Background: Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are the two most
common subtypes of non-small cell lung carcinoma (NSCLC), and the cures for them are quite different
from each other. Traditional morphological procedures could not effectively distinguish AC and SCC
because of their morphologically similar cells.
Objective: It is necessary to identify the genes which could effectively discriminate AC from SCC on
the molecular level.
Method: In this work, we apply the context likelihood of related algorithm to gene expression values to infer
AC and SCC networks, respectively. We calculate the values of four centrality measures (the average degree,
the average clustering coefficient, the average betwenness and the average coritivity) on both AC and SCC
networks. The structural key genes are defined as the genes which make great contributions to the topological
changes between two gene networks.
Results: We find that the values of the average degree and the average coritivity of AC networks are
much smaller than those of SCC networks. The degree and the coritivity are considered to be the
effective measures to select structural key genes. We obtain 18 structural key genes, five of which have
been previously identified as markers to distinguish between AC and SCC.
Conclusion: Our results show that the structural key genes which are found by the effective measures
may be used to distinguish the subtypes of NSCLC. The current method could be extended to other
complex diseases for distinguishing subtypes and detecting the molecular targets for targeted therapy.