Pathway cross-talk network strategy reveals key pathways in non-small cell lung cancer

The purpose of this study was to explore the pathway cross-talks and key pathways in non-small cell lung cancer (NSCLC) to better understand the underlying pathological mechanism.

Integrated gene expression data, pathway data and protein-protein interaction (PPI) data were assessed to identify the pathway regulatory interactions in NSCLC, and constructed the background and disease pathway crosstalk networks, respectively. In this work, the attractor method was implemented to identified the differential pathways, and the rank product (RP) algorithm was used to determine the importance of pathways.

Based on 787,896 PPI interactions from STRING database and 300 human pathways from KEGG, we constructed the back pathway cross-talk network with 300 nodes and 42239 edges. Integrating with expression data of NSCLC, each pathway cross-talk endowed with a weight value, and disease pathway cross-talks were identified. By RP algorithm and topology analysis of network, we selected 5 key pathways, including Alanine, DNA replication, Fanconi anemia pathway, Cell cycle and MicroRNAs in cancer under the pre-set thresholds.

We successfully revealed the disease pathway cross-talks and explored 5 key pathways in NSCLC, which may be the underlying therapeutic targets for lung cancer.