Prognostic model and immunomicroenvironment analysis of combined thyroid and breast cancer in females based on cuproptosis related miRNA

Abstract

Abstract Background Previous studies have shown that breast cancer and thyroid cancer are more common in females . Among them, the incidence of breast cancer in the female cancer research ranks first. Thyroid cancer is the most common endocrine malignancy in women. cuproptosis is a new type of programmed cell death discovered recently. The objective of this study was to evaluate the prognostic significance of cuproptosis related miRNA (CRM) in female breast and thyroid cancers and to explore potential associations between the two cancers. Methods： We extracted transcriptomic data and clinicopathological features of women with THCA and BRCA from the Cancer Genome Atlas (TCGA) database. Univariate Cox regression and LASSO analysis were used to establish the prognostic characteristics of CRM. In order to verify the accuracy of the model, Kaplan-Meyer (K-M) and transient receiver operating characteristics (ROC) analysis were used. We drew a column graph that included clinical features and miRNA features to refine the prediction of the patient prognosis model. Finally, we performed immunoinfiltration correlation analysis. Results： In this study, we constructed a prognostic profile of CRM containing 15 miRNAs. This CRM feature was an independent predictor of overall survival. In addition, risk score was a better independent prognostic factor than traditional clinicopathological features. The correlation and differentiation analysis of immune invasion found a strong positive correlation among immune cells such as aDCs and DCs, while Macrophages showed significant differences among the risk group. The study revealed that there was strong positive correlation between immune functions such as APC co stimulation and Check-point. Furthermore, indicators of APC co inhibition, APC co stimulation, Check-point, and Inflammation-promoting showed significant differences between risk groups. Based on risk score and immune score, we finally screened out 6 differential expression genes (DEGs) : such as PCOLCE, SV2C. These DEGs were significantly correlated with one or more immune cells and their functions during immune invasion. Conclusion： CRM features can be used as novel biomarkers to predict the prognosis of patients with breast cancer and thyroid cancer, and to predict the clinical outcome and treatment response of patients, thus providing basic insights for further research.