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Protein-based Prognostic Signature for Predicting the Survival and Immunotherapeutic Efficiency of Endometrial Carcinoma by Jinzhi Lai; Tianwen Xu; Hainan Yang is a Medicine article available to read on EtoBox.

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## Abstract ## Background Endometrial cancer (EC) is the most frequent malignancy of the female genital tract worldwide. Our study aimed to construct an effective protein prognostic signature to predict prognosis and immunotherapy responsiveness in patients with endometrial carcinoma. ## Methods Protein expression data, RNA expression profile data and mutation data were obtained from The Cancer Proteome Atlas (TCPA) and The Cancer Genome Atlas (TCGA). Prognosis-related proteins in EC patients were screened by univariate Cox regression analysis. Least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression analysis were performed to establish the protein-based prognostic signature. The CIBERSORT algorithm was used to quantify the proportions of immune cells in a mixed cell population. The Immune Cell Abundance Identifier (ImmuCellAI) and The Cancer Immunome Atlas (TCIA) web tools were used to predict the response to immunochemotherapy. The pRRophetic algorithm was used to estimate the sensitivity of chemotherapeutic and targeted agents. ## Results We constructed a prognostic signature based on 9 prognostic proteins, which could divide patients into

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Author

Jinzhi Lai; Tianwen Xu; Hainan Yang

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Medicine (Health Sciences)