Systematic Transcriptome and Regulatory Network Analyses Reveal the Hypoglycemic Mechanism of Dendrobium fimbriatum

The AR signaling pathway plays an important role in initiation and progression of many hormone-related cancers including prostate, bladder, kidney, lung, and breast cancer. However, the potential roles of androgen-responsive long noncoding RNAs (lncRNAs) in hormone-related cancers remained unclear. In the present study, we identified 469 novel androgen-responsive lncRNAs using microarray data. After validating the accuracy of the array data, we constructed a transcriptional network which contained more than 30 transcriptional factors using ChIP-seq data to explore upstream regulators of androgen-responsive lncRNAs. Next, we conducted bioinformatics analysis to identify lncRNA-miRNA-mRNA regulatory network. To explore the potential roles of androgen-responsive lncRNAs in hormone-related cancers, we performed coexpression network and PPI network analyses using TCGA data. GO and KEGG analyses showed these lncRNAs were mainly involved in regulating signal transduction, transcription, development, cell adhesion, immune response, cell differentiation, and MAPK signaling pathway. We also highlight the prognostic value of HPN-AS1, TPTEP1, and LINC00623 in cancer outcomes. Our results suggest that androgen-responsive lncRNAs played important roles in regulating hormone-related cancer progression and could be novel molecular biomarkers.

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Biomarker identification and trans-regulatory network analyses in esophageal adenocarcinoma and Barrett’s esophagus

World Journal of Gastroenterology ◽

10.3748/wjg.v25.i2.233 ◽

2019 ◽

Vol 25 (2) ◽

pp. 233-244 ◽

Cited By ~ 10

Author(s):

Jing Lv ◽

Lei Guo ◽

Ji-Han Wang ◽

Yu-Zhu Yan ◽

Jun Zhang ◽

...

Keyword(s):

Barrett’S Esophagus ◽

Esophageal Adenocarcinoma ◽

Regulatory Network ◽

Barrett's Esophagus ◽

Biomarker Identification ◽

Network Analyses

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Comparative proteomic and regulatory network analyses of the elongating pig conceptus

PROTEOMICS ◽

10.1002/pmic.200800776 ◽

2009 ◽

Vol 9 (10) ◽

pp. 2678-2694 ◽

Cited By ~ 17

Author(s):

Séverine A. Degrelle ◽

Le Ann Blomberg ◽

Wesley M. Garrett ◽

Robert W. Li ◽

Neil C. Talbot

Keyword(s):

Regulatory Network ◽

Network Analyses

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FlexFlux: combining metabolic flux and regulatory network analyses

BMC Systems Biology ◽

10.1186/s12918-015-0238-z ◽

2015 ◽

Vol 9 (1) ◽

Cited By ~ 25

Author(s):

Lucas Marmiesse ◽

Rémi Peyraud ◽

Ludovic Cottret

Keyword(s):

Regulatory Network ◽

Metabolic Flux ◽

Network Analyses

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Identification of Key Transcription Factors Related to Bacterial Spot Resistance in Pepper through Regulatory Network Analyses

Genes ◽

10.3390/genes12091351 ◽

2021 ◽

Vol 12 (9) ◽

pp. 1351

Author(s):

Qingquan Zhu ◽

Shenghua Gao ◽

Wenli Zhang

Keyword(s):

Transcription Factor ◽

Disease Resistance ◽

Resistance Genes ◽

Defense Response ◽

Regulatory Network ◽

Xanthomonas Campestris ◽

Economic Benefits ◽

Multiple Time ◽

Bacterial Spot ◽

Network Analyses

Bacterial spot (BS), caused by Xanthomonas campestris pv. Vesicatoria (Xcv), severely affects the quality and yield of pepper. Thus, breeding new pepper cultivars with enhanced resistance to BS can improve economic benefits for pepper production. Identification of BS resistance genes is an essential step to achieve this goal. However, very few BS resistance genes have been well characterized in pepper so far. In this study, we reanalyzed public multiple time points related to RNA-seq data sets from two pepper cultivars, the Xcv-susceptible cultivar ECW and the Xcv-resistant cultivar VI037601, post Xcv infection. We identified a total of 3568 differentially expressed genes (DEGs) between two cultivars post Xcv infection, which were mainly involved in some biological processes, such as Gene Ontology (GO) terms related to defense response to bacterium, immune system process, and regulation of defense response, etc. Through weighted gene co-expression network analysis (WGCNA), we identified 15 hub (Hub) transcription factor (TF) candidates in response to Xcv infection. We further selected 20 TFs from the gene regulatory network (GRN) potentially involved in Xcv resistance response. Finally, we predicted 4 TFs, C3H (p-coumarate 3-hydroxylase), ERF (ethylene-responsive element binding factor), TALE (three-amino-acid-loop-extension), and HSF (heat shock transcription factor), as key factors responsible for BS disease resistance in pepper. In conclusion, our study provides valuable resources for dissecting the underlying molecular mechanism responsible for Xcv resistance in pepper. Additionally, it also provides valuable references for mining transcriptomic data to identify key candidates for disease resistance in horticulture crops.

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Identification of lncRNA NR_028138.1 as a biomarker and construction of a ceRNA network for bipolar disorder

Scientific Reports ◽

10.1038/s41598-021-94122-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ling He ◽

Pengtao Zou ◽

Wanlei Sun ◽

Yonghui Fu ◽

Wenfeng He ◽

...

Keyword(s):

Bipolar Disorder ◽

Roc Curve ◽

Regulatory Network ◽

Noncoding Rnas ◽

Differentially Expressed ◽

Diagnostic Model ◽

Network Analyses ◽

Control Subjects ◽

Cerna Network ◽

Potential Biomarker

AbstractThe pathogenesis of bipolar disorder (BD), a chronic mood disorder, is largely unknown. Noncoding RNAs play important roles in the pathogenesis of BD. However, little is known about the correlations of long noncoding RNAs (lncRNAs) with BD. Illumina high-throughput sequencing in BD patients and normal controls was used to identify differentially expressed (DE) genes. Two-step real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to validate DE-RNAs in the first cohort (50 BD and 50 control subjects). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and lncRNA-mRNA coexpression and lncRNA-microRNA (miRNA)-messenger RNA (mRNA) competing endogenous RNA (ceRNA) network analyses were used to predict the functions of DE-RNAs. Receiver operating characteristic (ROC) curve analysis and logistic regression were applied to evaluate diagnostic performance in an additional testing group (80 BD and 66 control subjects). A total of 576 significantly DE-lncRNAs and 262 DE-mRNAs were identified in BD patients, and 95 lncRNA-miRNA-mRNA interactions were used to construct a ceRNA regulatory network. Analysis of the first cohort showed that six RNAs (NR_028138.1, TCONS_00018621, TCONS_00002186, TNF, PID1, and SDK1) were differentially expressed in the BD group (P < 0.01). NR_028138.1 was used to establish a BD diagnostic model (area under the ROC curve 0.923, P < 0.004, 95% CI: 0.830–0.999). Verification in the second cohort revealed uniformly significant differences in NR_028138.1 (P < 0.0001). This study constructed a ceRNA regulatory network and provided a hypothesis for the pathogenesis of BD. NR_028138.1 was identified as a central element involved in the transcriptional regulation in BD and a potential biomarker.

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Regulatory Network Analyses Reveal Genome-Wide Potentiation of LIF Signaling by Glucocorticoids and Define an Innate Cell Defense Response

PLoS Genetics ◽

10.1371/journal.pgen.1000224 ◽

2008 ◽

Vol 4 (10) ◽

pp. e1000224 ◽

Cited By ~ 31

Author(s):

David Langlais ◽

Catherine Couture ◽

Aurélio Balsalobre ◽

Jacques Drouin

Keyword(s):

Defense Response ◽

Regulatory Network ◽

Cell Defense ◽

Network Analyses ◽

Genome Wide

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Bioinformatics analyses of significant genes, related pathways and candidate prognostic biomarkers in Alzheimer disease

10.1101/2021.05.06.442918 ◽

2021 ◽

Author(s):

Basavaraj Mallikarjunayya Vastrad ◽

Chanabasayya Mallikarjunayya Vastrad

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

High Throughput Sequencing ◽

Functional Enrichment ◽

Neuronal System ◽

Rt Pcr ◽

Hub Genes ◽

Hub Gene ◽

Network Analyses ◽

Gene Regulatory

Alzheimers disease (AD) is one of the most common causes of dementia and frailty. This study aimed to use bioinformatics analysis to identify differentially expressed genes (DEGs) in AD. The Expression profiling by high throughput sequencing dataset GSE125583 was downloaded from the Gene Expression Omnibus (GEO) database and DEGs were identified. After assessment of Gene Ontology (GO) terms and pathway enrichment for DEGs, a protein protein interaction (PPI) network, module analysis, miRNA hub gene regulatory network construction and TF hub gene regulatory network were conducted via comprehensive target prediction and network analyses. Finally, we validated hub genes by receiver operating characteristic curve (ROC) and RT-PCR. In total, 956 DEGs were identified in the AD samples, including 479 up regulated genes and 477 down regulated genes. Functional enrichment analysis showed that these DEGs are mainly involved in the neuronal system, GPCR ligand binding, regulation of biological quality and cell communication. The hub genes of PAK1, ELAVL2, NSF, HTR2C, TERT, UBD, MKI67, HSPB1, PYHIN1 and TES might be associated with AD. The diagnostic value and expression levels of these hub genes in AD were further confirmed by ROC analysis and RT-PCR. In conclusion, we identified pathways and crucial candidate genes that affect the outcomes of patients with AD, and these genes might serve as potential therapeutic targets.

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