Linking Interactome to Disease

The introduction of high-throughput gene expression profiling technologies (DNA microarrays) in molecular biology and their expected applications to the clinic have allowed the design of predictive signatures linked to a particular clinical condition or patient outcome in a given clinical setting. However, it has been shown that such signatures are prone to several problems: (i) they are heavily unstable and linked to the set of patients chosen for training; (ii) data topology is problematic with regard to the data dimensionality (too many variables for too few samples); (iii) diseases such as cancer are provoked by subtle misregulations which cannot be readily detected by current analysis methods. To find a predictive signature generalizable for multiple datasets, a strategy of superimposition of a large scale of protein-protein interaction data (human interactome) was devised over several gene expression datasets (a total of 2,464 breast cancer tumors were integrated), to find discriminative regions in the interactome (subnetworks) predicting metastatic relapse in breast cancer. This method, Interactome-Transcriptome Integration (ITI), was applied to several breast cancer DNA microarray datasets and allowed the extraction of a signature constituted by 119 subnetworks. All subnetworks have been stored in a relational database and linked to Gene Ontology and NCBI EntrezGene annotation databases for analysis. Exploration of annotations has shown that this set of subnetworks reflects several biological processes linked to cancer and is a good candidate for establishing a network-based signature for prediction of metastatic relapse in breast cancer.

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Flexible web-based integration of distributed large-scale human protein interaction maps

Journal of Integrative Bioinformatics ◽

10.1515/jib-2007-51 ◽

2007 ◽

Vol 4 (1) ◽

pp. 40-50 ◽

Cited By ~ 1

Author(s):

Gautam Chaurasia ◽

Yasir Iqbal ◽

Christian Hänig ◽

Hanspeter Herzel ◽

Erich E. Wanker ◽

...

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Large Scale ◽

Human Interaction ◽

Human Protein ◽

Protein Interaction Data ◽

Interaction Data ◽

Protein Protein Interactions ◽

Human Interactome ◽

Protein Protein Interaction

Summary Protein-protein interactions constitute the backbone of many molecular processes. This has motivated the recent construction of several large-scale human protein-protein interaction maps [1-10]. Although these maps clearly offer a wealth of information, their use is challenging: complexity, rapid growth, and fragmentation of interaction data hamper their usability. To overcome these hurdles, we have developed a publicly accessible database termed UniHI (Unified Human Interactome) for integration of human protein-protein interaction data. This database is designed to provide biomedical researchers a common platform for exploring previously disconnected human interaction maps. UniHI offers researchers flexible integrated tools for accessing comprehensive information about the human interactome. Several features included in the UniHI allow users to perform various types of network-oriented and functional analysis. At present, UniHI contains over 160,000 distinct interactions between 17,000 unique proteins from ten major interaction maps derived by both computational and experimental approaches [1-10]. Here we describe the details of the implementation and maintenance of UniHI and discuss the challenges that have to be addressed for a successful integration of interaction data.

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Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Mathematics ◽

10.3390/math9070772 ◽

2021 ◽

Vol 9 (7) ◽

pp. 772

Author(s):

Seonghun Kim ◽

Seockhun Bae ◽

Yinhua Piao ◽

Kyuri Jo

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Large Scale ◽

Drug Response ◽

Response Prediction ◽

Biological Data ◽

Expression Data ◽

Convolutional Network ◽

Essential Information ◽

Protein Protein Interaction

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

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Key Genes and Prognostic Analysis in HER2+ Breast Cancer

Technology in Cancer Research & Treatment ◽

10.1177/1533033820983298 ◽

2021 ◽

Vol 20 ◽

pp. 153303382098329

Author(s):

Yujie Weng ◽

Wei Liang ◽

Yucheng Ji ◽

Zhongxian Li ◽

Rong Jia ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Differentially Expressed Genes ◽

Protein Interaction ◽

Differentially Expressed ◽

Protein Kinase Activity ◽

Protein Protein Interaction ◽

Risk Of Death ◽

Her2 Breast Cancer ◽

Key Genes

Human epidermal growth factor 2 (HER2)+ breast cancer is considered the most dangerous type of breast cancers. Herein, we used bioinformatics methods to identify potential key genes in HER2+ breast cancer to enable its diagnosis, treatment, and prognosis prediction. Datasets of HER2+ breast cancer and normal tissue samples retrieved from Gene Expression Omnibus and The Cancer Genome Atlas databases were subjected to analysis for differentially expressed genes using R software. The identified differentially expressed genes were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses followed by construction of protein-protein interaction networks using the STRING database to identify key genes. The genes were further validated via survival and differential gene expression analyses. We identified 97 upregulated and 106 downregulated genes that were primarily associated with processes such as mitosis, protein kinase activity, cell cycle, and the p53 signaling pathway. Visualization of the protein-protein interaction network identified 10 key genes ( CCNA2, CDK1, CDC20, CCNB1, DLGAP5, AURKA, BUB1B, RRM2, TPX2, and MAD2L1), all of which were upregulated. Survival analysis using PROGgeneV2 showed that CDC20, CCNA2, DLGAP5, RRM2, and TPX2 are prognosis-related key genes in HER2+ breast cancer. A nomogram showed that high expression of RRM2, DLGAP5, and TPX2 was positively associated with the risk of death. TPX2, which has not previously been reported in HER2+ breast cancer, was associated with breast cancer development, progression, and prognosis and is therefore a potential key gene. It is hoped that this study can provide a new method for the diagnosis and treatment of HER2 + breast cancer.

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Identification of biomarkers in breast cancer metastasis by integrating protein-protein interaction network and gene expression data

2011 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS) ◽

10.1109/gensips.2011.6169443 ◽

2011 ◽

Cited By ~ 3

Author(s):

Md Jamiul Jahid ◽

Jianhua Ruan

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Gene Expression Data ◽

Protein Interaction Network ◽

Cancer Metastasis ◽

Interaction Network ◽

Breast Cancer Metastasis ◽

Expression Data ◽

Protein Protein Interaction ◽

Protein Protein Interaction Network

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GENE FUNCTION PREDICTION BY A COMBINED ANALYSIS OF GENE EXPRESSION DATA AND PROTEIN-PROTEIN INTERACTION DATA

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720005001612 ◽

2005 ◽

Vol 03 (06) ◽

pp. 1371-1389 ◽

Cited By ~ 11

Author(s):

GUANGHUA XIAO ◽

WEI PAN

Keyword(s):

Gene Expression ◽

Protein Interaction ◽

Gene Function ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Function Prediction ◽

Protein Interaction Data ◽

Interaction Data ◽

Gene Function Prediction ◽

Protein Protein Interaction

Prediction of biological functions of genes is an important issue in basic biology research and has applications in drug discoveries and gene therapies. Previous studies have shown either gene expression data or protein-protein interaction data alone can be used for predicting gene functions. In particular, clustering gene expression profiles has been widely used for gene function prediction. In this paper, we first propose a new method for gene function prediction using protein-protein interaction data, which will facilitate combining prediction results based on clustering gene expression profiles. We then propose a new method to combine the prediction results based on either source of data by weighting on the evidence provided by each. Using protein-protein interaction data downloaded from the GRID database, published gene expression profiles from 300 microarray experiments for the yeast S. cerevisiae, we show that this new combined analysis provides improved predictive performance over that of using either data source alone in a cross-validated analysis of the MIPS gene annotations. Finally, we propose a logistic regression method that is flexible enough to combine information from any number of data sources while maintaining computational feasibility.

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