DElncRNAs) And mRNAs Research Articles

Expression Profiling of Coding and Noncoding RNAs in the Endometrium of Patients with Endometriosis.

The aim of this study was to identify differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) in the endometrium of individuals with and without endometriosis (EMS) during the proliferative (P) and secretory (S) phases of the menstrual cycle. Tissues were obtained from 18 control (CT; P-phase [pCT], n = 8; S-phase [sCT], n = 13) and 23 EMS patients (P-phase [pEMS], n = 13; S-phase [sEMS], n = 12). DElncRNAs and DEmRNAs were analyzed using total RNA-sequencing. In P-phase, expression of NONHSAG019742.2 and NONHSAT120701.2 was significantly higher in EMS than control patients, that of while NONHSAG048398.2 and NONHSAG016560.2 was lower in EMS patients. In S-phase, expression of NONHSAT000959.2, NONHSAT203423.1, and NONHSAG053769.2 was significantly increased in EMS patients, while that of NONHSAG012105.2 and NONHSAG020839.2 was lower. In addition, the expression of HSD11B2, THBS1, GPX3, and SHISA6 was similar to that of neighboring lncRNAs in both P- and S-phases. In contrast, ELP3 and NR4A1, respectively, were up- or downregulated in pEMS tissues. In sEMS, expression of LAMB3 and HIF1A was increased, while expression of PAM was reduced. Our findings on lncRNAs and mRNAs encourage not only exploration of the potential clinical applications of lncRNAs and mRNAs as prognostic or diagnostic biomarkers for EMS but also to gain valuable insights into its pathogenesis.

Differential expression of long non-coding RNAs in colon cancer: Insights from transcriptomic analysis

BackgroundColon Cancer (CC) incidence has sharply grown in recent years. Long non-coding RNAs (lncRNA) are produced by a group of non-protein-coding genes, and have important functions in controlling gene expression and impacting the biological features of various malignancies including CC. MethodsOur research focused on examining the function of lncRNAs in the development of colon cancer. To this end, we selected and analyzed a dataset (GSE104836) from the GEO database, which contained information about the expression of mRNAs and lncRNAs in both colon cancer tissues and normal adjacent paired tumor tissues. The DESeq2 R package in Bioconductor was used to identify differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) that showed differences in expression levels. Next, by literature review of previous studies, we chose two lncRNAs (FENDRR and LINC00092) for additional studies. To validate our findings, a series of tests were performed on a total of 31 tumor tissues and normal paired adjacent tumor tissues. The lncRNA expression levels were assessed in tumor tissues as well as in surrounding normal tumor tissues. ResultsThe data confirmed that just two particular lncRNAs, FENDRR and LINC00092, had considerably decreased expression levels throughout all stages of cancer. In addition, the survival assay was conducted using the GEPIA2 software, revealing that a reduced expression of FENDRR is correlated with a reduced overall survival. Furthermore, our investigation using receiver operating characteristic (ROC) methodology revealed that these two lncRNAs had significant discriminatory ability between colon cancer and normal tissues. To determine the cause of the decrease in the activity of these two long non-coding RNAs (lncRNAs), we used methylation-specific PCR (MSP) to examine the methylation pattern of their promoter regions. Our investigation revealed hypermethylation in the promoter regions of FENDRR and LINC00092 within tumor tissues compared to normal adjacent tumor tissues. ConclusionTaken together, our findings revealed the lncRNAs signatures as potential therapeutic targets and molecular diagnostic biomarkers in colon cancer. Furthermore, the evidence provided substantiates the important role of promoter methylation in regulating the expression levels for both of these lncRNAs.

Comprehensive analysis of differential long non-coding RNA and messenger RNA expression in cholelithiasis using high-throughput sequencing and bioinformatics.

The etiology of gallstone disease (GSD) has not been fully elucidated. Consequently, the primary objective of this study was to scrutinize and provisionally authenticate the distinctive expression profiles of long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) in GSD. RiboNucleic Acid (RNA) sequencing was used on four paired human gallbladder samples for the purpose of this study. Differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were identified and subjected to analysis of their biological functions. The Pearson's correlation coefficients between DElncRNAs and DEmRNAs were computed to construct a co-expression network delineating their associations. Furthermore, both cis- and trans-regulatory networks of selected lncRNAs were established and visualized. Additionally, a competing endogenous RNA (ceRNA) regulatory network was constructed. To validate the RNA-sequencing data, we performed a Quantitative Real-time Polymerase Chain Reaction (RT-qPCR) on 10 paired human gallbladder samples, assessing the expressions of the top 4 DEmRNAs and DElncRNAs in gallstone and control samples. A total of 934 DEmRNAs and 304DElncRNAs were successfully identified. Functional enrichment analysis indicated a predominant involvement in metabolic-related biological functions. Correlation analysis revealed a strong association between the expressions of 597 DEmRNAs and 194 DElncRNAs. Subsequently, both a cis-lncRNA-mRNA and a trans-lncRNA-Transcription Factor (TF)-mRNA regulatory network were meticulously constructed. Additionally, a ceRNA network, comprising of 24 DElncRNAs, 201 DEmRNAs, and 120 predicted miRNAs, was established. Furthermore, using RT-qPCR, we observed significant upregulation of AC004692.4, HECW1-IT1, SFRP4, and COMP, while LINC01564, SLC26A3, RP1-27K12.2, and GSTA2 exhibited marked downregulation in gallstone samples. Importantly, these findings were consistent with the sequencing. We conducted a screening process to identify DElncRNAs and DEmRNAs in GSD. This approach contributes to a deeper understanding of the genetic factors involved in the etiology of gallstones.

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

TFP5 is a Cdk5 inhibitor peptide, which could restore insulin production. However, the role of TFP5 in diabetic nephropathy (DN) is still unclear. 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. We evaluated the role of TFP5 in DN pathology and performed RNA sequencing in C57BL/6Jcontrol 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. 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 inthe model group compared to control group, and 123 up-regulated and 153 down-regulated DEmRNAs in theTFP5 group compared to themodel 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. Overall, the present study provides novel insights into molecular mechanisms of TFP5 treatment in DN.

Crucial role of lncRNA NONHSAG037054.2 and GABPA, and their related functional networks, in ankylosing spondylitis.

Long non-coding RNAs (lncRNAs) have been previously researched in ankylosing spondylitis (AS). Nevertheless, there are few studies of lncRNAs and mRNAs associated with the pathogenesis of AS. Differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) between AS and normal samples were assessed using the R limma package. DOSE packages and 'clusterProfiler' were exploited for gene enrichment analysis. The functional association of proteins and protein interactions was assessed using the STRING database. To investigate the important genes and subnetworks in the protein-protein interaction network, the MCODE plug-in in the Cytoscape software was utilized. The gene mRNA was examined via reverse transcription-quantitative PCR. In total, 152 DEmRNAs and 204 DElncRNAs were observed between normal and AS samples. A total of 68 candidate genes related to DElncRNA were identified. These candidate genes were enriched in 30 cellular component terms, 22 molecular functions, 83 biological processes, 9 Kyoto Encyclopedia of Genes and Genomes, and 36 disease ontology pathways. NONHSAG037054.2 was the most related lncRNA to genes, and GABPA was the most connected gene to lncRNA in AS. The NCBI/GenBank accession number of the lncRNA NONHSAG037054.2 was not found because it is not included in NCBI. The information of lncRNA NONHSAG037054.2 can be found at the website (http://www.noncode.org/show_gene.php?id=NONHSAG037054 and https://www.genecards.org/cgi-bin/carddisp.pl?gene=ACAP2-IT1). In total, 13 microRNAs (miRNAs) and 46 miRNAs associated with NONHSAG037054.2 and GABPA, respectively, were found. A total of 173 RNA-binding protein genes were associated with both NONHSAG037054.2 and GABPA. In addition, GABPA was downregulated in AS samples, suggesting it may have diagnostic value in AS. In conclusion, NONHSAG037054.2 and GABPA are associated with AS. GABPA was downregulated in AS, and it could serve as a novel diagnostic factor for AS.

Identification of AP002498.1 and LINC01871 as prognostic biomarkers and therapeutic targets for distant metastasis of colorectal adenocarcinoma.

Increasing evidence suggests that lncRNA (Long non-coding RNA, lncRNA)-mediated ceRNA (competing endogenous RNA, ceRNA) networks are involved in the occurrence and progression of colorectal cancer (CRC). However, the roles of the lncRNA-miRNA-mRNA ceRNA network in distant metastasis of CRC are still unclear. In this study, we constructed a specific ceRNA network to identify potential biomarkers and therapeutic targets for distant metastasis of CRC. Specifically, RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to screen for differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) related to metastasis. After validation and selection by qRT-PCR and univariate and multivariate analysis of the metastasis- and prognosis-related lncRNAs, the regulated microRNAs (miRNAs) and coexpressed mRNAs were used to construct a ceRNA network for distant metastasis of CRC. Two key distant metastasis-related DElncRNAs, AP002498.1 and LINC01871, were identified by univariate and multivariate analysis in combination with analyses of clinical data and expression levels. Furthermore, lncRNA-associated ceRNA subnetworks were constructed from the predicted miRNAs and 13 coexpressed DEmRNAs (SERPINA1, ITLN1, REG4, L1TD1, IGFALS, MUC5B, CIITA, CXCL9, CXCL10, GBP4, GNLY, IDO1, and NOS2). The AP002498.1- and LINC01871-associated ceRNA subnetworks regulated the expression of the target genes SERPINA1 and MUC5B and GNLY, respectively, through the associated miRNAs. The DElncRNA AP002498.1 and the LINC01871/miR-4644 and miR-185-5p/GNLY axes were identified as being closely associated with distant metastasis and could represent independent prognostic biomarkers or therapeutic targets in colorectal adenocarcinoma.

Comprehensive analysis of lncRNA-mRNA co-expression networks in HPV-driven cervical cancer reveals the pivotal function of LINC00511-PGK1 in tumorigenesis

BackgroundMounting evidence suggests that noncoding RNAs (lncRNAs) were involved in various human cancers. However, the role of these lncRNAs in HPV-driven cervical cancer (CC) has not been extensively studied. Considering that HR-HPV infections contribute to cervical carcinogenesis by regulating the expression of lncRNAs, miRNAs and mRNAs, we aim to systematically analyze lncRNAs and mRNAs expression profile to identify novel lncRNAs-mRNAs co-expression networks and explore their potential impact on tumorigenesis in HPV-driven CC. MethodsLncRNA/mRNA microarray technology was utilized to identify the differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) in HPV-16 and HPV-18 cervical carcinogenesis compared to normal cervical tissues. Venn diagram and weighted gene co-expression network analysis (WGCNA) were used to identify the hub DElncRNAs/DEmRNAs that were both significantly correlated with HPV-16 and HPV-18 CC patients. LncRNA-mRNA correlation analysis and functional enrichment pathway analysis were performed on these key DElncRNAs/DEmRNAs in HPV-16 and HPV-18 CC patients to explore their mutual mechanism in HPV-driven CC. A lncRNA-mRNA co-expression score (CES) model was established and validated by using the Cox regression method. Afterward, the clinicopathological characteristics were analyzed between CES-high and CES-low groups. In vitro, functional experiments were performed to evaluate the role of LINC00511 and PGK1 in cell proliferation, migration and invasion in CC cells. To understand whether LINC00511 play as an oncogenic role partially via modulating the expression of PGK1, rescue assays were used. ResultsWe identified 81 lncRNAs and 211 mRNAs that were commonly differentially expressed in HPV-16 and HPV-18 CC tissues compared to normal tissues. The results of lncRNA-mRNA correlation analysis and functional enrichment pathway analysis showed that the LINC00511-PGK1 co-expression network may make an important contribution to HPV-mediated tumorigenesis and be closely associated with metabolism-related mechanisms. Combined with clinical survival data, the prognostic lncRNA-mRNA co-expression score (CES) model based on LINC00511 and PGK1 could precisely predict patients’ overall survival (OS). CES-high patients had a worse prognosis than CES-low patients and the enriched pathways and potential targets of applicable drugs were explored in CES-high patients. In vitro experiments confirmed the oncogenic functions of LINC00511 and PGK1 in the progression of CC, and revealed that LINC00511 functions in an oncogenic role in CC cells partially via modulating the expression of PGK1. ConclusionsTogether, these data identify co-expression modules that provide valuable information to understand the pathogenesis of HPV-mediated tumorigenesis, which highlights the pivotal function of the LINC00511-PGK1 co-expression network in cervical carcinogenesis. Furthermore, our CES model has a reliable predicting ability that could stratify CC patients into low- and high-risk groups of poor survival. This study provides a bioinformatics method to screen prognostic biomarkers which leads to lncRNA-mRNA co-expression network identification and construction for patients’ survival prediction and potential drug applications in other cancers.

Identification of core cuprotosis-correlated biomarkers in abdominal aortic aneurysm immune microenvironment based on bioinformatics.

The occurrence of abdominal aortic aneurysms (AAAs) is related to the disorder of immune microenvironment. Cuprotosis was reported to influence the immune microenvironment. The objective of this study is to identify cuprotosis-related genes involved in the pathogenesis and progression of AAA. Differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) in mouse were identified following AAA through high-throughput RNA sequencing. The enrichment analyses of pathway were selected through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG). The validation of cuprotosis-related genes was conducted through immunofluorescence and western blot analyses. Totally, 27616 lncRNAs and 2189 mRNAs were observed to be differentially expressed (|Fold Change| ≥ 2 and q< 0.05) after AAA, including 10424 up-regulated and 17192 down-regulated lncRNAs, 1904 up-regulated and 285 down-regulated mRNAs. Gene ontology and KEGG pathway analysis showed that the DElncRNAs and DEmRNAs were implicated in many different biological processes and pathways. Furthermore, Cuprotosis-related genes (NLRP3, FDX1) were upregulated in the AAA samples compared with the normal one. Cuprotosis-related genes (NLRP3,FDX1) involved in AAA immune environment might be critical for providing new insight into identification of potential targets for AAA therapy.

Differential lncRNA/mRNA expression profiling and ceRNA network analyses in amniotic fluid from foetuses with ventricular septal defects.

Long noncoding RNAs (lncRNAs) have been shown to be involved in the regulation of numerous biological processes in embryonic development. We aimed to explore lncRNA expression profiles in ventricular septal defects (VSDs) and reveal their potential roles in heart development. Microarray analyses were performed to screen differentially expressed lncRNAs (DE-lncRNAs) and mRNAs (DE-mRNAs) in the amniotic fluid between the VSD group and the control group. Bioinformatics analyses were further used to identify the functional enrichment and signaling pathways of important mRNAs. Then, a coding-noncoding gene coexpression (CNC) network and competitive endogenous RNAs (ceRNA) network were drawn. Finally, qRT‒PCR was performed to verify several hub lncRNAs and mRNAs in the network. A total of 710 DE-lncRNAs and 397 DE-mRNAs were identified in the VSD group. GO and KEGG analyses revealed that the DE-mRNAs were enriched in cardiac development-related biological processes and pathways, including cell proliferation, cell apoptosis, and the Sonic Hedgehog signaling pathway. Four VSD related mRNAs was used to construct the CNC network, which included 149 pairs of coexpressing lncRNAs and mRNAs. In addition, a ceRNA network, including 15 lncRNAs, 194 miRNAs, and four mRNAs, was constructed to reveal the potential regulatory relationship between lncRNAs and protein-coding genes. Finally, seven RNAs in the ceRNA network were validated, including IDS, NR2F2, GPC3, LINC00598, GATA3-AS1, PWRN1, and LINC01551. Our study identified some lncRNAs and mRNAs may be potential biomarkers and therapeutic targets for foetuses with VSD, and described the lncRNA-associated ceRNA network in the progression of VSD.

Identification of key long non-coding RNA-associated competing endogenous RNA axes in Brodmann Area 10 brain region of schizophrenia patients

Schizophrenia (SCZ) is a serious mental condition with an unknown cause. According to the reports, Brodmann Area 10 (BA10) is linked to the pathology and cortical dysfunction of SCZ, which demonstrates a number of replicated findings related to research on SCZ and the dysfunction in tasks requiring cognitive control in particular. Genetics' role in the pathophysiology of SCZ is still unclear. Therefore, it may be helpful to understand the effects of these changes on the onset and progression of SCZ to find novel mechanisms involved in the regulation of gene transcription. In order to determine the molecular regulatory mechanisms affecting the SCZ, the long non-coding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs) axes in the BA10 area were determined using a bioinformatics approach in the present work. A microarray dataset (GSE17612) consisted of brain post-mortem tissues of the BA10 area from SCZ patients and matched healthy subjects was downloaded from the Gene Expression Omnibus (GEO) database. This dataset included probes for both lncRNAs and mRNAs. Using the R software's limma package, the differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were found. The RNA interactions were also discovered using the DIANA-LncBase and miRTarBase databases. In the ceRNA network, positive correlations between DEmRNAs and DElncRNAs were evaluated using the Pearson correlation coefficient. Finally, lncRNA-associated ceRNA axes were built by using the co-expression and DElncRNA-miRNA-DEmRNA connections. We identified the DElncRNA-miRNA-DEmRNA axes, which included two key lncRNAs (PEG3-AS1, MIR570HG), seven key miRNAs (hsa-miR-124-3p, hsa-miR-17-5p, hsa-miR-181a-5p, hsa-miR-191-5p, hsa-miR-26a-5p, hsa-miR-29a-3p, hsa-miR-29b-3p), and eight key mRNAs (EGR1, ETV1, DUSP6, PLOD2, CD93, SERPINB9, ANGPTL4, TGFB2). Furthermore, DEmRNAs were found to be enriched in the "AGE-RAGE signaling pathway in diabetic complications", "Amoebiasis", "Transcriptional misregulation in cancer", "Human T-cell leukemia virus 1 infection", and "MAPK signaling pathway". This study offers research targets for examining significant molecular pathways connected to the pathogenesis of SCZ, even though the function of these ceRNA axes still needs to be investigated.

Identification of potential regulatory long non-coding RNA-associated competing endogenous RNA axes in periplaque regions in multiple sclerosis.

Slow-burning inflammation at the lesion rim is connected to the expansion of chronic multiple sclerosis (MS) lesions. However, the underlying processes causing expansion are not clearly realized. In this context, the current study used a bioinformatics approach to identify the expression profiles and related lncRNA-associated ceRNA regulatory axes in the periplaque region in MS patients. Expression data (GSE52139) from periplaque regions in the secondary progressive MS spinal cord and controls were downloaded from the Gene Expression Omnibus database (GEO), which has details on mRNAs and lncRNAs. Using the R software's limma package, the differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were found. The RNA interactions were also found using the DIANA-LncBase, miRTarBase, and HMDD databases. The Pearson correlation coefficient was used to determine whether there were any positive correlations between DEmRNAs and DElncRNAs in the ceRNA network. Finally, lncRNA-associated ceRNA axes were created based on co-expression and connections between DElncRNA, miRNA, and DEmRNA. We used the Enrichr tool to enrich the biological process, molecular function, and pathways for DEmRNAs and DElncRNAs. A network of DEmRNAs' protein-protein interactions was developed, and the top five hub genes were found using Cytoscape and STRING. The current study indicates that 15 DEmRNAs, including FOS, GJA1, NTRK2, CTNND1, and SP3, are connected to the MS ceRNA network. Additionally, four DElncRNAs (such as TUG1, ASB16-AS1, and LINC01094) that regulated the aforementioned mRNAs by sponging 14 MS-related miRNAs (e.g., hsa-miR-145-5p, hsa-miR-200a-3p, hsa-miR-20a-5p, hsa-miR-22-3p, hsa-miR-23a-3p, hsa-miR-27a-3p, hsa-miR-29b-3p, hsa-miR-29c-3p, hsa-miR-34a-5p) were found. In addition, the analysis of pathway enrichment revealed that DEmRNAs were enriched in the pathways for the "MAPK signaling pathway", "Kaposi sarcoma-associated herpesvirus infection", "Human immunodeficiency virus one infection", "Lipid and atherosclerosis", and "Amphetamine addiction". Even though the function of these ceRNA axes needs to be investigated further, this study provides research targets for studying ceRNA-mediated molecular mechanisms related to periplaque demyelination in MS.

Identification and integrated analysis of lncRNAs and miRNAs in IPEC-J2 cells provide novel insight into the regulation of the innate immune response by PDCoV infection

BackgroundNoncoding RNAs (ncRNAs), including microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), are pivotal regulators involved in the pathogenic mechanism of multiple coronaviruses. Porcine deltacoronavirus (PDCoV) has evolved multiple strategies to escape the innate immune response of host cells, but whether ncRNAs are involved in this process during PDCoV infection is still unknown.ResultsIn this study, the expression profiles of miRNAs, lncRNAs and mRNAs in IPEC-J2 cells infected with PDCoV at 0, 12 and 24 hours postinfection (hpi) were identified through small RNA and RNA sequencing. The differentially expressed miRNAs (DEmiRNAs), lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were screened from the comparison group of IPEC-J2 cells at 0 and 12 hpi as well as the comparison group of IPEC-J2 cells at 12 and 24 hpi. The target genes of these DEncRNAs were predicted. The bioinformatics analysis of the target genes revealed multiple significantly enriched functions and pathways. Among them, the genes that were associated with innate immunity were specifically screened. The expression of innate immunity-related ncRNAs and mRNAs was validated by RT–qPCR. Competing endogenous RNA (ceRNA) regulatory networks among innate immunity-related ncRNAs and their target mRNAs were established. Moreover, we found that the replication of PDCoV was significantly inhibited by two innate immunity-related miRNAs, ssc-miR-30c-3p and ssc-miR-374b-3p, in IPEC-J2 cells.ConclusionsThis study provides a data platform to conduct studies of the pathogenic mechanism of PDCoV from a new perspective and will be helpful for further elucidation of the functional role of ncRNAs involved in PDCoV escaping the innate immune response.

Identification of a lncRNA AC011511.5- Mediated Competitive Endogenous RNA Network Involved in the Pathogenesis of Allergic Rhinitis.

LncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) networks are thought to be involved in regulating the development of various inflammatory diseases. Up to now, the mechanism of such a network in allergic rhinitis (AR) remains unclear. In the study, we investigated the differential expression of lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) by performing a microarray analysis of peripheral blood obtained from AR patients and healthy control subjects. StarBase 2.0 was used to predict miRNAs that might interact with various DElncRNAs and DEmRNAs. We constructed a ceRNA network based on potential lncRNA-miRNA-mRNA interactions. The Cluster Profiler R package was used to perform a functional enrichment analysis of the hub-ceRNA, and Molecular Complex Detection (MCODE) was used for further identification of the hub-ceRNA network. The expression levels of genes contained in the hub-ceRNA network were validated by RT-PCR. In total, 247 DEmRNAs and 18 DelncRNAs were aberrantly expressed in the PBMCs of AR patients. A ceRNA network consisting of 3 lncRNAs, 45 miRNAs, and 75 mRNAs was constructed. A GO analysis showed that negative regulation of immune response, response to interferon-beta, and response to interferon-alpha were important terms. A KEGG pathway analysis showed that 75 mRNAs were significantly enriched in “NOD-like receptor signaling pathway” and “tryptophan metabolism”. Ultimately, a hub-ceRNA network was constructed based on 1 lncRNA (AC011511.5), 5 miRNAs (hsa-miR-576-5p, hsa-miR-520c-5p, hsa-miR-519b-5p, hsa-miR-519c-5p, and hsa-miR-518d-5p), and 2 mRNAs (ZFP36L1 and SNX27). Following further verification, we found that overexpression of lncRNA AC011511.5 or inhibitor of miR-576-5p upregulated SNX27 expression. The expression of SNX27 in the lncRNA AC011511.5 overexpression & miR-576-5p inhibitor group was not different from that in the miR-576-5p inhibitor group or lncRNA AC011511.5 overexpression group, indicating that overexpression of lncRNA AC011511.5 could not further upregulate the expression of SNX27 in miR-576-5p inhibitor Jurkat cells. This network may provide new insights to search for biomarkers that can be used for the diagnosis and clinical treatment of AR.

Long non-coding RNA-associated competing endogenous RNA axes in the olfactory epithelium in schizophrenia: a bioinformatics analysis

The etiology of schizophrenia (SCZ), as a serious mental illness, is unknown. The significance of genetics in SCZ pathophysiology is yet unknown, and newly identified mechanisms involved in the regulation of gene transcription may be helpful in determining how these changes affect SCZ development and progression. In the current work, we used a bioinformatics approach to describe the role of long non-coding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs) in the olfactory epithelium (OE) samples in order to better understand the molecular regulatory processes implicated in SCZ disorders in living individuals. The Gene Expression Omnibus database was used to obtain the OE microarray dataset (GSE73129) from SCZ sufferers and control subjects, which contained information about both lncRNAs and mRNAs. The limma package of R software was used to identify the differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs). RNA interaction pairs were discovered using the Human MicroRNA Disease Database, DIANA-LncBase, and miRTarBase databases. In this study, the Pearson correlation coefficient was utilized to find positive correlations between DEmRNAs and DElncRNAs in the ceRNA network. Eventually, lncRNA-associated ceRNA axes were developed based on co-expression relations and DElncRNA-miRNA-DEmRNA interactions. This work found six potential DElncRNA-miRNA-DEmRNA loops in SCZ pathogenesis, including, SNTG2-AS1/hsa-miR-7-5p/SLC7A5, FLG-AS1/hsa-miR-34a-5p/FOSL1, LINC00960/hsa-miR-34a-5p/FOSL1, AQP4-AS1/hsa-miR-335-5p/FMN2, SOX2-OT/hsa-miR-24-3p/NOS3, and CASC2/hsa-miR-24-3p/NOS3. According to the findings, ceRNAs in OE might be promising research targets for studying SCZ molecular mechanisms. This could be a great opportunity to examine different aspects of neurodevelopment that may have been hampered early in SCZ patients.

Dysregulated lncRNA and mRNA may promote the progression of ischemic stroke via immune and inflammatory pathways: results from RNA sequencing and bioinformatics analysis.

BackgroundLong non-coding RNAs (lncRNAs) are widely involved in gene transcription regulation and which act as epigenetic modifiers in many diseases.ObjectiveTo determine whether lncRNAs are involved in ischemic stroke (IS), we analyzed the expression profile of lncRNAs and mRNAs in IS.MethodsRNA sequencing was performed on the blood of three pairs of IS patients and healthy controls. Differential expression analysis was used to identify differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs). Based on the co-expression relationships between lncRNA and mRNA, a series of bioinformatics analysis including GO and KEGG enrichment analysis and PPI analysis, were conducted to predict the function of lncRNA.ResultsRNA sequencing produced a total of 5 DElncRNAs and 144 DEmRNAs. Influenza A pathway and Herpes simplex infection pathway were the most significant pathways. EP300 and NFKB1 were the most important target proteins, and Human leucocyte antigen (HLA) family were the key genes in IS.ConclusionsAnalysis of this study revealed that dysregulated lncRNAs in IS may lead to IS by affecting the immune and inflammation system.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13258-021-01173-1.

The Identification of Childhood Asthma Progression-Related lncRNAs and mRNAs Suitable as Biomarkers Using Weighted Gene Coexpression Network Analysis.

Background Asthma is a common chronic respiratory disease in children, seriously affecting children's health and growth. This bioinformatics study aimed to identify potential RNA candidates closely associated with childhood asthma development within current gene databases. Methods GSE65204 and GSE19187 datasets were screened and downloaded from the NCBI GEO database. Differentially expressed long noncoding RNAs (DE-lncRNAs) and mRNAs (DE-mRNAs) were identified using the Bioconductor limma package in R, and these DE-mRNAs were used to perform biological process (BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Thereafter, weighted gene coexpression network analysis (WGCNA) was utilized to screen the modules directly related to childhood asthma, and a coexpression network of DE-lncRNAs and DE-mRNAs was built. Finally, principal component analysis (PCA) was performed. Results In total, 7 DE-lncRNAs and 1060 DE-mRNAs, as well as 7 DE-lncRNAs and 1027 DE-mRNAs, were identified in GSE65204 and GSE19187, respectively. After comparison, 336 overlapping genes had the same trend of expression, including 2 overlapped DE-lncRNAs and 334 overlapped DE-mRNAs. These overlapped DE-mRNAs were enriched in 28 BP and 12 KEGG pathways. Eleven modules were obtained in GSE65204, and it was found that the purple, black, and yellow modules were significantly positively correlated with asthma development. Subsequently, a coexpression network including 63 DE-mRNAs and 2 DE-lncRNAs was built, and five KEGG pathways, containing 8 genes, were found to be directly associated with childhood asthma. The PCA further verified these results. Conclusion LncRNAs LINC01559 and SNHG8 and mRNAs VWF, LAMB3, LAMA4, CAV1, ALDH1A3, SMOX, GNG4, and PPARG were identified as biomarkers associated with the progression of childhood asthma.

Identification and Validation of an 8-lncRNA Signature That Predicts Prognosis in Patients With Esophageal Squamous Cell Carcinoma

Background: Esophageal squamous cell carcinoma (ESCC) is correlated with worse clinical prognosis and lacks available targeted therapy, which requires identification of reliable biomarkers for the diagnosis and treatment of ESCC. Methods: Here, we downloaded the GSE53625 dataset as training dataset and the RNA sequencing data from TCGA database as validation dataset to screen differentially expressed long non-coding RNAs (DElncRNAs) and mRNAs (DEmRNAs) with the criterion of FDR 1. Results : Total 1136 DERs, including 689 downregulated mRNAs, 318 upregulated mRNAs, 74 downregulated lncRNAs and 55 upregulated lncRNAs were obtained in GES53625 dataset. Bioinformatics analysis showed that three clinical features, including age, pathologic stage and risk score (RS) model status and 8 independent prognostic genes, including ADAMTS9-AS1, DLX6-AS1, LINC00470, LINC00520, LINC01497, LINC01749, MAMDC2-AS1 and SSTR5-AS1 were significantly correlated with prognosis. In the constructed co-expression of lncRNA-mRNA networks, we identified 13 significantly correlated biological processes and 5 KEGG signal pathways. Functionally, we further demonstrated that knockdown of LINC00470 significantly suppressed cell proliferation, G1/S transition, migration and invasion using CCK-8 assay, flow cytometry and transwell migration assay in two ESCC cell lines (EC9706 and TE-9). Conclusion: In conclusions, our eight-lncRNA signature and nomogram can provide theoretical guidance for further research on the molecular mechanism of ESCC and the screening of molecular markers. Funding: None Declaration of Interest: The authors declare no competing interests. Ethical Approval: The study was approved by the Ethics Committee of the Harbin Medical University Cancer Hospital.

Identification and validation of key non-coding RNAs and mRNAs using co-expression network analysis in pre-eclampsia.

Pre-eclampsia (PE) is a common complication of pregnancy, associated with maternal and fetal morbidity and mortality. In this study, we aimed to explore important long non-coding RNAs (lncRNAs) and their possible mechanisms in PE.GSE60438 expression profile including 25 PE samples and 23 normal samples were obtained from gene expression omnibus (GEO) database. After normalization with betaqn package in R, differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were identified using the limma package. Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway were analyzed using DAVID 6.7 and GSEA 3.0. LncRNAs-mRNAs coexpression was implemented using weighted gene co-expression network analysis (WGCNA). MicroRNAs linked with these DElncRNAs and DEmRNAs were predicted and a competitive endogenous RNA (ceRNA) network was built.A total of 53 DElncRNAs and 301 DEmRNAs were identified between control and PE samples. These DEmRNAs were enriched into pathways such as protein digestion and absorption, osteoclast differentiation. WGCNA constructed a lncRNA-mRNA coexpression network, among which SUMO1P3, NACAP1, NCF1C, ANXA2P1, GTF2IP1, NAPSB, OR7E37P were hub genes. ceRNA network was constructed together with microRNAs (miRNAs), and functional analysis indicated cellular membrane and sugar binding were involved in PE progression. Five lncRNAsANXA2P1, GTF2IP1, NACAP1, NCF1C and OR7E37P were successfully validated in our clinical specimens.The DElncRNAs, including ANXA2P1, GTF2IP1, NACAP1, NCF1C and OR7E37P might play important roles in PE. However, the exact mechanism of these lncRNAs in prediction and diagnosis of PE should be further explored.

Using lncRNA Sequencing to Reveal a Putative lncRNA-mRNA Correlation Network and the Potential Role of PCBP1-AS1 in the Pathogenesis of Cervical Cancer.

Background/AimsLong non-coding RNAs (lncRNAs) play important roles in many diseases and participate in posttranscriptional regulatory networks in tumors. However, the functions of major lncRNAs in cervical cancer are unclear. Therefore, the aim of this study was to construct a lncRNA-mRNA coexpression functional network and analyze lncRNAs that might contribute to the pathogenesis of cervical cancer.MethodsDifferentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) between three pairs of cervical cancer tissues and adjacent mucosa were identified by lncRNA microarray analysis. LncRNA-mRNA correlation analysis and functional enrichment were performed on the DEGs. From the correlation network, PCBP1-AS1 was selected as a candidate for further analysis. PCBP1-AS1 expression was examined by qPCR, and Kaplan–Meier survival, clinicopathology, GSEA, and immune infiltration analysis of PCBP1-AS1 were performed. The immune responses of PCBP1-AS1 expression in cervical cancer were analyzed using TIMER and western blot. PCBP1-AS1 was knocked down and overexpressed to evaluate its role in cell proliferation, migration, and invasion.ResultsA total of 130 lncRNAs were significantly differentially expressed in cervical cancer patient samples compared with control samples. Differentially expressed mRNAs in the lncRNA-mRNA interaction network were involved in the EMT process. Combined with the Kaplan–Meier survival analyses, the coexpression network revealed that PCBP1-AS1 was significantly associated with OS and clinicopathological parameters in cervical cancer patients. Moreover, PCBP1-AS1 expression was not only significantly increased in cervical cancer specimens but also associated with tumor stage, TNM, and invasion. GSEA revealed that PCBP1-AS1 is closely correlated with cell biological function via the p53 and notch signaling pathways. TIMER analysis revealed that the numbers of NK cells and M2 macrophages decreased when PCBP1-AS1 expression was high, which was consistent with the western blot results in clinical samples. Furthermore, in vitro experiments showed that high expression of PCBP1-AS1 promoted cell proliferation, migration, and invasion.ConclusionsTranscriptomic and lncRNA-mRNA correlation analyses revealed that PCBP1-AS1 plays a key role as an independent prognostic factor in patients with cervical cancer. The identification of PCBP1-AS1 as a new biomarker for cervical cancer could help explain how changes in the immune environment promote cervical cancer development.

Functional predication of differentially expressed circRNAs/lncRNAs in the prefrontal cortex of Nrf2-knockout mice.

In the central nervous system, nuclear factor erythroid-2-related factor 2 (Nrf2) protects neurons from oxidant injury, thereby ameliorating neurodegeneration. We explored the key circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs) involved in Nrf2-induced neuroprotection. We used microarrays to examine the circRNAs (DEcircRNAs), lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) differentially expressed between Nrf2 (+/+) and Nrf2 (-/-) mice and identified DEcircRNA/DElncRNA-miRNA-DEmRNA interaction networks. In total, 197 DEcircRNAs, 685 DElncRNAs and 356 DEmRNAs were identified in prefrontal cortical tissues from Nrf2 (-/-) mice. The expression patterns of selected DEcircRNAs (except for mmu_circ_0003404) and DElncRNAs in qRT-PCR analyses were generally consistent with the microarray analysis results. Functional annotation of the DEmRNAs in the DEcircRNA/DElncRNA-miRNA-DEmRNA networks indicated that five non-coding RNAs (mmu_circ_0000233, ENSMUST00000204847, NONMMUT024778, NONMMUT132160 and NONMMUT132168) may contribute to Nrf2 activity, with the help of mmu_circ_0015035 and NONMMUT127961. The results also revealed that four non-coding RNAs (cicRNA.20127, mmu_circ_0012936, ENSMUST00000194077 and NONMMUT109267) may influence glutathione metabolism. Additionally, 44 DEcircRNAs and 7 DElncRNAs were found to possess coding potential. These findings provide clues to the molecular pathways through which Nrf2 protects neurons.