Tuberculosis (TB), which is caused by the mycobacterium TB, is the major cause of human death worldwide. The aim of this study was to identify the biomarkers involved in child TB. Gene expression data were obtained from the Array Express Archive of Functional Genomics Data. Gene expression data and protein-protein interaction (PPI) data were downloaded to construct differential gene co-expression networks (DCNs). The Benjamini-Hochberg algorithm was used to correct the P-value. In total, 3,820 edges (PPIs) and 1,359 nodes (genes) were obtained from the human-related PPIs data and gene expression data at the criteria of absolute value of Pearson's correlation coefficient >0.8. The DCNs were formed by these edges and nodes. Thirteen seed genes were obtained by ranging z-scores. Eight significant multiple different modules were identified from DCNs using the statistical significant test. In conclusion, the seed genes and significant modules constitute potential biomarkers that reveal the underlying mechanisms in child TB. The new identified biomarkers may contribute to an understanding of TB and provide a new therapeutic method for the treatment of TB.
Keywords: differential gene co-expression networks; multiple differential modules; protein-protein interactions; tuberculosis.