CEA: Combination-based gene set functional enrichment analysis

Sun, Duanchen; Liu, Yinliang; Zhang, Xiang-Sun; Wu, Lingyun

doi:10.1038/s41598-018-31396-4

Cited by 6 publications

(4 citation statements)

References 50 publications

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“…The two aforementioned gene lists underline the potential limitations of a cluster based method such as that used here to assess the biological properties of the candidate gene sets. Specifically, these limitations relate to i) grouping of similar terms into a cluster and evaluating the enrichment of functional clusters instead of each individual term within the clusters and ii) the evaluation of the identified term clusters separately, while not taken into consideration the relationships between clusters 63 .…”

Section: Discussionmentioning

confidence: 99%

Discovery and characterization of functional modules associated with body weight in broilers

Tarsani

Kranis

Maniatis

et al. 2019

Sci Rep

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Aim of the present study was to investigate whether body weight (BW) in broilers is associated with functional modular genes. To this end, first a GWAS for BW was conducted using 6,598 broilers and the high density SNP array. The next step was to search for positional candidate genes and QTLs within strong LD genomic regions around the significant SNPs. Using all positional candidate genes, a network was then constructed and community structure analysis was performed. Finally, functional enrichment analysis was applied to infer the functional relevance of modular genes. A total number of 645 positional candidate genes were identified in strong LD genomic regions around 11 genome-wide significant markers. 428 of the positional candidate genes were located within growth related QTLs. Community structure analysis detected 5 modules while functional enrichment analysis showed that 52 modular genes participated in developmental processes such as skeletal system development. An additional number of 14 modular genes ( GABRG1, NGF, APOBEC2, STAT5B, STAT3, SMAD4, MED1, CACNB1, SLAIN2, LEMD2, ZC3H18, TMEM132D, FRYL and SGCB ) were also identified as related to body weight. Taken together, current results suggested a total number of 66 genes as most plausible functional candidates for the trait examined.

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Section: Discussionmentioning

confidence: 99%

Discovery and characterization of functional modules associated with body weight in broilers

Tarsani

Kranis

Maniatis

et al. 2019

Sci Rep

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“…According to functional enrichment analysis, multiple processes associated with gene expression and regulation and classical tumor signaling pathways were enriched, including many carcinogenic signals (31). These findings indicate that aberrant growth signals regulate the cell cycle and cell growth (32,33), and also promote chemotherapy resistance.…”

Section: Discussionmentioning

confidence: 94%

Bioinformatics analysis identifies potential chemoresistance‑associated genes across multiple types of cancer

et al. 2019

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Despite the fact that studies have revealed mechanisms underlying tumor chemoresistance, the functions of numerous potential chemoresistance-associated genes have yet to be elucidated. A bioinformatics analysis was conducted to screen differentially expressed genes (DEGs) across four types of chemoresistant tumors and functional enrichment analysis was used to examine the biological significance of these genes. Furthermore, a gene network was constructed using weighted gene co-expression network analysis to identify hub genes. A total of 6,015, 2,074, 2,141 and 954 differentially expressed genes were identified in estrogen receptor-negative breast cancer, ovarian cancer, rectal cancer and gastric cancer, respectively; however, only five of these DEGs were dysregulated in all four types of cancer. Functional enrichment analysis of the DEGs suggested that genomic stability and immune response are crucial determinants of tumor chemoresistance. In addition, 14, 8, 6 and 1 co-expressed gene modules were identified in estrogen receptor-negative breast cancer, ovarian cancer, rectal cancer and gastric cancer, respectively, and protein-protein interaction networks were created. The analysis identified only calcium-calmodulin-dependent protein kinase kinase 2, erythropoietin receptor, mitochondrial poly(A) RNA polymerase, α-parvin, and zinc finger and BTB domain-containing protein 44 to be dysregulated in all four cancer types, indicating varying mechanisms of chemoresistance in different tumor types. Furthermore, our analysis suggests that type I collagen α1, fibroblast growth factor 14 and major histocompatibility complex, class II, DR β1 potentially serve key roles in the development of chemoresistance. In conclusion, the present study proposes a simple and effective strategy for identifying genes involved in chemoresistance and predicting their potential functional roles, which may guide subsequent experimental studies.

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“…Uniprot contains millions of real and available protein data, which can be used for protein information analysis of different species (18,19). This Enrichment analysis can identify valuable functional classes or pathways from identified proteomic results or differentiated protein data (20).The functions were categorized according to their cellular components (CC), molecular functions (MF) and biological processes (BP). (Fig.4, table 1)In the higher ranking results, the words related to the "binding" appeared more frequently.…”

Section: Go Function and Kegg Pathway Analysis Of 109potential Targetsmentioning

confidence: 99%

Bioinformatics-based Prediction of the mechanism and targets for BushenjieduDecoction in preventing relapse of acute leukemia

Sun

Feng-ying

Wei

et al. 2020

Preprint

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Background: Leukemia is a lethal myeloproliferative disorder, its’ relapse following chemotherapy is the major concern in clinical practice. For a long time, we found that traditional Chinese medicines such as Bushenjiedudecoction (BSJD) have significant effects on delaying relapse. However, the underlying mechanisms are not clear, which limits the clinical application of BSJD decoction. Methods: Therefore, we tried to make some explorations in this study. We isolated mesenchymal stem cells (MSC) after treated them with BSJD for proteomic analysis. And then 109 targets were screened out through analysis of the shared proteins of that affected by BSJD and those related to leukemia. Subsequently, the data were analyzed by GO functions, KEGG pathways, PPI network and topological analysis, and then some nodes were selected for animal experiment. Results: As a result, we demonstrated the effective targets of BSJD on MSC through bioinformatics analysis and explored the potential mechanism of BSJD from its influence on niches.These targets contains Hspb1、Dnmt1、Mmp2、Thbs1、Crebbp、Hmgb1、Acta2、Cdkn1b、Atg7、Tsc2 and Icam1. Afterwards, we confirmed BSJD reduced the gene expression of ICAM-1 through cultured MSC in vitro.Conclusions: We screened the potential targets of BSJD on MSC through proteomics and bioinformatics analysis, and selected some genes for experimental verification. These studies demonstrated the effect of BSJD on MSC. We hope that this research method could provide a new way of systematically studying the effects of traditional Chinese medicine on diseases.

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CEA: Combination-based gene set functional enrichment analysis

Cited by 6 publications

References 50 publications

Discovery and characterization of functional modules associated with body weight in broilers

Discovery and characterization of functional modules associated with body weight in broilers

Bioinformatics analysis identifies potential chemoresistance‑associated genes across multiple types of cancer

Bioinformatics-based Prediction of the mechanism and targets for BushenjieduDecoction in preventing relapse of acute leukemia

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