scholarly journals Integrated Analysis of DNA Methylation and mRNA Expression Profiles Data to Identify Key Genes in Lung Adenocarcinoma

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Xiang Jin ◽  
Xingang Liu ◽  
Xiaodan Li ◽  
Yinghui Guan
Keyword(s):  
Dna Methylation ◽  
Lung Adenocarcinoma ◽  
Mrna Expression ◽  
Early Stage ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Integrated Analysis ◽  
Tissue Samples ◽  
Ppi Networks ◽  
Key Genes

Introduction. Lung adenocarcinoma (LAC) is the most frequent type of lung cancer and has a high metastatic rate at an early stage. This study is aimed at identifying LAC-associated genes.Materials and Methods. GSE62950 downloaded from Gene Expression Omnibus included a DNA methylation dataset and an mRNA expression profiles dataset, both of which included 28 LAC tissue samples and 28 adjacent normal tissue samples. The differentially expressed genes (DEGs) were screened by Limma package in R, and their functions were predicted by enrichment analysis using TargetMine online tool. Then, protein-protein interaction (PPI) network was constructed using STRING and Cytoscape. Finally, LAC-associated methylation sites were identified by CpGassoc package in R and mapped to the DEGs to obtain LAC-associated DEGs.Results. Total 913 DEGs were identified in LAC tissues. In the PPI networks,MAD2L1,AURKB,CCNB2,CDC20,andWNT3Ahad higher degrees, and the first four genes might be involved in LAC through interaction. Total 8856 LAC-associated methylation sites were identified and mapped to the DEGs. And there were 29 LAC-associated methylation sites located in 27 DEGs (e.g.,SH3GL2,BAI3,CDH13,JAM2,MT1A,LHX6,andIGFBP3).Conclusions. These key genes might play a role in pathogenesis of LAC.

scholarly journals Integrated analysis of miRNA and mRNA expression profiles in tilapia gonads at an early stage of sex differentiation

BMC Genomics ◽  
2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Wenjing Tao ◽  
Lina Sun ◽  
Hongjuan Shi ◽  
Yunying Cheng ◽  
Dongneng Jiang ◽  
...  
Keyword(s):  
Mrna Expression ◽  
Early Stage ◽  
Expression Profiles ◽  
Sex Differentiation ◽  
Integrated Analysis

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 Upregulation of LIMK1 Is Correlated With Poor Prognosis and Immune Infiltrates in Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Guojun Lu ◽  
Ying Zhou ◽  
Chenxi Zhang ◽  
Yu Zhang
Keyword(s):  
Lung Adenocarcinoma ◽  
Mrna Expression ◽  
Roc Curve ◽  
Poor Prognosis ◽  
Expression Profiles ◽  
Immune Therapy ◽  
Functional Enrichment ◽  
Normal Tissues ◽  
Ppi Networks ◽  
Kaplan Meier

BackgroundProtein-coding gene LIM Domain Kinase 1 (LIMK1) is upregulated in various tumors and reported to promote tumor invasion and metastasis. However, the prognostic values of LIMK1 and correlation with immune infiltrates in lung adenocarcinoma are still not understood. Therefore, we evaluated the prognostic role of LIMK1 and its correlation with immune infiltrates in lung adenocarcinoma.MethodsTranscriptional expression profiles of LIMK1 between lung adenocarcinoma tissues and normal tissues were downloaded from the Cancer Genome Atlas (TCGA). The LIMK1 protein expression was assessed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas. Receiver operating characteristic (ROC) curve was used to differentiate lung adenocarcinoma from adjacent normal tissues. Kaplan-Meier method was conducted to assess the effect of LIMK1 on survival. Protein-protein interaction (PPI) networks were constructed by the STRING. Functional enrichment analyses were performed using the “ClusterProfiler” package. The relationship between LIMK1 mRNA expression and immune infiltrates was determined by tumor immune estimation resource (TIMER) and tumor-immune system interaction database (TISIDB).ResultsThe expression of LIMK1 in lung adenocarcinoma tissues was significantly upregulated than those in adjacent normal tissues. Increased LIMK1 mRNA expression was associated with lymph node metastases and high TNM stage. The ROC curve analysis showed that with a cutoff level of 4.908, the accuracy, sensitivity, and specificity for LIMK1 differentiate lung adenocarcinoma from adjacent controls were 69.5, 93.2, and 71.9%, respectively. Kaplan-Meier survival analysis showed lung adenocarcinoma patients with high- LIMK1 had a worse prognosis than those with low- LIMK1 (43.1 vs. 55.1 months, P = 0.028). Correlation analysis indicated LIMK1 mRNA expression was correlated with tumor purity and immune infiltrates.ConclusionUpregulated LIMK1 is significantly correlated with poor survival and immune infiltrates in lung adenocarcinoma. Our study suggests that LIMK1 can be used as a biomarker of poor prognosis and potential immune therapy target in lung adenocarcinoma.

scholarly journals Integrated analysis of lncRNA-miRNA-mRNA ceRNA network and the potential prognosis indicators in sarcomas

2020 ◽  
Author(s):  
Ling Zhang ◽  
Lu Gao ◽  
Yu Zhao ◽  
Xuelei Ma
Keyword(s):  
Survival Analysis ◽  
Protein Interactions ◽  
Sequence Data ◽  
Gene Prediction ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Integrated Analysis ◽  
Pathway Enrichment Analysis ◽  
Tissue Samples ◽  
Cerna Network

Abstract The ceRNA network has been demonstrated to play crucial roles in multiple biological processes and the development of neoplasms, which have the potential to become diagnostic and prognosis markers and therapeutic targets. In this work, we comparing the expression profiles between sarcoma identified differentially expressed genes (DEGs), lncRNAs (DELs) and miRNAs (DEMs) in sarcomas and normal tissue samples in GEO datasets. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied to investigate the major functions of the overlapping DEGs. Then, lncRNA-miRNA interactions and miRNA-mRNA interactions were predicted, and a ceRNA regulatory network was constructed. In addition, the mRNAs included in ceRNA network were used to construct the protein-protein interactions network, and the survival analysis of sarcomas was performed according to the biomarkers included in the ceRNA network. According to the RNA sequence data from GEO dataset, 1296 DEGs were identified in sarcoma samples by combining the GO and Pathway enrichment analysis, 338 DELs were discovered after re-annotating the probes, and 36 DEGs were ascertained through intersecting two different expression miRNAs sets. Further, 448 miRNA-mRNA interactions and 454 miRNA-lncRNA interactions were obtained through target gene prediction, and then, we constructed a lncRNA-miRNA-mRNA ceRNA network containing 9 miRNAs, 69 lncRNAs and 113 mRNAs. PPI network showed that the hub up-regulated nodes include IGF1, PRKCB and GNAI3, and the hub down-regulated nodes include AR, CYCS and PPP1CB. Survival analysis revealed that the expression levels of 12 RNAs involved in the ceRNA network were associated with overall survival of sarcoma patients. Our study showed that the ceRNA network in sarcomas based on that lncRNA could serve as ceRNA and discovered the potential indicators for prognosis of sarcoma patients.

scholarly journals Identification of Methylation Markers for Diagnosis of Autism Spectrum Disorder

2021 ◽  
Author(s):  
Bei Zhang ◽  
Xiaoyuan Hu ◽  
Yuefei Li ◽  
Yongkang Ni ◽  
Lin Xue
Keyword(s):  
Autism Spectrum Disorder ◽  
Expression Profiles ◽  
Neurodevelopmental Disorder ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Sphingolipid Metabolism ◽  
Ppi Networks ◽  
Key Genes

Abstract Autism spectrum disorder (ASD) is a hereditary heterogeneous neurodevelopmental disorder characterized by social and speech dysplasia. We collected the expression profiles of ASD in GSE26415, GSE42133 and GSE123302, as well as methylation data of GSE109905. Differentially expressed genes (DEGs) between ASD and controls were obtained by differential expression analysis. Enrichment analysis identified the biological functions and signaling pathways involved by common genes in three groups of DEGs. PPI networks were used to identify genes with the highest connectivity as key genes. In addition, we identified methylation markers by associating differentially methylated positions (DMPs). Key methylation markers were identified using the LASSO model. ROC curves and nomograms were used to identify the diagnostic role of key methylation markers for ASD. A total of 57 common genes were identified in the three groups of DEGs. These genes were mainly enriched in Sphingolipid metabolism and PPAR signaling pathway. In the PPI network, we identified seven key genes with higher connectivity, and used qPCR experiments to verify the expressions. In addition, we identified 31 methylation markers and screened 3 key methylation markers (RUNX2, IMMP2L and MDM2) by LASSO model. They all had good diagnostic effects on ASD, and their methylation levels were closely related to the risk of ASD. Our analysis identified RUNX2, IMMP2L and MDM2 as possible diagnostic markers for ASD. Identifying different biomarkers and risk genes will contribute to the early diagnosis of ASD and the development of new clinical and drug treatments.

scholarly journals Use of integrative epigenetic and mRNA expression analyses to identify significantly changed genes and functional pathways in osteoarthritic cartilage

2018 ◽  
Vol 7 (5) ◽  
pp. 343-350 ◽  
Author(s):  
A. He ◽  
Y. Ning ◽  
Y. Wen ◽  
Y. Cai ◽  
K. Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Mrna Expression ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Signalling Pathway ◽  
Pathway Enrichment Analysis ◽  
Osteoarthritic Cartilage ◽  
Pathway Enrichment ◽  
Genome Wide

Aim Osteoarthritis (OA) is caused by complex interactions between genetic and environmental factors. Epigenetic mechanisms control the expression of genes and are likely to regulate the OA transcriptome. We performed integrative genomic analyses to define methylation-gene expression relationships in osteoarthritic cartilage. Patients and Methods Genome-wide DNA methylation profiling of articular cartilage from five patients with OA of the knee and five healthy controls was conducted using the Illumina Infinium HumanMethylation450 BeadChip (Illumina, San Diego, California). Other independent genome-wide mRNA expression profiles of articular cartilage from three patients with OA and three healthy controls were obtained from the Gene Expression Omnibus (GEO) database. Integrative pathway enrichment analysis of DNA methylation and mRNA expression profiles was performed using integrated analysis of cross-platform microarray and pathway software. Gene ontology (GO) analysis was conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Results We identified 1265 differentially methylated genes, of which 145 are associated with significant changes in gene expression, such as DLX5, NCOR2 and AXIN2 (all p-values of both DNA methylation and mRNA expression < 0.05). Pathway enrichment analysis identified 26 OA-associated pathways, such as mitogen-activated protein kinase (MAPK) signalling pathway (p = 6.25 × 10-4), phosphatidylinositol (PI) signalling system (p = 4.38 × 10-3), hypoxia-inducible factor 1 (HIF-1) signalling pathway (p = 8.63 × 10-3 pantothenate and coenzyme A (CoA) biosynthesis (p = 0.017), ErbB signalling pathway (p = 0.024), inositol phosphate (IP) metabolism (p = 0.025), and calcium signalling pathway (p = 0.032). Conclusion We identified a group of genes and biological pathwayswhich were significantly different in both DNA methylation and mRNA expression profiles between patients with OA and controls. These results may provide new clues for clarifying the mechanisms involved in the development of OA. Cite this article: A. He, Y. Ning, Y. Wen, Y. Cai, K. Xu, Y. Cai, J. Han, L. Liu, Y. Du, X. Liang, P. Li, Q. Fan, J. Hao, X. Wang, X. Guo, T. Ma, F. Zhang. Use of integrative epigenetic and mRNA expression analyses to identify significantly changed genes and functional pathways in osteoarthritic cartilage. Bone Joint Res 2018;7:343–350. DOI: 10.1302/2046-3758.75.BJR-2017-0284.R1.

Identify Key Genes by Weighted Gene Co-Expression Network Analysis for Lung Adenocarcinoma

Nano LIFE ◽  
2019 ◽  
Vol 09 (01n02) ◽  
pp. 1940002
Author(s):  
Jichen Xu ◽  
Xianchun Zong ◽  
Qianshu Ren ◽  
Hongyu Wang ◽  
Lijuan Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Lung Adenocarcinoma ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Oocyte Meiosis ◽  
Key Genes

The aim of this paper is to identify key genes in lung adenocarcinoma (LUAD) through weighted gene co-expression network analysis (WGCNA), and to further understand the molecular mechanism of LUAD. 107 gene expression profiles were downloaded from GSE10072 in the GEO database. We performed rigorous processing of the initial gene expression profile data. Subsequently, we used WGCNA to identify disease-driven modules and enforced functional enrichment analysis. The key genes were defined as the most connected genes in the driver module and were validated using the GSE75037 and TCGA database. GSE10072 removed 41 unpaired lung samples and 4 outliers. By analyzing the 62 samples using WGCNA, we obtained 26 modules and identified the brown and magenta modules as the driving modules for the LUAD. We found that the “Cell cycle”, “Oocyte meiosis” and “Progesterone-mediated oocyte maturation” pathways may be related to the occurrence of LUAD. GSE75037 removed 8 outlier and obtained 2909 differentially expressed genes (DEGs), 26 genes (9 genes in the brown module, 17 genes in the magenta module) overlap with key genes in the driver module. The results of the survival analysis suggest that 19 genes were significantly correlated with the patient’s survival time, including KPNA2, FEN1, RRM2, TOP2A, CENPF, MCM4, BIRC5, MELK, MAD2L1, CCNB1, CCNA2, KIF11, CDKN3, NUSAP1, CEP55, AURKA, NEK2, KIF14 and CDCA8, which may be potential biomarkers or therapeutic targets for LUAD. In this study, we provide a theoretical basis for further understanding the biological mechanism of LUAD through bioinformatics analysis of LUAD.

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