Regulatory effects of lncRNA PVT1 on transcriptome in human breast cancer MDA-MB-231 cell line determined by in silico analyses.

The current study intended to explore the interaction of the long non-coding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA) under the background of competitive endogenous RNA (ceRNA) network in endometriosis (EMs). The differentially expressed miRNAs (DEmiRs), differentially expressed lncRNA (DELs), and differentially expressed genes (DEGs) between EMs ectopic (EC) and eutopic (EU) endometrium based on three RNA-sequencing datasets (GSE105765, GSE121406, and GSE105764) were identified, which were used for the construction of ceRNA network. Then, DEGs in the ceRNA network were performed with Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein-protein interaction (PPI) analysis. Besides, the DEmiRs in the ceRNA network were validated in GSE124010. And the target DELs and DEGs of verified DEmiRs were validated in GSE86534. The correlation of verified DEmiRs, DEGs, and DELs was explored. Moreover, gene set enrichment analysis (GSEA) was applied to investigate the function of verified DEmiRs, DEGs, and DELs. Overall, 1352 DEGs and 595 DELs from GSE105764, along with 27 overlapped DEmiRs between GSE105765 and GSE121406, were obtained. Subsequently, a ceRNA network, including 11 upregulated and 16 downregulated DEmiRs, 7 upregulated and 13 downregulated DELs, 48 upregulated and 46 downregulated DEGs, was constructed. The GO and KEGG pathway analysis showed that this ceRNA network probably was associated with inflammation-related pathways. Furthermore, hsa-miR-182-5p and its target DELs (LINC01018 and SMIM25) and DEGs (BNC2, CHL1, HMCN1, PRDM16) were successfully verified in the validation analysis. Besides, hsa-miR-182-5p was significantly negatively correlated with these target DELs and DEGs. The GSEA analysis implied that high expression of LINC01018, SMIM25, and CHL1, and low expression of hsa-miR-182-5p would activate inflammation-related pathways in endometriosis EU samples.LINC01018 and SMIM25 might sponge hsa-miR-182-5p to upregulate downstream genes such as CHL1 to promote the development of endometriosis.

BackgroundIcariin presents protective effect in several kidney diseases. However, the role of icariin in contrast-induced acute kidney injury (CIAKI) is still unclear. This study aimed to investigate the effect of icariin in CIAKI, as well as exploring the underlying mechanism from the aspect of interaction between protein-coding genes and non-coding RNAs.MethodsThe effect of icariin was evaluated in both in vivo and in vitro CIAKI models. Rat kidneys were collected for genome-wide sequencing. The differentially expressed genes (DEGs) were screened and visualized by R software. The function annotation of DEGs was analyzed by Metascape. By Cytoscape software, the competing endogenous RNA (ceRNA) network was constructed, and hub genes were selected. Expressions of hub genes were validated by PCR. Association of hub genes in the ceRNA network and renal function was also examined.ResultsIcariin protected against CIAKI in both in vivo and in vitro models. Based on DEGs in icariin pretreated CIAKI rats, lncRNA- and circRNA-associated ceRNA networks were constructed, respectively. Function annotation showed the ceRNA networks were enriched in ERK1 and ERK2 cascade, MAPK signaling and NF-κB signaling. Further, two circRNAs, six lncRNAs, four miRNAs and nine mRNAs were selected as hub genes of the ceRNA network. Among them, eight mRNAs (Acot1, Cbwd1, Ly6i, Map3k14, Mettl2b, Nyap1, Set and Utp20) were negatively correlated with renal function, while one mRNA (Tmem44) was positively correlated with renal function.ConclusionIcariin presented a protective effect against CIAKI. The ceRNA network, involving Acot1, Cbwd1, Ly6i, Map3k14, Mettl2, Nyap1, Set, Tmem44 and Utp20, might partially contribute to the underlying mechanism of icariin protection by regulation of ERK1 and ERK2 cascade, MAPK signaling and NF-κB signaling.

Phospholipase C epsilon 1 (PLCE1) and the competing endogenous RNA (ceRNA) network are crucial for tumorigenesis and the progression of esophageal squamous cell carcinoma (ESCC). However, whether PLCE1 can regulate the ceRNA network in ESCC has not been clarified. In the present study, we aimed to identify the PLCE1-regulated ceRNA network and further elucidate the regulatory mechanisms by which ESCC is promoted. Microarray analysis was used to identify differentially expressed lncRNAs (DELs) and differentially expressed genes (DEGs) from three pairs of samples of PLCE-silenced Eca109 and control Eca109 cells. Next, the ceRNA regulatory network was established and visualized in Cytoscape, and functional enrichment analysis was performed to analyze DEGs from ceRNAs. Protein-protein interaction (PPI) networks among the DEGs were established by the STRING database to screen hub genes. Kaplan-Meier survival analysis was used to validate hub genes. Finally, PLCE1-related hub gene/lncRNA/miRNA axes were also constructed based on the ceRNA network. A total of 105 DELs and 346 DEGs were found to be dysregulated in the microarray data (|log2FC| >1.5, adjusted P<0.05). We constructed a PLCE1-regulated ceRNA network that incorporated 12 lncRNAs, 43 miRNAs, and 169 mRNAs. Functional enrichment analysis indicated that the DEGs might be associated with ESCC onset and development. A PPI network was established, and 9 hub genes [WD and tetratricopeptide repeats 1 (WDTC1), heat shock protein family A (Hsp70) member 5 (HSPA5), N-ethylmaleimide sensitive factor, vesicle fusing ATPase (NSF), fibroblast growth factor 2 (FGF2), cyclin dependent kinase inhibitor 1A (CDKN1A or P21), bone morphogenetic protein 2 (BMP2), complement C3 (C3), GM2 ganglioside activator (GM2A) and discs large MAGUK scaffold protein 4 (DLG4)] were determined from the network. Kaplan-Meier survival analysis validated four hub genes (BMP2, CDKN1A, GM2A, and DLG4) that were treated as prognostic factors. Ultimately, hub gene/lncRNA/miRNA subnetworks were obtained based on the 4 hub genes, 13 DEmiRNAs, and 10 DELs. In conclusion, the PLCE1-regulated ceRNA contributes to the onset and progression of ESCC and the underlying molecular mechanisms may provide insights into personalized prognosis and new therapies for ESCC patients.

Background: Increasing evidence has demonstrated that long non-coding RNAs (lncRNAs) play an important role in the competitive endogenous RNA (ceRNA) networks in that they regulate protein-coding gene expression by sponging microRNAs (miRNAs). However, the roles of specific lncRNA and its related competing endogenous RNAs (ceRNA) network in acute lymphocyte leukemia (ALL) are not fully understood. Aims: The aims of this study were to use RNA expression profile bioinformatics data from cases of ALL from the Cancer Genome Atlas (TCGA), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Gene Ontology (GO) databases to construct a ceRNA network of mRNAs, lncRNAs, and miRNAs. Methods: All patient databases were obtained from TCGA database. lncRNA and mRNA expression files included leukemia and corresponding normal samples were also downloaded from TCGA portal. An EdgeR (empirical analysis of digital gene expression data in R) package was used to identify the RNAseq data of acute lymphoblastic leukemia. Only the differential expression genes (DEGs) with adj.P.Val < 0.05 and |log2fold change (FC)| ≥ 2 were considered as significant. Based on bioinformatics generated from miRcode, starBase, and miRTarBase, we constructed an lncRNA-miRNA-mRNA network (ceRNA network) in ALL. Database for annotation, visualization and integrated discovery (DAVID), was used for GO analysis to understand the functions of targeted genes in terms of biological process (BP), cellular component (CC) and molecular function (MF). In addition, KEGG and an R Package, clusterProfiler, was distinguished pathway enrichment of each targeted gene. Cutoff value was set as P value < 0.05. Results: We found 755 differentially expressed lncRNAs and 6131 differentially expressed genes (DEGs). The functional enrichment indicated that the DEGs mainly regulated the pathways of programmed cell death, cell cycle, apoptosis and so on. Through integrated lncRNA-mRNA and miRNA-mRNA pairs, the ceRNA network was constructed. The resulting ceRNA network included 395 mRNAs, 135 lncRNAs and 31 miRNAs. 18 out of the 135 lncRNAs (RP11-497G19.2, LOC339535, LA16c-329F2.1, AK127309, XLOC_006664, RP1-16A9.1, RP11-69I8.3, LOC100507254, KB-1183D5.14, LOC100506305, RP11-87C12.5, WASIR1, RP3-523C21.1, RP11-325F22.2, AC002454.1, LOC286367, LOC286367, RP3-523C21.2) were identified and found to be associated with the complete remission rate of ALL patients (P < 0.05)Summary/Conclusion: Our results showed lncRNA expression patterns and a complex ceRNA network in ALL. Furthermore, we identified 18 lncRNAs as novel, potential prognostic biomarkers for ALL. However, futher studies on verification of ceRNA network are needed.

Background: The molecular biological mechanism of tubal factor infertility (TFI) is still unclear. Long noncoding RNAs (lncRNAs) are considered a major part of the competitive endogenous RNA (ceRNA) network and have attracted growing attention. Our study aimed to explore the regulatory mechanisms of lncRNAs associated with TFI and screen potential key genes related to TFI. Materials and Methods: Differentially expressed lncRNAs (DELs) and differentially expressed genes (DEGs) were identified by comparing normal and TFI expression patterns of lncRNAs and mRNAs in eutopic endometrial tissues obtained from 3 normal and 3 TFI patients during implantation. These data were used to develop a protein-protein interaction (PPI) network of DEGs using the STRING online software. The identified DELs and DEGs were then used to construct a ceRNA network, and the Network Analyzer Tool Kit in Cytoscape was used to analyze the ceRNA network topology and stability. Finally, the overlapping genes present in both the ceRNA and PPI networks were selected as the potential key genes related to TFI. Results: Ninety-six DEGs (59 up and 37 down) and 45 DELs (28 up and 17 down) were identified. Thirty-four DEGs were mapped in a PPI network. A ceRNA network, including two lncRNAs (LINC00305 and DLX6-AS1), four microRNAs (hsa-miR-20b-5p, hsa-miR-17-5p, hsa-miR-107, and hsa-miR-24-3p), and four mRNAs (MAP3K3, HMGB3, FAM103A1, and TMEM209), was successfully constructed. Importantly, a potential key gene (TMEM209) related to TFI was identified. Conclusion: The construction of a ceRNA network related to TFI may help elucidate the regulatory mechanism by which genes and lncRNAs function as ceRNA networks. Importantly, TMEM209 may be further evaluated as potential therapeutic targets for TFI.

Objective To elucidate the potential roles of the lncRNA-mediated competitive endogenous RNA (ceRNA) network in the pathogenesis of bronchopulmonary dysplasia (BPD), we performed an integrated bioinformatics analysis based on miRNA and mRNA microarray datasets between BPD and normal samples. Study design The mRNA and miRNA expression profiles of BPD were downloaded from the Gene Expression Omnibus (GEO) database to perform an integrated analysis. The limma package was used to identify differentially expressed genes (DEGs) and differentially expressed miRNA (DEmiRs), followed by functional enrichment analysis of DEGs. DEmiR–DEG and DEmiRNA–lncRNA interactions were predicted. Subsequently, the lncRNA-related ceRNA network was structured. Finally, a newborn BPD mouse model was established, and quantitative real-time PCR (qPCR) was used to validate the expression of the selected mRNAs, miRNAs, and lncRNAs. Results A total of 445 DEGs and 155 DEmiRs were obtained by comparing BPD samples and normal samples. Functional enrichment analysis showed that DEGs were primarily enriched in GO terms such as cell division and inflammatory response; and DEGs were mainly involved in the p53 signaling pathway. The miR17hg-miR-130b-3p-roundabout guidance receptor 2 (Robo2) and GM20455-miR-34a-5p-BMP/retinoic acid-inducible neural specific 1 (Brinp1) ceRNA axes were obtained by constructing the ceRNA network. In addition, the upregulation of Robo2 and miR17hg while the downregulation of miR-130b-3p; as well as the upregulation of Brinp1 and GM20455 but the downregulation of miR-34a-5p were validated by qPCR. Conclusion The miR17hg-miR-130b-3p-Robo2 and GM20455-miR-34a-5p-Brinp1 axes may serve important role in the development of BPD. These findings might provide novel insight for a comprehensive understanding of molecular mechanisms in BPD, and genes in the ceRNA network might be considered as potential biomarkers and therapeutic targets against BPD.

Relationship between M6A methylation regulator and prognosis in patients with hepatocellular carcinoma after transcatheter arterial chemoembolization

Integrated bioinformatics and validation to construct lncRNA-miRNA-mRNA ceRNA network in status epilepticus

Objective: To construct the competitive endogenous RNA (ceRNA) network related to gastric cancer and explore the molecular mechanism. Methods: The expression profiles of lncRNA, miRNA and mRNA in gastric cancer and paracancer tissues were analyzed by biochip technology, edgeR package in R software was used to filtrate differential expression genes (multiple change of >1.5 times, P<0.05) and volcano map was drawn. Based on the online miRNA-lncRNA prediction tool lncBase database and the miRNA Target gene prediction database (miRTarBase, target-scan, miRDB, starBase), the relationship between miRNA, lncRNA and mRNA was predicted. Cytoscape software was used to construct lncRNA-miRNA-mRNA ceRNA network and key genes (hub genes) were identified based on cytohubba calculation of degree score of each node. Then Hub genes related to the prognosis of gastric cancer were verified in the TCGA database. The GO and KEGG enrichment analysis of differentially expressed mRNA was performed using the online biological information annotation database DAVID, P<0.05 and false discovery rate (FDR)<0.05 were used as cut-off criteria. R software was used to download the RNA sequencing data and mirna-seq data of gastric cancer and adjacent tissues in TCGA database, edgeR package was used to screen out differentially expressed mRNA, miRNA and lncRNA, and some differentially expressed genes in our data were verified. In OncoLnc database, STAD project of TCGA data was selected and hub gene was input. Patients were divided into two groups based on the median value for hub genes and Kaplan-meier analysis was performed. Results: The differentially expressed 766 mRNA, 110 lncRNA and 10 miRNA were screened out, among them 90 mRNA, 4 lncRNA and 6 miRNA were used to construct the ceRNA network, and 2 of the 20 hub genes were related to the prognosis of patients. MLK7-AS1, SPP1, SULF1, hsa-miR-1307-3p were upregulated in gastric cancer tissues from our biochip, while MT2A, MT1X were downregulated, which were consistent with the results of TCGA gastric cancer database. The differentially expressed mRNAs were significantly enriched in the biological process (BP) and the mineral absorption pathway. CHST1 was negatively correlated while miR-183-5p was positively corelated with the survival of patients. Conclusion: The establishment of ceRNA network for gastric cancer is conducive to further understanding of the molecular biological mechanism. CHST1 and miR-183-5p can be used as prognostic factors of gastric cancer.

BackgroundBreast carcinoma is one of the most common tumors in women. The immune microenvironment, especially T cell infiltration, is related to the occurrence and prognosis of breast carcinoma. ObjectiveThis study investigated the gene expression patterns associated with tumor-infiltrating CD4+ and CD8+ T cells in invasive breast carcinomas. MethodsThe gene expression data and corresponding clinical phenotype data from the Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) were downloaded. The stromal and immune score were calculated using ESTIMATE. The differentially expressed genes (DEGs) with a high vs. low stromal score and a high vs. low immune score were screened and then functionally enriched. The tumor-infiltrating immune cells were investigated using the Cibersort algorithm, and the CD4+ and CD8+ T cell-related genes were identified using a Spearman correlation test of infiltrating abundance with the DEGs. Moreover, the miRNA-mRNA pairs and lncRNA-miRNA pairs were predicted to construct the competing endogenous RNAs (ceRNA) network. Kaplan-Meier (K-M) survival curves were also plotted. ResultsIn total, 478 DEGs with a high vs. low stromal score and 796 DEGs with a high vs. low immune score were identified. In addition, 39 CD4+ T cell-related genes and 78 CD8+ T cell-related genes were identified; of these, 14 genes were significantly associated with the prognosis of BRCA patients. Moreover, for CD4+ T cell-related genes, the chr22-38_28785274-29006793.1–miR-34a/c-5p–CAPN6 axis was identified from the ceRNA network, whereas the chr22-38_28785274-29006793.1–miR-494-3p–SLC9A7 axis was identified for CD8+ T cell-related genes. ConclusionsThe chr22-38_28785274-29006793.1–miR-34a/c-5p–CAPN6 axis and the chr22-38_28785274-29006793.1–miR-494-3p–SLC9A7 axis might regulate cellular activities associated with CD4+ and CD8+ T cell infiltration, respectively, in BRCA.

AIMTo identify and predict the competing endogenous RNA (ceRNA) networks in colorectal cancer (CRC) by bioinformatics analysis.METHODSIn the present study, we obtained CRC tissue and normal tissue gene expression profiles from The Cancer Genome Atlas project. Differentially expressed (DE) genes (DEGs) were identified. Then, upregulated and downregulated miRNA-centered ceRNA networks were constructed by analyzing the DEGs using multiple bioinformatics approaches. DEmRNAs in the ceRNA networks were identified in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using KEGG Orthology Based Annotation System 3.0. The interactions between proteins were analyzed using the STRING database. Kaplan-Meier survival analysis was conducted for DEGs and real time quantitative polymerase chain reaction (RT-qPCR) was also performed to validate the prognosis-associated lncRNAs in CRC cell lines.RESULTSEighty-one DElncRNAs, 20 DEmiRNAs, and 54 DEmRNAs were identified to construct the ceRNA networks of CRC. The KEGG pathway analysis indicated that nine out of top ten pathways were related with cancer and the most significant pathway was “colorectal cancer”. Kaplan-Meier survival analysis showed that the overall survival was positively associated with five DEGs (IGF2-AS, POU6F2-AS2, hsa-miR-32, hsa-miR-141, and SERPINE1) and it was negatively related to three DEGs (LINC00488, hsa-miR-375, and PHLPP2). Based on the STRING protein database, it was found that SERPINE1 and PHLPP2 interact with AKT1. Besides, SERPINE1 can interact with VEGFA, VTN, TGFB1, PLAU, PLAUR, PLG, and PLAT. PHLPP2 can interact with AKT2 and AKT3. RT-qPCR revealed that the expression of IGF2-AS, POU6F2-AS2, and LINC00488 in CRC cell lines was consistent with the in silico results.CONCLUSIONCeRNA networks play an important role in CRC. Multiple DEGs are related with clinical prognosis, suggesting that they may be potential targets in tumor diagnosis and treatment.

Integrated Analyses of Competing Endogenous RNA Network Reveal Potential Therapeutic Targets in Chronic Lymphocytic Leukemia

The aim of this study is to explore the prognostic value of vascular invasion (VI) in hepatocellular carcinoma (HCC) by searching for competing endogenous RNAs (ceRNA) network and constructing a new prognostic model for HCC. The differentially expressed genes (DEGs) between HCC and normal tissues were identified from GEO and TCGA. StarBase and miRanda prediction tools were applied to construct a circRNA-miRNA-mRNA network. The DEGs between HCC with and without VI were also identified. Then, the hub genes were screened to build a prognostic risk score model through the method of least absolute shrinkage and selection operator. The prognostic ability of the model was assessed using the Kaplan-Meier method and Cox regression analysis. In result, there were 221 up-regulated and 47 down-regulated differentially expressed circRNAs (DEcircRNAs) in HCC compared with normal tissue. A circRNA-related ceRNA network was established, containing 11 DEcircRNAs, 12 DEmiRNAs, and 161 DEmRNAs. Meanwhile, another DEG analysis revealed 625 up-regulated and 123 down-regulated DEGs between HCC with and without VI, and then a protein-protein interaction (PPI) network was built based on 122 VI-related DEGs. From the intersection of DEGs within the PPI and ceRNA networks, we obtained seven hub genes to build a novel prognostic risk score model. HCC patients with high-risk scores had shorter survival time and presented more advanced T/N/M stages as well as VI occurrence. In conclusion a novel prognostic model based on seven VI-associated DEGs within a circRNA-related ceRNA network was constructed in this study, with great ability to predict the outcome of HCC patients.

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. 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. 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. 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.

BackgroundAtrial fibrillation (AF) is the most common arrhythmia. Previous studies mainly focused on identifying potential diagnostic biomarkers and treatment strategies for AF, while few studies concentrated on post-operative AF (POAF), particularly using bioinformatics analysis and machine learning algorithms. Therefore, our study aimed to identify immune-associated genes and provide the competing endogenous RNA (ceRNA) network for POAF.MethodsThree GSE datasets were downloaded from the GEO database, and we used a variety of bioinformatics strategies and machine learning algorithms to discover candidate hub genes. These techniques included identifying differentially expressed genes (DEGs) and circRNAs (DECs), building protein-protein interaction networks, selecting common genes, and filtering candidate hub genes via three machine learning algorithms. To assess the diagnostic value, we then created the nomogram and receiver operating curve (ROC). MiRNAs targeting DEGs and DECs were predicted using five tools and the competing endogenous RNA (ceRNA) network was built. Moreover, we performed the immune cell infiltration analysis to better elucidate the regulation of immune cells in POAF.ResultsWe identified 234 DEGs (82 up-regulated and 152 down-regulated) of POAF via Limma, 75 node genes were visualized via PPI network, which were mainly enriched in immune regulation. 15 common genes were selected using three CytoHubba algorithms. Following machine learning selection, the nomogram was created based on the four candidate hub genes. The area under curve (AUC) of the nomogram and individual gene were all over 0.75, showing the ideal diagnostic value. The dysregulation of macrophages may be critical in POAF pathogenesis. A novel circ_0007738 was discovered in POAF and the ceRNA network was eventually built.ConclusionWe identified four immune-associated candidate hub genes (C1QA, C1R, MET, and SDC4) for POAF diagnosis through the creation of a nomogram and evaluation of its diagnostic value. The modulation of macrophages and the ceRNA network may represent further therapy methods.