Identifying disease related sub-pathways for analysis of genome-wide association studies

Li, Chunquan; Han, Junwei; Shang, Desi; Li, Jing; Wang, Yan; Wang, Yingying; Zhang, Yunpeng; Yao, Qianlan; Zhang, Chunlong; Li, Kongning; Li, Xia

doi:10.1016/j.gene.2012.04.051

Cited by 13 publications

(10 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, pathway databases, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) [153], can be used to cluster candidate genes into higher-level functions, disease processes, and organism behaviors. Li et al (2012) [154] applied this approach to the Wellcome Trust Case Control Consortium (WTCCC; 2007) [101] dataset. Significantly over-represented pathways in T1D included the Jak-STAT, calcium, MAPK, and Wnt signaling pathways, as well as tryptophan and pyrimidine metabolism.…”

Section: From Genes To Functionmentioning

confidence: 99%

From Markers to Molecular Mechanisms: Type 1 Diabetes in the Post-GWAS Era

Baxter

Jordan²

2012

Rev Diabet Stud

View full text Add to dashboard Cite

■ AbstractBy the year 2000, a draft of the human genome sequence was completed. Millions of single-nucleotide polymorphisms (SNPs) had been deposited into public databases, and high throughput technologies were under development for SNP genotyping. At that time, it was predicted that large case control association studies would provide far better resolution and power than genome-wide linkage studies. Type 1 diabetes was one of the first phenotypes to be examined by genome-wide association studies (GWAS), and to date over 50 genomic regions have been associated with the disease.In general, the great majority of these loci individually contribute a relatively small degree of risk, and most loci lie outside of coding sequences. The identification of molecular mechanisms from these genomic data therefore remains a significant challenge. Here, we summarize genetic candidate, linkage, and association studies of type 1 diabetes and discuss a potential strategy to identify mechanisms of disease from genomic data.

show abstract

Section: From Genes To Functionmentioning

confidence: 99%

From Markers to Molecular Mechanisms: Type 1 Diabetes in the Post-GWAS Era

Baxter

Jordan²

2012

Rev Diabet Stud

View full text Add to dashboard Cite

show abstract

“…We overcame this problem by incorporating gene expression data into pathway structure and using a greedy algorithm to search the subpathways associated with the prognosis of patients. Although a number of methods for identifying subpathways associated with cancer have been proposed, the advantage of our method is that it considered patient‐specific clinical and prognosis information in the identification of subpathways, and constructed subpathway activity profiles for each patient in the data set.…”

Section: Discussionmentioning

confidence: 99%

“…However, entire pathways are often too large to enable accurate interpretation of relevant biological phenomena. In recent years, key subpathway regions representative of the entire corresponding pathway are believed to be more useful in terms of interpreting relevant biological phenomena, and abnormalities of these subpathway regions may contribute to the aetiology of diseases . Subpathway‐GM was proposed to identify disease‐relevant subpathways by integrating information from genes and metabolites and pathway structure information within the given pathway.…”

Section: Introductionmentioning

confidence: 99%

Inference of patient‐specific subpathway activities reveals a functional signature associated with the prognosis of patients with breast cancer

Han

Liu

Jiang

et al. 2018

J Cellular Molecular Medi

Self Cite

View full text Add to dashboard Cite

Breast cancer is one of the most deadly forms of cancer in women worldwide. Better prediction of breast cancer prognosis is essential for more personalized treatment. In this study, we aimed to infer patient‐specific subpathway activities to reveal a functional signature associated with the prognosis of patients with breast cancer. We integrated pathway structure with gene expression data to construct patient‐specific subpathway activity profiles using a greedy search algorithm. A four‐subpathway prognostic signature was developed in the training set using a random forest supervised classification algorithm and a prognostic score model with the activity profiles. According to the signature, patients were classified into high‐risk and low‐risk groups with significantly different overall survival in the training set (median survival of 65 vs 106 months, P = 1.82e‐13) and test set (median survival of 75 vs 101 months, P = 4.17e‐5). Our signature was then applied to five independent breast cancer data sets and showed similar prognostic values, confirming the accuracy and robustness of the subpathway signature. Stratified analysis suggested that the four‐subpathway signature had prognostic value within subtypes of breast cancer. Our results suggest that the four‐subpathway signature may be a useful biomarker for breast cancer prognosis.

show abstract

“…Sequence patterns over-represented in the upregulated genes, but under-represented in the downregulation genes. The subpathway mining algorithm in SubpathwayMiner has been successful in identifying disease-related subpathways of biological significance [24,25]. One of the advantages of the subpathway method is that it identifies more pathways than the traditional pathway enrichment.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics Analyses of m-RNA Profiling Following Ezrin Knockdown in Esophageal Squamous Cell Carcinoma

Xie¹

2014

J Cancer Sci Ther

View full text Add to dashboard Cite

Ezrin is involves in multiple cancer cell functions. In this study, differentially expressed genes (DEGs) identified from mRNA expression profiling following Ezrin knockdown in esophageal squamous cell carcinoma (ESCC), were analyzed by multiple bioinformatics methods for a comprehensive understanding. Gene Ontology enrichment found significant terms related to immunity, stimulus response, extracellular matrix-binding and signal transduction. Functional Annotation Chart showed except GO categories, these DEGs were annotated by 72 functional category terms. Subpathway analyses revealed the DEGs were involved in 36 subpathways, overwhelming the traditional pathway enrichment. Promoter analyses showed specific sequence patterns and transcription factors correspond to the co-downregulation and co-upregulation of DEGs. MNB1A, DOF2, PBF, YY1, PRRX2, UBX and ARNT-AHR co-regulate the downregulated genes, whereas GATA2, ZNF42, deltaEF1, MYCN and ARNT co-regulate the upregulated genes. This paper provides a workflow for a better understanding the roles of Ezrin knockdown in ESCC by the bioinformatics analyses of its DEGs.

show abstract

Identifying disease related sub-pathways for analysis of genome-wide association studies

Cited by 13 publications

References 43 publications

From Markers to Molecular Mechanisms: Type 1 Diabetes in the Post-GWAS Era

From Markers to Molecular Mechanisms: Type 1 Diabetes in the Post-GWAS Era

Inference of patient‐specific subpathway activities reveals a functional signature associated with the prognosis of patients with breast cancer

Bioinformatics Analyses of m-RNA Profiling Following Ezrin Knockdown in Esophageal Squamous Cell Carcinoma

Contact Info

Product

Resources

About