scholarly journals Comprehensive Analysis of lncRNA–miRNA– mRNA Network Ascertains Prognostic Factors in Patients with Colon Cancer

2019 ◽  
Vol 18 ◽  
pp. 153303381985323 ◽  
Author(s):  
Zhenzhen Gao ◽  
Peng Fu ◽  
Zhengyi Yu ◽  
Fuxi Zhen ◽  
Yanhong Gu
Keyword(s):  
Colon Cancer ◽  
Gene Ontology ◽  
Differentially Expressed ◽  
Messenger Rnas ◽  
Competing Endogenous Rna ◽  
Normal Colon ◽  
Kaplan Meier ◽  
Non Coding Rna ◽  
Competing Endogenous Rnas ◽  
Non Coding Rnas

Background: Non-coding RNAs are competing endogenous RNAs in the occurrence and development of tumorigenesis; numerous microRNAs are aberrantly expressed in colon cancer tissues and play significant roles in oncogenesis development and metastasis. However, large clinical and RNA data are lacking to further confirm the exact role of these RNAs in tumors. This study aimed to ascertain differential RNA expression between colon cancer and normal colon tissues. Materials and Methods: RNA sequencing and clinical data of patients with colon cancer were procured from The Cancer Genome Atlas database; differentially expressed long non-coding RNA, differentially expressed messenger RNAs, and differentially expressed microRNAs were achieved using the limma package in edgeR to generate competing endogenous RNAs networks. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were conducted with ggplot2 package, the Kaplan-Meier survival method was used to predict survival in patients with colon cancer. Results: In total, 1174 differentially expressed long non-coding RNAs, 2068 differentially expressed messenger RNAs, and 239 differentially expressed microRNAs were generated between 480 colon cancer and 41 normal colon tissue samples. Three competing endogenous RNA networks were established. Gene Ontology analysis indicated that the genes of the up-regulated microRNA network were involved in negative regulation of transcription, DNA-template, and those of down-regulated microRNA network were involved in transforming growth factor β receptor signaling pathways, response to hypoxia, cell migration, while Kyoto Encyclopedia of Genes and Genomes analyses of these networks turned out to be negative. Three long non-coding RNAs (AP004609.1, ARHGEF26-AS1, and LINC00491), 3 microRNAs (miRNA-141, miRNA-216a, and miRNA-193b) and 3 RNAs (ULBP2, PHLPP2, and TPM2) were detected to be associated with prognosis by the Kaplan-Meier survival analysis. Additionally, univariate and multivariate Cox regression analyses showed that the microRNA-216a of the competing endogenous RNA might be an independent prognostic factor in colon cancer. Conclusions: This study constructed the non-coding RNA-related competing endogenous RNA networks in colon cancer and sheds lights on underlying biomarkers for colon cancer cohorts.

scholarly journals Identification of Non-coding RNA Biomarkers in Stress-induced Depression via Comprehensive Analysis of Competing Endogenous RNA Network

2020 ◽  
Author(s):  
xuanjun liu ◽  
Lan Yan ◽  
Chun Lin ◽  
Yiliang Zhang ◽  
Haofei Miao ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Comprehensive Analysis ◽  
Differentially Expressed ◽  
Psychiatric Disease ◽  
Circular Rnas ◽  
Messenger Rnas ◽  
Competing Endogenous Rna ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
Promising Perspective

Abstract BackgroundDepression is one of the most common psychiatric disease worldwide. Although the research about the pathogenesis of depression have achieved progress, the detailed effect of non-coding RNAs (ncRNAs) in depression are still not clearly elucidated. This study was aimed to identify non-coding RNA biomarkers in stress-induced depression via comprehensive analysis of competing endogenous RNA networkMethodsIn this present study, we acquired RNA expression from RNA seq expression profile in three mice with depressive-like behaviors using chronic restraint stress paradigm and three C57BL/6J wild-type mice as control mice. ResultsA total of 41 differentially expressed circular RNAs (circRNAs) and 181 differentially expressed messenger RNAs (mRNAs) were up-regulated, and 65 differentially expressed circRNAs and 289 differentially expressed mRNAs were down-regulated, which were selected by a threshold of fold change ≥2 and a p-value < 0.05. Gene Ontology was performed to analyze the biological functions, and we predicted potential signaling pathways based on Kyoto Encyclopedia of Genes and Genomes pathway database. In addition, we constructed a circRNA-microRNA (miRNA)-mRNA regulatory network to further identify non-coding RNAs biomarkers. ConclusionsOur findings provide a promising perspective for further research into molecular mechanisms of depression, and targeting circRNA -mediated competing endogenous RNA (ceRNA) network is a useful strategy to early recognize the depression.

scholarly journals Construction of asthma related competing endogenous RNA network revealed novel long non-coding RNAs and potential new drugs

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Yifang Liao ◽  
Ping Li ◽  
Yanxia Wang ◽  
Hong Chen ◽  
Shangwei Ning ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Repositioning ◽  
New Drugs ◽  
Differentially Expressed ◽  
Interaction Data ◽  
Competing Endogenous Rna ◽  
Non Coding Rna ◽  
Competing Endogenous Rna Network ◽  
Non Coding Rnas ◽  
Long Non Coding Rna

Abstract Background Asthma is a heterogeneous disease characterized by chronic airway inflammation. Long non-coding RNA can act as competing endogenous RNA to mRNA, and play significant role in many diseases. However, there is little known about the profiles of long non-coding RNA and the long non-coding RNA related competing endogenous RNA network in asthma. In current study, we aimed to explore the long non-coding RNA-microRNA-mRNA competing endogenous RNA network in asthma and their potential implications for therapy and prognosis. Methods Asthma-related gene expression profiles were downloaded from the Gene Expression Omnibus database, re-annotated with these genes and identified for asthma-associated differentially expressed mRNAs and long non-coding RNAs. The long non-coding RNA-miRNA interaction data and mRNA-miRNA interaction data were downloaded using the starBase database to construct a long non-coding RNA-miRNA-mRNA global competing endogenous RNA network and extract asthma-related differentially expressed competing endogenous RNA network. Finally, functional enrichment analysis and drug repositioning of asthma-associated differentially expressed competing endogenous RNA networks were performed to further identify key long non-coding RNAs and potential therapeutics associated with asthma. Results This study constructed an asthma-associated competing endogenous RNA network, determined 5 key long non-coding RNAs (MALAT1, MIR17HG, CASC2, MAGI2-AS3, DAPK1-IT1) and identified 8 potential new drugs (Tamoxifen, Ruxolitinib, Tretinoin, Quercetin, Dasatinib, Levocarnitine, Niflumic Acid, Glyburide). Conclusions The results suggested that long non-coding RNA played an important role in asthma, and these novel long non-coding RNAs could be potential therapeutic target and prognostic biomarkers. At the same time, potential new drugs for asthma treatment have been discovered through drug repositioning techniques, providing a new direction for the treatment of asthma.

scholarly journals Long-non Coding RNAs Related to Fat Deposition in Pigs Included lncRNA Corresponding to Human MALAT1

2021 ◽  
Author(s):  
Katarzyna Piórkowska ◽  
Kacper Żukowski ◽  
Katarzyna Ropka-Molik ◽  
Mirosław Tyra
Keyword(s):  
Regulatory Elements ◽  
Fat Deposition ◽  
Differentially Expressed ◽  
Rna Molecules ◽  
Non Coding Rna ◽  
Sequencing Method ◽  
Non Coding Rnas ◽  
Potential Interactions ◽  
Long Non Coding Rna ◽  
Generation Sequencing

Obesity is a problem in the last decades since the development of different technologies forced the submission of a faster pace of life, resulting in nutrition style changes. In turn, domestic pigs are an excellent animal model in recognition of adiposity-related processes, corresponding to the size of individual organs, the distribution of body fat in the organism, and similar metabolism. The present study applied the next-generation sequencing method to identify adipose tissue (AT) transcriptomic signals related to increased fat content by identifying differentially expressed genes (DEGs), included long-non coding RNA molecules. The Freiburg RNA tool was applied to recognise predicting hybridisation energy of RNA-RNA interactions. The results indicated several long non-coding RNAs (lncRNAs) whose expression was significantly positively or negatively associated with fat deposition. lncRNAs play an essential role in regulating gene expression by sponging miRNA, binding transcripts, facilitating translation, or coding other smaller RNA regulatory elements. In the pig fat tissue of obese group, increased expression of lncRNAs corresponding to human MALAT1 was observed that previously recognised in the obesity-related context. Moreover, hybridisation energy analyses pinpointed numerous potential interactions between identified differentially expressed lncRNAs, and obesity-related genes and miRNAs expressed in AT.

scholarly journals Machine learning reduced gene/non-coding RNA features that classify Schizophrenia patients accurately and highlight insightful gene clusters

2020 ◽  
Author(s):  
Yichuan Liu ◽  
Hui-Qi Qu ◽  
Xiao Chang ◽  
Lifeng Tian ◽  
Joseph Glessner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neurodevelopmental Disorder ◽  
Wnt Signaling Pathway ◽  
Gene Clusters ◽  
Differentially Expressed ◽  
Cell Junction ◽  
Mrna Levels ◽  
Rna Seq ◽  
Non Coding Rna ◽  
Non Coding Rnas

AbstractSchizophrenia (SCZ) is a chronic and severely disabling neurodevelopmental disorder that affects people worldwide. RNA-seq has been a powerful method to detect the differentially expressed genes/non-coding RNAs in patients; however, due to overfitting problems differentially expressed targets (DETs) cannot be used properly as biomarkers. In this study, dorsolateral prefrontal cortex (dlpfc) RNA-seq data from 254 individuals’ was obtained from the CommonMind consortium and analyzed with machine learning methods, including random forest, forward feature selection (ffs), and factor analysis, to reduce the numbers of gene/non-coding RNA feature vectors to overcome overfitting problem and explore involved functional clusters. In 2-fold shuffle testing, the average predictive accuracy for SCZ patients was 67% based on coding genes, and the 96% based on long non-coding RNAs (lncRNAs). Coding genes were further clustered into 14 factors and lncRNAs were clustered into 45 factors to represent the underlying features. The largest contribution factor for coding genes contains number of genes critical in neurodevelopment and previously reported in relation with various brain disorders. Genomic loci of lncRNAs were more insightful, enriched for genes critical in synapse function (p=7.3E-3), cell junction (p=0.017), neuron differentiation (p=8.3E-3), phosphorylation (8.2E-4), and involving the Wnt signaling pathway (p=0.029). Taken together, machine learning is a powerful algorithm to reduce functional biomarkers in SCZ patients. The lncRNAs capture the characteristics of SCZ tissue more accurately than mRNA as the formers regulate every level of gene expression, not limited to mRNA levels.

scholarly journals Competing endogenous RNA (ceRNA) hypothetic model based on comprehensive analysis of long non-coding RNA expression in lung adenocarcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8024 ◽  
Author(s):  
Xiwen Wang ◽  
Rui Su ◽  
Qiqiang Guo ◽  
Jia Liu ◽  
Banlai Ruan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Inverse Correlation ◽  
The Cancer Genome Atlas ◽  
Competing Endogenous Rna ◽  
Cerna Network ◽  
Kaplan Meier ◽  
Non Coding Rna ◽  
Pathway Analyses ◽  
Lung Adenocarcinomas ◽  
Cancer Tissues

Background Non-small cell lung cancer (NSCLC) is a major subtype of lung cancer with high malignancy and bad prognosis, consisted of lung adenocarcinomas (LUAD) and lung squamous cell carcinomas (LUSC) chiefly. Multiple studies have indicated that competing endogenous RNA (ceRNA) network centered long noncoding RNAs (lncRNAs) can regulate gene expression and the progression of various cancers. However, the research about lncRNAs-mediated ceRNA network in LUAD is still lacking. Methods In this study, we analyzed the RNA-seq database from The Cancer Genome Atlas (TCGA) and obtained dysregulated lncRNAs in NSCLC, then further identified survival associated lncRNAs through Kaplan–Meier analysis. Quantitative real time PCR (qRT-PCR) was performed to confirm their expression in LUAD tissues and cell lines. The ceRNA networks were constructed based on DIANA-TarBase and TargetScan databases and visualized with OmicShare tools. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to investigate the potential function of ceRNA networks. Results In total, 1,437 and 1,699 lncRNAs were found to be up-regulated in LUAD and LUSC respectively with 895 lncRNAs overlapping (|log2FC| > 3, adjusted P value <0.01). Among which, 222 lncRNAs and 46 lncRNAs were associated with the overall survival (OS) of LUAD and LUSC, and 18 out of 222 up-regulated lncRNAs were found to have inverse correlation with LUAD patients’ OS (|log2FC| > 3, adjusted P value < 0.02). We selected 3 lncRNAs (CASC8, LINC01842 and VPS9D1-AS1) out of these 18 lncRNAs and confirmed their overexpression in lung cancer tissues and cells. CeRNA networks were further constructed centered CASC8, LINC01842 and VPS9D1-AS1 with 3 miRNAs and 100 mRNAs included respectively. Conclusion Through comprehensively analyses of TCGA, our study identified specific lncRNAs as candidate diagnostic and prognostic biomarkers for LUAD. The novel ceRNA network we created provided more insights into the regulatory mechanisms underlying LUAD.

Whole Transcriptome Analysis: Implication to Estrous Cycle Regulation

2021 ◽  
Author(s):  
Xiaopeng An ◽  
Yue Zhang ◽  
Fu Li ◽  
Zhanhang Wang ◽  
Shaohua Yang ◽  
...  
Keyword(s):  
Estrous Cycle ◽  
Transcriptome Analysis ◽  
Circular Rna ◽  
Differentially Expressed ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
Whole Transcriptome Analysis ◽  
Cycle Regulation ◽  
Whole Transcriptome ◽  
Goat Ovary

Abstract BackgroundEstrous cycle is one of female characteristics after sexual maturity, including estrus (ES) and diestrus (DS) stages. Estrous cycle is important in female physiology and its disorder may lead to diseases. In the latest years, effects of non-coding RNAs and mRNA on estrous cycle start to arouse much concern, however, a whole transcriptome analysis among non-coding RNAs and mRNA has not been reported.ResultsHere we report a whole transcriptome analysis of goat ovary in estrus and diestrus periods. Estrus synchronization was conducted to induce the estrus phase and on day 32, the goats naturally shifted into diestrus stage. The ovary RNA of estrus and diestrus stages was respectively collected to perform RNA-sequencing. Then the circular RNA; microRNA; long non-coding RNA; mRNA databases of goat ovary were acquired, and the differentially expressions between estrus and diestrus stages were screened to construct circRNA-miRNA-mRNA/lncRNA and lncRNA-miRNA/mRNA networks, thus providing potential pathways that involved in the regulation of estrous cycle. Differentially expressed mRNAs, such as MMP9, TIMP1, 3BHSD and PTGIS, and differentially expressed microRNAs, such as miR-21-3p，miR-202-3p and miR-223-3p, which play key roles in estrous cycle regulation were extracted from the network.ConclusionsOur data provided the miRNA, circRNA, lncRNA and mRNA databases of goat ovary and each differentially expressed profile between ES and DS. Networks among differentially expressed miRNAs, circRNAs, lncRNAs and mRNAs were constructed to provide valuable resources for the study of estrous cycle and related diseases.

scholarly journals Comprehensive analysis of differentially expressed profiles of long non-coding RNAs and messenger RNAs in kaolin-induced hydrocephalus

Gene ◽  
2019 ◽  
Vol 697 ◽  
pp. 184-193 ◽  
Author(s):  
Yan-Hui Shi ◽  
Xin-Wei He ◽  
Feng-Di Liu ◽  
Yi-Sheng Liu ◽  
Yue Hu ◽  
...  
Keyword(s):  
Comprehensive Analysis ◽  
Differentially Expressed ◽  
Messenger Rnas ◽  
Non Coding Rnas
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