Identification of Novel Diagnostic Biomarkers in Prostate Adenocarcinoma Based on the Stromal-Immune Score and Analysis of the WGCNA and ceRNA Network

Prostate cancer is still a significant global health burden in the coming decade. Novel biomarkers for detection and prognosis are needed to improve the survival of distant and advanced stage prostate cancer patients. The tumor microenvironment is an important driving factor for tumor biological functions. To investigate RNA prognostic biomarkers for prostate cancer in the tumor microenvironment, we obtained relevant data from The Cancer Genome Atlas (TCGA) database. We used the bioinformatics tools Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm and weighted coexpression network analysis (WGCNA) to construct tumor microenvironment stromal-immune score-based competitive endogenous RNA (ceRNA) networks. Then, the Cox regression model was performed to screen RNAs associated with prostate cancer survival. The differentially expressed gene profile in tumor stroma was significantly enriched in microenvironment functions, like immune response, cancer-related pathways, and cell adhesion-related pathways. Based on these differentially expressed genes, we constructed three ceRNA networks with 152 RNAs associated with the prostate cancer tumor microenvironment. Cox regression analysis screened 31 RNAs as the potential prognostic biomarkers for prostate cancer. The most interesting 8 prognostic biomarkers for prostate cancer included lncRNA LINC01082, miRNA hsa-miR-133a-3p, and genes TTLL12, PTGDS, GAS6, CYP27A1, PKP3, and ZG16B. In this systematic study for ceRNA networks in the tumor environment, we screened out potential biomarkers to predict prognosis for prostate cancer. Our findings might apply a valuable tool to improve prostate cancer clinical management and the new target for mechanism study and therapy.

Relationship Between the Pyroptosis Pathway and Epilepsy: A Bioinformatic Analysis

PurposeThis study aimed to analyse the correlation between the pyroptosis pathway and epilepsy using bioinformatics analysis technology. We analyzed the expression of gasdermin D (GSDMD) and gasdermin E (GSDME), the key molecules of pyroptosis, in kainic acid-induced epileptic mice.MethodsWeighted gene co-expression network analysis (WGCNA) was used to construct a signed co-expression network from expression data to screen gene sets closely related to epilepsy. The correlation between the module and epilepsy was verified through module conservative analysis, gene ontology (GO) annotation analysis, and correlation analysis with known epilepsy genes. We obtained currently recognized pyroptosis-related molecules through literature review, and correlation analysis was used to evaluate their correlation with epilepsy. Differentially expressed gene (DEG) analysis was used to analyse expression changes of pyroptosis-related molecules at the transcriptome level, compared to the sham group. We subsequently established a kainic acid-induced status epilepticus (SE) model in mice and validated the mRNA and protein expression of GSDMD and GSDME, the key molecules of pyroptosis, by quantitative reverse transcription PCR (qRT-PCR) and western blotting (WB).ResultsUsing WGCNA, module conservative analysis, and correlation analysis with known epilepsy genes, we screened out a module (a gene set of interest) closely related to epilepsy that was prominently enriched in immune and inflammatory-related biological processes. Correlation analysis results suggest that pyroptosis-related molecules are closely related to this module, but have no obvious correlation with others. DEG analysis of molecules associated with pyroptosis suggests that most of the pyroptosis-related molecules had significantly increased expression after SE, such as IL1b, Casp1, Casp4, Pycard, Gsdmd, Nlrp3, Aim2, Mefv, Tlr2, Tlr3, and Tlr4. qRT-PCR and WB analysis confirmed that the mRNA and protein levels of GSDMD in the mouse hippocampus were significantly upregulated after SE. The mRNA expression of GSDME was not different between the epilepsy group and sham group. However, the WB results showed that the expression of full-length GSDME was decreased and GSDME-N-terminus were significantly increased after SE.ConclusionsOur study highlights that the pyroptosis pathway may be closely related to epilepsy. GSDMD and GSDME, the key executive molecules of pyroptosis, will help to understand the pathogenesis of epilepsy and aid in discovering new targets for anti-epileptic drug treatments.

Identification of Novel Prognostic Markers Associated With Laryngeal Squamous Cell Carcinoma Using Comprehensive Analysis

BackgroundLaryngeal squamous cell carcinoma (LSCC) is a leading malignant cancer of the head and neck. Patients with LSCC, in which the cancer has infiltrated and metastasized, have a poor prognosis. Therefore, there is an urgent need to identify more potential targets for drugs and biomarkers for early diagnosis.MethodsRNA sequence data from LSCC and patients’ clinical traits were obtained from the Gene Expression Omnibus (GEO) (GSE142083) and The Cancer Genome Atlas (TCGA) database. Differentially expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify hub genes. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, prognostic value analysis, receiver operating characteristic (ROC) curve analysis, gene mutation analysis, tumor-infiltrating immune cell abundance profile estimation, gene set variation analysis (GSVA), and gene set enrichment analysis (GSEA) were performed. Single-gene RNA sequencing data were obtained from the GSE150321 dataset. Cell proliferation and viability were confirmed by the CCK-8 assay and real-time PCR.ResultsA total of 701 DEGs, including 329 upregulated and 372 downregulated genes, were screened in the GSE142083 dataset. Using WGCNA, three modules were identified to be closely related to LSCC. After intersecting the DEGs and performing univariate and multivariate Cox analyses, a novel prognostic model based on three genes (SLC35C1, HOXB7, and TEDC2) for LSCC was established. Interfering TEDC2 expression inhibited tumor cell proliferation and migration.ConclusionsOur results show that SLC35C1, HOXB7, and TEDC2 have the potential to become new therapeutic targets and prognostic biomarkers for LSCC.

Regulatory Mechanisms of the Resistance to Common Bacterial Blight Revealed by Transcriptomic Analysis in Common Bean (Phaseolus vulgaris L.)

Common bean blight (CBB), primarily caused by Xanthomonas axonopodis pv. phaseoli (Xap), is one of the most destructive diseases of common bean (Phaseolus vulgaris L.). The tepary bean genotype PI 319443 displays high resistance to Xap, and the common bean genotypes HR45 and Bilu display high resistance and susceptibility to Xap, respectively. To identify candidate genes related to Xap resistance, transcriptomic analysis was performed to compare gene expression levels with Xap inoculation at 0, 24, and 48 h post inoculation (hpi) among the three genotypes. A total of 1,146,009,876 high-quality clean reads were obtained. Differentially expressed gene (DEG) analysis showed that 1,688 DEGs responded to pathogen infection in the three genotypes. Weighted gene coexpression network analysis (WGCNA) was also performed to identify three modules highly correlated with Xap resistance, in which 334 DEGs were likely involved in Xap resistance. By combining differential expression analysis and WGCNA, 139 DEGs were identified as core resistance-responsive genes, including 18 genes encoding resistance (R) proteins, 19 genes belonging to transcription factor families, 63 genes encoding proteins with oxidoreductase activity, and 33 plant hormone signal transduction-related genes, which play important roles in the resistance to pathogen infection. The expression patterns of 20 DEGs were determined by quantitative real-time PCR (qRT-PCR) and confirmed the reliability of the RNA-seq results.

Loss of SDHB Induces a Metabolic Switch in the hPheo1 Cell Line toward Enhanced OXPHOS

Background: Enzymes of tricarboxylic acid (TCA) have recently been recognized as tumor suppressors. Mutations in the SDHB subunit of succinate dehydrogenase (SDH) cause pheochromocytomas and paragangliomas (PCCs/PGLs) and predispose patients to malignant disease with poor prognosis. Methods: Using the human pheochromocytoma cell line (hPheo1), we knocked down SDHB gene expression using CRISPR-cas9 technology. Results: Microarray gene expression analysis showed that >500 differentially expressed gene targets, about 54%, were upregulated in response to SDHB knock down. Notably, genes involved in glycolysis, hypoxia, cell proliferation, and cell differentiation were up regulated, whereas genes involved in oxidative phosphorylation (OXPHOS) were downregulated. In vitro studies show that hPheo1 proliferation is not affected negatively and the cells that survive by shifting their metabolism to the use of glutamine as an alternative energy source and promote OXPHOS activity. Knock down of SDHB expression results in a significant increase in GLUD1 expression in hPheo1 cells cultured as monolayer or as 3D culture. Analysis of TCGA data confirms the enhancement of GLUD1 in SDHB mutated/low expressed PCCs/PGLs. Conclusions: Our data suggest that the downregulation of SDHB in PCCs/PGLs results in increased GLUD1 expression and may represent a potential biomarker and therapeutic target in SDHB mutated tumors and SDHB loss of activity-dependent diseases.

Establishment of a Risk Score Model for Early Prediction of Severe H1N1 Influenza

H1N1 is the most common subtype of influenza virus circulating worldwide and can cause severe disease in some populations. Early prediction and intervention for patients who develop severe influenza will greatly reduce their mortality. In this study, we conducted a comprehensive analysis of 180 PBMC samples from three published datasets from the GEO DataSets. Differentially expressed gene (DEG) analysis and weighted correlation network analysis (WGCNA) were performed to provide candidate DEGs for model building. Functional enrichment and CIBERSORT analyses were also performed to evaluate the differences in composition and function of PBMCs between patients with severe and mild disease. Finally, a risk score model was built using lasso regression analysis, with six genes (CX3CR1, KLRD1, MMP8, PRTN3, RETN and SCD) involved. The model performed moderately in the early identification of patients that develop severe H1N1 disease.

Identification of Latent Diagnostic Biomarkers and Biological Pathways in Dermatomyositis Based on WGCNA

Introduction. Dermatomyositis (DM) is a chronic autoimmune disease of predominantly lymphocytic infiltration mainly involving the transverse muscle. Its pathogenesis is remaining unknown. This research is designed to probe the latent pathogenesis of dermatomyositis, identify potential biomarkers, and reveal the pathogenesis of dermatomyositis through information biology analysis of gene chips. Methods. In this study, we utilised the GSE14287 and GSE11971 datasets rooted in the Gene Expression Omnibus (GEO) databank, which included a total of 62 DM samples and 9 normal samples. The datasets were combined, and the differentially expressed gene sets were subjected to weighted gene coexpression network analysis, and the hub gene was screened using a protein interaction network from genes in modules highly correlated with dermatomyositis progression. Results. A total of 3 key genes—myxovirus resistance-2 (MX2), oligoadenylate synthetase 1 (OAS1), and oligoadenylate synthetase 2 (OAS2)—were identified in combination with cell line samples, and the expressions of the 3 genes were verified separately. The results showed that MX2, OAS1, and OAS2 were highly expressed in LPS-treated cell lines compared to normal cell lines. The results of pathway enrichment analysis of the genes indicated that all 3 genes were enriched in the cytosolic DNA signalling and cytokine and cytokine receptor interaction signalling pathways; the results of functional enrichment analysis showed that all 3 were enriched in interferon-α response and interferon-γ response functions. Conclusions. This is important for the study of the pathogenesis and objective treatment of dermatomyositis and provides important reference information for the targeted therapy of dermatomyositis.

TimesVector-Web: A Web Service for Analysing Time Course Transcriptome Data with Multiple Conditions

From time course gene expression data, we may identify genes that modulate in a certain pattern across time. Such patterns are advantageous to investigate the transcriptomic response to a certain condition. Especially, it is of interest to compare two or more conditions to detect gene expression patterns that significantly differ between them. Time course analysis can become difficult using traditional differentially expressed gene (DEG) analysis methods since they are based on pair-wise sample comparison instead of a series of time points. Most importantly, the related tools are mostly available as local Software, requiring technical expertise. Here, we present TimesVector-web, which is an easy to use web service for analysing time course gene expression data with multiple conditions. The web-service was developed to (1) alleviate the burden for analyzing multi-class time course data and (2) provide downstream analysis on the results for biological interpretation including TF, miRNA target, gene ontology and pathway analysis. TimesVector-web was validated using three case studies that use both microarray and RNA-seq time course data and showed that the results captured important biological findings from the original studies.

Integrated Metabolomics and Transcriptomic Analysis of Hepatopancreas in Different Living Status Macrobrachium nipponense in Response to Hypoxia

As the basic element of aerobic animal life, oxygen participates in most physiological activities of animals. Hypoxia stress is often the subject of aquatic animal research. Macrobrachium nipponense, an economically important aquatic animal in southern China, has been affected by hypoxia for many years and this has resulted in a large amount of economic loss due to its sensitivity to hypoxia; Metabolism and transcriptome data were combined in the analysis of the hepatopancreas of M. nipponense in different physiological states under hypoxia; A total of 108, 86, and 48 differentially expressed metabolites (DEMs) were found in three different comparisons (survived, moribund, and dead shrimps), respectively. Thirty-two common DEMs were found by comparing the different physiological states of M. nipponense with the control group in response to hypoxia. Twelve hypoxia-related genes were identified by screening and analyzing common DEMs. GTP phosphoenolpyruvate carboxykinase (PEPCK) was the only differentially expressed gene that ranked highly in transcriptome analysis combined with metabolome analysis. PEPCK ranked highly both in transcriptome analysis and in combination with metabolism analysis; therefore, it was considered to have an important role in hypoxic response. This manuscript fills the one-sidedness of the gap in hypoxia transcriptome analysis and reversely deduces several new genes related to hypoxia from metabolites. This study contributes to the clarification of the molecular process associated with M. nipponense under hypoxic stress.

HIF-Dependent CKB Expression Promotes Breast Cancer Metastasis, Whereas Cyclocreatine Therapy Impairs Cellular Invasion and Improves Chemotherapy Efficacy

The oxygen-responsive hypoxia inducible factor (HIF)-1 promotes several steps of the metastatic cascade. A hypoxic gene signature is enriched in triple-negative breast cancers (TNBCs) and is correlated with poor patient survival. Inhibiting the HIF transcription factors with small molecules is challenging; therefore, we sought to identify genes downstream of HIF-1 that could be targeted to block invasion and metastasis. Creatine kinase brain isoform (CKB) was identified as a highly differentially expressed gene in a screen of HIF-1 wild type and knockout mammary tumor cells derived from a transgenic model of metastatic breast cancer. CKB is a cytosolic enzyme that reversibly catalyzes the phosphorylation of creatine, generating phosphocreatine (PCr) in the forward reaction, and regenerating ATP in the reverse reaction. Creatine kinase activity is inhibited by the creatine analog cyclocreatine (cCr). Loss- and gain-of-function genetic approaches were used in combination with cCr therapy to define the contribution of CKB expression or creatine kinase activity to cell proliferation, migration, invasion, and metastasis in ER-negative breast cancers. CKB was necessary for cell invasion in vitro and strongly promoted tumor growth and lung metastasis in vivo. Similarly, cyclocreatine therapy repressed cell migration, cell invasion, the formation of invadopodia and lung metastasis. Moreover, in common TNBC cell line models, the addition of cCr to conventional cytotoxic chemotherapy agents was either additive or synergistic to repress tumor cell growth.