scholarly journals Identification and Validation of Immune-Related Prognostic Genes in the Tumor Microenvironment of Colon Adenocarcinoma

2022 ◽  
Vol 12 ◽  
Author(s):  
Shenghua Pan ◽  
Tingting Tang ◽  
Yanke Wu ◽  
Liang Zhang ◽  
Zekai Song ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Tumor Microenvironment ◽  
Signaling Pathway ◽  
Colon Adenocarcinoma ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Kaplan Meier

The tumor microenvironment (TME) has been shown to be involved in angiogenesis, tumor metastasis, and immune response, thereby affecting the treatment and prognosis of patients. This study aims to identify genes that are dysregulated in the TME of patients with colon adenocarcinoma (COAD) and to evaluate their prognostic value based on RNA omics data. We obtained 512 COAD samples from the Cancer Genome Atlas (TCGA) database and 579 COAD patients from the independent dataset (GSE39582) in the Gene Expression Omnibus (GEO) database. The immune/stromal/ESTIMATE score of each patient based on their gene expression was calculated using the ESTIMATE algorithm. Kaplan–Meier survival analysis, Cox regression analysis, gene functional enrichment analysis, and protein–protein interaction (PPI) network analysis were performed. We found that immune and stromal scores were significantly correlated with COAD patients’ overall survival (log rank p < 0.05). By comparing the high immune/stromal score group with the low score group, we identified 688 intersection differentially expressed genes (DEGs) from the TCGA dataset (663 upregulated and 25 downregulated). The functional enrichment analysis of intersection DEGs showed that they were mainly enriched in the immune process, cell migration, cell motility, Toll-like receptor signaling pathway, and PI3K-Akt signaling pathway. The hub genes were revealed by PPI network analysis. Through Kaplan–Meier and Cox analysis, four TME-related genes that were significantly related to the prognosis of COAD patients were verified in GSE39582. In addition, we uncovered the relationship between the four prognostic genes and immune cells in COAD. In conclusion, based on the RNA expression profiles of 1091 COAD patients, we screened four genes that can predict prognosis from the TME, which may serve as candidate prognostic biomarkers for COAD.

scholarly journals Identification of Potential Target Genes in HER-2 Positive Breast Cancer by Bioinformatics Analysis.

2020 ◽  
Author(s):  
Song Wang ◽  
Yi Quan ◽  
Hongying Lyu ◽  
Jian Deng
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Signaling Pathway ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Her 2 ◽  
Positive Breast Cancer

Abstract Background: HER-2 positive breast cancer has a high risk of for relapse, metastasis and drug resistance, and is correlated with a poor prognosis. Thus, the study objective was to reveal target genes and key pathways in HER-2 subtype breast cancer. Methods: The gene expression dataset (GSE29431) was downloaded from the Gene Expression Omnibus database(GEO), and the differentially expressed genes (DEGs) were determined using LIMMA package in R software. Subsequently, Functional enrichment analysis were performed in ClusterProfiler package of R platform. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to construct a Protein-Protein Interaction (PPI) network of DEGs. Module analysis and target genes were identified by Cytoscape software. Further more, The influence of target genes on overall survival (OS) was assessed using the Kaplan-Meier plotter database.Results: The differential expression analysis revealed 96 genes were up-regulated while 407 genes were down-regulated in HER-2 positive breast cancer tissue compared to normal breast tissue. Functional enrichment analysis showed that the DEGs were mainly involved in regulation of lipid metabolic process, PPAR signaling pathway and PI3K-Akt signaling pathway. PPI network construction revealed a total of 199 nodes and 560 edges, and 12 target genes were identified by the highest value of degree. In addition, target genes were associated with worse overall prognosis, including NUSAP1, PTTG1, CEP55, TOP2A, CCNB1, CENPF, MELK, AURKA, UBE2C, BUB1B, KIF20A and RRM2.Conclusion: The present study identified 12 target genes associated with the development of HER-2 subtype breast cancer, which may help to provide new biomarkers and therapeutic targets.

scholarly journals Construction of Gene Modules and Analysis of Prognostic Biomarkers for Cervical Cancer by Weighted Gene Co-Expression Network Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiamei Liu ◽  
Shengye Liu ◽  
Xianghong Yang
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Network Analysis ◽  
Cancer Progression ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Gene Modules

BackgroundDespite advances in the understanding of neoplasm, patients with cervical cancer still have a poor prognosis. Identifying prognostic markers of cervical cancer may enable early detection of recurrence and more effective treatment.MethodsGene expression profiling data were acquired from the Gene Expression Omnibus database. After data normalization, genes with large variation were screened out. Next, we built co-expression modules by using weighted gene co-expression network analysis to investigate the relationship between the modules and clinical traits related to cervical cancer progression. Functional enrichment analysis was also applied on these co-expressed genes. We integrated the genes into a human protein-protein interaction (PPI) network to expand seed genes and build a co-expression network. For further analysis of the dataset, the Cancer Genome Atlas (TCGA) database was used to identify seed genes and their correlation to cervical cancer prognosis. Verification was further conducted by qPCR and the Human Protein Atlas (HPA) database to measure the expression of hub genes.ResultsUsing WGCNA, we identified 25 co-expression modules from 10,016 genes in 128 human cervical cancer samples. After functional enrichment analysis, the magenta, brown, and darkred modules were selected as the three most correlated modules for cancer progression. Additionally, seed genes in the three modules were combined with a PPI network to identify 31 tumor-specific genes. Hierarchical clustering and Gepia results indicated that the expression quantity of hub genes NDC80, TIPIN, MCM3, MCM6, POLA1, and PRC1 may determine the prognosis of cervical cancer. Finally, TIPIN and POLA1 were further filtered by a LASSO model. In addition, their expression was identified by immunohistochemistry in HPA database as well as a biological experiment.ConclusionOur research provides a co-expression network of gene modules and identifies TIPIN and POLA1 as stable potential prognostic biomarkers for cervical cancer.

scholarly journals The Role of Circular RNA CDR1as/ciRS-7 in Regulating Tumor Microenvironment: A Pan-Cancer Analysis

Biomolecules ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 429 ◽  
Author(s):  
Zou ◽  
Zheng ◽  
Deng ◽  
Yang ◽  
Xie ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Signaling Pathway ◽  
Malignant Tumors ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Circular Rna ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Receptor Interaction

Circular RNA CDR1as/ciRS-7 functions as an oncogenic regulator in various cancers. However, there has been a lack of systematic and comprehensive analysis to further elucidate its underlying role in cancer. In the current study, we firstly performed a bioinformatics analysis of CDR1as among 868 cancer samples by using RNA-seq datasets of the MiOncoCirc database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), CIBERSORT, Estimating the Proportion of Immune and Cancer cells (EPIC), and the MAlignant Tumors using Expression data (ESTIMATE) algorithm were applied to investigate the underlying functions and pathways. Functional enrichment analysis suggested that CDR1as has roles associated with angiogenesis, extracellular matrix (ECM) organization, integrin binding, and collagen binding. Moreover, pathway analysis indicated that it may regulate the TGF-β signaling pathway and ECM-receptor interaction. Therefore, we used CIBERSORT, EPIC, and the ESTIMATE algorithm to investigate the association between CDR1as expression and the tumor microenvironment. Our data strongly suggest that CDR1as may play a specific role in immune and stromal cell infiltration in tumor tissue, especially those of CD8+ T cells, activated NK cells, M2 macrophages, cancer-associated fibroblasts (CAFs) and endothelial cells. Generally, systematic and comprehensive analyses of CDR1as were conducted to shed light on its underlying pro-cancerous mechanism. CDR1as regulates the TGF-β signaling pathway and ECM-receptor interaction to serve as a mediator in alteration of the tumor microenvironment.

scholarly journals Identification of Key Genes Affecting the Tumor Microenvironment and Prognosis of Triple-Negative Breast Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiarong Yi ◽  
Wenjing Zhong ◽  
Haoming Wu ◽  
Jikun Feng ◽  
Xiazi Zouxu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Tumor Microenvironment ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Key Genes

Although the tumor microenvironment (TME) plays an important role in the development of many cancers, its roles in breast cancer, especially triple-negative breast cancer (TNBC), are not well studied. This study aimed to identify genes related to the TME and prognosis of TNBC. Firstly, we identified differentially expressed genes (DEG) in the TME of TNBC, using Expression data (ESTIMATE) datasets obtained from the Cancer Genome Atlas (TCGA) and Estimation of Stromal and Immune cells in Malignant Tumor tissues. Next, survival analysis was performed to analyze the relationship between TME and prognosis of TNBC, as well as determine DEGs. Genes showing significant differences were scored as alternative genes. A protein-protein interaction (PPI) network was constructed and functional enrichment analysis conducted using the DEG. Proteins with a degree greater than 5 and 10 in the PPI network correspond with hub genes and key genes, respectively. Finally, CCR2 and CCR5 were identified as key genes in TME and prognosis of TNBC. Finally, these results were verified using Gene Expression Omnibus (GEO) datasets and immunohistochemistry of TNBC patients. In conclusion, CCR2 and CCR5 are key genes in the TME and prognosis of TNBC with the potential of prognostic biomarkers in TNBC.

scholarly journals Protein Network Analysis to Prioritize Key Genes and Pathway for Stress-Mediated Neurodegeneration

2018 ◽  
Vol 11 (1) ◽  
pp. 240-251 ◽  
Author(s):  
Neha Srivastava ◽  
Bhartendu Nath Mishra ◽  
Prachi Srivastava
Keyword(s):  
Oxidative Stress ◽  
Gene Ontology ◽  
Transcription Factor ◽  
Network Analysis ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Node Degree ◽  
Key Genes

Background:Oxidative Stress (OS) has been implicated in the pathophysiology of many neurodegenerative diseases. OS can cause cellular damage that results in cell death due to overproduction of reactive oxygen species (ROS) that may play the crucial role in the disease progression. An impaired mechanism in correlation with reduced expression of antioxidant proteins is the very common feature among most of the age-related disorders. Variousin-vitroandin-vivostudies suggest the major contribution of oxidative stress in neurodegeneration. Role of Nrf2 gene is well established as a neuroprotective gene especially in concern with stress-mediated neurodegeneration. Nrf2 is a bZIP transcription factor that forms the heterodimer with small Maf protein and transcription factor AP1 that regulates transcription by binding to ARE which coordinates the transcription of genes involved in phase II detoxification and an antioxidant defense that is used to protect the cell from oxidative stress.Aim:The currentinsilicostudy was attempted to prioritize key genes and pathway in stress-mediated neurodegeneration through network-based analysis.Methods:Protein-protein interaction network was constructed and analyzed using 63 Nrf2 regulating candidate genes obtained from NCBI database based on literature studies usingSTRING 10.0database andCytoscape v 3.6.0software plug-inNetwork Analyzer.Further, the functional enrichment analysis of identified gene was done usingPANTHER GENE ONTOLOGYsoftware and DAVID tool.Results:Based on network topological parameter, TP53, JUN, MYC, NFE2L2, AKT1, PIK3CA & UBC were identified as the key gene in the network. Among them, TP53 gene was obtained as a super hub gene with the highest Betweenness Centrality (BC) and node degree. The functional enrichment analysis was done usingPANTHER GENE ONTOLOGYsoftware and DAVID tool reveals their significant role in neurotrophin signaling pathway, MAPK signaling pathway, cellular response to stress & in the regulation of stress.Conclusion:The network analysis will help in prioritizing genes in the pathway that helps in understanding the underlying mechanism of disease. Thus, further study on these genes and their biological mechanism and pathway may, therefore, provide a potential target for the treatment of stress-mediated neurodegeneration.

scholarly journals Identification of Key Genes and Pathways Associated with Sex Differences in Osteoarthritis Based on Bioinformatics Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Shiying Wang ◽  
Huanmei Wang ◽  
Wei Liu ◽  
Biaofang Wei
Keyword(s):  
Gene Expression ◽  
Sex Differences ◽  
Focal Adhesion ◽  
Osteoclast Differentiation ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Key Genes

Sex differences have been suggested to play critical roles in the pathophysiology of osteoarthritis (OA), resulting in sex-specific prevalence and incidence. However, their roles in the development of OA remain largely unknown. The aim of this study was to screen out key genes and pathways mediating biological differences between OA females after menopause and OA males. First, the gene expression data of GSE36700 and GSE55457 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between sexes were identified using R software, respectively. The overlapping DEGs were obtained. Then, protein-protein interactive (PPI) network was constructed to further analyze interactions between the overlapping DEGs. Finally, enrichment analyses were separately performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes tools. In our results, a total of 278 overlapping DEGs were identified between OA females after menopause and OA males, including 219 upregulated and 59 downregulated genes. In the PPI network, seven hub genes were identified, including EGF, ERBB2, CDC42, PIK3R2, LCK, CBL, and STAT1. Functional enrichment analysis revealed that these genes were mainly enriched in PI3K-Akt signaling pathway, osteoclast differentiation, and focal adhesion. In conclusion, the results in the current study suggest that pathways of PI3K-Akt, osteoclast differentiation, and focal adhesion may play important roles in the development of OA females after menopause. EGFR, ERBB2, CDC42, and STAT1 may be key genes related to OA progression in postmenopausal women and may be promising therapeutic targets for OA.

scholarly journals Gene correlation network analysis to identify regulatory factors in sciatic nerve injury

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Liuxun Li ◽  
Xiaokang Du ◽  
Haiqian Ling ◽  
Yuhang Li ◽  
Xuemin Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Sciatic Nerve ◽  
Signaling Pathway ◽  
Nerve Injury ◽  
Sciatic Nerve Injury ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Adult Group ◽  
Hub Genes

Abstract Background Sciatic nerve injury (SNI), which frequently occurs under the traumatic hip and hip fracture dislocation, induces serious complications such as motor and sensory loss, muscle atrophy, or even disabling. The present work aimed to determine the regulating factors and gene network related to the SNI pathology. Methods Sciatic nerve injury dataset GSE18803 with 24 samples was divided into adult group and neonate group. Weighted gene co-expression network analysis (WGCNA) was carried out to identify modules associated with SNI in the two groups. Moreover, differentially expressed genes (DEGs) were determined from every group, separately. Subsequently, co-expression network and protein–protein interaction (PPI) network were overlapped to identify hub genes, while functional enrichment and Reactome analysis were used for a comprehensive analysis of potential pathways. GSE30165 was used as the test set for investigating the hub gene involvement within SNI. Gene set enrichment analysis (GSEA) was performed separately using difference between samples and gene expression level as phenotype label to further prove SNI-related signaling pathways. In addition, immune infiltration analysis was accomplished by CIBERSORT. Finally, Drug–Gene Interaction database (DGIdb) was employed for predicting the possible therapeutic agents. Results 14 SNI status modules and 97 DEGs were identified in adult group, while 15 modules and 21 DEGs in neonate group. A total of 12 hub genes was overlapping from co-expression and PPI network. After the results from both test and training sets were overlapped, we verified that the ten real hub genes showed remarkably up-regulation within SNI. According to functional enrichment of hub genes, the above genes participated in the immune effector process, inflammatory responses, the antigen processing and presentation, and the phagocytosis. GSEA also supported that gene sets with the highest significance were mostly related to the cytokine–cytokine receptor interaction. Analysis of hub genes possible related signaling pathways using gene expression level as phenotype label revealed an enrichment involved in Lysosome, Chemokine signaling pathway, and Neurotrophin signaling pathway. Immune infiltration analysis showed that Macrophages M2 and Regulatory T cells may participate in the development of SNI. At last, 25 drugs were screened from DGIdb to improve SNI treatment. Conclusions The gene expression network is determined in the present work based on the related regulating factors within SNI, which sheds more light on SNI pathology and offers the possible biomarkers and therapeutic targets in subsequent research.

scholarly journals Identification of key gene in colorectal cancer using bioinformatics analysis.

2020 ◽  
Author(s):  
Rongrong Xiao ◽  
Ping Wang ◽  
Tian Xia ◽  
Chun-Yi Li ◽  
Ting Jiang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Tumor Microenvironment ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes

Abstract Background Tumor microenvironment plays important roles in the development of cancer. The aim of our study was to examine the expression of genes in colorectal cancer and also to evaluate the association value between expression level of these genes and clinical features. Methods We combined The Cancer Genome Atlas (TCGA) datasets to identify differentially expressed genes in colon cancer. Using these differentially expressed genes, we constructed protein-protein interaction network and conducted functional enrichment analysis. Genes with degree beyond 10 in the PPI network were regarded as hub genes. Then, we verified of the expression of molecules in Oncomine datasets and conducted Kaplan-Meier curve and log-rank test and functional enrichment analysis on these hub genes. Finally, we analyzed the relationship clinicopathological features analysis with the key gene. Results There were 719 differentially expressed genes identified to be associated with colon cancer microenvironment. We screened out 10 hub genes by construction of PPI network. The functions of these hub genes were enriched in cytokine-cytokine receptor interaction, alcoholism and systemic lupus erythematosus, which provided further insight into the roles of these genes in the tumor microenvironment. GNG4, with the highest degrees in the PPI network, were highly exprepressed in metastasis(P = 9.5-05) ,N1(P = 0.0025) and N2(,0.037).It was a relationship with stage. It was significantly different between with stage I and IV, II and III, II and IV,III and IV (P = 0.0015,0.029,3.9-05,0.00074,0.01,respectively) Conclusions We identified GNG4 can be regarded as a prognostic biomarker in colon cancer.

scholarly journals Bioinformatic analysis identifies potential key genes of epilepsy

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0254326
Author(s):  
Yike Zhu ◽  
Dan Huang ◽  
Zhongyan Zhao ◽  
Chuansen Lu
Keyword(s):  
Network Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
P Value ◽  
Ppi Network ◽  
Functional Roles ◽  
Protein Protein Interaction

Background Epilepsy is one of the most common brain disorders worldwide. It is usually hard to be identified properly, and a third of patients are drug-resistant. Genes related to the progression and prognosis of epilepsy are particularly needed to be identified. Methods In our study, we downloaded the Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE143272. Differentially expressed genes (DEGs) with a fold change (FC) >1.2 and a P-value <0.05 were identified by GEO2R and grouped in male, female and overlapping DEGs. Functional enrichment analysis and Protein-Protein Interaction (PPI) network analysis were performed. Results In total, 183 DEGs overlapped (77 ups and 106 downs), 302 DEGs (185 ups and 117 downs) in the male dataset, and 750 DEGs (464 ups and 286 downs) in the female dataset were obtained from the GSE143272 dataset. These DEGs were markedly enriched under various Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. 16 following hub genes were identified based on PPI network analysis: ADCY7, C3AR1, DEGS1, CXCL1 in male-specific DEGs, TOLLIP, ORM1, ELANE, QPCT in female-specific DEGs and FCAR, CD3G, CLEC12A, MOSPD2, CD3D, ALDH3B1, GPR97, PLAUR in overlapping DEGs. Conclusion This discovery-driven study may be useful to provide a novel insight into the diagnosis and treatment of epilepsy. However, more experiments are needed in the future to study the functional roles of these genes in epilepsy.

scholarly journals Identification of Key Candidate Biomarkers and Pathways in Atherosclerosis

2020 ◽  
Author(s):  
Zhijun Meng ◽  
Jia Gao ◽  
Hongping Liang ◽  
Caihong Liu ◽  
Jianli Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathway ◽  
Early Stage ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Effective Prevention ◽  
Diagnostic Indicator

Abstract Background Atherosclerosis is the leading cause of cardiovascular disease worldwide for which lacks effective prevention and therapeutic strategy. Therefore, clinical indicators for early diagnosis and screening are in great need. The present study aimed to elucidate the key genetic signatures and pathways identifying the key candidate biomarker in atherosclerosis by integrative bioinformatics analysis combining with experimental assay. Methods The gene expression profiles (GSE30169, GSE6584) were achieved from the Gene Expression Omnibus database. Functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to examine the biological functions of identified differentially expressed genes (DEGs). A protein-protein interaction (PPI) network was mapped using Cytoscape software. Results 91 DEGs were identified, including 68 up-regulated genes and 23 down-regulated genes. Functional enrichment analysis indicated that DEGs genes were significantly enriched in ferroptosis, TNF signaling pathway, IL-17 signaling pathway. 12 nodes with the highest degrees were selected as hub genes. CCL2, CXCL1, IL6, and DUSP1 can serve as a sensitive diagnostic indicator for the early stage of atherosclerosis; CEBPB and HMOX1 can serve as a diagnostic indicator for diabetic atherosclerosis; TRIB3 is a sensitive marker indicating atherosclerosis risks in diabetic women group.Conclusions In conclusion, we have identified key candidate genes that indicate the diagnosis of patients with atherosclerosis, and these genes may serve as potential therapeutic or drug development targets for atherosclerosis.

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