Conclusion
The IRS model can be used for survival prediction and GNAI2 serves as a candidate therapeutic target for GC patients.
Methods
This study evaluated the effect of GNAI2 on the tumor microenvironment (TME) in GC, constructed an immune risk score (IRS) model based on differentially-expressed immune genes, and systematically correlated GNAI2 and epigenetic factor expression patterns with TME and IRS. Also, RT-qPCR, flow cytometry, Western blotting (WB), and transwell assays were carried out to explore the regulatory mechanism of GNAI2 in GC.
Purpose
Although the G protein subunit α i2 (GNAI2) is upregulated in multiple cancers, its prognostic value and exact role in the development of gastric cancer (GC) remain largely unknown.
Results
High GNAI2 expression was associated with poor prognosis. Cytokine activation, an increase in tumor-infiltrating immune cells (TIIC), and the accumulation of regulatory T cells in the tumor immune cycle were all promoted by the TME, which was significantly associated with GNAI2 expression. Two different differentially expressed mRNA (DER) modification patterns were determined. These two DERs-clusters had significantly different TME cell infiltrations and were classified as either noninflamed or immune-inflamed phenotypes. The IRS model constructed using differentially expressed genes (DEGs) had great potential in predicting GC prognosis. The IRS model was also used in assessing clinicopathological features, such as microsatellite instability (MSI) status, epithelial-mesenchymal transition (EMT) status, clinical stages, tumor mutational burden (TMB), and tumor immune dysfunction and exclusion (TIDE) scores. Low IRS scores were associated with high immune checkpoint gene expression. Cell and animal studies confirmed that GNAI2 activated PI3K/AKT pathway and promoted the growth and migration of GC cells.