Comput Biol Chem - Revealing weak differential gene expressions and their reproducible functions associated with breast cancer metastasis.

Tópicos

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Resumo

Based on microarray data, a basic task is to extract differentially expressed (DE) genes between disease states and their associated functions to understand disease mechanisms. However, few such analyses have been conducted for breast cancer metastasis, possibly owing to the uncertainty of the disease state assignment for patients, which may lead to an extremely low power of detecting DE genes. In this study, we analyzed five datasets composed of metastatic and non-metastatic breast primary cancer samples. For two datasets in which few DE genes could be detected by the conventional false discovery rate control approach, a clustering approach was applied to select a group of genes with large differential expression changes between two groups of samples, in which the powers of identifying DE genes increased greatly. Then, we showed that each of the five DE gene lists captured a part of the differential expression signals from which we were able to extract metastasis-associated functions non-randomly reproducible across different datasets. Our results highlighted that many general biological processes (such as 'cell division', 'cell cycle', 'microtubule-based processes' and 'chromosome segregation'), rather than only their sub-processes, may be globally altered during the course of breast cancer metastasis, characterizing cancer metastasis as a 'systems disease'.

Resumo Limpo

base microarray data basic task extract differenti express de gene diseas state associ function understand diseas mechan howev analys conduct breast cancer metastasi possibl owe uncertainti diseas state assign patient may lead extrem low power detect de gene studi analyz five dataset compos metastat nonmetastat breast primari cancer sampl two dataset de gene detect convent fals discoveri rate control approach cluster approach appli select group gene larg differenti express chang two group sampl power identifi de gene increas great show five de gene list captur part differenti express signal abl extract metastasisassoci function nonrandom reproduc across differ dataset result highlight mani general biolog process cell divis cell cycl microtubulebas process chromosom segreg rather subprocess may global alter cours breast cancer metastasi character cancer metastasi system diseas

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