Background: Adenocarcinoma in situ (AIS) is a pre-invasive lesion in the lung and a subtype of lung adenocarcinoma. The patients with AIS can be cured by resecting the lesion completely. In contrast, the patients with invasive lung adenocarcinoma have very poor 5-year survival rate. AIS can develop into invasive lung adenocarcinoma. The investigation and comparison of AIS and invasive lung adenocarcinoma at the genomic level can deepen our understanding of the mechanisms underlying lung cancer development.
Results: In this study, we identified 61 lung adenocarcinoma (LUAD) invasive-specific differentially expressed genes, including nine long non-coding RNAs (lncRNAs) based on RNA sequencing techniques (RNA-seq) data from normal, AIS, and invasive tissue samples. These genes displayed concordant differential expression (DE) patterns in the independent stage III LUAD tissues obtained from The Cancer Genome Atlas (TCGA) RNA-seq dataset. For individual invasive-specific genes, we constructed subnetworks using the Genetic Algorithm (GA) based on protein-protein interactions, protein-DNA interactions and lncRNA regulations. A total of 19 core subnetworks that consisted of invasive-specific genes and at least one putative lung cancer driver gene were identified by our study. Functional analysis of the core subnetworks revealed their enrichment in known pathways and biological progresses responsible for tumor growth and invasion, including the VEGF signaling pathway and the negative regulation of cell growth.
Conclusions: Our comparison analysis of invasive cases, normal and AIS uncovered critical genes that involved in the LUAD invasion progression. Furthermore, the GA-based network method revealed gene clusters that may function in the pathways contributing to tumor invasion. The interactions between differentially expressed genes and putative driver genes identified through the network analysis can offer new targets for preventing the cancer invasion and potentially increase the survival rate for cancer patients.