ceRNA Network Research Articles

Identifying hub genes for chemo-radiotherapy sensitivity in cervical cancer: a bi-dataset in silico analysis

PurposeTo identify the hub genes that associated with chemo-radiotherapy sensitivity for cervical cancer and to explore the relationship between hub genes and various cellular processes and potential mechanism of cervical cancer.MethodsThe gene expression data of 21 patients with CESC and the mRNA expression profiles of 296 patients with CESC were obtained from the Gene Expression Omnibus(GEO) and The Cancer Genome Atlas (TCGA) databases, respectively. The potential functions and regulatory mechanisms of differentially expressed genes (DEGs) were identified using GO and KEGG enrichment analyses. Hub genes were identified using random survival forest analysis. The relationship between hub genes and various cellular processes was comprehensively analyzed. The expression of hub genes was assessed using clinical data extracted from the Human Protein Atlas (HPA) database.ResultsA total of 139 and 13 DEGs were found to be upregulated and downregulated, respectively, in CESC. The six hub genes, namely, SELP, PIM2, CCL19, SDS, NRP1, and SF3A2, were significantly correlated with immune cell infiltration, chemotherapy sensitivity, disease-related genes, and enriched signaling pathways (all p-value < 0.05). A nomogram and calibration curve were generated using the six hub genes to predict prognosis with high accuracy. A regulatory network comprising TFs (ZBTB3) and mRNAs (NRP1/PIM2/SELP) and several competitive endogenous RNA (ceRNA) networks comprising mRNAs, miRNAs, and lncRNAs were constructed. Data from HPA indicated that the protein expression of the six hub genes differed significantly between patients with CESC and healthy individuals.ConclusionUpregulation of SELP, PIM2, CCL19, SDS, NRP1, and SF3A2 is associated with radiotherapy sensitivity and is involved in various cellular processes in CESC. These six genes may serve as biomarkers for predicting the radiotherapy response and prognosis in patients with CESC.

Identifying potential key ferroptosis-related genes and therapeutic drugs in sepsis-induced ARDS by bioinformatics and experimental verification.

Acute respiratory distress syndrome (ARDS) is a serious pathological process with high mortality. Ferroptosis is pivotal in sepsis, whose regulatory mechanisms in sepsis-induced ARDS remains unknown. We aimed to determine key ferroptosis-related genes in septic ARDS and investigate therapeutic traditional Chinese medicine (TCM). Sepsis-induced ARDS dataset obtained from Gene Expression Omnibus (GEO) was analyzed to identify ferroptosis-related differentially expressed genes (FRDEGs). Enrichment analysis and protein-protein interaction (PPI) network construction were performed to identify hub genes. Immune cells infiltration was analyzed and competitive endogenous RNA (ceRNA) network was constructed. The diagnostic value of hub genes in septic ARDS was analyzed and the occurrence of ferroptosis and the expression of hub genes were detected. TCM targeting hub genes was predicted via SymMap database and was verified. 16 FRDEGs were obtained, among which the top four genes (IL1B, TXN, MAPK3, HSPB1) were selected as hub genes, which may be potential diagnostic markers of septic ARDS. Immunoassay showed that sepsis-induced ARDS and hub genes were closely related to immune cells. The ceRNA network showed 26 microRNAs and 38 long noncoding RNA (lncRNAs). Ferroptosis occurred and the expressions of IL1B, MAPK3 and TXN were increased in septic ARDS mice and LPS-challenged human pulmonary alveolar epithelial cells (HPAEpiCs). Sea buckthorn alleviated septic lung injury and affected hub genes expression. Ferroptosis-related genes of IL1B, MAPK3 and TXN serve as potential diagnostic genes for sepsis-induced ARDS. Sea buckthorn may be therapeutic medication for ARDS. This study provides a new direction for septic ARDS treatment.

Identification of competing endogenous RNA networks associated with circRNA and lncRNA in TCDD-induced cleft palate development

2,3,7,8 -tetrachlorodibenzo-p-dioxin (TCDD) is a teratogen that can induce cleft palate formation, a common birth defect. Competing endogenous RNAs (ceRNAs), including circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs), indirectly regulate gene expression via sharing microRNAs (miRNAs). Nevertheless, the mechanism by which they act as ceRNAs to regulate palatal development remains to be explored in greater detail. Here, the cleft palate model of C57BL/6 N pregnant mice was constructed by gavage of TCDD (64 ug/kg) on gestation day (GD) 10.5, and the palatal shelves were taken on gestation day (GD) 14.5 for whole-transcriptome sequencing to investigate the underlying mechanisms of the roles of circRNAs and lncRNAs as ceRNAs in cleft palate. Sequencing results revealed that 293 lncRNA, 589 circRNA, 47 miRNA, and 138 messenger RNA (mRNA) were significantly dysregulated, and the cytochrome P450 (CYP) enzymes and the aryl hydrocarbon receptor (AhR) pathway play key roles in the induction of cleft palate upon exposure to TCDD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed the function of TCDD function was mainly related to the metabolic processes of intracellular compounds, including the metabolic processes of cellular aromatic compounds and the metabolism of exogenous drugs by cytochrome P450, etc. Furthermore, quantitative reverse transcription polymerase chain reaction (qRT-PCR) indicated that the circRNA_1781/miR-30c-1–3p/PKIB and XR_380026.2/miR-1249–3p/DNAH10 ceRNA networks were hypothesized to be a hub involved in palatal development suggesting that the circRNA_1781/miR-30c-1–3p/PKIB and XR_380026.2/miR-1249–3p/DNAH10 ceRNA networks may be critical for palatogenesis, setting the foundation for the investigation of cleft palate.

Identification of key immune-related genes and potential therapeutic targets in immune checkpoint inhibitor–associated myocarditis

Abstract Background Immune checkpoint inhibitors (ICIs) are widely used in cancer treatment; however, the emergence of ICI-associated myocarditis (ICI-MC) presents a severe and potentially fatal complication with poorly understood pathophysiological mechanisms. This study aimed to identify crucial immune-related genes in ICI-MC and uncover potential therapeutic targets using bioinformatics. Methods Using the GSE180045 dataset, which includes three groups—Group A: ICI patients without immune adverse events, Group B: ICI patients with non-myocarditis immune adverse events, and Group C: ICI patients with myocarditis—we analyzed differentially expressed genes (DEGs) between ICI-MC samples (Group C) and non-myocarditis controls (Groups A and B). These DEGs were then cross-referenced with 1796 immune-related genes from the immPort database to identify immune-related DEGs. We conducted functional enrichment analyses (Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, gene set enrichment analysis), constructed a protein–protein interaction network, and identified hub genes. Validation using the GSE4172 dataset led to the identification of optimal feature genes from the overlap between hub genes and DEGs. Predictions of target MicroRNAs (miRNAs) were made, and a competing endogenous RNA (ceRNA) network was constructed. Target drugs for hub genes were predicted using the Connectivity Map database. Results We identified 58 DEGs between ICI-MC and controls, which led to the identification of 32 immune-related DEGs after intersection with 1796 immune-related genes. Functional analyses revealed enrichment in cell lysis, CD8+ T-cell receptor, natural killer cell–mediated cytotoxicity, and RAGE signaling. Notably upregulated hub genes included IL7R, PRF1, GNLY, CD3G, NKG7, GZMH, GZMB, KLRB1, KLRK1, and CD247. In the validation dataset, 407 DEGs were uncovered, resulting in the identification of 3 optimal feature genes (KLRB1, NKG7, GZMH). The predicted target miRNAs, lincRNAs, and circRNAs constituted a comprehensive ceRNA network. Among the top 10 drugs with elevated connectivity scores was acetohydroxamic acid, indicating a need for caution in ICI treatment. Conclusion KG7, GZMH, and KLRB1 were identified as pivotal immune-related genes in ICI-MC. Biological enrichments included pathways involved in cell lysis, the CD8+ T-cell receptor pathway, natural killer cell–mediated cytotoxicity, RAGE signaling, and proinflammatory responses. The ceRNA network illuminated the role of critical molecules and underscored the importance of avoiding drugs such as acetohydroxamic acid in ICI treatment. Key message What is already known on this topic Myocarditis is recognized as a serious ICI-associated toxicity, seemingly infrequent yet often fulminant and lethal. The underlying mechanisms of ICI-associated myocarditis remain not fully understood. Although the significance of T cells and cytotoxic T lymphocyte–associated protein 4 (CTLA-4) is evident, the inciting antigens, the reasons for their recognition, and the mechanisms causing cardiac cell injury are not well characterized. An improved understanding of ICI-associated myocarditis will provide insights into the equilibrium between the immune and cardiovascular systems. What this study adds Our study further validates the significance of T cells and CTLA-4 in ICI-associated myocarditis. More importantly, we identified three genes—NKG7, GZMH, and KLRB1—essential for the development of ICI-MC and proposed ceRNA networks involving these three key genes. How this study might affect research, practice or policy The newly discovered key genes and their intricate molecular interactions offer a comprehensive perspective on the mechanisms underlying ICI-MC. Furthermore, our findings advise caution regarding the use of drugs like acetohydroxamic acid during ICI treatment. As our understanding of these regulatory networks deepens, our study provides valuable insights that could inform future therapeutic strategies for ICI-MC.

Construction of circRNA-mediated ceRNA network and immunoassay for investigating pathogenesis of COPD.

The chronic respiratory condition known as chronic obstructive pulmonary disease (COPD) was one of the main causes of death and disability worldwide. This study aimed to explore and elucidate new targets and molecular mechanisms of COPD by constructing competitive endogenous RNA (ceRNA) networks. GSE38974 and GSE106986 were used to select DEGs in COPD samples and normal samples. Cytoscape software was used to construct and present protein-protein interaction (PPI) network, mRNA-miRNA co-expression network and ceRNA network. The CIBERSORT algorithm and the Lasso model were used to screen the immune infiltrating cells and hub genes associated with COPD, and the correlation between them was analyzed. COPD cell models were constructed in vitro and the expression level of ceRNA network factors mediated by hub gene was detected by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). In this study, 852 differentially expressed genes were screened in the GSE38974 dataset, including 439 upregulated genes and 413 downregulated genes. Gene clustering analysis of PPI network results was performed using the Minimum Common Tumor Data Element (MCODE) in Cytoscape, and seven hub genes were screened using five algorithms in cytoHubba. CCL20 was verified as an important hub gene based on mRNA-miRNA co-expression network, GSE106986 database validation and the analysis of ROC curve results. Finally, we successfully constructed the circDTL-hsa-miR-330-3p-CCL20 network by Cytoscape. Immune infiltration analysis suggested that CCL20 can co-regulate immune cell migration and infiltration through chemokines CCL7 and CXCL3. In vitro experiments, the expression of circDTL and CCL20 was increased, while the expression of hsa-miR-330-3p was decreased in the COPD cell model. By constructing the circDTL-hsa-miR-330-3p-CCL20 network, this study contributes to a better understanding of the molecular mechanism of COPD development, which also provides important clues for the development of new therapeutic strategies and drug targets.

Comprehensive analysis of an mRNA co-expression network and a ceRNA network reveals potential prognostic biomarkers in oral squamous cell carcinoma

BackgroundOral squamous cell carcinoma (OSCC) is a prevalent and aggressive oral cancer with a poor prognosis. Its polygenic risk is likely influenced by complex transcriptional disorders involving networks of co-expressed and functionally related genes, though such investigations are limited in OSCC.MethodsWe analyzed the GSE37991 dataset, comprising 40 OSCC and 40 normal oral tissue samples from the Gene Expression Omnibus. Tumor-specific modules were identified using weighted correlation network analysis (WGCNA), leading to the selection of hub mRNAs and lncRNAs. These lncRNAs were used to construct lncRNA–mRNA and competing endogenous RNA networks. We further examined the expression profiles and survival data of these genes from the Cancer Genome Atlas. Prognostic markers were identified and validated through 5-year survival analysis and Cox proportional hazards modeling. RT-qPCR was used to validate the expression levels in clinical OSCC tissues.ResultsWe identified 1847 differentially expressed genes in OSCC tissues. WGCNA revealed four OSCC-specific modules, screening 120 hub mRNAs and five hub lncRNAs. Two prognostic markers (AQP5, IL-26) from hub mRNAs and three (FRMD5, INHBB, GUCY1A3) from the lncRNA–mRNA network were associated with survival. Validation showed lower expression of AQP5 and GUCY1A3, and higher expression of FRMD5 and INHBB in OSCC compared to normal tissues.ConclusionThis study enhances our understanding of transcriptional dysregulation in OSCC and may highlights AQP5, IL-26, FRMD5, INHBB, and GUCY1A3 as promising prognostic biomarkers.

Unraveling the role of lactate-related genes in myocardial infarction

BackgroundLactate is a crucial intermediary, facilitating communication between myocardial energy metabolism and microenvironmental regulation. The present study aimed to investigate the relationship between lactate-related genes (LRGs) and myocardial infarction (MI). MethodsA total of 23 LRGs exhibited differential expression between individuals with MI and healthy controls. Lasso regression analysis and validation with the GSE61144 dataset identified three hub genes: COX20, AGK, and PDHX. Single-gene GSEA of these genes revealed strong enrichment in pathways related to amino acid metabolism, cell cycle, and immune functions. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was utilized to validate the expression levels of the hub genes. ResultsImmune infiltration analysis revealed differences in CD4+ T and CD8+ T cells between the MI and control groups. Additionally, 67 candidate drugs targeting the three hub LRGs were identified, and a ceRNA network was constructed to explore the intricate interactions among these genes. ConclusionsThese findings enhance the understanding of MI and have potential therapeutic implications.

Using Bioinformatics and Machine Learning to Predict the Genetic Characteristics of Ferroptosis-Cuproptosis-Related Genes Associated with Sleep Deprivation.

Sleep deprivation (SD), a common sleep disease in clinic, has certain risks, and its pathogenesis is still unclear. This study aimed to identify ferroptosis-cuproptosis-related genes (FCRGs) associated with SD through bioinformatics and machine learning, thus elucidating their biological significance and clinical value. SD-DEGs were obtained from GEO. We intersected key WGCNA module genes of DE-FCRGs with SD-DEGs to obtain SD-DE-FCRGs. GO and KEGG analyses were performed. Machine learning was used to screen SD-DE-FCRGs, and filtered genes were intersected to obtain SD characteristic genes. ROC curves were used to evaluate the accuracy of SD characteristic genes. CIBERSORT was used to analyze the correlation between SD-DE-FCRGs and immune cells. We constructed a ceRNA network of SD-DE-FCRGs and used DGIbd to predict gene drug targets. The 156 DEGs were identified from GSE98566. Five SD-DE-FCRGs from DE- FCRGs and SD-DEGs were analyzed via WGCNA, and enrichment analysis involved mainly ribosome regulation, mitochondrial pathways, and neurodegenerative diseases. Machine learning was used to obtain Four SD-DE-FCRGs (IKZF1, JCHAIN, MGST3, and UQCR11), and these gene analyses accurately evaluated the distribution model (AUC=0.793). Immune infiltration revealed that SD hub genes were correlated with most immune cells. Unsupervised cluster analysis revealed significant differential expression of immune-related genes between two subtypes. GSVA and GSEA revealed that enriched biological functions included oxidative phosphorylation, ribonucleic acid, metabolic diseases, activation of oxidative phosphorylation, and other pathways. Four SD-DE-FCRGs associated with 29 miRNAs were identified via the construction of a ceRNA network. The important target lenalidomide of IKZF1 was predicted. We first used bioinformatics and machine learning to screen four SD-DE-FCRGs. These genes may affect the involvement of infiltrating immune cells in pathogenesis of SD by regulating FCRGs. We predicted that lenalidomide may target IKZF1 from SD-DE-FCRGs.

Integrated analysis of lncRNA-miRNA-mRNA ceRNA network in neurodegenerative diseases

BackgroundNeurodegenerative diseases are one of the main causes of physical or behavioral complications which is considered as one of the main health concerns of the elderly population. However, treatment options for neurological diseases are still limited. Recent advances in bioinformatics studies provide an opportunity to understand the mechanisms of these diseases to identify therapeutic targets. In this research, the mRNAs involved in Alzheimer's, Multiple sclerosis, Parkinson's, and Huntington's neurological diseases, which are regulated by upstream factors, have been investigated. Only 2% of all transcripts of a gene are translated into protein and the rest are converted into miRNAs, lncRNAs or circRNAs. miRNAs have crucial role in regulating mRNAs and in a similar sequence lncRNAs or circRNAs are crucial in regulating miRNAs, which disrupts gene expression. ResultsTo discover above relations in neurodegenerative disease, miRNA-mRNA and lncRNA-miRNA bipartite networks were constructed and then were integrated to construct lncRNA-miRNA-mRNA tripartite networks. Constructing these networks leads to understand deeply about the structure of this mechanism and introducing new biomarkers for the studied diseases. In the next step, enrichment analysis was performed to recognize the genes involved in crucial pathways. Finally, the obtained biomarkers were investigated over the previous studies to prove the accuracy of proposed method. ConclusionsIn conclusion, for all four diseases, several numbers of mRNAs, miRNAs and lncRNAs were identified, which are introduced as biomarkers extracted by this study.

Long Non-coding RNAs Influence Aging Process of Sciatic Nerves in SD Rats.

To investigate the long non-coding RNAs (lncRNAs) changes in the sciatic nerve (SN) in Sprague Dawley (SD) rats during aging. Eighteen healthy SD rats were selected at the age of 1 month (1M) and 24 months (24M) and SNs were collected. High-throughput transcriptome sequencing and bioinformatics analysis were performed. Protein-protein interaction (PPI) networks and competing endogenous RNA (ceRNA) networks were established according to differentially expressed genes (DEGs). As the length of lncRNAs increased, its proportion to the total number of lncRNAs decreased. A total of 4079 DElncRNAs were identified in Con vs. 24M. GO analysis was primarily clustered in nerve and lipid metabolism, extracellular matrix, and vascularization-related fields. There were 17 nodes in the PPI network of the target genes of up-regulating genes including Itgb2, Lox, Col11a1, Wnt5a, Kras, etc. Using quantitative RT-PCR, microarray sequencing accuracy was validated. There were 169 nodes constructing the PPI network of down-regulated target genes, mainly including Col1a1, Hmgcs1, Hmgcr. CeRNA interaction networks were constructed. Lipid metabolism, angiogenesis, and ECM fields might play an important role in the senescence process in SNs. Col3a1, Serpinh1, Hmgcr, and Fdps could be candidates for nerve aging research.

Advanced Insights into Competitive Endogenous RNAs (ceRNAs) Regulated Pathogenic Mechanisms in Metastatic Triple-Negative Breast Cancer (mTNBC).

Triple-negative breast cancer is aggressive and challenging to treat because of a lack of targets and heterogeneity among tumors. A paramount factor in the mortality from breast cancer is metastasis, which is driven by genetic and phenotypic alterations that drive epithelial-mesenchymal transition, stemness, survival, migration and invasion. Many genetic and epigenetic mechanisms have been identified in triple-negative breast cancer that drive these metastatic phenotypes; however, this knowledge has not yet led to the development of effective drugs for metastatic triple-negative breast cancer (mTNBC). One that may not have received enough attention in the literature is post-translational regulation of broad sets of cancer-related genes through inhibitory microRNAs and the complex competitive endogenous RNA (ceRNA) regulatory networks they are influenced by. This field of study and the resulting knowledge regarding alterations in these networks is coming of age, enabling translation into clinical benefit for patients. Herein, we review metastatic triple-negative breast cancer (mTNBC), the role of ceRNA network regulation in metastasis (and therefore clinical outcomes), potential approaches for therapeutic exploitation of these alterations, knowledge gaps and future directions in the field.

To investigate the role and potential mechanism of has_circ_RBMS3 in bone metastasis of breast cancer based on bioinformatics.

Circular RNAs (circRNAs) play a crucial regulatory role in malignant tumor metastasis. This study focused on the role of bone metastasis-related circRBMS3 in breast cancer. Two circRNA microarray datasets were obtained from the GEO database and overlapped bone metastasis-related circRNAs in breast cancer. CircRBMS3 expression was validated in bone metastasis tissues by RT-qPCR. Cellular CCK-8 assay and Transwell assays were performed to measure the effect of circRBMS3 in breast cancer cells. Bioinformatic analyses were performed to identify the binding miRNAs of circRBMS3 and downstream mRNAs. Online database STRING and Cytoscape software were used to analyze PPI interaction and conduct the ceRNA network. GEO database analysis showed that circRBMS3 was one of the upregulated circRNAs among all the metastatic cells. CircRBMS3 was increased in bone metastasis breast cancer tissues compared to non-bone metastasis tissues and associated with poor 3-year overall survival. CircRBMS3 knockdown repressed breast cancer cell proliferation, migration, and invasion, as well as bone resorption gene and osteoclast phenotype gene expression. CircRBMS3 was found to bind withmiR-654-3p. Subsequently, downstream mRNAs were predicted, and the circRBMS3 miR-654-3p-mRNA network was established. In conclusion, circRBMS3 expression was upregulated in bone metastasis breast cancer and might be a potential prognostic marker for patients. Silencing circRBMS3 restrained breast cancer cell proliferation, migration, and invasion, as well as associated with bone metastasis. The circRBMS3-miR-654-3p-mRNAs network elucidated potential mechanisms underlying bone metastasis in breast cancer.

Construction of Prognostic ceRNA Network Landscape in Breast Cancer to Explore Impacting Genes on Drug Response by Integrative Bioinformatics Analysis

Background: Breast cancer accounts for 30% of all new female cancers yearly. Bioinformatics serves us to find new biomarkers and facilitate future experimental research. Exploring a distinct network of competing endogenous RNA (ceRNA) that includes potential prognostic, diagnostic, and therapeutic biomarkers is captivating. Methods: Differentially expressed lncRNAs, mRNAs, and miRNAs were collected using Gene Expression Omnibus datasets. DEGs were validated based on TCGA. Functional analysis and pathway activity were also done. Drug sensitivity analyses were done, and IC50 vs. gene expression plots were depicted. Results: A total of 696 mRNAs, 48 lncRNAs, and, 43 miRNAs were identified to have significant differential expression in cancerous breast tissue than normal breast tissue samples. Functional analysis showed significant pathway enrichments in cancer. We found that 13 individual genes, lncRNAs, and miRNAs, CDC6, ERBB2, EZR, HELLS, MAPK13, MCM2, MMP1, SLC7A5, TINCR, TRIP13, hsa-miR-376a, hsa-miR-21, hsa-miR-454 were significantly predictive of poor overall survival and AKAP12, CXCL12, FGF2, IRS2, LINC00342, LINC01140, MEG3, MIR250HG, NAV3, NDRG2, NEAT1, TGFBR3 and, hsa-miR-29c were associated with favorable overall survival. We reached a set of five genes (EGR1, NFIB, TGFBR3, SMARCA4, and MCM2) that exhibit altered expression patterns in breast cancer, resulting in increased susceptibility of cancer cells to certain drug treatments. Conclusion: We successfully made a unique ce-network, providing new clues to understand the regulatory functions of non-coding RNAs (miRNAs and lncRNAs) in the pathogenesis and prognosis of breast cancer and will facilitate further experimental studies to develop new biomarkers in the diagnosis, prognosis and, therapy of breast cancer.

Comprehensive analysis of lncRNA-mediated ceRNA network in renal cell carcinoma based on GEO database.

Renal cell carcinoma (RCC) ranks among the leading causes of cancer-related mortality. Despite extensive research, the precise etiology and progression of RCC remain incompletely elucidated. Long noncoding RNA (lncRNA) has been identified as competitive endogenous RNA (ceRNA) capable of binding to microRNA (miRNA) sites, thereby modulating the expression of messenger RNAs (mRNA) and target genes. This regulatory network is known to exert a pivotal influence on cancer initiation and progression. However, the specific role and functional significance of the lncRNA-miRNA-mRNA ceRNA network in RCC remain poorly understood. The RCC transcriptome data was obtained from the gene expression omnibus database. The identification of differentially expressed long noncoding RNAs (DElncRNAs), differentially expressed miRNAs, and differentially expressed mRNAs (DEmRNAs) between RCC and corresponding paracancer tissues was performed using the "Limma" package in R 4.3.1 software. We employed a weighted gene co-expression network analysis to identify the key DElncRNAs that are most relevant to RCC. Subsequently, we utilized the encyclopedia of RNA interactomes database to predict the interactions between these DElncRNAs and miRNAs, and the miRDB database to predict the interactions between miRNAs and mRNAs. Therefore, key DElncRNAs were obtained to verify the expression of their related genes in the The Cancer Genome Atlas database and to analyze the prognosis. The construction of RCC-specific lncRNA-miRNA-mRNA ceRNA network was carried out using Cytoscape 3.7.0. A total of 286 DElncRNAs, 56 differentially expressed miRNAs, and 2065 DEmRNAs were identified in RCC. Seven key DElncRNAs (GAS6 antisense RNA 1, myocardial infarction associated transcript, long intergenic nonprotein coding RNA 921, MMP25 antisense RNA 1, Chromosome 22 Open Reading Frame 34, MIR34A host gene, MIR4435-2 host gene) were identified using weighted gene co-expression network analysis and encyclopedia of RNA interactomes databases. Subsequently, a network diagram comprising 217 nodes and 463 edges was constructed based on these key DElncRNAs. The functional analysis of DEmRNAs in the ceRNA network was conducted using Kyoto Encyclopedia of Genes and Genomes and gene ontology. We constructed RCC-specific ceRNA networks and identified the crucial lncRNAs associated with RCC using bioinformatics analysis, which will help us further understand the pathogenesis of this disease.

Bioinformatics analysis of neutrophil-associated hub genes and ceRNA network construction in septic cardiomyopathy.

Septic cardiomyopathy (SCM) is a critical sepsis complication characterized by reversible cardiac depression during early septic shock. Neutrophils, integral to innate immunity, can mediate organ damage when abnormal, but their specific role in sepsis-induced myocardial damage remains elusive. Our study focuses on elucidating the role of Neutrophil-Related Genes (NRGs) in SCM, finding early diagnosis and treatment biomarkers. We identified shared differentially expressed genes (DEGs) from datasets GSE79962 and GSE44363 and pinpointed hub DEGs using the cytoHubba plugin in Cytoscape software. The Neutrophil-Related Hub Gene (NRHG) MRC1 was identified via intersecting hub DEGs with NRGs from WGCNA. We validated MRC1's abnormal expression in SCM using our data and external datasets. Furthermore, a neutrophil-related ceRNA network (AC145207.5/ miR-23a-3p/MRC1) was constructed and validated. Our findings reveal MRC1 as a potential NRHG in SCM pathogenesis, offering insights into neutrophil-mediated mechanisms in SCM and providing a novel molecular target for early diagnosis and intervention in SCM.

The construction and experimental verification of a 6-LncRNA model based on Lactic acid metabolism in the tumor microenvironment of Wilms tumor

BackgroundLactic acid (LA) can promote the malignant progression of tumors through the crosstalk with the tumor microenvironment (TME). However, the function of long non-coding RNAs (lncRNAs) related to LA metabolism in Wilms tumor (WT) remains unclear. MethodsGene expression data and clinical data of WT patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Through the ESTIMATE algorithm and Pearson correlation analysis, lncRNAs related to tumor immunity and LA metabolism were screened. Subsequently, Cox regression analysis and Lasso Cox regression analysis were used to construct a model. Furthermore, candidate genes were identified and a competitive endogenous RNA (ceRNA) network was conducted to explore the specific mechanism of characteristic genes. Finally, based on the strong clinical relevance of UNC5B-AS1, its expression and function were experimentally verified. ResultsThe immune score and stromal score were found to be closely related to the prognosis of WT. Eventually, a prognostic model (TME-LA-LM) consisting of 6 lncRNAs was successfully identified. The model demonstrated favorable predictive ability and accuracy, with significant variation in immune infiltration and drug susceptibility observed between risk groups. Additionally, the study revealed the involvement of 2 candidate genes and 5 microRNAs (miRNAs) in the tumor’s development. Notably, UNC5B-AS1 was highly expressed and found to promote the proliferation and migration of tumor cells. ConclusionThis study, for the first time, elucidated the prognostic signatures of WT using lncRNAs related to TME and LA metabolism. The fundings of this research offer valuable insights for future studies on immunotherapy, personalized chemotherapy and mechanism research.

Identification and functional characterization of differentially expressed circRNAs in high glucose treated endothelial cells: Construction of circRNA-miRNA-mRNA network

BackgroundEndothelial dysfunction is a complication of diabetes mellitus (DM), characterized by impaired endothelial function in both microvessels and macrovessels, closely linked to atherosclerosis (AS). Endothelial dysfunction, characterized by impaired endothelial cell (EC) function, is a pivotal factor in AS and DM. Circular RNAs (circRNAs) are endogenous non-coding RNAs that can act as competing endogenous RNAs (ceRNAs) and regulate gene expression. However, the role of circRNAs in ECs dysfunction and AS under high glucose (HG) condition remains elusive. MethodsWe performed high-throughput sequencing to identify differentially expressed (DE) circRNAs in human umbilical vein endothelial cells (HUVEC) exposed to HG, one risk factors of endothelial dysfunction and AS. We then validated eight candidate circRNAs by qRT-PCR and functional analysis, directing our attention to hsa_circ_0122319. Moreover, microarray analysis identified the differential expression profiles of miRNAs and mRNAs regulated by hsa_circ_0122319. Subsequently, the construction of the ceRNAs network employed bioinformatic analysis and Cytoscape software. Furthermore, the role of the PI3K-Akt signaling pathway in regulating ceRNAs was evaluated. ResultsWe detected 917 DE circRNAs in HG treated HUVEC. The parental genes of these circRNAs were enriched in cell cycle, cellular senescence and endocytosis related pathways. The differential expression of hsa_circ_0122319 was confirmed to be most obvious at the cellular level and in clinical samples by qPCR experiments. After overexpression of hsa_circ_0122319, 49 DE miRNAs and 459 DE mRNAs were identified using microarray analysis. Subsequently, a ceRNAs network was constructed, comprising hsa_circ_0122319, 8 miRNAs, and 41 mRNAs. ConclusionIn summary, our study delves into the role of circRNAs in endothelial dysfunction associated with DM and AS. Through high-throughput sequencing and validation, we identified hsa_circ_0122319 as a pivotal regulator of ECs function under HG conditions. It also showed that hsa_circ_0123319 has the potential to serve as a biomarker for DM and its vascular complications, and provides new evidence for future exploration of the intricate molecular mechanisms of endothelial dysfunction in the progression of DM and AS.

Unveiling circRNA-mediated ceRNA networks in ischemic stroke by integrative analysis of multi-source gene expression profiling

BackgroundCircular RNAs (circRNAs) are emerging as potential therapeutic targets for ischemic stroke (IS) due to their regulatory roles in inflammation and apoptosis. This study aimed to develop a comprehensive and robust IS-specific competing endogenous RNA (ceRNA) network to facilitate the identification of novel diagnostic and therapeutic targets. MethodsWe integrated expression data from 15 IS studies using the Rank-In algorithm to minimize batch effects. Differentially expressed circRNAs, miRNAs, and mRNAs were identified by comparing IS and control samples. Functional enrichment analysis of differentially expressed circRNA host genes revealed significantly enriched pathways and Gene Ontology (GO) terms relevant to IS pathogenesis. We further predicted miRNA-circRNA and mRNA-miRNA interactions, enabling the construction of a comprehensive ceRNA network to identify circRNA-related genes with diagnostic potential for IS. ResultsIntegrated analysis revealed 199 differentially expressed circRNAs, 103 miRNAs, and 1736 mRNAs in IS patients. Functional enrichment analysis implicated these molecules in relevant pathways like the neurotrophin signaling pathway and p53 signaling pathway. The constructed circRNA-miRNA-mRNA regulatory network provided insights into potential mechanisms underlying IS. Three circRNA-related genes (RGS2, CDK5R1, and NSF) displayed promising diagnostic potential for IS when combined. ConclusionsWe successfully constructed a robust and informative IS-specific ceRNA network by integrating data from diverse sources. This network identified differentially expressed RNAs and revealed enriched pathways potentially involved in IS pathogenesis. Notably, our analysis identified CDK5R1, RGS2, and NSF as potential diagnostic biomarkers for IS. This study sheds light on a circRNA-mediated regulatory network with potential diagnostic and therapeutic implications for ischemic stroke.

FAM136A as a Diagnostic Biomarker in Esophageal Cancer: Insights into Immune Infiltration, m6A Modification, Alternative Splicing, Cuproptosis, and the ceRNA Network.

FAM136A promotes the progression and metastasis of various tumors. However, there are few studies on the role of FAM136A in esophageal cancer (ESCA). The TCGA, GTEx, and GEO databases are employed to analyze the expression of FAM136A in ESCA, and qPCR and TMA experiments are performed for validation. Enrichment analyzes are performed to investigate the association of FAM136A expression with immune features, m6A modification, alternative splicing, cuproptosis, and the ceRNA network via bioinformatics analysis. FAM136A is highly expressed in ESCA and correlated with lymph node metastasis and overall survival (OS). Bioinformatics analysis suggested that FAM136A may participate in the following processes to promote ESCA development and progression: 1) Promotion of mast cells infiltration to influence the ESCA immune microenvironment, 2) HNRNPC upregulation to regulate m6A modification, 3) ALYREF upregulation to increase the occurrence of retained intron (RI) events, 4) CDK5RAP1 upregulation to achieve inhibition of tumor cell apoptosis, and 5) promotion of ESCA progression through the lncRNA SNHG15/hsa-miR-29c-3p/FAM136A ceRNA network. FAM136A is a potential biomarker for ESCA diagnosis and treatment response evaluation, and the underlying mechanisms may be associated with immune infiltration, m6A modification, alternative splicing, cuproptosis, and the ceRNA regulatory network.

Evaluation of UCA1/miR-138/CDK6 Network in the Patients with Laryngeal Squamous Cell Carcinoma

Objective: Noncoding RNAs (ncRNAs) have the potential to be diagnostic and therapeutic targets, in many cancer types. miRNA ‐ mRNA ceRNA (competing endogenous RNA) network in cancer has been an interesting topic that has been studied in recent years. We aimed to investigate the relationship of UCA1/miR-138/CDK6 network with laryngeal squamous cell carcinoma (LSCC). Material and Methods: We studied with the cancerous tissue and as the control; neighboring normal tissue samples of the patients with diagnosis of LSCC. UCA1, miR-138 and CDK6 expression analysis was made by quantitative real-time PCR (qRT-PCR) method after RNA isolation of the samples. Results: Expressions of cancerous tissue samples and normal adjacent tissue samples as controls were compared. CDK6 and UCA1 levels were increased and miR-138 levels were found to be decreased in cancer tissues but not statistically significant. Conclusions: To the best of our knowledge there is no study to provide an expression profile of the UCA1/CDK6/miR-138 network for LSCC patients. Although we could not obtain statistically significant results, our results were similar with the UCA1, miR‐138, and CDK6 axis literature. Further studies with larger patient samples and LSCC cell lines may confirm the function of this axis, which might be a diagnostic and therapeutic target for LSCC.