Analysis of differentially expressed long non-coding RNAs in LPS-induced human HMC3 microglial cells.

BACKGROUND: Long non-coding RNAs (lncRNAs) are emerging as key modulators of inflammatory gene expression, but their roles in neuroinflammation are poorly understood. Here, we identified the inflammation-related lncRNAs and correlated mRNAs of the lipopolysaccharide (LPS)-treated human microglial cell line HMC3. We explored their potential roles and interactions using bioinformatics tools such as gene ontology (GO), kyoto encyclopedia of genes and genomes (KEGG), and weighted gene co-expression network analysis (WGCNA). RESULTS: We identified 5 differentially expressed (DE) lncRNAs, 4 of which (AC083837.1, IRF1-AS1, LINC02605, and MIR3142HG) are novel for microglia. The DElncRNAs with their correlated DEmRNAs (99 total) fell into two network modules that both were enriched with inflammation-related RNAs. However, treatment with the anti-inflammatory agent JQ1, an inhibitor of the bromodomain and extra-terminal (BET) protein BRD4, neutralized the LPS effect in only one module, showing little or even enhancing effect on the other. CONCLUSIONS: These results provide insight into, and a resource for studying, the regulation of microglia-mediated neuroinflammation and its potential therapy by small-molecule BET inhibitors.

Role of differentially expressed genes and long non-coding RNAs in papillary thyroid carcinoma diagnosis, progression, and prognosis.

Currently, the combination of ultrasonography and fine-needle aspiration biopsy (FNAB) can not discriminate between benign and malignant tumor of thyroid in some cases. The main issue in assessing the patients with thyroid nodules is to distinguish thyroid cancer from benign nodules, and reduce diagnostic surgery. To identify potential molecular biomarkers for patients with indeterminate FNAB, we explored the differentially expressed genes (DEGs) and differentially expressed long non-coding RNAs (DElncRNAs) in TCGA database between 318 papillary thyroid carcinoma (PTC) tissues and 35 normal thyroid gland tissues by DESeq R. Furthermore, DEGs were verified by gene expression profile GSE33630. Ten top DEGs and DElncRNAs were identified as candidate biomarkers for diagnosis and Lasso (Least Absolute Shrinkage and Selection Operator) logistic regression analysis were performed to improve the diagnostic accuracy of them. Besides, partial molecular biomarkers of top DEGs and DElncRNAs were closely related to the tumor stage (T), lymph node metastasis (N), metastasis (M) and pathological stage of PTC, which could reflect behavior of tumor progression. According to multivariate Cox analysis, the combination of two DEGs (METTL7B and KCTD16) and two DElncRNAs (LINC02454 and LINC02471) could predict the outcome in a more exact way. In conclusion, top DEGs and DElncRNAs could raise diagnosis of PTC in indeterminate FNAB specimens, and some could function as molecule biomarkers for tumor progression and prognosis.

Whole transcriptome mapping reveals the lncRNA regulatory network of TFP5 treatment in diabetic nephropathy.

BACKGROUND: TFP5 is a Cdk5 inhibitor peptide, which could restore insulin production. However, the role of TFP5 in diabetic nephropathy (DN) is still unclear. OBJECTIVE: This study aims to characterize the transcriptome profiles of mRNA and lncRNA in TFP5-treated DN mice to mine key lncRNAs associated with TFP5 efficacy. METHODS: We evaluated the role of TFP5 in DN pathology and performed RNA sequencing in C57BL/6J control mice, C57BL/6J db/db model mice, and TFP5 treatment C57BL/6J db/db model mice. The differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were analyzed. WGCNA was used to screen hub-gene of TFP5 in treatment of DN. RESULTS: Our results showed that TFP5 therapy ameliorated renal tubular injury in DN mice. In addition, compared with the control group, the expression profile of lncRNAs in the model group was significantly disordered, while TFP5 alleviated the abnormal expression of lncRNAs. A total of 67 DElncRNAs shared among the three groups, 39 DElncRNAs showed a trend of increasing in the DN group and decreasing after TFP treatment, while the remaining 28 showed the opposite trend. DElncRNAs were enriched in glycosphingolipid biosynthesis signaling pathways, NF-κB signaling pathways, and complement activation signaling pathways. There were 1028 up-regulated and 1117 down-regulated DEmRNAs in the model group compared to control group, and 123 up-regulated and 153 down-regulated DEmRNAs in the TFP5 group compared to the model group. The DEmRNAs were involved in PPAR and MAPK signaling pathway. We confirmed that MSTRG.28304.1 is a key DElncRNA for TFP5 treatment of DN. TFP5 ameliorated DN maybe by inhibiting MSTRG.28304.1 through regulating the insulin resistance and PPAR signaling pathway. The qRT-PCR results confirmed the reliability of the sequencing data through verifying the expression of ENSMUST00000211209, MSTRG.31814.5, MSTRG.28304.1, and MSTRG.45642.14. CONCLUSION: Overall, the present study provides novel insights into molecular mechanisms of TFP5 treatment in DN.

Identification of key genes for hypertrophic cardiomyopathy using integrated network analysis of differential lncRNA and gene expression.

Objective: Hypertrophic cardiomyopathy (HCM) is a complex heterogeneous heart disease. Recent reports found that long non-coding RNAs (lncRNAs) play an important role in the progression of cardiovascular diseases. The present study aimed to identify the novel lncRNAs and messenger RNAs (mRNAs) and determine the key pathways involved in HCM. Methods: The lncRNA and mRNA sequencing datasets of GSE68316 and GSE130036 were downloaded from the Gene Expression Omnibus (GEO) database. An integrated co-expression network analysis was conducted to identify differentially expressed lncRNAs and differentially expressed mRNAs in patients with HCM. Then, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were explored to identify the biological functions and signaling pathways of the co-expression network. Protein-protein interaction (PPI) and hub gene networks were constructed by using Cytoscape software. Plasma samples of patients with HCM and the GSE89714 dataset were used to validate the bioinformatics results. Results: A total of 1,426 differentially expressed long non-coding RNAs (lncRNAs) and 1,715 differentially expressed mRNAs were obtained from GSE68316, of which 965 lncRNAs and 896 mRNAs were upregulated and 461 lncRNAs and 819 mRNAs were downregulated. A total of 469 differentially expressed lncRNAs and 2,407 differentially expressed mRNAs were screened from GSE130036, of which 183 lncRNAs and 1,283 mRNAs were upregulated and 286 lncRNAs and 1,124 mRNAs were downregulated. A co-expression network was constructed and contained 30 differentially expressed lncRNAs and 63 differentially expressed mRNAs, which were primarily involved in 'G-protein beta/gamma-subunit complex binding,' 'polyubiquitin modification-dependent protein binding,' 'Apelin signaling pathway,' and 'Wnt signaling pathway.' The 10 hub genes in the upregulated network [G Protein Subunit Alpha I2 (GNAI2), G Protein Subunit Alpha I1 (GNAI1), G Protein Subunit Alpha I3 (GNAI3), G Protein Subunit Gamma 2 (GNG2), G Protein Subunit Beta 1 (GNB1), G Protein Subunit Gamma 13 (GNG13), G Protein Subunit Gamma Transducin 1 (GNGT1), G Protein Subunit Gamma 12 (GNG12), AKT Serine/Threonine Kinase 1 (AKT1) and GNAS Complex Locus (GNAS)] and the 10 hub genes in the downregulated network [Nucleotide-Binding Oligomerization Domain Containing Protein 2 (NOD2), Receptor-Interacting Serine/Threonine Kinase 2 (RIPK2), Nucleotide-Binding Oligomerization Domain Containing Protein 1 (NOD1), Mitochondrial Antiviral Signaling Protein (MAVS), Autophagy Related 16-Like 1 (ATG16L1), Interferon Induced With Helicase C Domain 1 (IFIH1), Autophagy Related 5 (ATG5), TANK-Binding Kinase 1 (TBK1), Caspase Recruitment Domain Family Member 9 (CARD9), and von Willebrand factor (VWF)] were screened using cytoHubba. The expression of LA16c-312E8.2 and RP5-1160K1.3 in the plasma of patients with HCM was elevated, and the expression of the MIR22 host gene (MIR22HG) was decreased, which was consistent with our analysis, while the expression of LINC00324 and Small Nucleolar RNA Host Gene 12 (SNHG12) was not significantly different between the two groups. Verification analyses performed on GSE89714 showed the upregulated mRNAs of Chloride Voltage-Gated Channel 7 (CLCN7), N-Acetylglucosamine-1-Phosphate Transferase Subunit Gamma (GNPTG), Unk Like Zinc Finger (UNKL), Adenosine Monophosphate Deaminase 2 (AMPD2), GNAI3, WD Repeat Domain 81 (WDR81), and Serpin Family F Member 1 (SERPINF1) and downregulated mRNAs of TATA-Box Binding Protein Associated Factor 12 (TAF12) co-expressed with five crucial lncRNAs. Moreover, GNAI2, GNAI3, GNG12, and vWF were upregulated and GNAS was downregulated in the top 10 hub genes of upregulated and downregulated PPI networks. Conclusion: These findings from integrative biological analysis of lncRNA-mRNA co-expression networks explored the key genes and pathways and provide new insights into the understanding of the mechanism and discovering new therapeutic targets for HCM. Three differentially expressed pivotal lncRNAs (LA16c-312E8.2, RP5-1160K1.3, and MIR22HG) in the co-expression network may serve as biomarkers and intervention targets for the diagnosis and treatment of HCM.

Meta-analysis of transcriptomics data identifies potential biomarkers and their associated regulatory networks in gallbladder cancer.

Aim: This study aimed to identify key genes, non-coding RNAs, and their possible regulatory interactions during gallbladder cancer (GBC). Background: The early detection of GBC, i.e. before metastasis, is restricted by our limited knowledge of molecular markers and mechanism(s) involved during carcinogenesis. Therefore, identifying important disease-associated transcriptome-level alterations can be of clinical importance. Methods: In this study, six NCBI-GEO microarray dataseries of GBC and control tissue samples were analyzed to identify differentially expressed genes (DEGs) and non-coding RNAs {microRNAs (DEmiRNAs) and long non-coding RNAs (DElncRNAs)} with a computational meta-analysis approach. A series of bioinformatic methods were applied to enrich functional pathways, create protein-protein interaction networks, identify hub genes, and screen potential targets of DEmiRNAs and DElncRNAs. Expression and interaction data were consolidated to reveal putative DElncRNAs:DEmiRNAs:DEGs interactions. Results: In total, 351 DEGs (185 downregulated, 166 upregulated), 787 DEmiRNAs (299 downregulated, 488 upregulated), and 7436 DElncRNAs (3127 downregulated, 4309 upregulated) were identified. Eight genes (FGF, CDK1, RPN2, SEC61A1, SOX2, CALR, NGFR, and NCAM) were identified as hub genes. Genes associated with ubiquitin ligase activity, N-linked glycosylation, and blood coagulation were upregulated, while those for cell-cell adhesion, cell differentiation, and surface receptor-linked signaling were downregulated. DEGs-DEmiRNAs-DElncRNAs interaction network identified 46 DElncRNAs to be associated with 28 DEmiRNAs, consecutively regulating 27 DEGs. DEmiRNAs-hsa-miR-26b-5p and hsa-miR-335-5p; and DElnRNAs-LINC00657 and CTB-89H12.4 regulated the highest number of DEGs and DEmiRNAs, respectively. Conclusion: The current study has identified meaningful transcriptome-level changes and gene-miRNA-lncRNA interactions during GBC and laid a platform for future studies on novel prognostic and diagnostic markers in GBC.

Identification and in vitro validation of diagnostic and prognostic biomarkers for lung squamous cell carcinoma.

Background: The aim of the present study was to find diagnostic and prognostic biomarkers for lung squamous cell carcinoma (LUSC) and to validate key biomarkers in vitro. Methods: RNA sequencing was used to identify differentially expressed mRNAs (DEmRNAs) and differentially expressed long non-coding RNAs (DElncRNAs) in LUSC tissues. RNA sequencing results were validated using a published dataset. Diagnostic and prognostic values of candidate genes were evaluated by receiver-operating characteristic (ROC) curve analysis and survival analysis, respectively. To determine the effect of MIR205HG in LUSC, MIR205HG expression was knocked down in NCI-H520 cells. 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and Transwell assay were used to respectively detect the effect of MIR205HG on cell proliferation and migration. Results: In total, 1,946 DEmRNAs and 428 DElncRNAs were identified in LUSC compared with normal tissues. A total of 851 DElncRNA-DEmRNA co-expression pairs were obtained. With the exception of NEAT1, MCM2, SERPINB5, ITGB8, CASC19, and MIR205HG were upregulated in LUSC. ROC curve analysis indicated that MCM2, SERPINB5, ITGB8, CASC19, and MIR205HG could predict LUSC. Survival analysis suggested that SERPINB5, NEAT1, and MIR205HG had potential prognostic value for LUSC. MIR205HG knockdown inhibited cell proliferation and migration, and significantly reduced the expression of ITGB8. Conclusions: The findings of the present study could help determine the pathogenesis of LUSC and provide new and accurate therapeutic targets for its treatment.

LncRNAs KB-1836B5, LINC00566 and FAM27L are associated with the survival time of patients with ovarian cancer.

Ovarian cancer (OvCa) is the most common gynecological malignancy type in the United States in 2014. Functions of long non-coding RNAs (lncRNAs) in OvCa have attracted increasing attention from researchers. The present study aimed to identify an lncRNA-based signature for survival prediction in patients with OvCa. On the basis of lncRNA expression profiles from The Cancer Genome Atlas data portal, differentially expressed lncRNAs (DELs) were selected from patients with good prognosis and poor prognosis in the training set, from which the prognostic lncRNAs were identified using univariate and multivariate Cox regression analyses and used to construct a risk scoring system. The prognostic power of this lncRNA signature was tested in the training set and validated in validation dataset and entire dataset. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the genes significantly associated with ≥1 prognostic lncRNA, and a total of 112 DELs were identified. LncRNAs KB-1836B5, long intergenic non-protein coding RNA 566 (LINC00566) and family with sequence similarity E5 (FAM27L) were determined to be prognostic lncRNAs. A three-lncRNAs signature-based risk scoring system was developed, which classified the patients from the training set into high-risk and low-risk groups with significantly different overall survival time. Risk stratification capability of the three-lncRNAs signature was validated in the validation and entire set. Multivariate Cox regression and data stratification analyses determined that the three-lncRNAs signature was independent of other clinical variables. GO and KEGG pathway enrichment analyses determined that the three prognostic lncRNAs may be involved in a number of metabolic processes and signaling pathways, including the mechanistic target of rapamycin signaling pathway, ubiquitin-mediated proteolysis, and complement and coagulation cascades pathways. In conclusion, the results of the present study demonstrated that the three-lncRNAs signature may be an independent biomarker for predicting prognosis in patients with OvCa.