LINCRNA01094 Promotes Renal Interstitial Fibrosis via the Mir-513b-5p/MELK/Smad3 Axis.

BACKGROUND: Chronic Kidney Disease (CKD) is a common chronic disease that is a threat to human health. Accumulating evidence showed that long noncoding RNAs (lncRNAs) are associated with various diseases and can function as competing endogenous RNAs (ceRNAs). However, the roles and functions of the lncRNA‒miRNA-mRNA network in CKD are still unclear. METHODS: In this study, we performed differential expression analysis of lncRNAs, miRNAs, and mRNAs in CKD using the datasets GSE66494 and GSE80247 from the Gene Expression Omnibus. A total of 33 lncRNAs, 20 miRNAs, and 240 mRNAs were differentially expressed between CKD patients and healthy controls. Two ceRNA interaction modules composed of 11 hub nodes, namely, 2 lncRNAs (LINC01086, LINC01094), 2 miRNAs (hsa-miR-197-3p, hsamiR- 513b-5p) and 7 mRNAs (CENPF, TOP2A, ARHGAP11A, CEP55, MELK, DTL, and ANLN) were constructed. In vitro knockdown of LINC01094 expression in renal tubular epithelial HK2 cells significantly attenuated the phenotype of TGFß1-induced cell fibrosis. RESULTS: The results of RNA immunoprecipitation (RIP) experiments and dual-luciferase reporter experiments based on constructed mutants confirmed that LINC01094 could mediate MELK expression by sponging miR-513b-5p. CONCLUSION: Our observations indicated that lowering the expression of LINC01094 can significantly attenuate the TGFß1-induced fibrosis phenotype in HK2 cells and renal inflammation through the miR-513b-5p/MELK/Smad3 signalling axis.

Identification and Validation of Aging- and Endoplasmic Reticulum Stress-Related Genes in Periodontitis Using a Competing Endogenous RNA Network.

Periodontitis is a multifactorial chronic inflammatory disease that destroy periodontium. Apart from microbial infection and host immune responses, emerging evidence shows aging and endoplasmic reticulum stress (ER stress) play a key role in periodontitis pathogenesis. The aim of this study is to identify aging-related genes (ARGs) and endoplasmic reticulum stress-related genes (ERGs) in periodontitis. Data were obtained from the Gene Expression Omnibus (GEO), Human Ageing Genomic Resources (HAGR) and GeneCards databases to identify differentially expressed mRNAs/miRNAs/lncRNAs (DEmRNAs/DEmiRNAs/DElncRNAs), ARGs and ERGs, respectively. We used the MultiMiR database for the reverse prediction of miRNAs and predicted miRNA-lncRNA interactions using the STARBase database. Afterwards, we constructed a mRNA-miRNA-lncRNA ceRNA network. A total of 10 hub genes, namely LCK, LYN, CXCL8, IL6, HCK, IL1B, BTK, CXCL12, GNAI1 and FCER1G, and 5 DEmRNAs-ARGs-ERGs were then discovered. Further, weighted gene co-expression network analysis (WGCNA) and single sample gene set enrichment analysis (ssGSEA) were performed to explore co-expression modules and immune infiltration respectively. Finally, we used transmission electron microscope (TEM), inverted fluorescence microscopy, quantitative real-time polymerase chain reaction (qRT-PCR) and Western Blot to verify the bioinformatic results in periodontal ligament stem cells (PDLSCs) infected with Porphyromonas gingivalis (P. gingivalis). The experimental results broadly confirmed the accuracy of bioinformatic analysis. The present study established an aging- and ER stress-related ceRNA network in periodontitis, contributing to a deeper understanding of the pathogenesis of periodontitis.

Identification and validation of the TRHDE-AS1/hsa-miR-449a/ADAMTS5 axis as a novel prognostic biomarker in prostate cancer.

Despite advancements in cancer research, the prognostic implications of competing endogenous RNA (ceRNA) networks in prostate cancer (PCa) remain incompletely understood. This study aimed to elucidate the prognostic relevance of ceRNA networks in PCa, utilizing a comprehensive bioinformatics approach alongside experimental validation. After searching The Cancer Genome Atlas (TCGA) database, RNA sequencing (RNA-Seq) data were extracted to identify differentially expressed RNAs (DERs) between 491 PCa samples and 51 normal prostate tissues, following which a comprehensive bioinformatics strategy was implemented to construct a ceRNA network. An optimal prognostic signature comprising these DERs was then established and validated using TCGA data. In addition, functional validation was performed through RNA pull-down, dual-luciferase reporter assays, quantitative real-time PCR, and western blot analysis conducted in PC-3 and DU145 cell lines, thereby complementing the bioinformatics analysis. A total of 613 DERs, comprising 103 long noncoding RNAs (lncRNAs), 60 microRNAs (miRNAs), and 450 messenger RNAs (mRNAs), were identified and utilized in constructing a ceRNA network, which encompassed 23 lncRNAs, 9 miRNAs, and 52 mRNAs. An optimal prognostic signature was established, including VPS9D1 antisense RNA 1 (VPS9D1-AS1), miR-449a, cyclin-dependent kinase 5 regulatory subunit 1 (CDK5R1), targeting protein for Xklp2 (TPX2), solute carrier family 7 member 11 (SLC7A11), copine7 (CPNE7), and maternal embryonic leucine zipper kinase (MELK), yielding area under the curve (AUC) values exceeding 0.8 across training, validation, and entire datasets. Our experiments results revealed an interaction between lncRNA TRHDE antisense RNA 1 (TRHDE-AS1) and miR-449a and that miR-449a could target the ADAM metallopeptidase with thrombospondin type 1 motif 5 (ADAMTS5) mRNA. Knockdown of miR-449a significantly impeded cell proliferation, G1/S transition, migration and invasion, and promoted apoptosis in PC-3 and DU145 cells. Furthermore, knockdown of miR-449a notably downregulated protein expression of CDK4, cyclin D1, N-cadherin and vimentin, while upregulating protein expression of cleaved caspase-3 and E-cadherin. This study contributes to a deeper understanding of the prognostic-linked ceRNA network in PCa, providing fundamental insights that could improve diagnostic and therapeutic approaches for PCa management.

Discovery and validation of molecular patterns and immune characteristics in the peripheral blood of ischemic stroke patients.

Background: Stroke is a disease with high morbidity, disability, and mortality. Immune factors play a crucial role in the occurrence of ischemic stroke (IS), but their exact mechanism is not clear. This study aims to identify possible immunological mechanisms by recognizing immune-related biomarkers and evaluating the infiltration pattern of immune cells. Methods: We downloaded datasets of IS patients from GEO, applied R language to discover differentially expressed genes, and elucidated their biological functions using GO, KEGG analysis, and GSEA analysis. The hub genes were then obtained using two machine learning algorithms (least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE)) and the immune cell infiltration pattern was revealed by CIBERSORT. Gene-drug target networks and mRNA-miRNA-lncRNA regulatory networks were constructed using Cytoscape. Finally, we used RT-qPCR to validate the hub genes and applied logistic regression methods to build diagnostic models validated with ROC curves. Results: We screened 188 differentially expressed genes whose functional analysis was enriched to multiple immune-related pathways. Six hub genes (ANTXR2, BAZ2B, C5AR1, PDK4, PPIH, and STK3) were identified using LASSO and SVM-RFE. ANTXR2, BAZ2B, C5AR1, PDK4, and STK3 were positively correlated with neutrophils and gamma delta T cells, and negatively correlated with T follicular helper cells and CD8, while PPIH showed the exact opposite trend. Immune infiltration indicated increased activity of monocytes, macrophages M0, neutrophils, and mast cells, and decreased infiltration of T follicular helper cells and CD8 in the IS group. The ceRNA network consisted of 306 miRNA-mRNA interacting pairs and 285 miRNA-lncRNA interacting pairs. RT-qPCR results indicated that the expression levels of BAZ2B, C5AR1, PDK4, and STK3 were significantly increased in patients with IS. Finally, we developed a diagnostic model based on these four genes. The AUC value of the model was verified to be 0.999 in the training set and 0.940 in the validation set. Conclusion: Our research explored the immune-related gene expression modules and provided a specific basis for further study of immunomodulatory therapy of IS.

CAPN2 correlates with insulin resistance states in PCOS as evidenced by multi-dataset analysis.

OBJECTIVE: IR emerges as a feature in the pathophysiology of PCOS, precipitating ovulatory anomalies and endometrial dysfunctions that contribute to the infertility challenges characteristic of this condition. Despite its clinical significance, a consensus on the precise mechanisms by which IR exacerbates PCOS is still lacking. This study aims to harness bioinformatics tools to unearth key IR-associated genes in PCOS patients, providing a platform for future therapeutic research and potential intervention strategies. METHODS: We retrieved 4 datasets detailing PCOS from the GEO, and sourced IRGs from the MSigDB. We applied WGCNA to identify gene modules linked to insulin resistance, utilizing IR scores as a phenotypic marker. Gene refinement was executed through the LASSO, SVM, and Boruta feature selection algorithms. qPCR was carried out on selected samples to confirm findings. We predicted both miRNA and lncRNA targets using the ENCORI database, which facilitated the construction of a ceRNA network. Lastly, a drug-target network was derived from the CTD. RESULTS: Thirteen genes related to insulin resistance in PCOS were identified via WGCNA analysis. LASSO, SVM, and Boruta algorithms further isolated CAPN2 as a notably upregulated gene, corroborated by biological verification. The ceRNA network involving lncRNA XIST and hsa-miR-433-3p indicated a possible regulatory link with CAPN2, supported by ENCORI database. Drug prediction analysis uncovered seven pharmacological agents, most being significant regulators of the endocrine system, as potential candidates for addressing insulin resistance in PCOS. CONCLUSIONS: This study highlights the pivotal role of CAPN2 in insulin resistance within the context of PCOS, emphasizing its importance as both a critical biomarker and a potential therapeutic target. By identifying CAPN2, our research contributes to the expanding evidence surrounding the CAPN family, particularly CAPN10, in insulin resistance studies beyond PCOS. This work enriches our understanding of the mechanisms underlying insulin resistance, offering insights that bridge gaps in the current scientific landscape.

Integrated analysis of a competing endogenous RNA network reveals a ferroptosis-related 6-lncRNA prognostic signature in clear cell renal cell carcinoma.

BACKGROUND: Establishing a robust signature for prognostic prediction and precision treatment is necessary due to the heterogeneous prognosis and treatment response of clear cell renal cell carcinoma (ccRCC). OBJECTIVES: This study set out to elucidate the biological functions and prognostic role of ferroptosis-related long non-coding RNAs (lncRNAs) based on a synthetic analysis of competing endogenous RNA networks in ccRCC. MATERIAL AND METHODS: Ferroptosis-related genes were obtained from the FerrDb database. The expression data and matched clinical information of lncRNAs, miRNAs and mRNAs from The Cancer Genome Atlas (TCGA) database were obtained to identify differentially expressed RNAs. The lncRNA-miRNA-mRNA ceRNA network was established utilizing the common miRNAs that were predicted in the RNAHybrid, StarBase and TargetScan databases. Then, using progressive univariate Cox regression, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis of gene expression data and clinical information, a ferroptosis-related lncRNA prognosis signature was constructed based on the lncRNAs in ceRNA. Finally, the influence of independent lncRNAs on ccRCC was explored. RESULTS: A total of 35 ferroptosis-related mRNAs, 356 lncRNAs and 132 miRNAs were sorted out after differential expression analysis in the TCGA-KIRC. Subsequently, overlapping lncRNA-miRNA and miRNA-mRNA interactions among the RNAHybrid, StarBase and TargetScan databases were constructed and identified; then a ceRNA network with 77 axes related to ferroptosis was established utilizing mutual miRNAs in 2 interaction networks as nodes. Next, a 6-ferroptosis-lncRNA signature including PVT1, CYTOR, MIAT, SNHG17, LINC00265, and LINC00894 was identified in the training set. Kaplan-Meier analysis, PCA, t-SNE analysis, risk score curve, and receiver operating characteristic (ROC) curve were performed to confirm the validity of the signature in the training set and verified in the validation set. Finally, single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) analysis showed that the signature was related to immune cell infiltration. CONCLUSIONS: Our research underlines the role of the 6-ferroptosis-lncRNA signature as a predictor of prognosis and a therapeutic alternative for ccRCC.

Construction of an immune-related prognostic signature and lncRNA-miRNA-mRNA ceRNA network in acute myeloid leukemia.

The progression of acute myeloid leukemia (AML) is influenced by the immune microenvironment in the bone marrow and dysregulated intracellular competing endogenous RNA (ceRNA) networks. Our study utilized data from UCSC Xena, The Cancer Genome Atlas Program, the Gene Expression Omnibus, and the Immunology Database and Analysis Portal. Using Cox regression analysis, we identified an immune-related prognostic signature. Genomic analysis of prognostic messenger RNA (mRNA) was conducted through Gene Set Cancer Analysis (GSCA), and a prognostic ceRNA network was constructed using the Encyclopedia of RNA Interactomes. Correlations between signature mRNAs and immune cell infiltration, checkpoints, and drug sensitivity were assessed using R software, gene expression profiling interactive analysis (GEPIA), and CellMiner, respectively. Adhering to the ceRNA hypothesis, we established a potential long noncoding RNA (lncRNA)/microRNA (miRNA)/mRNA regulatory axis. Our findings pinpointed 9 immune-related prognostic mRNAs (KIR2DL1, CSRP1, APOBEC3G, CKLF, PLXNC1, PNOC, ANGPT1, IL1R2, and IL3RA). GSCA analysis revealed the impact of copy number variations and methylation on AML. The ceRNA network comprised 14 prognostic differentially expressed lncRNAs (DE-lncRNAs), 6 prognostic DE-miRNAs, and 3 prognostic immune-related DE-mRNAs. Correlation analyses linked these mRNAs' expression to 22 immune cell types and 6 immune checkpoints, with potential sensitivity to 27 antitumor drugs. Finally, we identified a potential LINC00963/hsa-miR-431-5p/CSRP1 axis. This study offers innovative insights for AML diagnosis and treatment through a novel immune-related signature and ceRNA axis. Identified novel biomarkers, including 2 mRNAs (CKLF, PNOC), 1 miRNA (hsa-miR-323a-3p), and 10 lncRNAs (SNHG25, LINC01857, AL390728.6, AC127024.5, Z83843.1, AP002884.1, AC007038.1, AC112512, AC020659.1, AC005921.3) present promising candidates as potential targets for precision medicine, contributing to the ongoing advancements in the field.

CircRNA expression profiles and regulatory networks in the vitreous humor of people with high myopia.

Myopia is a global health and economic issue. Circular RNAs (circRNAs) have been shown to play an important role in the pathogenesis of many ocular diseases. We first evaluated the circRNA profiles and possible roles in vitreous humor samples of individuals with high myopia by a competitive endogenous RNA (ceRNA) array. Vitreous humor samples were collected from 15 high myopic (5 for ceRNA array, and 10 for qPCR) and 15 control eyes (5 for ceRNA array, and 10 for qPCR) with idiopathic epiretinal membrane (ERM) and macular hole (MH). 486 circRNAs (339 upregulated and 147 downregulated) and 264 mRNAs (202 upregulated and 62 downregulated) were differentially expressed between the high myopia and control groups. The expression of hsa_circ_0033079 (hsa-circDicer1), hsa_circ_0029989 (hsa-circNbea), hsa_circ_0019072 (hsa-circPank1) and hsa_circ_0089716 (hsa-circEhmt1) were validated by qPCR. Pearson analysis and multivariate regression analysis showed positive and significant correlations for axial length with hsa-circNbea and hsa-circPank1. KEGG analysis showed that the target genes of circRNAs were enriched in the mTOR, insulin, cAMP, and VEGF signaling pathways. GO analysis indicated that circRNAs mainly targeted transcription, cytoplasm, and protein binding. CircRNA-associated ceRNA network analysis and PPI network analysis identified several critical genes for myopia. The expression of circNbea, circPank1, miR-145-5p, miR-204-5p, Nras, Itpr1 were validated by qPCR in the sclera of form-deprivation myopia (FDM) mice model. CircPank1/miR-145-5p/NRAS and circNbea/miR-204-5p/ITPR1 were identified and may be important in the progression of myopia. Our findings suggest that circRNAs may contribute to the pathogenesis of myopia and may serve as potential biomarkers.

An Important Role in Novel Immune Mechanism and Diagnostic Model of Ankylosing Spondylitis: The CeRNA-ADRB2 Network.

Objective: The mechanism of ankylosing spondylitis (AS) remains unclear, and clinical diagnosis still pose challenges. This study aims to explore potential regulatory mechanisms underlying AS and develop a novel diagnostic model. Methods: Interspinous ligament (ISL) tissues were collected from control samples and ankylosing spondylitis with kyphotic deformity (AS-KD) samples during surgery, followed by high-throughput sequencing. By integrating gene expression profiles from publicly available AS peripheral blood (PB) samples, differentially expressed immune genes (DEIRGs) were identified. Through gene set enrichment analysis(GSEA), gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, the regulatory mechanisms of the immune gene family in AS were explored. A diagnostic model for AS were constructed and validated it externally. Additionally, a competing endogenous RNA(ceRNA)-protein regulatory network was built for key immune genes. Results: Adrenergic receptor beta 2 (ADRB2) was downregulated in both ISL and PB samples. It was enriched in common pathways, including natural killer cell-mediated cytotoxicity, B cell receptor signaling pathway, Th1 and Th2 cell differentiation. Using the LASSO algorithm, 12 DEIRGs were identified, including the downregulated ADRB2. Based on the DEIRGs family, a novel diagnostic model was constructed with an AUC of 0.87 for the validation set and 0.7 for the test set. The AUC for ADRB2 alone was 0.75. Subgrouping AS based on these immune genes revealed a close association with neutrophils. GSEA and KEGG analysis of ISL, PB, and subgrouping of AS showed that ADRB2 may be involved in regulating the T cell receptor signaling pathway. Immune infiltration analysis indicated a decrease in CD8+ T cell infiltration, which was positively correlated with ADRB2. ADRB2 in AS-KD was regulated by multiple ceRNA-protein (lncRNA-[hsa-miR-513a-5p]-mRNA-protein). Conclusion: The immune gene family, especially ADRB2, participates in the mechanism and contributes to the diagnosis of AS.

Comprehensive Analysis of a Competing Endogenous RNA Co-Expression Network in Chronic Obstructive Pulmonary Disease.

Purpose: Chronic obstructive pulmonary disease (COPD) is the main cause of mortality world widely. Non-coding RNAs (lncRNAs) and associated competitive endogenous RNAs (ceRNAs) networks were recently proved to lead to mRNA gene expression downregulation but were still unclear in COPD. This study aims to investigate and elucidate the mechanisms underlying the involvement of ceRNA co-expression networks in COPD pathogenesis. Methods: Obtained expression signature of data from the Gene Expression Omnibus database and compared the differentially expression of mRNAs and miRNAs between COPD patients and healthy smokers. Predicted the miRNA-lncRNA and miRNA-mRNA interaction using online library and employed CIBERSORT to measure the proportions of the 22 immune cells in the COPD and control groups. Results: Established a ceRNA-network comprising 11 lncRNAs, 5 miRNAs, and 16 mRNAs. Using the weighted correlation network analysis method, we identified hub genes and hub miRNAs and obtained one core sub-network, XIST, FGD5-AS1, KCNQ1OT1, HOXA11-AS, LINC00667, H19, PRKCQ-AS1, NUTM2A-AS1/has-mir-454-3p/ZNF678, PRRG4. COPD patients had different proportions of immune cells than controls, and these variations were associated with the magnitude of pulmonary function parameters. Conclusion: The ceRNA-network, particularly the core sub-network, may be a putative goal for COPD, in which specific immune cells were involved.

Role of ceRNA network in inflammatory cells of rheumatoid arthritis. / CeRNAç½‘ç»œåœ¨ç±»é£Žæ¹¿å ³èŠ‚ç‚Žçš„ç‚Žç—‡ç»†èƒžä¸­çš„ä½œç”¨.

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease caused by inflammatory cells. Various inflammatory cells involved in RA include fibroblast-like synoviocytes, macrophages, CD4+T-lymphocytes, B lymphocytes, osteoclasts and chondrocytes. The close interaction between various inflammatory cells leads to imbalance of immune response and disorder of the expression of mRNA in inflammatory cells. It helps to drive production of pro-inflammatory cytokines and stimulate specific antigen-specific T- and B-lymphocytes to produce autoantibodies which is an important pathogenic factor for RA. Competing endogenous RNA (ceRNA) can regulate the expression of mRNA by competitively binding to miRNA. The related ceRNA network is a new regulatory mechanism for RNA interaction. It has been found to be involved in the regulation of abnormal biological processes such as proliferation, apoptosis, invasion and release of inflammatory factors of RA inflammatory cells. Understanding the ceRNA network in 6 kinds of RA common inflammatory cells provides a new idea for further elucidating the pathogenesis of RA, and provides a theoretical basis for the discovery of new biomarkers and effective therapeutic targets.

Construction of ceRNA regulatory networks for osteoporosis.

Osteoporosis increases the risk of fracture. Improving the diagnosis and treatment of osteoporosis has clinical applications. The differentially expressed genes (DEcircRs, DEmRs, DEmiRs) of osteoporotic patients and controls were analyzed using the GEO database, and enrichment analysis of DEmRs was performed. circRNAs and mRNAs, which were predicted to have a target relationship with DEmRs, were obtained to compare competing endogenous RNA (ceRNA) regulatory networks by comparison with differentially expressed genes. Molecular experiments were utilized to validate the expression of genes within the network. The interactions between genes within the ceRNA network were validated by luciferase reporter assays. Following overexpression of circ_0070304 in bone marrow mesenchymal stem cells (BMSCs), the osteogenic differentiation of the cells was assessed by Alizarin Red staining. A total of 110 intersectional DEmRs between patients with osteoporosis and controls from GSE35958 and GSE56815, which were mainly enriched in estrogen, the thyroid hormone signaling pathway, and adherens junctions were identified. A ceRNA network [circ_0070304/miR­183­5p/ring finger and CCCH­type domains 2 (RC3H2)] was then constructed. Circ_0070304 acted as a sponge for miR­183­5p and regulated RC3H2 expression. Overexpression of circ_0070304 upregulated ROCK1 and induced osteogenic differentiation. The ceRNA regulatory network that was obtained is expected to be a new target for osteoporosis treatment and to provide new insights into the diagnosis and treatment of osteoporosis in greater depth.

Characterization of the circulating transcriptome expression profile and identification of novel miRNA biomarkers in hypertrophic cardiomyopathy.

BACKGROUND: Hypertrophic cardiomyopathy (HCM), one of the most common genetic cardiovascular diseases, but cannot be explained by single genetic factors. Circulating microRNAs (miRNAs) are stable and highly conserved. Inflammation and immune response participate in HCM pathophysiology, but whether the miRNA profile changes correspondingly in human peripheral blood mononuclear cells (PBMCs) with HCM is unclear. Herein, we aimed to investigate the circulating non-coding RNA (ncRNA) expression profile in PBMCs and identify potential miRNAs for HCM biomarkers. METHODS: A Custom CeRNA Human Gene Expression Microarray was used to identify differentially expressed (DE) mRNAs, miRNAs, and ncRNAs (including circRNA and lncRNA) in HCM PBMCs. Weighted correlation network analysis (WGCNA) was used to identify HCM-related miRNA and mRNA modules. The mRNAs and miRNAs from the key modules were used to construct a co-expression network. Three separate machine learning algorithms (random forest, support vector machine, and logistic regression) were applied to identify potential biomarkers based on miRNAs from the HCM co-expression network. Gene Expression Omnibus (GEO) database (GSE188324) and experimental samples were used for further verification. Gene set enrichment analysis (GSEA) and competing endogenous RNA (ceRNA) network was used to determine the potential functions of the selected miRNAs in HCM. RESULTS: We identified 1194 DE-mRNAs, 232 DE-miRNAs and 7696 DE-ncRNAs in HCM samples compared with normal controls from the microarray data sets. WGCNA identified key miRNA modules and mRNA modules evidently associated with HCM. We constructed a miRNA‒mRNA co-expression network based on these modules. A total of three hub miRNAs (miR-924, miR-98 and miR-1) were identified by random forest, and the areas under the receiver operator characteristic curves of miR-924, miR-98 and miR-1 were 0.829, 0.866, and 0.866, respectively. CONCLUSIONS: We elucidated the transcriptome expression profile in PBMCs and identified three hub miRNAs (miR-924, miR-98 and miR-1) as potential biomarkers for HCM detection.

The non-coding competing endogenous RNAs in acute myeloid leukemia: biological and clinical implications.

Acute myeloid leukemia (AML) is a hematologic carcinoma that has seen a considerable improvement in patient prognosis because of genetic diagnostics and molecularly-targeted therapies. Nevertheless, recurrence and drug resistance remain significant obstacles to leukemia treatment. It is critical to investigate the underlying molecular mechanisms and find solutions. Non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), circular RNAs, long non-coding RNAs, and pseudogenes, have been found to be crucial components in driving cancer. The competing endogenous RNA (ceRNA) mechanism has expanded the complexity of miRNA-mediated gene regulation. A great deal of literature has shown that ncRNAs are essential to the biological functions of the ceRNA network (ceRNET). NcRNAs can compete for the same miRNA response elements to influence miRNA-target RNA interactions. Recent evidence suggests that ceRNA might be a potential biomarker and therapeutic strategy. So far, however, there have been no comprehensive studies on ceRNET about AML. What is not yet clear is the clinical application of ceRNA in AML. This study attempts to summarize the development of research on the related ceRNAs in AML and the roles of ncRNAs in ceRNET. We also briefly describe the mechanisms of ceRNA and ceRNET. What's more significant is that we explore the clinical value of ceRNAs to provide accurate diagnostic and prognostic biomarkers as well as therapeutic targets. Finally, limitations and prospects are considered.

SNHG 12 and hsa-miR-140-5P may play an important role in the ceRNA network related to hypertrophic cardiomyopathy.

Background: Competing endogenous RNA (ceRNA) networks play important roles in the mechanism and development of a variety of diseases. This study aimed to construct a ceRNA network of hypertrophic cardiomyopathy (HCM). Methods: We searched the Gene Expression Omnibus (GEO) database and then analyzed the RNAs of 353 samples to explore differentially expressed lncRNAs (DELs), microRNAs (miRNAs; DEMs), and messenger RNAs (DEmRNAs) during the progression of HCM. Weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and transcription factor (TF) prediction of miRNAs were also performed, and the GO terms, KEGG pathway terms, protein-protein interaction (PPI) network, and Pearson correlation network of the DEGs were visualized with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and through Pearson analysis. In addition, a ceRNA network related to HCM was constructed on the basis of the DELs, DEMs, and DEs. Finally, the function of the ceRNA network was explored via GO and KEGG enrichment analyses. Results: Through our analysis, 93 DELs (77 upregulated and 16 downregulated), 163 DEMs (91 upregulated and 72 downregulated), and 432 DEGs (238 upregulated and 194 downregulated) were screened. The functional enrichment analysis results for miRNAs showed that the miRNAs were mainly related to the VEGFR signaling network and the INFr pathway and were mainly regulated by TFs such as SOX1, TEAD1, and POU2F1. Gene set enrichment analysis (GSEA), GO analysis, and KEGG enrichment analysis showed that the DEGs were enriched in the Hedgehog signaling pathway, IL-17 signaling pathway, and TNF signaling pathway. In addition, a ceRNA network including 8 lncRNAs (e.g., LINC00324, SNHG12, and ALMS1-IT1), 7 miRNAs (e.g., hsa-miR-217, hsa-miR-184, and hsa-miR-140-5p), and 52 mRNAs (e.g., IGFBP5, TMED5, and MAGT1) was constructed. The results revealed that SNHG12, hsa-miR-140-5p, hsa-miR-217, TFRC, HDAC4, TJP1, IGFBP5, and CREB5 may form an important network involved in the pathology of HCM. Conclusions: The novel ceRNA network that we have demonstrated will provide new research points about molecular mechanisms of HCM.

Comprehensive analysis of autophagy-related gene expression profiles identified five gene biomarkers associated with immune infiltration and advanced plaques in carotid atherosclerosis.

BACKGROUND: Autophagy plays an important role in the progression of carotid atherosclerosis (CAS). This study aimed to identify hub autophagy-related genes (ATGs) associated with CAS. METHODS: GSE43292 and GSE28829 datasets of early and advanced CAS plaques were enrolled from the Gene Expression Omnibus (GEO) database. A comprehensive analysis of differentially expressed ATGs (DE-ATGs) was conducted. Functional enrichment assay was used to explore biological functions of DE-ATGs. The hub ATGs were identified by protein-protein interaction (PPI) network. Immunohistochemistry (IHC) and Real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR) were used to validate hub ATGs at the protein level and mRNA level. Correlation analysis of hub ATGs with immune cells was also conducted. In addition, a competitive endogenous RNA (ceRNA) network was constructed, and diagnostic value of hub ATGs was evaluated. RESULTS: A total of 19 DE-ATGs were identified in early and advanced CAS plaques. Functional enrichment analysis of DE-ATGs suggested that they were closely correlated to autophagy, apoptosis, and lipid regulation. Moreover, 5 hub ATGs, including TNFSF10, ITGA6, CTSD, CCL2, and CASP1, were identified and further verified by IHC. The area under the curve (AUC) values of the 5 hub ATGs were 0.818, 0.732, 0.792, 0.814, and 0.812, respectively. Competing endogenous RNA (ceRNA) networks targeting the hub ATGs were also constructed. In addition, the 5 hub ATGs were found to be closely associated with immune cell infiltration in CAS. CONCLUSION: In this study, we identified 5 hub ATGs including CASP1, CCL2, CTSD, ITGA6 and TNFSF10, which could serve as candidate diagnostic biomarkers and therapeutic targets.

Constructed the ceRNA network and predicted a FEZF1-AS1/miR-92b-3p/ZIC5 axis in colon cancer.

The purpose of this study was to identify the role of FEZF1-AS1 in colon cancer and predicted the underlying mechanism. We extracted sequencing data of colon cancer patients from The Cancer Genome Atlas database, identified the differential expression of long noncoding RNA, microRNA, and messenger RNA, constructed a competitive endogenous RNA network, and then analyzed prognosis. Then, we used the enrichment analysis databases for functional analysis. Finally, we studied the FEZF1-AS1/miR-92b-3p/ZIC5 axis. We detected the expression of FEZF1-AS1, miR-92b-3p, and ZIC5 via quantitative reverse transcription-PCR, transfected colon cancer cell RKO with lentivirus and conducted FEZF1-AS1 knockdown, and performed cancer-related functional assays. It indicated that many RNA in the competitive endogenous RNA network, such as ZIC5, were predicted to be related to overall survival of colon cancer patients, and enrichment analysis showed cancer-related signaling pathways, such as PI3K/AKT signaling pathway. The expression of FEZF1-AS1 and ZIC5 was significantly higher and that of miR-92b-3p was lower in the colon cancer than in the normal colon tissues. FEZF1-AS1 promoted the migration, proliferation, as well as invasion of RKO. According to the prediction, FEZF1-AS1 and ZIC5 might competitively bind to miR-92b-3p, leading to the weakening of the inhibitory impact of miR-92b-3p on ZIC5 and increasing expression of ZIC5, thus further activating the PI3K/AKT signaling pathway, which led to the occurrence and development of colon cancer. The study suggested that FEZF1-AS1 might be an effective diagnosis biomarker for colon cancer.

New insights into COL26A1 in thyroid carcinoma: prognostic prediction, functional characterization, immunological drug target and ceRNA network.

Background: Thyroid carcinoma (THCA) is one of the most commonly diagnosed malignancies. Collagen is the main component in extracellular matrix. Rising studies have determined the oncogenic effect of collagen in cancer progression, which is intriguing to be further explored. Collagen type XXVI alpha 1 chain (COL26A1) is a newly discovered collagen subtype, functions of which still remain poorly demonstrated in THCA. Methods: Based on the transcriptome data from The Cancer Genome Atlas (TCGA) and other public databases, we conducted investigations of COL26A1 in THCA with respects to diagnostic/prognostic prediction, functional characterization, immune infiltration, chemical drug target and non-coding RNA regulatory network. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) and western blot were used to verify the expression of COL26A1 in THCA. Results: COL26A1 was significantly upregulated in THCA, and the high COL26A1 expression inferred poor prognosis [hazard ratio (HR) =4.76; 95% confidence interval (CI): 1.36-16.73; P=0.015]. The diagnostic area under the curve (AUC) of COL26A1 achieved 0.736 (95% CI: 0.669-0.802). COL26A1 was also identified as an independent prognostic predictor for THCA (HR =3.928; 95% CI: 3.716-4.151; P<0.001). Besides, logistic regression analysis indicated that age >45 years [odds ratio (OR) =1.532; 95% CI: 1.081-2.176; P=0.017], pathological stage III (OR =2.055; 95% CI: 1.314-3.184; P=0.001), tall cell subtype (OR =5.533; 95% CI: 2.420-14.957; P<0.001), residual tumor R1 (OR =1.844; 95% CI: 1.035-3.365; P=0.041) and extrathyroidal extension (OR =1.800; 95% CI: 1.225-1.660; P=0.003) were risk factors associated with high COL26A1 expression in THCA. Functional characterizations implied that COL26A1 was associated with immunological processes and oncogenic signaling pathways. High COL26A1 expression was accompanied by more abundant infiltration of immune cells and higher stromal/immune score. In addition, most immune checkpoints were significantly positively co-expressed with COL26A1, including PD-1, PD-L1 and CTLA4. Drugs including trichloroethylene, acetamide and thioacetamide etc. that can decrease the expression of COL26A1 were also identified. The predicted long noncoding RNA (lncRNA)-microRNA (miRNA)-COL26A1 regulatory axes were successfully deciphered. qRT-PCR and western blot verified the upregulation of COL26A1 in THCA. Conclusions: Our work has primarily appraised COL26A1 as a promising biomarker for diagnosis/prognosis and immuno/therapeutic target in THCA.

Construction of a potentially functional lncRNA-miRNA-mRNA network in sepsis by bioinformatics analysis.

Objective: Sepsis is a common disease in internal medicine, with a high incidence and dangerous condition. Due to the limited understanding of its pathogenesis, the prognosis is poor. The goal of this project is to screen potential biomarkers for the diagnosis of sepsis and to identify competitive endogenous RNA (ceRNA) networks associated with sepsis. Methods: The expression profiles of long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and messenger RNAs (mRNAs) were derived from the Gene Expression Omnibus (GEO) dataset. The differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs) and mRNAs (DEmRNAs) were screened by bioinformatics analysis. DEmRNAs were analyzed by protein-protein interaction (PPI) network analysis, transcription factor enrichment analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Set Enrichment Analysis (GSEA). After the prediction of the relevant database, the competitive ceRNA network is built in Cytoscape. The gene-drug interaction was predicted by DGIgb. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to confirm five lncRNAs from the ceRNA network. Results: Through Venn diagram analysis, we found that 57 DElncRNAs, 6 DEmiRNAs and 317 DEmRNAs expressed abnormally in patients with sepsis. GO analysis and KEGG pathway analysis showed that 789 GO terms and 36 KEGG pathways were enriched. Through intersection analysis and data mining, 5 key KEGG pathways and related core genes were revealed by GSEA. The PPI network consists of 247 nodes and 1,163 edges, and 50 hub genes are screened by the MCODE plug-in. In addition, there are 5 DElncRNAs, 6 DEmiRNAs and 28 DEmRNAs in the ceRNA network. Drug action analysis showed that 7 genes were predicted to be molecular targets of drugs. Five lncRNAs in ceRNA network are verified by qRT-PCR, and the results showed that the relative expression of five lncRNAs was significantly different between sepsis patients and healthy control subjects. Conclusion: A sepsis-specific ceRNA network has been effectively created, which is helpful to understand the interaction between lncRNAs, miRNAs and mRNAs. We discovered prospective sepsis peripheral blood indicators and proposed potential treatment medicines, providing new insights into the progression and development of sepsis.