Identification of Key Pathways and Genes in Polycystic Ovary Syndrome via Integrated Bioinformatics Analysis and Prediction of Small Therapeutic Molecules

Abstract To add a enhance understanding of polycystic ovary syndrome (PCOS) at the molecular level, this investigation aimed to find the genes and crucial pathways linked with PCOS by using integrated bioinformatics analysis. Based on the expression profiling by high throughput sequencing data GSE84958 derived from the Gene Expression Omnibus, the differentially expressed genes (DEGs) between PCOS samples and normal controls were identified. With DEGs, we performed a series of functional enrichment analyses. Then, a protein–protein interaction (PPI) network, miRNA - target genes and TF - target gene networks were constructed and visualized, with which the hub gene nodes were screened out. Finally, validation of hub genes was performed by using receiver operating characteristic (ROC) and RT-PCR. Molecular docking studies performed. A total of 739 DEGs were screened out, among which 360 genes were up regulated and 379 genes were down regulated. GO enrichment analysis indicated that up regulated genes were mainly involved in peptide metabolic process, organelle envelope and RNA binding and the down regulated genes were significantly enriched in plasma membrane bounded cell projection organization, neuron projection and DNA-binding transcription factor activity, RNA polymerase II-specific. REACTOME pathway enrichment analysis showed that the up regulated genes were mainly enriched in translation and respiratory electron transport and the down regulated genes were mainly enriched in generic transcription pathway and transmembrane transport of small molecules. The top 10 hub genes in the constructed PPI network, miRNA - target gene network and TF - target gene network were SAA1, ADCY6, POLR2K, RPS15, RPS15A, CTNND1, ESR1, NEDD4L, KNTC1 and NGFR. The modules analysis showed that genes in the top 2 significant modules of PPI network were mainly associated with respiratory electron transport and signalling by NGF, respectively. We find a series of crucial genes along with the pathways that were most closely related with PCOS initiation and advancement. Our investigations provide a more detailed molecular mechanism for the progression of PCOS, detail information on the potential biomarkers and therapeutic targets.

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Identification of key pathways and genes in polycystic ovary syndrome via integrated bioinformatics analysis and prediction of small therapeutic molecules

Reproductive Biology and Endocrinology ◽

10.1186/s12958-021-00706-3 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Praveenkumar Devarbhavi ◽

Lata Telang ◽

Basavaraj Vastrad ◽

Anandkumar Tengli ◽

Chanabasayya Vastrad ◽

...

Keyword(s):

Polycystic Ovary Syndrome ◽

Gene Network ◽

Mirna Target ◽

Target Gene ◽

Bioinformatics Analysis ◽

Polycystic Ovary ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Ovary Syndrome

AbstractTo enhance understanding of polycystic ovary syndrome (PCOS) at the molecular level; this investigation intends to examine the genes and pathways associated with PCOS by using an integrated bioinformatics analysis. Based on the expression profiling by high throughput sequencing data GSE84958 derived from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) between PCOS samples and normal controls were identified. We performed a functional enrichment analysis. A protein-protein interaction (PPI) network, miRNA- target genes and TF - target gene networks, were constructed and visualized, with which the hub gene nodes were identified. Validation of hub genes was performed by using receiver operating characteristic (ROC) and RT-PCR. Small drug molecules were predicted by using molecular docking. A total of 739 DEGs were identified, of which 360 genes were up regulated and 379 genes were down regulated. GO enrichment analysis revealed that up regulated genes were mainly involved in peptide metabolic process, organelle envelope and RNA binding and the down regulated genes were significantly enriched in plasma membrane bounded cell projection organization, neuron projection and DNA-binding transcription factor activity, RNA polymerase II-specific. REACTOME pathway enrichment analysis revealed that the up regulated genes were mainly enriched in translation and respiratory electron transport and the down regulated genes were mainly enriched in generic transcription pathway and transmembrane transport of small molecules. The top 10 hub genes (SAA1, ADCY6, POLR2K, RPS15, RPS15A, CTNND1, ESR1, NEDD4L, KNTC1 and NGFR) were identified from PPI network, miRNA - target gene network and TF - target gene network. The modules analysis showed that genes in modules were mainly associated with the transport of respiratory electrons and signaling NGF, respectively. We find a series of crucial genes along with the pathways that were most closely related with PCOS initiation and advancement. Our investigations provide a more detailed molecular mechanism for the progression of PCOS, detail information on the potential biomarkers and therapeutic targets.

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Identification of potential and novel target genes in pituitary prolactinoma by bioinformatics analysis

10.1101/2020.12.21.423732 ◽

2020 ◽

Author(s):

Vikrant Ghatnatti ◽

Basavaraj Vastrad ◽

Swetha Patil ◽

Chanabasayya Vastrad ◽

Iranna Kotturshetti

Keyword(s):

Gene Ontology ◽

Signaling Pathways ◽

Target Gene ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Condensed Chromosome ◽

Go Enrichment ◽

Go Enrichment Analysis

AbstractPituitary prolactinoma is one of the most complicated and fatally pathogenic pituitary adenomas. Therefore, there is an urgent need to improve our understanding of the underlying molecular mechanism that drives the initiation, progression, and metastasis of pituitary prolactinoma. The aim of the present study was to identify the key genes and signaling pathways associated with pituitary prolactinoma using bioinformatics analysis. Transcriptome microarray dataset GSE119063 was acquired from Gene Expression Omnibus datasets, which included 5 pituitary prolactinoma samples and 4 normal pituitaries samples. We screened differentially expressed genes (DEGs) with limma and investigated their biological function by pathway and Gene Ontology (GO) enrichment analysis. A protein-protein interaction (PPI) network of the up and down DEGs were constructed and analyzed by HIPPIE and Cytoscape software. Module analyses were performed. In addition, a target gene - miRNA network and target gene - TF network of the up and down DEGs were constructed by NetworkAnalyst and Cytoscape software. The set of DEGs exhibited an intersection consisting of 989 genes (461 up-regulated and 528 down-regulated), which may be associated with pituitary prolactinoma. Pathway enrichment analysis showed that the 989 DEGs were significantly enriched in the retinoate biosynthesis II, signaling pathways regulating pluripotency of stem cells, ALK2 signaling events, vitamin D3 biosynthesis, cell cycle and aurora B signaling. Gene Ontology (GO) enrichment analysis also showed that sensory organ morphogenesis, extracellular matrix, hormone activity, nuclear division, condensed chromosome and microtubule binding. In the PPI network and modules, SOX2, PRSS45, CLTC, PLK1, B4GALT6, RUNX1 and GTSE1 were considered as hub genes. In the target gene miRNA network and target gene - TF network, LINC00598, SOX4, IRX1 and UNC13A were considered as hub genes. Using integrated bioinformatics analysis, we identified candidate genes in pituitary prolactinoma, which may improve our understanding of the mechanisms of the pathogenesis and integration; genes may be therapeutic targets and prognostic markers for pituitary prolactinoma.

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Identification of genes, pathways and transcription factor-miRNA-target gene networks and experimental verification in venous thromboembolism

Scientific Reports ◽

10.1038/s41598-021-95909-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yiming Su ◽

Qiyi Li ◽

Zhiyong Zheng ◽

Xiaomin Wei ◽

Peiyong Hou

Keyword(s):

Transcription Factor ◽

Venous Thromboembolism ◽

Cellular Response ◽

Gene Network ◽

Mirna Target ◽

Target Gene ◽

Vascular Endothelial Cell ◽

Differentially Expressed ◽

Ppi Network ◽

Mirna Target Gene

AbstractVenous thromboembolism (VTE) is a complex, multifactorial life-threatening disease that involves vascular endothelial cell (VEC) dysfunction. However, the exact pathogenesis and underlying mechanisms of VTE are not completely clear. The aim of this study was to identify the core genes and pathways in VECs that are involved in the development and progression of unprovoked VTE (uVTE). The microarray dataset GSE118259 was downloaded from the Gene Expression Omnibus database, and 341 up-regulated and 8 down-regulated genes were identified in the VTE patients relative to the healthy controls, including CREB1, HIF1α, CBL, ILK, ESM1 and the ribosomal protein family genes. The protein–protein interaction (PPI) network and the transcription factor (TF)-miRNA-target gene network were constructed with these differentially expressed genes (DEGs), and visualized using Cytoscape software 3.6.1. Eighty-nine miRNAs were predicted as the targeting miRNAs of the DEGs, and 197 TFs were predicted as regulators of these miRNAs. In addition, 237 node genes and 4 modules were identified in the PPI network. The significantly enriched pathways included metabolic, cell adhesion, cell proliferation and cellular response to growth factor stimulus pathways. CREB1 was a differentially expressed TF in the TF-miRNA-target gene network, which regulated six miRNA-target gene pairs. The up-regulation of ESM1, HIF1α and CREB1 was confirmed at the mRNA and protein level in the plasma of uVTE patients. Taken together, ESM1, HIF1α and the CREB1-miRNA-target genes axis play potential mechanistic roles in uVTE development.

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Integrative Analyses of Genes Associated with Fulminant Type 1 Diabetes

Journal of Immunology Research ◽

10.1155/2020/1025857 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Xiaofeng Ye ◽

Tianshu Zeng ◽

Wen Kong ◽

Lu-lu Chen

Keyword(s):

Type 1 Diabetes ◽

Gene Network ◽

Target Gene ◽

Molecular Mechanisms ◽

Healthy Controls ◽

Ppi Network ◽

Hub Genes ◽

Fulminant Type 1 Diabetes ◽

Fulminant Type

Objective. Fulminant type 1 diabetes (FT1D) is a type of type 1 diabetes, which is characterized by rapid onset of disease and severe metabolic disorders. We intend to screen for crucial genes and potential molecular mechanisms in FT1D in this study. Method. We downloaded GSE44314, which includes six healthy controls and five patients with FT1D, from the GEO database. Identification of differentially expressed genes (DEGs) was performed by NetworkAnalyst. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were screened by an online tool—Database for Annotation, Visualization, and Integration Discovery (DAVID). Protein-protein interaction (PPI) network and hub genes among DEGs were analyzed by NetworkAnalyst. And we also use NetworkAnalyst to find out the microRNAs (miRNAs) and transcription factors (TFs) which regulate the expression of DEGs. Result. We identified 130 DEGs (60 upregulated and 70 downregulated DEGs) between healthy controls and FT1D patients. GO analysis results revealed that DEGs were mostly enriched in generation of precursor metabolites and energy, neurohypophyseal hormone activity, and mitochondrial inner membrane. KEGG pathway analysis demonstrated that DEGs were mostly involved in nonalcoholic fatty liver disease. Results indicated that NCOA1, SRF, ERBB3, EST1, TOP1, UBE2S, INO80, COX7C, ITGAV, and COX6C were the top hub genes in the PPI network. Furthermore, we recognized that LDLR, POTEM, IFNAR2, BAZ2A, and SRF were the top hub genes in the miRNA-target gene network, and SRF, TSPAN4, CD59, ETS1, and SLC25A25 were the top hub genes in the TF-target gene network. Conclusion. Our study pinpoints key genes and pathways associated with FT1D by a sequence of bioinformatics analysis on DEGs. These identified genes and pathways provide more detailed molecular mechanisms of FT1D and may provide novel therapeutic targets.

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Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysis

10.1101/2021.06.08.447531 ◽

2021 ◽

Author(s):

Basavaraj Mallikarjunayya Vastrad ◽

Chanabasayya Mallikarjunayya Vastrad

Keyword(s):

Gene Regulatory Network ◽

Regulatory Network ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Hub Gene ◽

Network Modules ◽

Gene Regulatory

Type 1 diabetes mellitus (T1DM) is a metabolic disorder for which the underlying molecular mechanisms remain largely unclear. This investigation aimed to elucidate essential candidate genes and pathways in T1DM by integrated bioinformatics analysis. In this study, differentially expressed genes (DEGs) were analyzed using DESeq2 of R package from GSE162689 of the Gene Expression Omnibus (GEO). Gene ontology (GO) enrichment analysis, REACTOME pathway enrichment analysis, and construction and analysis of protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network, and validation of hub genes were then performed. A total of 952 DEGs (477 up regulated and 475 down regulated genes) were identified in T1DM. GO and REACTOME enrichment result results showed that DEGs mainly enriched in multicellular organism development, detection of stimulus, diseases of signal transduction by growth factor receptors and second messengers, and olfactory signaling pathway. The top hub genes such as MYC, EGFR, LNX1, YBX1, HSP90AA1, ESR1, FN1, TK1, ANLN and SMAD9 were screened out as the critical genes among the DEGs from the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Receiver operating characteristic curve (ROC) analysis and RT-PCR confirmed that these genes were significantly associated with T1DM. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the advancement and progression of T1DM, and certain genes might be used as candidate target molecules to diagnose, monitor and treat T1DM.

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Identification of differentially expressed genes in Cushing's disease by integrated bioinformatics analysis

10.21203/rs.3.rs-29947/v1 ◽

2020 ◽

Author(s):

Tingting Lv ◽

Haoying Yu ◽

Shuyue Ren ◽

Jingrong Wang ◽

Lan Sun ◽

...

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Cushing’S Disease ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Cushing's Disease ◽

Age Group ◽

Ppi Network ◽

Hub Genes ◽

Differential Genes

Abstract Background Cushing's disease is a rare and little-known disease, and the individualization of drug treatment varies greatly. Studies have shown that the gene expression profile of Cushing's disease is related to its clinical characteristics. Therefore, the study aims to identify key differential genes between the age and size of tumors through bioinformatics technology, thus providing a theoretical basis for personalized targeted therapy of Cushing's disease. Methods Downloading the gene expression microarray (GSE93825) data from the Gene Expression Omnibus (GEO) database and obtaining differentially expressed genes (DEGs) of different tumor sizes and ages through GEO2R. The DAVID database, Cytoscape and String platforms were utilized for functional enrichment analysis and protein-protein interaction (PPI) network analysis on selected differential genes. Results First, 96 DEGs were identified between macroadenoma (MAC) and microadenoma (MIC), which initially proved the different gene expression characteristics between them. Second, a total of 2128 DEGs were identified in MAC age group. The top five hub genes of the PPI network were GNGT2, LPAR3, PDYN, GRM3, and HTR1D. A total of 16 DEGs were identified in MIC age group. In addition, 88 DEGs were identified in younger MAC and MIC groups. The top five hub genes included LEP, PTGS2, STAT6, CXCL12, and ITPKB. 299 DEGs were identified in senior MAC and MIC groups. The first five hub genes were CCR7, LPAR2, CXCR5, ADCY3, and TAS2R14. By virtue of DAVID and Cytoscape software, the function enrichment analysis and core module analysis were performed successfully. Conclusions In summary, our research shows through bioinformatics analysis that different gene expression profiles of Cushing's disease are related to the size and age of the tumor, which may provide new insights into the molecular pathogenesis of Cushing's disease. These hub genes may be used for accurate diagnosis and treatment of Cushing's disease.

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Identification of Key Genes Potentially Related to Triple Receptor Negative Breast Cancer by Microarray Analysis

10.1101/2020.12.21.423796 ◽

2020 ◽

Author(s):

Basavaraj Vastrad ◽

Chanabasayya Vastrad ◽

Iranna Kotturshetti

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cell Cycle ◽

Immune Response ◽

Target Gene ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Go Enrichment ◽

Go Enrichment Analysis

AbstractTriple receptor negative breast cancer (TNBC) is the type of gynecological cancer in the elderly women. This study is aimed to explore molecular mechanism of TNBC via bioinformatics analysis. The gene expression profiles of GSE88715 (including 38 TNBC and 38 normal control) was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using the limma package in R software. Pathway and gene ontology (GO) enrichment analysis were performed based on various pathway dabases and GO database. Then, InnateDb interactome database, Cytoscape and PEWCC1 were applied to construct the protein-protein interaction (PPI) network and screen hub genes. Similarly, miRNet database, NetworkAnalyst database and Cytoscape were applied to construct the target gene - miRNA network and target gene - TF network, and screen targate genes. Pathway and GO enrichment analysis was further performed for hub genes, gene clusters identified via module analysis and targate genes. The expression of hub genes with prognostic values was validated on the UALCAN, cBio Portal, The Human Protein Atlas, receiver operator characteristic (ROC) curve analysis, RT-PCR analysis and immune infiltration analysis. A total of 949 DEGs were identified in TNBC (469 up regulated genes, and 480 down regulated genes), and they were mainly enriched in the terms of phospholipases, toxoplasmosis, immune response, cell surface, glycolysis, biosynthesis of amino acids, carboxylic acid metabolic process and organic substance catabolic process extracellular space. Hub genes including UBD, HLA-B, MYC and HSP90AB1 were identified via PPI network and modules, which were mainly enriched in immune response, antigen processing and presentation, cell cycle and pathways in cancer. Targate genes including CCDC80, PEG10, HOPX and CCNA2 were identified via target gene - miRNA network and target gene - TF network, which were mainly enriched in extracellular structure organization, validated targets of C-MYC transcriptional activation, ensemble of genes encoding core extracellular matrix including ECM glycoproteins and cell cycle. The top five significantly overexpressed mRNA (ADAM15, BATF, NOTCH3, ITGAX and SDC1) and the top five significantly underexpressed mRNA (RPL4, EEF1G, RPL3, RBMX and ABCC2) were selected for further validation in TNBCpatients and healthy controls. Analysis of the expression of genes in the various databases showed that ADAM15, BATF, NOTCH3, ITGAX, SDC1, RPL4, EEF1G, RPL3, RBMX and ABCC2 expressions have a cancer specific pattern in TNBC. Collectively, ADAM15, BATF, NOTCH3, ITGAX, SDC1, RPL4, EEF1G, RPL3, RBMX and ABCC2 may be useful candidate biomarkers for TNBC diagnosis, prognosis and theraputic targates.

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EXPLORING HUB GENES IN LUNG CANCER USING INTEGRATED BIOINFORMATICS ANALYSIS

10.1101/2021.08.22.457301 ◽

2021 ◽

Author(s):

swaminathan venkataramanan

Keyword(s):

Lung Cancer ◽

Bioinformatics Analysis ◽

Primary Lung Cancer ◽

Enrichment Analysis ◽

Expression Level ◽

Gene Identification ◽

Ppi Network ◽

Hub Genes ◽

Hub Gene ◽

Pathway Enrichment

Lung cancer is a serious health issue worldwide causing death men than women. The environmental and genetic factored in metastasis primary lung cancer. The survival rate patient improved by identification hub genes lung cancer with bioinformatics tools. The present study was to determine the biological pathway and PPI network by identification the biomarker hub genes lung cancer. The GSE84797, GSE28827, and GSE115456 were retrieved from GEO attained upregulated and downregulated by GEO2R tool. Enrichment analysis DEGs analyzed used DAVID server. PPI network and hub genes was constructed by STRING, Cytoscape and cytoHubba. The OS and expression level were retrieved by KM plotter server and GEPIA2. Results obtained 3 DEGs dataset by GEO2R with -1.0 ≤ logFC≥1.0.Upregulated and downregulated DEGs presence for GO and pathway enrichment analysis.The PPI network and hub genes identification done with cutoff >0.9 obtained KRT5, KRT13, KRT17, KRTI6 KRTI5 , C5, PPBP, CXCL2, CCR9, and CCR7 .The OS done analyzed showed hazard ratio while expression level presented the different gene of LUSC/LUAD and normal tissues.The hub genes help in understanding the system biology of cancer through the identification network of protein interaction and hub gene identification lung cancer for classified the medicine and early diagnosis cancer.

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Identification of miRNA-target gene regulatory networks in liver fibrosis based on bioinformatics analysis

PeerJ ◽

10.7717/peerj.11910 ◽

2021 ◽

Vol 9 ◽

pp. e11910

Author(s):

Yang Tai ◽

Chong Zhao ◽

Jinhang Gao ◽

Tian Lan ◽

Huan Tong

Keyword(s):

Immune Response ◽

Liver Cirrhosis ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Ppi Network ◽

Hub Genes ◽

Differentially Expressed Mirnas ◽

And Control

Background Liver cirrhosis is one of the leading causes of death worldwide. MicroRNAs (miRNAs) can regulate liver fibrosis, but the underlying mechanisms are not fully understood, and the interactions between miRNAs and mRNAs are not clearly elucidated. Methods miRNA and mRNA expression arrays of cirrhotic samples and control samples were acquired from the Gene Expression Omnibus database. miRNA-mRNA integrated analysis, functional enrichment analysis and protein-protein interaction (PPI) network construction were performed to identify differentially expressed miRNAs (DEMs) and mRNAs (DEGs), miRNA-mRNA interaction networks, enriched pathways and hub genes. Finally, the results were validated with in vitro cell models. Results By bioinformatics analysis, we identified 13 DEMs between cirrhotic samples and control samples. Among these DEMs, six upregulated (hsa-miR-146b-5p, hsa-miR-150-5p, hsa-miR-224-3p, hsa-miR-3135b, hsa-miR-3195, and hsa-miR-4725-3p) and seven downregulated (hsa-miR-1234-3p, hsa-miR-30b-3p, hsa-miR-3162-3p, hsa-miR-548aj-3p, hsa-miR-548x-3p, hsa-miR-548z, and hsa-miR-890) miRNAs were further validated in activated LX2 cells. miRNA-mRNA interaction networks revealed a total of 361 miRNA-mRNA pairs between 13 miRNAs and 245 corresponding target genes. Moreover, PPI network analysis revealed the top 20 hub genes, including COL1A1, FBN1 and TIMP3, which were involved in extracellular matrix (ECM) organization; CCL5, CXCL9, CXCL12, LCK and CD24, which participated in the immune response; and CDH1, PECAM1, SELL and CAV1, which regulated cell adhesion. Functional enrichment analysis of all DEGs as well as hub genes showed similar results, as ECM-associated pathways, cell surface interaction and adhesion, and immune response were significantly enriched in both analyses. Conclusions We identified 13 differentially expressed miRNAs as potential biomarkers of liver cirrhosis. Moreover, we identified 361 regulatory pairs of miRNA-mRNA and 20 hub genes in liver cirrhosis, most of which were involved in collagen and ECM components, immune response, and cell adhesion. These results would provide novel mechanistic insights into the pathogenesis of liver cirrhosis and identify candidate targets for its treatment.

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Differential Expression and Bioinformatics Analysis of circRNA in Non-small Cell Lung Cancer

Frontiers in Genetics ◽

10.3389/fgene.2020.586814 ◽

2020 ◽

Vol 11 ◽

Author(s):

Qiuwen Sun ◽

Xia Li ◽

Muchen Xu ◽

Li Zhang ◽

Haiwei Zuo ◽

...

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Small Cell Lung Cancer ◽

Target Genes ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Small Cell ◽

Ppi Network ◽

Small Cell Lung ◽

Hub Genes

Circular RNA (CircRNA) plays an important role in tumorigenesis and progression of non-small cell lung cancer (NSCLC), but the pathogenesis of NSCLC caused by circRNA has not been fully elucidated. This study aimed to investigate differentially expressed circRNAs and identify the underlying pathogenesis hub genes of NSCLC by comprehensive bioinformatics analysis. Data of gene expression microarrays (GSE101586, GSE101684, and GSE112214) were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs (DECs) were obtained by the “limma” package of R programs and the overlapping operation was implemented of DECs. CircBase database and Cancer-Specific CircRNA database (CSCD) were used to find miRNAs binding to DECs. Target genes of the found miRNAs were identified utilizing Perl programs based on miRDB, miRTarBase, and TargetScan databases. Functional and enrichment analyses of selected target genes were performing using the “cluster profiler” package. Protein-protein interaction (PPI) network was constructed by the Search Tool for the STRING database and module analysis of selected hub genes was performed by Cytoscape 3.7.1. Survival analysis of hub genes were performed by Gene Expression Profiling Interactive Analysis (GEPIA). Respectively, 1 DEC, 249 DECs, and 101 DECs were identified in GSE101586, GSE101684, and GSE112214. A total of eight overlapped circRNAs, 43 miRNAs and 427 target genes were identified. Gene Ontology (GO) enrichment analysis showed these target genes were enriched in biological processes of regulation of histone methylation, Ras protein signal transduction and covalent chromatin modification etc. Pathway enrichment analysis showed these target genes are mainly involved in AMPK signaling pathway, signaling pathways regulating pluripotency of stem cells and insulin signaling pathway etc. A PPI network was constructed based on 427 target genes of the 43 miRNAs. Ten hub genes were found, of which the expression of MYLIP, GAN, and CDC27 were significantly related to NSCLC patient prognosis. Our study provide a deeper understanding the circRNAs-miRNAs-target genes by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of NSCLC. MYLIP, GAN, and CDC27 genes might serve as novel biomarker for precise treatment and prognosis of NSCLC in the future.

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