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Identifying breast cancer biomarkers

ABSTRACT: Background: Breast cancer is a heterogeneous neoplasm. Distinct subtypes of breast cancer have been defined, suggesting the existence of molecular differences contributing to their clinical outcomes. However, the molecular differences between HER2 positive and negative breast cancer tumors remain unclear. Objective: The aim of this study was to identify a gene expression profile for breast tumors based on HER2 status. Material and methods: The HER2 status was determined by immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) in 54 breast tumor samples. Using Affymetrix microarray data from these breast tumors, we established the expression profiling of breast cancer based on HER2 IHC and FISH results. To validate microarray experiment data, real-time quantitative reverse transcription-PCR was performed. Results: We found significant differences between the HER2-positive and HER2-negative breast tumor samples, which included overexpression of HER2, as well as other genes located on 17q12, and genes functionally related to migration. Conclusion: Our study shows the potential of integrated genomics profiling to shed light on the molecular knowledge of HER2-positive breast tumors. The tumor samples under study correspond to 54 primary breast carcinomas. They included 15 cases with a HER2 IHC3+ score with HER2 gene amplification, 13 cases with IHC2+ score with amplification and 13 without HER2 gene amplification, and 13 cases IHC0/1+ score without HER2 gene amplification. 12 samples of breast normal tissues from breast cancer patients were also included as a reference. Neither overexpression nor amplification of HER2 was observed.

ORGANISM(S): Homo sapiens

SUBMITTER: Lopez Francisco

PROVIDER: S-ECPF-GEOD-29431 | biostudies-other |

REPOSITORIES: biostudies-other

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Project description:BackgroundMining gene regulatory network (GRN) is an important avenue for addressing cancer mechanism. Mutations in cancer genome perturb GRN and cause a rewiring in an orchestrated network. Hence, the exploration of gene regulatory network rewiring is significant to discover potential biomarkers and indicators for discriminating cancer phenotypes.ResultsHere, we propose a new bioinformatics method of identifying biomarkers based on network rewiring in different states. It firstly reconstructs GRN in different phenotypic conditions from gene expression data with a priori background network. We employ the algorithm based on path consistency algorithm and conditional mutual information to delete false-positive regulatory interactions between independent nodes/genes or not closely related gene pairs. And then a differential gene regulatory network (D-GRN) is constructed from the rewiring parts in the two phenotype-specific GRNs. Community detection technique is then applied for D-GRN to detect functional modules. Finally, we apply logistic regression classifier with recursive feature elimination to select biomarker genes in each module individually. The extracted feature genes result in a gene set of biomarkers with impressing ability to distinguish normal samples from controls. We verify the identified biomarkers in external independent validation datasets. For a proof-of-concept study, we apply the framework to identify diagnostic biomarkers of breast cancer. The identified biomarkers obtain a maximum AUC of 0.985 in the internal sample classification experiments. And these biomarkers achieve a maximum AUC of 0.989 in the external validations.ConclusionIn conclusion, network rewiring reveals significant differences between different phenotypes, which indicating cancer dysfunctional mechanisms. With the development of sequencing technology, the amount and quality of gene expression data become available. Condition-specific gene regulatory networks that are close to the real regulations in different states will be established. Revealing the network rewiring will greatly benefit the discovery of biomarkers or signatures for phenotypes. D-GRN is a general method to meet this demand of deciphering the high-throughput data for biomarker discovery. It is also easy to be extended for identifying biomarkers of other complex diseases beyond breast cancer.

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Identifying breast cancer biomarkers

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