The MK2 pathway is linked to G-CSF, cytokine production and metastasis in gastric cancer: a novel intercorrelation analysis approach

Abstract

Abstract Background Gastric cancer is associated with chronic inflammation, but there is still much to understand about the tumor microenvironment and the underlying tumor-promoting mechanisms. The Map kinase-activated protein kinase 2 (MK2) pathway is a regulator of inflammatory cytokine production that we have been studying in gastrointestinal cancers. Here, we set out to determine the significance of this gene in gastric cancer along with its downstream mediators and if there were differences in the primary tumors with and without metastasis. Methods Human gastric cancer tissues with and without metastasis were examined for MK2 expression and cytokine profile in organ culture supernatants. Advanced statistical methods including a lower triangular correlation matrix, novel rooted correlation network, linear and logistic regression modeling along with Kruskal–Wallis testing with Sidak correction for multiple testing were applied to gain understanding of cytokines/chemokines linked to metastasis. Results The MK2 pathway is strongly linked with metastasis and a panel of cytokines. Gene expression was able to classify gastric cancer metastasis 85.7% of the time. A significant association with a panel of cytokines was found, including G-CSF, GM-CSF, Mip-1β, IFN-α, MCP-1, IL-1β, IL-6, and TNF-α. Mip-1β was found to have the strongest association with MK2 and metastasis after Sidak correction for multiple testing. Conclusions MK2 gene expression and a novel associated cytokine panel are linked to gastric cancer metastasis. G-CSF is the strongest cytokine to differentiate between metastasis and non-metastasis patients and had the lowest P value, while Mip-1β showed the strongest association with MK2 and metastasis after Sidak correction. MK2 and associated cytokines are potential biomarkers for gastric cancer metastasis. The novel intercorrelation analysis approach is a promising method for understanding the complex nature of cytokine/chemokine regulation and links to disease outcome.