scholarly journals Identification of Issue Inhibitor of Metalloproteinase 1 (TIMP1) as a Potential Biomarker for the Diagnosis, Pathogenesis and Prognosis of Colorectal Cancer via Integrated Bioinformatic Analysis

2020 ◽  
Author(s):  
Guona Li ◽  
Mengmeng Kang ◽  
Siyuan Sheng ◽  
Ziyi Chen ◽  
Kunshan Li ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Network Analysis ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Molecular Therapy ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Pathway Enrichment ◽  
Potential Biomarker

Abstract Background: Colorectal cancer (CRC) is a common malignant tumor of the digestive system. It is crucial to screen potential biomarkers for the diagnosis, pathogenesis, and prognosis of CRC because there are limited clinical symptoms associated with this cancer. Therefore, we attempted to identify biomarkers associated with the occurrence and progression of CRC by utilizing bioinformatic analysis and to elucidate a molecular mechanism for the diagnosis and treatment of CRC. Methods: Two independent gene expression profile datasets of colonic neoplasms (GSE44076 and GSE37182) were collected from public GEO datasets, which included 182 tumor tissues and 236 normal tissues. Next, differentially expressed genes (DEGs) between CRC colonic samples and non-CRC colonic samples were obtained via GEO2R online tools. Subsequently, hub genes were selected by several analyses of DEGs, including GO pathway enrichment analysis, KEGG pathway enrichment analysis, and PPI network analysis. Finally, the correlation between the hub genes and the occurrence of CRC was tested by harnessing survival analysis and ROC curve analysis. Results: Sixty-one shared DEGs were screened, including 44 high-expression genes and 17 low-expression genes, in CRC samples. Four genes (MYC, TIMP1, MMP7, and COL1A1) were considered to be hub genes because they exhibited higher connectivity degree scores through PPI network analysis. More importantly, there was a significant correlation between increased expression of TIMP1 and reduced survival time in patients with colorectal cancer. Conclusion: By using bioinformatic analysis, this study suggested that Timp-1 may represent a potential biomarker for the diagnosis and prognosis of targeted molecular therapy for CRC.

scholarly journals Identification of Differentially Expressed Genes and associated pathways common to Eyelid and Non-Ocular Basal Cell Carcinoma to understand the Molecular Biology of BCC

2021 ◽  
Author(s):  
Perumal Jayaraj ◽  
Seema Sen ◽  
Pranjal Vats ◽  
Shefali Dahiya ◽  
Vanshika Mohindroo
Keyword(s):  
Basal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Basal Cell ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Malignant Neoplasms ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Pathway Enrichment

Background: Eyelid BCC accounts for more than 90% of Eyelid malignant neoplasms. Various aberrant signalling pathways and genes in Non-Ocular BCC have been found whereas Eyelid bcc remains elusive. Objective: This study aims to find the common DEGs of Eyelid and Non-Ocular BCC using bioinformatic analysis and text mining to gain more insights into the molecular aspects common to both BCC non-ocular and Eyelid BCC and to identify common potential prognostic markers. Material and method: The Gene Expression profiles of Eyelid BCC (GSE103439) and Non-Ocular BCC (GSE53462) were obtained from the NCBI GEO database followed by identification of common DEGs. Protein-Protein interaction and Pathway Enrichment analysis of these screened genes was done using bioinformatic tools like STRING, Cytoscape and BiNGO, DAVID, KEGG respectively. Results: A total of 181 genes were found common in both datasets. A PPI network was formed for the screened genes and 20 HUB genes were sorted which included CTNNB1, MAPK14, BTRC, EGFR, ADAM17. Pathway enrichment of HUB genes showed that they were dysregulated in carcinogenic and apoptotic pathways that seem to play a role in the progression of both the BCC. Conclusion: The result and findings of bioinformatic analysis highlighted the molecular pathways and genes enriched in both Eyelid BCC as well as Non- Ocular BCC. The identified pathways should be studied further to recognise common molecular events that would lead to the progression of BCC. This may provide a window to explore the prognostic and therapeutic strategies common to both BCC. Keywords: Basal cell carcinoma (BCC), Cancer, Microarray, Ophthalmology, Tumour marker

scholarly journals Prediction and Validation of Hub Genes Associated with Colorectal Cancer by Integrating PPI Network and Gene Expression Data

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Yongfu Xiong ◽  
Wenxian You ◽  
Rong Wang ◽  
Linglong Peng ◽  
Zhongxue Fu
Keyword(s):  
Colorectal Cancer ◽  
Mrna Expression ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Advanced Tumor Stage ◽  
Ppi Network ◽  
Hub Genes ◽  
Clinical Value ◽  
Advanced Tumor

Although hundreds of colorectal cancer- (CRC-) related genes have been screened, the significant hub genes still need to be further identified. The aim of this study was to identify the hub genes based on protein-protein interaction network and uncover their clinical value. Firstly, 645 CRC patients’ data from the Tumor Cancer Genome Atlas were downloaded and analyzed to screen the differential expression genes (DEGs). And then, the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed, and PPI network of the DEGs was constructed by Cytoscape software. Finally, four hub genes (CXCL3, ELF5, TIMP1, and PHLPP2) were obtained from four subnets and further validated in our clinical setting and TCGA dataset. The results showed that mRNA expression of CXCL3, ELF5, and TIMP1 was increased in CRC tissues, whereas PHLPP2 mRNA expression was decreased. More importantly, high expression of CXCL3, ELF5, and TIMP1 was significantly associated with lymphatic invasion, distance metastasis, and advanced tumor stage. In addition, a shorter overall survival was observed in patients with increased CXCL3, TIMP1, and ELF5 expression and decreased PHLPP2 expression. In conclusion, the four hub genes screened by our strategy could serve as novel biomarkers for prognosis prediction of CRC patients.

scholarly journals Multi‐omics analysis reveals the interaction between the complement system and the coagulation cascade in the development of endometriosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liang Yu ◽  
Huaji Shen ◽  
Xiaohan Ren ◽  
Anqi Wang ◽  
Shu Zhu ◽  
...  
Keyword(s):  
Complement System ◽  
Quantitative Polymerase Chain Reaction ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Coagulation Cascade ◽  
Coagulation System ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Pathway Enrichment

AbstractEndometriosis (EMS) is a disease that shows immune dysfunction and chronic inflammation characteristics, suggesting a role of complement system in its pathophysiology. To find out the hub genes and pathways involved in the pathogenesis of EMs, three raw microarray datasets were recruited from the Gene Expression Omnibus database (GEO). Then, a series of bioinformatics technologies including gene ontology (GO), Hallmark pathway enrichment, protein–protein interaction (PPI) network and gene co-expression correlation analysis were performed to identify hub genes. The hub genes were further verified by the Real-time quantitative polymerase chain reaction (RT-PCR) and Western Blot (WB). We identified 129 differentially expressed genes (DEGs) in EMs, of which 78 were up-regulated and 51 were down-regulated. Through GO functional enrichment analysis, we found that the DEGs are mainly enriched in cell adhesion, extracellular matrix remodeling, chemokine regulation, angiogenesis regulation, epithelial cell proliferation, et al. In Hallmark pathway enrichment analysis, coagulation pathway showed great significance and the terms in which included the central complement factors. Moreover, the genes were dominating in PPI network. Combined co-expression analysis with experimental verification, we found that the up-regulated expression of complement (C1S, C1QA, C1R, and C3) was positively related to tissue factor (TF) in EMs. In this study, we discovered the over expression complement and the positive correlation between complement and TF in EMs, which suggested that interaction of complement and coagulation system may play a role within the pathophysiology of EMS.

scholarly journals Weighted gene co-expression network analysis identifies modules and functionally enriched pathways in the lactation process

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohammad Farhadian ◽  
Seyed Abbas Rafat ◽  
Bahman Panahi ◽  
Christopher Mayack
Keyword(s):  
Network Analysis ◽  
Regulatory Networks ◽  
Kegg Pathway ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Phenotypic Traits ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Pathway Enrichment ◽  
Dependent Protein

AbstractThe exponential growth in knowledge has resulted in a better understanding of the lactation process in a wide variety of animals. However, the underlying genetic mechanisms are not yet clearly known. In order to identify the mechanisms involved in the lactation process, various mehods, including meta-analysis, weighted gene co-express network analysis (WGCNA), hub genes identification, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment at before peak (BP), peak (P), and after peak (AP) stages of the lactation processes have been employed. A total of 104, 85, and 26 differentially expressed genes were identified based on PB vs. P, BP vs. AP, and P vs. AP comparisons, respectively. GO and KEGG pathway enrichment analysis revealed that DEGs were significantly enriched in the “ubiquitin-dependent ERAD” and the “chaperone cofactor-dependent protein refolding” in BP vs. P and P vs. P, respectively. WGCNA identified five significant functional modules related to the lactation process. Moreover, GJA1, AP2A2, and NPAS3 were defined as hub genes in the identified modules, highlighting the importance of their regulatory impacts on the lactation process. The findings of this study provide new insights into the complex regulatory networks of the lactation process at three distinct stages, while suggesting several candidate genes that may be useful for future animal breeding programs. Furthermore, this study supports the notion that in combination with a meta-analysis, the WGCNA represents an opportunity to achieve a higher resolution analysis that can better predict the most important functional genes that might provide a more robust bio-signature for phenotypic traits, thus providing more suitable biomarker candidates for future studies.

scholarly journals Bioinformatic analysis reveals hub genes and pathways that promote melanoma metastasis

2020 ◽  
Author(s):  
Wenxing Su ◽  
Yi Guan ◽  
Biao Huang ◽  
Juanjuan Wang ◽  
Yuqian Wei ◽  
...  
Keyword(s):  
Differentially Expressed Gene ◽  
Primary Melanoma ◽  
Enrichment Analysis ◽  
Gene Interaction ◽  
Underlying Mechanism ◽  
Bioinformatic Analysis ◽  
Pathway Enrichment Analysis ◽  
Melanoma Metastasis ◽  
Ppi Network ◽  
Hub Genes

Abstract Background: Melanoma has the highest mortality rate of all skin tumors, and metastases are the major cause of death from it. The molecular mechanism leading to melanoma metastasis is currently unclear. Methods: With the goal of revealing the underlying mechanism, three data sets with accession numbers GSE8401, GSE46517 and GSE7956 were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed gene (DEG) of primary melanoma and metastatic melanoma, three kinds of analyses were performed, namely functional annotation, protein‐protein interaction (PPI) network and module construction, and co-expression and drug-gene interaction prediction analysis. Results: A total of 41 up-regulated genes and 79 down-regulated genes was selected for subsequent analyses. Results of pathway enrichment analysis showed that extracellular matrix organization and proteoglycans in cancer are closely related to melanoma metastasis. In addition, seven pivotal genes were identified from PPI network, including CXCL8, THBS1, COL3A1, TIMP3, KIT, DCN, and IGFBP5, which have all been verified in the TCGA database and clinical specimens, but only CXCL8, THBS1 and KIT had significant differences in expression. Conclusions: To conclude, CXCL8, THBS1 and KIT may be the hub genes in the metastasis of melanoma and thus may be regarded as therapeutic targets in the future.

scholarly journals Bioinformatic analysis reveals hub genes and pathways that promote melanoma metastasis

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Wenxing Su ◽  
Yi Guan ◽  
Biao Huang ◽  
Juanjuan Wang ◽  
Yuqian Wei ◽  
...  
Keyword(s):  
Differentially Expressed Gene ◽  
Primary Melanoma ◽  
Enrichment Analysis ◽  
Gene Interaction ◽  
Underlying Mechanism ◽  
Bioinformatic Analysis ◽  
Pathway Enrichment Analysis ◽  
Melanoma Metastasis ◽  
Ppi Network ◽  
Hub Genes

Abstract Background Melanoma has the highest mortality rate of all skin tumors, and metastases are the major cause of death from it. The molecular mechanism leading to melanoma metastasis is currently unclear. Methods With the goal of revealing the underlying mechanism, three data sets with accession numbers GSE8401, GSE46517 and GSE7956 were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed gene (DEG) of primary melanoma and metastatic melanoma, three kinds of analyses were performed, namely functional annotation, protein-protein interaction (PPI) network and module construction, and co-expression and drug-gene interaction prediction analysis. Results A total of 41 up-regulated genes and 79 down-regulated genes was selected for subsequent analyses. Results of pathway enrichment analysis showed that extracellular matrix organization and proteoglycans in cancer are closely related to melanoma metastasis. In addition, seven pivotal genes were identified from PPI network, including CXCL8, THBS1, COL3A1, TIMP3, KIT, DCN, and IGFBP5, which have all been verified in the TCGA database and clinical specimens, but only CXCL8, THBS1 and KIT had significant differences in expression. Conclusions To conclude, CXCL8, THBS1 and KIT may be the hub genes in the metastasis of melanoma and thus may be regarded as therapeutic targets in the future.

scholarly journals Screening Novel Drug Candidates for Kidney Renal Clear Cell Carcinoma Treatment: A Study on Differentially Expressed Genes through the Connectivity Map Database

2021 ◽  
pp. 1-12
Author(s):  
Bin Gao ◽  
Lijuan Wang ◽  
Na Zhang ◽  
Miaomiao Han ◽  
Yubo Zhang ◽  
...  
Keyword(s):  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Enrichment Analysis ◽  
Renal Clear Cell Carcinoma ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Connectivity Map ◽  
Hub Genes ◽  
Pathway Enrichment ◽  
Drug Candidates

<b><i>Objective:</i></b> Kidney renal clear cell carcinoma (KIRC) is a common cancer with high morbidity and mortality in renal cancer. Thus, the transcriptome data of KIRC patients in The Cancer Genome Atlas (TCGA) database were analyzed and drug candidates for the treatment of KIRC were explored through the connectivity map (CMap) database. <b><i>Methods:</i></b> The transcriptome data of KIRC patients were downloaded from TCGA database, and KIRC-associated hub genes were screened out through differential analysis and protein-protein interaction (PPI) network analysis. Afterward, the CMap database was used to select drug candidates for KIRC treatment, and the drug-targeted genes were obtained through the STITCH database. A PPI network was constructed by combining drug-targeted genes with hub genes that affected the pathogenesis of KIRC to obtain final hub genes. Finally, combining hub genes and KIRC-associated hub genes, the pathways affected by drugs were explored by pathway enrichment analysis. <b><i>Results:</i></b> A total of 2,312 differentially expressed genes were found in patients, which were concentrated in immune cell activity, cytokine, and chemokine secretion pathways. Drug screening disclosed 5 drug candidates for KIRC treatment: fedratinib, Ly344864, geldanamycin, AS-605240, and luminespib. Based on drug-targeted genes and KIRC-associated hub genes, 16 hub genes were screened out. Pathway enrichment analysis revealed that drugs mainly affected pathways such as neuroactive ligand pathways, cell adhesion, and chemokines. <b><i>Conclusion:</i></b> The above results indicated that fedratinib, LY 344864, geldanamycin, AS-605240, and luminespib could be used as candidates for KIRC therapy. The findings from this study will make contributions to the treatment of KIRC in the future.

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.

scholarly journals Identification and Interaction Analysis of Molecular Markers in Myocardial Infarction by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Hub Gene ◽  
Pathway Enrichment ◽  
Network Modules ◽  
Gene Regulatory

AbstractMyocardial infarction (MI) is the leading cardiovascular diseases in worldwide, yet relatively little is known about the genes and signaling pathways involved in MI progression. The present investigation aimed to elucidate potential crucial candidate genes and pathways in MI. expression profiling by high throughput sequencing dataset (GSE132143) was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 20 MI samples and 12 normal control samples. Differentially expressed genes (DEGs) were identified using t-tests in the DESeq2 R package. These DEGs were subsequently investigated by Gene Ontology (GO) and pathway enrichment analysis, a protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed and analyzed. Hub genes were validated by receiver operating characteristic curve (ROC) analysis. In total, 958 DEGs were identified, of which 480 were up regulated and 478 were down regulated. GO and pathway enrichment analysis results revealed that the DEGs were mainly enriched in, immune system, neuronal system, response to stimulus, and multicellular organismal process. A PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network was constructed by using Cytoscape software, and CFTR, CDK1, RPS13, RPS15A, RPS27, NOTCH1, MRPL12, NOS2, CCDC85B and ATN1 were identified as the hub genes. Our results highlight the important roles of the genes including CFTR, CDK1, RPS13, RPS15A, RPS27, NOTCH1, MRPL12, NOS2, CCDC85B and ATN1 in MI pathogenesis or therapeutic management.

scholarly journals Bioinformatic analysis reveals hub genes and pathways that promote melanoma metastasis

2020 ◽  
Author(s):  
wenxing su ◽  
yi guan ◽  
biao huang ◽  
juanjuan wang ◽  
yuqian wei ◽  
...  
Keyword(s):  
Differentially Expressed Gene ◽  
Primary Melanoma ◽  
Enrichment Analysis ◽  
Gene Interaction ◽  
Underlying Mechanism ◽  
Bioinformatic Analysis ◽  
Pathway Enrichment Analysis ◽  
Melanoma Metastasis ◽  
Ppi Network ◽  
Hub Genes

Abstract Background: Melanoma has the highest mortality rate of all skin tumors, and metastases are the major cause of death from it. The molecular mechanism leading to melanoma metastasis is currently unclear. Methods: With the goal of revealing the underlying mechanism, three data sets with accession numbers GSE8401, GSE46517 and GSE7956 were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed gene (DEG) of primary melanoma and metastatic melanoma, three kinds of analyses were performed, namely functional annotation, protein‐protein interaction (PPI) network and module construction, and co-expression and drug-gene interaction prediction analysis. Results: A total of 41 up-regulated genes and 79 down-regulated genes was selected for subsequent analyses. Results of pathway enrichment analysis showed that extracellular matrix organization and proteoglycans in cancer are closely related to melanoma metastasis. In addition, seven pivotal genes were identified from PPI network, including CXCL8, THBS1, COL3A1, TIMP3, KIT, DCN, and IGFBP5, which have all been verified in the TCGA database and clinical specimens, but only CXCL8, THBS1 and KIT had significant differences in expression. Conclusions: To conclude, CXCL8, THBS1 and KIT may be the hub genes in the metastasis of melanoma and thus may be regarded as therapeutic targets in the future.

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