scholarly journals Contribution of endothelial cell-derived transcriptomes to the colon cancer based on bioinformatics analysis

2021 ◽  
Vol 18 (6) ◽  
pp. 7280-7300
Author(s):  
Jie Wang ◽  
◽  
Md. Nazim Uddin ◽  
Rehana Akter ◽  
Yun Wu ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Endothelial Cells ◽  
Bioinformatics Analysis ◽  
Immune Cell ◽  
M2 Macrophage ◽  
Colon Tumor ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Immunosuppressive Cells

<abstract> <p>Colon tumor endothelial cells (CTECs) plays substantial roles to induce immune invasion, angiogenesis and metastasis. Thus, identification of the CTECs-derived transcriptomes could be helpful for colon cancer diagnosis and potential therapy. </p> <sec><title>Methods</title><p> By analysis of CTECs-derived gene expression profiling dataset, we identified differentially expressed genes (DEGs) between CTECs and colon normal endothelial cells (CNECs). In addition, we identified the significant pathways and protein-protein interaction (PPI) network that was significantly associated with the DEGs. Furthermore, we identified hub genes whose expression was significantly associated with prognosis and immune cell infiltrations in colon cancer. Finally, we identified the significant correlations between the prognostic hub genes and immune-inhibitory markers in colon cancer. </p></sec> <sec><title>Results</title><p>We identified 362 DEGs in CTECs relative to the CNECs, including117 up-regulated genes and 245 down-regulated genes in the CTECs. In addition, we identified significantly up-regulated pathways in CTECs that were mainly involved in cancer and immune regulation. Furthermore, we identified hub genes (such as <italic>SPARC, COL1A1, COL1A2</italic> and <italic>IGFBP3</italic>) that are associated with prognosis and immune cells infiltrations in colon cancer. Interestingly, we found that prognosis-associated hub genes (<italic>SPARC, COL1A1, COL1A2</italic> and <italic>IGFBP3</italic>) are positively correlated with immune-inhibitory markers of various immunosuppressive cells, including TAM, M2 macrophage, Tregs and T cell exhaustion. Finally, our findings revealed that prognosis-associated upregulated hub genes are positively correlated with immune checkpoint markers, including PD-L1 and PD-L2 and the immunosuppressive markers including TGFB1 and TGFBR1.</p></sec> <sec><title>Conclusions</title><p>The identification of CTECs-specific transcriptomes may provide crucial insights into the colon tumor microenvironment that mediates the development of colon cancer.</p></sec> </abstract>

scholarly journals Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Weishuang Xue ◽  
Jinwei Li ◽  
Kailei Fu ◽  
Weiyu Teng
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Peripheral Blood ◽  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

scholarly journals Establishment and Validation of a Genetic Label Associated With M2 Macrophage Infiltration to Predict Survival in Colon Cancer Patients and Assist Immunotherapy

2021 ◽  
Author(s):  
Boyang Xu ◽  
Ziqi Peng ◽  
Guanyu Yan ◽  
Ningning Wang ◽  
Moye Chen ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Microsatellite Instability ◽  
Cancer Patients ◽  
Immune Cell ◽  
Checkpoint Inhibitors ◽  
Macrophage Infiltration ◽  
M2 Macrophage ◽  
High Morbidity ◽  
M2 Macrophages ◽  
Hub Genes

Abstract Background: Colon cancer is a kind of malignant tumor with high morbidity and mortality. Researchers have tried to interpret it from different perspectives and divide it into different subtypes in order to achieve individualized treatment. With the rise of immunotherapy, its value in the field of tumor has initially emerged. Based on the above background, from the perspective of immune infiltration, this study classified colon cancer according to the infiltration of M2 macrophages in patients with colon cancer and further explored it.Methods: Cibersort was used to analyze the level of immune cell infiltration in colon cancer patients in the TCGA database. WGCNA, Consensus Clustering analysis, Lasso analysis, and univariate KM analysis were used to screen and verify the hub genes associated with M2 macrophages. PCA was used to establish the M2 macrophage-related score—M2I Score. The correlation between M2I Score and somatic cell variation and microsatellite instability were analysed. Furthermore the correlation between M2 macrophage score and differences in immunotherapy sensitivity was also explored. Results: M2 macrophage infiltration was associated with poor prognosis. Four hub genes (ANKS4B, CTSD, TIMP1, and ZNF703) were selected as the progression-related genes associated with M2 macrophages. A stable and accurate M2I Score for M2 macrophages used in COAD was constructed based on four hub genes. M2I Score was positively correlated with tumor mutation load (TMB). The M2I Score of MSI-H group was higher than that of MSI-L group and MSS group. Combine with the TCIA database, we concluded that patients with a high M2I Score were more sensitive to PD-1 inhibitors and PD-1 inhibitors combined with CTLA-4 inhibitors. The low rating group may have better efficacy without immune checkpoint inhibitors or with CTLA4 inhibitors alone.Conclusion: Four prognostic hub genes associated with M2 macrophages were screened to establish the M2I Score and divided the patients into two subgroups: high M2I Score group and low M2I Score group. TMB, microsatellite instability and sensitivity to immunotherapy were higher in the high-rated group. PD-1 inhibitors or PD-1 combined with CTLA-4 inhibitors are preferred for patients in the high-rated group who are more sensitive to immunotherapy.

scholarly journals Establishment and Validation of a Genetic Label Associated With M2 Macrophage Infiltration to Predict Survival in Patients With Colon Cancer and to Assist in Immunotherapy

2021 ◽  
Vol 12 ◽  
Author(s):  
Boyang Xu ◽  
Ziqi Peng ◽  
Guanyu Yan ◽  
Ningning Wang ◽  
Moye Chen ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Immune Cell ◽  
Checkpoint Inhibitors ◽  
The Cancer Genome Atlas ◽  
Macrophage Infiltration ◽  
M2 Macrophage ◽  
High Morbidity ◽  
Consensus Clustering ◽  
M2 Macrophages ◽  
Hub Genes

BackgroundColon cancer is a malignant tumor with high morbidity and mortality. Researchers have tried to interpret it from different perspectives and divided it into different subtypes to facilitate individualized treatment. With the rise in the use of immunotherapy, its value in the field of tumor has begun to emerge. From the perspective of immune infiltration, this study classified colon cancer according to the infiltration of M2 macrophages in patients with colon cancer and further explored the same.MethodsCibersort algorithm was used to analyze the level of immune cell infiltration in patients with colon cancer in The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network analysis (WGCNA), Consensus Clustering analysis, Lasso analysis, and univariate Kaplan–Meier analysis were used to screen and verify the hub genes associated with M2 macrophages. Principal component analysis (PCA) was used to establish the M2 macrophage-related score (M2I Score). The correlation between M2I Score and somatic cell variation and microsatellite instability (MSI) were analyzed. Furthermore, the correlation between M2 macrophage score and differences in immunotherapy sensitivity was also explored.ResultsM2 macrophage infiltration was associated with poor prognosis. Four hub genes (ANKS4B, CTSD, TIMP1, and ZNF703) were identified as the progression-related genes associated with M2 macrophages. A stable and accurate M2I Score for M2 macrophages used in colon adenocarcinoma was determined based on four hub genes. The M2I Score was positively correlated with the tumor mutation load (TMB). The M2I Score of the group with high instability of microsatellites was higher than that of the group with low instability of microsatellites and microsatellite-stable group. Combined with the Cancer Immunome Atlas database, we concluded that patients with high M2I Scores were more sensitive to programmed cell death protein 1 (PD-1) inhibitors and PD-1 inhibitors combined with cytotoxic T-lymphocyte–associated antigen 4 (CTLA-4) inhibitors. The low-rating group may have better efficacy without immune checkpoint inhibitors or with CTLA4 inhibitors alone.ConclusionFour prognostic hub genes associated with M2 macrophages were screened to establish the M2I Score. Patients were divided into two subgroups: high M2I Score group and low M2I Score group. TMB, MSI, and sensitivity to immunotherapy were higher in the high-rated group. PD-1 inhibitors or PD-1 combined with CTLA-4 inhibitors are preferred for patients in the high-rated group who are more sensitive to immunotherapy.

scholarly journals Identification of key genes in allergic rhinitis by bioinformatics analysis

2021 ◽  
Vol 49 (7) ◽  
pp. 030006052110295
Author(s):  
Yunfei Zhang ◽  
Yue Huang ◽  
Wen-xia Chen ◽  
Zheng-min Xu
Keyword(s):  
Allergic Rhinitis ◽  
Antigen Processing ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Validation Dataset ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Pathway Analyses ◽  
Antigen Processing And Presentation

Objective This study aimed to explore the potential molecular mechanism of allergic rhinitis (AR) and identify gene signatures by analyzing microarray data using bioinformatics methods. Methods The dataset GSE19187 was used to screen differentially expressed genes (DEGs) between samples from patients with AR and healthy controls. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were applied for the DEGs. Subsequently, a protein–protein interaction (PPI) network was constructed to identify hub genes. GSE44037 and GSE43523 datasets were screened to validate critical genes. Results A total of 156 DEGs were identified. GO analysis verified that the DEGs were enriched in antigen processing and presentation, the immune response, and antigen binding. KEGG analysis demonstrated that the DEGs were enriched in Staphylococcus aureus infection, rheumatoid arthritis, and allograft rejection. PPI network and module analysis predicted seven hub genes, of which six ( CD44, HLA-DPA1, HLA-DRB1, HLA-DRB5, MUC5B, and CD274) were identified in the validation dataset. Conclusions Our findings suggest that hub genes play important roles in the development of AR.

scholarly journals Identification of Transcriptional Markers and microRNA–mRNA Regulatory Networks in Colon Cancer by Integrative Analysis of mRNA and microRNA Expression Profiles in Colon Tumor Stroma

Cells ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 1054 ◽  
Author(s):  
Md. Nazim Uddin ◽  
Mengyuan Li ◽  
Xiaosheng Wang
Keyword(s):  
Colon Cancer ◽  
Mirna Expression ◽  
Expression Profiling ◽  
Regulatory Networks ◽  
Tumor Stroma ◽  
Receptor Interaction ◽  
Colon Tumor ◽  
Ppi Network ◽  
Hub Genes ◽  
Mirna Expression Profiling

The aberrant expression of microRNAs (miRNAs) and genes in tumor microenvironment (TME) has been associated with the pathogenesis of colon cancer. An integrative exploration of transcriptional markers (gene signatures) and miRNA–mRNA regulatory networks in colon tumor stroma (CTS) remains lacking. Using two datasets of mRNA and miRNA expression profiling in CTS, we identified differentially expressed miRNAs (DEmiRs) and differentially expressed genes (DEGs) between CTS and normal stroma. Furthermore, we identified the transcriptional markers which were both gene targets of DEmiRs and hub genes in the protein–protein interaction (PPI) network of DEGs. Moreover, we investigated the associations between the transcriptional markers and tumor immunity in colon cancer. We identified 17 upregulated and seven downregulated DEmiRs in CTS relative to normal stroma based on a miRNA expression profiling dataset. Pathway analysis revealed that the downregulated DEmiRs were significantly involved in 25 KEGG pathways (such as TGF-β, Wnt, cell adhesion molecules, and cytokine–cytokine receptor interaction), and the upregulated DEmiRs were involved in 10 pathways (such as extracellular matrix (ECM)-receptor interaction and proteoglycans in cancer). Moreover, we identified 460 DEGs in CTS versus normal stroma by a meta-analysis of two gene expression profiling datasets. Among them, eight upregulated DEGs were both hub genes in the PPI network of DEGs and target genes of the downregulated DEmiRs. We found that three of the eight DEGs were negative prognostic factors consistently in two colon cancer cohorts, including COL5A2, EDNRA, and OLR1. The identification of transcriptional markers and miRNA–mRNA regulatory networks in CTS may provide insights into the mechanism of tumor immune microenvironment regulation in colon cancer.

scholarly journals Analysis of Potential Hub Genes and Related Transcription Factors in Rosacea Patients using Bioinformatics Analysis

2021 ◽  
Author(s):  
Xin Wang ◽  
Wenfang Dong ◽  
Huan Wang ◽  
Jianjun You ◽  
Ruobing Zheng ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Regulatory Network ◽  
Bioinformatics Analysis ◽  
Biological Functions ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
String Databases ◽  
Cell Signaling Pathways

Abstract Objective The aim of this study is to discover the adipocyte genes and pathways involved in rosacea using bioinformatics analysis.Methods The GSE65914 gene expression profile was obtained. The GEO2R tool was used to screen out differentially expressed genes (DEGs). It was further analyzed with Gene Ontology (GO) to explore functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) to explore cell signaling pathways. Protein-protein interaction (PPI) networks among the DEGs were found by STRING databases and visualized in Cytoscape software. The related transcription factors regulatory network of the DEGs were also constructed.Results A total of 254 DEGs, including 72 up-regulated genes and 182 down-regulated genes, were obtained in rosacea samples. The biological functions of DEGs are mainly involved in the inflammatory response and chemokine activity. A PPI network consisting of 217 nodes and 710 edges was constructed using STRING, and ten hub genes were identified with Cytoscape software. Some transcriptional factors were also found to interact with these hub DEGs.Conclusion In this study, we obtained ten hub genes, including CXCL8, CCR5, CXCR4, CXCL10, MMP9, CD2, CCL19, CXCL9, CCL5, CD3D, which play an essential role in the pathology of rosacea, and these genes may provide a basis for the screening of treatment biomarkers for rosacea in the future.

scholarly journals Identification of hub genes in colon cancer via bioinformatics analysis

2020 ◽  
Vol 48 (9) ◽  
pp. 030006052095323
Author(s):  
Jun Liu ◽  
Gui-Li Sun ◽  
Shang-Ling Pan ◽  
Meng-Bin Qin ◽  
Rong Ouyang ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Organic Anion ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Venn Diagram ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes

Objectives This study aimed to investigate hub genes and their prognostic value in colon cancer via bioinformatics analysis. Methods Differentially expressed genes (DEGs) of expression profiles (GSE33113, GSE20916, and GSE37364) obtained from Gene Expression Omnibus (GEO) were identified using the GEO2R tool and Venn diagram software. Function and pathway enrichment analyses were performed, and a protein–protein interaction (PPI) network was constructed. Hub genes were verified based on The Cancer Genome Atlas (TCGA) and Human Protein Atlas (HPA) databases. Results We identified 207 DEGs, 62 upregulated and 145 downregulated genes, enriched in Gene Ontology terms “organic anion transport,” “extracellular matrix,” and “receptor ligand activity”, and in the Kyoto Encyclopedia of Genes and Genomes pathway “cytokine-cytokine receptor interaction.” The PPI network was constructed and nine hub genes were selected by survival analysis and expression validation. We verified these genes in the TCGA database and selected three potential predictors ( ZG16, TIMP1, and BGN) that met the independent predictive criteria. TIMP1 and BGN were upregulated in patients with a high cancer risk, whereas ZG16 was downregulated. The immunostaining results from HPA supported these findings. Conclusion This study indicates that these hub genes may be promising prognostic indicators or therapeutic targets for colon cancer.

scholarly journals CD3D, GZMK, and KLRB1 Are Potential Markers for Early Diagnosis of Rheumatoid Arthritis, Especially in Anti-Citrullinated Protein Antibody-Negative Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Junqin Lu ◽  
Yihui Bi ◽  
Yapeng Zhu ◽  
Shi Huipeng ◽  
Wenxiu Duan ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Early Diagnosis ◽  
Bioinformatics Analysis ◽  
High Sensitivity ◽  
Ppi Network ◽  
Hub Genes ◽  
Network Construction ◽  
Protein Protein Interaction ◽  
Citrullinated Protein ◽  
Coexpression Network Analysis

Early diagnosis and monitoring of rheumatoid arthritis (RA) progress are critical for effective treatment. In clinic, the detection of rheumatoid factor (RF) and anti-citrullinated protein antibodies (ACPA) are usually combined to diagnose early RA. However, the poor specificity of RF and high heterogeneity of ACPA make the early diagnosis of RA still challenging. Bioinformatics analysis based on high-throughput omics is an emerging method to identify novel and effective biomarkers, which has been widely used in many diseases. Herein, utilizing an integrated strategy based on expression correlation analysis and weighted gene coexpression network analysis (WGCNA), we identified 76 RA-trait different expression genes (DEGs). Combined with protein-protein interaction (PPI) network construction and clustering, new hub genes associated in RA synovia, CD3D, GZMK, and KLRB1, were identified. We verified the specificity of these genes in the synovium of RA patients through three external datasets. We also observed high sensitivity and specificity of them for ACPA-negative patients. CD3D, GZMK, and KLRB1 are potentially key mediators of RA pathogenesis and markers for RA diagnosis.

scholarly journals Identification of Key Pathways and Genes in Nasopharyngeal Carcinoma Based on Bioinformatics Analysis

2020 ◽  
Author(s):  
Hao Li ◽  
Shimin Zong ◽  
Yingying Wen ◽  
Peiyu Du ◽  
Wenting Yu ◽  
...  
Keyword(s):  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Cell Cycle Checkpoints ◽  
Ppi Network ◽  
Hub Genes ◽  
Tissue Samples ◽  
Protein Protein Interaction ◽  
Nasopharyngeal Tissue ◽  
Microarray Datasets ◽  
Selection Of

Abstract Purpose: The purpose of this study is to identify novel molecular markers and potential molecular targets for NPC based on bioinformatics analysis.Methods: We used bioinformatics to analyze one miRNA and two mRNA expression microarray datasets from the Gene Expression Omnibus database. The study included nasopharyngeal tissue samples from 57 patients with NPC and 32 patients without NPC. Fifty-one screened differentially expressed genes (DEGs) were evaluated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway enrichment analyses, and a protein-protein interaction (PPI) network was constructed. Results: The GO analysis results showed that the DEGs were mainly related to cell cycle checkpoints, cell division, and DNA synthesis during DNA repair. The KEGG analysis results suggested that the DEGs were mainly associated with extracellular matrix receptor interactions. In the PPI network, we identified RAD51AP1, MAD2L1, SPP1, CCNE2, CNTNAP2, and MELK as hub genes, clustered a key module, and identified eight key transcription factors: TFII-I, Pax-5, STAT4, GR-alpha, YY1, C/EBPβ, GRβ, and TFIID. Conclusion: The hub genes and signaling pathways identified above may play an important role in NPC development and provide ideas for the selection of valuable prognostic markers and the development of new molecular-targeted drugs.

scholarly journals Analysis of differential gene expression between right-sided and left-sided colon cancer by bioinformatics analysis

2020 ◽  
Author(s):  
Qiangwei Chi ◽  
Shizuan Chen ◽  
Shaotang Li
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Bioinformatics Analysis ◽  
Disease Free Survival ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks

Abstract Background Colon cancer is a common tumor of the digestive tract worldwide. Recent researches have revealed that colon cancer exhibits distinct differences in clinical and biological characteristics depending on the location of the tumor. However, the underlying genetic and molecular mechanism of the differences between right-sided colon cancer (RCC) and left-sided colon cancer (LCC) are not fully understood. This study aimed to identify molecular potential biomarkers and therapeutic targets for precise treatment of right-sided and left-sided colon cancer using bioinformatics analysis. Methods The gene microarray profile, named GSE44076, from the Gene Expression Omnibus (GEO) public database was downloaded and processed to then select differentially expressed genes (DEGs) on the base of two sample groups of RCC and LCC. Also, gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein–protein interaction (PPI) network construction, module analysis, validation of hub genes, and survival analysis. Results Finally, we obtained 2259 DEGs between RCC and LCC, 1300 of which were upregulated in RCC and 945 of which were upregulated in LCC. The results of GO and KEGG analysis of the DEGs indicated that the biological functions of DEGs in RCC and LCC were significantly different. CTLA4, IL10, IL2RB, IFNG, NCAM1, EGFR, MYC, SRC, CUL3, and NCBP2 were identified from the PPI networks as the hub genes of RCC and LCC. Among the hub genes, the log-rank tests for overall survival (OS) and disease free survival (DFS) were applied. Moreover, all hub genes, except CUL3, had differential expression levels of miRNA between tumor group and normal group. Conclusion These hub genes and pathways identified based on bioinformatics analysis might conduce to explain the differences between RCC and LCC, and most of the hub genes were specific to the malignant tissues. Notably, these hub genes, especially the genes associated with immunotherapy such as CTLA4, might be potential specific targets or prognostic markers for precise treatment of colon cancer.

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