scholarly journals ITGB2 as a Novel Biomarker Correlating With Prognosis and Immune Infiltrates in Ovarian Cancer

2021 ◽  
Author(s):  
chanyuan li ◽  
Ting Wan ◽  
Ting Deng ◽  
Junya Cao ◽  
He Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Protein Interaction ◽  
Ppi Network ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Protein Protein Interaction ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

Abstract Background: Epithelial ovarian cancer is nowadays one of the malignancies in women, this study aimed to identify novel biomarkers to predict prognosis and immunotherapy efficacy.Methods: The differentially expressed genes (DEGs) obtained from online database Gene Expression Omnibus (GEO)were screened via GEO2R and Venn diagram software, gene enrichment was analysed by Gene Ontology(GO) function and Kyoto Encyclopedia of Genes and Genomes(KEGG), then protein protein interaction(PPI)network and Cytoscape software were used to confirm the genes closely related to ovarian cancer. Survival analysis of hub genes were obtained from Kaplan–Meier plotter, with their differential expression in specimen validated by Gene Expression Profiling Interactive Analysis (GEPIA) and an integrated repository portal for tumor-immune system interactions (TISIDB). Finally, we used the Tumor Immune Estimation Resource 2.0 (TIMER2.0) and application Estimate the Proportion of Immune and Cancer cells (EPIC) to search the immune infiltration characteristics of the genes.Results: 355 DEGs between epithelial ovarian cancer and normal ovarian tissue were screened out. These DEGs were associated with extracellular exosome, bicellular tight junction and cell-cell junction, and remarkably enriched in molecules of cell adhesion and leukocyte transendothelial migration activity. Ten hub genes were identified via protein protein interaction (PPI) network: PTAFR, HLA-DRA, OAS2, OAS3, PTPN6, LYN, VAMP8, IRF6, ITGB2, CD47. Furthermore, the Kaplan–Meier plotter was conducted, overexpression of four genes was positively connected to poor prognosis in ovarian cancer:OAS2, OAS3, ITGB2, CD47,which were also correlated with immune infiltrates in ovarian cancer and had the highest degree of correlation with tumor associated macrophages (TAMs) infiltration, among which ITGB2 was highly correlated with TAMs infiltration level.Conclusion: ITGB2, OAS2, OAS3, and CD47 are upregulated with unfavorable prognosis in ovarian cancer, and ITGB2 may act as a novel prognostic biomarker with immune infiltration values.

Identification of Key Biomarkers Associated with Survival and Prognosis in Colon Adenocarcinoma

2021 ◽  
Author(s):  
Yanjie Zhou ◽  
Lu Jiang ◽  
Jiang Lin ◽  
Wendong Tang ◽  
Wenqian Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Protein Interaction ◽  
Colon Adenocarcinoma ◽  
High Rate ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Core Genes

Abstract Background: Colorectal cancer (CRC) has a high rate of relapse and recurrence that result in poor prognosis and unsatisfactory outcomes. Colon adenocarcinoma (COAD) is the most prevalent type of CRC. It is crucial to identify novel molecular biomarkers for the early diagnosis, prognosis evaluation and disease monitoring of COAD.Methods: Three gene expression profile data were downloaded from the Gene Expression Omnibus(GEO), and the differential expression genes(DEGs) were identified by GEO2R. Gene Ontology (GO) and KEGG pathway enrichment analysis were conducted by WebGestalt online tool. String database and Cytoscape software were used for protein–protein interaction (PPI) network construction and module analysis. The top 20 Hub Genes were screened from the PPI network using MCC algorithm on CytoHubba app of Cytoscape software, and were verified by ONCOMINE database then. The core genes affecting CRC prognosis were screened by GEPIA2 survival analysis web tool. Finally, the expression level and clinical indicators including core genes was analyzed by TCGA-COAD dataset.Results: In total, 413 differentially expressed genes (DEGs) were identified, and the GO and KEGG enrichment analyses of DEGs were processed. After, the protein–protein interaction (PPI) network was constructed and 20 hub genes were identified. Furthermore, three core genes were selected via survival analysis . Finally, the diagnostic and prognostic value of these core genes was verified by clinical analysis of TCGA-COAD dataset.Conclusion: SPP1, GRP and GNGT1 were all over-expressed in COAD, and may be regarded as novel diagnostic and prognostic biomarkers for COAD.

The Screening and Identification of Key Biomarkers in Adrenocortical Carcinoma: Evidence from a Bioinformatics Analysis

2022 ◽  
Vol 12 (3) ◽  
pp. 523-532
Author(s):  
Xin Yan ◽  
Chunfeng Liang ◽  
Xinghuan Liang ◽  
Li Li ◽  
Zhenxing Huang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Gene Ontology ◽  
Protein Interaction ◽  
Adrenocortical Carcinoma ◽  
Gene Expression Omnibus ◽  
Heparin Binding ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Upregulated Genes

<sec> <title>Objective:</title> This study aimed to identify the potential key genes associated with the progression and prognosis of adrenocortical carcinoma (ACC). </sec> <sec> <title>Methods:</title> Differentially expressed genes (DEGs) in ACC cells and normal adrenocortical cells were assessed by microarray from the Gene Expression Omnibus database. The biological functions of the classified DEGs were examined by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein–protein interaction (PPI) network was mapped using Cytoscape software. MCODE software was also used for the module analysis and then 4 algorithms of cytohubba software were used to screen hub genes. The overall survival (OS) examination of the hub genes was then performed by the ualcan online tool. </sec> <sec> <title>Results:</title> Two GSEs (GSE12368, GSE33371) were downloaded from GEO including 18 and 43 cases, respectively. One hundred and sixty-nine DEGs were identified, including 57 upregulated genes and 112 downregulated genes. The Gene Ontology (GO) analyses showed that the upregulated genes were significantly enriched in the mitotic cytokines is, nucleus and ATP binding, while the downregulated genes were involved in the positive regulation of cardiac muscle contraction, extracellular space, and heparin-binding (P < 0.05). The Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway examination showed significant pathways including the cell cycle and the complement and coagulation cascades. The protein– protein interaction (PPI) network consisted of 162 nodes and 847 edges, including mitotic nuclear division, cytoplasmic, protein kinase binding, and cell cycle. All 4 identified hub genes (FOXM1, UBE2C, KIF11, and NDC80) were associated with the prognosis of adrenocortical carcinoma (ACC) by survival analysis. </sec> <sec> <title>Conclusions:</title> The present study offered insights into the molecular mechanism of adrenocortical carcinoma (ACC) that may be beneficial in further analyses. </sec>

scholarly journals Nomogram Models Based on Gene Expression in Prediction of Breast Cancer Bone Metastasis

2021 ◽  
Author(s):  
Teng-di Fan ◽  
Di-kai Bei ◽  
Song-wei Li
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Bone Metastasis ◽  
Gene Signature ◽  
Functional Enrichment ◽  
Training Set ◽  
Hub Genes ◽  
Kaplan Meier ◽  
Patient Prognosis ◽  
Kaplan Meier Plotter

Abstract Objective: To design a weighted co-expression network and build gene expression signature-based nomogram (GESBN) models for predicting the likelihood of bone metastasis in breast cancer (BC) patients. Methods: Dataset GSE124647 was used as a training set, and GSE14020 was taken as a validation set. In the training cohort, limma package in R was adopted to obtain differentially expressed genes (DEGs) between BC non-bone metastasis and bone metastasis patients, which were used for functional enrichment analysis. After weighted co-expression network analysis (WGCNA), univariate Cox regression and Kaplan-Meier plotter analyses were performed to screen potential prognosis-related genes. Then, GESBN models were constructed and evaluated. Further, the expression levels of genes in the models were explored in the training set, which was validated in GSE14020. Finally, the prognostic value of hub genes in BC was explored. Results: A total of 1858 DEGs were obtained. WGCNA result showed that the blue module was most significantly related to bone metastasis and prognosis. After survival analyses, GAJ1, SLC24A3, ITGBL1, and SLC44A1 were subjected to construct a GESBN model for overall survival. While GJA1, IGFBP6, MDFI, ITGFBI, ANXA2, and SLC24A3 were subjected to build a GESBN model for progression-free survival. Kaplan-Meier plotter and receiver operating characteristic analyses presented the reliable prediction ability of the models. Besides, GJA1, IGFBP6, ITGBL1, SLC44A1, and TGFBI expressions were significantly different between the two groups in GSE124647 and GSE14020. The hub genes had a significant impact on patient prognosis. Conclusion: Both the four-gene signature and six-gene signature could accurately predict patient prognosis, which may provide novel treatment insights for BC bone metastasis.

scholarly journals Identification of Lung-Cancer-Related Genes with the Shortest Path Approach in a Protein-Protein Interaction Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Bi-Qing Li ◽  
Jin You ◽  
Lei Chen ◽  
Jian Zhang ◽  
Ning Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Shortest Path ◽  
Protein Interaction ◽  
Expression Profiles ◽  
Shortest Paths ◽  
Gene Expression Profiles ◽  
Cancer Genes ◽  
Ppi Network ◽  
Protein Protein Interaction

Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI) network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra’s algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutationPvalue less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.

scholarly journals Integrated bioinformatics approach to understand immune-related key genes and pathways in chronic spontaneous urticaria

2020 ◽  
Author(s):  
wenxing su ◽  
biao huang ◽  
ying zhao ◽  
xiaoyan zhang ◽  
lu chen ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Protein Interaction ◽  
Chronic Spontaneous Urticaria ◽  
Signal Pathways ◽  
Radical Cure ◽  
Ppi Network ◽  
Erythroid Progenitor ◽  
Immune Infiltration ◽  
Protein Protein Interaction ◽  
Key Genes

Abstract Background Chronic spontaneous urticaria (CSU) refers to recurrent urticaria that lasts for more than 6 weeks in the absence of an identifiable trigger. Due to its recurrent wheal and severe itching, CSU seriously affects patients' life quality. There is currently no radical cure for it and its vague pathogenesis limits the development of targeted therapy. With the goal of revealing the underlying mechanism, two data sets with accession numbers GSE57178 and GSE72540 were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed genes (DEGs) of CSU skin lesion samples and healthy controls, four kinds of analyses were performed, namely functional annotation, protein-protein interaction (PPI) network and module construction, co-expression and drug-gene interaction prediction analysis, and immune and stromal cells deconvolution analyses. Results 92 up-regulated genes and 7 down-regulated genes were selected for subsequent analyses. Through the enrichment analysis of the core modules, three signal pathways were found to be closely related to the occurrence and development of CSU, including TNF signaling pathway, NF-κB signaling pathway and Jak-STAT signaling pathway. Referring to protein-protein interaction (PPI) network analysis and GeneCards database, we identified four key genes, IL6, TLR4, ICAM1, and PTGS2. In addition, according to the results of immune infiltration analysis, CSU tissue generally contained a higher proportion of dendritic cells, Th2 cells, mast cells, megakaryocyte-erythroid progenitor, preadipocytes, and macrophages M1. Conclusions To conclude, the key genes and pathways identified from CSU lesions and normal controls along with the immune infiltration profile may provide new insights into the development of CSU.

scholarly journals Identification and Analysis of Genes Associated with Epithelial Ovarian Cancer by Integrated Bioinformatics Methods

2020 ◽  
Author(s):  
Ting Gui ◽  
Chenhe Yao ◽  
Binghan Jia ◽  
Keng Shen
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Epithelial Ovarian Cancer ◽  
Protein Interactions ◽  
Therapeutic Targets ◽  
Progression Free Survival ◽  
The Cancer Genome Atlas ◽  
Ppi Network ◽  
Hub Genes ◽  
Free Survival

Abstract Background: Though considerable efforts have been made to improve the treatment of epithelial ovarian cancer (EOC), the prognosis of patients has remained poor. Identifying differentially expressed genes (DEGs) involved in EOC progression and exploiting them as novel biomarkers or therapeutic targets for EOC is highly valuable. Methods: Overlapping DEGs were screened out from three independent gene expression omnibus (GEO) datasets and subjected to Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses. The protein-protein interactions (PPI) network of DEGs was constructed in the STRING database. The top 20 hub genes were selected using cytoHubba. The expression of hub genes was detected in GEPIA, Oncomine, and human protein atlas (HPA) databases. The relationship of hub genes with the pathological stage and the overall survival and progression-free survival in EOC patients was investigated using the cancer genome atlas data. Results: A total of 306 DEGs were identified, including 265 up-regulated and 41 down-regulated. Through the PPI network analysis, the top 20 genes were screened out, among which 4 hub genes were selected after literature retrieval, including CDC45, CDCA5, KIF4A, ESPL1. The four genes were up-regulated in EOC tissues and the expression of these four genes decreased gradually with the continuous progression of EOC. Survival curves illustrated that patients with a lower level of CDCA5 and ESPL1 had better overall survival and progression-free survival. Conclusions: Two hub genes, CDCA5 and ESPL1, identified as playing tumor-promotive roles, could be utilized as potential novel therapeutic targets for EOC treatment.

scholarly journals Bioinformatics Approach to Identify Common Gene Signatures of Patients With Coronavirus 2019 and Lung Adenocarcinoma

2021 ◽  
Author(s):  
Xiao Liang ◽  
Yali Chen ◽  
Yuchao Fan
Keyword(s):  
Lung Adenocarcinoma ◽  
Severe Disease ◽  
Enrichment Analysis ◽  
Immune Checkpoints ◽  
Specific Gene ◽  
Ppi Network ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Protein Protein Interaction ◽  
Tf Gene

Abstract Coronavirus disease 2019 (COVID-19) continues as a global pandemic. Patients with lung cancer infected with COVID-19 may develop severe disease or die. Treating such patients severely burdens overwhelmed healthcare systems. Here we identified potential pathological mechanisms shared between patients with COVID-19 and lung adenocarcinoma (LUAD). Co-expressed, differentially expressed genes (DEGs) in patients with COVID-19 and LUAD were identified and used to construct a protein-protein interaction (PPI) network and to perform enrichment analysis. We used the NetworkAnalyst platform to establish a co-regulatory of the co-expressed DEGs, and we used Spearman’s correlation to evaluate the significance of associations of hub genes with immune infiltration and immune checkpoints. Analysis of three datasets identified 112 shared DEGs, which were used to construct a protein-PPI network. Subsequent enrichment analysis revealed co-expressed genes related to biological process (BP), molecular function (MF), cellular component (CC) as well as to pathways, specific organs, cells and diseases. Ten co-expressed hub genes were employed to construct a gene-miRNA, transcription factor (TF)-gene and TF-miRNA network. Hub genes were significantly associated with immune infiltration and immune checkpoints. Finally methylation level of hub genes in LUAD was obtained via UALCAN database. The present multi-dimensional study reveals commonality in specific gene expression by patients with COVID-19 and LUAD. These findings provide insights into developing strategies for optimising the management and treatment of patients with LUAD with COVID-19.

scholarly journals Bromine domain protein 4 is an important marker for prognosis of ovarian cancer and tumor immune infiltration

2021 ◽  
Author(s):  
Chujia Chen ◽  
Zhiyong Yang ◽  
Qiuchan Zhao ◽  
Bangming Xu ◽  
Donglin Cao
Keyword(s):  
Ovarian Cancer ◽  
Immune Cell ◽  
Expression Level ◽  
Immune Markers ◽  
Immune Infiltration ◽  
Normal Tissues ◽  
Kaplan Meier ◽  
Domain Protein ◽  
Kaplan Meier Plotter

Abstract Background Ovarian cancer (OC) is one of the most common malignant gynecological tumors, but its pathogenesis is unclear. Bromine domain protein 4 (BRD4) is involved in the malignant transformation of cells, as well as the invasion and metastasis of tumor cells. The biological role of BRD4 in ovarian cancer is yet to be determined. Methods The differential expression of BRD4 in OC and corresponding normal tissues was evaluated by exploring the Tumor Immune Assessment Resources (TIMER) and the Oncomine database. The correlation between the expression level of BRD4 and the prognosis of OC patients was evaluated using the Kaplan-Meier Plotter database. Using TIMER, we further studied the correlation between BRD4 and tumor immune cell infiltration. Results The expression of BRD4 was significantly higher in patients with OC, and high BRD4 expression was closely related to low overall survival rate. The BRD4 expression was associated with the levels of immune markers of macrophages, dendritic cells, neutrophils, and various effector T cells. Taken together, these findings show that BRD4 expression is significantly related to immune infiltration in OC and suggest that BRD4 might play an important role in the immune evasion of OC cells. Conclusion The expression level of BRD4 in OC tissues is significantly upregulated, and its high expression is significantly associated with poor prognosis of patients and is closely related to tumor immune infiltration. These results suggest that BRD4 can be used as a prognostic marker and a marker of immune infiltration in OC.

scholarly journals Exploration and validation of related hub gene expression during SARS-CoV-2 infection of human bronchial organoids

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Ke-Ying Fang ◽  
Wen-Chao Cao ◽  
Tian-Ao Xie ◽  
Jie Lv ◽  
Jia-Xin Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Social Order ◽  
Expression System ◽  
Differentially Expressed ◽  
Dimensional Structure ◽  
Hub Genes ◽  
R Software ◽  
Protein Protein Interaction

Abstract Background In the novel coronavirus pandemic, the high infection rate and high mortality have seriously affected people’s health and social order. To better explore the infection mechanism and treatment, the three-dimensional structure of human bronchus has been employed in a better in-depth study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods We downloaded a separate microarray from the Integrated Gene Expression System (GEO) on a human bronchial organoids sample to identify differentially expressed genes (DEGS) and analyzed it with R software. After processing with R software, Gene Ontology (GO) and Kyoto PBMCs of Genes and Genomes (KEGG) were analyzed, while a protein–protein interaction (PPI) network was constructed to show the interactions and influence relationships between these differential genes. Finally, the selected highly connected genes, which are called hub genes, were verified in CytoHubba plug-in. Results In this study, a total of 966 differentially expressed genes, including 490 upregulated genes and 476 downregulated genes were used. Analysis of GO and KEGG revealed that these differentially expressed genes were significantly enriched in pathways related to immune response and cytokines. We construct protein-protein interaction network and identify 10 hub genes, including IL6, MMP9, IL1B, CXCL8, ICAM1, FGF2, EGF, CXCL10, CCL2, CCL5, CXCL1, and FN1. Finally, with the help of GSE150728, we verified that CXCl1, CXCL8, CXCL10, CCL5, EGF differently expressed before and after SARS-CoV-2 infection in clinical patients. Conclusions In this study, we used mRNA expression data from GSE150819 to preliminarily confirm the feasibility of hBO as an in vitro model to further study the pathogenesis and potential treatment of COVID-19. Moreover, based on the mRNA differentiated expression of this model, we found that CXCL8, CXCL10, and EGF are hub genes in the process of SARS-COV-2 infection, and we emphasized their key roles in SARS-CoV-2 infection. And we also suggested that further study of these hub genes may be beneficial to treatment, prognostic prediction of COVID-19.

scholarly journals Bioinformatics Analysis of Potential Key Genes in Trastuzumab-Resistant Gastric Cancer

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Guangda Yang ◽  
Liumeng Jian ◽  
Xiangan Lin ◽  
Aiyu Zhu ◽  
Guohua Wen
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Prognostic Value ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Trastuzumab Resistance ◽  
Hub Genes ◽  
Her2 Positive ◽  
Protein Protein Interaction ◽  
Stem Cell Pluripotency

Background. This study was performed to identify genes related to acquired trastuzumab resistance in gastric cancer (GC) and to analyze their prognostic value. Methods. The gene expression profile GSE77346 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained by using GEO2R. Functional and pathway enrichment was analyzed by using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Search Tool for the Retrieval of Interacting Genes (STRING), Cytoscape, and MCODE were then used to construct the protein-protein interaction (PPI) network and identify hub genes. Finally, the relationship between hub genes and overall survival (OS) was analyzed by using the online Kaplan-Meier plotter tool. Results. A total of 327 DEGs were screened and were mainly enriched in terms related to pathways in cancer, signaling pathways regulating stem cell pluripotency, HTLV-I infection, and ECM-receptor interactions. A PPI network was constructed, and 18 hub genes (including one upregulated gene and seventeen downregulated genes) were identified based on the degrees and MCODE scores of the PPI network. Finally, the expression of four hub genes (ERBB2, VIM, EGR1, and PSMB8) was found to be related to the prognosis of HER2-positive (HER2+) gastric cancer. However, the prognostic value of the other hub genes was controversial; interestingly, most of these genes were interferon- (IFN-) stimulated genes (ISGs). Conclusions. Overall, we propose that the four hub genes may be potential targets in trastuzumab-resistant gastric cancer and that ISGs may play a key role in promoting trastuzumab resistance in GC.

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