Weighed Gene Co-expression Network Analysis Research Articles

An immune biomarker associated with EMT serves as a predictor for prognosis and drug response in bladder cancer.

Bladder cancer (BLCA), which develops from the upper endometrial of the bladder, is the sixth most prevalent cancer across the globe. WDHD1 (WD repeat and HMG-box DNA binding protein 1 gene) directly affects signaling, the cell cycle, and the development of the cell skeleton. Uncertainty surrounds WDHD1's function in BLCA immunity and prognosis, though. Using weighed gene co-expression network analysis (WGCNA), initially, we first identified 32 risk factors in genes with differential expression for this investigation. Then, using a variety of bioinformatic techniques and experimental validation, we examined the connections between WDHD1 and BLCA expression, clinical pathological traits, WDHD1-related proteins, upper-skin-intermediate conversion (EMT), immune cell immersion, convergence factors, immune markers, and drug sensitivity. The findings demonstrated that we constructed a 32-gene risk-predicting model where WDHD1 was elevated as a representative gene expression in BLCA and related to a range of clinical traits. Furthermore, high WDHD1 expression was a standalone predictor associated with a worse survival rate. The most commonly recruited cells and their evolutionary patterns were highlighted to better comprehend WDHD1's function in cancer. High WDHD1 expression was associated with many aspects of immunology. Finally, the study found that individuals with high expression of WDHD1 were drug-sensitive to four different broad-spectrum anti-cancer drugs. These results describe dynamic changes in the tumor microenvironment in BLCA and provide evidence for the hypothesis that WDHD1 is a novel biomarker of tumor development. WDHD1 may therefore be a useful target for the detection and management of BLCA.

Identification of shared gene signatures in major depressive disorder and triple-negative breast cancer.

Patients with major depressive disorder (MDD) have an increased risk of breast cancer (BC), implying that these two diseases share similar pathological mechanisms. This study aimed to identify the key pathogenic genes that lead to the occurrence of both triple-negative breast cancer (TNBC) and MDD. Public datasets GSE65194 and GSE98793 were analyzed to identify differentially expressed genes (DEGs) shared by both datasets. A protein-protein interaction (PPI) network was constructed using STRING and Cytoscape to identify key PPI genes using cytoHubba. Hub DEGs were obtained from the intersection of hub genes from a PPI network with genes in the disease associated modules of the Weighed Gene Co-expression Network Analysis (WGCNA). Independent datasets (TCGA and GSE76826) and RT-qPCR validated hub gene expression. A total of 113 overlapping DEGs were identified between TNBC and MDD. The PPI network was constructed, and 35 hub DEGs were identified. Through WGCNA, the blue, brown, and turquoise modules were recognized as highly correlated with TNBC, while the brown, turquoise, and yellow modules were similarly correlated with MDD. Notably, G3BP1, MAF, NCEH1, and TMEM45A emerged as hub DEGs as they appeared both in modules and PPI hub DEGs. Within the GSE65194 and GSE98793 datasets, G3BP1 and MAF exhibited a significant downregulation in TNBC and MDD groups compared to the control, whereas NCEH1 and TMEM45A demonstrated a significant upregulation. These findings were further substantiated by TCGA and GSE76826, as well as through RT-qPCR validation. This study identified G3BP1, MAF, NCEH1 and TMEM45A as key pathological genes in both TNBC and MDD.

Identification of an 8 HPV-related RNA signature as a novel prognostic biomarker for squamous cell carcinoma of the head and neck.

Squamous cell carcinoma of the head and neck (SCCHN) is a commonly detected cancer worldwide. Human papillomavirus (HPV) is emerging as an important risk factor affecting SCCHN prognosis. Therefore, identification of HPV status is essential for effective therapies in SCCHN. The aim of this study was to investigate the prognostic value of HPV-associated RNA biomarkers for SCCHN. The clinical data, survival data, and RNA-seq data of SCCHN were downloaded from The Cancer Genome Atlas database. Before the differential expression analysis, the heterogeneity between the 2 groups (HPV+ vs HPV-) of samples was analyzed using principal component analysis. The differentially expressed genes (DEGs) between HPV+ and HPV- SCCHN samples were analyzed using the R edgeR package. The Gene Ontology functional annotations, including biological process, molecular function and cellular component (CC), and Kyoto Encyclopedia of Genes And Genomes pathways enriched by the DEGs were analyzed using DAVID. The obtained matrix was analyzed by weighed gene coexpression network analysis. A total of 350 significant DEGs were identified through differential analysis, and these DEGs were significantly enriched in functions associated with keratinization, and the pathway of neuroactive ligand-receptor interaction. Moreover, 72 hub genes were identified through weighed gene coexpression network analysis. After the hub genes and DEGs were combined, we obtained 422 union genes, including 65 survival-associated genes. After regression analysis, a HPV-related prognostic model was established, which consisted of 8 genes, including Clorf105, CGA, CHRNA2, CRIP3, CTAG2, ENPP6, NEFH, and RNF212. The obtained regression model could be expressed by an equation as follows: risk score = 0.065 × Clorf105 + 0.012 × CGA + 0.01 × CHRNA2 + 0.047 × CRIP3 + 0.043 × CTAG2-0.034 × ENPP6 - 0.003 × NEFH - 0.068 × RNF212. CGA interacted with 3 drugs, and CHRNA2 interacted with 11 drugs. We have identified an 8 HPV-RNA signature associated with the prognosis of SCCHN patients. Such prognostic model might serve as possible candidate biomarker and therapeutic target for SCCHN.

Identification of the cuproptosis-related hub genes and therapeutic agents for sarcopenia.

Background: Along with acceleration of population aging, the increasing prevalence of sarcopenia has posed a heavy burden on families as well as society. In this context, it is of great significance to diagnose and intervene sarcopenia as early as possible. Recent evidence has indicated the role of cuproptosis in the development of sarcopenia. In this study, we aimed to seek the key cuproptosis-related genes that can be used for identification and intervention of sarcopenia. Methods: The GSE111016 dataset was retrieved from GEO. The 31 cuproptosis-related genes (CRGs) were obtained from previous published studies. The differentially expressed genes (DEGs) and Weighed gene co-expression network analysis (WGCNA) were subsequently analyzed. The core hub genes were acquired by the intersection of DEGs, WGCNA and CRGs. Through logistic regression analysis, we established a diagnostic model of sarcopenia based on the selected biomarkers and was validated in muscle samples from GSE111006 and GSE167186. In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis were performed on these genes. Furthermore, the gene set enrichment analysis (GSEA), and immune cell infiltration were also conducted on the identified core genes. Finally, we screened the potential drugs targeting the potential biomarkers of sarcopenia. Results: A total of 902 DEGs and WGCNA containing 1,281 significant genes were preliminarily selected. Intersection of DEGs, WGCNA and CRGs yielded four core genes (PDHA1, DLAT, PDHB, and NDUFC1) as potential biomarkers for the prediction of sarcopenia. The predictive model was established and validated with high AUC values. KEGG pathway and Gene Ontology biological analysis indicated these core genes may play a crucial role in energy metabolism in mitochondria, oxidation process, and aging-related degenerative diseases. In addition, the immune cells may be involved in the development of sarcopenia through mitochondrial metabolism. Finally, metformin was identified as a promising strategy of sarcopenia treatment via targeting NDUFC1. Conclusion: The four cuproptosis-related genes PDHA1, DLAT, PDHB and NDUFC1 may be the diagnostic biomarkers for sarcopenia, and metformin holds great potential to be developed as a therapy for sarcopenia. These outcomes provide new insights for better understanding of sarcopenia and innovative therapeutic approaches.

Gene co-expression network analysis for the selection of candidate early warning indicators of heat and nutrient stress in Posidonia oceanica

The continuous worldwide seagrasses decline calls for immediate actions in order to preserve this precious marine ecosystem. The main stressors that have been linked with decline in seagrasses are 1) the increasing ocean temperature due to climate change and 2) the continuous inputs of nutrients (eutrophication) associated with coastal human activities. To avoid the loss of seagrass populations, an “early warning” system is needed. We used Weighed Gene Co-expression Network Analysis (WGCNA), a systems biology approach, to identify potential candidate genes that can provide an early warning signal of stress in the Mediterranean iconic seagrass Posidonia oceanica, anticipating plant mortality. Plants were collected from both eutrophic (EU) and oligotrophic (OL) environments and were exposed to thermal and nutrient stress in a dedicated mesocosm. By correlating the whole-genome gene expression after 2-weeks exposure with the shoot survival percentage after 5-weeks exposure to stressors, we were able to identify several transcripts that indicated an early activation of several biological processes (BP) including: protein metabolic process, RNA metabolic process, organonitrogen compound biosynthetic process, catabolic process and response to stimulus, which were shared among OL and EU plants and among leaf and shoot apical meristem (SAM), in response to excessive heat and nutrients. Our results suggest a more dynamic and specific response of the SAM compared to the leaf, especially the SAM from plants coming from a stressful environment appeared more dynamic than the SAM from a pristine environment. A vast list of potential molecular markers is also provided that can be used as targets to assess field samples.

Effects of SVEP1 on Lung Squamous Cell Carcinoma and its Association with Tumor Mutation Burden, Prognosis, and Immune Regulation.

The mutated genes in lung squamous cell carcinoma were investigated for their possible association with tumor mutation burden, microsatellite instability, and cancer prognosis. Our study aims to evaluate the value of the candidate genes as a potential biomarker of lung squamous cell carcinoma and pan-cancer analysis. The landscape of the tumor microenvironment and infiltrating lymphocytes in lung squamous cell carcinoma was calculated using ESTIMATE and CIBERSORT algorithm. Weighed gene co-expression network analysis was used to screen key modules related to immune cell infiltration. Somatic mutations were found by data analysis from the TCGA and ICGC databases. Mann-Whitney U test was used to evaluate the tumor mutation burden difference between patients with mutant and wild-type SVEP1 genes. The Kaplan-Meier method was used to examine the prognosis of the patients with mutations. The effects of SVEP1 expression on tumor mutation burden and immunity in different cancers were determined by pan-cancer analysis. SVEP1 mutation was found to be associated with a higher tumor mutation burden and prognosis. SVEP1 mutation might be involved in the possible biological process of the anti-tumor immune response. SVEP1 is related to different degrees of immune infiltration in cancer. Moreover, the miRNA-SVEP1 targeting network was used to illuminate the possible mechanisms. SVEP1 mutation and its mRNA expression are related to tumor mutation burden and cancer immunity in lung squamous cell carcinoma. Our findings reveal the underlying mechanisms, indicating that SVEP1 may be a prognostic marker of lung squamous cell carcinoma.

Sclerospora graminicola Suppresses Plant Defense Responses by Disrupting Chlorophyll Biosynthesis and Photosynthesis in Foxtail Millet.

Downy mildew of foxtail millet is an important oomycete disease caused by Sclerospora graminicola, affecting the yield and quality of the crop. Foxtail millet infected with S. graminicola exhibit symptoms of leaf yellowing and leaf cracking. To uncover the pathogenic mechanism of this disease, we explored the effects on chlorophyll synthesis and photosynthesis of foxtail millet leaves infected by S. graminicola. An elite foxtail millet variety, JG21, susceptible to S. graminicola, was used as for this study. S. graminicola inhibited chlorophyll synthesis and caused loose mesophyll cell arrangement. In addition, some cells were severely vacuolated in S. graminicola-infected foxtail millet leaves at the early stages of infection. S. graminicola could invade the mesophyll cells through haustoria which destroyed the chloroplast structure at the middle stages of infection causing significant accumulation of osmiophilic particles (OPs) and disintegrated chloroplast grana lamellae. Furthermore, foxtail millet leaves split longitudinally at the later stages of infection. Chlorophyll and carotenoid contents in infected leaves decreased significantly compared with those in the control. Net photosynthetic rate (Pn) of leaves and stomatal conductance showed a downward trend, and intercellular carbon dioxide concentrations increased significantly following the infection with S. graminicola. A total of 1,618 differentially expressed genes (DEGs) were detected between the control group and the treatment groups using RNA sequencing (RNA-Seq) among S1–S5 stages. DEGs associated with “photosynthesis” and “light reaction” were enriched. Gene expression patterns showed that 91.3% of 23 genes related to chlorophyll synthesis and photosynthesis, were significantly down-regulated than the control during S1–S5 stages. Based on the gene expression dataset, weighed gene co-expression network analysis (WGCNA) with 19 gene co-expression modules related to photosynthesis revealed six hub genes related to chlorophyll synthesis, which were suppressed during infection. The results suggest that infection of S. graminicola led to weak chlorophyll synthesis and rapid chloroplasts disappearance in foxtail millet. The defense responses and resistance of foxtail millet to S. graminicola were inhibited because chloroplast structure and function were destroyed in leaves, and the sexual reproduction in S. graminicola could be completed rapidly.

MIR100HG Regulates CALD1 Gene Expression by Targeting miR-142-5p to Affect the Progression of Bladder Cancer Cells in vitro, as Revealed by Transcriptome Sequencing.

Background/Aim: The role of long non-coding RNA (lncRNA) and competing endogenous RNAs (ceRNA) networks in bladder cancer, especially the function of lncRNA-miRNA-mRNA regulatory network in bladder cancer, are still relatively poorly understood. This research mainly used transcriptome sequencing to screen key lncRNAs and ceRNAs, explore their pathogenic mechanism in bladder cancer, and search for potential diagnostic and therapeutic targets. Methods: High-throughput transcriptome sequencing, combined with the limma package, Kaplan-Meier curve analysis, lncRNA-mRNA coexpression network, univariate Cox analysis, multivariate Cox analysis, protein-protein interaction (PPI), functional enrichment, weighed gene co-expression network analysis (WGCNA), ceRNA network and quantitative PCR (qPCR) analyses were performed to assess and screen differentially expressed lncRNAs and mRNAs. Then, the effects of MIR100HG on the proliferation, migration and invasion of the bladder cancer cell line 5,637 were evaluated using cell counting kit-8(CCK-8), wound-healing and transwell assays, respectively. A dual luciferase reporter assay was used to validate the MIR100HG/miR-142-5p and miR-142-5p/CALD1 targeting relationship, and the regulatory relationship among MIR100HG/miR-142-5p/CALD1 expression was explored using qPCR and western blot. Results: A total of 127 differentially expressed lncRNAs and 620 differentially expressed mRNAs were screened. Based on the survival prognosis analysis, Cox analysis, lncRNA-mRNA network, PPI network and WGCNA, we obtained 3 key lncRNAs and 13 key mRNAs, as well as the MIR100HG/miR-142-5p/CALD1 key regulatory axis. qPCR results showed that compared with the adjacent tissues, the expression of MIR100HG and CALD1 was up-regulated, and the expression of miR-142-5p was down-regulated. Moreover, MIR100HG expression was positively correlated with the tumor grade and clinical grade of patients with bladder cancer. Overexpression of MIR100HG effectively promoted the proliferation, migration and invasion of 5,637 cells, inhibited the expression of miR-142-5p, and induced the expression of CALD1 in 5,637 cells. In addition, miR-142-5p inhibited CALD1 expression in bladder cancer cells through a direct association, and reversed the proliferation and CALD1 expression in 5,637 cells overexpressing of MIR100HG. Conclusion: MIR100HG regulates CALD1 expression by targeting miR-142-5p to inhibit the proliferation, migration and invasion of bladder cancer cells. MIR100HG is an independent prognostic factor for bladder cancer, with potential as a biomarker for the diagnosis and treatment of bladder cancer.

Transcriptomic analysis of grapevine in response to ABA application reveals its diverse regulations during cold acclimation and deacclimation

Abscisic acid (ABA) plays crucial regulatory roles in cold acclimation and deacclimation of grapevine, making it a potential tool to be utilized in vineyards for the acquisition of preferred phenotypes in winter and spring. To understand the function of ABA, we conducted experiments during cold acclimation and deacclimation and evaluated the impact of exogenous ABA on the grapevine transcriptome. RNA-seq data were collected periodically hours or days after ABA treatment. Transcriptomic data were analyzed using principal component analysis (PCA), hierarchical clustering, unsupervised weighed gene co-expression network analysis (WGCNA), contrast-based differentially expressed genes (DEGs) identification and pre-ranked gene set enrichment analysis (GSEA). Our results suggest that ABA functions differently during cold acclimation and deacclimation by selectively regulating key pathways including auxin/indole acetic acid (IAA) metabolism, galactose metabolism and ribosome biogenesis. We also identified the activation of several apparent negative feedback systems that regulated ABA-induced transcriptomic changes, suggesting the existence of a balancing system in response to excessive ABA. This balancing systems potentially eliminates the long-term negative effect on grapevine growing from using ABA in the field. These findings advance our understanding about the regulation of grapevine physiology during dormancy and supports the potential of applying ABA as a cultural practice to mitigate cold injury in winter and spring.

CALD1 promotes the expression of PD-L1 in bladder cancer via the JAK/STAT signaling pathway.

BackgroundBladder cancer (BC) is a common malignant neoplasm with a high rate of recurrence and progression, despite optimal treatment. There is a pressing need to identify new effective biomarkers for the targeted treatment of BC.MethodsThe key gene CALD1 was screened via weighed gene co-expression network analysis (WGCNA) from encoding protein genes of BC. Clinical and prognostic significance was explored in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Cell Counting Kit-8 (CCK-8), flow cytometry, transwell chamber experiment and nude mouse xenograft assay were performed to test cell growth, apoptosis, migration, invasion and tumorigenesis capacities. Immune correlation was analyzed in The Tumor Immune Estimation Resource (TIMER) database. Relevant signaling pathways were explored using gene set enrichment analysis (GSEA).ResultsIncreased expression of CALD1 was significantly correlated with histological grade, clinical stage, T stage, and lymphatic metastasis. Kaplan-Meier survival curves showed that high CALD1 expression was associated with poor overall survival (OS) and disease-free survival (DFS) in TCGA database, and with poor OS in the four GEO databases. CALD1 promotes growth, migration, invasion, and cell cycle of tumor cell, and inhibits tumor cell apoptosis in vitro and in vivo. CADL1 expression was positively correlated with increased CD274 levels (r=0.357, P=9.71e−14). JAK/STAT signaling pathway was significantly enriched in the high CALD1 expression group. CALD1-mediated PD-L1 overexpression (OE) was via the activation of the JAK/STAT signaling pathway; this effect was blocked by the specific JAK inhibitor Ruxolitinib.ConclusionsCALD1 is a potential molecular marker associated with prognosis. It promotes the malignant progression of BC and upregulates the PD-L1 expression via the JAK/STAT signaling pathway.

Identification of Key Genes Involved in Pancreatic Ductal Adenocarcinoma with Diabetes Mellitus Based on Gene Expression Profiling Analysis.

This study aimed to identify key genes involved in the progression of diabetic pancreatic ductal adenocarcinoma (PDAC). Two gene expression datasets (GSE74629 and GSE15932) were obtained from Gene Expression Omnibus. Then, differentially expressed genes (DEGs) between diabetic PDAC and non-diabetic PDAC were identified, followed by a functional analysis. Subsequently, gene modules related to DM were extracted by weighed gene co-expression network analysis. The protein-protein interaction (PPI) network for genes in significant modules was constructed and functional analyses were also performed. After that, the optimal feature genes were screened by support vector machine (SVM) recursive feature elimination and SVM classification model was built. Finally, survival analysis was conducted to identify prognostic genes. The correlations between prognostic genes and other clinical factors were also analyzed. Totally, 1546 DEGs with consistent change tendencies were identified and functional analyses showed they were strongly correlated with metabolic pathways. Furthermore, there were two significant gene modules, in which RPS27A and UBA52 were key genes. Functional analysis of genes in two gene modules revealed that these genes primarily participated in oxidative phosphorylation pathway. Additionally, 21 feature genes were closely related with diabetic PDAC and the corresponding SVM classifier markedly distinguished diabetic PDAC from non-diabetic PDAC patients. Finally, decreased KIF22 and PYGL levels had good survival outcomes for PDAC. Four genes (RPS27A, UBA52, KIF22 and PYGL) might be involved in the pathogenesis of diabetic PDAC. Furthermore, KIF22 and PYGL acted as prognostic biomarkers for diabetic PDAC.

Identification of Potential Biomarkers Associated with Acute Myocardial Infarction by Weighted Gene Coexpression Network Analysis.

Background In the general population, acute myocardial infarction (AMI) represents a significant cause of mortality. This study is aimed at identifying novel diagnostic biomarkers to aid in treating and diagnosing AMI. Methods The Gene Expression Omnibus (GEO) database was explored to extract two microarray datasets, GSE66360 and GSE48060, which were subsequently merged into a single cohort. Both AMI and control samples were analyzed for differentially expressed genes (DEGs), which were subsequently subjected to weighed gene coexpression network analysis (WGCNA) to identify the most significant module. Gene Ontology (GO) and pathway analyses subsequently carried out the most significant gene modules along with construction of a protein-protein interaction network (PPI). Cytoscape plugin cytoHubba allowed for the prediction of the top 4 key genes according to the network maximal clique centrality (MCC) algorithm. The expression levels and diagnostic value of the four key genes were additionally verified in the GSE62646 dataset. Results A WCGNA analysis revealed 878 DEGs which were clustered into 6 modules. The module with the most significance in AMI was colored blue. Subsequent GO and KEGG pathway enrichment analysis on blue module genes revealed that they were primarily enriched in the inflammation-related pathways. These findings, in combination with PPI and coexpression networks, resulted in the identification of the top four genes by cytoHubba, which included leukocyte immunoglobulin-like receptor B2 (LILRB2), toll-like receptor 2 (TLR2), neutrophil cytosolic factor 2 (NCF2), and S100A9. Among them, LILRB2, NCF2, and S100A9 were validated in the GSE62646 dataset. Conclusions The results suggested that LILRB2, NCF2, and S100A9 could be potential gene biomarkers for AMI.

Upregulation of glutaminase 2 and neutrophil cytosolic factor 2 is associated with the poor prognosis of glioblastoma.

Background: This study aimed to identify glioblastoma prognosis-associated genes with potential diagnosis or prognosis values using integrated bioinformatics analysis. Results: In total, 1831 differentially expressed genes (DEGs) between the glioblastoma and control samples were identified and were clustered into seven weighed gene co-expression network analysis (WGCNA)modules. These DEGs were associated with different functional categories and pathways. Nine prognosis-associated DEGs (including glutaminase 2 [GLS2] and neutrophil cytosolic factor 2 [NCF2]) were identified, and the higher expression levels of GLS2 and NCF2 genes were associated with the poor prognosis of glioblastoma in 'The Cancer Genome Atlas' cohort and a clinical cohort. Conclusion: These results showed that the two genes play novel roles in the etiological and development of glioblastoma.

Novel genes associated with folic acid-mediated metabolism in mouse: A bioinformatics study.

Folic acid plays an essential role in the central nervous system and cancer. This study aimed to screen genes related to folic acid metabolism. Datasets (GSE80587, GSE65267 and GSE116299) correlated to folic acid were screened in the Gene Expression Omnibus. Weighed gene co-expression network analysis was performed to identify modules associated with sample traits of folic acid and organs (brain, prostate and kidney). Functional enrichment analysis was performed for the eigengenes in modules that were significantly correlated with sample traits. Accordingly, the hub genes and key nodes in the modules were identified using the protein interaction network. A total of 17,252 genes in three datasets were identified. One module, which included 97 genes that were highly correlated with sample traits (including folic acid treatment [cor = -0.57, P = 3e-04] and kidney [cor = -0.68, p = 4e-06]), was screened out. Hub genes, including tetratricopeptide repeat protein 38 (Ttc38) and miR-185, as well as those (including Sema3A, Insl3, Dll1, Msh4 and Snai1) associated with "neuropilin binding", "regulation of reproductive process" and "vitamin D metabolic process", were identified. Genes, including Ttc38, Sema3A, Insl3, Dll1, Msh4 and Snai1, were the novel factors that may be associated with the development of the kidneys and related to folic acid treatment.

Identifying the mRNAs associated with Bladder cancer recurrence.

To identify the mRNAs associated with bladder cancer (BC) recurrence. The transcription profile of GSE31684 including 39 recurrent BC tumor samples and 54 non-recurrent BC tumor samples as well as transcription profile of GSE13507 including 36 recurrent BC tumor samples and 67 non-recurrent BC tumor samples were downlaoded from the Gene Expression Omnibus. Then, the differentially expressed genes (DEGs) were identified using linear models for microarray data (limma) and the intersections of DEGs from the two datasets were further screened. The weighed gene co-expression network analysis (WGCNA) was used to screen the modules related to BC recurrence. Protein-protein interaction (PPI) network analysis was used to analyze the genes interaction. Their functions were predicted by Gene Ontology and KEGG pathway enrichment. Moreover, The Comparative Toxicogenomics Database 2017 update (CTD) was used to search the BC related pathway. The univariate cox regression analysis was used to identify DEGs associated to the recurrence. Kaplan-Meier plots were used to illustrate recurrence free survival time (RFS). A total of 692 intersections DEGs were screened. WGCNA showed that 7 modules (2279 genes) were stable in both the datasets. A total of 169 intersection DEGs were mapped to the 7 modules. There existed 149 interaction relationships among 81 proteins (18 down-regulated and 63 up-regulated DEGs) in the PPI network. Two KEGG pathways including Focal adhesion and ECM-receptor interaction were enriched which were also found in the CTD. The univariate cox regression analysis showed that 3 DEGs (COL4A1, COL1A2 and COL5A1) were significant related to the prognosis. Multivariate cox regression analysis revealed that pathologic_N (N0-N1 vs N2-N3, p= 0.033) were independent prognostic factors for overall survival in patients with BC. COL4A1, COL1A2 and COL5A1 could be associated with BC recurrence.

Weighed Gene Coexpression Network Analysis Screens the Potential Long Noncoding RNAs and Genes Associated with Progression of Coronary Artery Disease.

Background Coronary artery disease (CAD) is a type of heart disease with a high morbidity rate. This study is aimed at identifying potential biomarkers closely related to the progression of CAD. Materials and Methods A microarray dataset of GSE59867 was downloaded from a public database, Gene Expression Omnibus, which included 46 cases of stable CAD without a history of myocardial infarction (MI), 30 cases of MI without heart failure (HF), and 34 cases of MI with HF. Differentially expressed long noncoding RNAs (DElncRNAs) and mRNAs (DEmRNAs) were identified by the limma package, and functions of DEmRNAs were annotated by Gene Ontology and KEGG pathways. In addition, weighed gene coexpression network analysis (WGCNA) was used to construct a coexpression network of DEmRNAs, and a disease-related lncRNAs-mRNAs-pathway network was constructed. Finally, the datasets of GSE61145 and GSE57338 were used to verify the expression levels of the above highly correlated candidates. Results A total of 2362 upregulated mRNAs and 2816 downregulated mRNAs, as well as 235 upregulated lncRNAs and 113 downregulated lncRNAs were screened. These genes were significantly enriched in “cytokine-cytokine receptor interaction,” “RIG-I-like receptor signaling pathway,” and “natural killer cell-mediated cytotoxicity.” Five modules including 1201 DEmRNAs were enriched in WGCNA. A coexpression network including 19 DElncRNAs and 413 DEmRNAs was constructed. These genes were significantly enriched in “phosphatidylinositol signaling system,” “insulin signaling pathway,” and “MAPK signaling pathway”. Disease-related gene-pathway network suggested FASN in “insulin signaling pathway,” DGKZ in “phosphatidylinositol signaling system,” and TNFRSF1A in “MAPK signaling pathway” were involved in MI. Conclusion FASN, DGKZ, and TNFRSF1A were revealed to be CAD progression-associated genes by WGCNA coexpression network analysis.

Identification of key miRNAs in the progression of hepatocellular carcinoma using an integrated bioinformatics approach.

BackgroudIt has been shown that aberrant expression of microRNAs (miRNAs) and transcriptional factors (TFs) is tightly associated with the development of HCC. Therefore, in order to further understand the pathogenesis of HCC, it is necessary to systematically study the relationship between the expression of miRNAs, TF and genes. In this study, we aim to identify the potential transcriptomic markers of HCC through analyzing common microarray datasets, and further establish the differential co-expression network of miRNAs–TF–mRNA to screen for key miRNAs as candidate diagnostic markers for HCC.MethodWe first downloaded the mRNA and miRNA expression profiles of liver cancer from the GEO database. After pretreatment, we used a linear model to screen for differentially expressed genes (DEGs) and miRNAs. Further, we used weighed gene co-expression network analysis (WGCNA) to construct the differential gene co-expression network for these DEGs. Next, we identified mRNA modules significantly related to tumorigenesis in this network, and evaluated the relationship between mRNAs and TFs by TFBtools. Finally, the key miRNA was screened out in the mRNA–TF–miRNA ternary network constructed based on the target TF of differentially expressed miRNAs, and was further verified with external data set.ResultsA total of 465 DEGs and 215 differentially expressed miRNAs were identified through differential genes expression analysis, and WGCNA was used to establish a co-expression network of DEGs. One module that closely related to tumorigenesis was obtained, including 33 genes. Next, a ternary network was constructed by selecting 256 pairs of mRNA–TF pairs and 100 pairs of miRNA–TF pairs. Network mining revealed that there were significant interactions between 18 mRNAs and 25 miRNAs. Finally, we used another independent data set to verify that miRNA hsa-mir-106b and hsa-mir-195 are good classifiers of HCC and might play key roles in the progression of HCC.ConclusionOur data indicated that two miRNAs—hsa-mir-106b and hsa-mir-195—are identified as good classifiers of HCC.

Transcriptome analysis of tobacco root response to different concentrations of nitrate

AbstractWe aimed to explore genes and transcription factors (TFs) in tobacco (Nicotiana tabacum L.) that were closely related to responses to different concentrations of nitrate (NO3−) in tobacco root. Cultivar K326 was used in this study. The RNA sequence data generated from K326 treated with high or low concentration of NO3− supplied at different time points (0, 6, 12, and 24 h) were analyzed to determine the transcriptome response of tobacco root to NO3− supply. The expression of numerous genes was altered after high and low NO3− treatment. Further analysis revealed 1981 and 3573 differentially expressed genes (DEGs) from low and high NO3− treatment groups, respectively. Comparative analysis identified 567 low‐NO3−‐specific, 2159 high‐NO3−‐specific, and 1414 commonly expressed genes. Combining short time‐series expression miner (STEM) and weighed gene co‐expression network analysis (WGCNA) results, 136 and 416 DEGs were identified from low and high NO3− groups, respectively, among which, 12 were TFs. In the constructed protein−protein interaction (PPI) network, three DEGs in high NO3− group (LOC107807186, LOC107792457, and LOC107786314) exhibited highest degrees. Within low NO3− group, LOC107798206, LOC107815159, and LOC107779796 had highest degrees. Among the 12 TFs, LOC107778750, LOC107810552, and LOC107804260 had the highest degrees. Our data provides valuable information about genes, especially the 12 TFs, which were induced by different concentrations of NO3− in tobacco root. These findings may be useful for identification of high or low NO3− treatment‐induced genes and TFs, as well as for understanding potential gene regulation mechanisms in response to NO3− supply in tobacco.

Identification of Hub Genes and Potential Molecular Mechanisms in Patients with HBV-Associated Acute Liver Failure.

Hepatitis B virus (HBV) infection is a major cause of acute liver failure (ALF) in China, and mortality rates are high among patients who do not receive a matched liver transplant. This study aimed to determine potential mechanisms involved in HBV-ALF pathogenesis. Gene expression profiles under access numbers GSE38941 and GSE14668 were downloaded from the Gene Expression Omnibus database, including cohorts of HBV-ALF liver tissue and normal samples. Differentially expressed genes (DEGs) with false discovery rates (FDR) <0.05 and |log2(fold change)| >1 as thresholds were screened using the Limma package. Gene modules associated with stable disease were mined using weighed gene co-expression network analysis (WGCNA). A co-expression network was constructed and DEGs were analyzed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. A gene-based network was constructed to explore major factors associated with disease progression. We identified 2238 overlapping DEGs as crucial gene cohorts in ALF development. Based on a WGCNA algorithm, 10 modules (modules 1-10) were obtained that ranged from 75 to 1078 genes per module. Cyclin-dependent kinase 1 (CDK1), cyclin B1 (CCNB1), and cell-division cycle protein 20 (CDC20) hub genes were screened using the co-expression network. Furthermore, 17 GO terms and 6 KEGG pathways were identified, such as cell division, immune response process, and antigen processing and presentation. Two overlapping signaling pathways that are crucial factors in HBV-ALF were screened using the Comprehensive Toxicogenomics Database (CTD). Several candidate genes including HLA-E, B2M, HLA-DPA1, and SYK were associated with HBV-ALF progression. Natural killer cell-mediated cytotoxicity and antigen presentation contributed to the progression of HBV-ALF. The HLA-E, B2M, HLA-DPA1, and SYK genes play critical roles in the pathogenesis and development of HBV-ALF.

Identification of candidate genes and miRNAs associated with neuropathic pain induced by spared nerve injury.

Neuropathic pain (NP) is a complex, chronic pain condition caused by injury or dysfunction affecting the somatosensory nervous system. This study aimed to identify crucial genes and miRNAs involved in NP. Microarray data (access number GSE91396) were downloaded from the Gene Expression Omnibus (GEO). Murine RNA-seq samples from three brain regions [nucleus accumbens, (NAc); medial prefrontal cortex, (mPFC) and periaqueductal gray, (PAG)] were compared between the spared nerve injury (SNI) model and a sham surgery. After data normalization, differentially expressed RNAs were screened using the limma package and functional enrichment analysis was performed with Database for Annotation, Visualization and Integrated Discovery. The microRNA (miRNA/miR)-mRNA regulatory network and miRNA-target gene-pathway regulatory network were constructed using Cytoscape software. A total of 2,776 differentially expressed RNAs (219 miRNAs and 2,557 mRNAs) were identified in the SNI model compared with the sham surgery group. A total of two important modules (red and turquoise module) were found to be related to NP using weighed gene co-expression network analysis (WGCNA) for the 2,325 common differentially expressed RNAs in three brain regions. The differentially expressed genes (DEGs) in the miRNA-mRNA regulatory network were significantly enriched in 21 Gene Ontology terms and five pathways. A total of four important DEGs (CXCR2, IL12B, TNFSF8 and GRK1) and five miRNAs (miR-208a-5p, miR-7688-3p, miR-344f-3p, miR-135b-3p and miR-135a-2-3p) were revealed according to the miRNA-target gene-pathway regulatory network to be related to NP. Four important DEGs (CXCR2, IL12B, TNFSF8 and GRK1) and five miRNAs (miR-208a-5p, miR-7688-3p, miR-344f-3p, miR-135b-3p and miR-135a-2-3p) were differentially expressed in SNI, indicating their plausible roles in NP pathogenesis.