scholarly journals Low expression of CHRDL1 and SPARCL1 predicts poor prognosis of lung adenocarcinoma based on comprehensive analysis and immunohistochemical validation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Huan Deng ◽  
Qingqing Hang ◽  
Dijian Shen ◽  
Yibi Zhang ◽  
Ming Chen
Keyword(s):  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Transforming Growth Factor ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Prognostic Indicators ◽  
Hub Genes ◽  
Coexpressed Genes

Abstract Purpose Exploring the molecular mechanisms of lung adenocarcinoma (LUAD) is beneficial for developing new therapeutic strategies and predicting prognosis. This study was performed to select core genes related to LUAD and to analyze their prognostic value. Methods Microarray datasets from the GEO (GSE75037) and TCGA-LUAD datasets were analyzed to identify differentially coexpressed genes in LUAD using weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. Functional enrichment analysis was conducted, and a protein–protein interaction (PPI) network was established. Subsequently, hub genes were identified using the CytoHubba plug-in. Overall survival (OS) analyses of hub genes were performed. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas (THPA) databases were used to validate our findings. Gene set enrichment analysis (GSEA) of survival-related hub genes were conducted. Immunohistochemistry (IHC) was carried out to validate our findings. Results We identified 486 differentially coexpressed genes. Functional enrichment analysis suggested these genes were primarily enriched in the regulation of epithelial cell proliferation, collagen-containing extracellular matrix, transforming growth factor beta binding, and signaling pathways regulating the pluripotency of stem cells. Ten hub genes were detected using the maximal clique centrality (MCC) algorithm, and four genes were closely associated with OS. The CPTAC and THPA databases revealed that CHRDL1 and SPARCL1 were downregulated at the mRNA and protein expression levels in LUAD, whereas SPP1 was upregulated. GSEA demonstrated that DNA-dependent DNA replication and catalytic activity acting on RNA were correlated with CHRDL1 and SPARCL1 expression, respectively. The IHC results suggested that CHRDL1 and SPARCL1 were significantly downregulated in LUAD. Conclusions Our study revealed that survival-related hub genes closely correlated with the initiation and progression of LUAD. Furthermore, CHRDL1 and SPARCL1 are potential therapeutic and prognostic indicators of LUAD.

scholarly journals Bioinformatics analysis of microarray data to identify the candidate biomarkers of lung adenocarcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7313 ◽  
Author(s):  
Tingting Guo ◽  
Hongtao Ma ◽  
Yubai Zhou
Keyword(s):  
Survival Analysis ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Prion Diseases ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes

Background Lung adenocarcinoma (LUAD) is the major subtype of lung cancer and the most lethal malignant disease worldwide. However, the molecular mechanisms underlying LUAD are not fully understood. Methods Four datasets (GSE118370, GSE85841, GSE43458 and GSE32863) were obtained from the gene expression omnibus (GEO). Identification of differentially expressed genes (DEGs) and functional enrichment analysis were performed using the limma and clusterProfiler packages, respectively. A protein–protein interaction (PPI) network was constructed via Search Tool for the Retrieval of Interacting Genes (STRING) database, and the module analysis was performed by Cytoscape. Then, overall survival analysis was performed using the Kaplan–Meier curve, and prognostic candidate biomarkers were further analyzed using the Oncomine database. Results Totally, 349 DEGs were identified, including 275 downregulated and 74 upregulated genes which were significantly enriched in the biological process of extracellular structure organization, leukocyte migration and response to peptide. The mainly enriched pathways were complement and coagulation cascades, malaria and prion diseases. By extracting key modules from the PPI network, 11 hub genes were screened out. Survival analysis showed that except VSIG4, other hub genes may be involved in the development of LUAD, in which MYH10, METTL7A, FCER1G and TMOD1 have not been reported previously to correlated with LUAD. Briefly, novel hub genes identified in this study will help to deepen our understanding of the molecular mechanisms of LUAD carcinogenesis and progression, and to discover candidate targets for early detection and treatment of LUAD.

scholarly journals High Expression of UBB, RAC1, and ITGB1 Predicts Worse Prognosis among Nonsmoking Patients with Lung Adenocarcinoma through Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Huan Deng ◽  
Yichao Huang ◽  
Li Wang ◽  
Ming Chen
Keyword(s):  
Extracellular Matrix ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Hub Genes ◽  
Go Enrichment

Purpose. The molecular mechanism underlying the tumorigenesis and progression of lung adenocarcinoma (LUAD) in nonsmoking patients remains unclear. This study was conducted to select crucial therapeutic and prognostic biomarkers for nonsmoking patients with LUAD. Methods. Microarray datasets from the Gene Expression Omnibus (GSE32863 and GSE75037) were analyzed for differentially expressed genes (DEGs). Gene Ontology (GO) enrichment analysis of DEGs was performed, and protein-protein interaction network was then constructed using the Search Tool for the Retrieval of Interacting Genes and Cytoscape. Hub genes were then identified by the rank of degree. Overall survival (OS) analyses of hub genes were performed among nonsmoking patients with LUAD in Kaplan-Meier plotter. The Cancer Genome Atlas (TCGA) and The Human Protein Atlas (THPA) databases were applied to verify hub genes. In addition, we performed Gene Set Enrichment Analysis (GSEA) of hub genes. Results. We identified 1283 DEGs, including 743 downregulated and 540 upregulated genes. GO enrichment analyses showed that DEGs were significantly enriched in collagen-containing extracellular matrix and extracellular matrix organization. Moreover, 19 hub genes were identified, and 12 hub genes were closely associated with OS. Although no obvious difference was detected in ITGB1, the downregulation of UBB and upregulation of RAC1 were observed in LUAD tissues of nonsmoking patients. Immunohistochemistry in THPA database confirmed that UBB and ITGB1 were downregulated, while RAC1 was upregulated in LUAD. GSEA suggested that ribosome, B cell receptor signaling pathway, and cell cycle were associated with UBB, RAC1, and ITGB1 expression, respectively. Conclusions. Our study provides insights into the underlying molecular mechanisms of the carcinogenesis and progression of LUAD in nonsmoking patients and demonstrated UBB, RAC1, and ITGB1 as therapeutic and prognostic indicators for nonsmoking LUAD. This is the first study to report the crucial role of UBB in nonsmoking LUAD.

scholarly journals Identification of Key Modules and Hub Genes in Hypertrophic Cardiomyopathy Based on Integrative Weighted Gene Co-expression Network Analysis

2021 ◽  
Author(s):  
Jun Jiang ◽  
Delong Chen ◽  
Siyuan Xie ◽  
Qichao Dong ◽  
Yi Yu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Hedgehog Signaling ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Remodeling Of Extracellular Matrix

Abstract BackgroundHypertrophic cardiomyopathy (HCM) is a heterogeneously inherited cardiac disorder with unclear biological pathogenesis. This study aims to identify the key modules and genes involved in the development of HCM.MethodsUsing weighted gene co-expression network analysis (WGCNA) algorithm, we constructed integrative co-expression networks for the two large sample HCM datasets separately. After selecting clinically significant modules with the same clinical trait, functional enrichment analysis was performed to detect their common pathways. Based on the intramodular connectivity (IC), the shared hub genes were generated, validated, and further explored in gene set enrichment analysis (GSEA).ResultsThe orange and pink modules in GSE141910, the green and brown modules in GSE36961 were mostly related to HCM. Functional enrichment analysis suggested that HCM might exhibit enhanced processes including remodeling of extracellular matrix, activation of abnormal protein signaling, aggregation of calcium ion, and organization of cytoskeleton. SMOC2, COL16A1, RASL11B, TUBA3D, IL18R1 were defined as real hub genes due to their top IC values, significantly different expression levels, and excellent diagnostic performance in both datasets. Moreover, GSEA analysis demonstrated that pathways of the five hub genes were mainly involved in neuroactive ligand-receptor interaction, ECM-receptor interaction, Hedgehog signaling pathway.ConclusionOur study provides more comprehensive insights into the molecular mechanisms of HCM, identifies five hub genes as candidate biomarkers for HCM, which might be theoretically feasible for targeted therapy against HCM.

scholarly journals Identification of Key Genes and Pathways Associated with Differences of Subchondral Bone in Osteoarthritis

2020 ◽  
Author(s):  
Yiyuan Zhang ◽  
Rongguo Yu ◽  
Jiayu Zhang ◽  
Eryou Feng ◽  
Haiyang Wang ◽  
...  
Keyword(s):  
Subchondral Bone ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Common Chronic Disease ◽  
Drug Candidates ◽  
Therapeutic Modalities ◽  
Pathogenic Genes

Abstract BackgroundOsteoarthritis (OA) is a common chronic disease worldwide. Subchondral bone is an important pathological change in OA and responds more rapidly to adverse loading and events compared to cartilage. However, the pathogenic genes and pathways of subchondral bone are largely unclear.ObjectiveThis study aimed to identify signature differences in genes involved in knee lateral tibial (LT) and medial tibial (MT) plateaus of subchondral bone tissue while exploring their potential molecular mechanisms via bioinformatics analysis.MethodsFirst, the gene expression data of GSE51588 was downloaded from the GEO database. Differentially expressed genes (DEGs) between knee LT and MT were identified, and functional enrichment analyses were performed. Then, a protein-protein interactive network was constructed in order to acquire the hub genes, and modules analysis was conducted using STRING and Cytoscape for further analysis. The enriched hub genes were queried in DGIdb database to find suitable drug candidates in OA.ResultsA total of 202 DEGs (112 upregulated genes and 84 downregulated genes) were determined. In the PPI network, ten hub genes were identified. Five significant modules were identified using the MCODE plugin unit. Functional enrichment analysis revealed the most important signaling pathways. Six of the ten hub genes were targetable by a total of 35 drugs, suggesting their possible therapeutic use for OA .ConclusionsThe identified hub genes and functional enrichment pathways were implicated in the development and progression of subchondral bone in OA, thus improving our understanding of OA and offering molecular targets for future therapeutic modalities.

scholarly journals Construction and Analysis of Survival-Associated Competing Endogenous RNA Network in Lung Adenocarcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Lixian Chen ◽  
Zhonglu Ren ◽  
Yongming Cai
Keyword(s):  
Prognostic Factors ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Competing Endogenous Rna ◽  
Cox Regression Analysis ◽  
Cerna Network

Increasing evidence has shown that noncoding RNAs play significant roles in the initiation, progression, and metastasis of tumours via participating in competing endogenous RNA (ceRNA) networks. However, the survival-associated ceRNA in lung adenocarcinoma (LUAD) remains poorly understood. In this study, we aimed to investigate the regulatory mechanisms underlying ceRNA in LUAD to identify novel prognostic factors. mRNA, lncRNA, and miRNA sequencing data obtained from the GDC data portal were utilized to identify differentially expressed (DE) RNAs. Survival-related RNAs were recognized using univariate Kaplan-Meier survival analysis. We performed functional enrichment analysis of survival-related mRNAs using the clusterProfiler package of R and STRING. lncRNA-miRNA and miRNA-mRNA interactions were predicted based on miRcode, Starbase, and miRanda. Subsequently, the survival-associated ceRNA network was constructed for LUAD. Multivariate Cox regression analysis was used to identify prognostic factors. Finally, we acquired 15 DE miRNAs, 49 DE lncRNAs, and 843 DE mRNAs associated with significant overall survival. Functional enrichment analysis indicated that survival-related DE mRNAs were enriched in cell cycle. The survival-associated lncRNA-miRNA-mRNA ceRNA network was constructed using five miRNAs, 49 mRNAs, and 21 lncRNAs. Furthermore, seven hub RNAs (LINC01936, miR-20a-5p, miR-31-5p, TNS1, TGFBR2, SMAD7, and NEDD4L) were identified based on the ceRNA network. LINC01936 and miR-31-5p were found to be significant using the multifactorial Cox regression model. In conclusion, we successfully constructed a survival-related lncRNA-miRNA-mRNA ceRNA regulatory network in LUAD and identified seven hub RNAs, which provide novel insights into the regulatory molecular mechanisms associated with survival of LUAD, and identified two independent prognostic predictors for LUAD.

scholarly journals Screening a prognosis-related target gene in patients with HER-2 positive breast cancer by bioinformatics analysis

2021 ◽  
Author(s):  
Song Wang ◽  
Yi Quan
Keyword(s):  
Breast Cancer ◽  
Target Gene ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Study Objective ◽  
Her 2 ◽  
Positive Breast Cancer

Objective: HER-2 positive breast cancer has a high risk of for relapse, metastasis and drug resistance, and is related to a poor prognosis. Thus, the study objective was to determine a target gene and explore the associated molecular mechanisms in HER-2 positive breast cancer. Methods: Three RNA expression profiles were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), and were used to identify differentially expressed genes (DEGs) using R software. A Protein-Protein Interaction (PPI) network was constructed and hub genes were determined. Subsequently, the relationship between clinical parameters and hub genes was examined to screen target gene. Next, DNA methylation and genomic alterations of the target gene were evaluated. To further explore potential molecular mechanisms, genes co-expressed with the target gene were performed functional enrichment analysis Results: The differential expression analysis revealed 217 DEGs in HER-2 positive breast cancer tissues compared to normal breast tissues. RRM2 was the only hub gene closely associated with lymphatic metastasis and prognosis in HER-2 positive breast cancer. Additionally, RRM2 was frequently often amplified and negatively associated with the methylation level. Functional enrichment analysis showed that the co-expression genes were mainly involved in cell cycle. Conclusions: The present study identified RRM2 as a target gene associated with the initiation, progression and prognosis of HER-2 positive breast cancer, which may contribute to provide a new biomarker and therapeutic target.

scholarly journals Predictive Value of CCNB1, BUB1B and TTK in the Progression and Prognosis of Lung Adenocarcinoma

2020 ◽  
Author(s):  
Lecai Xiong ◽  
Yuquan Bai ◽  
Minglin Zhu ◽  
Zetian Yang ◽  
Jinping Zhao ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Cycle ◽  
Lung Adenocarcinoma ◽  
Expression Profiles ◽  
Disease Free Survival ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Lung Cancer Patients

Lung cancer predominates in cancer-related deaths worldwide, with lung adenocarcinoma (LUAD) being a common histological subtype of lung cancer. The aim at this study was to search for biomarkers associated with the progression and prognosis of LUAD. We have integrated the expression profiles of 1174 lung cancer patients from five GEO datasets (GSE18842, GSE19804, GSE30219, GSE40791 and GSE68465) and identified a set of differentially expressed genes. Functional enrichment analysis showed that these genes are closely related to the progression of LUAD, such as cell cycle, mitosis and adhesion. Cytoscape software was used to establish a protein-protein interaction (PPI) network to analyze important modules using Molecular Complex Detection (MCODE), and finally CCNB1, BUB1B and TTK were selected for further study. The study found that compared with non-tumor lung tissue, CCNB1, BUB1B and TTK are highly expressed in LUAD. Kaplan-Meier analysis showed that CCNB1, BUB1B and TTK were negatively correlated with the overall survival and disease-free survival of patients. Gene set enrichment analysis (GSEA) demonstrated that for the samples of any hub gene highly expressed, most of the functional gene sets enriched in cell cycle. In summary, CCNB1, BUB1B and TTK can be used as biomarkers of poor prognosis of LUAD. The high expression of CCNB1, BUB1B and TTK can accelerate the progression of LUAD and lead to shorter survival, suggesting that they may be potential targets for treatment in LUAD.

scholarly journals Hub genes of stroke identified by weighted gene co-expression network analysis

2019 ◽  
Author(s):  
Junhong Li ◽  
Yang Zhai ◽  
Peng Wu ◽  
Yueqiang Hu ◽  
Wei Chen ◽  
...  
Keyword(s):  
Network Analysis ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Neuron Death ◽  
Roc Curve Analysis ◽  
Precise Diagnosis

Abstract Background Microarray-based gene expression profiling has been widely used in biomedical research. Weighted gene co-expression network analysis (WGCNA) can link microarray data directly to clinical traits and to identify rules for predicting pathological stage and prognosis of disease, it has been found useful in many biological processes. Stroke is one of the most common diseases worldwide, yet molecular mechanisms of its pathogenesis are largely unknown. We aimed to construct gene co-expression networks to identify key modules and hub genes associated with the pathogenesis of stroke.Results In this study, we screened out the differentially expressed genes from gene microarray expression profiles, then constructed the free-scale gene co-expression networks to explore the associations between gene sets and clinical features, and to identify key modules and hub genes. Subsequently, functional enrichment and the receiver operating characteristic (ROC) curve analysis were performed. And the results show that a total of 11,747 most variant genes were used for co-expression network construction. Pink and yellow modules were found to be the most significantly related to stroke. Functional enrichment analysis showed that the pink module was mainly involved in regulation of neuron regeneration, and the repair of DNA damage, while the yellow module was mainly enriched in ion transport system dysfunction which were correlated with neuron death. A total of 8 hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) were identified and validated at transcriptional levels (other datasets) and by existing literatures.Conclusions Eight hub genes (PRR11, NEDD9, Notch2, RUNX1-IT1, ANP32A-IT1, ASTN2, SAMHD1 and STIM1) may serve as biomarkers and therapeutic targets for precise diagnosis and treatment of stroke in the future.

scholarly journals Bioinformatics Analysis Reveals Diagnostic Markers and Vital Pathways Involved in Acute Coronary Syndrome

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Mingshuang Li ◽  
Conglin Ren ◽  
Chenxia Wu ◽  
Xinyao Li ◽  
Xinyi Li ◽  
...  
Keyword(s):  
Acute Coronary Syndrome ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
Functional Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Coronary Syndrome ◽  
Incidence And Mortality ◽  
Expression Matrix

Background. Acute coronary syndrome (ACS) has a high incidence and mortality rate. Early detection and intervention would provide clinical benefits. This study aimed to reveal hub genes, transcription factors (TFs), and microRNAs (miRNAs) that affect plaque stability and provide the possibility for the early diagnosis and treatment of ACS. Methods. We obtained gene expression matrix GSE19339 for ACS patients and healthy subjects from public database. The differentially expressed genes (DEGs) were screened using Limma package in R software. The biological functions of DEGs were shown by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Protein-protein interaction (PPI) network was mapped in Cytoscape, followed by screening of hub genes based on the Molecular Complex Detection (MCODE) plug-in. Functional Enrichment analysis tool (FunRich) and Database for Annotation, Visualization and Integrated Discovery (DAVID) were used to predict miRNAs and TFs, respectively. Finally, GSE60993 expression matrix was chosen to plot receiver operating characteristic (ROC) curves with the aim of further assessing the reliability of our findings. Results. We obtained 176 DEGs and further identified 16 hub genes by MCODE. The results of functional enrichment analysis showed that DEGs mediated inflammatory response and immune-related pathways. Among the predicted miRNAs, hsa-miR-4770, hsa-miR-5195, and hsa-miR-6088 all possessed two target genes, which might be closely related to the development of ACS. Moreover, we identified 11 TFs regulating hub gene transcriptional processes. Finally, ROC curves confirmed three genes with high confidence (area under the curve > 0.9), including VEGFA, SPP1, and VCAM1. Conclusion. This study suggests that three genes (VEGFA, SPP1, and VCAM1) were involved in the molecular mechanisms of ACS pathogenesis and could serve as biomarkers of disease progression.

scholarly journals Weighted Gene Co-Expression Network Analysis and Treatment Strategies of Tumor Recurrence-Associated Hub Genes in Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhengze Shen ◽  
Shengwei Liu ◽  
Jie Liu ◽  
Jingdong Liu ◽  
Caoyuan Yao
Keyword(s):  
Network Analysis ◽  
Lung Adenocarcinoma ◽  
Tumor Recurrence ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Gene Interaction Network ◽  
Tissue Samples ◽  
Hub Gene

Despite the recent progress of lung adenocarcinoma (LUAD) therapy, tumor recurrence remained to be a challenging factor that impedes the effectiveness of treatment. The objective of the present study was to predict the hub genes affecting LUAD recurrence via weighted gene co-expression network analysis (WGCNA). Microarray samples from LUAD dataset of GSE32863 were analyzed, and the modules with the highest correlation to tumor recurrence were selected. Functional enrichment analysis was conducted, followed by establishment of a protein–protein interaction (PPI) network. Subsequently, hub genes were identified by overall survival analyses and further validated by evaluation of expression in both myeloid populations and tissue samples of LUAD. Gene set enrichment analysis (GSEA) was then carried out, and construction of transcription factors (TF)–hub gene and drug–hub gene interaction network was also achieved. A total of eight hub genes (ACTR3, ARPC5, RAB13, HNRNPK, PA2G4, WDR12, SRSF1, and NOP58) were finally identified to be closely correlated with LUAD recurrence. In addition, TFs that regulate hub genes have been predicted, including MYC, PML, and YY1. Finally, drugs including arsenic trioxide, cisplatin, Jinfukang, and sunitinib were mined for the treatment of the eight hub genes. In conclusion, our study may facilitate the invention of targeted therapeutic drugs and shed light on the understanding of the mechanism for LUAD recurrence.

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