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A supervised network analysis on gene expression profiles of breast tumors predicts a 41-gene prognostic signature of the transcription factor MYB across molecular subtypes.

ABSTRACT: BACKGROUND: MYB is predicted to be a favorable prognostic predictor in a breast cancer population. We proposed to find the inferred mechanism(s) relevant to the prognostic features of MYB via a supervised network analysis. METHODS: Both coefficient of intrinsic dependence (CID) and Galton Pierson's correlation coefficient (GPCC) were combined and designated as CIDUGPCC. It is for the univariate network analysis. Multivariate CID is for the multivariate network analysis. Other analyses using bioinformatic tools and statistical methods are included. RESULTS: ARNT2 is predicted to be the essential gene partner of MYB. We classified four prognostic relevant gene subpools in three breast cancer cohorts as feature types I-IV. Only the probes in feature type II are the potential prognostic feature of MYB. Moreover, we further validated 41 prognosis relevant probes to be the favorable prognostic signature. Surprisingly, two additional family members of MYB are elevated to promote poor prognosis when both levels of MYB and ARNT2 decline. Both MYBL1 and MYBL2 may partially decrease the tumor suppressive activities that are predicted to be up-regulated by MYB and ARNT2. CONCLUSIONS: The major prognostic feature of MYB is predicted to be determined by the MYB subnetwork (41 probes) that is relevant across subtypes.

SUBMITTER: Liu LY

PROVIDER: S-EPMC3930188 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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A supervised network analysis on gene expression profiles of breast tumors predicts a 41-gene prognostic signature of the transcription factor MYB across molecular subtypes.

Liu Li-Yu D LY Chang Li-Yun LY Kuo Wen-Hung WH Hwa Hsiao-Lin HL Chang King-Jen KJ Hsieh Fon-Jou FJ

Computational and mathematical methods in medicine 20140203

<h4>Background</h4>MYB is predicted to be a favorable prognostic predictor in a breast cancer population. We proposed to find the inferred mechanism(s) relevant to the prognostic features of MYB via a supervised network analysis.<h4>Methods</h4>Both coefficient of intrinsic dependence (CID) and Galton Pierson's correlation coefficient (GPCC) were combined and designated as CIDUGPCC. It is for the univariate network analysis. Multivariate CID is for the multivariate network analysis. Other analys ...[more]

PMID: 24639887

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Project description:Purpose HER2 gene amplification or protein overexpression (HER2+) defines a clinically challenging subgroup of breast cancer with variable prognosis and response to therapy. We aimed to investigate the heterogeneous biological appearance and clinical behavior of HER2+ tumors using molecular profiling. Materials and Methods Hierarchical clustering of gene expression data from 58 HER2-amplified tumors of various stage, histological grade and estrogen receptor (ER) status was used to construct a HER2-derived prognostic predictor that was further evaluated in several large independent breast cancer data sets. Results Unsupervised analysis identified three subtypes of HER2+ tumors with mixed stage, histological grade and ER-status. One subtype had a significantly worse clinical outcome. A prognostic predictor was created based on differentially expressed genes between the subtype with worse outcome and the other subtypes. The predictor was able to define patient groups with better and worse outcome in HER2+ breast cancer across multiple independent breast cancer data sets and identify a sizable HER2+ group with long disease-free survival and low mortality. Significant correlation to prognosis was also observed in basal-like, ER−, lymph node positive or high-grade tumors, irrespective of HER2-status. The predictor included genes associated to immune response, tumor invasion and metastasis. Conclusion The HER2-derived prognostic predictor provides further insight into the heterogeneous biology of HER2+ tumors and may become useful for improved selection of patients that need additional treatment with new drugs targeting the HER2 pathway. Array comparative genomic hybridization (aCGH) identified 58 breast tumors with amplification of HER2 from a larger cohort of approx 500 tumors breast. Global gene expression profiles were obtained using 70-mer oligonucleotide microarrays. Unsupervised hierarchical clustering of the 58 tumors, using Pearson correlation and complete linkage, identified three main clusters. One cluster showed significantly poorer clinical outcome. Significance of microarray (SAM) analysis was performed to identify 158 genes separating the poor outcome cluster compared to the other two clusters. Gene expression centroids, based on the 158 genes, were created for each cluster for validation in independent breast cancer data sets.

Project description:BackgroundLung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients.MethodsRaw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature.ResultsA prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041-39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779-3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis.ConclusionOur study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

Dataset Information

A supervised network analysis on gene expression profiles of breast tumors predicts a 41-gene prognostic signature of the transcription factor MYB across molecular subtypes.

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A supervised network analysis on gene expression profiles of breast tumors predicts a 41-gene prognostic signature of the transcription factor MYB across molecular subtypes.

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