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Background: The pathogenesis of colorectal cancer (CRC) is influenced by various risk factors, and genetic alterations in progression of colon polyps. The expression patterns of microRNA-548 (miR-548) in colorectal tissues have been sufficiently characterized. The aim of this study is to clarify the role of miR-548aa in tumorigenesis, gene targeting, predictive value and its expression levels in tumoral versus adjacent marginal tissues in CRC patients., Methods and Results: This study included 35 CRC patients who underwent surgery to remove their tumor tissue. Tumor samples and adjacent marginal tissue were collected and gene expression was analyzed through real-time PCR. The correlation between miR-548aa expression levels and clinical parameters were investigated. Our findings showed a significant increase in the expression of miR-548aa in tumoral tissues compared to marginal tissues (p < 0.05). The upregulation of miR-548aa was significantly detected in CRC samples, showing an area under the curve of 0.89 (p = 0.002), indicating strong sensitivity and specificity for CRC diagnosis. To prediction of miRNA target genes and construction of regulatory networks of miR-548aa, we used bioinformatics tools including TargetScan and miRDB. Protein-protein interaction (PPI) network was constructed through STRING database and visualized using Cytoscape software., Conclusions: Dysregulation of miR-548aa is closely related to tumorigenesis in colorectal tissues, which affects disease progression and clinical outcomes. The miR-548aa can be introduced as a proposed biomolecule involved in mechanism of tumorigenesis, gene targeting and predictive value for CRC diagnosis and a target for therapeutic intervention., Competing Interests: Declarations. Ethical approval: The research study was approved by the ethics committee of Ardabil University of Medical Sciences (IR.ARUMS.REC.1400.310), Ardabil, Iran, based on the ethical principles of human research and experimentation expressed in the 1995 Declaration of Helsinki and its later amendments. Informed consent for participation in the study was obtained in writing from each subject. All authors have read and approved the final manuscript. Competing interests: The authors declare no competing interests., (© 2025. The Author(s), under exclusive licence to Springer Nature B.V.)

Purpose: Oocyte aging is a significant factor in the negative reproductive outcomes of older women. However, the pathogenesis of oocyte aging remains unclear. This study aimed to identify the hub genes involved in oocyte aging via bioinformatics methods., Methods: The oocyte aging datasets GSE155179 and GSE158802 were obtained from the GEO database and analyzed as the training set. The GSE164371 dataset was then defined as the validation set. Differentially expressed genes were analyzed via the limma package and weighted gene coexpression network analysis, and intersected with cellular senescence-associated genes from the Cell Senescence database. The hub genes were identified via three machine learning algorithms, namely, support vector machine recursive feature elimination, random forest, and least absolute shrinkage and selection operator logistic, which were also confirmed via the validation set. Finally, a microRNA-mRNA‒transcription factor regulatory network and single-gene gene set enrichment analysis were performed to clarify the pathogenesis of oocyte aging., Results: A competing endogenous RNA network of GSE155179 and GSE158802 with 124 mRNAs, 31 long noncoding RNAs, and 31 miRNAs was constructed. Two modules with 814 genes were considered the key modules of oocyte aging. PDIK1L, SIRT1, and MCU were subsequently identified as hub genes; on the basis of these hub genes, a regulatory network of oocyte aging with 8 miRNAs, 3 mRNAs, and 227 TFs was ultimately constructed., Conclusions: This study contributes to a deeper understanding of oocyte aging and may aid in the development of therapeutic approaches to improve reproductive outcomes in older women., Competing Interests: Declarations. Ethics approval: Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and the institutional requirements. Competing interests: The authors declare no competing interests. Disclaimer: We declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

MicroRNA (miRNA) sponges synergistically modulate physiological and pathological processes in the form of modules or clusters. Here, we present a protocol for inferring and analyzing miRNA sponge modules in heterogeneous data using the R package miRSM 2.0. We describe steps for identifying gene modules, inferring miRNA sponge modules at multi-sample and single-sample levels, and performing modular analysis. From the perspective of computational biology, miRSM 2.0 has the potential to advance our understanding of the role of miRNA sponges in diseases. For complete details on the use and execution of this protocol, please refer to Zhang et al. 1 , 2 , 3 ., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Acute respiratory distress syndrome (ARDS) is a serious acute lung injury, and can develop into pulmonary fibrosis (PLF). Circular RNAs (circRNAs) regulatory network in ARDS is important. The study explored the role of hsa&#95;circ&#95;0006892 in the occurrence of ARDS and the development of PLF., Methods: Hsa&#95;circ&#95;0006892 levels were verified in serum samples of 203 ARDS patients with or without PLF, and the diagnostic value was evaluated through ROC. Cox regression analysis was performed to identify PLF-related factors. The downstream target genes were predicted online. The function and pathway of key genes were annotated through GO and KEGG pathway analysis. Protein-protein interaction (PPI) analysis was performed for the examination of protein interactions., Results: qRT-PCR determined the downregulation of hsa&#95;circ&#95;0006892 in the serum of both ARDS and PLF patients. Hsa&#95;circ&#95;0006892 can differentiate ARDS from controls, and independently related to the development of PLF. Nine targeted related miRNAs were integrated with dysregulated miRNAs from GSE27430 dataset. Clinically, miR-486-3p was the only miRNA that was significantly different in both ARDS and PLF groups, and was determined to be the target of hsa&#95;circ&#95;0006892. 180 target genes of miR-486-3p were predicted, which were integrated with ARDS and PLF-related GSE84439 and GSE38958 datasets. Go and KEGG pathway analysis identified Ras signaling pathway as the most commonly enriched pathway in the overlapped genes., Conclusions: The present results identified the differentially expressed hsa&#95;circ&#95;0006892 in ARDS and PLF, and suggested a possible molecular mechanism of hsa&#95;circ&#95;0006892/miR-486-3p axis., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Prostate cancer is among the most common cancers in males. Recent application of system biology methods has resulted in identification of key genes in the process of carcinogenesis. In the current study, we selected two datasets related to prostate cancer (PCa) and performed bulk RNA-seq analysis by selecting samples with Gleason scores greater than 7 and combining them. Subsequently, using several systems biology approaches, we constructed the ceRNA network and ultimately identified key axes related to PCa. Our analyses revealed importance of ADAMTS9-AS2/miR-150/PRKCA, ADAMTS9-AS2/miR-150/MMP14, MEG3/miR-150/PRKCA and MEG3/miR-150/MMP14 with miR-150 being a central component. Remarkably, miR-150 exhibited strong statistical significance in survival analyses. Further, analyzing expression levels from TCGA datasets, the expression of the identified genes associates significantly with prostate cancer compared to normal tissue confirming the bioinformatic analyses. Therefore, these genes can be regarded as prognostic markers in prostate cancer and the pathways are potential targets for therapeutic interventions., Competing Interests: Declaration of Competing Interest The authors declare they have nothing to report., (Copyright © 2024 The Authors. Published by Elsevier GmbH.. All rights reserved.)

Background: As noncoding RNAs, circular RNAs (circRNAs) can act as microRNA (miRNA) sponges due to their abundant miRNA binding sites, allowing them to regulate gene expression and influence disease development. Accurately identifying circRNA-miRNA associations (CMAs) is helpful to understand complex disease mechanisms. Given that biological experiments are time consuming and labor intensive, alternative computational methods to predict CMAs are urgently needed., Results: This study proposes a novel computational model named CMAGN, which incorporates several advanced computational methods, for predicting CMAs. First, similarity networks for circRNAs and miRNAs are constructed according to their sequences. Graph attention autoencoder is then applied to these networks to generate the first representations of circRNAs and miRNAs. The second representations of circRNAs and miRNAs are obtained from the CMA network via node2vec. The similarity networks of circRNAs and miRNAs are reconstructed on the basis of these new representations. Finally, network consistency projection is applied to the reconstructed similarity networks and the CMA network to generate a recommendation matrix., Conclusion: Five-fold cross-validation of CMAGN reveals that the area under ROC and PR curves exceed 0.96 on two widely used CMA datasets, outperforming several existing models. Additional tests elaborate the reasonability of the architecture of CMAGN and uncover its strengths and weaknesses., (© 2024. The Author(s).)

Background: The competitive endogenous RNA (ceRNA) hypothesis suggests that microRNAs (miRNAs) mediate a regulatory relation between long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) which share similar miRNA response elements (MREs) to bind to the same miRNA. Since the ceRNA hypothesis was proposed, several studies have been conducted to construct a network of lncRNAs, miRNAs and mRNAs in cancer. However, most cancer-related ceRNA networks are intended for representing a general relation of RNAs in cancer rather than for a patient-specific relation. Due to the heterogeneous nature of cancer, lncRNA-miRNA-mRNA interactions can vary in different patients., Results: We have developed a new method for constructing a ceRNA network of lncRNAs, miRNAs and mRNAs, which is specific to an individual cancer patient and for finding prognostic biomarkers consisting of lncRNA-miRNA-mRNA triplets. We tested our method on extensive data sets of three types of cancer (breast cancer, liver cancer, and lung cancer) and obtained potential prognostic lncRNA-miRNA-mRNA triplets for each type of cancer., Conclusions: Analysis of expression patterns of the RNAs involved in the triplets and survival rates of cancer patients revealed several interesting findings. First, even for the same cancer type, prognostic lncRNA-miRNA-mRNA triplets can be different depending on whether lncRNA and mRNA show opposite or similar expression patterns. Second, prognostic lncRNA-miRNA-mRNA triplets are often more predictive of survival rates than RNA pairs or individual RNAs. Our approach will be useful for constructing patient-specific lncRNA-miRNA-mRNA networks and for finding prognostic biomarkers from the networks., (© 2024. The Author(s).)

Neuroblastoma (NB) is one of the leading causes of cancer-associated death in children. MYCN amplification is a prominent genetic marker for NB, and its targeting to halt NB progression is difficult to achieve. Therefore, an in-depth understanding of the molecular interactome of NB is needed to improve treatment outcomes. Analysis of NB multi-omics unravels valuable insight into the interplay between MYCN transcriptional and miRNA post-transcriptional modulation. Moreover, it aids in the identification of various miRNAs that participate in NB development and progression. This study proposes an integrated computational framework with three levels of high-throughput NB data (mRNA-seq, miRNA-seq, and methylation array). Similarity Network Fusion (SNF) and ranked SNF methods were utilized to identify essential genes and miRNAs. The specified genes included both miRNA-target genes and transcription factors (TFs). The interactions between TFs and miRNAs and between miRNAs and their target genes were retrieved where a regulatory network was developed. Finally, an interaction network-based analysis was performed to identify candidate biomarkers. The candidate biomarkers were further analyzed for their potential use in prognosis and diagnosis. The candidate biomarkers included three TFs and seven miRNAs. Four biomarkers have been previously studied and tested in NB, while the remaining identified biomarkers have known roles in other types of cancer. Although the specific molecular role is yet to be addressed, most identified biomarkers possess evidence of involvement in NB tumorigenesis. Analyzing cellular interactome to identify potential biomarkers is a promising approach that can contribute to optimizing efficient therapeutic regimens to target NB vulnerabilities., (© 2024. The Author(s).)

The etiology of epilepsy is ascribed to the synchronized aberrant neuronal activity within the brain. Circular RNAs (circRNAs), a class of non-coding RNAs characterized by their circular structures and covalent linkage, exert a substantial influence on this phenomenon. CircRNAs possess stereotyped replication, transience, repetitiveness, and paroxysm. Additionally, MicroRNA (miRNA) plays a crucial role in the regulation of diverse pathological processes, including epilepsy. CircRNA is of particular significance due to its ability to function as a competing endogenous RNA, thereby sequestering or inhibiting miRNA activity through binding to target mRNA. Our review primarily concentrates on elucidating the pathological and functional roles, as well as the underlying mechanisms, of circRNA-miRNA-mRNA networks in epilepsy. Additionally, it explores the potential utility of these networks for early detection and therapeutic intervention., (© 2024 The Authors. CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

One of the prime reasons of increasing breast cancer mortality is metastasizing cancer cells. Owing to the side effects of clinically available drugs to treat breast cancer metastasis, it is of utmost importance to understand the underlying biogenesis of breast cancer tumorigenesis. In-silico identification of potential RNAs might help in utilizing the miR-27 family as a therapeutic target in breast cancer. The experimentally verified common interacting mRNAs for miR27 family are retrieved from three publicly available databases- TargetScan, miRDB and miRTarBase. Finally on comparing the common genes with HCMDB and GEPIA data, four breast cancer-associated differentially expressed metastatic mRNAs (GATA3, ENAH, ITGA2 and SEMA4D) are obtained. Corresponding to the miR27 family and associated mRNAs, interacting drugs are retrieved from Sm2mir and CTDbase, respectively. The interaction network-based approach was utilized to obtain the hub RNAs and triad modules by employing the 'Cytohubba' and 'MClique' plugins, respectively in Cytoscape. Further, sample-, subclass- and promoter methylation-based expression analyses reveals GATA3 and ENAH to be the most significant mRNAs in breast cancer metastasis having >10% genetic alteration in both METABRIC Vs TCGA datasets as per their oncoprint analysis via cBioPortal. Additionally, survival analysis in Oncolnc reveals SEMA4D as survival biomarker. Interactions among the miR27 family, their target mRNAs and drugs interacting with miRNAs and mRNAs can be extensively explored in both in-vivo and in-vitro setups to assess their therapeutic potential in the diminution of breast cancer., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Sepsis is a complex clinical syndrome caused by a dysregulated host immune response to infection. This study aimed to identify a competing endogenous RNA (ceRNA) network that can greatly contribute to understanding the pathophysiological process of sepsis and determining sepsis biomarkers. Methods: The GSE100159, GSE65682, GSE167363, and GSE94717 datasets were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene coexpression network analysis was performed to find modules possibly involved in sepsis. A long noncoding RNA-microRNA-messenger RNA (lncRNA-miRNA-mRNA) network was constructed based on the findings. Single-cell analysis was performed. Human umbilical vein endothelial cells were treated with lipopolysaccharide (LPS) to create an in vitro model of sepsis for network verification. Reverse transcription-polymerase chain reaction, fluorescence in situ hybridization, and luciferase reporter genes were used to verify the bioinformatic analysis. Result: By integrating data from three GEO datasets, we successfully constructed a ceRNA network containing 18 lncRNAs, 7 miRNAs, and 94 mRNAs based on the ceRNA hypothesis. The lncRNA ZFAS1 was found to be highly expressed in LPS-stimulated endothelial cells and may thus play a role in endothelial cell injury. Univariate and multivariate Cox analyses showed that only SLC26A6 was an independent predictor of prognosis in sepsis. Overall, our findings indicated that the ZFAS1 /hsa-miR-449c-5p/ SLC26A6 ceRNA regulatory axis may play a role in the progression of sepsis. Conclusion: The sepsis ceRNA network, especially the ZFAS1 /hsa-miR-449c-5p/ SLC26A6 regulatory axis, is expected to reveal potential biomarkers and therapeutic targets for sepsis management.

Purpose: To identify potential biomarkers and the molecular mechanisms associated with repeated implantation failure (RIF), three microarray datasets, GSE71331 (lncRNA + mRNA), GSE111974 (lncRNA + mRNA), and GSE71332 (miRNA), were retrieved from the Gene Expression Omnibus (GEO) database., Methods: The differentially expressed mRNAs (DEMs), lncRNAs (DElncRNAs), and miRNAs (DEmiRNAs) between normal control samples (C group) and RIF samples (RIF group) were identified, and then a module partition analysis was performed based on weighted correlation network analysis (WGCNA). Following enrichment analysis of the genes, the lncRNA-miRNA-mRNA interactions (ceRNA) were examined. The mRNAs in the ceRNA network were evaluated using the GSE58144 dataset. Finally, the key RNAs were verified using reverse transcription-quantitative polymerase chain reaction (RT-qPCR)., Results: Fifty-three DEmiRNAs, 327 DEMs, and 13 DElncRNAs were identified between the C and RIF groups. According to WGCNA, the magenta module was positively correlated with RIF disease status. The lncRNA-mRNA interaction analysis based on genes in the magenta module revealed the intersecting lncRNAs, including peptidylprolyl isomerase E-like pseudogene (PPIEL) and the testis-specific transcript, y-Linked 14 (TTTY14); these lncRNAs are mainly involved in functions, such as plasma membrane organization. The ceRNA network analysis revealed several interactions, such as TTTY14-miR-6088-semaphorin 5 A (SEMA5A). Finally, SEMA5A and the zinc finger protein 555 (ZNF555) were identified to be significantly upregulated in the RIF group compared with those in the C group in the GSE58144 dataset. The RT-qPCR results aligned with the above results., Conclusions: Overall, TTTY14, ZNF555, SEMA5A, PPIEL, and miR-6088 could serve as novel biomarkers of RIF., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Circular RNAs (circRNAs) function as miRNA sponges by adsorbing microRNAs (miRNAs), thereby regulating messenger RNA (mRNA) expression. The circRNA-miRNA-mRNA regulatory network associated with type 2 diabetes mellitus (T2DM) has rarely been explored. A circRNA-miRNA-mRNA regulatory network associated with T2DM was established to help deepen our understanding of the molecular mechanism of and therapeutic targets for T2DM., Methods: Differentially expressed circRNAs (DEcircRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) were derived from the Gene Expression Omnibus (GEO) microarray datasets GSE114248, GSE51674 and GSE95849, respectively. A circRNA-miRNA-mRNA regulatory network associated with T2DM and its subnetwork were constructed. The hub genes were screened using a protein-protein interaction (PPI) network. Finally, a hub gene-related network was constructed. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed., Results: The circRNA-miRNA-mRNA network included 9 circRNAs, 24 miRNAs and 320 mRNAs. When four key circRNAs (circMYO9B, circGRAMD1B, circTHAP4 and circTMC7) were chosen, the subnetwork contained 4 circRNAs, 18 miRNAs and 307 mRNAs. Afterwards, 8 hub genes (SIRT1, GNG7, KDR, FOS, SIN3B, STAT1, SP1, and MAPK3) were extracted from the PPI network. GO and KEGG pathway analyses revealed that the network might be involved in oxidative stress responses, regulation of inflammation, neovascularization, endocrine and cancer-related processes, etc., Conclusions: A circRNA-miRNA-hub gene regulatory network was constructed, and the potential functions of the hub genes were analyzed. Four important circRNAs (circMYO9B, circGRAMD1B, circTHAP4 and circTMC7) might be involved in the occurrence and development of T2DM, and this finding provides new insight into the molecular mechanism of and therapeutic targets for T2DM and its complications. Future studies are needed to validate the sponge effects and mechanisms of these 4 circRNAs.

Background: Diabetes is a spectrum of metabolic diseases affecting millions of people worldwide. The loss of pancreatic β-cell mass by either autoimmune destruction or apoptosis, in type 1-diabetes (T1D) and type 2-diabetes (T2D), respectively, represents a pathophysiological process leading to insulin deficiency. Therefore, therapeutic strategies focusing on restoring β-cell mass and β-cell insulin secretory capacity may impact disease management. This study took advantage of powerful integrative bioinformatic tools to scrutinize publicly available diabetes-associated gene expression data to unveil novel potential molecular targets associated with β-cell dysfunction., Methods: A comprehensive literature search for human studies on gene expression alterations in the pancreas associated with T1D and T2D was performed. A total of 6 studies were selected for data extraction and for bioinformatic analysis. Pathway enrichment analyses of differentially expressed genes (DEGs) were conducted, together with protein-protein interaction networks and the identification of potential transcription factors (TFs). For noncoding differentially expressed RNAs, microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), which exert regulatory activities associated with diabetes, identifying target genes and pathways regulated by these RNAs is fundamental for establishing a robust regulatory network., Results: Comparisons of DEGs among the 6 studies showed 59 genes in common among 4 or more studies. Besides alterations in mRNA, it was possible to identify differentially expressed miRNA and lncRNA. Among the top transcription factors (TFs), HIPK2, KLF5, STAT1 and STAT3 emerged as potential regulators of the altered gene expression. Integrated analysis of protein-coding genes, miRNAs, and lncRNAs pointed out several pathways involved in metabolism, cell signaling, the immune system, cell adhesion, and interactions. Interestingly, the GABAergic synapse pathway emerged as the only common pathway to all datasets., Conclusions: This study demonstrated the power of bioinformatics tools in scrutinizing publicly available gene expression data, thereby revealing potential therapeutic targets like the GABAergic synapse pathway, which holds promise in modulating α-cells transdifferentiation into β-cells., (© 2024. The Author(s).)

Growing evidence has shown that besides the protein coding genes, the non-coding elements of the genome are indispensable for maintaining the property of self-renewal in human embryonic stem cells and in cell fate determination. However, the regulatory mechanisms and the landscape of interactions between the coding and non-coding elements is poorly understood. In this work, we used weighted gene co-expression network analysis (WGCNA) on transcriptomic data retrieved from RNA-seq and small RNA-seq experiments and reconstructed the core human pluripotency network (called PluriMLMiNet) consisting of 375 mRNA, 57 lncRNA and 207 miRNAs. Furthermore, we derived networks specific to the naïve and primed states of human pluripotency (called NaiveMLMiNet and PrimedMLMiNet respectively) that revealed a set of molecular markers (RPS6KA1, ZYG11A, ZNF695, ZNF273, and NLRP2 for naive state, and RAB34, TMEM178B, PTPRZ1, USP44, KIF1A and LRRN1 for primed state) which can be used to distinguish the pluripotent state from the non-pluripotent state and also to identify the intra-pluripotency states (i.e., naïve and primed state). The lncRNA DANT1 was found to be a crucial as it formed a bridge between the naive and primed state-specific networks. Analysis of the genes neighbouring DANT1 suggested its possible role as a competing endogenous RNA (ceRNA) for the induction and maintenance of human pluripotency. This was computationally validated by predicting the missing DANT1-miRNA interactions to complete the ceRNA circuit. Here we first report that DANT1 might harbour binding sites for miRNAs hsa-miR-30c-2-3p, hsa-miR-210-3p and hsa-let-7b-5p which may influence pluripotency., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest. The work was carried out by AG while working as a JRF at the University of Allahabad., (Copyright © 2023 International Society of Differentiation. Published by Elsevier B.V. All rights reserved.)

Objectives: To utilize an integrative strategy to construct functional miRNA-mRNA regulatory networks by combining the reverse expression relationships between miRNAs and targets and computational predictions for gestational diabetes mellitus (GDM)., Material and Methods: A total of three microarray or RNA-seq datasets (GSE98043, GSE19649 and GSE92772) of plasma samples comparing GDM pregnant women and healthy control pregnant women were downloaded from the GEO database. The differentially expressed genes (DEmRNAs) and the differentially expressed miRNAs (DEmiRNAs) were analyzed. The target genes of DEmiRNAs were identified using two independent and complementary types of information: computational target predictions and expression relationships. An interaction network was constructed to identify hub genes of GDM. Another dataset (GSE92772) was used to externally verify the predictive ability of the hub genes., Results: A total of 264 DEmiRNAs and 1217 DEmRNAs were identified with Hsa-miR-146a-3p ranked first of DEmiRNAs. Functions of GDM-related miRNAs were mainly enriched in the glypican pathway, proteoglycan syndecan-mediated signaling events, and syndecan-1-mediated signaling events. A total of 47 target genes, including TRAF6, were shared between the computational target predictions and DEmRNAs and were identified as target genes of hsa-miR-146a-3p. The interaction network analysis identified TRAF6, CASP8, PTPN6, and CHD3 as hub genes involved in the pathophysiological process of GDM. Next, independent external validation was performed using the GSE19649 dataset. The expression of TRAF6, CASP8 and CHD3 in eight pairs of GDM blood samples was confirmed to be higher than that in healthy pregnant women blood samples with a AUC of 0.813, 0.813, and 0.703, respectively., Conclusions: Our preliminary analysis revealed that miR-146a-3p/TRAF6 might play a central role in the pathogenesis of GDM. Three hub genes, TRAF6, CASP8, and CHD3, were identified and independently externally validated as potential GDM noninvasive serum markers for future biomarkers research.

Background: Alzheimer's disease (AD) is the most prevalent neurological disorder worldwide, affecting approximately 24 million individuals. Despite more than a century of research on AD, its pathophysiology is still not fully understood., Objective: Recently, genetic studies of AD have focused on analyzing the general expression profile by employing high-throughput genomic techniques such as microarrays. Current research has leveraged bioinformatics advancements in genetic science to build upon previous efforts., Methods: Data from the GSE118553 dataset used in this investigation, and the analyses carried out using programs such as Limma and BioBase. Differentially expressed genes (DEGs) and differentially expressed microRNAs (DEmiRs) associated with AD identified in the studied areas of the brain. Target genes of the DEmiRs identified using the MultiMiR package. Gene ontology (GO) completed using the Enrichr website, and the protein-protein interaction (PPI) network for these genes drawn using STRING and Cytoscape software., Results: The findings introduced DEGs including CTNNB1, PAK2, MAP2K1, PNPLA6, IGF1R, FOXL2, DKK3, LAMA4, PABPN1, and GDPD5, and DEmiRs linked to AD (miR-106A, miR-1826, miR-1253, miR-10B, miR-18B, miR-101-2, miR-761, miR-199A1, miR-379 and miR-668), (miR-720, miR-218-2, miR-25, miR-602, miR-1226, miR-548K, miR-H1, miR-410, miR-548F2, miR-181A2), (miR-1470, miR-651, miR-544, miR-1826, miR-195, miR-610, miR-599, miR-323, miR-587 and miR-340), and (miR-1282, miR-1914, miR-642, miR-1323, miR-373, miR-323, miR-1322, miR-612, miR-606 and miR-758) in cerebellum, frontal cortex, temporal cortex, and entorhinal cortex, respectively., Conclusions: The majority of the genes and miRNAs identified by our findings may be employed as biomarkers for prediction, diagnosis, or therapy response monitoring.

Background: Previous studies on transcriptional profiles suggested dysregulation of multiple RNA species in Alzheimer's disease. However, despite recent investigations revealing various aspects of circular RNA (circRNA)-associated competing endogenous RNA (ceRNA) networks in Alzheimer's Disease (AD) pathogenesis, few genome-wide studies have explored circRNA-associated profiles in AD patients exhibiting varying degrees of cognitive loss., Objective: To investigate the potential pathogenesis-related molecular biological changes in the various stages of AD progression., Methods: Whole transcriptome sequencing was performed on the peripheral blood of 7 normal cognition (NC) subjects, 8 patients with mild cognitive impairment, 8 AD patients with mild dementia (miD), and 7 AD patients with moderate dementia (moD). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to predict the potential functions of the maternal genes of microRNAs (miRNAs), circRNAs and long non-coding RNAs (lncRNAs). The construction of ceRNA network was performed between the NC group and each diseased group based on the differently expressed RNAs., Results: In total, 3568 messenger RNAs (mRNAs), 142 miRNAs, 990 lncRNAs, and 183 circRNAs were identified as significantly differentially expressed across the four groups. GO and KEGG enrichment analysis revealed the significant roles of GTPase activity and the MAPK signaling pathway in AD pathogenesis. A circRNA-miRNA-lncRNA ceRNA pathway, characterized by the downregulated hsa-miR-7-5p and upregulated hsa&#95;circ&#95;0001170, was identified based on the differentially expressed RNAs between the NC group and the moD group., Conclusion: The study suggests that circRNAs may be independent of mRNAs in AD pathogenesis and holds promise as potential biomarkers for AD clinical manifestations and pathological changes., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Primary ovarian insufficiency (POI) is a common condition leading to the pathological decline of ovarian function in women of reproductive age, resulting in amenorrhea, hypogonadism, and infertility. Biochemical premature ovarian insufficiency (bPOI) is an intermediate stage in the pathogenesis of POI in which the fertility of patients has been reduced. Previous studies suggest that granulosa cells (GCs) play an essential role in the pathogenesis of POI, but their pathogenetic mechanisms remain unclear. To further explore the potential pathophysiological mechanisms of GCs in POI, we constructed a molecular long non-coding RNA (lncRNA)-microRNA (miRNA)-messenger RNA (mRNA) network using GC expression data collected from biochemical premature ovarian failure (bPOI) patients in the GEO database. We discovered that the GCs of bPOI patients had differential expression of 131 mRNAs, 191 lncRNAs, and 28 miRNAs. By systematic network analysis, we identified six key genes, including SRSF1, PDIA5, NEURL1B, UNK, CELF2, and CFL2, and five hub miRNAs, namely hsa-miR-27a-3p, hsa-miR-24-3p, hsa-miR-22-3p, hsa-miR-129-5p, and hsa-miR-17-5p, and the results suggest that the expression of these key genes may be regulated by two hub miRNAs, hsa-miR-27a-3p and hsa-miR-17-5p. Additionally, a POI model in vitro was created to confirm the expression of a few important genes. In this study, we discovered a unique lncRNA-miRNA-mRNA network based on the ceRNA mechanism in bPOI for the first time, and we screened important associated molecules, providing a partial theoretical foundation to better understand the pathogenesis of POI., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: Recurrent implantation failure (RIF) affects up to 10% of in vitro fertilization (IVF) patients worldwide. However, the pathogenesis of RIF remains unclear. This study was aimed at identifying hub transcription factors (TFs) of RIF in bioinformatics approaches., Methods: The GSE111974 (mRNA), GSE71332 (miRNA), and GSE103465 (mRNA) datasets were downloaded from the Gene Expression Omnibus database from human endometrial tissue using R version 4.2.1 and used to identify differentially expressed TFs (DETFs), differentially expressed miRNAs, and differentially expressed genes for RIF, respectively. DETFs were subjected to functional enrichment analysis and the protein-protein interaction network analysis using the Search Tool for the Retrieval of Interacting Genes (version 11.5) database. Hub TFs were identified using the cytoHubb plug-in, after which a hub TF-miRNA-mRNA network was constructed using Cytoscape v3.8.2., Results: Fifty-seven DETFs were identified, in which Gene Ontology analysis revealed to be mainly involved in the regulation of transcription. Kyoto Encyclopedia of Genes and Genomes pathway analysis suggested that DETFs were enriched in transcriptional misregulation in cancer, aldosterone synthesis and secretion, AMPK signaling pathway, and cGMP-PKG signaling pathway. EOMES, NKX2-1, and POU5F1 were identified as hub TFs, and a hub TF-miRNA-mRNA regulatory network was constructed using these three hub TFs, four miRNAs, and four genes., Conclusion: Collectively, we identified three promising molecular biomarkers for the diagnosis of RIF, which may further be potential therapeutic targets. This study provides novel insights into the molecular mechanisms underlying RIF. However, further experiments are required to verify these results., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Introduction: To retrieve, and classify PCa miRNAs and identify the functional relationship between miRNAs and their targets through literature collection with computational analysis., Background: MicroRNAs play a role in gene regulation, which can either repress or activate the gene. Hence, the functions of miRNAs are dependent on the target gene. This study will be the first of its kind to combine computational analysis with corpus PCa data. Effectively, our study reported the huge number of miRNAs associated with PCa along with functional information., Objective: The identification and classification of previously known full PCa miRNAs and their targets were made possible by mining the literature data. Systems Biology and curated data mining assisted in identifying optimum miRNAs and their target genes for PCa therapy., Methods: PubMed database was used to collect the PCa literature up to December 2021. Pubmed. mineR package was used to extract the microRNAs associated articles and manual curation was performed to classify the microRNAs based on the function in PCa. PPI was constructed using the STRING database. Pathway analysis was performed using PANTHER and ToppGene Suite Software. Functional analysis was performed using ShinyGO software. Cluster analysis was performed using MCODE 2.0, and Hub gene analysis was performed using cytoHubba. The genemiRNA network was reconstructed using Cytoscape., Results: Unique PCa miRNAs were retrieved and classified from mined PCa literature. Six hundred and five unique miRNAs from 250 articles were considered as oncomiRs to trigger PCa. One hundred and twenty unique miRNAs from 118 articles were considered Tumor Suppressor miRNAs to suppress the PCa. Twenty-four unique miRNAs from 22 articles were utilized as treatment miRNAs to treat PCa. miRNAs target genes and their significant pathways, functions and hub genes were identified., Conclusion: miR-27a, miR-34b, miR-495, miR-23b, miR-100, miR-218, Let-7a family, miR-27a- 5p, miR-34c, miR-34a, miR-143/-145, miR-125b, miR-124 and miR-205 with their target genes AKT1, SRC, CTNNB1, HRAS, MYC and TP53 are significant PCa targets., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Objective: The prevalence of autism spectrum disorder (ASD) in children has been increasing year by year, which has seriously affected the quality of life of children. There are many theories about the cause of ASDs, with some studies suggesting that it may be related to gene expression levels or inflammation and immune system dysfunction. But the exact mechanism is not fully understood., Methods: profile of gene expression The protein interaction network (PPI) of differentially expressed genes was created using the STRING web tool and GSE77103, which was chosen from the gene expression omnibus (GEO) database. Using the CytoHubba plugin of Cytoscape program, the hub genes were examined. The hub gene regulatory network for miRNA-mRNA was then built., Results: We identified 551 differentially expressed genes(DEGs) in 8 children with ASD and normal children. In addition, we screened out 10 hub genes (MX1, ISG15, IRF7, DDX58, IFIT1, BCL2L1, HPGDS, CTSD, PTGS2 and CD68) that were most associated with the development of ASDs. Then, microRNAs (miRNAs) closely related to hub genes (such as has-miR-27a-5p) were screened, and the miRNA-mRNA regulatory network was constructed., Conclusion: In this study, a total of 10 hub genes were identified, including MX1, ISG15, IRF7, DDX58, IFIT1, BCL2L1, HPGDS, CTSD, PTGS2 and CD68, which are closely related to ASD. These genes may play a key role in the occurrence and progression of ASD. In addition, we also revealed some miRNAs that regulate the hub genes of ASD. These results may deepen our understanding of ASD and provide potential biomarkers and targets for future treatment of patients with ASD.

Colorectal cancer (CRC) is a serious global health concern, with a high incidence and mortality rate. Although there have been advancements in the early detection and treatment of CRC, therapy resistance is common. MicroRNAs (miRNAs), a type of small non-coding RNA that regulates gene expression, are key players in the initiation and progression of CRC. Recently, there has been growing attention to the complex interplay of miRNAs in cancer development. miRNAs are powerful RNA molecules that regulate gene expression and have been implicated in various physiological and pathological processes, including carcinogenesis. By identifying current challenges and limitations of treatment strategies and suggesting future research directions, this review aims to contribute to ongoing efforts to enhance CRC diagnosis and treatment. It also provides a comprehensive overview of the role miRNAs play in CRC carcinogenesis and explores the potential of miRNA-based therapies as a treatment option. Importantly, this review highlights the exciting potential of targeted modulation of miRNA function as a therapeutic approach for CRC., (© 2023. The Author(s), under exclusive licence to Springer Nature B.V.)

MiRNAs are reported to be linked to the pathogenesis of human complex diseases. Disease-related miRNAs may serve as novel bio-marks and drug targets. This work focuses on designing a multi-relational Graph Convolutional Network model to predict miRNA-disease associations (HGCNMDA) from a Heterogeneous network. HGCNMDA introduces a gene layer to construct a miRNA-gene-disease heterogeneous network. We refine the features of nodes into initial and inductive features so that the direct and indirect associations between diseases and miRNA can be considered simultaneously. Then HGCNMDA learns feature embeddings for miRNAs and disease through a multi-relational graph convolutional network model that can assign appropriate weights to different types of edges in the heterogeneous network. Finally, the miRNA-disease associations were decoded by the inner product between miRNA and disease feature embeddings. We apply our model to predict human miRNA-disease associations. The HGCNMDA is superior to the other state-of-the-art models in identifying missing miRNA-disease associations and also performs well on recommending related miRNAs/diseases to new diseases/ miRNAs. The codes are available at https://github.com/weiba/HGCNMDA.

The interaction between long non-coding RNAs (lncRNAs), miRNAs and mRNAs has implications in the pathogenesis of different cancer, including breast cancer. In the current study, we developed an in-silico approach to ascertain the competing endogenous RNA (ceRNA) network in breast cancer. Our approach led to identification of 1816 differentially expressed (DE) mRNAs, including 1039 downregulated DEmRNAs (such as LEP and ADIPOQ) and 777 upregulated DEmRNAs (such as COL11A1 and COL10A1), 19 DElncRNAs, including 15 downregulated DElncRNAs (such as CARMN and COPG2IT1) and 4 upregulated DElncRNAs (such as MALAT1 and NRAV) and 27 DEmiRNAs, including 15 downregulated DEmiRNAs (such as MIR452 and MIR224) and 12 upregulated DEmiRNAs (such as MIR6787 and MIR21). Pathway analysis revealed down-regulation of PPAR, Fatty acid metabolism, Adipocytokine, Vascular smooth muscle contraction and Metabolism of xenobiotics by cytochrome P450, while up-regulation of Pyrimidine metabolism, p53 signaling pathway, Cell cycle, Oocyte meiosis and RNA transport pathways in breast cancer. Finally, we constructed an lncRNA/miRNA/mRNA ceRNA network consisted of 2 lncRNAs, 15 mRNAs, and 4 miRNAs. This network represents an appropriate target for design of anti-cancer modalities and documentation of novel markers for breast cancer., Competing Interests: Declaration of Competing Interest The authors declare they have no conflict of interest., (Copyright © 2023 Elsevier GmbH. All rights reserved.)

Uterine Corpus Endometrial Carcinoma (UCEC) is one of the major malignant tumors of the female reproductive system. However, there are limitations in the currently available diagnostic approaches for UCEC. Long non-coding RNAs (lncRNAs) play important roles in regulating biological processes as competitive endogenous RNA (ceRNA) in tumors. To study the potential of lncRNAs as non-invasive diagnostic tumor markers, RNA-sequencing dataset of UCEC patients from The Cancer Genome Atlas was used to identify differentially expressed genes. A lncRNA-miRNA-mRNA ceRNA network was constructed by differentially expressed lncRNAs, miRNAs and miRNAs. Pathway enrichment and functional analysis for the mRNAs in the constructed ceRNA network provide the direction of future research for UCEC by demonstrating the most affected processes and pathways. Seven potential lncRNA biomarkers (C20orf56, LOC100144604, LOC100190940, LOC151534, LOC727677, FLJ35390, LOC158572) were validated in UCEC patients by quantitative real-time PCR. Notably, LOC100190940 and LOC158572 were identified as novel RNA molecules with unknown functions. Receiver operating characteristic (ROC) curve analysis demonstrated that the combined 7 lncRNAs had a high diagnostic value for UCEC patients with area under curve (AUC) of 0.941 (95% CI: 0.875-0.947). Our study highlights the potential of the validated 7 lncRNAs panel as diagnostic biomarkers in UCEC, providing new insights into the UCEC pathogenesis., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Construction of synthetic circuits that can reprogram genetic networks and signal pathways is a long-term goal for manipulation of biosystems. However, it is still highly challenging to build artificial genetic communications among endogenous RNA species due to their sequence independence and structural diversities. Here we report an RNA-based synthetic circuit that can establish regulatory linkages between expression of endogenous genes in both Escherichiacoli and mammalian cells. This design employs a displacement-assembly approach to modulate the activity of guide RNA for function control of CRISPR/Cas9. Our experiments demonstrate the great effectiveness of this RNA circuit for building artificial connections between expression of originally unrelated genes. Both exogenous and naturally occurring RNAs, including small/microRNAs and long mRNAs, are capable of controlling expression of another endogenous gene through this approach. Moreover, an artificial signal pathway inside mammalian cells is also successfully established to control cell apoptosis through our designed synthetic circuit. This study provides a general strategy for constructing synthetic RNA circuits, which can introduce artificial connections into the genetic networks of mammalian cells and alter the cellular phenotypes., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Acute Myeloid Leukemia (AML) can be detected based on morphology, cytochemistry, immunological markers, and cytogenetics. MicroRNAs (miRNAs) influence key biological pathways in multiple haematological malignancies including AML. In this work, we have analysed the miRNome and the transcriptome of normal and AML samples and have identified the significant set of miRNA-target mRNA pairs present within AML- Peripheral Blood and AML- Bone Marrow samples from both tissue and cell lines. The miRNA target genes are further filtered based on their functional significance in AML system. These filtered genes constitute the set of selected miRNA target features, which have been finally used for developing machine learning based prediction tool, 'TbAMLPred' for preliminary detection of AML. This model implements both unsupervised clustering and supervised classification algorithms that would increase the reliability of prediction. Our results show that the selected miRNA target-based features can separate the control and disease samples linearly. Overall, we put forward 'TbAMLPred' for a non-invasive mode of preliminary AML diagnosis in future. Github link for accessing TbAMLPred: https://github.com/zglabDIB/TbAMLPred., Competing Interests: Declaration of Competing Interest The authors have no competing ineterst to declare., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: Calcific aortic valve disease (CAVD) is the most common native valve disease. Valvular interstitial cell (VIC) osteogenic differentiation and valvular endothelial cell (VEC) dysfunction are key steps in CAVD progression. Circular RNA (circRNAs) is involved in regulating osteogenic differentiation with mesenchymal cells and is associated with multiple disease progression, but the function of circRNAs in CAVD remains unknown. Here, we aimed to investigate the effect and potential significance of circRNA-miRNA-mRNA networks in CAVD., Methods: Two mRNA datasets, one miRNA dataset, and one circRNA dataset of CAVD downloaded from GEO were used to identify DE-circRNAs, DE-miRNAs, and DE-mRNAs. Based on the online website prediction function, the common mRNAs (FmRNAs) for constructing circRNA-miRNA-mRNA networks were identified. GO and KEGG enrichment analyses were performed on FmRNAs. In addition, hub genes were identified by PPI networks. Based on the expression of each data set, the circRNA-miRNA-hub gene network was constructed by Cytoscape (version 3.6.1)., Results: 32 DE-circRNAs, 206 DE-miRNAs, and 2170 DE-mRNAs were identified. Fifty-nine FmRNAs were obtained by intersection. The KEGG pathway analysis of FmRNAs was enriched in pathways in cancer, JAK-STAT signaling pathway, cell cycle, and MAPK signaling pathway. Meanwhile, transcription, nucleolus, and protein homodimerization activity were significantly enriched in GO analysis. Eight hub genes were identified based on the PPI network. Three possible regulatory networks in CAVD disease were obtained based on the biological functions of circRNAs including: hsa&#95;circ&#95;0026817-hsa-miR-211-5p-CACNA1C, hsa&#95;circ&#95;0007215-hsa-miR-1252-5p-MECP2, and hsa&#95;circ&#95;0007215-hsa-miR-1343-3p- RBL1., Conclusion: The present bionformatics analysis suggests the functional effect for the circRNA-miRNA-mRNA network in CAVD pathogenesis and provides new targets for therapeutics., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2023 Linghong Song et al.)

Background: Epilepsy is a neurological disease characterized by neural network dysfunction. Although most reports indicate that the pathological process of epilepsy is related to inflammation, synaptic plasticity, cell apoptosis, and ion channel dysfunction, the underlying molecular mechanisms of epilepsy are not fully understood., Methods: This review summarizes the latest literature on the roles and characteristics of long noncoding RNAs (lncRNAs) in the pathogenesis of epilepsy., Results: lncRNAs are a class of long transcripts without protein-coding functions that perform important regulatory functions in various biological processes. lncRNAs are involved in the regulation of the pathological process of epilepsy and are abnormally expressed in both patients and animal models. This review provides an overview of research progress in epilepsy, the multifunctional features of lncRNAs, the lncRNA expression pattern related to epileptogenesis and status epilepticus, and the potential mechanisms for the two interactions contributing to epileptogenesis and progression., Conclusion: lncRNAs can serve as new diagnostic markers and therapeutic targets for epilepsy in the future., (© 2023. Fondazione Società Italiana di Neurologia.)

Competition for intracellular resources, also known as gene expression burden, induces coupling between independently co-expressed genes, a detrimental effect on predictability and reliability of gene circuits in mammalian cells. We recently showed that microRNA (miRNA)-mediated target downregulation correlates with the upregulation of a co-expressed gene, and by exploiting miRNAs-based incoherent-feed-forward loops (iFFLs) we stabilise a gene of interest against burden. Considering these findings, we speculate that miRNA-mediated gene downregulation causes cellular resource redistribution. Despite the extensive use of miRNA in synthetic circuits regulation, this indirect effect was never reported before. Here we developed a synthetic genetic system that embeds miRNA regulation, and a mathematical model, MIRELLA, to unravel the miRNA (MI) RolE on intracellular resource aLLocAtion. We report that the link between miRNA-gene downregulation and independent genes upregulation is a result of the concerted action of ribosome redistribution and 'queueing-effect' on the RNA degradation pathway. Taken together, our results provide for the first time insights into the hidden regulatory interaction of miRNA-based synthetic networks, potentially relevant also in endogenous gene regulation. Our observations allow to define rules for complexity- and context-aware design of genetic circuits, in which transgenes co-expression can be modulated by tuning resource availability via number and location of miRNA target sites., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Background: Nasopharyngeal carcinoma (NPC) represents a highly aggressive malignant tumor. Competing endogenous RNAs (ceRNA) regulation is a common regulatory mechanism in tumors. The ceRNA network links the functions between mRNAs and ncRNAs, thus playing an important regulatory role in diseases. This study screened the potential key genes in NPC and predicted regulatory mechanisms using bioinformatics analysis. Methods: The merged microarray data of three NPC-related mRNA expression microarrays from the Gene Expression Omnibus (GEO) database and the expression data of tumor samples or normal samples from the nasopharynx and tonsil in The Cancer Genome Atlas (TCGA) database were both subjected to differential analysis and Weighted Gene Co-expression Network Analysis (WGCNA). The results from two different databases were intersected with WGCNA results to obtain potential regulatory genes in NPC, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. The hub-gene in candidate genes was discerned through Protein-Protein Interaction (PPI) analysis and its upstream regulatory mechanism was predicted by miRwalk and circbank databases. Results: Totally 68 upregulated genes and 96 downregulated genes in NPC were screened through GEO and TCGA. According to WGCNA, the NPC-related modules were screened from GEO and TCGA analysis results, and the genes in the modules were obtained. After the results of differential analysis and WGCNA were intersected, 74 differentially expressed candidate genes associated with NPC were discerned. Finally, fibronectin 1 (FN1) was identified as a hub-gene in NPC. Prediction of upstream regulatory mechanisms of FN1 suggested that FN1 may be regulated by ceRNA mechanisms involving multiple circRNAs, thereby influencing NPC progression through ceRNA regulation. Conclusion: FN1 is identified as a key regulator in NPC development and is likely to be regulated by numerous circRNA-mediated ceRNA mechanisms., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Chen, Shi, Ren, Wan, Zhuo, Zeng, SangDan and Wang.)

Competing endogenous RNAs (ceRNAs) have gained attention in cancer research owing to their involvement in microRNA-mediated gene regulation. Previous studies have identified ceRNA networks of individual cancers. Nevertheless, none of these studies has investigated different cancer stages. We identify stage-specific ceRNAs in breast cancer using the cancer genome atlas data. Moreover, we investigate the molecular functions and prognostic ability of ceRNAs involved in stage I-IV networks. We identified differentially expressed candidate ceRNAs using edgeR and limma R packages. A three-step analysis was used to identify statistically significant ceRNAs of each stage. Survival analysis and functional enrichment analysis were conducted to identify molecular functions and prognostic ability. We found five genes and one long non-coding RNA unique to the stage IV ceRNA network. These genes have been described in previous breast cancer studies. Genes acted as ceRNAs are enriched in cancer-associated pathways. Two, three, and three microRNAs from stages I, II, and III were prognostic from the Kaplan-Meier survival analysis. Our results reveal a set of unique ceRNAs in metastatic breast cancer. Further experimental work is required to evaluate their role in metastasis. Moreover, identifying stage-specific ceRNAs will improve the understanding of personalised therapeutics in breast cancer., (© 2023. The Author(s).)

Background: Irritable bowel syndrome (IBS) is the most common gastrointestinal disease worldwide, with diarrhea-predominant irritable bowel syndrome (IBS-D) being the prevalent subtype. However, its pathogenesis remains unclear. Research has increasingly focused on identifying genetic factors in the mechanisms underlying IBS., Objective: We aimed to explore key gene nodes and potential microRNA-mRNA regulatory pairs of IBS-D using bioinformatics methods., Methods: We downloaded the GSE36701 microarray dataset from the Gene Expression Omnibus database and obtained 1358 differentially expressed mRNAs by analyzing mRNA profiles using the GEO2R analysis tool. Based on our previous study, we used TargetScan, miTarBase, and miRDB to predict the downstream genes of three known microRNAs (hsa-let-7b-5p, hsa-miR-19b-3p, and hsamiR- 20a-5p), and the microRNA-mRNA regulatory network was visualized using Cytoscape., Results: A total of 795 downstream target genes were found in TargetScan, miRTarBase, and miRDB databases, and 50 candidate genes were obtained. The Metascape and STRING databases were used to perform enrichment analysis and construct a protein-protein interaction network of candidate genes. Finally, we constructed a network of 3 microRNAs and 50 candidate mRNAs, among which 28 negative relation ship pairs and 5 key axes (hsa-miR-20a-5p/VEGFA, hsa-let-7b- 5p/MSN, hsa-let-7b-5p /PPP1R16B, hsa-19b-3p/ITGA2, and hsa-19b-3p/PIK3R3) were identified., Conclusion: We report five novel microRNA-mRNA regulatory axes in IBS-D pathogenesis and speculated that PIK3R3, negatively regulated by hsa-miR-19b-3p, may regulate NF-κB production through the PI3K/Akt pathway, which accounts for the occurrence of clinical symptoms in IBS-D patients. Our findings may offer key biomarkers for IBS-D diagnosis and treatment., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

MicroRNAs (miRNAs) play important role in regulating cellular metabolism, and are currently being explored in cancer. As metabolic reprogramming in cancer is a major mediator of phenotypic plasticity, understanding miRNA-regulated metabolism will provide opportunities to identify miRNA targets that can regulate oncogenic phenotypes by taking control of cellular metabolism. In the present work, we studied the effect of differentially expressed miRNAs on metabolism, and associated oncogenic phenotypes in glioblastoma (GBM) using patient-derived data. Networks of differentially expressed miRNAs and metabolic genes were created and analyzed to identify important miRNAs that regulate major metabolism in GBM. Graph network-based approaches like network diffusion, backbone extraction, and different centrality measures were used to analyze these networks for identification of potential miRNA targets. Important metabolic processes and cellular phenotypes were annotated to trace the functional responses associated with these miRNA-regulated metabolic genes and associated phenotype networks. miRNA-regulated metabolic gene subnetworks of cellular phenotypes were extracted, and important miRNAs regulating these phenotypes were identified. The most important outcome of the study is the target miRNA combinations predicted for five different oncogenic phenotypes that can be tested experimentally for miRNA-based therapeutic design in GBM. Strategies implemented in the study can be used to generate testable hypotheses in other cancer types as well, and design context-specific miRNA-based therapy for individual patient. Their usability can be further extended to other gene regulatory networks in cancer and other genetic diseases., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Diffuse large B-cell lymphoma (DLBCL) is an aggressive B-cell lymphoma with high heterogeneity. There is an unmet need to investigate valid indicators for the diagnosis and therapy of DLBCL., Methods: GEO database was utilized to screen for differentially expressed genes (DEGs) and differential miRNAs in DLBCL tissues. The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were applied to analyse DEGs. Then multiple databases were searched for related miRNAs within DLBCL, TNF receptor-associated factor 5 (TRAF5) and NF-kappa B (NF- κ B) signaling pathways. The KOBAS database was used to assist in the screening of miRNAs of interest and construct the regulatory network of miRNA-mRNA. Finally, the expression level and diagnostic performance of miRNAs were analyzed with GEO datasets, and DEGs were identified from the GEPIA database., Results: DEGs were significantly concentrated in the NF- κ B signaling pathway and cytokine-cytokine receptor interaction, and involved in the process of immune response and protein binding. MiR-15a-5p, miR-147a, miR-192-5p, miR-197-3p, miR-532-5p, and miR-650 were revealed to be targeting TRAF5 and participating in NF- κ B signaling pathway and might impact the apoptosis and signal transduction of DLBCL. In the GEPIA database, TRAF5 was significantly overexpressed in DLBCL. The expression of miR-197-3p was upregulated within GEO datasets, while the rest of the miRNAs were downregulated in DLBCL., Conclusions: Subsets of miRNAs may participate in the NF- κ B signaling pathway by co-targeting TRAF5 and could be prospective biomarkers exploring the pathogenesis of DLBCL., Competing Interests: The authors declare that there are no conflicts of interest., (Copyright © 2022 Chunyao Li et al.)

Objective: This study aimed to investigate the molecular regulatory mechanisms underpinning Duchenne muscular dystrophy (DMD)., Methods: Using microarray data, differentially expressed long noncoding RNAs (DELs) and DMD-related differentially expressed mRNAs (DEMs) were screened based on the comparative toxicogenomics database, using a cutoff of |log 2 fold change| > 1 and false discovery rate (FDR) < 0.05. Then, protein-protein interaction (PPI), coexpression network of lncRNA-mRNA, and DMD-related lncRNA-mRNA pathway networks were constructed, and functional analyses of the genes in the network were performed. Finally, the proportions of immune cells infiltrating the muscle tissues in DMD were analyzed, and the correlation between the immune cells and expression of the DELs/DEMs was studied., Results: A total of 46 DELs and 313 DMD-related DEMs were identified. The PPI network revealed STAT1 , VEGFA , and CCL2 to be the top three hub genes. The DMD-related lncRNA-mRNA pathway network comprising two pathways, nine DELs, and nine DMD-related DEMs showed that PYCARD , RIPK2 , and CASP1 were significantly enriched in the NOD-like receptor signaling pathway, whereas MAP2K2 , LUM , RPS6 , PDCD4 , TWIST1 , and HIF1A were significantly enriched with proteoglycans in cancers. The nine DELs in this network were DBET, MBNL1-AS1, MIR29B2CHG, CCDC18-AS1, FAM111A-DT, GAS5, LINC01290, ATP2B1-AS1, and PSMB8-AS1., Conclusion: The nine DMD-related DEMs and DELs identified in this study may play important roles in the occurrence and progression of DMD through the two pathways of the NOD-like receptor signaling pathway and proteoglycans in cancers., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Bing Xu and Chunlei Zheng.)

The deterioration and metastasis of cancer involve various aspects of genomic changes, including genomic DNA changes, epigenetic modifications, gene expression, and other complex interactions. Therefore, integrating single-cell multi-omics data to construct gene regulatory networks containing more omics information is of great significance for understanding the pathogenesis of cancer. In this article, an algorithm integrating single-cell RNA sequencing data and DNA methylation data to construct a gene regulatory network based on the back-propagation (BP) neural network (scBPGRN) is proposed. This algorithm uses biweight extreme correlation coefficients to measure the correlation between factors and uses neural networks to calculate generalized weights to construct gene regulation networks. Finally, the node strength is calculated to identify the genes associated with cancer. We apply the scBPGRN algorithm to hepatocellular carcinoma (HCC) data. We construct a regulatory network and identify top-ranked genes, such as MYCBP, KLHL35, PRKCZ, and SERPINA6, as the key HCC-related genes. We analyze the top 100 genes, and the HCC-related genes are concentrated in the top 20. In addition, the single cell data is found to consist of two subpopulations. We also apply scBPGRN to two subpopulations. We analyze the top 50 genes in them, and the HCC-related genes are concentrated in the top 20. The consequences of functional enrichment analysis indicate that the gene regulatory network we have constructed is valid. Our results have been verified in several pieces of literature. This study provides a reference for the integration of single-cell multi-omics data to construct gene regulatory networks., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background: Acute kidney injury is a common clinical problem with no sensitive and specific diagnostic biomarkers and definitive treatments. The underlying molecular mechanisms of acute kidney injury are unclear. Therefore, it is pivotal to explore the underlying mechanisms and screen for novel diagnostic biomarkers, and therapeutic targets., Methods: The present study identified 15 hub genes by WGCNA analysis. LASSO-based logistic regression analysis was used to select key features and construct a diagnostic model of AKI. In addition, GO and KEGG analyses were performed and TF-mRNA and miRNA-mRNA network analysis and immune infiltration analysis of hub genes were performed to reveal the underlying mechanisms of AKI., Results: A diagnostic model was constructed by LASSO-based logistic regression analysis and was validated by RT-qPCR based on 15 hub genes. GO and KEGG analyses revealed DEGs were enriched in oxidation-reduction process, cell adhesion, proliferation, migration, and metabolic process. The enriched TFs were BRD2, EP300, ETS1, MYC, SPI1, and ZNF263. The enriched miRNAs were miR-181c-5p, miR-218-5p, miR-485-5p, miR-532-5p and miR-6884-5p. The immune infiltration analysis showed that Macrophages M2 was decreasing significantly revealing a protective factor for further AKI treatment., Conclusions: The present study identified 15 hub genes based on WGCNA. Development and validation of a potentially diagnostic model based on 15 hub genes. In addition, exploring the interaction between transcriptional factors and 15 hub genes, and miRNA-mRNA relationship pairs. Furthermore, immune infiltration analysis was performed by analyzing gene expression profiles of AKI. Our study provides some basis for further experimental studies., (© 2022 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC.)

MicroRNA (miRNA) is a class of non-coding single-stranded RNA molecules encoded by endogenous genes with a length of about 22 nucleotides. MiRNAs have been successfully identified as differentially expressed in various cancers. There is evidence that disorders of miRNAs are associated with a variety of complex diseases. Therefore, inferring potential miRNA-disease associations (MDAs) is very important for understanding the aetiology and pathogenesis of many diseases and is useful to disease diagnosis, prognosis and treatment. First, We creatively fused multiple similarity subnetworks from multi-sources for miRNAs, genes and diseases by multiplexing technology, respectively. Then, three multiplexed biological subnetworks are connected through the extended binary association to form a tripartite complete heterogeneous multiplexed network (Tri-HM). Finally, because the constructed Tri-HM network can retain subnetworks' original topology and biological functions and expands the binary association and dependence between the three biological entities, rich neighbourhood information is obtained iteratively from neighbours by a non-equilibrium random walk. Through cross-validation, our tri-HM-RWR model obtained an AUC value of 0.8657, and an AUPR value of 0.2139 in the global 5-fold cross-validation, which shows that our model can more fully speculate disease-related miRNAs.

Cancer is not only a very aggressive but also a very diverse disease. Recent advances in high-throughput omics technologies of cancer have enabled biomedical researchers to have more opportunities for studying its multi-level biological regulatory mechanism. However, there are few methods to explore the underlying mechanism of cancer by identifying its multidimensional molecular regulatory modules from the multidimensional omics data of cancer. In this paper, we propose a sparse orthogonality-regularized joint non-negative matrix factorization (SOJNMF) algorithm which can integratively analyze multidimensional omics data. This method can not only identify multidimensional molecular regulatory modules, but reduce the overlap rate of features among the multidimensional modules while ensuring the sparsity of the coefficient matrix after decomposition. Gene expression data, miRNA expression data and gene methylation data of liver cancer are integratively analyzed based on SOJNMF algorithm. Then, we obtain 238 multidimensional molecular regulatory modules. The results of permutation test indicate that different omics features within these modules are significantly correlated in statistics. Meanwhile, the results of functional enrichment analysis show that these multidimensional modules are significantly related to the underlying mechanism of the occurrence and development of liver cancer.

Ischemic stroke (IS) is the most common type of stroke, and its pathophysiological process is more complex. In recent years, the key regulatory roles of non-coding RNA (miRNA, circRNA) and mRNA in the development of IS have attracted more attention. In the process of cerebral ischemia/reperfusion injury, circRNA can regulate nerves, blood vessels and immune system through miRNA/mRNA axis, so as to affect the neurovascular unit of IS. The combination of these noncoding RNAs and mRNAs can be used as non-invasive biomarkers and therapeutic tools for IS diagnosis, prognosis and brain injury. Therefore, it is very important to study the potential molecular mechanism, activation pathway and treatment methods of circRNA/miRNA/mRNA network in IS. This review will focus on the latest progress of circRNA/miRNA/mRNA regulatory network, we have also included some circRNAs, which does not mediate through a miRNA, so we also include circRNA -mRNA network. And explore the application prospect of these RNAs as potential therapeutic targets in the prevention and treatment of IS., Competing Interests: Declaration of competing interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Background: Salt-sensitivity hypertensives (SSH) are an independent risk factor for cardiovascular disease. However, the mechanism of SSH is not clear. This study is aimed at constructing a competing endogenous RNA (ceRNA) network related to SSH., Methods: Data sets were collected from the Gene Expression Omnibus database (GEO) to extract data on salt sensitivity RNA of patients with or without hypertensives in GSE135111. Firstly, we analyzed differentially expressed genes (DEGs, log2FC ≥ 0.5 and P < 0.05) and differentially expressed lncRNAs (DELs, log2FC ≥1 and P <0.05) between SSH and salt-sensitive normotension (SSN). Then, the gene ontology (GO), KEGG pathway enrichment analysis, and PPI network construction of DEGs were performed, and the hub genes in the PPI network by cytoHubba (12 methods) were screened out. Finally, a ceRNA network was constructed based on lncRNA-miRNA-mRNA pairs and hub genes., Results: 163 DEGs and 65 DELs were screened out. The GO and KEGG pathway analyses of DEGs were mainly enriched in metabolism (e.g., insulin secretion and cellular response to glucagon stimulus and peptidyl-tyrosine dephosphorylation,) and plasma membrane signaling (e.g., cell adhesion and chemical synaptic transmission and integral component of membrane). Additionally, a ceRNA network, including 1 mRNA (EGLN3), 2 miRNAs (hsa-miR-17-5p and hsa-miR-20b-5p), and 1 lncRNA (C1orf143) was successfully constructed., Conclusions: In conclusion, the proposed ceRNA network may help elucidate the regulatory mechanism by which lncRNAs function as ceRNAs and contribute to the pathogenesis of SSH. Importantly, candidate lncRNAs, miRNAs, and mRNAs can be further evaluated as a potential therapeutic targets for SSH., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2022 Xiu-Juan Liu et al.)

MicroRNAs are key components of cellular regulatory networks, and breakdown in miRNA function causes cascading effects leading to pathophenotypes. A better understanding of the role of miRNAs in diseases is essential for human health. Here, we have devised a method for comprehensively mapping the associations between miRNAs and diseases by merging on a common key between two curated omics databases. The resulting bidirectional resource, miR2Trait, is more detailed than earlier catalogs, uncovers new relationships, and includes analytical utilities to interrogate and extract knowledge from these datasets. miR2Trait provides resources to compute the disease enrichment of a user-given set of miRNAs and analyze the miRNA profile of a specified diseasome. Reproducible examples demonstrating use-cases for each of these resource components are illustrated. Furthermore we used these tools to construct pairwise miRNA-miRNA and disease-disease enrichment networks, and identified 23 central miRNAs that could underlie major regulatory functions in the human genome. miR2Trait is available as an open-source command-line interface in Python3 (URL: https://github.com/miR2Trait) with a companion wiki documenting the scripts and data resources developed, under MIT license for commercial and non-commercial use. A minimal web-based implementation has been made available at https://sas.sastra.edu/pymir18. Supplementary information is available at: https://doi.org/10.6084/m9.figshare.8288825.v3., Competing Interests: The authors declare there are no competing interests., (©2022 Babu and Palaniappan.)

MicroRNAs represent major regulatory components of the disease epigenome and they constitute powerful biomarkers for the accurate diagnosis and prognosis of various diseases, including cancers. The advent of high-throughput technologies facilitated the generation of a vast amount of miRNA-cancer association data. Computational approaches have been utilized widely to effectively analyze and interpret these data towards the identification of miRNA signatures for diverse types of cancers. Herein, a novel computational workflow was applied to discover core sets of miRNA interactions for the major groups of neoplastic diseases by employing network-based methods. To this end, miRNA-cancer association data from four comprehensive publicly available resources were utilized for constructing miRNA-centered networks for each major group of neoplasms. The corresponding miRNA-miRNA interactions were inferred based on shared functionally related target genes. The topological attributes of the generated networks were investigated in order to detect clusters of highly interconnected miRNAs that form core modules in each network. Those modules that exhibited the highest degree of mutual exclusivity were selected from each graph. In this way, neoplasm-specific miRNA modules were identified that could represent potential signatures for the corresponding diseases., Competing Interests: Gökhan Karakülah is an Academic Editor for PeerJ., (©2022 Arshinchi Bonab et al.)

Background: Recently, Inflammatory Bowel Disease (IBD) has been proven as a risk factor for the increasing incidence of cervical cancer (CC) development. In this study, we identify these potential hub genes and their significant pathways that commonly interact between IBD and CC and these pathological mechanisms. To this end, we use bioinformatics and systems biology approaches to analyze the miRNA-mRNA, TFs-mRNA regulatory network., Methods and Findings: The reanalysis dataset from Gene Expression Omnibus (GEO) and the cancer genome atlas (TCGA) found these common differentially expressed genes (DEGs) between IBD and CC, clustered via weighted gene co-expression network analysis, and the vital modules significantly related to cervical cancer were identified. These hub genes of the key module were identified and explored in biological mechanism pathway analysis. Organelle fission, nuclear envelope, protein serine/threonine kinase activity, and the Human T-cell leukemia virus 1 infection pathway were the major enriched pathways for the common DEGs. Due to the high connectivity, the common DEGs with protein-protein interaction (PPI) network disclosed hub proteins (CDK1, MAD2L1, and CCNB1). This study also showed the classification algorithms of ten hub genes (MAD2L1, CCNB2, CDK1, CCNA2, BUB1B, KIF11, TTK, BUB1, CCNB1, ASPM) with accuracy >0.90 suggesting the novel biomarker potential of the hub genes. The microRNAs (miRNA), and transcription factors (TFs) mRNA regulatory network, five transcription factors, and twelve miRNAs are strongly linked to three hub genes. Gene drug interaction analysis found seven drugs compound that interacts with the hub gene., Conclusions: In the current study, our procedure has hypothesized the comprehensive understanding of disease mechanisms vital for both CC and IBD that may mediate their interaction. Our results suggest the further investigation of the molecules for the treatment of IBD and CC., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Craniofacial structures change dynamically in morphology during development through the coordinated regulation of various cellular molecules. However, it remains unclear how these complex mechanisms are regulated in a spatiotemporal manner. Here we applied natural cubic splines to model gene and microRNA (miRNA) expression from embryonic day (E) 10.5 to E14.5 in the proximal and distal regions of the maxillary processes to identify spatiotemporal patterns of gene and miRNA expression, followed by constructing corresponding regulatory networks. Three major groups of differentially expressed genes (DEGs) were identified, including 3,927 temporal, 314 spatial, and 494 spatiotemporal DEGs. Unsupervised clustering further resolved these spatiotemporal DEGs into 8 clusters with distinct expression patterns. Interestingly, we found 2 clusters of differentially expressed miRNAs: 1 had 80 miRNAs monotonically decreasing and the other had 97 increasing across developmental stages. To evaluate the phenotypic relevance of these DEGs during craniofacial development, we integrated data from the CleftGeneDB database and constructed the regulatory networks of genes related to orofacial clefts. Our analysis revealed 2 hub miRNAs, mmu-miR-325-3p and mmu-miR-384-5p, that repressed cleft-related genes Adamts3 , Runx2 , Fgfr2 , Acvr1 , and Edn2 , while their expression increased over time. On the contrary, 2 hub miRNAs, mmu-miR-218-5p and mmu-miR-338-5p, repressed cleft-related genes Pbx2 , Ermp1 , Snai1 , Tbx2 , and Bmi1 , while their expression decreased over time. Our experiments indicated that these miRNA mimics significantly inhibited cell proliferation in mouse embryonic palatal mesenchymal (MEPM) cells and O9-1 cells through the regulation of genes associated with cleft palate and validated the role of our regulatory networks in orofacial clefts. To facilitate interactive exploration of these data, we developed a user-friendly web tool to visualize the gene and miRNA expression patterns across developmental stages, as well as the regulatory networks (https://fyan.shinyapps.io/facebase&#95;shiny/). Taken together, our results provide a valuable resource that serves as a reference map for future research in craniofacial development.

Background: Colorectal cancer (CRC) is currently the fourth most common cancer worldwide. The roles of exosomal competing endogenous RNAs (ceRNAs) in CRC remain unclear. In this study, we constructed an exosomal ceRNA network to identify the core ceRNAs and investigate the diagnostic biomarkers in CRC. Methods and Patients: Serum exosomes were isolated from four CRC patients and two healthy donors by ultracentrifugation, and then subjected to RNA isolation, sequencing and microarray. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analyses were performed to identify functional enrichment implications of differentially expressed exosomal mRNAs. TargetScan and miRanda were used for identifying the miRNA-mRNA and miRNA-LncRNA interactions. The predicted lncRNAs and mRNAs were intersected with the differentially expressed genes, for which the screening criterion was fold change >1.5 in the microarray. Differentially expressed exosomal miRNAs were identified in the GSE71008 dataset, and differentially expressed mRNAs (DEmRNAs) were further summarized from The Cancer Genome Atlas (TCGA) database. Results: A total of 1186 exosomal DEmRNAs, 2088 exosomal DElncRNAs and 29 exosomal miRNAs were detected in CRC patients compared to the healthy donors. Functional enrichment analysis suggested that exosomal DEmRNAs might participate in pathways related to carcinogenesis and development of cancer. An exosomal ceRNA regulatory network of CRC was constructed based on 40 lncRNAs, two miRNAs, and five mRNAs. Exosomal miR-150-5p and miR-10b-5p expression levels were increased in healthy donors compared with CRC patients in the GSE71008 dataset, and five DEmRNAs ( TOMM70A , RBM48 , BEND3 , RHOBTB1 , and ADAMTS2 ) were significantly upregulated in TCGA database. Two potential exosomal regulatory axes of lncRNA G016261-miR-150-5p- RBM48 and lncRNA XLOC&#95;011677-miR-10b-5p- BEND3 were identified from the network. Conclusion: The current study revealed potential molecular biological regulation pathways and diagnostic biomarkers through the exosomal ceRNA regulatory network., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhao, Song, Song and Xie.)

Background: In periodontitis, noncoding RNAs may play a regulatory role in the immune microenvironment through competitive endogenous RNA. We aimed to profile noncoding RNA expression and construct immune-related ceRNA network in periodontitis., Methods: Five inflamed periodontal tissue and five healthy gingivae were collected for whole-transcriptome sequencing. Differential gene, functional enrichment, and protein-protein interaction network analysis were performed to explore the function of differentially expressed genes. CIBERSORTx was used to analyze level of immune cell infiltration in the periodontal tissue. An immune-related competitive endogenous RNA network was constructed and expression of key regulators in the network was validated., Results: Compared with healthy gingiva, 200 mRNAs, 90 long noncoding RNAs, 65 microRNAs, and 518 circular RNAs were differentially expressed, and cell chemotaxis was significantly enhanced in inflamed periodontal tissue. Immune cell infiltration analysis showed that neutrophils, macrophages M1, T follicular helper cells, and naive B cells were significantly increased in periodontitis. Key regulators including JUN, FOS, THBS1, KLF2, WIF1, were identified and their expression was then validated., Conclusion: We constructed an immune-related competitive endogenous RNA network in periodontal tissue, which provided new insights into immune homeostasis in periodontitis and laid a foundation for further study of noncoding RNAs. Key regulators in this network may be promising targets for future periodontitis treatment., (© 2022. The Author(s).)

Wilms tumor (WT) is the most common genitourinary renal tumor that typically occurs in children under 15 and is thought to be linked to somatic and germline mutations. However, the specific functional role of competing endogenous RNAs (ceRNAs) and their potential implications in WT remain unclear. In this study, we developed an lncRNA-mediated (long noncoding RNA-mediated) ceRNA network via the R packages for WT with expression data obtained from the tumor alterations relevant for genomics-driven therapy (TARGET) database. Unsupervised hierarchical clustering analysis revealed that the WT specimens could be clearly distinguished from healthy specimens with respect to the expression of disordered RNAs. A total of 1,607 differentially expressed (DE) lncRNAs, 116 DE microRNAs (DEmiRNAs), and 3,262 DE messenger RNAs (DEmRNAs) were identified as WT-specific RNAs, and a lncRNA-miRNA-mRNA ceRNA network with 159 DElncRNAs, 18 DEmiRNAs, 131 DEmRNAs, and 792 interactions was constructed. According to the clinical survival data, 12 DElncRNAs, 5 DEmRNAs, and 2 DEmiRNAs were selected from the ceRNA network that could significantly impact the overall survival of WT patients ( P < 0.05). Functional enrichment analysis showed that the biological processes and pathways of DEmRNAs, such as cell cycle and virus infection, may be associated with WT. The present study constructed a dysregulated lncRNA-mediated ceRNA network in WT and discovered that lncRNA-mediated ceRNAs may serve as important regulators in WT development and progression. Survival-associated RNAs may serve as new potential biomarkers, suggesting that the constructed ceRNA network in WT might be important for determining optimal therapeutic strategies., Competing Interests: The authors declare that there are no conflicts of interest., (Copyright © 2022 Biao An et al.)