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Immune landscape of advanced gastric cancer tumor microenvironment identifies immunotherapeutic relevant gene signature

Advanced gastric cancer (AGC) is a disease with poor prognosis due to the current lack of effective therapeutic strategies. Immune checkpoint blockade treatments have shown effect responses in patient subgroups but biomarkers remain challenging. Traditional classifcation of gastric cancer (GC) is based on genomic profiling and molecular features. | Zhang et al. BMC Cancer 2021 21 1324 https doi.org 10.1186 s12885-021-09065-z RESEARCH Open Access Immune landscape of advanced gastric cancer tumor microenvironment identifies immunotherapeutic relevant gene signature Simeng Zhang1 2 3 4 Mengzhu Lv5 Yu Cheng1 2 3 4 Shuo Wang1 2 3 4 Ce Li1 2 3 4 and Xiujuan Qu1 2 3 4 Abstract Background Advanced gastric cancer AGC is a disease with poor prognosis due to the current lack of effective therapeutic strategies. Immune checkpoint blockade treatments have shown effective responses in patient sub- groups but biomarkers remain challenging. Traditional classification of gastric cancer GC is based on genomic profil- ing and molecular features. Therefore it is critical to identify the immune-related subtypes and predictive markers by immuno-genomic profiling. Methods Single-sample gene-set enrichment analysis ssGSEA and ESTIMATE algorithm were used to identify the immue-related subtypes of AGC in two independent GEO datasets. Weighted gene co-expression network analysis WGCNA and Molecular Complex Detection MCODE algorithm were applied to identify hub-network of immune- related subtypes. Hub genes were confirmed by prognostic data of KMplotter and GEO datasets. The value of hub- gene in predicting immunotherapeutic response was analyzed by IMvigor210 datasets. MTT assay Transwell migra- tion assay and Western blotting were performed to confirm the cellular function of hub gene in vitro. Results Three immune-related subtypes Immunity_H Immunity_M and Immunity_L of AGC were identified in two independent GEO datasets. Compared to Immunity_L the Immuntiy_H subtype showed higher immune cell infiltration and immune activities with favorable prognosis. A weighted gene co-expression network was constructed based on GSE62254 dataset and identified one gene module which was significantly correlated with the Immunity_H subtype. A Hub-network which represented high immune activities was extracted based on topological features and .