scholarly journals Database Mining of Genes of Prognostic Value for the Prostate Adenocarcinoma Microenvironment Using the Cancer Gene Atlas

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Xin Zhao ◽  
Daixing Hu ◽  
Jia Li ◽  
Guozhi Zhao ◽  
Wei Tang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Immune Cell ◽  
Kegg Pathway ◽  
Clinical Sample ◽  
Expression Profiles ◽  
Prostate Adenocarcinoma ◽  
Cancer Gene ◽  
Hub Genes ◽  
Ppi Networks ◽  
Pathway Analyses

Background. Prostate adenocarcinoma (PRAD) is a common malignant tumor in elderly men. Our research uses The Cancer Gene Atlas (TCGA) database to find potential related genes for predicting the prognosis of patients with PRAD. Methods. We downloaded gene expression profiles and clinical sample information from TCGA for 490 patients with PRAD (patient age: 41-78 years). We calculated stromal and immune scores using the ESTIMATE algorithm to predict the level of stromal and immune cell infiltration. We categorized patients with PRAD in TCGA into high and low score arrays according to their median immune/stromal scores and identified differentially expressed genes (DEGs) that were significantly correlated with the prognosis of PRAD. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. The association between DEGs and overall survival was investigated by weighted Kaplan–Meier survival analysis and multivariate analysis. Furthermore, the protein-protein interaction network (PPI) of DEGs was constructed using the STRING tool. Finally, the hub genes were identified by analyzing the degree of association of PPI networks. Results. We found that 8 individual DEGs, C6, S100A12, MLC1, PAX5, C7, FAM162B, CAMK1G, and TCEAL5, were significantly predictive of favorable overall survival and one DEG, EPYC, was associated with poor overall survival. GO and KEGG pathway analyses revealed that the DEGs were associated with immune responses. Moreover, 30 hub genes were obtained using the PPI network of DEGs: ITGAM, CD4, CD3E, IL-10, LCP2, ITGB2, ZAP-70, C3, CCL19, CXCL13, CXCL9, BTK, CCL21, CD247, CD28, CD3D, FCER1G, PTPRC, TYROBP, CCR5, ITK, CCL13, CCR1, CCR2, CD79B, CYBB, IL2RG, JAK3, PLCG2, and CD19. These prominent nodes had the most associations with other genes, indicating that they might play crucial roles in the prognosis of PRAD. Conclusions. We extracted a list of genes associated with the prostate adenocarcinoma microenvironment, which might contribute to the prediction and interpretation of PRAD prognosis.

Identification of hub genes specific to pulmonary metastasis in osteosarcoma through integrated bioinformatics analysis

2021 ◽  
pp. 1-11
Author(s):  
Yinan Chai ◽  
Lihan Xu ◽  
Rui He ◽  
Liangjun Zhong ◽  
Yuying Wang
Keyword(s):  
Pulmonary Metastasis ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Experimental Studies ◽  
Genetic Alterations ◽  
Mapk Signaling ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Pathway Analyses ◽  
Geo Database

BACKGROUND: Pulmonary metastasis is the most frequent cause of death in osteosarcoma (OS) patients. Recently, several bioinformatics studies specific to pulmonary metastatic osteosarcoma (PMOS) have been applied to identify genetic alterations. However, the interpretation and reliability of the results obtained were limited for the independent database analysis. OBJECTIVE: The expression profiles and key pathways specific to PMOS remain to be comprehensively explored. Therefore, in our study, three original datasets of GEO database were selected. METHODS: Initially, three microarray datasets (GSE14359, GSE14827, and GSE85537) were downloaded from the GEO database. Differentially expressed genes (DEGs) between PMOS and nonmetastatic osteosarcoma (NMOS) were identified and mined using DAVID. Subsequently, GO and KEGG pathway analyses were carried out for DEGs. Corresponding PPI network of DEGs was constructed based on the data collected from STRING datasets. The network was visualized with Cytoscape software, and ten hub genes were selected from the network. Finally, survival analysis of these hub genes also used the TARGET database. RESULTS: In total, 569 upregulated and 1238 downregulated genes were filtered as DEGs between PMOS and NMOS. Based on the GO analysis result, these DEGs were significantly enriched in the anatomical structure development, extracellular matrix, biological adhesion, and cell adhesion terms. Based on the KEGG pathway analysis result, these DEGs were mainly enriched in the pathways in cancer, PI3K-Akt signaling, MAPK signaling, focal adhesion, cytokine-cytokine receptor interaction, and IL-17 signaling. Hub genes (ANXA1 and CXCL12) were significantly associated with overall survival time in OS patient. CONCLUSION: Our results may provide new insight into pulmonary metastasis of OS. However, experimental studies remain necessary to elucidate the biological function and mechanism underlying PMOS.

scholarly journals Identification of miRNA-mRNA network and immune-related gene signatures in IgA nephropathy by integrated bioinformatics analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shi-Yao Wei ◽  
Shuang Guo ◽  
Bei Feng ◽  
Shang-Wei Ning ◽  
Xuan-Yi Du
Keyword(s):  
T Cells ◽  
Iga Nephropathy ◽  
Immune Cells ◽  
Regulatory Network ◽  
Target Genes ◽  
Immune Cell ◽  
Expression Profiles ◽  
Specific Treatment ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background IgA nephropathy (IgAN) is the most common form of primary glomerulonephritis worldwide, and its diagnosis depends mainly on renal biopsy. However, there is no specific treatment for IgAN. Moreover, its causes and underlying molecular events require further exploration. Methods The expression profiles of GSE64306 and GSE93798 were downloaded from the Gene Expression Omnibus (GEO) database and used to identify the differential expression of miRNAs and genes, respectively. The StarBase and TransmiR databases were employed to predict target genes and transcription factors of the differentially expressed miRNAs (DE-miRNAs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to predict biological functions. A comprehensive analysis of the miRNA-mRNA regulatory network was constructed, and protein–protein interaction (PPI) networks and hub genes were identified. CIBERSORT was used to examine the immune cells in IgAN, and correlation analyses were performed between the hub genes and infiltrating immune cells. Results Four downregulated miRNAs and 16 upregulated miRNAs were identified. Forty-five and twelve target genes were identified for the upregulated and downregulated DE-miRNAs, respectively. CDKN1A, CDC23, EGR1, HIF1A, and TRIM28 were the hub genes with the highest degrees of connectivity. CIBERSORT revealed increases in the numbers of activated NK cells, M1 and M2 macrophages, CD4 naive T cells, and regulatory T cells in IgAN. Additionally, HIF1A, CDC23, TRIM28, and CDKN1A in IgAN patients were associated with immune cell infiltration. Conclusions A potential miRNA-mRNA regulatory network contributing to IgAN onset and progression was successfully established. The results of the present study may facilitate the diagnosis and treatment of IgAN by targeting established miRNA-mRNA interaction networks. Infiltrating immune cells may play significant roles in IgAN pathogenesis. Future studies on these immune cells may help guide immunotherapy for IgAN patients.

scholarly journals Bioinformatics Analysis and Identification of Genes and Pathways in Ischemic Cardiomyopathy

2021 ◽  
Author(s):  
Jing Cao ◽  
Zhaoya Liu ◽  
Jie Liu ◽  
Chan Li ◽  
Guogang Zhang ◽  
...  
Keyword(s):  
Network Analysis ◽  
Ischemic Cardiomyopathy ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Ppi Networks ◽  
Regulated Expression ◽  
Limma Package ◽  
Pathway Analyses

Abstract BackgroundIschemic cardiomyopathy (ICM) is considered to be the common cause of heart failure, which has high prevalence and mortality. This study aimed to investigate the different expressed genes (DEGs) and pathways in the pathogenesis of ICM using bioinformatics analysis.MethodsThe control and ICM datasets GSE116250，GSE46224 and GSE5406 were collected from the gene expression omnibus (GEO) database. DEGs were identified using limma package of R software and co-expressed genes were identified with Venn diagrams. Then, the gene otology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to explored the biological functions and signaling pathways. Protein-protein interaction (PPI) networks were assembled with Cytoscape software to identify hub genes related to the pathogenesis of ICM.ResultsA total of 844 DEGs were screened from GSE116250, of which 447 up-regulated and 397 down-regulated genes respectively. A total of 99 DEGs were singled out from GSE46224, of which 58 up-regulated and 41 down-regulated genes respectively. 30 DEGs were screened from GSE5406, including 10 genes with up-regulated expression and 20 genes with down-regulated expression. 5 up-regulated and 3 down-regulated co-expressed DEGs were intersected in three datasets. GO and KEGG pathway analyses revealed that DEGs mainly enriched in collagen fibril organization, protein digestion and absorption, AGE-RAGE signaling pathway and other related pathways. Collagen alpha-1(III) chain (COL3A1), collagen alpha-2(I) chain (COL1A2) and lumican (LUM) are the three hub genes in all three datasets through PPI network analysis. The expression of 5 DEGs (SERPINA3, FCN3, COL3A1, HBB, MXRA5) in heart tissues by qRT-PCR results were consistent with our GEO analysis, while expression of 3 DEGs (ASPN, LUM, COL1A2) were opposite with GEO analysis.ConclusionsThese findings from this bioinformatics network analysis investigated key hub genes, which contributed to better understand the mechanism and new therapeutic targets of ICM.

scholarly journals Identification of key biomarkers and immune infiltration in systemic lupus erythematosus by integrated bioinformatics analysis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Xingwang Zhao ◽  
Longlong Zhang ◽  
Juan Wang ◽  
Min Zhang ◽  
Zhiqiang Song ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Immune Cell ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Tissue Destruction ◽  
Systemic Lupus ◽  
Ppi Networks

Abstract Background Systemic lupus erythematosus (SLE) is a multisystemic, chronic inflammatory disease characterized by destructive systemic organ involvement, which could cause the decreased functional capacity, increased morbidity and mortality. Previous studies show that SLE is characterized by autoimmune, inflammatory processes, and tissue destruction. Some seriously-ill patients could develop into lupus nephritis. However, the cause and underlying molecular events of SLE needs to be further resolved. Methods The expression profiles of GSE144390, GSE4588, GSE50772 and GSE81622 were downloaded from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs) between SLE and healthy samples. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed by metascape etc. online analyses. The protein–protein interaction (PPI) networks of the DEGs were constructed by GENEMANIA software. We performed Gene Set Enrichment Analysis (GSEA) to further understand the functions of the hub gene, Weighted gene co‐expression network analysis (WGCNA) would be utilized to build a gene co‐expression network, and the most significant module and hub genes was identified. CIBERSORT tools have facilitated the analysis of immune cell infiltration patterns of diseases. The receiver operating characteristic (ROC) analyses were conducted to explore the value of DEGs for SLE diagnosis. Results In total, 6 DEGs (IFI27, IFI44, IFI44L, IFI6, EPSTI1 and OAS1) were screened, Biological functions analysis identified key related pathways, gene modules and co‐expression networks in SLE. IFI27 may be closely correlated with the occurrence of SLE. We found that an increased infiltration of moncytes, while NK cells resting infiltrated less may be related to the occurrence of SLE. Conclusion IFI27 may be closely related pathogenesis of SLE, and represents a new candidate molecular marker of the occurrence and progression of SLE. Moreover immune cell infiltration plays important role in the progession of SLE.

A Comprehensive Data Analysis of Differentially Regulated Genes in Melanoma

2020 ◽  
Author(s):  
Yanjie Han ◽  
Xinxin Li ◽  
Jiliang Yan ◽  
Chunyan Ma ◽  
Xin Wang ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Growth Factor Receptor ◽  
Principal Component ◽  
Distant Metastases ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background: Melanoma is the most deadly tumor in skin tumors and is prone to distant metastases. The incidence of melanoma has increased rapidly in the past few decades, and current trends indicate that this growth is continuing. This study was aimed to explore the molecular mechanisms of melanoma pathogenesis and discover underlying pathways and genes associated with melanoma.Methods: We used high-throughput expression data to study differential expression profiles of related genes in melanoma. The differentially expressed genes (DEGs) of melanoma in GSE15605, GSE46517, GSE7553 and the Cancer Genome Atlas (TCGA) datasets were analyzed. Differentially expressed genes (DEGs) were identified by paired t-test. Then the DEGs were performed cluster and principal component analyses and protein–protein interaction (PPI) network construction. After that, we analyzed the differential genes through bioinformatics and got hub genes. Finally, the expression of hub genes was confirmed in the TCGA databases and collected patient tissue samples.Results: Total 144 up-regulated DEGs and 16 down-regulated DEGs were identified. A total of 17 gene ontology analysis (GO) terms and 11 pathways were closely related to melanoma. Pathway of pathways in cancer was enriched in 8 DEGs, such as junction plakoglobin (JUP) and epidermal growth factor receptor (EGFR). In the PPI networks, 9 hub genes were obtained, such as loricrin (LOR), filaggrin (FLG), keratin 5 (KRT5), corneodesmosin (CDSN), desmoglein 1 (DSG1), desmoglein 3 (DSG3), keratin 1 (KRT1), involucrin (IVL) and EGFR. The pathway of pathways in cancer and its enriched DEGs may play important roles in the process of melanoma. The hub genes of DEGs may become promising melanoma candidate genes. Five key genes FLG, DSG1, DSG3, IVL and EGFR were identified in the TCGA database and melanoma tissues.Conclusions: The results suggested that FLG, DSG1, DSG3, IVL and EGFR might play important roles and potentially be valuable in the prognosis and treatment of melanoma.

scholarly journals Identification of Key Genes and Prognostic Value Analysis in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Meng Wang ◽  
Licheng Wang ◽  
Shusheng Wu ◽  
Dongsheng Zhou ◽  
Xianming Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Expression Profiles ◽  
Cyclin B1 ◽  
Ppi Network ◽  
Hub Genes ◽  
Altered Expression ◽  
Value Analysis ◽  
Kaplan Meier ◽  
Key Genes

Emerging evidence indicates that various functional genes with altered expression are involved in the tumor progression of human cancers. This study is aimed at identifying novel key genes that may be used for hepatocellular carcinoma (HCC) diagnosis, prognosis, and targeted therapy. This study included 3 expression profiles (GSE45267, GSE74656, and GSE84402), which were obtained from the Gene Expression Omnibus (GEO). GEO2R was used to analyze the differentially expressed genes (DEGs) between HCC and normal samples. The functional and pathway enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the identified DEGs was constructed using the Search Tool for the Retrieval of Interacting Gene, and hub genes were identified. ONCOMINE and CCLE databases were used to verify the expression of the hub genes in HCC tissues and cells. Kaplan-Meier plotter was used to assess the effects of the hub genes on the overall survival of HCC patients. A total of 99 DEGs were identified from the 3 expression profiles. These DEGs were enriched with functional processes and pathways related to HCC pathogenesis. From the PPI network, 5 hub genes were identified. The expression of the 5 hub genes was all upregulated in HCC tissues and cells compared with the control tissues and cells. Kaplan-Meier survival curves indicated that high expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), MAD2 mitotic arrest deficient-like 1 (MAD2L1), and topoisomerase IIα (TOP2A) predicted poor overall survival in HCC patients (all log-rank P<0.01). These results revealed that the DEGs may serve as candidate key genes during HCC pathogenesis. The 5 hub genes, including CDK1, CCNB1, CCNB2, MAD2L1, and TOP2A, may serve as promising prognostic biomarkers in HCC.

scholarly journals Bioinformatics Analysis for Identifying Micro-RNAs, Long Noncoding RNAs, Transcription Factors, Immune Genes Regulatory Networks, and Potential Therapeutic Agents for Diabetic Cardiomyopathy Using an Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Jiefang Zhou ◽  
Xiaowei Ji ◽  
Xiuwei Shen ◽  
Kefeng Yan ◽  
Peng Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Diabetic Cardiomyopathy ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Therapeutic Agents ◽  
Hub Genes ◽  
Ppi Networks ◽  
Micro Rnas ◽  
Rna Interaction

Abstract Objectives We identified functional genes and studied the underlying molecular mechanisms of diabetic cardiomyopathy (DCM) using bioinformatics tools. Methods Original gene expression profiles were obtained from the GSE21610 and GSE112556 datasets. We used GEO2R to screen the differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on DEGs. Protein–protein interaction (PPI) networks of DEGs were constructed using STRING and hub genes of signaling pathways were identified using Cytoscape. Aberrant hub gene expression was verified using The Cancer Genome Atlas dataset. Connectivity Map was used to predict the drugs that could treat DCM. Results The DEGs in DCM were mainly enriched in the nuclei and cytoplasm and involved in DCM- and chemokine-related signaling pathways. In the PPI network, 32 nodes were chosen as hub nodes and an RNA interaction network was constructed with 517 interactions. The expression of key genes (JPIK3R1, CCR9, XIST, WDFY3.AS2, hsa-miR-144-5p, and hsa-miR-146b-5p) was significantly different between DCM and normal tissues. Danazol, ikarugamycin, and semustine were identified as therapeutic agents against DCM using CMAP. Conclusion The identified hub genes could be associated with DCM pathogenesis and the above drugs could be used for treating DCM.

scholarly journals Screening of Hub Genes Associated with Lung Adenocarcinoma by Integrated Bioinformatic Analysis

2021 ◽  
Author(s):  
Zimeng Wei ◽  
Min Zhao ◽  
Linnan Zang
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Analysis Tool ◽  
Hub Genes ◽  
Kegg Analysis ◽  
Ppi Networks

Abstract Background Lung adenocarcinoma (LUAD) is the main histological subtype of lung cancer. However, the molecular mechanism underlying LUAD is not yet clearly defined, but elucidating this process in detail would be of great significance for clinical diagnosis and treatment. Methods Gene expression profiles were retrieved from Gene Expression Omnibus database (GEO), and the common differentially expressed genes (DEGs) were identified by online GEO2R analysis tool. Subsequently, the enrichment analysis of function and signaling pathways of DEGs in LUAD were performed by gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomics (KEGG) analysis. The protein-protein interaction (PPI) networks of the DEGs were established through the Search Tool for the Retrieval of Interacting Genes (STRING) database and hub genes were screened by plug-in CytoHubba in Cytoscape. Afterwards, we detected the expression of hub genes in LUAD and other cancers via GEPIA, Oncomine and HPA databases. Finally, Kaplan-Meier plotter were performed to analyze the prognosis efficacy of hub genes. Results 74 up-regulated and 238 down-regulated DEGs were identified. As for the up-regulated DEGs, KEGG analysis results revealed they were mainly enrolled in protein digestion and absorption. However, the down-regulated DEGs were primarily enriched in cell adhesion molecules. Subsequently, 9 hub genes: KIAA0101, CDCA7, TOP2A, CDC20, ASPM, TPX2, CENPF, UBE2T and ECT2, were identified and showed higher expression in both LUAD and other cancers. Finally, all these hub genes were found significantly related to the prognosis of LUAD (p < 0.05). Conclusions Our results screened out the hub genes and pathways that were related to the development and prognosis of LUAD, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of LUAD.

scholarly journals CXCR4 Negatively Correlates with NKT Cell Infiltration During Gastric Precancerous Lesions Progression to Early Gastric Cancer

2020 ◽  
Author(s):  
Xiaotao Jiang ◽  
Kunhai Zhuang ◽  
Kailin Jiang ◽  
Yi Wen ◽  
Linling Xie ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Early Gastric Cancer ◽  
Immune Cells ◽  
Immune Cell ◽  
Precancerous Lesions ◽  
Expression Profiles ◽  
Cell Infiltration ◽  
Hub Genes ◽  
Nkt Cell ◽  
Hub Gene

Abstract Background Immune microenvironment in gastric precancerous lesions (GPL) and early gastric cancer (EGC) still remain largely unknown. This study aims to identify key immune cells and hub genes associated with GPL progression to EGC. Methods Immune Cell Abundance Identifier (ImmuCellAI) algorithm was used to quantify the proportions of immune cells of GPL and GC samples based on gene expression profiles. Key immune cells associated with GPL progression to EGC were identified using one‐way analysis of variance (ANOVA) test and Spearman’s correlation test. Weighted gene co-expression analysis (WGCNA) and pathway enrichment were adopted to identify hub gene co-expression network and hub genes associated with the key immune cells infiltration. The pattern of key immune cells infiltration, hub genes expression and their correlation were verified in an independent GPL-EGC cohort and GC datasets.Results NKT cell was found gradually decreased during GPL progression to EGC and negatively correlated with tumorigenesis. According to WGCNA and hub genes screening, CXCR4, having a poor prognosis, increased with GPL progression, positively correlated with tumorigenesis and negatively correlated with NKT cell infiltration significantly, was identified as the real hub gene. The negative correlation between CXCR4 and NKT cell infiltration was successfully verified in an independent GPL-EGC cohort and GC datasets.Conclusion CXCR4 and NKT cell are possible to serve as biomarkers in monitoring GPL progression to EGC. Besides, CXCR4 may be involved in regulating NKT cell infiltration during GPL progression to EGC, which may provide a new immunotherapeutic target.

scholarly journals Identification of Molecular Markers Associated with Diagnosis and Prognosis in Lung Adenocarcinoma by Bioinformatics Analysis

2020 ◽  
Author(s):  
Zimeng Wei ◽  
Linnan Zang ◽  
Min Zhao
Keyword(s):  
Gene Expression ◽  
Molecular Markers ◽  
Lung Adenocarcinoma ◽  
Expression Profiles ◽  
Diagnosis And Treatment ◽  
Analysis Tool ◽  
Hub Genes ◽  
Kegg Analysis ◽  
Diagnosis And Prognosis ◽  
Ppi Networks

Abstract Background：Lung adenocarcinoma (LUAD) is the main histological subtype of lung cancer. However, the molecular mechanism underlying LUAD is not yet clearly defined, but elucidating this process in detail would be of great significance for clinical diagnosis and treatment. Our aim is to identify the candidate key genes and pathways associated with diagnosis and prognosis in LUAD.Methods：In this study, three gene expression profiles GSE118370, GSE32863 and GSE43458 were retrieved from Gene Expression Omnibus database (GEO), and the common differentially expressed genes (DEGs) were identified by online GEO2R analysis tool. Subsequently, the enrichment analysis of function and signaling pathways of DEGs in LUAD were performed by gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomics (KEGG) analysis. The protein-protein interaction (PPI) networks of the DEGs were established through the Search Tool for the Retrieval of Interacting Genes (STRING) database and hub genes were screened by plug-in CytoHubba in Cytoscape. Afterwards, the miRNAs and the hub genes network was constructed via miRWalk. Finally, receiver operating characteristic (ROC) curve and Kaplan-Meier plotter were performed to analyze the diagnosis and prognosis efficacy of hub genes. Results: A total of 311 DEGs were identified, including 74 up-regulated and 238 down-regulated genes. GO analysis results showed that DEGs were mainly enriched in biological processes including composition of extracellular matrix, regulation of angiogenesis and so on. KEGG analysis results revealed DEGs were mainly enrolled in cell adhesion signaling pathway. Subsequently, 10 hub genes, CDC20, CENPF, TPX2, TOP2A, KIAA0101, CDCA7, ASPM, ECT2, UBE2T and COL1A1, were identified. And TOP2A, CDCA7, TPX2 and COL1A1 showed strong relationships with each other and the miRNAs nearby in miRNAs-mRNA network obtained by miRWalk website. Finally, all these 10 hub genes were found significantly related to the diagnosis and prognosis of LUAD (p<0.05). Conclusions: The identification of hub genes in this study will help us to understand the pathogenesis of LUAD, especially the molecular mechanisms of its development. Our results suggested that TOP2A, CDCA7, TPX2 and COL1A1 might present predictive value for the development and prognosis in LUAD, and might be used as potential molecular markers for the diagnosis and treatment of LUAD.

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