Construction Of ceRNA Network Research Articles

Construction of ceRNA network mediated by circRNAs screening from microarray and identification of novel biomarkers for myasthenia gravis

BackgroundRecent studies have revealed that circRNA can serve as ceRNA to participate in multiple autoimmune diseases. Our study aims to explore the key circRNA as ceRNA and biomarker for MG. Methods: We used circRNA microarray to explore differentially expressed circRNAs (DECs) from MG and compare with control. Then, we predicted the target miRNA associated with DECs and screened miRNAs by the algorithm of random walk with restart (RWR). Next, we constructed the circRNA-miRNA-mRNA ceRNA regulated network (CMMC) to identify the hub objects. Following, we detected the expression of hub-circRNAs by RT-PCR. We verify has_circ_0004183 (circFRMD4) sponging miR-145-5p regulate cells proliferation using luciferase assay and CCK-8. Results: We found that the expression level of circFRMD4 and has_circ_0035381 (circPIGB) were upregulated and has_circ_0089153(circ NUP214) had the lowest expression level in MG. Finally, we proved circFRMD4 sponging miR-145-5p regulate Jurkat cells proliferation. CircFRMD4 take part in the genesis and development of MG via circFRMD4/miR145-5p axis. Conclusions: We found that circFRMD4, circPIGB and circNUP214 can be considered as valuable potential novel biomarkers for AchR + MG. CircFRMD4 participate in the development of AchR + MG via targeting binding with miR-145-5p.

Study on analgesic effect of Shentong Zhuyu Decoction in neuropathic pain rats by network pharmacology and RNA-Seq

Ethnopharmacological relevanceShentong Zhuyu Decoction (STZYD) is a traditional prescription for promoting the flow of Qi and Blood which is often used in the treatment of low back and leg pain clinicall with unclear mechanism. Neuropathic pain (NP) is caused by disease or injury affecting the somatosensory system. LncRNAs may play a key role in NP by regulating the expression of pain-related genes through binding mRNAs or miRNAs sponge mechanisms. Aim of the studyTo investigate the effect and potential mechanism of STZYD on neuropathic pain. MethodsChronic constriction injury (CCI) rats, a commonly used animal model, were used in this study. The target of STZYD in NP was analyzed by network pharmacology, and the analgesic effect of STZYD in different doses (H-STZYD, M-STZYD, L-STZYD) on CCI rats was evaluated by Mechanical withdrawal thresholds (MWT) and thermal withdrawal latency (TWL). Meanwhile, RNA-seq assay was used to detect the changed mRNAs and lncRNAs in CCI rats after STZYD intervention. GO analysis, KEGG pathway analysis, and IPA analysis were used to find key target genes and pathways, verified by qPCR and Western Blot. The regulatory effect of lncRNAs on target genes was predicted by co-expression analysis and ceRNA network construction. ResultsWe found that STZYD can improve hyperalgesia in CCI rats, and H-STZYD has the best analgesic effect. The results of network pharmacological analysis showed that STZYD could play an analgesic role in CCI rats through the MAPK/ERK/c-FOS pathway. By mRNA-seq and lncRNA-seq, we found that STZYD could regulate the expression of Cnr1, Cacng5, Gucy1a3, Kitlg, Npy2r, and Grm8, and inhibited the phosphorylation level of ERK in the spinal cord of CCI rats. A total of 27 lncRNAs were associated with the target genes and 30 lncRNAs, 83 miRNAs and 5 mRNAs participated in the ceRNA network. ConclusionSTZYD has the effect of improving hyperalgesia in CCI rats through the MAPK/ERK/c-FOS pathway, which is related to the regulation of lncRNAs to Cnr1 and other key targets.

Whole Transcriptome Sequencing of Peripheral Blood Identifies the Alzheimer's Disease-Related circRNA-miRNA-lncRNA Pathway.

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. To investigate the potential pathogenesis-related molecular biological changes in the various stages of AD progression. 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. In total, 3568 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_circ_0001170, was identified based on the differentially expressed RNAs between the NC group and the moD group. The study suggests that circRNAs may be independent of messenger RNAs (mRNAs) in AD pathogenesis and holds promise as potential biomarkers for AD clinical manifestations and pathological changes.

Exploring the association between circRNA expression and pediatric obesity based on a case–control study and related bioinformatics analysis

ObjectiveOur present study utilized case–control research to explore the relationship between specific circRNAs and pediatric obesity through a literature review and bioinformatics and to predict their possible biological functions, providing ideas for epigenetic mechanism studies of pediatric obesity.MethodsCircRNAs related to pediatric obesity were preliminarily screened by a literature review and qRT–PCR. CircRNA expression in children with obesity (n = 75) and control individuals (n = 75) was confirmed with qRT–PCR in a case–control study. This was followed by bioinformatics analyses, such as GO analysis, KEGG pathway analysis, and ceRNA network construction. Multivariate logistic regression was utilized to analyze the effects of circRNAs on obesity. A receiver operating characteristic (ROC) curve was also drawn to explore the clinical application value of circRNAs in pediatric obesity.ResultsHas_circ_0046367 and hsa_circ_0000284 were separately validated to be statistically downregulated and upregulated, respectively, in the peripheral blood mononuclear cells of children with obesity and revealed as independent indicators of increased CHD risk [hsa_circ_0046367 (OR = 0.681, 95% CI: 0.480 ~ 0.967) and hsa_circ_0000284 (OR = 1.218, 95% CI: 1.041 ~ 1.424)]. The area under the ROC curve in the combined analysis of hsa_circ_0046367 and hsa_circ_0000284 was 0.706 (95% CI: 0.623 ~ 0.789). Enrichment analyses revealed that these circRNAs were actively involved in neural plasticity mechanisms, cell secretion and signal regulation.ConclusionThe present research revealed that low expression of hsa_circ_0046367 and high expression of hsa_circ_0000284 are risk factors for pediatric obesity and that neural plasticity mechanisms are closely related to obesity.

LncRNA-XR_002792574.1-mediated ceRNA network reveals potential biomarkers in myopia-induced retinal ganglion cell damage

BackgroundLong noncoding RNAs (lncRNAs) play a key role in the occurrence and progression of myopia. However, the function of lncRNAs in retinal ganglion cells (RGCs) in the pathogenesis of myopia is still unknown. The aim of our study was to explore the lncRNA-mediated competing endogenous RNA (ceRNA) network in RGCs during the development of myopia.MethodsRNA sequencing was performed to analyze lncRNA and mRNA expression profiles in RGCs between guinea pigs with form-deprived myopia (FDM) and normal control guinea pigs, and related ceRNA networks were constructed. Then, potentially important genes in ceRNA networks were verified by qRT‒PCR, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to explore biological functions in the RGCs of FDM guinea pigs. The important genes and related signaling pathways were further verified by qRT‒PCR, immunohistochemistry, immunofluorescence and Western blot in myopia in FDM guinea pigs, FDM mice, and highly myopic adults.ResultsThe distribution of RGCs was uneven, the number of RGCs was decreased, and RGC apoptosis was increased in FDM guinea pigs. In total, 873 lncRNAs and 2480 mRNAs were determined to be differentially expressed genes in RGCs from normal control and FDM guinea pigs. Via lncRNA-mediated ceRNA network construction and PCR verification, we found that lncRNA-XR_002792574.1 may be involved in the development of myopia through the miR-760-3p/Adcy1 pathway in RGCs. Further verification in FDM guinea pigs, FDM mice, and highly myopic adults demonstrated that the lncRNA-XR_002792574.1/miR-760-3p/Adcy1 axis in RGCs might be related to cGMP/PKG, the apelin signaling pathway and scleral remodeling.ConclusionWe demonstrated that the lncRNA-XR_002792574.1/miR-760-3p/Adcy1 axis in RGCs might be related to myopia. On the one hand, the lncRNA-XR_002792574.1/miR-760-3p/Adcy1 axis might inhibit the cGMP/PKG and apelin signaling pathways in RGCs, thereby causing RGC damage in myopia. On the other hand, the lncRNA-XR_002792574.1/miR-760-3p/Adcy1 axis may cause myopic scleral remodeling through the ERK-MMP-2 pathway. These findings may reveal novel potential targets in myopia and provide reference value for exploration and development of gene editing therapeutics for hereditary myopia.

Bioinformatics analysis identifies potential m6A hub genes in the pathogenesis of intracerebral hemorrhage

BackgroundIntracerebral hemorrhage (ICH) is a type of stroke associated with a high rate of disability and mortality. The role of N6-methyladenosine (m6A) in ICH remains unclear. MethodsScreening of m6A DEGs by differentially expressed genes (DEGs) analysis. m6A hub genes in ICH were identified by protein-protein interaction (PPI) networks. Pearson correlation tests were used to explore the relationship between m6A hub genes and DNA methylation. m6A hub genes were examined by ROC curves for their ability to predict ICH occurrence. Immune cell infiltration and m6A hub gene correlation in ICH were analysed using the CIBERSORT algorithm. Construction of ceRNA networks and enrichment analysis by GO/KEGG. ResultsA total of 12 m6A regulatory enzymes were differentially expressed after ICH. the PPI network screened three m6A hub genes, including YTHDF2, FTO and HNRNPA2B1. A high expression of YTHDF2 was associated with DNA hypomethylation after ICH and could better predict the development of ICH. yTHDF2 was associated with high infiltration of M1 macrophages after ICH. A ceRNA network was constructed based on the m6A central gene with target genes enriched in transcriptional regulation and the LKB1 signalling pathway. ConclusionM6A modifications are involved in the progression of ICH. YTHDF2, an m6A key gene, may regulate ICH progression by promoting infiltration of M1 macrophages or through the ceRNA network.

Identification of hub genes in bladder transitional cell carcinoma through ceRNA network construction integrated with gene network analysis.

Bladder transitional cell carcinoma (BTCC) forms more than 90% of bladder cancer cases. It brings challenges to the early diagnosis and therapy of BTCC, due to lack of efficient screening biomarkers. We used weighted gene co-expression network analysis (WGCNA) combined competing endogenous RNA (ceRNA) network construction depending on TCGA datasets to investigate potential hub genes and regulatory pathways associated with occurrence and progression of BTCC. We further used real-time polymerase chain reaction (RT-PCR) to validate the relative expression genes correlated with BTCC. By WGCNA, the gene co-expression module with 11 genes was found corelated with BTCC tumour stage and prognosis after survival analyses. Ultimately, we put 100 highly stage-related genes into the above constructed ceRNA network and then constructed another new network. Among them, all elements in AC112721.1/LINC00473/AC128709.1-hsa-mir-195-RECK and LINC00460-hsa-mir-429-ZFPM2 axes were simultaneously corelated with overall survival. RT-PCR showed that AKAP12 was downregulated in tumour tissues. The hub genes screened out in the present study may provide ideals for further treatment on BTCC.

Construction of ceRNA network and identification of hub differentially expressed genes associated with breast cancer using reanalysis of microarray dataset: A systems biology approach

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.

Gender-specific lncRNA-miRNA-mRNA regulatory network to reveal potential genes for primary open-angle glaucoma

BackgroundInvestigation of biomarkers may facilitate understanding the mechanisms of primary open-angle glaucoma (POAG) and developing therapeutic targets. This study aimed to identify potential genes based on competing endogenous RNA (ceRNA) network for POAG. MethodsBased on long noncoding RNAs (lncRNAs), microRNAs (miRNAs) and messenger RNAs (mRNAs) from the Gene Expression Omnibus (GEO) database, we identified differential expressed lncRNAs (DELs), differential expressed miRNAs (DEMis) and differential expressed mRNAs (DEMs) and then constructed a ceRNA network. Through weighted gene co-expression network analysis (WGCNA), we identified gender-specific genes for gender-associated ceRNA network construction, followed by the protein-protein interaction (PPI) network and functional enrichment analysis to screen hub genes and reveal their functions. The expression levels of hub genes were measured in steroid-induced ocular hypertension (SIOH) mice. ResultsA total of 175 DELs, 727 DEMs and 45 DEMis were screened between control and POAG samples. Seven modules were identified through WGCNA and one module was associated with gender of POAG patients. We discovered 41 gender-specific genes for gender-associated ceRNA construction and then identified 8 genes (NAV3, C1QB, RXRB, P2RY4, ADAM15, VAV3, ZNF207 and TOP1), which were enriched in cell cycle-related pathways and immune-related pathways. C1QB, RXRB, Top1 and ZNF207 were highly interacted with other proteins. The expression levels of NAV3 and C1QB were downregulated in SIOH, while the levels of RXRB, P2RY4, ADAM15, VAV3, ZNF207 and TOP1 were upregulated in SIOH. ConclusionThis study identifies hub genes associated with the pathogenesis of gender-specific POAG and provides potential biomarkers for POAG.

Novel ceRNA network construction associated with programmed cell death in acute rejection of heart allograft in mice.

T cell-mediated acute rejection(AR) after heart transplantation(HT) ultimately results in graft failure and is a common indication for secondary transplantation. It's a serious threat to heart transplant recipients. This study aimed to explore the novel lncRNA-miRNA-mRNA networks that contributed to AR in a mouse heart transplantation model. The donor heart from Babl/C mice was transplanted to C57BL/6 mice with heterotopic implantation to the abdominal cavity. The control group was syngeneic heart transplantation with the same kind of mice donor. The whole-transcriptome sequencing was performed to obtain differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs) and lncRNAs (DElncRNAs) in mouse heart allograft. The biological functions of ceRNA networks was analyzed by GO and KEGG enrichment. Differentially expressed ceRNA involved in programmed cell death were further verified with qRT-PCR testing. Lots of DEmRNAs, DEmiRNAs and DElncRNAs were identified in acute rejection and control after heart transplantation, including up-regulated 4754 DEmRNAs, 1634 DElncRNAs, 182 DEmiRNAs, and down-regulated 4365 DEmRNAs, 1761 DElncRNAs, 132 DEmiRNAs. Based on the ceRNA theory, lncRNA-miRNA-mRNA regulatory networks were constructed in allograft acute rejection response. The functional enrichment analysis indicate that the down-regulated mRNAs are mainly involved in cardiac muscle cell contraction, potassium channel activity, etc. and the up-regulated mRNAs are mainly involved in T cell differentiation and mononuclear cell migration, etc. The KEGG pathway enrichment analysis showed that the down-regulated DEmRNAs were mainly enriched in adrenergic signaling, axon guidance, calcium signaling pathway, etc. The up-regulated DEmRNAs were enriched in the adhesion function, chemokine signaling pathway, apoptosis, etc. Four lncRNA-mediated ceRNA regulatory pathways, Pvt1/miR-30c-5p/Pdgfc, 1700071M16Rik/miR-145a-3p/Pdgfc, 1700071M16Rik/miR-145a-3p/Tox, 1700071M16Rik/miR-145a-3p/Themis2, were finally validated. In addition, increased expression of PVT1, 1700071M16Rik, Tox and Themis2 may be considered as potential diagnostic gene biomarkers in AR. We speculated that Pvt1/miR-30c-5p/Pdgfc, 1700071M16Rik/miR-145a-3p/Pdgfc, 1700071M16Rik/miR-145a-3p/Tox and 1700071M16Rik/miR-145a-3p/Themis2 interaction pairs may serve as potential biomarkers in AR after HT.

Integrated bioinformatics and validation reveal SOX12 as potential biomarker in colon adenocarcinoma based on an immune infiltration-related ceRNA network.

The primary objective of this study was to construct competing endogenous RNA (ceRNA) networks and evaluate the prognostic significance of tumor-infiltrating immune cells (TIICs) and key biomarkers within the ceRNA networks in colon adenocarcinoma (COAD) patients. Comprehensive bioinformatics tools were used to screen differentially expressed genes (DEGs), miRNAs (DEMs), and lncRNAs (DELs) related to COAD, leading to the creation of ceRNA networks. The CIBERSORT technique was employed to assess the significance of TIICs in COAD, and an immune-related prognosis prediction model was subsequently developed. Co-expression analyses were conducted to determine the relationship between key genes in ceRNA networks and immunologically significant TIICs. The study also utilized 5 GEO datasets and web-based databases to externally validate the findings. The study revealed a statistically significant relationship between key hub genes and immune cells, as determined through co-expression analysis. Two hub regulators (SOX12 and H19) demonstrated significant prognostic value in the ceRNA-related prognostic model, and their elevated expression levels were verified across multiple CRC cell lines. Additionally, the knockdown of SOX12 led to a suppression of proliferation, migration, and invasion in colon cancer cells. Through the construction of ceRNA networks and evaluation of TIICs, the study successfully established two risk score models and nomograms. These models serve as valuable tools for understanding the molecular processes and predicting the prognosis of COAD patients. Further validation of hub regulators SOX12 and H19 substantiates their potential role as key biomarkers in COAD.

Identification of immune-related lncRNA in sepsis by construction of ceRNA network and integrating bioinformatic analysis.

Sepsis is a high mortality disease which seriously threatens human life and health, for which the pathogenetic mechanism still unclear. There is increasing evidence showed that immune and inflammation responses are key players in the development of sepsis pathology. LncRNAs, which act as ceRNAs, have critical roles in various diseases. However, the regulatory roles of ceRNA in the immunopathogenesis of sepsis have not yet been elucidated. In this study, we aimed to identify immune biomarkers associated with sepsis. We first generated a global immune-associated ceRNA (IMCE) network based on data describing interactions pairs of gene-miRNA and miRNA-lncRNA. Afterward, we excavated a dysregulated sepsis immune-associated ceRNA (SPIMC) network from the global IMCE network by means of a multi-step computational approach. Functional enrichment indicated that lncRNAs in SPIMC network have pivotal roles in the immune mechanism underlying sepsis. Subsequently, we identified module and hub genes (CD4 and STAT4) via construction of a sepsis immune-related PPI network. Then, we identified hub genes based on the modular structure of PPI network and generated a ceRNA subnetwork to analyze key lncRNAs associated with sepsis. Finally, 6 lncRNAs (LINC00265, LINC00893, NDUFA6-AS1, NOP14-AS1, PRKCQ-AS1 and ZNF674-AS1) that identified as immune biomarkers of sepsis. Moreover, the CIBERSORT algorithm and the infiltration of circulating immune cells types were performed to identify the inflammatory state of sepsis. Correlation analyses between immune cells and sepsis immune biomarkers showed that the LINC00265 was strongly positive correlated with the macrophages M2 (r = 0.77). Collectively, these results may suggest that these lncRNAs (LINC00265, LINC00893, NDUFA6-AS1, NOP14-AS1, PRKCQ-AS1 and ZNF674-AS1) played important roles in the immune pathogenesis of sepsis and provide potential therapeutic targets for further researches on immune therapy treatment in patients with sepsis.

Identification of biomarkers associated with diagnosis of postmenopausal osteoporosis patients based on bioinformatics and machine learning.

Background: Accumulating evidence suggests that postmenopausal osteoporosis (PMOP) is a common chronic systemic metabolic bone disease, but its specific molecular pathogenesis remains unclear. This study aimed to identify novel genetic diagnostic markers for PMOP. Methods: In this paper, we combined three GEO datasets to identify differentially expressed genes (DEGs) and performed functional enrichment analysis of PMOP-related differential genes. Key genes were analyzed using two machine learning algorithms, namely, LASSO and the Gaussian mixture model, and candidate biomarkers were found after taking the intersection. After further ceRNA network construction, methylation analysis, and immune infiltration analysis, ACACB and WWP1 were finally selected as diagnostic markers. Twenty-four clinical samples were collected, and the expression levels of biomarkers in PMOP were detected by qPCR. Results: We identified 34 differential genes in PMOP. DEG enrichment was mainly related to amino acid synthesis, inflammatory response, and apoptosis. The ceRNA network construction found that XIST-hsa-miR-15a-5p/hsa-miR-15b-5p/hsa-miR-497-5p and hsa-miR-195-5p-WWP1/ACACB may be RNA regulatory pathways regulating PMOP disease progression. ACACB and WWP1 were identified as diagnostic genes for PMOP, and validated in datasets and clinical sample experiments. In addition, these two genes were also significantly associated with immune cells, such as T, B, and NK cells. Conclusion: Overall, we identified two vital diagnostic genes responsible for PMOP. The results may help provide potential immunotherapeutic targets for PMOP.

Construction of ceRNA network based on RNA-seq for identifying prognostic lncRNA biomarkers in Perthes disease.

Introduction: Legg-Calvé-Perthes disease or Perthes disease is a condition that occurs in children aged 2 to 15years, and is characterized by osteonecrosis of the femoral head, which results in physical limitations. Despite ongoing research, the pathogenesis and molecular mechanisms underlying the development of Perthes disease remain unclear. In order to obtain further insights, the expression patterns of long non-coding RNAs (lncRNAs), miRNAs, and mRNAs in a rabbit model of Perthes disease were analyzed in this study by transcriptome sequencing. Methods and results: The results of RNA-seq analyses revealed that 77 lncRNAs, 239 miRNAs, and 1027 mRNAs were differentially expressed in the rabbit model. This finding suggested that multiple genetic pathways are involved in the development of Perthes disease. A weighted gene co-expression network analysis (WGCNA) network was subsequently constructed using the differentially expressed mRNAs (DEmRNAs), and network analysis revealed that the genes associated with angiogenesis and platelet activation were downregulated, which was consistent with the findings of Perthes disease. A competing endogenous RNA (ceRNA) network was additionally constructed using 29 differentially expressed lncRNAs (including HIF3A and LOC103350994), 28 differentially expressed miRNAs (including ocu-miR-574-5p and ocu-miR-324-3p), and 76 DEmRNAs (including ALOX12 and PTGER2). Disscusion: The results obtained herein provide novel perspectives regarding the pathogenesis and molecular mechanisms underlying the development of Perthes disease. The findings of this study can pave the way for the development of effective therapeutic strategies for Perthes disease in future.

Establishment and verification of prognostic model and ceRNA network analysis for colorectal cancer liver metastasis

ObjectsColorectal cancer (CRC) is one of the most common cancers in the world. Approximately two-thirds of patients with CRC will develop colorectal cancer liver metastases (CRLM) at some point in time. In this study, we aimed to construct a prognostic model of CRLM and its competing endogenous RNA (ceRNA) network.MethodsRNA-seq of CRC, CRLM and normal samples were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database. Limma was used to obtain differential expression genes (DEGs) between CRLM and CRC from sequencing data and GSE22834, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses were performed, respectively. Univariate Cox regression analysis and lasso Cox regression models were performed to screen prognostic gene features and construct prognostic models. Functional enrichment, estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) algorithm, single-sample gene set enrichment analysis, and ceRNA network construction were applied to explore potential mechanisms.ResultsAn 8-gene prognostic model was constructed by screening 112 DEGs from TCGA and GSE22834. CRC patients in the TCGA and GSE29621 cohorts were stratified into either a high-risk group or a low-risk group. Patients with CRC in the high-risk group had a significantly poorer prognosis compared to in the low-risk group. The risk score was identified as an independent predictor of prognosis. Functional analysis revealed that the risk score was closly correlated with various immune cells and immune-related signaling pathways. And a prognostic gene-associated ceRNA network was constructed that obtained 3 prognosis gene, 14 microRNAs (miRNAs) and 7 long noncoding RNAs (lncRNAs).ConclusionsIn conclusion, a prognostic model for CRLM identification was proposed, which could independently identify high-risk patients with low survival, suggesting a relationship between local immune status and prognosis of CRLM. Moreover, the key prognostic genes-related ceRNA network were established for the CRC investigation. Based on the differentially expressed genes between CRLM and CRC, the prognosis model of CRC patients was constructed.

Landscape of co-expressed genes between the myocardium and blood in sepsis and ceRNA network construction: a bioinformatic approach

Septic cardiomyopathy is a serious complication of sepsis. The mechanism of disease pathogenesis, which is caused by infection, is well researched. Despite ongoing efforts, there are no viable biological markers in the peripheral blood for early detection and diagnosis of septic cardiomyopathy. We aimed to uncover potential biomarkers of septic cardiomyopathy by comparing the covaried genes and pathways in the blood and myocardium of sepsis patients. Gene expression profiling of GSE79962, GSE65682, GSE54514, and GSE134364 was retrieved from the GEO database. Student’s t-test was used for differential expression analysis. K-means clustering analysis was applied for subgroup identification. Least absolute shrinkage and selection operator (LASSO) and logistic regression were utilized for screening characteristic genes and model construction. Receiver operating characteristic (ROC) curves were generated for estimating the diagnostic efficacy. For ceRNA information prediction, miWalk and lncBase were applied. Cytoscape was used for ceRNA network construction. Inflammation-associated genes were upregulated, while genes related to mitochondria and aerobic metabolism were downregulated in both blood and the myocardium. Three groups with a significantly different mortality were identified by these covaried genes, using clustering analysis. Five characteristic genes—BCL2A1, CD44, ADGRG1, TGIF1, and ING3—were identified, which enabled the prediction of mortality of sepsis. The pathophysiological changes in the myocardium of patients with sepsis were also reflected in peripheral blood to some extent. The co-occurring pathological processes can affect the prognosis of sepsis. Thus, the genes we identified have the potential to become biomarkers for septic cardiomyopathy.

CeRNA network construction and identification of hub genes as novel therapeutic targets for age-related cataracts using bioinformatics.

The aim of this study is to investigate the genetic and epigenetic mechanisms involved in the pathogenesis of age-related cataract (ARC). We obtained the transcriptome datafile of th ree ARC samples and three healthy, age-matched samples and used differential expression analyses to identify the differentially expressed genes (DEGs). The differential lncRNA-associated competing endogenous (ceRNA) network, and the protein-protein network (PPI) were constructed using Cytoscape and STRING. Cluster analyses were performed to identify the underlying molecular mechanisms of the hub genes affecting ARC progression. To verify the immune status of the ARC patients, immune-associated analyses were also conducted. The PPI network identified the FOXO1 gene as the hub gene with the highest score, as calculated by the Maximal Clique Centrality (MCC) algorithm. The ceRNA network identified lncRNAs H19, XIST, TTTY14, and MEG3 and hub genes FOXO1, NOTCH3, CDK6, SPRY2, and CA2 as playing key roles in regulating the pathogenesis of ARC. Additionally, the identified hub genes showed no significant correlation with an immune response but were highly correlated with cell metabolism, including cysteine, methionine, and galactose. The findings of this study may provide clues toward ARC pathogenic mechanisms and may be of significance for future therapeutic research.

Machine learning-based immune prognostic model and ceRNA network construction for lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a malignant tumor with a high lethality rate. Immunotherapy has become a breakthrough in cancer treatment and improves patient survival and prognosis. Therefore, it is necessary to find new immune-related markers. However, the current research on immune-related markers in LUAD is not sufficient. Therefore, there is a need to find new immune-related biomarkers to help treat LUAD patients. In this study, a bioinformatics approach combined with a machine learning approach screened reliable immune-related markers to construct a prognostic model to predict the overall survival (OS) of LUAD patients, thus promoting the clinical application of immunotherapy in LUAD. The experimental data were obtained from The Cancer Genome Atlas (TCGA) database, including 535 LUAD and 59 healthy control samples. Firstly, the Hub gene was screened using a bioinformatics approach combined with the Support Vector Machine Recursive Feature Elimination algorithm; then, a multifactorial Cox regression analysis by constructing an immune prognostic model for LUAD and a nomogram to predict the OS rate of LUAD patients. Finally, the regulatory mechanism of Hub genes in LUAD was analyzed by ceRNA. Five genes, ADM2, CDH17, DKK1, PTX3, and AC145343.1, were screened as potential immune-related genes in LUAD. Among them, ADM2 and AC145343.1 had a good prognosis in LUAD patients (HR < 1) and were novel markers. The remaining three genes screened were associated with poor prognosis in LUAD patients (HR > 1). In addition, the experimental results showed that patients in the low-risk group had better OS rates than those in the high-risk group (P < 0.001). In this paper, we propose an immune prognostic model to predict OS rate in LUAD patients and show the correlation between five immune genes and the level of immune-related cell infiltration. It provides new markers and additional ideas for immunotherapy in patients with LUAD.

Prediction of the Prognosis of Clear Cell Renal Cell Carcinoma by Cuproptosis-Related lncRNA Signals Based on Machine Learning and Construction of ceRNA Network.

Clear cell renal cell carcinoma's (ccRCC) occurrence and development are strongly linked to the metabolic reprogramming of tumors, and thus far, neither its prognosis nor treatment has achieved satisfying clinical outcomes. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively, provided us with information on the RNA expression of ccRCC patients and their clinical data. Cuproptosis-related genes (CRGS) were discovered in recent massive research. With the help of log-rank testing and univariate Cox analysis, the prognostic significance of CRGS was examined. Different cuproptosis subtypes were identified using consensus clustering analysis, and GSVA was used to further investigate the likely signaling pathways between various subtypes. Univariate Cox, least absolute shrinkage and selection operator (Lasso), random forest (RF), and multivariate stepwise Cox regression analysis were used to build prognostic models. After that, the models were verified by means of the C index, Kaplan-Meier (K-M) survival curves, and time-dependent receiver operating characteristic (ROC) curves. The association between prognostic models and the tumor immune microenvironment as well as the relationship between prognostic models and immunotherapy were next examined using ssGSEA and TIDE analysis. Four online prediction websites-Mircode, MiRDB, MiRTarBase, and TargetScan-were used to build a lncRNA-miRNA-mRNA ceRNA network. By consensus clustering, two subgroups of cuproptosis were identified that represented distinct prognostic and immunological microenvironments. A prognostic risk model with 13 CR-lncRNAs was developed. The immune microenvironment and responsiveness to immunotherapy are substantially connected with the model, which may reliably predict the prognosis of patients with ccRCC.

Construction of ceRNA Networks at Different Stages of Somatic Embryogenesis in Garlic.

LncRNA (long non-coding RNA) and mRNA form a competitive endogenous RNA (ceRNA) network by competitively binding to common miRNAs. This network regulates various processes of plant growth and development at the post-transcriptional level. Somatic embryogenesis is an effective means of plant virus-free rapid propagation, germplasm conservation, and genetic improvement, which is also a typical process to study the ceRNA regulatory network during cell development. Garlic is a typical asexual reproductive vegetable. Somatic cell culture is an effective means of virus-free rapid propagation in garlic. However, the ceRNA regulatory network of somatic embryogenesis remains unclear in garlic. In order to clarify the regulatory role of the ceRNA network in garlic somatic embryogenesis, we constructed lncRNA and miRNA libraries of four important stages (explant stage: EX; callus stage: AC; embryogenic callus stage: EC; globular embryo stage: GE) in the somatic embryogenesis of garlic. It was found that 44 lncRNAs could be used as precursors of 34 miRNAs, 1511 lncRNAs were predicted to be potential targets of 144 miRNAs, and 45 lncRNAs could be used as eTMs of 29 miRNAs. By constructing a ceRNA network with miRNA as the core, 144 miRNAs may bind to 1511 lncRNAs and 12,208 mRNAs. In the DE lncRNA-DE miRNA-DE mRNA network of adjacent stages of somatic embryo development (EX-VS-CA, CA-VS-EC, EC-VS-GE), by KEGG enrichment of adjacent stage DE mRNA, plant hormone signal transduction, butyric acid metabolism, and C5-branched dibasic acid metabolism were significantly enriched during somatic embryogenesis. Since plant hormones play an important role in somatic embryogenesis, further analysis of plant hormone signal transduction pathways revealed that the auxin pathway-related ceRNA network (lncRNAs-miR393s-TIR) may play a role in the whole stage of somatic embryogenesis. Further verification by RT-qPCR revealed that the lncRNA125175-miR393h-TIR2 network plays a major role in the network and may affect the occurrence of somatic embryos by regulating the auxin signaling pathway and changing the sensitivity of cells to auxin. Our results lay the foundation for studying the role of the ceRNA network in the somatic embryogenesis of garlic.