A new approach: Evaluation of necroptosis and immune status enables prediction of the tumor microenvironment and treatment targets in pancreatic cancer

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Abstract
Growing evidence indicates a potential correlation between necroptosis and pancreatic cancer, and the relationship between necroptosis, immune infiltration and the microenvironment in pancreatic cancer has drawn increasing attention. However, two-dimensional phenotype and prognostic assessment systems based on a combination of necroptosis and immunity have not been explored. In our present study, we explored the pancancer genomics signature of necroptosis-related molecules, identifying necroptosis-related molecule mutation profiles, expression profiles, and correlations between expression levels and methylation/CNV levels. We identified distinct necroptotic as well as immune statuses in pancreatic cancer, and a high necroptosis phenotype and high immunity phenotype both indicated better prognosis than a low necroptosis phenotype and low immunity phenotype. The two-dimensional phenotype we constructed has ideal discriminative effects on pancreatic cancer prognosis, inflammation, and the immune microenvironment. The "high-necroptosis and high-immunity (HNHI)" group exhibited the best prognosis and the highest proportion of infiltrating immune cells. The NI score can be used to predict patient prognosis and is correlated with the immune microenvironment score, chemotherapeutic drug IC50, and tumor mutational burden. In addition, it may be useful for predicting the effect of individualized chemotherapy and immunotherapy. Our study also revealed that SLC2A1 is associated with both necroptosis and immunity and acts as a potential oncogene in pancreatic cancer. In conclusion, the two-dimensional phenotype and NI score we developed are promising tools for clinical multiomics applications and prediction of chemotherapy and immunotherapy response and present benefits in terms of precision medicine and individualized treatment decision-making for pancreatic cancer patients.

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