Dataset Information

A Steiner tree-based method for biomarker discovery and classification in breast cancer metastasis.

ABSTRACT: Metastatic breast cancer is a leading cause of cancer-related deaths in women worldwide. DNA microarray has become an important tool to help identify biomarker genes for improving the prognosis of breast cancer. Recently, it was shown that pathway-level relationships between genes can be incorporated to build more robust classification models and to obtain more useful biological insight from such models. Due to the unavailability of complete pathways, protein-protein interaction (PPI) network is becoming more popular to researcher and opens a new way to investigate the developmental process of breast cancer.In this study, a network-based method is proposed to combine microarray gene expression profiles and PPI network for biomarker discovery for breast cancer metastasis. The key idea in our approach is to identify a small number of genes to connect differentially expressed genes into a single component in a PPI network; these intermediate genes contain important information about the pathways involved in metastasis and have a high probability of being biomarkers.We applied this approach on two breast cancer microarray datasets, and for both cases we identified significant numbers of well-known biomarker genes for breast cancer metastasis. Those selected genes are significantly enriched with biological processes and pathways related to cancer carcinogenic process, and, importantly, have much higher stability across different datasets than in previous studies. Furthermore, our selected genes significantly increased cross-data classification accuracy of breast cancer metastasis.The randomized Steiner tree based approach described in this study is a new way to discover biomarker genes for breast cancer, and improves the prediction accuracy of metastasis. Though the analysis is limited here only to breast cancer, it can be easily applied to other diseases.

SUBMITTER: Jahid MJ

PROVIDER: S-EPMC3481447 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Steiner tree-based method for biomarker discovery and classification in breast cancer metastasis.

Jahid Md Jamiul MJ Ruan Jianhua J

BMC genomics 20121026

<h4>Background</h4>Metastatic breast cancer is a leading cause of cancer-related deaths in women worldwide. DNA microarray has become an important tool to help identify biomarker genes for improving the prognosis of breast cancer. Recently, it was shown that pathway-level relationships between genes can be incorporated to build more robust classification models and to obtain more useful biological insight from such models. Due to the unavailability of complete pathways, protein-protein interacti ...[more]

PMID: 23134806

Similar Datasets

Project description:BackgroundCirculating tumor cells (CTCs) has been demonstrated as a promising liquid biopsy marker for breast cancer (BC). However, the intra-patient heterogeneity of CTCs remains a challenge to clinical application. We aim at profiling aggressive CTCs subpopulation in BC utilizing the distinctive metabolic reprogramming which is a hallmark of metastatic tumor cells.MethodsOncomine, TCGA and Kaplan-Meier plotter databases were utilized to analyze expression and survival relevance of the previously screened metastasis-promoting metabolic markers (PGK1/G6PD) in BC patients. CTCs detection and metabolic classification were performed through micro-filtration and multiple RNA in situ hybridization using CD45 and PGK1/G6PD probes. Blood samples were collected from 64 BC patients before treatment for CTCs analysis. Patient characteristics were recorded to evaluate clinical applications of CTCs metabolic subtypes, as well as morphological EMT subtypes classified by epithelial (EpCAM/CKs) and mesenchymal (Vimentin/Twist) markers.ResultsPGK1 and G6PD expressions were up-regulated in invasive BC tissues compared with normal mammary tissues. Increased tissue expressions of PGK1 or G6PD indicated shortened overall and relapse-free survival of BC patients (P < 0.001). Blood GM+CTCs (DAPI+CD45-PGK1/G6PD+) was detectable (range 0-54 cells/5 mL) in 61.8% of tCTCs > 0 patients. Increased GM+CTCs number and positive rate were correlated with tumor metastasis and progression (P < 0.05). The GM+CTCs ≥ 2/5 mL level presented superior AUC of ROC at 0.854 (95% CI 0.741-0.968) in the diagnosis of BC metastasis (sensitivity/specificity: 66.7%/91.3%), compared with that of tCTCs (0.779) and CTCs-EMT subtypes (E-CTCs 0.645, H-CTCs 0.727 and M-CTCs 0.697). Moreover, GM+CTCs+ group had inferior survival with decreased 2 years-PFS proportion (18.5%) than GM+CTCs- group (87.9%; P = 0.001).ConclusionsThis work establishes a PGK1/G6PD-based method for CTCs metabolic classification to identify the aggressive CTCs subpopulation. Metabolically active GM+CTCs subtype is suggested a favorable biomarker of distant metastasis and prognosis in BC patients.

Project description:Breast cancers can be classified by hierarchical clustering using an "intrinsic" gene list into one of at least five molecular subtypes: basal-like, HER2, luminal A, luminal B, and normal breast-like. Five different intrinsic gene lists composed of varying numbers of genes have been used for molecular subtype identification and classification of breast cancers. The aim of this study was to determine the objectivity and interobserver reproducibility of the assignment of molecular subtype classes by hierarchical cluster analysis. Three publicly available breast cancer datasets (n = 779) were subjected to two-way average-linkage hierarchical cluster analysis using five distinct intrinsic gene lists. We used free-marginal Kappa statistics to analyze interobserver agreement among five breast cancer researchers for the whole classification and for each molecular subtype separately according to each intrinsic gene list for each breast cancer dataset. None of the classification systems tested produced almost perfect agreement (Kappa ≥ 0.81) among observers. However, substantial interobserver agreement (70.8% to 76.1% of the samples and free-marginal Kappa scores from 0.635 to 0.701) was consistently observed in all datasets for four molecular subtypes (luminal, basal-like, HER2, and normal breast-like). When luminal cancers were subdivided (luminal A, B, and C), none of the classification systems produced substantial agreement (Kappa ≥ 0.61) in all the datasets analyzed. Analysis of each subtype separately revealed that only two (basal-like and HER2) could be reproducibly identified by independent observers (Kappa ≥ 0.81). Assignment of molecular subtype classes of breast cancer based on the analysis of dendrograms obtained with hierarchical cluster analysis is subjective and shows modest interobserver reproducibility. For the development of a molecular taxonomy, objective definitions for each molecular subtype and standardized methods for their identification are required.

Dataset Information

A Steiner tree-based method for biomarker discovery and classification in breast cancer metastasis.

Publications

A Steiner tree-based method for biomarker discovery and classification in breast cancer metastasis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets