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Background Infiltrating immune and stromal cells are important components of the endometrial cancer (EC) microenvironment, which has a significant effect on the biological behavior of EC, suggesting that unique immune-related genes may be associated with the prognosis of EC. However, the association of immune-related genes with the prognosis of EC has not been elucidated. We attempted to identify immune-related genes with potentially prognostic value in EC using The Cancer Genome Atlas database and the relationship between immune microenvironment and EC. Methods We analyzed 578 EC samples from TCGA database and used weighted gene co-expression network analysis to screen out immune-related genes. We constructed a protein–protein interaction network and analyzed it using STRING and Cytoscape. Immune-related genes were analyzed through conjoint Cox regression and random forest algorithm analysis were to identify a multi-gene prediction model and stratify low-risk and high-risk groups of EC patients. Based on these data, we constructed a nomogram prediction model to improve prognosis assessment. Evaluation of Immunological, gene mutations and gene enrichment analysis were applied on these groups to quantify additional differences. Results Using conjoint Cox regression and random forest algorithm, we found that TRBC2, TRAC, LPXN, and ARHGAP30 were associated with the prognosis of EC and constructed four gene risk models for overall survival and a consistent nomogram. The time-dependent receiver operating characteristic curve analysis revealed that the area under the curve for 1-, 3-, and 5-y overall survival was 0.687, 0.699, and 0.76, respectively. These results were validated using a validation cohort. Immune-related pathways were mostly enriched in the low-risk group, which had higher levels of immune infiltration and immune status. Conclusion Our study provides new insights for novel biomarkers and immunotherapy targets in EC.

Background Fluoropyrimidine plus platinum chemotherapy remains the standard first line treatment for gastric cancer (GC). Guidelines exist for the clinical interpretation of four DPYD genotypes related to severe fluoropyrimidine toxicity within European populations. However, the frequency of these single nucleotide polymorphisms (SNPs) in the Latin American population is low ( Methods Retrospectively, 224 clinical records of GC patient were screened, of which 93 patients were incorporated into the study. Eleven SNPs with minor allelic frequency above 5% in GSTP1, ERCC2, ERCC1, TP53, UMPS, SHMT1, MTHFR, ABCC2 and DPYD were assessed. Association between patient clinical characteristics and toxicity was estimated using logistic regression models and classification algorithms. Results Reported grade ≤ 2 and 3–4 toxicities were 64.6% (61/93) and 34.4% (32/93) respectively. Selected DPYD SNPs were associated with higher toxicity (rs1801265; OR = 4.20; 95% CI = 1.70–10.95, p = 0.002), while others displayed a trend towards lower toxicity (rs1801159; OR = 0.45; 95% CI = 0.19–1.08; p = 0.071). Combination of paired SNPs demonstrated significant associations in DPYD (rs1801265), UMPS (rs1801019), ABCC2 (rs717620) and SHMT1 (rs1979277). Using multivariate logistic regression that combined age, sex, peri-operative chemotherapy, 5-FU regimen, the binary combination of the SNPs DPYD (rs1801265) + ABCC2 (rs717620), and DPYD (rs1801159) displayed the best predictive performance. A nomogram was constructed to assess the risk of developing overall toxicity. Conclusion Pending further validation, this model could predict chemotherapy associated toxicity and improve GC patient quality of life.