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

Cai, Bin; Jiang, Xia

doi:10.4137/cin.s13882

Cited by 9 publications

(10 citation statements)

References 36 publications

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“…In the last decades, many studies have focused on the pathways related to cell growth and death in tumor formation. More and more results showed that cell cycle pathway and p53 signaling pathway played a key role in the formation of malignant tumors (9,(13)(14)(15). In this study, the researchers observed that cell cycle pathway (all P value < 0.001, from GSEA results of four independent microarray data) and p53 signaling pathway (all P value < 0.001, from GSEA results of four independent microarray data) were positively regulated in NSCLC.…”

Section: Discussionmentioning

confidence: 63%

“…Among some frequently used gene set analysis methods, gene set enrichment analysis (GSEA) is the most well-known and widely-used approach (7,8), through which the significant difference in expression of pre-defined gene set between two groups of data can be identified. The pre-defined gene set can be a set of genes in a gene ontology category, in a biological pathway, or can be user-defined (9). Recently, using GSEA method, some biological pathways, such as Ras signaling pathway and Wnt signaling pathway were identified to be significantly regulated in NSCLC (9,10), and explained the biological mechanisms of NSCLC.…”

Section: Introductionmentioning

confidence: 99%

“…The pre-defined gene set can be a set of genes in a gene ontology category, in a biological pathway, or can be user-defined (9). Recently, using GSEA method, some biological pathways, such as Ras signaling pathway and Wnt signaling pathway were identified to be significantly regulated in NSCLC (9,10), and explained the biological mechanisms of NSCLC. However, these studies mainly aimed at lung squamous cell carcinoma (LUSC), which is one of the major subtypes of NSCLC, or immune gene sets in females with NSCLC (9,10).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, using GSEA method, some biological pathways, such as Ras signaling pathway and Wnt signaling pathway were identified to be significantly regulated in NSCLC (9,10), and explained the biological mechanisms of NSCLC. However, these studies mainly aimed at lung squamous cell carcinoma (LUSC), which is one of the major subtypes of NSCLC, or immune gene sets in females with NSCLC (9,10). The identified biological pathways represented a fraction of the pathways implicated in NSCLC, and the biological pathways implicated in NSCLC need to be further identified systematically.…”

Section: Introductionmentioning

confidence: 99%

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Cross-Sectional Study of Gene Expression Analysis Identifies Critical Biological Pathways and Key Genes Implicated in Non-Small Cell Lung Cancer

Wang

et al. 2018

Iran Red Crescent Med J

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Background: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, which accounts for about 85% of all lung cancer types. However, critical biological pathways and key genes implicated in NSCLC remain ambiguous. Objectives: The present study aimed at identifying the critical biological pathways and key genes implicated in NSCLC, and providing insight in the molecular mechanism underlying NSCLC. Methods: In this case-control bioinformatics study, the researchers used four microarray data of NSCLC from public gene expression omnibus (GEO) database at the national center for biotechnology information (NCBI) website. The microarray data came from studies of American, Spanish, and Taiwanese NSCLC patients, and in total contained 190 NSCLC tissue and 180 normal lung tissue. A standardized microarray preprocessing and gene set enrichment analysis (GSEA) were used to analyze each microarray data and obtained significantly regulated pathways. Venn analysis was used to identify the common significantly regulated biological pathways. Protein and protein interaction (PPI) network analysis was used to identify the key genes within common significantly regulated pathways. The PPI information was retrieved from STRING database, and cytoscape software was used to construct and visualize the PPI network. Results: Through integrating GSEA results of four microarray data, finally, the researchers identified 22 common up-regulated and 85 common down-regulated pathways. Many genes within 107 common significantly regulated pathways were significantly enriched within cell cycle pathway (P value of 2.58e-79) and focal adhesion pathway (P value of 2.44e-81). The PPI network showed that up-regulated CDK1 (P value = 1.33e-18 and logFC = 1.41) and down-regulated PIK3R1 (P value = 5.09e-22 and logFC = -1.13) genes shared the most abundant edges, and were associated with NSCLC. Conclusions: This cross-sectional study showed increased concordance between gene expression profiling data. These identified pathways and genes provide some insight in the molecular mechanisms of NSCLC, and the genes may serve as candidate diagnostic and therapeutic targets of NSCLC.

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Section: Discussionmentioning

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cross-Sectional Study of Gene Expression Analysis Identifies Critical Biological Pathways and Key Genes Implicated in Non-Small Cell Lung Cancer

Wang

et al. 2018

Iran Red Crescent Med J

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“…The top ranked pathways were the viral carcinogenesis pathway, the p53 signaling pathway, and the PI3K-Akt signaling pathway. [29] Another study investigated the regulatory effect of SOX2 on FOXA1 and found that SOX2 was a negative upstream regulator of FOXA1 . These two genes were observed to be altered in 46.4% of 179 TCGA LUSC samples, and the findings of the study may possess future promise in improving the diagnostic efficiency for LUSCs as SOX2 alterations are common for LUSC.…”

Section: Additional Research Based On Tcga Lung Cancer Data Setsmentioning

confidence: 99%

The impact of the Cancer Genome Atlas on lung cancer

Chang

Lee

Huang

2015

Translational Research

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The Cancer Genome Atlas (TCGA) has profiled over 10,000 samples derived from 33 types of cancer to date, with the goal of improving our understanding of the molecular basis of cancer and advancing our ability to diagnose, treat, and prevent cancer. This review focuses on lung cancer as it is the leading cause of cancer-related mortality worldwide in both men and women. Particularly, non-small cell lung cancers (including lung adenocarcinoma and lung squamous cell carcinoma) were evaluated. Our goal is to demonstrate the impact of TCGA on lung cancer research under four themes: namely, diagnostic markers, disease progression markers, novel therapeutic targets, and novel tools. Examples were given related to DNA mutation, copy number variation, mRNA, and microRNA expression along with methylation profiling.

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T2-DAG: a powerful test for differentially expressed gene pathways via graph-informed structural equation modeling

Jin

Wang

2021

Bioinformatics

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Motivation A major task in genetic studies is to identify genes related to human diseases and traits to understand functional characteristics of genetic mutations and enhance patient diagnosis. Compared with marginal analyses of individual genes, identification of gene pathways, i.e. a set of genes with known interactions that collectively contribute to specific biological functions, can provide more biologically meaningful results. Such gene pathway analysis can be formulated into a high-dimensional two-sample testing problem. Given the typically limited sample size of gene expression datasets, most existing two-sample tests tend to have compromised powers because they ignore or only inefficiently incorporate the auxiliary pathway information on gene interactions. Results We propose T2-DAG, a Hotelling’s T2-type test for detecting differentially expressed gene pathways, which efficiently leverages the auxiliary pathway information on gene interactions from existing pathway databases through a linear structural equation model. We further establish its asymptotic distribution under pertinent assumptions. Simulation studies under various scenarios show that T2-DAG outperforms several representative existing methods with well-controlled type-I error rates and substantially improved powers, even with incomplete or inaccurate pathway information or unadjusted confounding effects. We also illustrate the performance of T2-DAG in an application to detect differentially expressed KEGG pathways between different stages of lung cancer. Availability and implementation The R (R Development Core Team, 2021) package T2DAG which implements the proposed T2-DAG test is available on Github at https://github.com/Jin93/T2DAG. Supplementary information Supplementary data are available at Bioinformatics online.

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

Cited by 9 publications

References 36 publications

Cross-Sectional Study of Gene Expression Analysis Identifies Critical Biological Pathways and Key Genes Implicated in Non-Small Cell Lung Cancer

Cross-Sectional Study of Gene Expression Analysis Identifies Critical Biological Pathways and Key Genes Implicated in Non-Small Cell Lung Cancer

The impact of the Cancer Genome Atlas on lung cancer

T2-DAG: a powerful test for differentially expressed gene pathways via graph-informed structural equation modeling

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