Identification of novel key lncRNAs involved in periodontitis by weighted gene co‐expression network analysis

Jin, Sanli; Zhou, Ruihao; Guan, Xiaoyan; Zhou, Jianguo; Liu, Jianguo

doi:10.1111/jre.12693

Cited by 34 publications

(27 citation statements)

References 42 publications

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“…The clinical information of all GC patients are included in Supplementary Table 2. We identified the outlier samples (to be excluded) by hierarchical cluster analysis of the normal and diseased data sets via the hclust function in WGCNA (Jin et al, 2019). We identified differentially expressed mRNAs (DEGs) and lncRNAs (DElncRNAs) using the Linear Models for Microarray data (Limma) package (Ritchie et al, 2015) in R. Genes with more than a 2-fold difference in expression were regarded as differentially expressed genes (DEGs, adjusted p.value <0.05).…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

WGCNA Co-Expression Network Analysis Reveals ILF3-AS1 Functions as a CeRNA to Regulate PTBP1 Expression by Sponging miR-29a in Gastric Cancer

Ren

Shang

et al. 2020

Front. Genet.

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Gastric cancer (GC) is one of the most common types of human cancers worldwide. However, the detail mechanisms underlying GC progression remained to be investigated. The present study identified 2823 differently expressed mRNAs and 441 differently expressed lncRNAs in GC. WGCNA was conducted to identify highly correlated lncRNAs and mRNAs. Bioinformatics analysis observed that these dysregulated lncRNAs were significantly associated with the regulation of angiogenesis, cell division, cell-cell adhesion, blood vessel development, adaptive immune response, gastric acid secretion, immune response. Co-expression analysis identified ILF3-AS1 was a key lncRNA involved in regulating GC progression. Loss of function assays showed that knockdown of ILF3-AS1 significantly suppressed GC cell proliferation and metastasis. Mechanically, the results indicate that ILF3-AS1 could enhance PTBP3 expression as an miR-29a sponge, thereby promoting the proliferation and metastasis of GC cells. Our work suggests that the ILF3-AS1/miR-29a/PTBP3 axis may be a potential target for the clinical diagnosis and treatment of GC.

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Section: Data Acquisition and Processingmentioning

confidence: 99%

WGCNA Co-Expression Network Analysis Reveals ILF3-AS1 Functions as a CeRNA to Regulate PTBP1 Expression by Sponging miR-29a in Gastric Cancer

Ren

Shang

et al. 2020

Front. Genet.

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“…among network analysis methods, WGcna is a systems biology method for describing correlation patterns among genes across microarray samples (10). These methods have been widely applied in various biological conditions, such as periodontitis (24), osteoarthritis (25) and liver regeneration (26). in the present study, WGcna and the limma method were used together to identify differentially expressed hub lncrnas in HcM.…”

Section: Discussionmentioning

confidence: 99%

Identification of circulating hub long noncoding RNAs associated with hypertrophic cardiomyopathy using weighted correlation network analysis

et al. 2020

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Hypertrophic cardiomyopathy (HcM) is one of the most commonly inherited heart diseases and the leading cause of sudden cardiac death among adolescents and young adults. circulating long noncoding rnas (lncrnas) have demonstrated potential as diagnostic and therapeutic targets in several cardiovascular diseases. However, the circulating extracellular lncRNA expression profile of patients with HCM remains unclear. Plasma lncrna expression was evaluated in patients with HcM and healthy controls using a human lncrna microarray. a weighted correlation network analysis (WGcna) and linear models for microarray data (limma) were used. GSe68316 data from cardiac tissue in the Gene expression omnibus database were analysed for further validation. using WGcna, two modules (referred to as the magenta and the light-yellow module) were identified that were positively associated with HcM. Gene ontology analysis revealed that lncrnas in the magenta module targeted 'heart growth'. using limma, a total of 290 lncrnas were differentially expressed (210 upregulated and 80 downregulated) in the plasma of HcM patients, compared with controls. Moreover, combined WGcna and limma analysis demonstrated that 27 hub lncrnas in the magenta module and 13 hub lncrnas in the light-yellow module were significantly upregulated, compared with the controls. Moreover, of the 40 differentially expressed hub lncRNAs identified in the two modules, three circulating lncrnas (lnc-P2rY6-1:1, enST00000488040 and ENST00000588047) were also significantly upregulated in the HcM cardiac tissue validation dataset. These lncrnas may serve as biomarkers and therapeutic targets for precise diagnosis and treatment of HcM.

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“…Only miRNAs with target RNAs in these four databases were used to construct the ceRNA network. We built the ceRNA network based on the abovementioned data and used the ggalluvial (version 0.9.1) package to visualize the network (Jin et al, 2019).…”

Section: Functional Enrichment Of the De-mirna Signaturementioning

confidence: 99%

“…An original list of the DE-mRNAs of the corresponding DE-miRNAs was predicted through miRTarBase. In addition, we obtained the differentially expressed mRNAs for periodontitis by analyzing the GSE16134 in our previous work (Jin et al, 2019). In order to appropriately restrict the number of DE-mRNAs for periodontitis, the original list of DE-mRNAs was overlapped with the differentially expressed mRNAs from GSE16134; the overlapping mRNAs between these two cohorts are shown in Supplementary Table 3.…”

Section: Gene Ontology and Kyoto Encyclopedia Of Genes And Genomes Anmentioning

confidence: 99%

Development of an miRNA-Array-Based Diagnostic Signature for Periodontitis

et al. 2020

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Periodontitis progression is accompanied by irreversible alveolar bone absorption and leads to tooth loss. Early diagnosis is important for tooth stability and periodontal tissue preservation. However, there is no recognized miRNA diagnostic signature with convincing sensitivity and specificity for periodontitis. In this study, we obtained miRNA array expression profiles of periodontitis from the Gene Expression Omnibus (GEO) database. After screening for differentially expressed miRNAs, the least absolute shrinkage and selection operator (LASSO) method was performed to identify and construct a 17-miRNA-based diagnostic signature (hsa-miR-3917, hsa-mir-4271, hsa-miR-3156, hsa-miR-3141, hsa-miR-1246, hsa-miR-125a-5p, hsa-miR-671-5p, hcmv-mir-UL70, hsa-miR-650, hsa-miR-497-3p, hsa-miR-145-3p, hsa-miR-141-3p, hsa-miR-210-3p, hsa-miR-204-3p, hsa-miR-203a-5p, hsa-miR-99a-3p, and hsa-miR-30a-3p). Periodontal tissue samples with higher risk scores were more likely to show symptoms of periodontitis. Then, the receiver operating characteristic (ROC) curves were used to assess the diagnostic value of the miRNA signature, which indicated that the optimum cutoff value in periodontitis diagnosis was 0.5056 with an area under the ROC curve (AUC) of 0.996, a sensitivity of 97.3%, a specificity of 100.0% in the training cohort; in the testing cohort, the corresponding values were as follows: an AUC of 0.998, a sensitivity of 97.9%, and a specificity of 91.7%. We next evaluated the efficacy of the signature in differentiating disease subtype and affected range. Furthermore, we conducted functional enrichment analysis of the 17 miRNA-targeted mRNAs, including the regulation of mTOR activity and cell autophagy, Th1/Th2 cell balance and immunoregulation, cell apoptosis, and so on. In summary, our study identified and validated a 17-miRNA diagnostic signature with convincing AUC, sensitivity, and specificity for periodontitis.

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Identification of novel key lncRNAs involved in periodontitis by weighted gene co‐expression network analysis

Cited by 34 publications

References 42 publications

WGCNA Co-Expression Network Analysis Reveals ILF3-AS1 Functions as a CeRNA to Regulate PTBP1 Expression by Sponging miR-29a in Gastric Cancer

WGCNA Co-Expression Network Analysis Reveals ILF3-AS1 Functions as a CeRNA to Regulate PTBP1 Expression by Sponging miR-29a in Gastric Cancer

Identification of circulating hub long noncoding RNAs associated with hypertrophic cardiomyopathy using weighted correlation network analysis

Development of an miRNA-Array-Based Diagnostic Signature for Periodontitis

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