Detection of the tipping point of metastasis of non-small cell lung carcinoma is crucial so that it does not lead to irreversible damage. In contrast to the single biomarker approach, identification of dynamic network biomarkers (DNB) which represent the dynamic change in the expression during disease progression is being increasingly used to study of tipping point in different cancers. In our study, stage-wise gene expression data from a lung cancer database with in silico dynamic network method revealed AZIN1 centric network of seven dynamically interacting genes (AZIN1, PGAM1, CNOT9, HMGCR, EIF3H, DNTTIP1, and ST7). These are involved in purine ribonucleoside monophosphate and purine ribonucleoside triphosphate metabolic pathways. Drastic changes in their gene expressions were observed at 2A and 2B transition stage indicating it to be a tipping point for metastasis. Further, in vitro gene expression studies in NCI-460 cells confirmed the interaction of networking genes. Silencing AZIN1 expression diminished DNTTIP1 and PGAM1 expression almost completely while significantly affecting other genes; further confirming a novel interaction of these genes in non-small cell lung carcinoma (NSCLC). Therefore, AZIN1 centric network of seven genes could serve as an important predictive DNB for the tipping point before NSCLC metastasis.
Key words: Dynamic Network Biomarker, function enrichment, Non-small cell lung carcinoma, text mining, fluorescence imaging, siRNA silencing, AZIN1 centric network
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