Investigation of a common gene expression signature in gastrointestinal cancers using systems biology approaches

2017 ◽  
Vol 13 (11) ◽  
pp. 2277-2288 ◽  
Author(s):  
Kaveh Baghaei ◽  
Nazanin Hosseinkhan ◽  
Hamid Asadzadeh Aghdaei ◽  
M. R. Zali
Keyword(s):  
Gene Expression ◽  
Systems Biology ◽  
Mortality Rate ◽  
Gene Expression Signature ◽  
Gastrointestinal Cancers ◽  
Common Gene ◽  
Expression Signature ◽  
Liver Cancers ◽  
Gi Cancers

According to GLOBOCAN 2012, the incidence and the mortality rate of colorectal, stomach and liver cancers are the highest among the total gastrointestinal (GI) cancers.

scholarly journals Aneuploid IMR90 cells induced by depletion of pRB, DNMT1 and MAD2 show a common gene expression signature

Genomics ◽  
2020 ◽  
Vol 112 (3) ◽  
pp. 2541-2549
Author(s):  
Danilo Cilluffo ◽  
Viviana Barra ◽  
Sergio Spatafora ◽  
Claudia Coronnello ◽  
Flavia Contino ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Signature ◽  
Common Gene ◽  
Expression Signature

scholarly journals Identification of a Common Gene Expression Signature in Dilated Cardiomyopathy Across Independent Microarray Studies

2006 ◽  
Vol 48 (8) ◽  
pp. 1610-1617 ◽  
Author(s):  
Andreas S. Barth ◽  
Ruprecht Kuner ◽  
Andreas Buness ◽  
Markus Ruschhaupt ◽  
Sylvia Merk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dilated Cardiomyopathy ◽  
Gene Expression Signature ◽  
Common Gene ◽  
Expression Signature ◽  
Microarray Studies

scholarly journals Systems Biology Approaches to Investigate Genetic and Epigenetic Molecular Progression Mechanisms for Identifying Gene Expression Signatures in Papillary Thyroid Cancer

2019 ◽  
Vol 20 (10) ◽  
pp. 2536
Author(s):  
Shan-Ju Yeh ◽  
Chien-Yu Lin ◽  
Cheng-Wei Li ◽  
Bor-Sen Chen
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Systems Biology ◽  
Papillary Thyroid Cancer ◽  
Gene Expression Signature ◽  
Detection Methods ◽  
Specific Gene ◽  
Potential Candidate ◽  
Papillary Thyroid ◽  
Expression Signature

Thyroid cancer is the most common endocrine cancer. Particularly, papillary thyroid cancer (PTC) accounts for the highest proportion of thyroid cancer. Up to now, there are few researches discussing the pathogenesis and progression mechanisms of PTC from the viewpoint of systems biology approaches. In this study, first we constructed the candidate genetic and epigenetic network (GEN) consisting of candidate protein–protein interaction network (PPIN) and candidate gene regulatory network (GRN) by big database mining. Secondly, system identification and system order detection methods were applied to prune candidate GEN via next-generation sequencing (NGS) and DNA methylation profiles to obtain the real GEN. After that, we extracted core GENs from real GENs by the principal network projection (PNP) method. To investigate the pathogenic and progression mechanisms in each stage of PTC, core GEN was denoted in respect of KEGG pathways. Finally, by comparing two successive core signaling pathways of PTC, we not only shed light on the causes of PTC progression, but also identified essential biomarkers with specific gene expression signature. Moreover, based on the identified gene expression signature, we suggested potential candidate drugs to prevent the progression of PTC with querying Connectivity Map (CMap).

scholarly journals Prognostic value of a novel glycolysis-related gene expression signature for gastrointestinal cancer in the Asian population

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rong Xia ◽  
Hua Tang ◽  
Jiemiao Shen ◽  
Shuyu Xu ◽  
Yinyin Liang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prognostic Value ◽  
Gene Expression Signature ◽  
Asian Population ◽  
Data Sets ◽  
Related Gene ◽  
Tissue Samples ◽  
Expression Signature ◽  
Normal Tissues ◽  
Gi Cancers

Abstract Background Globally, gastrointestinal (GI) cancer is one of the most prevalent malignant tumors. However, studies have not established glycolysis-related gene signatures that can be used to construct accurate prognostic models for GI cancers in the Asian population. Herein, we aimed at establishing a novel glycolysis-related gene expression signature to predict the prognosis of GI cancers. Methods First, we evaluated the mRNA expression profiles and the corresponding clinical data of 296 Asian GI cancer patients in The Cancer Genome Atlas (TCGA) database (TCGA-LIHC, TCGA-STAD, TCGA-ESCA, TCGA-PAAD, TCGA-COAD, TCGA-CHOL and TCGA-READ). Differentially expressed mRNAs between GI tumors and normal tissues were investigated. Gene Set Enrichment Analysis (GSEA) was performed to identify glycolysis-related genes. Then, univariate, LASSO regression and multivariate Cox regression analyses were performed to establish a key prognostic glycolysis-related gene expression signature. The Kaplan-Meier and receiver operating characteristic (ROC) curves were used to evaluate the efficiency and accuracy of survival prediction. Finally, a risk score to predict the prognosis of GI cancers was calculated and validated using the TCGA data sets. Furthermore, this risk score was verified in two Gene Expression Omnibus (GEO) data sets (GSE116174 and GSE84433) and in 28 pairs of tissue samples. Results Prognosis-related genes (NUP85, HAX1, GNPDA1, HDLBP and GPD1) among the differentially expressed glycolysis-related genes were screened and identified. The five-gene expression signature was used to assign patients into high- and low-risk groups (p < 0.05) and it showed a satisfactory prognostic value for overall survival (OS, p = 6.383 × 10–6). The ROC curve analysis revealed that this model has a high sensitivity and specificity (0.757 at 5 years). Besides, stratification analysis showed that the prognostic value of the five-gene signature was independent of other clinical characteristics, and it could markedly discriminate between GI tumor tissues and normal tissues. Finally, the expression levels of the five prognosis-related genes in the clinical tissue samples were consistent with the results from the TCGA data sets. Conclusions Based on the five glycolysis-related genes (NUP85, HAX1, GNPDA1, HDLBP and GPD1), and in combination with clinical characteristics, this model can independently predict the OS of GI cancers in Asian patients.

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