scholarly journals A Comprehensive Bioinformatics Analysis of TIMP2 in Multiple Malignancies

2021 ◽  
Author(s):  
Dan-Dan Wang ◽  
Wen-Xiu Xu ◽  
Wen-Quan Chen ◽  
Su-Jin Yang ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Immune Cells ◽  
Cancer Survival ◽  
Bioinformatics Analysis ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Tissue Inhibitor Of Metalloproteinase ◽  
Transcriptional Level ◽  
Gene Markers ◽  
Online Tool ◽  
Multiple Malignancies

Abstract Background: Tissue inhibitor of metalloproteinase-2 (TIMP2), an endogenous inhibitor of matrix metalloproteinases, has been disclosed to participate in the development and carcinogenesis of multiple malignancies. However, the prognosis of TIMP2 in different cancers and its correlation with tumor microenvironment and immunity have not been clarified.Methods: In this study, we conducted a comprehensive bioinformatics analysis to evaluate the prognostic and therapeutic value of TIMP2 in cancer patients by utilizing a series of databases, including ONCOMINE, GEPIA, cBioPortal, GeneMANIA, Metascape, and Sangerbox online tool. The expression of TIMP2 in different cancers were analyzed by Oncomine, TCGA and GTEx databases and mutation status of TIMP2 in cancers was then verified using cBioportal database. The protein-protein interaction (PPI) network of the TIMP family was exhibited by GeneMANIA. The prognosis of TIMP2 in cancers was performed though GEPIA database and cox regression. Additionally, the correlations between TIMP2 expression and immunity (immune cells, gene markers of immune cells, TMB, MSI, and neoantigen) were explored using Sangerbox online tool.Results: The transcriptional level of TIMP2 in most cancerous tissues were significantly elevated. Survival analysis revealed that elevated expression of TIMP2 was associated with unfavorable survival outcome in multiple cancers. Enrichment analysis demonstrated the possible mechanisms of TIMPs and their associated genes mainly involved in pathways including extracellular matrix (ECM) regulators, degradation of ECM and ECM disassembly, and several other signaling pathways. Conclusions: Our findings systematically dissected that TIMP2 was a potential prognostic maker in various cancers and use the inhibitor of TIMP2 may be an effective strategy for cancer therapy to improve the poor cancer survival and prognostic accuracy, but concrete mechanisms need to be validated by subsequent experiments.

scholarly journals A Comprehensive Bioinformatics Analysis of TIMP2 in Multiple Malignancies

2021 ◽  
Author(s):  
Dan-Dan Wang ◽  
Wen-Xiu Xu ◽  
Wen-Quan Chen ◽  
Su-Jin Yang ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Immune Cells ◽  
Cancer Survival ◽  
Bioinformatics Analysis ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Tissue Inhibitor Of Metalloproteinase ◽  
Transcriptional Level ◽  
Gene Markers ◽  
Online Tool ◽  
Multiple Malignancies

Abstract Background: Tissue inhibitor of metalloproteinase-2 (TIMP2), an endogenous inhibitor of matrix metalloproteinases, has been disclosed to participate in the development and carcinogenesis of multiple malignancies. However, the prognosis of TIMP2 in different cancers and its correlation with tumor microenvironment and immunity have not been clarified.Methods: In this study, we conducted a comprehensive bioinformatics analysis to evaluate the prognostic and therapeutic value of TIMP2 in cancer patients by utilizing a series of databases, including ONCOMINE, GEPIA, cBioPortal, GeneMANIA, Metascape, and Sangerbox online tool. The expression of TIMP2 in different cancers were analyzed by Oncomine, TCGA and GTEx databases and mutation status of TIMP2 in cancers was then verified using cBioportal database. The protein-protein interaction (PPI) network of the TIMP family was exhibited by GeneMANIA. The prognosis of TIMP2 in cancers was performed though GEPIA database and cox regression. Additionally, the correlations between TIMP2 expression and immunity (immune cells, gene markers of immune cells, TMB, MSI, and neoantigen) were explored using Sangerbox online tool.Results: The transcriptional level of TIMP2 in most cancerous tissues were significantly elevated. Survival analysis revealed that elevated expression of TIMP2 was associated with unfavorable survival outcome in multiple cancers. Enrichment analysis demonstrated the possible mechanisms of TIMPs and their associated genes mainly involved in pathways including extracellular matrix (ECM) regulators, degradation of ECM and ECM disassembly, and several other signaling pathways. Conclusions: Our findings systematically dissected that TIMP2 was a potential prognostic maker in various cancers and use the inhibitor of TIMP2 may be an effective strategy for cancer therapy to improve the poor cancer survival and prognostic accuracy, but concrete mechanisms need to be validated by subsequent experiments.

scholarly journals Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

2020 ◽  
Vol 40 (8) ◽  
Author(s):  
Sihan Chen ◽  
Guodong Cao ◽  
Wei Wu ◽  
Yida Lu ◽  
Xiaobo He ◽  
...  
Keyword(s):  
Cox Regression ◽  
Mechanistic Model ◽  
Single Gene ◽  
Colon Adenocarcinoma ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Gene Markers ◽  
Cox Regression Analysis

Abstract Colon adenocarcinoma (COAD) is a malignant gastrointestinal tumor, often occurring in the left colon, which is regulated by glycolysis-related processes. In past studies, multiple genes that influence the prognosis for survival have been discovered through bioinformatics analysis. However, the prediction of disease prognosis using a single gene is not an accurate method. In the present study, a mechanistic model was established to achieve better prediction for the prognosis of COAD. COAD-related data downloaded from The Cancer Genome Atlas (TCGA) were correlated with the glycolysis process using gene set enrichment analysis (GSEA) to determine the glycolysis-related genes that regulate COAD. Using COX regression analysis, glycolysis-related genes associated with the prognosis of COAD were identified, and the genes screened to establish a predictive model. The risk scores of this model were correlated with relevant clinical data to obtain a connection diagram between the model and survival rate, tumor characteristic data, etc. Finally, genes in the model were correlated with cells in the tumor microenvironment, finding that they affected specific immune cells in the model. Seven genes related to glycolysis were identified (PPARGC1A, DLAT, 6PC2, P4HA1, STC2, ANKZF1, and GPC1), which affect the prognosis of patients with COAD and constitute the model for prediction of survival of COAD patients.

scholarly journals CLDN6 and CLDN10 are Associated with Immune Infiltration of Ovarian Cancer: A Study of Claudin Family

2020 ◽  
Author(s):  
Peipei Gao ◽  
Ting Peng ◽  
Canhui Cao ◽  
Shitong Lin ◽  
Ping Wu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
T Cell ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Markers ◽  
Immune Infiltration ◽  
Kaplan Meier ◽  
The Relationship

Abstract Background: Claudin family is a group of membrane proteins related to tight junction. There are many studies about them in cancer, but few studies pay attention to the relationship between them and the tumor microenvironment. In our research, we mainly focused on the genes related to the prognosis of ovarian cancer, and explored the relationship between them and the tumor microenvironment of ovarian cancer.Methods: The cBioProtal provided the genetic variation pattern of claudin gene family in ovarian cancer. The ONCOMINE database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to exploring the mRNA expression of claudins in cancers. The prognostic potential of these genes was examined via Kaplan-Meier plotter. Immunologic signatures were enriched by gene set enrichment analysis (GSEA). The correlations between claudins and the tumor microenvironment of ovarian cancer were investigated via Tumor Immune Estimation Resource (TIMER).Results: In our research, claudin genes were altered in 363 (62%) of queried patients/samples. Abnormal expression levels of claudins were observed in various cancers. Among them, we found that CLDN3, CLDN4, CLDN6, CLDN10, CLDN15 and CLDN16 were significantly correlated with overall survival of patients with ovarian cancer. GSEA revealed that CLDN6 and CLDN10 were significantly enriched in immunologic signatures about B cell, CD4 T cell and CD8 T cell. What makes more sense is that CLDN6 and CLDN10 were found related to the tumor microenvironment. CLDN6 expression was negatively correlated with immune infiltration level in ovarian cancer, and CLDN10 expression was positively correlated with immune infiltration level in ovarian cancer. Further study revealed the CLDN6 expression level was negatively correlated with gene markers of various immune cells in ovarian cancer. And, the expression of CLDN10 was positive correlated with gene markers of immune cells in ovarian cancer.Conclusions: CLDN6 and CLDN10 were prognostic biomarkers, and correlated with immune infiltration in ovarian cancer. Our results revealed new roles for CLDN6 and CLDN10, and they were potential therapeutic targets in the treatment of ovarian cancer.

scholarly journals DOCK2 is a Biomarker for Predicting the Prognosis of Lung Adenocarcinoma and Associated with Immune Infiltration

2021 ◽  
Author(s):  
Jie He ◽  
Tongtong Zhang ◽  
Jian Sun ◽  
Guangnan Liu
Keyword(s):  
Immune Response ◽  
Lung Adenocarcinoma ◽  
Immune Cells ◽  
Bioinformatics Analysis ◽  
Cox Regression ◽  
Clinical Sample ◽  
Clinical Samples ◽  
Data Set ◽  
Immune Infiltration ◽  
Low Expression

Abstract Background: dedicator of cytokinesis 2 is an atypical guanine exchange factor, which is particularly expressed in hematopoietic cells and modulates the activation along with the migration of immune cells by activating Ras--related C3 botulinum toxin substrate (Rac). Nevertheless, the role of DOCK2 in lung adenocarcinoma (LUAD) remains unclear.Methods: Herein, we performed bioinformatics analysis of lung adenocarcinoma data abstracted from TCGA (The Cancer Genome Altas) and GEO (Gene Expression Omnibus) data resources, and combined with web tools consisting of LinkedOmics, TIMER, and TISIDB. Finally, combined with clinical lung adenocarcinoma samples, we verified the expression of DOCK2 in tissue and its effect on the prognosis of lung adenocarcinoma.Results: In the TCGA lung adenocarcinoma data set, the expression of DOCK2 was down-regulated in tumor tissues and verified in multiple independent cohorts. In addition, the low expression of DOCK2 indicates a poor overall survival(OS) in both TCGA and other GEO data sets and in our clinical samples. COX regression data illustrated that the low expression of DOCK2 was an independent predictor for OS. Functional network analysis shows that DOCK2 participates in immune response through interleukin production, neuroinflammatory response, acquired immune response, leukocyte migration and activation of lymph node cells, and is related to multiple immune-related pathways. Besides, the expression of DOCK2 was remarkably related with many kinds of tumor infiltrating immune cells.Conclusion: combined with bioinformatics analysis and clinical sample verification, our study shows that DOCK2 can independently estimate the prognosis of lung adenocarcinoma and is related to immune infiltration. As a promising prognostic indicator and potential target of immunotherapy, the potential effect of DOCK2 on lung adenocarcinoma and its molecular mechanism are worthy of further discussion.

scholarly journals Profiles of Immune Infiltration and Prognostic Immunoscore in Lung Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yanyan Li ◽  
Liping Tao ◽  
Weiyang Cai
Keyword(s):  
Lung Adenocarcinoma ◽  
Immune Cells ◽  
Cox Regression ◽  
Meta Analysis ◽  
Immune Therapy ◽  
Interaction Network ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Immune Microenvironment

Lung tissue is abundant with immune cells that form a powerful first defense against exotic particles and microbes. The malignant phenotype of lung adenocarcinoma (LUAD) is defined not only by intrinsic tumor cells but also by tumor-infiltrating immune cells (TIICs) recruited to the immune microenvironment. Understanding more about the immune microenvironment of LUAD could function in sorting out patients more likely with high risk and benefit from immunotherapy. Twenty-two types of TIICs were estimated based on large public LUAD cohorts from the TCGA and GEO datasets using the CIBERSORT algorithm. Then principal component analysis (PCA), meta-analysis, and single-sample gene set enrichment analysis (ssGSEA) were used to measure and evaluate the specific immune responses and relative mechanisms. Moreover, an immunoscore model based on the percent of immune cells was constructed via the univariate and multivariate Cox regression models, which provided an in-depth overview of the LUAD immune microenvironment and shed light on the immune regulatory mechanism. The differential expression genes (DEGs) were acquired based on the immunoscore model, and prognostic immune-related genes were further identified. GSEA and the protein–protein interaction network (PPI) further revealed that these genes were mostly enriched in many immune-related biological processes. It is hoped that this immune landscape could provide a more accurate understanding for LUAD development and tumor immune therapy.

scholarly journals The Novel Ferroptosis-related Gene Markers That Can Predict the Survival in Gastric Cancer Patients

2020 ◽  
Author(s):  
Bang Chen ◽  
Xin Xu ◽  
ShaoFu Zhu ◽  
ShiYi Yang ◽  
Kang Yang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Risk Score ◽  
Cancer Biology ◽  
Cox Regression ◽  
Tumor Development ◽  
Enrichment Analysis ◽  
Gene Markers ◽  
Basic Part ◽  
Cox Regression Analysis ◽  
Gastric Cancer Patients

Abstract Background: Gastric cancer(GC) refers to malignant tumor that derived from gastric epithelial cells. Ferroptosis is another programmed cell demise mode that is Fe-dependent, unique concerning apoptosis, cell necrosis, and autophagy. Current research demonstrates that ferroptosis assumes a basic part of cancer biology. Extracellular matrix(ECM) has been confirmed to play an essential role in the proliferation, apoptosis, metabolism and differentiation of tumor cells. As an important component of the tumor microenvironment, ECM interacts with the immune microenvironment and affects tumor development and progression. Methods: GC data were downloaded from the TCGA database. Furthermore, 259 ferroptosis-related genes were acquired with the FerrDb database. COX regression analysis was used to screen ferroptosis-related genes related to GC's prognosis, and these genes constructed the prediction model. The risk score of the model and clinical data of GC were further analyzed to get the correlation between the model and the overall survival(OS) rate and clinicopathological features. Finally, GO and KEGG enrichment analysis was carried out on the genes of the model. To further analyze the correlation between the genes in the model and tumor immunity, ssGSEA was used to score immune cells and calculate immune-related pathways' activity quantitatively. Results: A prognosis model was constructed according to the 11 ferroptosis-related genes related to prognosis to predict the prognosis of GC patients better. According to univariate and multivariate, risk score can be regarded as an independent predictor.Conclusions: we identified 11 ferroptosis-related genes (NOX4, NOX5, SLC1A5, GLS2, MYB, TGFBR1, NF2, ZFP36, DUSP1, SLC1A4, SP1), which affected the prognosis of GC patients.

scholarly journals Integrated Bioinformatics Analysis Identifies Heat Shock Factor 2 as a Prognostic Biomarker Associated With Immune Cell Infiltration in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Yumei Fan ◽  
Jiajie Hou ◽  
Xiaopeng Liu ◽  
Bihui Han ◽  
Yanxiu Meng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Heat Shock ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Critical Role ◽  
Enrichment Analysis ◽  
Normal Liver ◽  
Heat Shock Factor ◽  
Gene Set Enrichment Analysis

Hepatocellular carcinoma (HCC) is one of the most common malignancies and ranks as the second leading cause of cancer-related mortality worldwide. Heat shock factor 2 (HSF2) is a transcription factor that plays a critical role in development, particularly corticogenesis and spermatogenesis. However, studies examining the expression and prognostic value of HSF2 and its association with tumor-infiltrating immune cells in HCC are still rare. In the present study, we found that HSF2 expression was significantly upregulated in HCC tissues compared with normal liver tissues using the TCGA, ICGC, GEO, UALCAN, HCCDB and HPA databases. High HSF2 expression was associated with shorter survival of patients with HCC. Cox regression analyses and nomogram were used to evaluate the association of HSF2 expression with the prognosis of patients with HCC. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and gene set enrichment analysis (GSEA) revealed that HSF2 was associated with various signaling pathways, including the immune response. Notably, HSF2 expression was significantly correlated with the infiltration levels of different immune cells using the TIMER database and CIBERSORT algorithm. HSF2 expression also displayed a significant correlation with multiple immune marker sets in HCC tissues. Knockdown of HSF2 significantly inhibited the proliferation, migration, invasion and colony formation ability of HCC cells. In summary, we explored the clinical significance of HSF2 and provided a therapeutic basis for the early diagnosis, prognostic judgment, and immunotherapy of HCC.

scholarly journals Increased Expression Together with the Gene Regulation Network of NEIL3 Predicts Poor Prognosis and Correlates with Immune Infiltrates in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Bo Hu ◽  
Xiao-Bo Yang ◽  
Xinting Sang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Gene Regulation ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Immune Gene ◽  
Data Set ◽  
Gene Markers

Abstract Background: Abnormal Nei endonuclease VIII-like 3 (NEIL3)expression is associated with carcinogenesis. Methods: We used sequencing data from the Cancer Genome Atlas database, analyzed NEIL3 expression, gene regulation networks and the correlation with immune infiltrates in hepatocellular carcinoma (HCC). Clinicopathologic characteristics associated with overall survival in TCGA patients using Cox regression and the Kaplan-Meier method. Gene Set Enrichment Analysis was performed using TCGA data set. LinkedOmics was used to identify differential gene expression with NEIL3 and to analyze Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. Gene enrichment analysis examined target networks of kinases and transcription factors.Correlations between NEIL3 expression and cancer immune infiltrates and immune gene markers were analyzed by TIMER and GEPIA. Results: We found that overexpressed NEIL3 predicted poor prognosis. Functional network analysis suggested that NEIL3 regulates the DNA replication and cell cycle signaling via pathways involving several cancer-related kinases and E2F Transcription Factor 1.NEIL3 was also found to be associated with the infiltration of several immune cells. Conclusions: Our results demonstrate that data mining efficiently reveals information about NEIL3 expression, potential regulatory networks and the relationship with immune infiltration in HCC, laying a foundation for further study of the role of NEIL3 in carcinogenesis.

scholarly journals EMILIN2 Correlated With Its Methylation and Immune Infiltration Could Be an Independent Prognostic Biomarker in LGG

2021 ◽  
Author(s):  
Li-chong Wang ◽  
Zhe Zhang ◽  
Zi-long Tan ◽  
Qiao-li Lv ◽  
Shu-hui Chen ◽  
...  
Keyword(s):  
Immune Cells ◽  
Poor Prognosis ◽  
Bioinformatics Analysis ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Low Grade ◽  
Central Nervous System Neoplasms ◽  
Immune Infiltration ◽  
Nervous System Neoplasms ◽  
Slow Growing

Abstract Low-grade gliomas (LGGs) are slow-growing brain cancer in central nervous system neoplasms. EMILIN2 is an extracellular matrix (ECM) protein which could influence the progress of some tumour which is unclear in LGG. In our study, the methylation, expression, prognosis and immune value of EMILIN2 were analysed in LGG through bioinformatics analysis. we first analysed the LGG data from TCGA and discovered that the EMILIN2 expression, negatively correlated to the EMILIN2 methylation could predict a poor prognosis and associated with different clinical parameters. Moreover, univariate and multivariate Cox regression were performed in CGGA showed that the EMILIN2 could be an independent prognostic biomarker in LGG. Finally, EMILIN2 expression showed a correlation with gene makers in some immune cells which identified the significance of EMILIN2 in immune infiltration. In conclusion, EMILIN2 could act as an independent prognostic biomarker which might be associated with the malignancy and development of gliomas and play a crucial role in glioma in immune infiltration.

scholarly journals Development of a prognostic model based on immunogenomic landscape analysis of hepatocellular carcinoma

2021 ◽  
Author(s):  
ligong lu ◽  
Shaoqing Liu ◽  
Shengni Hua ◽  
Zhenlin Zhang ◽  
Meixiao Zhan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prediction Model ◽  
Risk Score ◽  
Immune Cells ◽  
Bioinformatics Analysis ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Prognostic Indicators

Abstract Background Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer, and the systematic exploration of its prognostic indicators is urgently needed. In this study, we obtained 12 IRGs for the construction of a risk score prediction model in HCC by bioinformatics analysis. Methods Differentially expressed genes were screened using the R software edgeR package. Functional enrichment analysis was performed through gene ontology analyses as well as the Kyoto Encyclopedia of Genes and Genomes pathway analysis. Single factor and multi-factor Cox analysis were employed for survival analysis. We used the Timer software to examine the correlation between risk score and tumor-infiltrating immune cells. Results We identified 3,215 up-regulated and 1,044 down-regulated genes in HCC tissues based on a cohort from The Cancer Genome Atlas (TCGA). Differentially expressed immune-related genes (IRGs) and survival-associated IRGs were further identified. We also integrated multivariate Cox regression analyses to obtain 12 IRGs for the construction of a risk score prediction model, whose performance was verified using the Kaplan-Meier survival and receiver operating characteristic curve analyses. Our findings suggest that the risk score was associated with clinical characteristics and the infiltration of immune cells in HCC patients. Conclusions We obtained a risk score prediction model of 12 IRGs in HCC by bioinformatics analysis and confirmed its performance.

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