Integrative lncRNA-mRNA co-expression network analysis identifies novel lncRNA E2F3-IT1 for rheumatoid arthritis.
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ABSTRACT:
SUBMITTER: Wu LF
PROVIDER: S-EPMC7905107 | biostudies-literature | 2021 Feb
REPOSITORIES: biostudies-literature
Publications
Integrative lncRNA-mRNA co-expression network analysis identifies novel lncRNA E2F3-IT1 for rheumatoid arthritis.
Wu Long-Fei LF Mo Xing-Bo XB He Jia-Hui JH He Pei P Lu Xin X Deng Hong-Wen HW Deng Fei-Yan FY Lei Shu-Feng SF
Clinical and translational medicine 20210201 2
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Project description:BackgroundsRheumatoid arthritis (RA) is a chronic inflammatory and autoimmune disease. Current studies suggest that long noncoding RNAs (lncRNAs) may be key regulators in pathogenesis.MethodsAnalyzed lncRNAs and mRNAs using microarrays to find key differentially expressed lncRNAs in RA. GO and KEGG enrichment analysis together with coding non-coding co-expression (CNC) network was used for comprehensive analysis. Verify that their expression levels are consistent with the chip results by qRT-PCR.ResultsThere are 268 differentially expressed lncRNAs (DELs) and 286 differentially expressed mRNAs (DEMs). We found 8 core lncRNAs through the CNC network. Eight highly significantly differentially expressed lncRNAs corrected with microarray profiles. The functions and associated pathways of significantly differentially expressed lncRNAs were predicted by GO and KEGG analysis. They may be involved in the pathogenesis of RA.ConclusionThe differential expression profiles of lncRNAs and mRNAs in the collagen-induced arthritis rat model preliminarily predicted functions through comprehensive analysis. However, its exact role and specific mechanism remain to be further studied.
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Project description:Hedyotis diffusa Willd (HDW) possesses heat-clearing, detoxification, anti-cancer, and anti-inflammatory properties. However, its effects on rheumatoid arthritis (RA) remain under-researched. In this study, we identified potential targets of HDW and collected differentially expressed genes of RA from the GEO dataset GSE77298, leading to the construction of a drug-component-target-disease regulatory network. The intersecting genes underwent GO and KEGG analysis. A PPI protein interaction network was established in the STRING database. Through LASSO, RF, and SVM-RFE algorithms, we identified the core gene MMP9. Subsequent analyses, including ROC, GSEA enrichment, and immune cell infiltration, correlated core genes with RA. mRNA-miRNA-lncRNA regulatory networks were predicted using databases like TargetScan, miRTarBase, miRWalk, starBase, lncBase, and the GEO dataset GSE122616. Experimental verification in RA-FLS cells confirmed HDW's regulatory impact on core genes and their ceRNA expression. We obtained 11 main active ingredients of HDW and 180 corresponding targets, 2150 RA-related genes, and 36 drug-disease intersection targets. The PPI network diagram and three machine learning methods screened to obtain MMP9, and further analysis showed that MMP9 had high diagnostic significance and was significantly correlated with the main infiltrated immune cells, and the molecular docking verification also showed that MMP9 and the main active components of HDW were well combined. Next, we predicted 6 miRNAs and 314 lncRNAs acting on MMP9, and two ceRNA regulatory axes were obtained according to the screening. Cellular assays indicated HDW inhibits RA-FLS cell proliferation and MMP9 protein expression dose-dependently, suggesting HDW might influence RA's progression by regulating the MMP9/miR-204-5p/MIAT axis. This innovative analytical thinking provides guidance and reference for the future research on the ceRNA mechanism of traditional Chinese medicine in the treatment of RA.
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Project description:Long non-coding RNAs (lncRNAs) widely regulate gene expression and play important roles in the pathogenesis of human diseases, including malignant tumors. However, the functions of most lncRNAs remain to be elucidated. In order to study and screen novel lncRNAs with important functions in the carcinogenesis of nasopharyngeal carcinoma (NPC), we constructed a lncRNA expression profile of 10 NPC tissues and 6 controls through a gene microarray. We identified 1,276 lncRNAs, of which most are unknown, with different expression levels in the healthy and NPC tissues. In order to shed light on the functions of these unknown lncRNAs, we first constructed a co-expression network of lncRNAs and mRNAs using bioinformatics and systematic biological approach. Moreover, mRNAs were clustered and enriched by their biological functions, and those lncRNAs have similar expression trends with mRNAs were defined as functional molecules with potential biological significance. The module may help identify key lncRNAs in the carcinogenesis of NPC and provide clues for in-depth study of their functions and associated signaling pathways. We suggest the newly identified lncRNAs may have clinic value as biomarkers and therapeutic targets for NPC diagnosis and treatment.
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Project description:Increasing evidence has revealed close relationships between long non-coding RNAs (lncRNAs) and chemoresistance in multiple types of tumors; however, functional lncRNAs in breast cancer (BC) have not been completely identified. In this study, we aimed to identify novel lncRNAs that might play critical roles in doxorubicn resistance, which could reveal potential biomarkers of BC. Using a BC dataset (GSE81971), we identified 452 lncRNAs that were upregulated and 659 that were downregulated; furthermore, there were 1896 differentially expressed mRNAs, of which 1137 were upregulated and 758 were downregulated in MCF-7/ADR cells compared with the expression in MCF-7 cells. We constructed an lncRNA-mRNA network by integrating probe reannotation and regulatory interactions. To elucidate the key lncRNAs in BC, we further analyzed dysregulated lncRNA-mRNA crosstalk, and six candidate lncRNAs (lnc-TRDMT1-5, ZNF667-AS1, lnc-MPPE1-13, DSCAM-AS1:5, DSCAM-AS1:2, and lnc-CFI-3) were identified. Notably, the expression level of lnc-TRDMT1-5 was significantly upregulated in resistant cells compared with sensitive cells, and its levels were increased in BC tissues compared with adjacent tissues. Levels were positively associated with estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) expression levels. High expression of lnc-TRDMT1-5 predicted poor prognosis in ER-positve and HER2-positive BC patients, especially in patients with chemoresistance. Bioinformatic and functional analysis revealed that lnc-TRDMT1-5 was involved in many crucial pathways in cancer, such as the PI3K/AKT and Wnt signaling pathways. Subcellular localization predicted that lnc-TRDMT1-5 was located in the cytoplasm, and the lncRNA-miRNA-mRNA network showed that lnc-TRDMT1-5 might serve as a regulator in BC. Here, our results demonstrated a dysregulated lncRNA-mRNA network that might provide new treatment strategies for chemoresistant BC, and the results identified a new lncRNA, lnc-TRDMT1-5, with oncogenic and prognostic functions in human BC.
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Project description:BackgroundIntracerebral hemorrhage (ICH) is a severe subtype of stroke lacking effective pharmacological targets. Long noncoding RNA (lncRNA) has been confirmed to participate in the pathophysiological progress of various neurological disorders. However, how lncRNA affects ICH outcomes in the acute phase is not completely clear. In this study, we aimed to reveal the relationship of lncRNA-miRNA-mRNA following ICH.MethodWe conducted the autologous blood injection ICH model and extracted total RNAs on day 7. Microarray scanning was used to obtain mRNA and lncRNA profiles, which were validated by RT-qPCR. GO/KEGG analysis of differentially expressed mRNAs was performed using the Metascape platform. We calculated the Pearson correlation coefficients (PCCs) of lncRNA-mRNA for co-expression network construction. A competitive endogenous (Ce-RNA) network was established based on DIANALncBase and miRDB database. Finally, the Ce-RNA network was visualized and analyzed by Cytoscape.ResultsIn total, 570 differentially expressed mRNAs and 313 differentially expressed lncRNAs were identified (FC ≥ 2 and value of p <0.05). The function of differentially expressed mRNAs was mainly enriched in immune response, inflammation, apoptosis, ferroptosis, and other typical pathways. The lncRNA-mRNA co-expression network contained 57 nodes (21 lncRNAs and 36 mRNAs) and 38 lncRNA-mRNA pairs. The ce-RNA network was generated with 303 nodes (29 lncRNAs, 163 mRNAs, and 111 miRNAs) and 906 edges. Three hub clusters were selected to indicate the most significant lncRNA-miRNA-mRNA interactions.ConclusionOur study suggests that the top differentially expressed RNA molecules may be the biomarker of acute ICH. Furthermore, the hub lncRNA-mRNA pairs and lncRNA-miRNA-mRNA correlations may provide new clues for ICH treatment.
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Project description:Despite the well-established role of long non-coding RNAs (lncRNAs) across various biological processes, their mechanisms in acute myocardial infarction (AMI) are not fully elucidated. The GSE34198 dataset from the Gene Expression Omnibus (GEO) database, which comprised 49 specimens from individuals with AMI and 47 specimens from controls, was extracted and analysed using the weighted gene co-expression network analysis (WGCNA) package. Twenty-seven key genes were identified through a combination of the degree and gene significance (GS) values, and the CDC42 (degree = 64), JAK2 (degree = 41), and CHUK (degree = 30) genes were identified as having the top three-degree values among the 27 genes. Potential interactions between lncRNA, miRNAs and mRNAs were predicted using the starBase V3.0 database, and a lncRNA-miRNA-mRNA triple network containing the lncRNA XIST, twenty-one miRNAs and three hub genes (CDC42, JAK2 and CHUK) was identified. RT-qPCR validation showed that the expression of the JAK2 and CDC42 genes and the lncRNA XIST was noticeably increased in samples from patients with AMI compared to normal samples. Pearson's correlation analysis also proved that JAK2 and CDC42 expression levels correlated positively with lncRNA XIST expression levels. The area under ROC curve (AUC) of lncRNA XIST was 0.886, and the diagnostic efficacy of the lncRNA XIST was significantly better than that of JAK2 and CDC42. The results suggested that the lncRNA XIST appears to be a risk factor for AMI likely through its ability to regulate JAK2 and CDC42 gene expressions, and it is expected to be a novel and reliable biomarker for the diagnosis of AMI.
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Project description:BackgroundCutaneous squamous cell carcinoma (cSCC) is the second most common type of skin cancer, the prognosis for patients with metastatic cSCC remains relatively poor. Thus, there is an urgent need to identify new diagnostic, prognostic, and therapeutic targets and pathways in cSCC.ResultsIt detected a total of 37,507 lncRNA probes and 32,825 mRNA probes and found 3593 differentially expressed lncRNAs and 3236 differentially expressed mRNAs. It has been found that mRNAs ACY3, NR1D1, MZB1 has co-expression relationship with six lncRNAs, GXYLT1P3, LINC00348, LOC101928131, A-33-p3340852, A-21-p0003442 and LOC644838.ConclusionsThe aim of this study is to identify cSCC-specific lncRNAs and indicated that six unstudied lncRNAs may serve an important role in endoplasmic reticulum stress apoptosis, autophagy and the progression of cSCC by modulating ACY3, NR1D1 and MZB1.
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Project description:Nasopharyngeal carcinoma is a type of head and neck cancer with a high incidence in men. In the past decades, the survival rate of NPC has remained around 70%, but it often leads to treatment failure due to its distant metastasis or recurrence. The lncRNA-mRNA regulatory network has not been fully elucidated. We downloaded the NPC-related gene expression datasets GSE53819 and GSE12452 from the Gene Expression Omnibus database; GSE53819 included 18 NPC tissues and 18 normal tissues, and GSE12452 included 31 NPC tissues and 10 normal tissues. Weighted gene co-expression network analysis was performed on mRNA and lncRNA to screen out modules that were highly correlated with tumor progression. The two datasets were subjected to differential analysis after removing batch effects, and then Venn diagrams were used to screen for overlapping genes in the module genes and differential genes. The lncRNA-mRNA co-expression network was then constructed, and key mRNAs were identified by MCODE analysis and expression analysis. GSEA analysis and qRT-PCR were performed on key mRNAs. Through a series of analyses, we speculated that BTK, CD72, PTPN6, and VAV1 may be independent predictors of the prognosis of NPC patients.Taken together, our study provides potential candidate biomarkers for NPC diagnosis, prognosis, or precise treatment.
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