Differentially Expressed mRNAs Research Articles

Long-stranded non-coding RNAs temporal-specific expression profiles reveal longissimus dorsi muscle development and intramuscular fat deposition in Tianzhu white yak.

The process of muscle development and intramuscular fat (IMF) deposition is quite complex and controlled by both mRNAs and ncRNAs. Long-stranded non-coding RNAs (LncRNAs) are involved in various biological processes in mammals while also playing a critical role in muscle development and fat deposition. In the present study, RNA-Seq was used to comprehensively study the expression of lncRNAs and mRNAs during muscle development and intramuscular fat deposition in postnatal Tianzhu white yaks at three stages, including 6 mo of age (calve, n = 6), 30 mo of age (young cattle, n = 6) and 54 mo of age (adult cattle, n = 6). The results indicated that a total of 2,101 lncRNAs and 20,855 mRNAs were screened across the three stages, of which the numbers of differential expression (DE) lncRNAs and DE mRNAs were 289 and 1,339, respectively, and DE lncRNAs were divided into eight different expression patterns based on expression trends. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that some DE mRNAs overlapped with target genes of lncRNAs, such as NEDD4L, SCN3B, AGT, HDAC4, DES, MYH14, KLF15 (muscle development), ACACB, PCK2, LIPE, PIK3R1, PNPLA2, and MGLL (intramuscular fat deposition). These DE mRNAs were significantly enriched in critical muscle development and IMF deposition-related pathways and GO terms, such as AMPK signaling pathway, PI3K-Akt signaling pathway, PPAR signaling pathway, etc. In addition, lncRNA-mRNA co-expression network analysis revealed that six lncRNAs (MSTRG.20152.2, MSTRG.20152.3, XR_001351700.1, MSTRG.8190.1, MSTRG.4827.1, and MSTRG.11486.1) may play a major role in Tianzhu white yak muscle development and lipidosis deposition. Therefore, this study enriches the database of yak lncRNAs and could help to further explore the functions and roles of lncRNAs in different stages of muscle development and intramuscular fat deposition in the Tianzhu white yak.

Expression profiles of circular RNAs and interaction networks of competing endogenous RNAs in neurogenic bladder of rats following suprasacral spinal cord injury.

Neurogenic bladder (NB) following suprasacral spinal cord injury (SSCI) is an interstitial disease with the structural remodeling of bladder tissue and matrix over-deposition. Circular RNAs (circRNAs) are involved in fibrotic disease development through their post-transcriptional regulatory functions. This study aimed to use transcriptome high-throughput sequencing to investigate the process of NB and bladder fibrosis after SSCI. Spinal cord transection at the T10-T11 level was used to construct the SSCI model in rats (10-week-old female Wistar rats, weighing 200 ± 20 g). The bladders were collected without (sham group) and with (SSCI 1-3 groups) NB status. Morphological examination was conducted to assess the extent of bladder fibrosis. Additionally, RNA sequencing was utilized to determine mRNAs and circRNAs expression patterns. The dynamic changes of differentially expressed mRNAs (DEMs) and circRNAs (DECs) in different periods of SSCI were further analyzed. Bladder weight, smooth muscle cell hypertrophy, and extracellular matrix gradually increased after SSCI. Compared with the sham group, 3,255 DEMs and 1,339 DECs, 3,449 DEMs and 1,324 DECs, 884 DEMs, and 1,151 DECs were detected in the SSCI 1-3 groups, respectively. Specifically, circRNA3621, circRNA0617, circRNA0586, and circRNA4426 were significant DECs common to SSCI 1-3 groups compared with the sham group. Moreover, Gene Ontology (GO) enrichment suggested that inflammatory and chronic inflammatory responses were the key events in NB progression following SSCI. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment associated with the "Chemokine signaling pathway", the "IL-17 signaling pathway", and the "TGF-beta signaling pathway" suggests their potential involvement in regulating biological processes. The circRNA-miRNA-mRNA interaction networks of DECs revealed rno-circ-2239 (micu2) as the largest node, indicating that the rno-circ-2239-miRNA-mRNA-mediated network may play a critical role in the pathogenesis of SSCI-induced NB. This study offers a comprehensive outlook on the possible roles of DEMs and DECs in bladder fibrosis and NB progression following SSCI. These findings have the potential to serve as novel biomarkers and therapeutic targets.

Long non-coding RNAs as potential biomarkers or therapeutic targets in gastric cancer.

This study aimed to find lncRNAs and mRNAs that were expressed differently by combining microarray datasets from different studies. This was done to find important target genes in gastric cancer for anti-cancer therapy. Gastric cancer (GC) is the fourth most frequent and second-most deadly malignancy worldwide. Thus, genetic diagnosis and treatment should focus on genetic and epigenetic variables. Based on several studies, disordered expression of non-coding RNAs (ncRNAs), such as lncRNAs, regulate gastric cancer invasion and metastasis. Besides, lncRNAs cooperatively regulate gene expression and GC progression. We obtained differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) from three GC tissue microarray datasets by meta-analysis and screened genes using the "Limma" package. Then, using the RNAInter database, we allocated DEmRNAs to each DElncRNA. ClusterProfiler and GOplot programs were used to analyze function enrichment pathways and gene ontologies for final DEmRNAs. A total of 9 differentially expressed lncRNAs (DElncRNAs) (5 up-regulated and 4 down-regulated), and 856 DEmRNAs (451 up-regulated and 405 down-regulated) between tumor and adjacent normal samples were found. Finally, 117 differentially expressed mRNAs were predicted as interactors of six DElncRNAs (H19, WT1-AS, EMX2OS, HOTAIR, ZEB1-AS1, and LINC00261). In order to promote cancer therapeutics and give knowledge on the process of carcinogenesis, our study projected a network of drug-gene interactions for discovered genes and presented relevant prospective biomarkers for the prognosis of patients with stomach cancer.

Construction of ceRNA Regulatory Network in Human Oocytes.

Cryogenic freezing, often known as cryopreservation, is a technique for preserving human oocytes. In this study, differentially expressed long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) were identified using the human frozen oocyte dataset GSE69768 from the Gene Expression Database (GEO). Subsequently, combined with the microRNA atlas database, the miRNAs combined with differentially expressed lncRNAs (DElncRNAs) were predicted, and the lncRNA-miRNA-mRNA interaction relationship and competitive endogenous (ceRNA) regulatory network were obtained. The results revealed that multiple DElncRNAs and DEmRNAs were involved in the ceRNA network of the human oocyte. Finally, GO functional annotation and KEGG pathway enrichment analysis were performed on the differentially expressed mRNA (DEmRNA) in the ceRNA network, and the biological processes and pathways that may be related to the ceRNA network in frozen oocytes were explored. In conclusion, in the ceRNA network for human oocyte, lncRNA, mRNA, and miRNA do not each operate via a distinct, independent mechanism. Not only does the RNA-RNA contact involve the ceRNA regulatory mechanism, but it also involves interactions between proteins that are encoded by genes. Furthermore, the negative effects of inter-individual variations and instability on the quality of high-throughput detection cannot be completely ruled out due to the paucity of human oocyte data containing both mRNA and lncRNA expression profiles.

Comprehensive analysis of the molecular mechanism for gastric cancer based on competitive endogenous RNA network

Objective: To explore the regulatory mechanism of competitive endogenous RNAs (ceRNA) in gastric cancer (GC) and to predict the prognosis of GC. Materials and Methods: Expression profiles of long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs were obtained from The Cancer Genome Atlas platform. Differentially expressed RNAs (DERNAs) were screened to construct a lncRNA-miRNA-mRNA ceRNA network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed on the ceRNA network-related differentially expressed mRNAs (DEmRNAs). Next, the DERNAs were subjected to Cox regression and survival analyses to identify crucial prognostic factors for patients with GC. Results: We detected 1029 differentially expressed lncRNAs, 104 differentially expressed miRNAs, and 1659 DEmRNAs in patients with GC. Next, we performed bioinformatic analysis to construct the lncRNA-miRNA-mRNA ceRNA network, which included 10 miRNAs, 65 lncRNAs, and 10 mRNAs. Subsequently, KaplanMeier (K-M) analysis showed that the survival rate of the high-risk group was significantly lower than that of the low-risk group, and the area under the curve value of the receiver operating characteristic curve revealed that the polygenic model had good predictive ability. The results indicated that ADAMTS9-AS1, ATAD2, and CADM2 might be potential therapeutic targets and prognostic biomarkers for GC. Conclusions: Our study has implications for predicting prognosis and monitoring surveillance of GC and provides a new theoretical and experimental basis for the clinical prognosis of GC.

Construction of atrial fibrillation‐related circRNA/lncRNA‐miRNA‐mRNA regulatory network and analysis of potential biomarkers

The specific pathogenesis of atrial fibrillation (AF) remains unclear. In this study, we examined the expression of differential messenger RNAs (mRNAs), circular RNAs (circRNAs), and long-stranded noncoding RNAs (lncRNAs) from human peripheral blood mononuclear cells to initially construct a circRNA/lncRNA-miRNA-mRNA ceRNA regulatory network to explore the pathogenesis of AF and to screen for potential biomarkers. A total of four pairs of AF cases and healthy subjects were selected to detect differentially expressed mRNAs, circRNAs, and lncRNAs in peripheral blood mononuclear cells by microarray analysis. And 20 pairs of peripheral blood from AF patients and healthy subjects were selected for validation of mRNA, circRNA, and lncRNA by quantitative real-time PCR (qRT-PCR).The relevant ceRNA networks were constructed by GO and KEGG and correlation analysis. The results showed that compared with healthy subjects, there were 813 differentially expressed mRNAs (DEmRNAs) in peripheral blood monocytes of AF, including 445 upregulated genes and 368 downregulated genes, 120 differentially expressed circRNAs (DEcircRNAs), including 65 upregulated and 55 downregulated, 912 differentially expressed lncRNAs (DElncRNAs), including 531 upregulated and 381 downregulated lncRNAs. GO and KEGG analysis of DERNA revealed the biological processes and pathways involved in AF. Based on microarray data and predicted miRNAs, a ceRNA network containing 34 mRNAs, 212 circRNAs, 108 lncRNAs, and 38 miRNAs was constructed. We revealed a novel ceRNA network in AF and showed that downregulated XIST, circRNA_2773, and CADM1 were negatively correlated with miR-486-5p expression and had a potential targeting relationship with miR-486-5p.

Bioinformatics-based study of the ceRNA network and the sub-network of the Ziyin Buyang Formula in premature ovarian failure due to ovarian tumour therapy

Ovarian tumour therapy has been known to cause ovary ageing. The competitive endogenous RNA (ceRNA) network plays an indispensable role in various diseases. We constructed the ceRNA network to examine the role of circular RNA (circRNA) in the ovaries of older women and elucidate the intervention mechanism of the Ziyin Buyang Formula. Ovarian tissue datasets were obtained from the Gene Expression Omnibus (GEO) database and screened for differentially expressed circRNAs (DECs), differentially expressed miRNA (DEMis) and differentially expressed mRNAs (DEMs). The circBase and Cancer-Specific CircRNA Databse 2.0 (CSCD) database were used to predict the miRNA response elements (MREs) of DECs. Interacting miRNAs were obtained by intersecting MREs and DEMis. Based on the interaction between circRNA-miRNA and miRNA-mRNA, the node circRNA, miRNA and mRNA were obtained. The ceRNA network was constructed using the Cytoscape software. The targets of the Ziyin Buyang Formula were obtained from the Traditional Chinese Medicine Systems Pharmacology database and related literature. The sub-networks of the interventions of the Ziyin Buyang Formula were constructed according to the target genes and the corresponding relationships in the ceRNA network. A total of seven circRNAs, six miRNAs and 224 mRNAs were distinguished as nodes from 226 DECs, 35 DEMis, 1697 DEMs, 2320 MREs, 19 intersecting miRNAs and 765 intersecting mRNAs. Moreover, 96 chemical components, 239 targets and four target genes were identified in the ceRNA network of the Ziyin Buyang Formula. This study constructed a ceRNA network for ovary ageing due to ovarian tumour therapy, and the sub-network of the interventions of Ziyin Buyang Formula was predicted, providing a novel perspective for the effective treatment of ovary ageing due to ovarian tumour therapy.

Identification of Alternative Splicing and LncRNA Genes in Brain Tissues of Fetal Mice at Different Developmental Stages.

Brain development is an extremely complex and precisely regulated process, with about one-third of genes expressed and precisely regulated during brain development. This study aims to explore the molecular mechanisms involved in brain development. We first established the expression profile of long non-coding RNAs (lncRNAs) and mRNAs in brain tissues of fetal mice at 12.5d, 14.5d and 16.5d through high-throughput sequencing. Second, the associated functions, pathways, and networks of the co-differentially expressed lncRNAs and mRNAs were identified via Gene Ontology (GO), pathway analysis, and PPI network. After bioinformatic analysis and screening, 8 differentially expressed lncRNAs and mRNAs with the same genetic origin were verified by RT-qPCR analysis in brain tissues of fetal mice at different developmental stages. The data revealed that there were 972 co-differentially expressed lncRNAs and 992 codifferentially expressed mRNAs in brain tissues of fetal mice at 12.5d, 14.5d and 16.5d. And we discovered 125 differentially expressed lncRNAs and mRNAs, which have the same genetic origin, in brain tissues of fetal mice at 12.5d, 14.5d and 16.5d through sequencing results and bioinformatics analysis. Besides, we proved that 8 lncRNAs, which have had the same genetic origin as differentially expressed mRNAs, were prominently downregulated, while their maternal genes were upregulated during brain development in fetal mice. Our results preliminarily illustrated the differentially expressed lncRNAs and mRNAs, both of which were derived from the same parent genes, during brain development in fetal mice, which suggests that alternative splicing of lncRNA exists during brain development. Besides, our study provides a perspective on critical genes for brain development, which might be the underlying therapeutic targets for developmental brain diseases in children.

PS-B11-8: ONE NOVEL MIRNA WITH 4 MRNA TARGETS WAS IDENTIFIED IN PERIPHERAL BLOOD MONONUCLEAR CELLS OF HYPERTENSIVE PATIENTS WITH METABOLIC SYNDROME

Objective: Hypertension (HTN), metabolic syndrome (MetS) and chronic kidney disease (CKD) are global health disorders that are epidemiologically associated. The immune system plays a role in hypertension and target organ damage. MicroRNAs are crucial post-transcriptional regulators of immune cell development and function. We hypothesized that microRNAs play a role in activation of immune cells in hypertension with target organ damage in humans. Design and method: Normotensive subjects (NTN) and patients with HTN associated or not with at least 2 other features of the MetS or with CKD were studied (n = 15–16). Peripheral blood mononuclear cells (PBMCs) were isolated, RNA extracted, small and total RNA sequencing (RNA-seq) done using Illumina HiSeq-2500 and data analyzed using a systems biology approach. Differentially expressed (DE) microRNAs and mRNAs were identified with fold change (FC) > 2 and > 1.5, respectively, and P < 0.005. The most representative DE miRNA isoforms with RNA-seq count number (CN) > 500, and with predicted targets by TargetScan with CN> 300 were validated by reverse transcription-quantitative PCR (RT-qPCR) in PBMCs. Results: RNA-seq identified DE microRNAs and mRNAs in HTN (22 and 19), MetS (57 and 401) and CKD (6 and 26) compared to NTN. Three of 14 selected miRNAs were validated by RT-qPCR. miR-409–5p and miR-411–5p were decreased respectively by 46% (P < 0.05) and 60% (P < 0.001), whereas the novel miR-pl-86 was increased 2-fold (P < 0.05) in MetS vs NTN. RT-qPCR data showed that tubulin alpha 1a (TUBA1A) predicted as mRNA target for miR-409–5p and miR-411–5p was increased 1.9-fold in MetS vs NTN group (P < 0.05). However, there was no correlation between RT-qPCR and RNA-seq data for TUBA1A. RT-qPCR data validated the decrease of 4 predicted miR-pl-86 targets. WD repeat domain 89 (WDR89), Dmx like 1 (DMXL1), zinc finger protein 600 (ZNF600) and NOC3-like DNA replication regulator (NOC3L) were decreased by ≧ 50% in MetS vs NTN (P < 0.05). RT-qPCR data demonstrated a correlation with RNA-seq data for miR-pl-86 (R2 = 0.37, P < 5.5E-07, n = 56), WDR89 (R2 = 0.29, P < 1.4E-05, n = 57), DMXL1 (R2 = 0.33, P < 2.7E-06, n = 57), ZNF600 (R2 = 0.25, P < 7.9E-05, n = 57), and NOC3L (R2 = 0.27, P < 3.5E-05, n = 57). Conclusion: This study identified one up-regulated novel microRNA and 4 down-regulated mRNA targets in PBMCs of patients with HTN and MetS. The role of this miRNA and mRNAs in immune cells of HTN patients with MetS needs to be determined.

Potential Common Molecular Mechanisms Between Periodontitis and Hepatocellular Carcinoma: A Bioinformatic Analysis and Validation.

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and has a poor prognosis. Periodontitis, or tooth loss, is considered to be related to hepatocarcinogenesis and its poor prognosis. This study aimed to explore potential associations and cross-talk mechanisms between periodontitis and HCC. Periodontitis and HCC microarray datasets were acquired from the Gene Expression Omnibus (GEO) database and were analyzed to obtain differentially expressed (DE) lncRNAs, miRNAs and mRNAs. Functional enrichment analysis was used to detect the functions of these mRNAs. Then, a ceRNA network of periodontitis-related HCC was constructed. Least absolute shrinkage and selection operator (LASSO) regression, random forest algorithm, and support vector machine-recursive feature elimination (SVM-RFE) were performed to explore the diagnostic significance of mRNAs in periodontitis-related HCC. Cox regression analyses were conducted to screen mRNAs with prognostic significance in HCC. Quantitative real-time PCR (qRT-PCR) and immunohistochemistry (IHC) were conducted to validate the expression of these mRNAs in HCC tissues. A ceRNA network was constructed. Functional enrichment analysis indicated that the network is associated with immune and inflammatory responses, the cell cycle and liver metabolic function. LASSO, random forest algorithm and SVM-RFE showed the diagnostic significance of DE mRNAs in HCC. Cox regression analyses revealed that MSH2, GRAMD1C and CTHRC1 have prognostic significance for HCC, and qRT-PCR and IHC validated this finding. Periodontitis may affect the occurrence of HCC by changing the immune and inflammatory response, the cell cycle and liver metabolic function. MSH2, GRAMD1C and CTHRC1 are potential prognostic biomarkers for HCC.

LncRNA RP11-1100L3.8 Involves in the Pathogenesis of Multiple Myeloma by Regulating NR4A1.

Although numerous studies have revealed that various long-non coding RNA (lncRNA) are implicated in multiple myeloma (MM) regulation, MM lncRNA profile and novel functional lncRNAs in MM need to be elucidated. Herein, lncRNAs and mRNAs (messenger ribonucleic acids) patterns in MM were evaluated using RNA-sequencing (RNAseq). Differentially expressed (DE) genes were defined and a complex regulatory network based on validation and predication was shaped. LncRNA-seq data analysis identified 539 DE lncRNAs and RP11-1100L3.8 was the most up-regulated known lncRNA. Subsequently, the upregulation and clinical RP11-1100L3.8 utilization value was verified in an expanded cohort. Based on the results of Cis nearby-targets and co-expression analysis, 1 correlation pair RP11-1100L3.8-nuclear receptor subfamily 4 group A member 1 (NR4A1) was defined. It is worth noting that NR4A1 is one of the top 5 significantly up-regulated DE mRNAs in MM patients. Moreover, it was found that NR4A1 overexpression is associated with poor prognosis in MM patients, making it suitable as biomarker. Additionally, spearman correlation analysis revealed the positive association between RP11-1100L3.8 and NR4A1 in MM patients. Furthermore, the dominant NR4A1 interacted genes were predicated and it was found that the genes containing NR4A1 were remarkably enriched in phosphatidylinositol 3-kinase (PI3K)-AKT (protein kinase B) signaling pathway. In addition, in vitro experiment suggested that RP11-1100L3.8 downregulation decreased NR4A1 expression in U266 and RPMI 8226 MM cells. RP11-1100L3.8 inhibition declined proliferation and promoted apoptosis in MM cells, which were rescued by NR4A1 overexpression. Moreover, it was found that RP11-1100L3.8 inhibition impeded PI3K and AKT phosphorylation and rapamycin mammalian target in MM cells, which was rescued by NR4A1 overexpression. This study identifies RP11-1100L3.8 as a potential MM biomarker, and it may be involved in MM pathophysiology by regulating NR4A1-mediated PI3K-AKT signaling pathway. This study provides a novel biomarker candidate for MM therapy.

Comparative molecular analysis of oral submucous fibrosis and other organ fibrosis based on weighted gene co-expression network analysis.

There is currently a lack of economic and suitable animal models that can accurately recapitulate the oral submucous fibrosis (OSF) disease state for indepth study. This is one of the primary reasons for the limited therapeutic methods available for OSF. Based on the underlying logic of pan-cancer analysis, this study systematically compares OSF and the other four types of organ fibrosis from the aspects of molecules, signaling pathways, biological processes, etc. A comprehensive analysis of the similarities and differences between OSF and other organ fibrosis is helpful for researchers to discover some general rules of fibrosis disease and may provide new ideas for studying OSF. Microarray data of the GSE64216, GSE76882, GSE171294, GSE92592, and GSE90051 datasets were downloaded from GEO. Differentially expressed mRNAs (DEmRNAs) of each type of fibrosis were identified by Limma package. Weighted gene co-expression network analysis (WGCNA) was used to identify each type of fibrosis-related module. The similarities and differences of each fibrosis-related-module genes were analyzed by function and pathway enrichment analysis. A total of 6 057, 10 910, 27 990, 10 480, and 4 801 DEmRNAs were identified in OSF, kidney intestinal fibrosis (KIF), liver fibrosis (LF), idiopathic pulmonary fibrosis (IPF), and skin fibrosis (SF), respectively. By using WGCNA, each type of fibrosis-related module was identified. The co-expression networks for each type of fibrosis were constructed respectively. Except that KIF and LF have 5 common hub genes, other fibrotic diseases have no common hub genes with each other. The common pathways of OSF, KIF, LF, IPF, and SF mainly focus on immune-related pathways. OSF and the other 4 types of fibrotic diseases are tissue- and organ-specific at the molecular level, but they share many common signaling pathways and biological processes, mainly in inflammation and immunity.

Construction and function analysis of the LncRNA-miRNA-mRNA competing endogenous RNA network in autoimmune hepatitis

AimsTo construct the lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network based on our microarray chip data for providing new insights into the pathogenesis of autoimmune hepatitis.MethodsThe ceRNA pairs were obtained by calculating the co-expression relationships among the differentially expressed lncRNAs (DELs), differentially expressed microRNAs (DEMis), and differentially expressed mRNAs (DEMs) with Pearson correlation analysis and hypergeometric distribution. The data of the differentially expressed genes were obtained from our previous studies in the concanavalin A-induced AIH mouse model. The biological functions of the ceRNA network were revealed by carrying out the GO and KEGG enrichment analysis. The expression of some differentially expressed genes constructed in the ceRNA pair was validated, and the correlation to liver injury was analyzed.ResultsThe mRNAs constructed in the ceRNA network were most significantly annotated in the GO terms of “inflammatory response” and enriched in “Cytokine-cytokine receptor interaction” and “MAPK signaling pathway”. The differences in the expression of Gm38975, mmu-miR-125a-3p, and Map3k13 between the model group and control group were significant, and the expression of these genes at a transcriptional level was positively or negatively correlated to the activity of ALT and AST as well as the amount of MDA and NO.ConclusionOur work is the first in its kind to predict and illustrate the comprehensive lncRNA-miRNA-mRNA ceRNA network associated with the etiopathogenesis of AIH. This study indicates to lay the foundation for revealing the potential roles of ceRNAs in the occurrence of AIH and provide novel treatment targets for this disease.

Comparative Transcriptomics Reveals the microRNA-Mediated Immune Response of Large Yellow Croaker (Larimichthys crocea) to Pseudomonas plecoglossicida Infection

Visceral white nodules disease (VWND), caused by Pseudomonas plecoglossicida, is a common disease among cage-farmed large yellow croaker (Larimichthys crocea) in China. However, comprehensive investigations of the molecular defensive mechanisms used by L. crocea in response to P. plecoglossicida infection remain relatively rare. Here, we constructed transcriptomes of the L. crocea spleen at 12 h and 24 h after P. plecoglossicida challenge. We identified 518 novel miRNAs and 823 known miRNAs in the spleen of L. crocea. Between the challenge and control groups, 32 differentially expressed miRNAs (DEmiRNAs), predicted to target 356 genes, and 1152 differentially expressed mRNAs (DEmRNAs) were identified at 12 h post-infection, while 33 DEmiRNAs, predicted to target 278 genes, and 1067 DEmRNAs were identified at 24 h post-infection. Gene ontology (GO) analysis showed that 146 and 126 GO terms were significantly enriched in the target genes at 12 h and 24 h, respectively. Twenty-eight and four immune-associated Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were significantly enriched in the target genes at 12 h and 24 h, respectively. Three immune-associated pathways were among those most enriched in the target genes: Toll-like receptor signaling, endocytosis, and C-type lectin receptor signaling. Network analysis identified 47 DEmRNA-DEmiRNA pairs. In particular, the immune-related genes TLR5S and PIGR were targeted by the miRNAs lcr-miR-7132c and dre-miR-183-5p, respectively. Dual-luciferase assays verified that lcr-miR-7132c downregulated TLR5S, suggesting that this miRNA may participate in regulating the immune response of L. crocea to P. plecoglossicida infection through the TLR5S-mediated signaling pathway. Our results help to clarify the miRNA-mediated immune response of L. crocea to P. plecoglossicida infection.

Identifying Genes that Affect Differentiation of Human Neural Stem Cells and Myelination of Mature Oligodendrocytes

Human neural stem cells (NSCs) are self-renewing, multipotent cells of the central nervous system (CNS). They are characterized by their ability to differentiate into a range of cells, including oligodendrocytes (OLs), neurons, and astrocytes, depending on exogenous stimuli. An efficient and easy directional differentiation method was developed for obtaining large quantities of high-quality of human OL progenitor cells (OPCs) and OLs from NSCs. RNA sequencing, immunofluorescence staining, flow cytometry, western blot, label-free proteomic sequencing, and qPCR were performed in OL lines differentiated from NSC lines. The changes in the positive rate of typical proteins were analyzed expressed by NSCs, neurons, astrocytes, OPCs, and OLs. We assessed Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of differentially expressed (DE) messenger RNAs (mRNAs) related to the differentiation of NSCs and the maturation of OLs. The percentage of NSCs differentiated into neurons, astrocytes, and OLs was 82.13%, 80.19%, and 90.15%, respectively. We found that nestin, PAX6, Musashi, and vimentin were highly expressed in NSCs; PDGFR-α, A2B5, NG2, OLIG2, SOX10, and NKX2-2 were highly expressed in OPCs; and CNP, GALC, PLP1, and MBP were highly expressed in OLs. RNA sequencing, western blot and qPCR revealed that ERBB4 and SORL1 gradually increased during NSC–OL differentiation. In conclusion, NSCs can differentiate into neurons, astrocytes, and OLs efficiently. PDGFR-α, APC, ID4, PLLP, and other markers were related to NSC differentiation and OL maturation. Moreover, we refined a screening method for ERBB4 and SORL1, which may underlie NSC differentiation and OL maturation.Graphical Potential unreported genes and proteins may regulate differentiation of human neural stem cells into oligodendrocyte lineage. Neural stem cells (NSCs) can differentiate into neurons, astrocytes, and oligodendrocyte (OLs) efficiently. By analyzing the DE mRNAs and proteins of NSCs and OLs lineage, we could identify reported markers and unreported markers of ERBB4 and SORL1 that may underlie regulate NSC differentiation and OL maturation.

Developing and validating a survival prediction model based on blood exosomal ceRNA network in patients with PAAD

BackgroundAmong the most lethal cancers, pancreatic adenocarcinoma (PAAD) is an essential component of digestive system malignancies that still lacks effective diagnosis and treatment methods. As exosomes and competing endogenous RNA (ceRNA) regulatory networks in tumors go deeper, we expect to construct a ceRNA regulatory network derived from blood exosomes of PAAD patients by bioinformatics methods and develop a survival prediction model based on it.MethodsBlood exosome sequencing data of PAAD patients and normal controls were downloaded from the exoRbase database, and the expression profiles of exosomal mRNA, lncRNA, and circRNA were differentially analyzed by R. The related mRNA, circRNA, lncRNA, and their corresponding miRNA prediction data were imported into Cytoscape software to visualize the ceRNA network. Then, we conducted GO and KEGG enrichment analysis of mRNA in the ceRNA network. Genes that express differently in pancreatic cancer tissues compared with normal tissues and associate with survival (P < 0.05) were determined as Hub genes by GEPIA. We identified optimal prognosis-related differentially expressed mRNAs (DEmRNAs) and generated a risk score model by performing univariate and multivariate Cox regression analyses.Results205 DEmRNAs, 118 differentially expressed lncRNAs (DElncRNAs), and 98 differentially expressed circRNAs (DEcircRNAs) were screened out. We constructed the ceRNA network, and a total of 26 mRNA nodes, 7 lncRNA nodes, 6 circRNA nodes, and 16 miRNA nodes were identified. KEGG enrichment analysis showed that the DEmRNAs in the regulatory network were mainly enriched in Human papillomavirus infection, PI3K-Akt signaling pathway, Osteoclast differentiation, and ECM-receptor interaction. Next, six hub genes (S100A14, KRT8, KRT19, MAL2, MYO5B, PSCA) were determined through GEPIA. They all showed significantly increased expression in cancer tissues compared with control groups, and their high expression pointed to adverse survival. Two optimal prognostic-related DEmRNAs, MYO5B (HR = 1.41, P < 0.05) and PSCA (HR = 1.10, P < 0.05) were included to construct the survival prediction model.ConclusionIn this study, we successfully constructed a ceRNA regulatory network in blood exosomes from PAAD patients and developed a two-gene survival prediction model that provided new targets which shall aid in diagnosing and treating PAAD.

Circular RNAs expression profiles and bioinformatics analysis in bronchopulmonary dysplasia.

Bronchopulmonary dysplasia (BPD) has long been considered the most challenging chronic lung disease for neonatologists and researchers due to its complex pathological mechanisms and difficulty in prediction. Growing evidence indicates that BPD is associated with the dysregulation of circular RNAs (circRNAs). Therefore, we aimed to explore the expression profiles of circRNAs and investigate the underlying molecular network associated with BPD. Peripheral blood was collected from very-low-birth-weight (VLBW) infants at 5-8 days of life to extract PBMCs. Microarray analysis and qRT-PCR tests were performed to determine the differentially expressed circRNAs (DEcircRNAs) between BPD and non-BPD VLBW infants. Simultaneous analysis of GSE32472 was conducted to obtain differentially expressed mRNAs (DEmRNA) from BPD infants. The miRNAs were predicted by DEcircRNAs and DEmRNAs of upregulated, respectively, and then screened for overlapping ones. GO and KEGG analysis was performed following construction of the competing endogenous RNA regulatory network (ceRNA) for further investigation. A total of 65 circRNAs (52 upregulated and 13 downregulated) were identified as DEcircRNAs between the two groups (FC >2.0 and p.adj <0.05). As a result, the ceRNA network was constructed based on three upregulated DEcircRNAs validated by qRT-PCR (hsa_circ_0007054, hsa_circ_0057950, and hsa_circ_0120151). Bioinformatics analysis indicated these DEcircRNAs participated in response to stimulus, IL-1 receptor activation, neutrophil activation, and metabolic pathways. In VLBW infants with a high risk for developing BPD, the circRNA expression profiles in PBMCs were significantly altered in the early post-birth period, suggesting immune dysregulation caused by infection and inflammatory response already existed.

Expression analysis of hsa_circ_0020397, hsa_circ_0005986, hsa_circ_0003028, and hsa_circ_0006990 in renal cell carcinoma

Renal cell carcinoma (RCC) is a prevalent heterogeneous kidney cancer. So far, different genes have been reported for RCC development. However, its particular molecular mechanism remains unclear. Circular RNAs (circRNAs), a class of non-coding RNAs, are involved in numerous biological processes in different malignancies such as RCC. This study aims to assess the expression and underlying mechanism of four circRNAs (hsa_circ_0020397, hsa_circ_0005986, hsa_circ_0003028, hsa_circ_0006990) with possible new roles in RCC. In the experimental step, we investigated the expression of these four circRNAs in our RCC samples using quantitative real-time polymerase chain reaction. In the bioinformatics step, the differential expressed mRNAs (DEmRNAs), and miRNAs (DEmiRNAs) were obtained from the GEO datasets using the GEO2R tool. A protein-protein interaction network was constructed using the STRING database, and hub genes were identified by Cytoscape. Molecular pathways associated with hub genes were detected using KEGG pathway enrichment analysis. Then, we utilized the ToppGene database to detect the relationships between DEmiRNAs and hub genes. Furthermore, interactions between circRNAs and DEmiRNAs were predicted by the StarBase and circinteractome databases. Finally, a circRNA-DEmiRNA-hub gene triple network was constructed. Our results revealed that the expression of hsa_circ_0020397, hsa_circ_0005986, and hsa_circ_0006990 was downregulated in RCC tissues. Moreover, these circRNAs had a significantly lower expression in patients with a history of kidney disease. Furthermore, hsa_circ_0003028 and hsa_circ_0006990 showed higher expression in the tumor of participants with Lymphovascular/perineural invasion and oncocytoma type, respectively. Based on bioinformatic results, 15 circRNA-DEmiRNA-hub gene ceRNA regulatory axes were predicted, which included three hub genes, five miRNAs, and four selected circRNAs. In conclusion, the current work is the first to emphasize the expression of the hsa_circ_0020397, hsa_circ_0005986, hsa_circ_0003028, and hsa_circ_0006990 in RCC patients presents a novel perspective on the molecular processes underlying the pathogenic mechanisms of RCC.

The Identification and Analysis of MicroRNAs Combined Biomarkers for Hepatocellular Carcinoma Diagnosis.

Hepatocellular carcinoma (HCC) is a common malignant tumor with high morbidity and mortality globally. Compared with traditional diagnostic methods, microRNAs (miRNAs) are novel biomarkers with higher accuracy. We aimed to identify combinatorial biomarkers of miRNAs to construct a classification model for the diagnosis of HCC. The mature miRNA expression profile data of six cancers (liver, lung, gastric, breast, prostate, and colon) were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database with accession number GSE36915, GSE29250, GSE99417, GSE41970, GSE64333 and GSE35982. The messenger RNA (mRNA) expression profile data of these six cancers were obtained from TCGA. Three R software packages, student's t-test, and a normalized foldchange method were utilized to identify HCC-specific differentially expressed miRNAs (DEMs). Using all combinations of obtained HCC-specific DEMs as input features, we constructed a classification model by support vector machine searching for the optimal combination. Furthermore, target genes prediction was conducted on the miRWalk 2.0 website to obtain differentially expressed mRNAs (DEmRNAs), and KEGG pathway enrichment was analyzed on the DAVID website. The optimal combination consisted of four miRNAs (hsa-miR-130a-3p, hsa-miR-450b-5p, hsa-miR-136-5p, and hsa-miR-24-1-5p), of which the last one has not been currently reported to be relevant to HCC. The target genes of hsa-miR-24-1-5p (CDC7, ACACA, CTNNA1, and NF2) were involved in the cell cycle, AMPK signaling pathway, Hippo signaling pathway, and insulin signaling pathway, which affect the proliferation, metastasis, and apoptosis of cancer cells. Moreover, the area under the receiver operating characteristic curves of the four miRNAs were all higher than 0.85. These results suggest that the miRNAs combined biomarkers were reliable for the diagnosis of HCC. Hsa-miR-24-1-5p was a novel biomarker for HCC diagnosis identified in this study.

Construction of an Expression Classifier Based on an Immune-related Ten-gene Panel for Rapid Diagnosis of Papillary Thyroid Carcinoma Risks

Background: Molecular alterations have been recognized as valuable diagnostic biomarkers for papillary thyroid carcinoma (PTC). Objectives: This study aimed to identify immune-related gene signatures associated with PTC progression using a computational pipeline and to develop an expression-based panel for rapid PTC risk classification. Methods: RNA-seq data and clinical information for PTC samples were downloaded from The Cancer Genome Atlas, followed by an analysis of differentially expressed (DE) RNAs among high-risk PTC, low-risk PTC, and normal groups. Immune cell infiltration and protein–protein interaction analyses were performed to obtain DE RNAs related to immunity. Then, a competing endogenous RNA (ceRNA) network was constructed to identify hub genes for the construction of a diagnostic model, which was evaluated by a receiver operator characteristic curve. A manually curated independent sample cohort was constructed to validate the model Results: By analyzing the immune cell infiltration, we found that the infiltration of plasma cells and CD8+ T cells was more abundant in the high-risk groups, and 68 DE mRNAs were found to be significantly correlated with these immune cells. Then a ceRNA network containing 10 immune-related genes was established. The ten-gene panel (including DEPDC1B, ELF3, VWA1, CXCL12, SLC16A2, C1QC, IPCEF1, ITM2A, UST, and ST6GAL1) was used to construct a diagnostic model with specificity (66.3%), sensitivity (83.3%), and area under the curve (0.762) for PTC classification. DEPDC1B and SLC16A2 were experimentally validated to be differentially expressed between high-risk and low-risk patients. Conclusion: The 10 immune-related gene panels can be used to evaluate the risk of PTC during pointof- care testing with high specificity and sensitivity.