the PI3K–AKT signaling pathway may play important roles in LC.ConclusionSeveral potential genes and pathways related to TB-associated LC were identified, including EGFR and three target genes not found in previous studies. These genes are related to cell proliferation, colony formation, migration, and invasion, and provide a direction for future research into the mechanisms of LC co-occurring with TB. The PI3K–AKT signaling pathway was also identified as a potential key pathway involved in LC development.

Conserved regulatory switches for the transition from natal down to juvenile feather in birds

The transition from natal downs for heat conservation to juvenile feathers for simple flight is a remarkable environmental adaptation process in avian evolution. However, the underlying epigenetic mechanism for this primary feather transition is mostly unknown. Here we conducted time-ordered gene co-expression network construction, epigenetic analysis, and functional perturbations in developing feather follicles to elucidate four downy-juvenile feather transition events. We report that extracellular matrix reorganization leads to peripheral pulp formation, which mediates epithelial-mesenchymal interactions for branching morphogenesis. α-SMA (ACTA2) compartmentalizes dermal papilla stem cells for feather renewal cycling. LEF1 works as a key hub of Wnt signaling to build rachis and converts radial downy to bilateral symmetry. Novel usage of scale keratins strengthens feather sheath with SOX14 as the epigenetic regulator. We show that this primary feather transition is largely conserved in chicken (precocial) and zebra finch (altricial) and discuss the possibility that this evolutionary adaptation process started in feathered dinosaurs.

Role of CENPF and NDC80 in the rehabilitation nursing of hepatocellular carcinoma and cirrhosis: An observational study.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors globally and often develops on the foundation of chronic liver disease or cirrhosis. Cirrhosis is a clinically prevalent chronic progressive liver disease characterized by diffuse liver damage resulting from long-term or repeated actions of 1 or more etiological factors. However, the impact of CENPF and nuclear division cycle 80 (NDC80) genes on rehabilitation nursing of HCC and cirrhosis remains unclear. HCC and cirrhosis datasets GSE63898 and GSE89377 profile files were downloaded from the gene expression omnibus database generated on platforms GPL13667 and GPL6947, respectively. Differentially expressed genes (DEGs) screening, weighted gene co-expression network analysis (WGCNA), construction and analysis of protein-protein interaction (PPI) networks, functional enrichment analysis, gene set enrichment analysis (GSEA), survival analysis, immune infiltration analysis, and comparative toxicogenomics database (CTD) analysis were conducted. Gene expression heatmaps were plotted. miRNAs regulating central DEGs were selected through TargetScan. A total of 626 DEGs were identified. According to gene ontology (GO) analysis, they were primarily enriched in small molecule metabolic processes, drug metabolic processes, binding of identical proteins, and lipid metabolic processes. Kyoto Encyclopedia of Gene and Genome (KEGG) analysis results indicated that the target genes were mainly enriched in metabolic pathways, phagosomes, glycine, serine, and threonine metabolism. The construction and analysis of the PPI network revealed 3 core genes (NDC80, CENPF, RRM2). Gene expression heatmaps showed that core genes (CENPF, NDC80) were highly expressed in HCC and cirrhosis samples. CTD analysis found that 2 genes (CENPF and NDC80) were associated with liver, jaundice, ascites, fever, dyspepsia, and hepatic encephalopathy. CENPF and NDC80 are highly expressed in HCC and cirrhosis, and CENPF and NDC80 might be the biomarkers of rehabilitation nursing of HCC and cirrhosis.

ICARUS v3, a massively scalable web server for single-cell RNA-seq analysis of millions of cells.

In recent years, improvements in throughput of single-cell RNA-seq have resulted in a significant increase in the number of cells profiled. The generation of single-cell RNA-seq datasets comprising >1 million cells is becoming increasingly common, giving rise to demands for more efficient computational workflows. We present an update to our single-cell RNA-seq analysis web server application, ICARUS (available at https://launch.icarus-scrnaseq.cloud.edu.au) that allows effective analysis of large-scale single-cell RNA-seq datasets. ICARUS v3 utilizes the geometric cell sketching method to subsample cells from the overall dataset for dimensionality reduction and clustering that can be then projected to the large dataset. We then extend this functionality to select a representative subset of cells for downstream data analysis applications including differential expression analysis, gene co-expression network construction, gene regulatory network construction, trajectory analysis, cell-cell communication inference, and cell cluster associations to GWAS traits. We demonstrate analysis of single-cell RNA-seq datasets using ICARUS v3 of 1.3 million cells completed within the hour. ICARUS is available at https://launch.icarus-scrnaseq.cloud.edu.au.

Identification of important genes related to anoikis in acute myocardial infarction.

Acute myocardial infarction (AMI) increasingly precipitates severe heart failure, with diagnoses now extending to progressively younger demographics. The focus of this study was to pinpoint critical genes linked to both AMI and anoikis, thereby unveiling potential novel biomarkers for AMI detection and intervention. Differential analysis was performed to identify significant differences in expression, and gene functionality was explored. Weighted gene coexpression network analysis (WGCNA) was used to construct gene coexpression networks. Immunoinfiltration analysis quantified immune cell abundance. Protein-protein interaction (PPI) analysis identified the proteins that interact with theanoikis. MCODE identified key functional modules. Drug enrichment analysis identified relevant compounds explored in the DsigDB. Through WGCNA, 13 key genes associated with anoikis and differentially expressed genes were identified. GO and KEGG pathway enrichment revealed the regulation of apoptotic signalling pathways and negative regulation of anoikis. PPI network analysis was also conducted, and 10 hub genes, such as IL1B, ZAP70, LCK, FASLG, CD4, LRP1, CDH2, MERTK, APOE and VTN were identified. IL1B were correlated with macrophages, mast cells, neutrophils and Tcells in MI, and the most common predicted medications were roxithromycin, NSC267099 and alsterpaullone. This study identified key genes associated with AMI and anoikis, highlighting their role in immune infiltration, diagnosis and medication prediction. These findings provide valuable insights into potential biomarkers and therapeutic targets for AMI.

In-depth analysis of immune cell landscapes reveals differences between lung adenocarcinoma and lung squamous cell carcinoma

BackgroundLung cancer is the leading cause of cancer deaths globally, with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) being major subtypes. Immunotherapy has emerged as a promising approach for the treatment of lung cancer, but understanding the underlying mechanisms of immune dysregulation is crucial for the development of effective therapies. This study aimed to investigate the distinctive cellular features of LUAD and LUSC and identify potential biomarkers associated with the pathogenesis and clinical outcomes of each subtype.MethodsWe used digital cytometry techniques to analyze the RNA-Seq data of 1128 lung cancer patients from The Cancer Genome Atlas (TCGA) database. The abundance of cell subtypes and ecotypes in LUAD and LUSC patients was quantified. Univariate survival analysis was used to investigate their associations with patient overall survival (OS). Differential gene expression analysis and gene co-expression network construction were carried out to explore the gene expression patterns of LUSC patients with distinct survival outcomes. Scratch wound-healing assay, colony formation assay, and transwell assay were used to validate the candidate drugs for LUSC treatment.ResultsWe found differential expression of cell subtypes between LUAD and LUSC, with certain cell subtypes being prognostic for survival in both subtypes. We also identified differential gene expression and gene co-expression modules associated with macrophages.3/PCs.2 ratio in LUSC patients with distinct survival outcomes. Furthermore, ecotype ratios were found to be prognostic in both subtypes and machine learning models showed that certain cell subtypes, such as epithelial.cells.1, epithelial.cells.5, and endothelial.cells.2 are important for predicting LUSC. Ginkgolide B and triamterene can inhibit the proliferation, invasion, and migration of LUSC cell lines.ConclusionWe provide insight into the distinctive cellular features of LUAD and LUSC, and identify potential biomarkers associated with the pathogenesis and clinical outcomes of each subtype. Ginkgolide B and triamterene could be promising drugs for LUSC treatment.

Network Analysis of Publicly Available RNA-seq Provides Insights into the Molecular Mechanisms of Plant Defense against Multiple Fungal Pathogens in Arabidopsis thaliana.

Fungal pathogens can have devastating effects on global crop production, leading to annual economic losses ranging from 10% to 23%. In light of climate change-related challenges, researchers anticipate an increase in fungal infections as a result of shifting environmental conditions. However, plants have developed intricate molecular mechanisms for effective defense against fungal attacks. Understanding these mechanisms is essential to the development of new strategies for protecting crops from multiple fungi threats. Public omics databases provide valuable resources for research on plant-pathogen interactions; however, integrating data from different studies can be challenging due to experimental variation. In this study, we aimed to identify the core genes that defend against the pathogenic fungi Colletotrichum higginsianum and Botrytis cinerea in Arabidopsis thaliana. Using a custom framework to control batch effects and construct Gene Co-expression Networks in publicly available RNA-seq dataset from infected A. thaliana plants, we successfully identified a gene module that was responsive to both pathogens. We also performed gene annotation to reveal the roles of previously unknown protein-coding genes in plant defenses against fungal infections. This research demonstrates the potential of publicly available RNA-seq data for identifying the core genes involved in defending against multiple fungal pathogens.

Global co-expression network for key factor selection on environmental stress RNA-seq dataset in Capsicum annuum

Environmental stresses significantly affect plant growth, development, and productivity. Therefore, a deeper understanding of the underlying stress responses at the molecular level is needed. In this study, to identify critical genetic factors associated with environmental stress responses, the entire 737.3 Gb clean RNA-seq dataset across abiotic, biotic stress, and phytohormone conditions in Capsicum annuum was used to perform individual differentially expressed gene analysis and to construct gene co-expression networks for each stress condition. Subsequently, gene networks were reconstructed around transcription factors to identify critical factors involved in the stress responses, including the NLR gene family, previously implicated in resistance. The abiotic and biotic stress networks comprise 233 and 597 hubs respectively, with 10 and 89 NLRs. Each gene within the NLR groups in the network exhibited substantial expression to particular stresses. The integrated analysis strategy of the transcriptome network revealed potential key genes for complex environmental conditions. Together, this could provide important clues to uncover novel key factors using high-throughput transcriptome data in other species as well as plants.

Identification of two immune subtypes and four hub immune-related genes in ovarian cancer through multiple analysis.

Immune classification of ovarian cancer (OV) becomes more and more influential for its immunotherapy. However, current studies had few immune subtypes of OV. It is urgent to explore the immune subtypes and deeper hub immune-related genes (IRGs) of OV for follow-up treatment. A total number of 379 OV samples were obtained from UCSC online website. Single sample gene set enrichment analysis of 29 immune gene sets was used for identifying immune subtypes of OV and gene set variation analysis were used for exploring the hallmarks and Kyoto Encyclopedia of Genes and Genomes pathways of immune types. Two immunity subtypes (Immunity_H and Immunity_L) were identified by single sample gene set enrichment analysis. The OV patients in Immunity_H group had longer overall survival compared with those in Immunity_L group. The Immunity_H had higher stromal score, immune score and estimate score and the tumor purity had the adverse tendency. Besides, the gene set variation analysis enrichment results showed positive relationship between improved immunoreaction and pathways correlated to classical signaling pathway (PI3K/AKT/MTOR, P53, TNFA/NFkB signaling pathways) and immune responses (T/B cell receptor signaling pathways and primary immunodeficiency). Furthermore, 4 hub IRGs (CCR5, IL10RA, ITGAL and PTPRC) were jointly dug by weighted gene co-expression network construction and Cytoscape. Our team also explored the mutations of 4 hub IRGs and PTPRC showed nearly 7% amplification. Besides, 8 immune-checkpoint genes had higher expression in Immuity_H group compared with Immuity_L group, except CD276. The correlation between PD-1/PD-L1 and 4 hub IRGs were explored and gene set enrichment analysis were conducted to explore the underlying mechanisms of PTPRC in OV. Finally, western-blotting showed PTPRC could regulate immune checkpoint PD-L1 expression via JAK-STAT signaling pathway. In a word, 2 immune subtypes and 4 hub IRGs of OV were identified by multiple analysis.

Hub Gene Mining and Co-Expression Network Construction of Low-Temperature Response in Maize of Seedling by WGCNA.

Weighted gene co-expression network analysis (WGCNA) is a research method in systematic biology. It is widely used to identify gene modules related to target traits in multi-sample transcriptome data. In order to further explore the molecular mechanism of maize response to low-temperature stress at the seedling stage, B144 (cold stress tolerant) and Q319 (cold stress sensitive) provided by the Maize Research Institute of Heilongjiang Academy of Agricultural Sciences were used as experimental materials, and both inbred lines were treated with 5 °C for 0 h, 12 h, and 24 h, with the untreated material as a control. Eighteen leaf samples were used for transcriptome sequencing, with three biological replicates. Based on the above transcriptome data, co-expression networks of weighted genes associated with low-temperature-tolerance traits were constructed by WGCNA. Twelve gene modules significantly related to low-temperature tolerance at the seedling stage were obtained, and a number of hub genes involved in low-temperature stress regulation pathways were discovered from the four modules with the highest correlation with target traits. These results provide clues for further study on the molecular genetic mechanisms of low-temperature tolerance in maize at the seedling stage.

Gene co-expression network construction and analysis for identification of genetic biomarkers associated with glioblastoma multiforme using topological findings

BackgroundGlioblastoma multiforme (GBM) is one of the most malignant types of central nervous system tumors. GBM patients usually have a poor prognosis. Identification of genes associated with the progression of the disease is essential to explain the mechanisms or improve the prognosis of GBM by catering to targeted therapy. It is crucial to develop a methodology for constructing a biological network and analyze it to identify potential biomarkers associated with disease progression.MethodsGene expression datasets are obtained from TCGA data repository to carry out this study. A survival analysis is performed to identify survival associated genes of GBM patient. A gene co-expression network is constructed based on Pearson correlation between the gene’s expressions. Various topological measures along with set operations from graph theory are applied to identify most influential genes linked with the progression of the GBM.ResultsTen key genes are identified as a potential biomarkers associated with GBM based on centrality measures applied to the disease network. These genes are SEMA3B, APS, SLC44A2, MARK2, PITPNM2, SFRP1, PRLH, DIP2C, CTSZ, and KRTAP4.2. Higher expression values of two genes, SLC44A2 and KRTAP4.2 are found to be associated with progression and lower expression values of seven gens SEMA3B, APS, MARK2, PITPNM2, SFRP1, PRLH, DIP2C, and CTSZ are linked with the progression of the GBM.ConclusionsThe proposed methodology employing a network topological approach to identify genetic biomarkers associated with cancer.

GeCoNet-Tool: a software package for gene co-expression network construction and analysis

BackgroundNetwork analysis is a powerful tool for studying gene regulation and identifying biological processes associated with gene function. However, constructing gene co-expression networks can be a challenging task, particularly when dealing with a large number of missing values.ResultsWe introduce GeCoNet-Tool, an integrated gene co-expression network construction and analysis tool. The tool comprises two main parts: network construction and network analysis. In the network construction part, GeCoNet-Tool offers users various options for processing gene co-expression data derived from diverse technologies. The output of the tool is an edge list with the option of weights associated with each link. In network analysis part, the user can produce a table that includes several network properties such as communities, cores, and centrality measures. With GeCoNet-Tool, users can explore and gain insights into the complex interactions between genes.

Differential expression profiles and functional analysis of long non-coding RNAs in calcific aortic valve disease

AimTo evaluate the expression profile of long non-coding RNAs (lncRNAs) in calcific aortic valve disease (CAVD) and explore their potential mechanism of action.MethodsThe gene expression profiles (GSE153555, GSE148219, GSE199718) were downloaded from the Gene Expression Omnibus (GEO) database and FastQC was run for quality control checks. After filtering and classifying candidate lncRNAs by differentially expressed genes (DEGs) and weighted co-expression networks (WGCNA) in GSE153555, we predicted the potential cis- or trans-regulatory target genes of differentially expressed lncRNAs (DELs) by using FEELnc and established the competitive endogenous RNA (ceRNA) network by miRanda, more over functional enrichment was analyzed using the ClusterProfiler package in R Bioconductor. The hub cis- or trans-regulatory genes were verified in GSE148219 and GSE199718 respectively.ResultsThere were 340 up-regulated lncRNAs identified in AS group compared with the control group (|log2Fold Change| ≥ 1.0 and Padj ≤ 0.05), and 460 down-regulated lncRNAs. Based on target gene prediction and co-expression network construction, twelve Long non-coding RNAs (CDKN2B-AS1, AC244453.2, APCDD1L-DT, SLC12A5-AS1, TGFB3, AC243829.4, MIR4435-2HG, FAM225A, BHLHE40-AS1, LINC01614, AL356417.2, LINC01150) were identified as the hub cis- or trans-regulatory genes in the pathogenesis of CAVD which were validated in GSE148219 and GSE19971. Additionally, we found that MIR4435-2HG was the top hub trans-acting lncRNA which also plays a crucial role by ceRNA pattern.ConclusionLncRNAs may play an important role in CAVD and may provide a new perspective on the pathogenesis, diagnosis, and treatment of this disease. Further studies are required to illuminate the underlying mechanisms and provide potential therapeutic targets.

Transcriptomic analysis reveals the gene regulatory networks involved in leaf and root response to osmotic stress in tomato.

Tomato (Solanum lycopersicum L.) is a major horticultural crop that is cultivated worldwide and is characteristic of the Mediterranean agricultural system. It represents a key component of the diet of billion people and an important source of vitamins and carotenoids. Tomato cultivation in open field often experiences drought episodes, leading to severe yield losses, since most modern cultivars are sensitive to water deficit. Water stress leads to changes in the expression of stress-responsive genes in different plant tissues, and transcriptomics can support the identification of genes and pathways regulating this response. Here, we performed a transcriptomic analysis of two tomato genotypes, M82 and Tondo, in response to a PEG-mediated osmotic treatment. The analysis was conducted separately on leaves and roots to characterize the specific response of these two organs. A total of 6,267 differentially expressed transcripts related to stress response was detected. The construction of gene co-expression networks defined the molecular pathways of the common and specific responses of leaf and root. The common response was characterized by ABA-dependent and ABA-independent signaling pathways, and by the interconnection between ABA and JA signaling. The root-specific response concerned genes involved in cell wall metabolism and remodeling, whereas the leaf-specific response was principally related to leaf senescence and ethylene signaling. The transcription factors representing the hubs of these regulatory networks were identified. Some of them have not yet been characterized and can represent novel candidates for tolerance. This work shed new light on the regulatory networks occurring in tomato leaf and root under osmotic stress and set the base for an in-depth characterization of novel stress-related genes that may represent potential candidates for improving tolerance to abiotic stress in tomato.