scholarly journals Identification of Significant Genes in Lung Cancer of Nonsmoking Women via Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yu Wang ◽  
Sibo Hu ◽  
Xianguang Bai ◽  
Ke Zhang ◽  
Ruixue Yu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Tissue Samples ◽  
Screening Criteria ◽  
Hub Gene ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Limma Package ◽  
Key Genes ◽  
Upregulated Genes ◽  
Geo Database

Background. The aim of this study was to identify potential key genes, proteins, and associated interaction networks for the development of lung cancer in nonsmoking women through a bioinformatics approach. Methods. We used the GSE19804 dataset, which includes 60 lung cancer and corresponding paracancerous tissue samples from nonsmoking women, to perform the work. The GSE19804 microarray was downloaded from the GEO database and differentially expressed genes were identified using the limma package analysis in R software, with the screening criteria of p value < 0.01 and ∣ log 2   fold   change   FC ∣ > 2 . Results. A total of 169 DEGs including 130 upregulated genes and 39 downregulated were selected. Gene Ontology and KEGG pathway analysis were performed using the DAVID website, and protein-protein interaction (PPI) networks were constructed and the hub gene module was screened through STING and Cytoscape. Conclusions. We obtained five key genes such as GREM1, MMP11, SPP1, FOSB, and IL33 which were strongly associated with lung cancer in nonsmoking women, which improved understanding and could serve as new therapeutic targets, but their functionality needs further experimental verification.

scholarly journals Bioinformatics-Based Research on Key Genes and Pathways of Intervertebral Disc Degeneration

Cartilage ◽  
2020 ◽  
pp. 194760352097324
Author(s):  
Qi Yan ◽  
Quan Xiao ◽  
Jun Ge ◽  
Cenhao Wu ◽  
Yingjie Wang ◽  
...  
Keyword(s):  
Intervertebral Disc ◽  
Disc Degeneration ◽  
Gene Expression Omnibus ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Network Analyses ◽  
Ppi Networks ◽  
Limma Package ◽  
Key Genes ◽  
New Treatment

Objective To find out the pathways and key genes and to reveal disc degeneration pathogenesis based on bioinformatic analyses. Design The GSE70362 dataset was downloaded from the GEO (Gene Expression Omnibus) database. Differentially expressed genes (DEGs) between the patients having disc degeneration and healthy controls were screened by Limma package in R language. Critical genes were identified by adopting gene ontologies (GOs), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. Results We identified 112 DEGs, including 60 genes which were upregulated and 52 that were downregulated. Analyses, such as GO and KEGG demonstrated that the DEGs got enriched in 4 biological processes and 2 signaling pathways, mainly related to disc degeneration. The PPI network analyses identified 5 key proteins, CCND1 (cyclin D1), GATA3, TNFSF11, LEF1, and DKK1 (Dickkopf related protein 1). Conclusion In this study, the DEGs and pathways determined promoted us understand the disc degeneration mechanisms. Also, the study may contribute novel biomarkers for the diagnosis and prevention of disc degeneration, and seek new treatment methods to repair and even regenerate degenerative intervertebral disc.

Title: Weighted Gene Co-Expression Network Analysis Identifies Five Hub Genes Associated with Metastasis in Synovial Sarcoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Hongzeng Wu ◽  
Benzheng Zhang ◽  
Jiazheng Zhao ◽  
Yi Zhao ◽  
Xiaowei Ma ◽  
...  
Keyword(s):  
Synovial Sarcoma ◽  
Tumor Metastasis ◽  
Gene Expression Omnibus ◽  
Survival Ratio ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Metastatic Lesions ◽  
Ppi Networks ◽  
Malignant Soft Tissue ◽  
Geo Database

Background: Synovial sarcoma (SS) refers to a malignant soft tissue sarcoma (STS) which often occurs in children and adults and has a poor prognosis in elderly patients. Patients with local lesions can be treated with extensive surgical resection combined with adjuvant or radiotherapy, whereas about half of the cases have recurrent diseases and metastatic lesions, and five-year survival ratio is assessed within the range of 27% - 55% only. Method: We downloaded a set of expression profile data (GSE40021) related to SS metastasis based on the Gene Expression Omnibus (GEO) database, and selected distinctly represented genes (DEGs) related to tumor metastasis. WGCNA was used to emphasize the DEGs related to tumor metastasis and obtain co-expression modules. Then, the module most related to SS metastasis was screened out. The genes enriched in this module were analyzed by Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway improvement analysis. Cytoscape software was used for constructing protein-protein interaction (PPI) networks, and hub genes were screened in Oncomine analysis. Result: We selected 514 DEGs, consisting of 210 up-regulated genes and 304 down-regulated genes. Through WGCAN, we got seven co-expression modules and the module most related to SS metastasis was the turquoise module, which contained 66 genes. Finally, we screened out five hub genes (HJURP, NCAPG, TPX2, CENPA, NDC80) through CytoHubba and Oncomine analysis. Conclusion: In this study, we screened five hub genes that may help in clinical diagnosis and serve as the latent purpose of SS treatment.

scholarly journals Identification of Immune-Related Genes in Sepsis due to Community-Acquired Pneumonia

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yanyan Li ◽  
Jiqin Wang ◽  
Yuzhen Li ◽  
Chunyan Liu ◽  
Xia Gong ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Enrichment Analysis ◽  
Community Acquired Pneumonia ◽  
Related Research ◽  
Protein Protein Interaction ◽  
Limma Package ◽  
Key Genes ◽  
Immune Related Genes ◽  
Immune Score

Background. Immunosuppression has a key function in sepsis pathogenesis, so it is of great significance to find immune-related markers for the treatment of sepsis. Methods. Datasets of community-acquired pneumonia (CAP) with sepsis from the ArrayExpress database were extracted. Differentially expressed genes (DEGs) between the CAP group and normal group by Limma package were performed. After calculation of immune score through the ESTIMATE algorithm, the DEGs were selected between the high immune score group and the low immune score group. Enrichment analysis of the intersected DEGs was conducted. Further, the protein-protein interaction (PPI) of the intersected DEGs was drawn by Metascape tools. Related publications of the key DEGs were searched in NCBI PubMed through Biopython models, and RT-qPCR was used to verify the expression of key genes. Results. 360 intersected DEGs (157 upregulated and 203 downregulated) were obtained between the two groups. Meanwhile, the intersected DEGs were enriched in 157 immune-related terms. The PPI of the DEGs was performed, and 8 models were obtained. In sepsis-related research, eight genes were obtained with degree ≥ 10 , included in the models. Conclusion. CXCR3, CCR7, HLA-DMA, and GPR18 might participate in the mechanism of CAP with sepsis.

scholarly journals Four Key Genes are Biomarkers Associated with Immunity in Neuroglioma

2020 ◽  
Author(s):  
Xin Yang ◽  
Jia-Qi Hao ◽  
Yu Zhang ◽  
Jia-Ying Shi ◽  
Xiao-Lin Zhu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Immune Cells ◽  
Poor Prognosis ◽  
Intracranial Tumor ◽  
Ppi Networks ◽  
Key Genes ◽  
The Relationship ◽  
Geo Database ◽  
Differential Genes

Abstract Background: Glioma is the most common intracranial tumor, with glioblastoma being the most malignant. However, its treatment is very few, and targeted therapy is an important breakthrough in treatment. Methods: Numerous genes are differentially expressed during the progression of glioma, some of which may play a key role. To find key genes, we analyzed three multi-sample microarrays (GSE4290, GSE54004, and GSE29796) in the GEO database to obtain intersection differential genes among them. We entered all DEGs into the STRING database and characterized the protein interactions of these DEGs as visual PPI networks by Cytoscape software. Also, we used the GEPIA2 and CGGAdatabase to predict the relationship between key genes and the prognosis of glioma patients.Results: A total of 222 up-regulated genes and 127 down-regulated genes were identified. Four genes(FN1, LAMB1, FAM20C, and COL6A1) were significantly negatively correlated with malignant glioma survival. Expression levels of four genes increased with the glioma grade. All gene expression is more common in IDH wild glioma and are enriched in the Mesenchymal subtype(AUC>0.8). In addition,they can be defined as hazard factors for glioma. We found that these genes were co-expressed and jointly involved in the infiltration of immune cells in tumors. Conclusion: In conclusion, FN1, LAMB1, FAM20C, and COL6A1 is associated with poor prognosis in glioma patients. These genes might be clinical targets of glioma immunotherapy.

FYN and CD247: key Genes for Septic Shock Based on Bioinformatics and Meta-Analysis

2021 ◽  
Vol 24 ◽  
Author(s):  
Yue Jiang ◽  
Qian Miao ◽  
Lin Hu ◽  
Tingyan Zhou ◽  
Yingchun Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Septic Shock ◽  
Survival Analysis ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Key Genes ◽  
Protein Protein Interaction Network ◽  
Geo Database

Background: Septic shock is sepsis accompanied by hemodynamic instability and high clinical mortality. Material and Methods: GSE95233, GSE57065, GSE131761 gene-expression profiles of healthy control subjects and septic shock patients were downloaded from the Gene-Expression Omnibus (GEO) database, and differences of expression profiles and their intersection were analysed using GEO2R. Function and pathway enrichment analysis was performed on common differentially expressed genes (DEG), and key genes for septic shock were screened using a protein-protein interaction network created with STRING. Also, data from the GEO database were used for survival analysis for key genes, and a meta-analysis was used to explore expression trends of core genes. Finally, high-throughput sequencing using the blood of a murine sepsis model was performed to analyse the expression of CD247 and FYN in mice. Results: A total of 539 DEGs were obtained (p < 0.05). Gene ontology analysis showed that key genes were enriched in functions, such as immune response and T cell activity, and DEGs were enriched in signal pathways, such as T cell receptors. FYN and CD247 are in the centre of the protein-protein interaction network, and survival analysis found that they are positively correlated with survival from sepsis. Further, meta-analysis results showed that FYN could be useful for the prognosis of patients, and CD247 might distinguish between sepsis and systemic inflammatory response syndrome patients. Finally, RNA sequencing using a mouse septic shock model showed low expression of CD247 and FYN in this model. Conclusion: FYN and CD247 are expected to become new biomarkers of septic shock.

scholarly journals Bioinformatics Analysis to Identify Key Genes and Pathways Associated with Sex Differences in Rheumatoid Arthritis

2021 ◽  
Author(s):  
Siwei Su ◽  
Wenjun Jiang ◽  
Xiaoying Wang ◽  
Sen Du ◽  
Lu Zhou ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Ppi Networks ◽  
Males And Females ◽  
Key Genes

Abstract ObjectiveThis study aims to explore the key genes and investigated the different signaling pathways of rheumatoid arthritis (RA) between males and females.Data and MethodsThe gene expression data of GSE55457, GSE55584, and GSE12021 were obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using R software. Then, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis of DEGs were conducted via Database for Annotation, Visualization, and Integrated Discovery (DAVID). The protein-protein interaction (PPI) networks of DEGs were constructed by Cytoscape 3.6.0. ResultsA total of 416 upregulated DEGs and 336 downregulated DEGs were identified in males, and 744 upregulated DEGs and 309 downregulated DEGs were identified in females.IL6, MYC, EGFR, FOS and JUN were considered as hub genes in RA pathogenesis in males, while IL6, ALB, PTPRC, CXCL8 and CCR5 were considered as hub genes in RA pathogenesis in females. ConclusionIdentified DEG may be involved in the different mechanisms of RA disease progression between males and females, and they are treated as prognostic markers or therapeutic targets for males and females. The pathogenesis mechanism of RA is sex-dependent.

scholarly journals Are there common mechanisms of aneuploidy-related abortion and defects in fetus?

2020 ◽  
Author(s):  
Liangqun Xie ◽  
Chan Lu ◽  
Fang Huang ◽  
Xun Li ◽  
Jingrui Huang ◽  
...  
Keyword(s):  
Thyroid Hormone ◽  
Network Analysis ◽  
Correlation Network ◽  
Venn Diagram ◽  
Signalling Pathways ◽  
Common Mechanism ◽  
Hub Gene ◽  
Protein Protein Interaction ◽  
Weighted Correlation ◽  
Geo Database

Abstract Background: Monosomies and trisomies, as the most common aneuploid abnormalities, are the leading causes of miscarriages and fetal defects in humans. Although there is evidence suggested that aneuploid may have some common aspects, their common mechanism still remains unclear. This studies objective was to explore the common mechanism of monosomies and trisomies, with a purpose to identify some critical biomarkers and pathway so as to early diagnosis and effective therapy.Methods: We obtained the mRNA expression profile of GSE114559 including 101 samples data from GEO database. These data include normal, every monosomic, and trisomic transcriptome. We conducted Limma analysis by using the adj. p<0.05 and |FC|>1 criteria to identify all monosomy-related, trisomy-related differentially expressed genes (DEGs), and also to found their overlapping DEGs through Venn diagram. We then performed Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses, protein-protein interaction(PPI) network analysis to find the functional, pathways and hub genes in these DEGs. We carried out weighted correlation network analysis (WGCNA) to further detect the candidate genes and pathways related to all DEGs and their overlappling DEGs. Finally, we further used qPCR to certify pathological change of specific genes. Results: We identified all monosomy-related, trisomy-related DEGs, and their overlapping DEGs which were enriched by spliceosome, thyroid hormone, infection-related genes and signalling pathways. We also found that epigenetic related pathways were significantly enriched in the DEGs of monosomies by GO, KEGG. We explode the hub gene and module in the DEGs of monosomies and the overlapping DEGs by PPI. Then, we found that spliceosome, thyroid hormone, infection-related genes and signalling pathways were enriched in all DEGs group and the overlapping DEGs group by weighted correlation network analysis (WGCNA). Finally, we certified some hub gene in the trisomy 21, 47, XYY samples from clinical patients by qPCR which were consistent with results of PPI analysis.Conclusion: Our study indicates the potential common mechanism underlining spliceosome, thyroid hormone and infection-related signalling pathways for both monosomies and trisomies, and the mechanism underling epigenetic for monosomies.

scholarly journals Gene Co-Expression Network Analysis Confirms Four Independent Predictors of Survival In Ovarian Cancer Patients Under Optimal Debulking Surgery

2021 ◽  
Author(s):  
Cong Zhang ◽  
Tao Zhu ◽  
Ting Hu ◽  
Qian Sun
Keyword(s):  
Ovarian Cancer ◽  
Network Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Clusters ◽  
Debulking Surgery ◽  
Screening Criteria ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Optimal Debulking

Abstract Background: Serious ovarian cancer (OvCa) is the most common histological type of epithelial OvCa with poor prognosis. Despite received optimal cytoreduction and standard chemotherapy, a large proportion of patients are forced to recurrence or death within three years. To identify exact prognostic biomarkers associated with overall survival (OS) is urgent requirements of exploring rapid tumor progression mechanisms and developing novel strategies for immunotherapy.Methods: The gene expression profiles of GSE49997, GSE9891 and TCGA were screened through rigorous criteria using R software and Bioconductor package. Weighted gene co-expression network analysis (WGCNA) was constructed to figure out gene clusters associated with OS. Protein-protein interaction (PPI) networks were built through STRING website. Prognostic values of potential biomarkers were validated using forest map and Kaplan-Meier analysis.Results: According to screening criteria, 788 samples and 10402 genes were reserved as the modeling dataset. We detected five modules related to OS and intersected 108 genes through WGCNA after random sampling. PPI network analysis, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed potential mechanisms of above biomarkers. Conclusions: Four exact biomarkers (CANT1, P4HB, DUS1L and SIRT7) were confirmed as independent predictors of survival in OvCa patients with success of debulking surgery, which might provide promising biomarkers for prognostic judgement in ovarian cancer.

scholarly journals Identification of the Key Genes and Pathways in Esophageal Carcinoma

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Peng Su ◽  
Shiwang Wen ◽  
Yuefeng Zhang ◽  
Yong Li ◽  
Yanzhao Xu ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Esophageal Carcinoma ◽  
Gene Expression Omnibus ◽  
Expression Level ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Go Enrichment ◽  
Key Genes ◽  
Microarray Datasets ◽  
Upregulated Genes

Objective. Esophageal carcinoma (EC) is a frequently common malignancy of gastrointestinal cancer in the world. This study aims to screen key genes and pathways in EC and elucidate the mechanism of it.Methods. 5 microarray datasets of EC were downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) were screened by bioinformatics analysis. Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and protein-protein interaction (PPI) network construction were performed to obtain the biological roles of DEGs in EC. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression level of DEGs in EC.Results. A total of 1955 genes were filtered as DEGs in EC. The upregulated genes were significantly enriched in cell cycle and the downregulated genes significantly enriched in Endocytosis. PPI network displayed CDK4 and CCT3 were hub proteins in the network. The expression level of 8 dysregulated DEGs including CDK4, CCT3, THSD4, SIM2, MYBL2, CENPF, CDCA3, and CDKN3 was validated in EC compared to adjacent nontumor tissues and the results were matched with the microarray analysis.Conclusion. The significantly DEGs including CDK4, CCT3, THSD4, and SIM2 may play key roles in tumorigenesis and development of EC involved in cell cycle and Endocytosis.

scholarly journals Identification of Biomarkers and Enriched Pathways Involved in Lung Cancer

2020 ◽  
Author(s):  
Nikita Singh ◽  
Mukesh Kumar ◽  
Atanu Bhattacharjee ◽  
Prashant Kumar Sonker ◽  
Agni Saroj
Keyword(s):  
Lung Cancer ◽  
Close Association ◽  
Enrichment Analysis ◽  
Notch Signaling Pathway ◽  
P Value ◽  
Pathway Enrichment Analysis ◽  
Ampk Signaling ◽  
Protein Protein Interaction ◽  
Viral Carcinogenesis ◽  
Key Genes

Objective: The aim of study is to find key genes and enriched pathways associated with lung cancer. Participants and Methods: Differentially expressed genes (DEGs) data of 54674 genes based on stage, tumor and status of lung cancer was taken from 66 patients of African American (AAs) origin. 2392 DEGs were found based on stage, 13502 DEGs were found based on tumor, 2927 DEGs were found based on status having p value (p&lt;0.05). Results: Total 33 common DEGs were found from stage, tumor and status of lung cancer. Gene ontology (GO) and KEGG pathway enrichment analysis was performed and 49 significant pathways were obtained, out of which 10 pathways were found to be exclusively involved in lung cancer development. Protein-protein interaction (PPI) network analysis found 69 nodes and 324 edges and identified 10 hub genes based on their highest degrees. Module analysis of PPI found that &lsquo;Viral carcinogenesis&rsquo;, &lsquo;pathways in cancer&rsquo;, &lsquo;notch signaling pathway&rsquo;, &lsquo;AMPK signaling pathways&rsquo; had a close association with lung cancer. Conclusion: These identified DEGs regulate other genes which play important role in growth of lung cancer. The key genes and enriched pathways identified can thus help in better identification and prediction of lung cancer.

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