Gene Set Enrichment Analysis (GSEA) for Interpreting Gene Expression Profiles

Background: The presence of diverse types of nanomaterials (NMs) in commerce is growing at an exponential pace. As a result, human exposure to these materials in the environment is inevitable, necessitating the need for rapid and reliable toxicity testing methods to accurately assess the potential hazards associated with NMs. In this study, we applied biclustering and gene set enrichment analysis methods to derive essential features of altered lung transcriptome following exposure to NMs that are associated with lung-specific diseases. Several datasets from public microarray repositories describing pulmonary diseases in mouse models following exposure to a variety of substances were examined and functionally related biclusters of genes showing similar expression profiles were identified. The identified biclusters were then used to conduct a gene set enrichment analysis on pulmonary gene expression profiles derived from mice exposed to nano-titanium dioxide (nano-TiO2), carbon black (CB) or carbon nanotubes (CNTs) to determine the disease significance of these data-driven gene sets. Results: Biclusters representing inflammation (chemokine activity), DNA binding, cell cycle, apoptosis, reactive oxygen species (ROS) and fibrosis processes were identified. All of the NM studies were significant with respect to the bicluster related to chemokine activity (DAVID; FDR p-value = 0.032). The bicluster related to pulmonary fibrosis was enriched in studies where toxicity induced by CNT and CB studies was investigated, suggesting the potential for these materials to induce lung fibrosis. The pro-fibrogenic potential of CNTs is well established. Although CB has not been shown to induce fibrosis, it induces stronger inflammatory, oxidative stress and DNA damage responses than nano-TiO2 particles. Conclusion: The results of the analysis correctly identified all NMs to be inflammogenic and only CB and CNTs as potentially fibrogenic. In addition to identifying several previously defined, functionally relevant gene sets, the present study also identified two novel genes sets: a gene set associated with pulmonary fibrosis and a gene set associated with ROS, underlining the advantage of using a data-driven approach to identify novel, functionally related gene sets. The results can be used in future gene set enrichment analysis studies involving NMs or as features for clustering and classifying NMs of diverse properties.

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Gene set enrichment analysis highlights different gene expression profiles in whole blood samples X-irradiated with low and high doses

International Journal of Radiation Biology ◽

10.3109/09553002.2013.782448 ◽

2013 ◽

Vol 89 (8) ◽

pp. 628-638 ◽

Cited By ~ 44

Author(s):

Houssein El-Saghire ◽

Hubert Thierens ◽

Pieter Monsieurs ◽

Arlette Michaux ◽

Charlot Vandevoorde ◽

...

Keyword(s):

Gene Expression ◽

Whole Blood ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Blood Samples ◽

Whole Blood Samples ◽

High Doses

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Revealing Biological Pathways Implicated in Lung Cancer from TCGA Gene Expression Data Using Gene Set Enrichment Analysis

Cancer Informatics ◽

10.4137/cin.s13882 ◽

2014 ◽

Vol 13s1 ◽

pp. CIN.S13882 ◽

Cited By ~ 4

Author(s):

Binghuang Cai ◽

Xia Jiang

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Gene Expression Data ◽

Lung Squamous Cell Carcinoma ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Expression Data ◽

Gene Set Enrichment ◽

Gene Set ◽

Pathway Gene

Analyzing biological system abnormalities in cancer patients based on measures of biological entities, such as gene expression levels, is an important and challenging problem. This paper applies existing methods, Gene Set Enrichment Analysis and Signaling Pathway Impact Analysis, to pathway abnormality analysis in lung cancer using microarray gene expression data. Gene expression data from studies of Lung Squamous Cell Carcinoma (LUSC) in The Cancer Genome Atlas project, and pathway gene set data from the Kyoto Encyclopedia of Genes and Genomes were used to analyze the relationship between pathways and phenotypes. Results, in the form of pathway rankings, indicate that some pathways may behave abnormally in LUSC. For example, both the cell cycle and viral carcinogenesis pathways ranked very high in LUSC. Furthermore, some pathways that are known to be associated with cancer, such as the p53 and the PI3K-Akt signal transduction pathways, were found to rank high in LUSC. Other pathways, such as bladder cancer and thyroid cancer pathways, were also ranked high in LUSC.

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Relationship Between San-Huang-Xie-Xin-Tang and Its Herbal Components on the Gene Expression Profiles in HepG2 Cells

The American Journal of Chinese Medicine ◽

10.1142/s0192415x08006235 ◽

2008 ◽

Vol 36 (04) ◽

pp. 783-797 ◽

Cited By ~ 43

Author(s):

Wen-Yu Cheng ◽

Shih-Lu Wu ◽

Chien-Yun Hsiang ◽

Chia-Cheng Li ◽

Tung-Yuan Lai ◽

...

Keyword(s):

Gene Expression ◽

Hepg2 Cells ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Major Effect ◽

Gene Set Enrichment Analysis ◽

P53 Signaling ◽

The Relationship ◽

Herbal Components

Traditional Chinese medicine (TCM) has been used for thousands of years. Most Chinese herbal formulae consist of several herbal components and have been used to treat various diseases. However, the mechanisms of most formulae and the relationship between formulae and their components remain to be elucidated. Here we analyzed the putative mechanism of San-Huang-Xie-Xin-Tang (SHXXT) and defined the relationship between SHXXT and its herbal components by microarray technique. HepG2 cells were treated with SHXXT or its components and the gene expression profiles were analyzed by DNA microarray. Gene set enrichment analysis indicated that SHXXT and its components displayed a unique anti-proliferation pattern via p53 signaling, p53 activated, and DNA damage signaling pathways in HepG2 cells. Network analysis showed that most genes were regulated by one molecule, p53. In addition, hierarchical clustering analysis showed that Rhizoma Coptis shared a similar gene expression profile with SHXXT. These findings may explain why Rhizoma Coptis is the principle herb that exerts the major effect in the herbal formula, SHXXT. Moreover, this is the first report to reveal the relationship between formulae and their herbal components in TCM by microarray and bioinformatics tools.

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Expression Profiles and Prognostic Values of BOLA Family members in Ovarian Cancer

10.21203/rs.3.rs-95044/v1 ◽

2020 ◽

Author(s):

Mingyang Zhu ◽

Shiqi Xiao

Keyword(s):

Ovarian Cancer ◽

Metal Ion ◽

Expression Profiles ◽

Family Members ◽

Enrichment Analysis ◽

Genetic Alterations ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Gene Set ◽

Protein Disulfide Oxidoreductase

Abstract Background: The BOLA gene family, comprising 3 members, is mainly involved in the regulation of intracellular iron homeostasis. Emerging evidence suggests that BOLA family member 2 play vital roles in tumorigenesis and progression of hepatic cellular carcinoma. However, little known about its roles in ovarian cancer. Methods: In present study, we investigated the expression profiles, prognostic roles, and genetic alterations of three BOLA family members in patients with ovarian cancer through several public databases, containing Oncomine and Gene Expression Profiling Interactive Analysis, Human Protein Atlas, Kaplan–Meier plotter and cBioPortal. Then, we constructed the protein–protein interaction networks of BOLA proteins and their interactors by using String database and Cytoscape software. In addition, we performed the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment by the Annotation, Visualization, and Integrated Discovery database. Finally, we explored the mechanisms underlying the involvement of BOLA family members in OC by using gene set enrichment analysis. Results: The mRNA and protein expression levels of BOLA2 and BOLA3 were heavily higher in ovarian cancer tissues than that in normal ovarian tissues. Dysregulated mRNA expressions of three BOLA family members were significantly associated with prognosis in overall or subgroup analysis. Moreover, genetic alterations also occurred in three BOLA family members in ovarian cancer. Network analysis and enrichment analysis indicated that three BOLA family members and their 20 interactors were mainly associated with metal-ion binding and protein disulfide oxidoreductase activity. Gene set enrichment analysis indicated that BOLA family members were mainly associated with oxidative phosphorylation, proteasome, protein export and glutathione metabolism in ovarian cancer. Conclusions: In brief, the present comprehensive bioinformatics analysis revealed that BOLA1, 2, and 3 may be new prognostic biomarkers, and BOLA2 and BOLA3 may be a potential therapeutic target of precision therapy for patients with ovarian cancer, but further studies are demanded to certify this notion.

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A Nomogram Based on Glycolysis-related Gene Expression Profilings Serve as a Novel Prognosis Risk Classifiers for Human Hepatocellular Carcinoma

10.21203/rs.3.rs-324874/v1 ◽

2021 ◽

Author(s):

Lingyu Zhang ◽

Yu Li ◽

Yibei Dai ◽

Danhua Wang ◽

Xuchu Wang ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Expression Profiles ◽

Area Under The Curve ◽

Prognostic Index ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Related Gene ◽

Metabolic Pattern

Abstract Metabolic pattern reconstruction is an important element in tumor progression. The metabolism of tumor cells is characterized by the abnormal increase of anaerobic glycolysis, regardless of the higher oxygen concentration, resulting in a large accumulation of energy from glucose sources, and contributes to rapid cell proliferation and tumor growth which is further referenced as the Warburg effect. We tried to reconstruct the metabolic pattern in the progression of cancer to screen which genetic changes are specific in cancer cells. A total of 12 common types of solid tumors were enrolled in the prospective study. Gene set enrichment analysis (GSEA) was implemented to analyze 9 glycolysis-related gene sets, which are closely related to the glycolysis process. Univariate and multivariate analyses were used to identify independent prognostic variables for the construction of a nomogram based on clinicopathological characteristics and a glycolysis-related gene prognostic index (GRGPI). The prognostic model based on glycolysis genes has the highest area under the curve (AUC) in LIHC (Liver hepatocellular carcinoma). 8-gene signatures (AURKA, CDK1, CENPA, DEPDC1, HMMR, KIF20A, PFKFB4, STMN1) were related to overall survival (OS) and recurrence-free survival (RFS). Further analysis demonstrates that the prediction model can accurately distinguish between high- and low-risk cancer patients among patients in different clusters in LIHC. A nomogram with a well-fitted calibration curve based on gene expression profiles and clinical characteristics improves discrimination in internal and external cohorts. Furthermore, the altering expression of metabolic genes related to glycolysis may contribute to the reconstruction of the tumor-related microenvironment.

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