scholarly journals Identification of potential core genes in colorectal carcinoma and key genes in colorectal cancer liver metastasis using bioinformatics analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lipeng Niu ◽  
Ce Gao ◽  
Yang Li
Keyword(s):  
Liver Metastasis ◽  
Colorectal Carcinoma ◽  
Malignant Tumors ◽  
Metastatic Cancer ◽  
Core Gene ◽  
Ppi Network ◽  
Hub Genes ◽  
Potential Core ◽  
Normal Tissues ◽  
Colon Cancer Liver Metastasis

AbstractColorectal carcinoma (CRC) is one of the most prevalent malignant tumors worldwide. Meanwhile, the majority of CRC related deaths results from liver metastasis. Gene expression profile of CRC patients with liver Metastasis was identified using 4 datasets. The data was analyzed using GEO2R tool. GO and KEGG pathway analysis were performed. PPI network of the DEGs between 1 and 2 gene sets was also constructed. The set 1 is named between primary CRC tissues and metastatic CRC tissues. The set 2 is named between primary CRC tissues and normal tissues. Finally, the prognostic value of hub genes was also analyzed. 35 DEGs (set 1) and 142 DEGs (set 2) were identified between CRC liver metastatic cancer patients. The PPI network was constructed using the top 10 set 1 hub genes which included AHSG, SERPINC1, FGA, F2, CP, ITIH2, APOA2, HPX, PLG, HRG and set 2 hub genes which included TIMP1, CXCL1, COL1A2, MMP1, AURKA, UBE2C, CXCL12, TOP2A, ALDH1A1 and PRKACB. Therefore, ITIH2 might represent the potential core gene for colon cancer liver metastasis. COL1A2 behaves as a key gene in colorectal carcinoma.

scholarly journals Integrated WGCNA and PPI Network to Screen Hub Genes Signatures for Infantile Hemangioma

2021 ◽  
Vol 11 ◽  
Author(s):  
Miao Xu ◽  
Tianxiang Ouyang ◽  
Kaiyang Lv ◽  
Xiaorong Ma
Keyword(s):  
Differential Expression ◽  
Enrichment Analysis ◽  
Infantile Hemangioma ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
Network Analyses ◽  
Cycle Arrest ◽  
Auc Value

BackgroundInfantile hemangioma (IH) is characterized by proliferation and regression.MethodsBased on the GSE127487 dataset, the differentially expressed genes (DEGs) between 6, 12, or 24 months and normal samples were screened, respectively. STEM software was used to screen the continued up-regulated or down-regulated in common genes. The modules were assessed by weighted gene co-expression network analysis (WGCNA). The enrichment analysis was performed to identified the biological function of important module genes. The area under curve (AUC) value and protein-protein interaction (PPI) network were used to identify hub genes. The differential expression of hub genes in IH and normal tissues was detected by qPCR.ResultsThere were 5,785, 4,712, and 2,149 DEGs between 6, 12, and 24 months and normal tissues. We found 1,218 DEGs were up-regulated or down-regulated expression simultaneously in common genes. They were identified as 10 co-expression modules. Module 3 and module 4 were positively or negatively correlated with the development of IH, respectively. These two module genes were significantly involved in immunity, cell cycle arrest and mTOR signaling pathway. The two module genes with AUC greater than 0.8 at different stages of IH were put into PPI network, and five genes with the highest degree were identified as hub genes. The differential expression of these genes was also verified by qRTPCR.ConclusionFive hub genes may distinguish for proliferative and regressive IH lesions. The WGCNA and PPI network analyses may help to clarify the molecular mechanism of IH at different stages.

scholarly journals ­ Comprehensive analysis of key genes associated with ceRNA networks in nasopharyngeal carcinoma based on bioinformatics analysis

2020 ◽  
Author(s):  
Yuanji Xu ◽  
Xinyi Huang ◽  
Wangzhong Ye ◽  
Yangfan Zhang ◽  
Changkun Li ◽  
...  
Keyword(s):  
Protein Kinase ◽  
Nasopharyngeal Carcinoma ◽  
Molecular Mechanisms ◽  
Mapk Pathway ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Indicators ◽  
High Morbidity ◽  
Ppi Network ◽  
Hub Genes ◽  
Normal Tissues

Abstract Background Nasopharyngeal carcinoma (NPC) is an epithelial malignancy with high morbidity rates in the east and southeast Asia. The molecular mechanisms of NPC remain largely unknown. We explored the pathogenesis, potential biomarkers, and prognostic indicators of NPC. Methods We analyzed mRNAs, long non-coding RNAs (lncRNAs), and microRNAs (miRNAs) in the whole transcriptome sequencing dataset of our hospital (five normal tissues vs. five NPC tissues) and six microarray datasets (62 normal tissues vs. 334 NPC tissues) downloaded from the Gene Expression Omnibus (GSE12452, GSE13597, GSE95166, GSE126683, and GSE70970, GSE43039). Differential expression analyses, gene ontology (GO) enrichment, kyoto encyclopedia of genes and genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) were conducted. The lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) networks were constructed using the miRanda and TargetScan database, and a protein-protein interaction (PPI) network of differentially expressed genes (DEGs) was built using Search Tool for the Retrieval of Interacting Genes (STRING) software. Hub genes were identified using Molecular Complex Detection (MCODE), NetworkAnalyzer, and CytoHubba. Results We identified 61 mRNAs, 14miRNAs, and 10 lncRNAs as DEGs related to NPC. Changes in NPC were enriched in the chromosomal region, sister chromatid segregation, and nuclear chromosome segregation. GSEA indicated that the mitogen-activated protein kinase (MAPK) pathway, phosphatidylinositol-3 OH kinase/protein kinase B (PI3K-Akt) pathway, apoptotic pathway, and tumor necrosis factor (TNF) were involved in the initiation and development of NPC. Finally, 20 hub genes were screened out via the PPI network. Conclusions Our findings could offer insights into the biological mechanisms underlying NPC and identify potential therapeutic targets, biomarkers and prognostic indicators for NPC.

scholarly journals Gene network in pulmonary tuberculosis based on bioinformatic analysis

2020 ◽  
Author(s):  
Lili Li ◽  
Jian Lv ◽  
Yuan He ◽  
Zhihua Wang
Keyword(s):  
Pulmonary Tuberculosis ◽  
Chemokine Receptors ◽  
Gene Network ◽  
Kegg Pathway ◽  
Bioinformatic Analysis ◽  
Core Gene ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Causal Genes

Abstract Background: Pulmonary tuberculosis (PTB) is one of the serious infectious diseases worldwide; however, the gene network involved in the host response remain largely unclear. Methods: This study integrated two cohorts profile datasets GSE34608 and GSE83456 to elucidate the potential gene network and signaling pathways in PTB. Differentially expressed genes (DEGs) were obtained for Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using Metascape database. Protein-Protein Interaction (PPI) network of DEGs was constructed by the online database the Search Tool for the Retrieval of Interacting Genes (STRING). Modules were identified by the plug-in APP Molecular Complex Detection (MCODE) in Cytoscape. GO and KEGG pathway of Module 1 were further analyzed by STRING. Hub genes were selected for further expression validation in dataset GSE19439. The gene expression level was also investigated in the dataset GSE31348 to display the change pattern during the PTB treatment. Results: Totally, 180 shared DEGs were identified from two datasets. Gene function and KEGG pathway enrichment revealed that DEGs mainly enriched in defense response to other organism, response to bacterium, myeloid leukocyte activation, cytokine production, etc. Seven modules were clustered based on PPI network. Module 1 contained 35 genes related to cytokine associated functions, among which 14 genes, including chemokine receptors, interferon-induced proteins and Toll-like receptors, were identified as hub genes. Expression levels of the hub genes were validated with a third dataset GSE19439. The signature of this core gene network showed significant response to Mycobacterium tuberculosis (Mtb) infection, and correlated with the gene network pattern during anti-PTB therapy. Conclusions: Our study unveils the coordination of causal genes during PTB infection, and provides a promising gene panel for PTB diagnosis. As major regulators of the host immune response to Mtb infection, the 14 hub genes are also potential molecular targets for developing PTB drugs.

scholarly journals Identification of Potential Biomarkers and Therapy Targets involved in Gastric Cancer Using Bioinformatics Analysis

2021 ◽  
Author(s):  
YangYang Teng ◽  
Na Shan ◽  
GuangRong Lu ◽  
LeYi Ni ◽  
ZeJun Gao ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Bioinformatics Analysis ◽  
Risk Model ◽  
Malignant Tumors ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Normal Tissues ◽  
Therapy Targets ◽  
Selection Operator ◽  
Cancer Tissues

Abstract Gastric cancer remains one of the five major malignant tumors in the world, posing a great threat to public life and health. As gene sequencing technology develops, it is urgent to find out specific molecular markers for cancer therapy. In this study, datasets of GSE13911, GSE30727, GSE63089 and GSE118916 were investigated by bioinformatics analysis, and differentially expressed genes (DEGs) between GC tissues and normal tissues were screened for potential cancer therapeutic targets. Furthermore, the GSE63089 dataset was analyzed by Weighted Gene Co-expression Network Analysis (WGCNA), and the highly related genes were clustered. Then, the hub genes were searched using co-expression network and Molecular Complex Detection (MCODE) plug-in from Cytoscape software. Finally, ASPM, COL11A1 and CDC20 were obtained by intersection of hub genes and DEGs. The expressions of ASPM, COL11A1 and CDC20 gene in gastric cancer tissues and normal tissues from TCGA database were detected. For these genes, the least absolute shrink and selection operator (LASSO) Cox expression analysis was used to establish the prognostic risk model. COL11A1 and CDC20 genes were identified as candidate prognostic risk markers for GC. Analysis using qRT-PCR has shown that COL11A1 and CDC20 were significant differentially expressed between gastric cancer tissues and normal gastric tissues (P < 0.01). In conclusion, our study identifies specific DEGs involved in ECM process and metabolism by cytochrome P450 process, and these DEGs may be potential targets for GC therapy. The model constructed by COL11A1 and CDC20 genes can predict the prognosis risk of GC patients. Taken together, these findings provide reference for further analyses of key alterations during GC progression.

scholarly journals ­ Comprehensive analysis of key genes associated with ceRNA networks in nasopharyngeal carcinoma based on bioinformatics analysis

2020 ◽  
Author(s):  
Yuanji Xu ◽  
Xinyi Huang ◽  
Wangzhong Ye ◽  
Yangfan Zhang ◽  
Changkun Li ◽  
...  
Keyword(s):  
Protein Kinase ◽  
Nasopharyngeal Carcinoma ◽  
Molecular Mechanisms ◽  
Mapk Pathway ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Indicators ◽  
High Morbidity ◽  
Ppi Network ◽  
Hub Genes ◽  
Normal Tissues

Abstract Background. Nasopharyngeal carcinoma (NPC) is an epithelial malignancy with high morbidity rates in the east and southeast Asia. The molecular mechanisms of NPC remain largely unknown. We explored the pathogenesis, potential biomarkers, and prognostic indicators of NPC.Methods. We analyzed mRNAs, long non-coding RNAs (lncRNAs), and microRNAs (miRNAs) in the whole transcriptome sequencing dataset of our hospital (five normal tissues vs. five NPC tissues) and six microarray datasets (62 normal tissues vs. 334 NPC tissues) downloaded from the Gene Expression Omnibus (GSE12452, GSE13597, GSE95166, GSE126683, and GSE70970, GSE43039). Differential expression analyses, gene ontology (GO) enrichment, kyoto encyclopedia of genes and genomes (KEGG) analysis, and gene set enrichment analysis (GSEA) were conducted. The lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) networks were constructed using the miRanda and TargetScan database, and a protein-protein interaction (PPI) network of differentially expressed genes (DEGs) was built using Search Tool for the Retrieval of Interacting Genes (STRING) software. Hub genes were identified using Molecular Complex Detection (MCODE), NetworkAnalyzer, and CytoHubba.Results. We identified 61 mRNAs, 14miRNAs, and 10 lncRNAs as shared DEGs related to NPC in seven datasets. Changes in NPC were enriched in the chromosomal region, sister chromatid segregation, and nuclear chromosome segregation. GSEA indicated that the mitogen-activated protein kinase (MAPK) pathway, phosphatidylinositol-3 OH kinase/protein kinase B (PI3K-Akt) pathway, apoptotic pathway, and tumor necrosis factor (TNF) were involved in the initiation and development of NPC. Finally, 20 hub genes were screened out via the PPI network.Conclusions. Several DEGs and their biological processes, pathways, and interrelations were found in our current study by bioinformatics analyses. Our findings may offer insights into the biological mechanisms underlying NPC and identify potential therapeutic targets for NPC.

scholarly journals Gene network in pulmonary tuberculosis based on bioinformatic analysis

2020 ◽  
Author(s):  
Lili Li ◽  
Jian Lv ◽  
Yuan He ◽  
Zhihua Wang
Keyword(s):  
Pulmonary Tuberculosis ◽  
Chemokine Receptors ◽  
Gene Network ◽  
Kegg Pathway ◽  
Bioinformatic Analysis ◽  
Core Gene ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Causal Genes

Abstract Background: Pulmonary tuberculosis (PTB) is one of the serious infectious diseases worldwide; however, the gene network involved in the host response remain largely unclear. Methods: This study integrated two cohorts profile datasets GSE34608 and GSE83456 to elucidate the potential gene network and signaling pathways in PTB. Differentially expressed genes (DEGs) were obtained for Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using Metascape database. Protein-Protein Interaction (PPI) network of DEGs was constructed by the online database the Search Tool for the Retrieval of Interacting Genes (STRING). Modules were identified by the plug-in APP Molecular Complex Detection (MCODE) in Cytoscape. GO and KEGG pathway of Module 1 were further analyzed by STRING. Hub genes were selected for further expression validation in dataset GSE19439. The gene expression level was also investigated in the dataset GSE31348 to display the change pattern during the PTB treatment. Results: Totally, 180 shared DEGs were identified from two datasets. Gene function and KEGG pathway enrichment revealed that DEGs mainly enriched in defense response to other organism, response to bacterium, myeloid leukocyte activation, cytokine production, etc. Seven modules were clustered based on PPI network. Module 1 contained 35 genes related to cytokine associated functions, among which 14 genes, including chemokine receptors, interferon-induced proteins and Toll-like receptors, were identified as hub genes. Expression levels of the hub genes were validated with a third dataset GSE19439. The signature of this core gene network showed significant response to Mycobacterium tuberculosis (Mtb) infection, and correlated with the gene network pattern during anti-PTB therapy. Conclusions: Our study unveils the coordination of causal genes during PTB infection, and provides a promising gene panel for PTB diagnosis. As major regulators of the host immune response to Mtb infection, the 14 hub genes are also potential molecular targets for developing PTB drugs.

scholarly journals The Identification of Core Gene Expression Signature in Hepatocellular Carcinoma

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Ning Li ◽  
Ling Li ◽  
Yongshun Chen
Keyword(s):  
Oxidative Stress ◽  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Financial Burden ◽  
Gene Expression Signature ◽  
Core Gene ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction

Hepatocellular carcinoma (HCC) is one of the most common malignancies, which causes serious financial burden worldwide. This study aims to investigate the potential mechanisms contributing to HCC and identify core biomarkers. The HCC gene expression profile GSE41804 was picked out to analyze the differentially expressed genes (DEGs). Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out using DAVID. We constructed a protein-protein interaction (PPI) network to visualize interactions of the DEGs. The survival analysis of these hub genes was conducted to evaluate their potential effects on HCC. In this analysis, 503 DEGs were captured (360 downregulated genes and 143 upregulated genes). Meanwhile, 15 hub genes were identified. GO analysis showed that the DEGs were mainly enriched in oxidative stress, cell cycle, and extracellular structure. KEGG analysis suggested the DEGs were enriched in the absorption, metabolism, and cell cycle pathway. PPI network disclosed that the top3 modules were mainly enriched in cell cycle, oxidative stress, and liver detoxification. In conclusion, our analysis uncovered that the alterations of oxidative stress and cell cycle are two major signatures of HCC. TOP2A, CCNB1, and KIF4A might promote the development of HCC, especially in proliferation and differentiation, which could be novel biomarkers and targets for diagnosis and treatment of HCC.

scholarly journals Identification of Key Genes and Immune Infiltrate in Nonalcoholic Steatohepatitis: A Bioinformatic Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zhen-yu Jiang ◽  
Yi Zhou ◽  
Lu Zhou ◽  
Shao-wei Li ◽  
Bang-mao Wang
Keyword(s):  
T Cells ◽  
Nonalcoholic Steatohepatitis ◽  
Γδ T Cells ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Normal Tissues ◽  
Key Genes

Background. Nonalcoholic steatohepatitis (NASH) can progress to cirrhosis and hepatic carcinoma and is closely associated with changes in the neurological environment. The discovery of new biomarkers would aid in the treatment of NASH. Methods. Data GSE89632 were downloaded from the Gene Expression Omnibus (GEO) database, and R package “limma” was used to identify differentially expressed genes (DEGs) for NASH vs. normal tissues. The STRING database was used to construct a protein-protein interaction (PPI) network, and the Cytoscape software program (Version 3.80) was used to visualize the PPI network and identify key genes. The immune infiltration of NASH was determined using the R package “CIBERSORT”. Results. We screened 41 DEGs. GO and KEGG enrichment analyses of the DEGs revealed the enrichment of pathways related to NAFLD steatosis and inflammation. A PPI network analysis was also performed on the DEGs, and seven genes (MYC, CXCL8, FOS, SOCS1, SOCS3, IL6, and PTGS2) were identified as hub genes. An immune infiltration assessment revealed that macrophages M2, memory resting CD4+ T cells, and γΔ T cells play important roles in the immune microenvironment of NASH, which may be mediated by the seven identified hub genes.

scholarly journals Identification of Potential Core Genes in Parkinson’s Disease Using Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wei Quan ◽  
Jia Li ◽  
Xiya Jin ◽  
Li Liu ◽  
Qinghui Zhang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Substantia Nigra ◽  
Metabolic Pathways ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Potential Core ◽  
Core Genes

Purpose. This study aimed to explore new core genes related to the occurrence of Parkinson’s disease (PD) and core genes that can lead to the progression of PD. Methods. The expression profile data of GSE42966, which contained six substantia nigra tissues isolated from normal individuals and nine substantia nigra tissues isolated from patients with PD, were obtained from Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified, followed by functional enrichment analysis and protein-protein interaction (PPI) network construction. We then identified 10 hub genes and analyzed their expression in different Braak stages. Results. A total of 773 DEGs were identified that were significantly enriched in metabolic pathways. Ten hub genes were identified through the PPI network, namely, GNG3, MAPK1, FPR1, ATP5B, GNG2, PRKACA, HRAS, HSPA8, PSAP, and GABBR2. The expression of HRAS was different in patients with PD with Braak stages 3 and 4. Conclusion. These 10 hub genes and the metabolic pathways they are enriched in may be involved in the pathogenesis of PD. HRAS may have potential value in predicting the progression of PD.

scholarly journals Identification of 10 Hub genes related to the progression of colorectal cancer by co-expression analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9633
Author(s):  
Jie Meng ◽  
Rui Su ◽  
Yun Liao ◽  
Yanyan Li ◽  
Ling Li
Keyword(s):  
Colorectal Cancer ◽  
Network Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Data Sets ◽  
Ppi Network ◽  
Hub Genes ◽  
Normal Tissues ◽  
Go Enrichment ◽  
Go Enrichment Analysis

Background Colorectal cancer (CRC) is the third most common cancer in the world. The present study is aimed at identifying hub genes associated with the progression of CRC. Method The data of the patients with CRC were obtained from the Gene Expression Omnibus (GEO) database and assessed by weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses performed in R by WGCNA, several hub genes that regulate the mechanism of tumorigenesis in CRC were identified. Differentially expressed genes in the data sets GSE28000 and GSE42284 were used to construct a co-expression network for WGCNA. The yellow, black and blue modules associated with CRC level were filtered. Combining the co-expression network and the PPI network, 15 candidate hub genes were screened. Results After validation using the TCGA-COAD dataset, a total of 10 hub genes (MT1X, MT1G, MT2A, CXCL8, IL1B, CXCL5, CXCL11, IL10RA, GZMB, KIT) closely related to the progression of CRC were identified. The expressions of MT1G, CXCL8, IL1B, CXCL5, CXCL11 and GZMB in CRC tissues were higher than normal tissues (p-value < 0.05). The expressions of MT1X, MT2A, IL10RA and KIT in CRC tissues were lower than normal tissues (p-value < 0.05). Conclusions By combinating with a series of methods including GO enrichment analysis, KEGG pathway analysis, PPI network analysis and gene co-expression network analysis, we identified 10 hub genes that were associated with the progression of CRC.

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