Identification of a ferroptosis-related gene pair biomarker with immune infiltration landscapes in ischemic stroke: a bioinformatics-based comprehensive study

Background Ischemic stroke (IS) is a principal contributor to long-term disability in adults. A new cell death mediated by iron is ferroptosis, characterized by lethal aggregation of lipid peroxidation. However, a paucity of ferroptosis-related biomarkers early identify IS until now. This study investigated potential ferroptosis-related gene pair biomarkers in IS and explored their roles in immune infiltration. Results In total, we identified 6 differentially expressed ferroptosis-related genes (DEFRGs) in the metadata cohort. Of these genes, 4 DEFRGs were incorporated into the competitive endogenous RNA (ceRNA) network, including 78 lncRNA-miRNA and 16 miRNA-mRNA interactions. Based on relative expression values of DEFRGs, we constructed gene pairs. An integrated scheme consisting of machine learning algorithms, ceRNA network, and gene pair was proposed to screen the key DEFRG biomarkers. The receiver operating characteristic (ROC) curve witnessed that the diagnostic performance of DEFRG pair CDKN1A/JUN was superior to that of single gene. Moreover, the CIBERSORT algorithm exhibited immune infiltration landscapes: plasma cells, resting NK cells, and resting mast cells infiltrated less in IS samples than controls. Spearman correlation analysis confirmed a significant correlation between plasma cells and CDKN1A/JUN (CDKN1A: r = − 0.503, P < 0.001, JUN: r = − 0.330, P = 0.025). Conclusions Our findings suggested that CDKN1A/JUN could be a robust and promising gene-pair diagnostic biomarker for IS, regulating ferroptosis during IS progression via C9orf106/C9orf139-miR-22-3p-CDKN1A and GAS5-miR-139-5p/miR-429-JUN axes. Meanwhile, plasma cells might exert a vital interplay in IS immune microenvironment, providing an innovative insight for IS therapeutic target.

WT1-AS/IGF2BP2 Axis Is a Potential Diagnostic and Prognostic Biomarker for Lung Adenocarcinoma According to ceRNA Network Comprehensive Analysis Combined with Experiments

Lung adenocarcinoma (LUAD) is one of the most common malignancies, and there is still a lack of effective biomarkers for early detection and prognostic prediction. Here, we comprehensively analyze the characteristics of. an RNA sequencing data set of LUAD samples. In total, 395 long non-coding RNAs (lncRNAs), 89 microRNAs (miRNAs), and 872 mRNAs associated with c-Myc were identified, which were differentially expressed between tumor and normal tissues. The most relevant pathway was found to be WT1-AS–miR-200a-3p–IGF2BP2 according to the rules of competitive endogenous RNA (ceRNA) regulation. WT1-AS and IGF2BP2 expression were positively correlated and increased in LUAD samples, while miR-200a-3p had relatively low expression. The high expression of WT1-AS and IGF2BP2 was associated with poor prognosis in LUAD patients, while low expression of miR-200a-3p predicted reduced survival (p < 0.05). The analysis of the multi-gene regulation model indicated that the WT1-AS (downregulation)–miR-200a-3p (upregulation)–IGF2BP2 (downregulation) pattern significantly improved the survival of LUAD patients. Finally, reverse transcription-polymerase chain reaction (RT-PCR) and Western blotting were detected in LUAD cells, and the results are consistent with the bioinformatics analysis. In summary, the WT1-AS/IGF2BP2 axis is a potential prognostic biomarker in LUAD and is expected to become an effective target for diagnosis and treatment.

The Comprehensive Analysis of Competitive Endogenous RNA Networks and Tumor-Infiltrating Immune Cells in Gastric Cancer With Lymph Node Metastasis

Background: Gastric cancer is a kind of tumor with strong heterogeneity. Long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) play significant roles in the development of tumors. Methods: In this study, we divided all TCGA gastric cancer patients into the whole, intestinal and diffuse cohorts for further analysis, and constructed competitive endogenous RNA network and evaluated immune cells using CIBERSORTx. The support vector machines recursive feature elimination (SVM-RFE) was used for screening significant signatures and the support vector machines (SVM) for establishing model predicting the lymph node metastasis. Results: In this study, we divided all TCGA gastric cancer patients into the whole, intestinal and diffuse cohorts for further analysis, and constructed competitive endogenous RNA network and evaluated immune cells using CIBERSORTx. The support vector machines recursive feature elimination (SVM-RFE) was used for screening significant signatures and the support vector machines (SVM) for establishing model predicting the lymph node metastasis. The performance of SVM model was good in the intestinal and diffuse cohort, while the model in the whole cohort was relatively poor. Some important co-expression patterns between immune cells and ceRNAs network indicated significant correlation CD70 with dendritic cells and so on. Conclusion: Our research inferred competing endogenous RNA network of lymph node metastasis and built an excellent predicting model.

Comprehensive analysis of an immune infiltrate-related competitive endogenous RNA network reveals potential prognostic biomarkers for non-small cell lung cancer

Globally, non-small cell lung cancer (NSCLC) is the most common malignancy and its prognosis remains poor because of the lack of reliable early diagnostic biomarkers. The competitive endogenous RNA (ceRNA) network plays an important role in the tumorigenesis and prognosis of NSCLC. Tumor immune microenvironment (TIME) is valuable for predicting the response to immunotherapy and determining the prognosis of NSCLC patients. To understand the TIME-related ceRNA network, the RNA profiling datasets from the Genotype-Tissue Expression and The Cancer Genome Atlas databases were analyzed to identify the mRNAs, microRNAs, and lncRNAs associated with the differentially expressed genes. Weighted gene co-expression network analysis revealed that the brown module of mRNAs and the turquoise module of lncRNAs were the most important. Interactions among microRNAs, lncRNAs, and mRNAs were prognosticated using miRcode, miRDB, TargetScan, miRTarBase, and starBase databases. A prognostic model consisting of 13 mRNAs was established using univariate and multivariate Cox regression analyses and validated by the receiver operating characteristic (ROC) curve. The 22 immune infiltrating cell types were analyzed using the CIBERSORT algorithm, and results showed that the high-risk score of this model was related to poor prognosis and an immunosuppressive TIME. A lncRNA–miRNA–mRNA ceRNA network that included 69 differentially expressed lncRNAs (DElncRNAs) was constructed based on the five mRNAs obtained from the prognostic model. ROC survival analysis further showed that the seven DElncRNAs had a substantial prognostic value for the overall survival (OS) in NSCLC patients; the area under the curve was 0.65. In addition, the high-risk group showed drug resistance to several chemotherapeutic and targeted drugs including cisplatin, paclitaxel, docetaxel, gemcitabine, and gefitinib. The differential expression of five mRNAs and seven lncRNAs in the ceRNA network was supported by the results of the HPA database and RT-qPCR analyses. This comprehensive analysis of a ceRNA network identified a set of biomarkers for prognosis and TIME prediction in NSCLC.

A Competitive Endogenous RNA Network Based on Differentially Expressed lncRNA in Lipopolysaccharide‐Induced Acute Lung Injury in Mice

Non-coding RNAs have remarkable roles in acute lung injury (ALI) initiation. Nevertheless, the significance of long non-coding RNAs (lncRNAs) in ALI is still unknown. Herein, we purposed to identify potential key genes in ALI and create a competitive endogenous RNA (ceRNA) modulatory network to uncover possible molecular mechanisms that affect lung injury. We generated a lipopolysaccharide-triggered ALI mouse model, whose lung tissue was subjected to RNA sequencing, and then we conducted bioinformatics analysis to select genes showing differential expression (DE) and to build a lncRNA-miRNA (microRNA)- mRNA (messenger RNA) modulatory network. Besides, GO along with KEGG assessments were conducted to identify major biological processes and pathways, respectively, involved in ALI. Then, RT-qPCR assay was employed to verify levels of major RNAs. A protein-protein interaction (PPI) network was created using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and the hub genes were obtained with the Molecular Complex Detection plugin. Finally, a key ceRNA subnetwork was built from major genes and their docking sites. Overall, a total of 8,610 lncRNAs were identified in the normal and LPS groups. Based on the 308 DE lncRNAs [p-value &lt; 0.05, |log2 (fold change) | &gt; 1] and 3,357 DE mRNAs [p-value &lt; 0.05, |log2 (fold change) | &gt; 1], lncRNA-miRNA and miRNA-mRNA pairs were predicted using miRanda. The lncRNA-miRNA-mRNA network was created from 175 lncRNAs, 22 miRNAs, and 209 mRNAs in ALI. The RT-qPCR data keep in step with the RNA sequencing data. GO along with KEGG analyses illustrated that DE mRNAs in this network were mainly bound up with the inflammatory response, developmental process, cell differentiation, cell proliferation, apoptosis, and the NF-kappa B, PI3K-Akt, HIF-1, MAPK, Jak-STAT, and Notch signaling pathways. A PPI network on the basis of the 209 genes was established, and three hub genes (Nkx2-1, Tbx2, and Atf5) were obtained from the network. Additionally, a lncRNA-miRNA-hub gene subnetwork was built from 15 lncRNAs, 3 miRNAs, and 3 mRNAs. Herein, novel ideas are presented to expand our knowledge on the regulation mechanisms of lncRNA-related ceRNAs in the pathogenesis of ALI.

Revealing Key LncRNAs in Cytogenetically Normal Acute Myeloid Leukemia by Reconstruction of the LncRNA–miRNA–mRNA Network Based on the Competitive Endogenous RNA Theory

Background Cytogenetically normal acute myeloid leukemia(CN-AML) is a heterogeneous disease with different prognosis.Researches on prognostic indicators and therapy targets of CN-AML are still ongoing.Instead of protein-coding genes,more and more researches were focused on the non-coding RNAs especially long non-coding RNAs(lncRNAs) which may play an important role in the development and prognosis of AML.Although a large number of lncRNAs had been found, our knowledge of their function and pathological significance is still in its infancy.The purpose of this research is to identify the key lncRNAs and explore their function in CN-AML by reconstructing the lncRNA–miRNA–mRNA network based on the competitive endogenous RNA(ceRNA) theory. Results We reconstructed a global triple network based on the ceRNA theory using the data from National Center for Biotechnology Information Gene Expression Omnibus and published literature. According to the topological algorithm,we identified the key lncRNAs which had both the higher node degrees and the higher number of lncRNA–miRNA and miRNA–mRNA pairs in the ceRNA network. Meanwhile, Gene Ontology (GO) and pathway analysis were performed using databases such as DAVID,KOBAS and Cytoscape plug-in ClueGO respectively.The lncRNA–miRNA–mRNA network was composed of 90 lncRNAs,33mRNAs,26 miRNAs and 259 edges in the lncRNA upregulated group,and 18 lncRNAs,11 mRNAs,6 miRNAs and 45 edges in the lncRNA downregulated group.The functional assay showed that 53 pathways and 108 GO terms were enriched. Three lncRNAs(XIST,GABPB1-AS1,TUG1)could possibly be selected as key lncRNAs wihch may play an important role in the development of CN-AML.Particularly,GABPB1-AS1 was highly expressed in CN-AML by both bioinformatics analysis and experimental verification in AML cell line(THP-1) by quantitative real-time polymerase chain reaction.In addition, GABPB1-AS1 was also negatively correlated with overall survival of AML patients. Conclusion The lncRNA–miRNA–mRNA network revealed key lncRNAs and their functions in CN-AML.Particularly,lncRNA GABPB1-AS1 was firstly proposed in AML.We believe that GABPB1-AS1 is expected to become a candidate diagnostic biomarker or potential therapeutic target.

MiR-144 regulates adipogenesis by mediating formation of C/EBPα-FoxO1 protein complex

Excessive adipogenesis caused obesity, which was a serious risk of health and led to a series of diseases, including type II diabetes (T2D) for example. Adipocyte as the basic unit of adipose tissue has emerged as one of significant target of the treatment of obesity-related metabolic syndromes by revealed its adipogenic molecular mechanism. MicroRNAs (miRNAs) have been demonstrated involving adipogenesis, and played a crucial role in the competitive endogenous RNA (ceRNA) effect. Besides that, C/EBPα as a crucial adipogenic regulator still lacked epigenetic explanation during pre-adipocyte adipogenesis. In this study, we first verified FoxO1 was one of the ceRNA of C/EBPα. They co-regulated adipogenesis through formed a protein complex that directly bound to its promoter to activate AdipoQ, and AdipoQ (Adiponectin) was a negative adipocytokines that suppressed adipogenesis, which played an important role in retaining adipogensis balance. Moreover, an adipose tissue specific enriched miRNA, miR-144 was the key regulator of the ceRNA effect between C/EBPα and FoxO1, which mediated the C/EBPα-FoxO1 complex formation, thus altered AdipoQ, furthermore regulated pre-adipocyte adipogenesis. This research will provide a new supplementary idea of the C/EBPα epigenetic role in pre-adipocyte adipogenesis.

Study of Neuronal Apoptosis ceRNA Network in Hippocampal Sclerosis of Human Temporal Lobe Epilepsy by RNA-Seq

Hippocampal sclerosis (HS) is one of the most common pathological type of intractable temporal lobe epilepsy (TLE), often characterized by hippocampal atrophy, neuronal apoptosis, and gliogenesis. However, the molecular mechanisms of neuronal apoptosis in patients with HS are still not fully understood. We therefore conducted a pilot study focusing on the neuronal apoptosis ceRNA network in the sclerotic hippocampus of intractable TLE patients. In this research, RNA sequencing (RNA-seq) was utilized to quantify the expression levels of lncRNAs, miRNAs, and mRNAs in TLE patients with HS (HS-TLE) and without HS (non-HS-TLE), and reverse transcription-quantitative PCR (qRT-PCR). The interactions of differential expression (DE) lncRNAs-miRNAs or DEmiRNAs-mRNAs were integrated by StarBase v3.0, and visualized using Cytoscape. Subsequently, we annotate the functions of lncRNA-associated competitive endogenous RNA (ceRNA) network through analysis of their interactions with mRNAs. RNA-seq analyses showed 381 lncRNAs, 42 miRNAs, and 457 mRNAs were dysregulated expression in HS-TLE compared to non-HS-TLE. According to the ceRNA hypothesis, 5 HS-specific ceRNA network were constructed. Among them, the core ceRNA regulatory network involved in neuronal apoptosis was constituted by 10 DElncRNAs (CDKN2B-AS1, MEG3, UBA6-AS1, etc.), 7 DEmiRNAs (hsa-miR-155-5p, hsa-miR-195-5p, hsa-miR-200c-3p, etc.), and 3 DEmRNAs (SCN2A, DYRK2, and MAPK8), which belonging to apoptotic and epileptic terms. Our findings established the first ceRNA network of lncRNA-mediated neuronal apoptosis in HS-TLE based on transcriptome sequencing, which provide a new perspective on the disease pathogenesis and precise treatments of HS.

Comprehensive Analysis of REST/NRSF Gene in Glioma and Its ceRNA Network Identification

We sought to clarify the clinical relationship between REST/NRSF expression and the prognosis of glioma and explore the REST-associated competitive endogenous RNA (ceRNA) network in glioma. We downloaded RNA-seq, miRNA-seq and correlated clinical data of 670 glioma patients from The Cancer Genome Atlas and analyzed the correlation between REST expression, clinical characteristics and prognosis. Differentially expressed genes (DEGs) were identified with DESeq2 and analyzed with Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) using the Profiler package. Starbase was used to explore the regulatory interaction between REST and miRNAs or LncRNAs. The lncRNA-miRNA-REST ceRNA network was constructed with Cytoscape. RT-qPCR, WB, CCK8, wound-healing, and luciferase assays were performed to validate the ceRNA network. Results showed that REST expression was significantly higher in glioma patients than normal samples. Higher REST expression was significantly associated with worse overall survival, progression-free interval, and worse disease-specific survival in glioma patients. The DEGs of mRNA, miRNA, and lncRNA were identified, and GO and KEGG enrichment analyses were performed. Finally, REST-associated ceRNA networks, including NR2F2-AS1-miR129-REST and HOTAIRM1-miR137-REST, were experimentally validated. Thus, REST may be a prognostic biomarker and therapeutic target in glioma, and its regulatory network validated in this study may provide insights into glioma's molecular regulatory mechanisms.