Abstract:

Inflammatory responses have been identified as a critical factor in the development and progression of various types of tumors. These responses influence the tumor microenvironment, promoting tumor cell invasion and migration while concomitantly reducing the efficacy of tumor therapy. Inflammation is widely regarded as a significant risk factor for the development of endometrial cancer (EC). However, the precise mechanisms through which it influences the development of EC remain to be elucidated. In this study, we obtain RNA expression profiles of EC patients and related clinical information from The Cancer Genome Atlas (TCGA) database. We then screen key inflammation-related genes using survival analysis and the least absolute value shrinkage and selection operator (LASSO) algorithms. Based on this, we finally construct a prognostic risk scoring model containing nine non-zero coefficient IRGs and an alignment diagram prediction model. Survival analysis demonstrates that patients in the low-risk group exhibit a higher survival rate and more favorable prognosis. The predictive performance of both models was confirmed through the analysis of test sets and calibration curves. Subsequently, we obtain EC-related datasets from the Gene Expression Omnibus (GEO) database to serve as an external validation, thereby further substantiating the reliability of the models. Subsequent immune infiltration analysis revealed significant disparities among nine immune cell types between the high- and low-risk groups, with multiple immune cells correlating with tumor progression and prognosis. Concurrently, we perform drug sensitivity analysis, it reveals a significant correlation between one representative EC drug, tamoxifen, and one of the aforementioned IRGs. In summary, our study successfully constructs a risk score model and a column-line graph prediction model for EC. It is expected that these models will better predict the overall survival and provide new therapeutic targets for EC patients.

Key words: endometrial cancer, inflammation related genes, prognosis, immune infiltration, drug sensitivity