부산대학교 도서관

BackgroundSolid-predominant lung adenocarcinoma (SPA), which is one of the high-risk subtypes with poor prognosis and unsatisfactory response to chemotherapy and targeted therapy in lung adenocarcinoma, remains molecular profile unclarified. Weighted correlation network analysis (WGCNA) was used for data mining, especially for studying biological networks based on pairwise correlations between variables. This study aimed to identify disease-related protein co-expression networks associated with early-stage SPA.MethodsWe assessed cancerous cells laser-microdissected from formalin-fixed paraffin-embedded (FFPE) tissues of a SPA group (n = 5), referencing a low-risk subtype, a lepidic predominant subtype group (LPA) (n = 4), and another high-risk subtype, micropapillary predominant subtype (MPA) group (n = 3) and performed mass spectrometry-based proteomic analysis. Disease-related co-expression networks associated with the SPA subtype were identified by WGCNA and their upstream regulators and causal networks were predicted by Ingenuity Pathway Analysis.ResultsAmong the forty WGCNA network modules identified, two network modules were found to be associated significantly with the SPA subtype. Canonical enriched pathways were highly associated with cellular growth, proliferation, and immune response. Upregulated HLA class I molecules HLA-G and HLA-B implicated high mutation burden and T cell activation in the SPA subtype. Upstream analysis implicated the involvement of highly activated oncogenic regulators, MYC, MLXIPL, MYCN, the redox master regulator NFE2L2, and the highly inhibited LARP1, leading to oncogenic IRES-dependent translation, and also regulators of the adaptive immune response, including highly activated IFNG, TCRD, CD3-TCR, CD8A, CD8B, CD3, CD80/CD86, and highly inhibited LILRB2. Interestingly, the immune checkpoint molecule HLA-G, which is the counterpart of LILRB2, was highly expressed characteristically in the SPA subtype and might be associated with antitumor immunity.ConclusionOur findings provide a disease molecular profile based on protein co-expression networks identified for the high-risk solid predominant adenocarcinoma, which will help develop future therapeutic strategies.