Dataset Information

Prognostic factor analysis for breast cancer using gene expression profiles.

ABSTRACT:

Background

The survival of patients with breast cancer is highly sporadic, from a few months to more than 15 years. In recent studies, the gene expression profiling of tumors has been used as a promising means of predicting prognosis factors.

Methods

In this study, we used gene expression datasets of tumors to identify prognostic factors in breast cancer. We conducted log-rank tests and used unsupervised clustering methods to find reciprocally expressed gene sets associated with worse survival rates. Prognosis prediction scores were determined as the ratio of gene expressions.

Results

As a result, four prognosis prediction gene set modules were constructed. The four prognostic gene sets predicted worse survival rates in three independent gene expression data sets. In addition, we found that cancer patient with poor prognosis, i.e., triple-negative cancer, HER2-enriched, TP53 mutated and high-graded patients had higher prognosis prediction scores than those with other types of breast cancer.

Conclusions

In conclusion, based on a gene expression analysis, we suggest that our well-defined scoring method of the prediction of survival outcome may be useful for developing prognostic factors in breast cancer.

SUBMITTER: Joe S

PROVIDER: S-EPMC4959370 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prognostic factor analysis for breast cancer using gene expression profiles.

Joe Soobok S Nam Hojung H

BMC medical informatics and decision making 20160718

<h4>Background</h4>The survival of patients with breast cancer is highly sporadic, from a few months to more than 15 years. In recent studies, the gene expression profiling of tumors has been used as a promising means of predicting prognosis factors.<h4>Methods</h4>In this study, we used gene expression datasets of tumors to identify prognostic factors in breast cancer. We conducted log-rank tests and used unsupervised clustering methods to find reciprocally expressed gene sets associated with w ...[more]

PMID: 27454576

Similar Datasets

Project description:Breast cancer accounts for nearly half of all cancer-related deaths in women worldwide. However, the molecular mechanisms that lead to tumour development and progression remain poorly understood and there is a need to identify candidate genes associated with primary and metastatic breast cancer progression and prognosis. In this study, candidate genes associated with prognosis of primary and metastatic breast cancer were explored through a novel bioinformatics approach. Primary and metastatic breast cancer tissues and adjacent normal breast tissues were evaluated to identify biomarkers characteristic of primary and metastatic breast cancer. The Cancer Genome Atlas-breast invasive carcinoma (TCGA-BRCA) dataset (ID: HS-01619) was downloaded using the mRNASeq platform. Genevestigator 8.3.2 was used to analyse TCGA-BRCA gene expression profiles between the sample groups and identify the differentially-expressed genes (DEGs) in each group. For each group, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were used to determine the function of DEGs. Networks of protein-protein interactions were constructed to identify the top hub genes with the highest degree of interaction. Additionally, the top hub genes were validated based on overall survival and immunohistochemistry using The Human Protein Atlas. Of the top 20 hub genes identified, four (KRT14, KIT, RAD51, and TTK) were considered as prognostic risk factors based on overall survival. KRT14 and KIT expression levels were upregulated while those of RAD51 and TTK were downregulated in patients with breast cancer. The four proposed candidate hub genes might aid in further understanding the molecular changes that distinguish primary breast tumours from metastatic tumours as well as help in developing novel therapeutics. Furthermore, they may serve as effective prognostic risk markers based on the strong correlation between their expression and patient overall survival.

Project description:BackgroundMicroRNAs (miRNAs) are important key regulators in multiple cellular functions, due to their a crucial role in different physiological processes. MiRNAs are differentially expressed in specific tissues, during specific cell status, or in different diseases as tumours. RNA sequencing (RNA-seq) is a Next Generation Sequencing (NGS) method for the analysis of differential gene expression. Using machine learning algorithms, it is possible to improve the functional significance interpretation of miRNA in the analysis and interpretation of data from RNA-seq. Furthermore, we tried to identify some patterns of deregulated miRNA in human breast cancer (BC), in order to give a contribution in the understanding of this type of cancer at the molecular level.ResultsWe adopted a biclustering approach, using the Iterative Signature Algorithm (ISA) algorithm, in order to evaluate miRNA deregulation in the context of miRNA abundance and tissue heterogeneity. These are important elements to identify miRNAs that would be useful as prognostic and diagnostic markers. Considering a real word breast cancer dataset, the evaluation of miRNA differential expressions in tumours versus healthy tissues evidenced 12 different miRNA clusters, associated to specific groups of patients. The identified miRNAs were deregulated in breast tumours compared to healthy controls. Our approach has shown the association between specific sub-class of tumour samples having the same immuno-histo-chemical and/or histological features. Biclusters have been validated by means of two online repositories, MetaMirClust database and UCSC Genome Browser, and using another biclustering algorithm.ConclusionsThe obtained results with biclustering algorithm aimed first of all to give a contribute in the differential expression analysis in a cohort of BC patients and secondly to support the potential role that these non-coding RNA molecules could play in the clinical practice, in terms of prognosis, evolution of tumour and treatment response.

Project description:IntroductionGene expression profiling of breast carcinomas has increased our understanding of the heterogeneous biology of this disease and promises to impact clinical care. The aim of this study was to evaluate the prognostic value of gene expression-based classification along with established prognostic markers and mutation status of the TP53 gene (tumour protein p53) in a group of breast cancer patients with long-term (12 to 16 years) follow-up.MethodsThe clinical and histopathological parameters of 200 breast cancer patients were studied for their effects on clinical outcome using univariate/multivariate Cox regression. The prognostic impact of mutations in the TP53 gene, identified using temporal temperature gradient gel electrophoresis and sequencing, was also evaluated. Eighty of the samples were analyzed for gene expression using 42 K cDNA microarrays and the patients were assigned to five previously defined molecular expression groups. The strength of the gene expression based classification versus standard markers was evaluated by adding this variable to the Cox regression model used to analyze all samples.ResultsBoth univariate and multivariate analysis showed that TP53 mutation status, tumor size and lymph node status were the strongest predictors of breast cancer survival for the whole group of patients. Analyses of the patients with gene expression data showed that TP53 mutation status, gene expression based classification, tumor size and lymph node status were significant predictors of survival. Breast cancer cases in the 'basal-like' and 'ERBB2+' gene expression subgroups had a very high mortality the first two years, while the 'highly proliferating luminal' cases developed the disease more slowly, showing highest mortality after 5 to 8 years. The TP53 mutation status showed strong association with the 'basal-like' and 'ERBB2+' subgroups, and tumors with mutation had a characteristic gene expression pattern.ConclusionTP53 mutation status and gene-expression based groups are important survival markers of breast cancer, and these molecular markers may provide prognostic information that complements clinical variables. The study adds experience and knowledge to an ongoing characterization and classification of the disease.

Project description:Background: Gene expression profiling of breast carcinomas has increased our understanding of the heterogeneous biology of this disease and promises to impact clinical care. The aim of this study was to evaluate the prognostic value of gene expression-based classification along with established prognostic markers and mutation status of the TP53 gene, in a group of breast cancer patients with long-term (12-16 years) follow-up. Methods: The clinical and histopathological parameters of 200 breast cancer patients were studied for their effects on clinical outcome using univariate/multivariate Cox regression. The prognostic impact of mutations in the TP53 gene, identified using TTGE and sequencing, was also evaluated. Eighty of the samples were analyzed for gene expression using 42K cDNA microarrays and the patients were assigned to five previously defined molecular expression groups. The strength of the gene expression based classification versus standard markers was evaluated by adding this variable to the Cox regression model used to analyze all samples. Results: Both univariate and multivariate analysis showed that TP53 mutation status, tumor size and lymph node status were the strongest predictors of breast cancer survival for the whole group of patients. Analyses of the patients with gene expression data showed that TP53 mutation status, gene expression based classification, tumor size and lymph node status were significant predictors of survival. The TP53 mutation status showed strong association with the ?basal-like? and ?ERBB2+? gene expression subgroups, and tumors with mutation had a characteristic gene expression pattern. Conclusions: TP53 mutation status and gene-expression based groups are important survival markers of breast cancer, and these molecular markers may provide prognostic information that complements clinical variables. The study adds experience and knowledge to an ongoing characterization and classification of the disease. Experiment set consisting of 80 primary breast carcinomas collected at Ulleval University Hospital (ULL-samples), Oslo, Norway from 1990-94, and one normal sample from breast reduction surgery.

Dataset Information

Prognostic factor analysis for breast cancer using gene expression profiles.

Background

Methods

Results

Conclusions

Publications

Prognostic factor analysis for breast cancer using gene expression profiles.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets