Dynamic protein-protein interaction subnetworks of lung cancer in cases with smoking history / 癌症

Smoking is the primary cause of lung cancer and is linked to 85% of lung cancer cases. However, how lung cancer develops in patients with smoking history remains unclear. Systems approaches that combine human protein-protein interaction (PPI) networks and gene expression data are superior to traditional methods. We performed these systems to determine the role that smoking plays in lung cancer development and used the support vector machine (SVM) model to predict PPIs. By defining expression variance (EV), we found 520 dynamic proteins (EV>0.4) using data from the Human Protein Reference Database and Gene Expression Omnibus Database, and built 7 dynamic PPI subnetworks of lung cancer in patients with smoking history. We also determined the primary functions of each subnetwork: signal transduction, apoptosis, and cell migration and adhesion for subnetwork A; cell-sustained angiogenesis for subnetwork B; apoptosis for subnetwork C; and, finally, signal transduction and cell replication and proliferation for subnetworks D-G. The probability distribution of the degree of dynamic protein and static protein differed, clearly showing that the dynamic proteins were not the core proteins which widely connected with their neighbor proteins. There were high correlations among the dynamic proteins, suggesting that the dynamic proteins tend to form specific dynamic modules. We also found that the dynamic proteins were only correlated with the expression of selected proteins but not all neighbor proteins when cancer occurred.

Using the theory of coevolution to predict protein-protein interactions in non-small cell lung cancer / 癌症

Systems biology has become an effective approach for understanding the molecular mechanisms underlying the development of lung cancer. In this study, sequences of 100 non-small cell lung cancer (NSCLC)-related proteins were downloaded from the National Center for Biotechnology Information (NCBI) databases. The Theory of Coevolution was then used to build a protein-protein interaction (PPI) network of NSCLC. Adopting the reverse thinking approach, we analyzed the NSCLC proteins one at a time. Fifteen key proteins were identified and categorized into a special protein family F(K), which included Cyclin D1 (CCND1), E-cadherin (CDH1), Cyclin-dependent kinase inhibitor 2A (CDKN2A), chemokine (C-X-C motif) ligand 12 (CXCL12), epidermal growth factor (EGF), epidermal growth factor receptor (EGFR), TNF receptor superfamily, member 6(FAS), FK506 binding protein 12-rapamycin associated protein 1 (FRAP1), O-6-methylguanine-DNA methyltransferase (MGMT), parkinson protein 2, E3 ubiquitin protein ligase (PARK2), phosphatase and tensin homolog (PTEN), calcium channel voltage-dependent alpha 2/delta subunit 2 (CACNA2D2), tubulin beta class I (TUBB), SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a, member 2 (SMARCA2), and wingless-type MMTV integration site family, member 7A (WNT7A). Seven key nodes of the sub-network were identified, which included PARK2, WNT7A, SMARCA2, FRAP1, CDKN2A, CCND1, and EGFR. The PPI predictions of EGFR-EGF, PARK2-FAS, PTEN-FAS, and CACNA2D2-CDH1 were confirmed experimentally by retrieving the Biological General Repository for Interaction Datasets (BioGRID) and PubMed databases. We proposed that the 7 proteins could serve as potential diagnostic molecular markers for NSCLC. In accordance with the developmental mode of lung cancer established by Sekine et al., we assumed that the occurrence and development of lung cancer were linked not only to gene loss in the 3p region (WNT7A, 3p25) and genetic mutations in the 9p region but also to similar events in the regions of 1p36.2 (FRAP1), 6q25.2-q27 (PARK2), and 11q13 (CCND1). Lastly, the invasion or metastasis of lung cancer happened.