Dysregulated lncRNAs regulate human umbilical cord mesenchymal stem cell differentiation into insulin-producing cells by forming a regulatory network with mRNAs
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE247570 | GEO | 2023/11/16
REPOSITORIES: GEO
Similar Datasets
Project description:ObjectiveIn recent years, cell therapy has emerged as a new research direction in the treatment of diabetes. However, the underlying molecular mechanisms of mesenchymal stem cell (MSC) differentiation necessary to form such treatment have not been clarified.MethodsIn this study, human umbilical cord mesenchymal stem cells (HUC-MSCs) isolated from newborns were progressively induced into insulin-producing cells (IPCs) using small molecules. HUC-MSC (S0) and four induced stage (S1-S4) samples were prepared. We then performed transcriptome sequencing experiments to obtain the dynamic expression profiles of both mRNAs and long noncoding RNAs (lncRNAs).ResultsWe found that the number of differentially expressed lncRNAs and mRNAs trended downwards during differentiation. Gene Ontology (GO) analysis showed that the target genes of differentially expressed lncRNAs were associated with translation, cell adhesion, and cell connection. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the NF-KB signalling pathway, MAPK signalling pathway, HIPPO signalling pathway, PI3K-Akt signalling pathway, and p53 signalling pathway were enriched in these differentially expressed lncRNA-targeting genes. We also found that the coexpression of the lncRNA CTBP1-AS2 with PROX1 and the lncRNAs AC009014.3 and GS1-72M22.1 with JARID2 mRNA was related to the development of pancreatic beta cells. Moreover, the coexpression of the lncRNAs: XLOC_ 050969, LINC00883, XLOC_050981, XLOC_050925, MAP3K14- AS1, RP11-148K1.12, and CTD2020K17.3 with p53, regulated insulin secretion by pancreatic beta cells.ConclusionIn this study, HUC-MSCs combined with small molecule compounds were successfully induced into IPCs. Differentially expressed lncRNAs may regulate the insulin secretion of pancreatic beta cells by regulating multiple signalling pathways. The lncRNAs AC009014.3, Gs1-72m21.1, and CTBP1-AS2 may be involved in the development of pancreatic beta cells, and the lncRNAs: XLOC_050969, LINC00883, XLOC_050981, XLOC_050925, MAP3K14-AS1, RP11-148K1.12, and CTD2020K17.3 may be involved in regulating the insulin secretion of pancreatic beta cells, thus providing a lncRNA catalogue for future research regarding the mechanism of the transdifferentiation of HUC-MSCs into IPCs. It also provides a new theoretical basis for the transplantation of insulin-producing cells into diabetic patients in the future.
| S-EPMC10809572 | biostudies-literature
Project description:To analyze and construct a survival-related endogenous RNA (ceRNA) network in gastric cancer (GC) with lymph node metastasis, we obtained expression profiles of long non-coding RNAs (lncRNAs), mRNAs, and microRNAs (miRNAs) in GC from The Cancer Genome Atlas database. The edgeR package was used to screen differentially expressed lncRNAs, mRNAs, and miRNAs between GC patients with lymphatic metastasis and those without lymphatic metastasis. Then, we used univariate Cox regression analysis to identify survival-related differentially expressed RNAs. In addition, we used multivariate Cox regression analysis to screen lncRNAs, miRNAs, and mRNAs for use in the prognostic prediction models. The results showed that 2,247 lncRNAs, 155 miRNAs, and 1,253 mRNAs were differentially expressed between the two patient groups. Using univariate Cox regression analysis, we found that 395 lncRNAs, eight miRNAs, and 180 mRNAs were significantly related to the survival time of GC patients. We next created a survival-related network consisting of 59 lncRNAs, seven miRNAs, and 36 mRNAs. In addition, we identified eight RNAs associated with prognosis by multivariate Cox regression analysis, comprising three lncRNAs (AC094104.2, AC010457.1, and AC091832.1), two miRNAs (miR-653-5p and miR-3923), and three mRNAs (C5orf46, EPHA8, and HPR); these were used to construct the prognostic prediction models, and their risk scores could be used to assess GC patients' prognosis. In conclusion, this study provides new insights into ceRNA networks in GC and the screening of prognostic biomarkers for GC.
| S-EPMC7960915 | biostudies-literature
Project description:BackgroundEsophageal squamous cell carcinoma (ESCC) is the main subtype of esophageal carcinoma. Protein coding genes and non-coding RNAs can be powerful prognostic factors in multiple cancers, including ESCC. However, there is currently no model that integrates multiple types of RNA expression signatures to predict clinical outcomes.MethodsThe sequencing data (RNA-sequencing and miRNA-sequencing) and clinical data of ESCC patients were obtained from The Cancer Genome Atlas (TCGA) database, and Differential gene expression analysis, Cox regression analysis and Spearman correlation analysis were used to construct prognosis-related lncRNA-mRNA co-expression network and scoring system with multiple types of RNA. The potential molecular mechanisms of prognostic mRNAs were explored by functional enrichment analysis.ResultsA total of 62 prognostic lncRNAs, eight prognostic miRNAs and 66 prognostic mRNAs were identified in ESCC (P-value < 0.05) and a prognosis-related lncRNA-mRNA co-expression network was created. Five prognosis-related hub RNAs (CDCA2, MTBP, CENPE, PBK, AL033384.1) were identified. Biological process analysis revealed that mRNAs in prognosis-related co-expression RNA network were mainly enriched in cell cycle, mitotic cell cycle and nuclear division. Additionally, we constructed a prognostic scoring system for ESCC using ten signature RNAs (MLIP, TNFSF10, SIK2, LINC01068, LINC00601, TTTY14, AC084262.1, LINC01415, miR-5699-3p, miR-552-5p). Using this system, patients in the low-risk group had better long-term survival than those in the high-risk group (log-rank, P-value < 0.0001). The area under the ROC curve (AUCs) revealed that the accuracy of the prediction model was higher than the accuracy of single type of RNA prediction model.ConclusionIn brief, we constructed a prognostic scoring system based on multiple types of RNA for ESCC that showed high predicting prognosis performance, and deeply understood the regulatory mechanism of prognosis-related lncRNA-mRNA co-expression network.
| S-EPMC7017795 | biostudies-literature
Project description:Messenger RNA (mRNA) and long noncoding RNA (lncRNA) are two main subgroups of RNAs participating in transcription regulation. With the development of next generation sequencing, increasing lncRNAs are identified. Many hidden functions of lncRNAs are also revealed. However, the differences in lncRNAs and mRNAs are still unclear. For example, we need to determine whether lncRNAs have stronger tissue specificity than mRNAs and which tissues have more lncRNAs expressed. To investigate such tissue expression difference between mRNAs and lncRNAs, we encoded 9339 lncRNAs and 14,294 mRNAs with 71 expression features, including 69 maximum expression features for 69 types of cells, one feature for the maximum expression in all cells, and one expression specificity feature that was measured as Chao-Shen-corrected Shannon's entropy. With advanced feature selection methods, such as maximum relevance minimum redundancy, incremental feature selection methods, and random forest algorithm, 13 features presented the dissimilarity of lncRNAs and mRNAs. The 11 cell subtype features indicated which cell types of the lncRNAs and mRNAs had the largest expression difference. Such cell subtypes may be the potential cell models for lncRNA identification and function investigation. The expression specificity feature suggested that the cell types to express mRNAs and lncRNAs were different. The maximum expression feature suggested that the maximum expression levels of mRNAs and lncRNAs were different. In addition, the rule learning algorithm, repeated incremental pruning to produce error reduction algorithm, was also employed to produce effective classification rules for classifying lncRNAs and mRNAs, which gave competitive results compared with random forest and could give a clearer picture of different expression patterns between lncRNAs and mRNAs. Results not only revealed the heterogeneous expression pattern of lncRNA and mRNA, but also gave rise to the development of a new tool to identify the potential biological functions of such RNA subgroups.
| S-EPMC6274976 | biostudies-literature
Project description:Acute myocardial infarction (AMI) is a major cause of mortality in the general population. However, the molecular phenotypes and therapeutic targets of AMI patients remain unclear. By profiling genome-wide transcripts and microRNAs (miRNAs) in a cohort of 23 AMI patients and 23 non-AMI patients, we found 218 dysregulated genes identified in the infarcted heart tissues from AMI patients relative to non-AMI controls. Pathway enrichment analysis of the dysregulated genes pointed to cell signaling/communication, cell/organism defense and cell structure/motility. We next compared the expression profiles of potential regulating miRNAs, suggesting that dysregulation of a number of AMI-associated genes (e.g., IL12A, KIF1A, HIF1α and CDK13) may be attributed to the dysregulation of their respective regulating miRNAs. One potentially pathogenic miRNA-mRNA pair, miR-210-HIF1α, was confirmed in a mouse model of myocardial infarction (MI). Inhibition of miR-210 expression improved the survival and cardiac function of MI mice. In conclusion, we presented the pathologic relationships between miRNAs and their gene targets in AMI. Such deregulated microRNAs and mRNAs like miR-210 serve as novel therapeutic targets of AMI.
| S-EPMC4697709 | biostudies-literature
Project description:Chronic myelocytic leukemia (CML) is characterized by increased and unregulated growth of predominantly myeloid cells in the bone marrow, and accumulation of these cells in blood. We investigated the effects of an anti-malarial drug, dihydroartemisinin (DHA), on K562 CML cells. We identified 34 mRNAs and eight lncRNAs dysregulated following DHA treatment in pure and hemin-induced K562 cells. Up- or downregulation of these potential DHA targets increased with increasing DHA concentration. We also constructed and analyzed a DHA-related mRNA-lncRNA regulation network in K562 cells, and found that four DHA-modulated mRNAs regulated by four lncRNAs participated in the steroid biosynthesis pathway. Some estrogen-related drugs, such as tamoxifen, shared common targets with DHA. We inferred that DHA exerted anti-cancer effects on K562 cells by influencing estrogen levels. Our findings indicate that DHA has potential not only as an anti-malarial drug, but also as an anti-CML chemotherapeutic.
| S-EPMC5788658 | biostudies-literature
Project description:Non-coding RNAs (ncRNAs) have been previously reported to serve an important role in transcription. In addition, several studies have revealed that long ncRNAs (lncRNAs) have a crucial role in human diseases. However, the association between lncRNAs and inflammation‑induced intestinal macrophages in the intestinal mucosal barrier has remained elusive. In the present study, intestinal macrophages from healthy Sprague Dawley rats were divided into two groups: The experimental group, consisting of intestinal macrophages treated with 1 mg/l lipopolysaccharide (LPS) and the control group, composed of untreated cells. Differentially expressed (DE) lncRNAs and mRNAs between the control and experimental groups were identified using microarray profiling. The levels of DE mRNAs and lncRNAs were measured by reverse transcription‑quantitative PCR (RT‑qPCR). Furthermore, Gene Ontology (GO) and pathway enrichment analyses of DE mRNAs and lncRNAs were performed. To identify core regulatory factors among DE lncRNAs and mRNAs, a lncRNA‑mRNA network was constructed. A total of 357 DE lncRNAs and 542 DE mRNAs between the LPS‑treated and untreated groups were identified (fold-change >1.5; P<0.05). In addition, selected microarray data were confirmed by RT‑qPCR. GO analysis of the DE mRNAs indicated that the biological functions of the upregulated mRNAs included inflammatory response, immune response, metabolic process and signal transduction, whereas those of the downregulated mRNAs were metabolic process, cell cycle, apoptosis and inflammatory response. In addition, pathway enrichment analysis of the upregulated mRNAs revealed that the most enriched pathways were the NF‑κB signaling pathway, B‑cell receptor signaling pathway and apoptosis, while the downregulated mRNAs were significantly involved in metabolic pathways, the phosphatidylinositol signaling system, cytokine‑cytokine receptor interaction and the Toll‑like receptor signaling pathway. The lncRNA‑mRNA co‑expression network suggested that lncRNAs NONMMUT024673 and NONMMUT062258 may have an important role in LPS‑induced intestinal macrophages. The present study identified the DE profiles between LPS‑ and non‑LPS‑treated intestinal macrophages. These DE lncRNAs and mRNAs may be used as potential targets for attenuating excessive inflammatory response in intestinal mucosal barrier dysfunction.
| S-EPMC7533500 | biostudies-literature
Project description:The colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide, but the pathogenesis of CRC remains not well-known. Increasing studies have highlighted the critical roles of long noncoding RNAs (lncRNAs) in tumorigenesis and cancer cells metastasis, however, the expression pattern, biological roles of lncRNAs, and the mechanisms responsible for their function in CRC remain elusive. In this study, we performed a genome-wide comprehensive analysis of lncRNAs profiling and clinical relevance to identify novel lncRNAs for the further study in CRC. RNA sequencing and microarray data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were annotated and analyzed to find differentially expressed lncRNAs in CRC. Analysis of these datasets revealed that hundreds of lncRNAs expression are dysregulated in CRC tissues when compared with normal tissues. By genomic variation analyses, we identified that some of these lncRNAs dysregulation is associated with the copy number amplification or deletion. Moreover, many lncRNAs expression levels are significantly associated with CRC patients overall and recurrence-free survivals, such as H19, LEF1-AS1, and RP11-296E3.2. Furthermore, we identified one liver metastasis-associated lncRNA termed LUCAT1 in CRC by analyzing lncRNAs expression profiles in the CRC tissues from patients with liver metastasis compared with the CRC tissues without metastasis. Finally, loss-of-function assays determined that knockdown of LUCAT1 could impair CRC cells invasion. Taken together, aberrantly expressed lncRNAs may play critical roles in the development and liver metastasis of CRC, and our findings may provide useful resource for identification of novel biomarkers of CRC.
| S-EPMC5633553 | biostudies-literature
Project description:MSC-EV exerts their effects by transferring cargo components into target cells. Given that extracellular vesicles (EV) can deliver diverse cargo to recipient cells and elicit similar therapeutic effects, we investigate the cargo of both adipose-derived MSC-derived EV (AMSC-EV) and umbilical cord-derived MSC-derived EV (HUMSC-EV).As a crucial component of EV cargo, proteins are known to exert significant influence on EV functions. We performed a protein analysis of MSC-EV.
2025-05-07 | PXD056371 | Pride
Project description:Long non-coding RNAs (lncRNAs) regulate gene expression through different molecular mechanisms, including DNA binding via the formation of RNA:DNA:DNA triple helices (TPXs). Despite the increasing amount of experimental evidence, TPXs investigation remains challenging. Here we present 3plex, a software able to predict TPX interactions in silico. Given an RNA sequence and a set of DNA sequences, 3plex integrates 1) Hoogsteen pairing rules that describe the biochemical interactions between RNA and DNA nucleotides, 2) RNA secondary structure prediction and 3) determination of the TPX thermal stability derived from a collection of TPX experimental evidences. We systematically collected and uniformly re-analysed published experimental lncRNA binding sites on human and mouse genomes. We used these data to evaluate 3plex performance and showed that its specific features allow a reliable identification of TPX interactions. We compared 3plex with the other available software and obtained comparable or even better accuracy at a fraction of the computation time. Interestingly, by inspecting collected data with 3plex we found that TPXs tend to be shorter and more degenerated than previously expected and that the majority of analysed lncRNAs can directly bind to the genome by TPX formation. Those results suggest that an important fraction of lncRNAs can exert its biological function through this mechanism. The software is available at https://github.com/molinerisLab/3plex.
| S-EPMC10236371 | biostudies-literature