Identifying gender independent biomarkers responsible for human muscle aging using microarray data

The scope of this study is the identification of gender-independent muscle transcriptional differences between younger and older subjects using skeletal muscle gene expression profiles. Towards this end, a combination of statistical methods, functional analyses, and machine learning techniques were exploited, and applied on an integrative dataset of publicly available microarray data obtained from healthy males and females. Through the proposed framework, a set of 46 reliable genes was identified that comprise a candidate gender-independent aging signature in human skeletal muscle. The identification was based on differential expression, information gain content, and significance regarding their central regulatory role in the underlying active molecular networks in the GO. The resulted gene subset was also tested for its generalization potency regarding the classification task, through the use of a series of classifiers, and results show that high classification accuracies could be obtained. Therefore, the selected genes comprise a promising group of biomarkers of ageing in human skeletal muscle to be evaluated in future studies.