Development of a seven-gene tumor immune microenvironment prognostic signature for high-risk grade III endometrial cancer

Abstract

Uterine corpus endometrial carcinoma locally infiltrates numerous immune cells and other tumor immune microenvironment components. These cells are involved in malignant tumor growth and proliferation and the process of resistance toward immunotherapies. Here, we aimed to develop a tumor immune microenvironment-related prognostic signature for high-risk grade III endometrial carcinoma based on The Cancer Genome Atlas. The signature was systematically correlated with immune infiltration characteristics of the tumor microenvironment. The seven-gene Riskscore signature was robust and performed well in training, testing, and Gene Expression Omnibus-independent cohorts. A nomogram comprising the gene signature accurately predicted patient prognosis, with our model performing better than other endometrial cancer-related signatures. Analysis of the IMvigor210 immunotherapy cohort revealed that subgroups with a low Riskscore had a better prognosis than subgroups with a high Riskscore. Subgroups with a low Riskscore exhibited immune cell infiltration and inflammatory profiles, whereas subgroups with a high Riskscore experienced progressive disease. The receiver operating characteristic curve indicated that risk score, neoantigen, and tumor mutation burden models together accurately predicted treatment response. Taken together, we developed a tumor microenvironment-based seven-gene prognostic stratification system to predict the prognosis of patients with high-risk endometrial cancer and guide more effective immunotherapy strategies.

Immunologic Signatures across Molecular Subtypes and Potential Biomarkers for Sub-Stratification in Endometrial Cancer

“…Activated M1 macrophages release pro-inflammatory factors and suppress tumor growth. High infiltration of M1 macrophages has been described in EC patients with a better prognosis [ 31 ].…”

Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases

“…Although the accuracy of the diagnosis and treatment of EC has made great progress in recent years, the molecular mechanism remains unknown. Abnormal gene expression and immune response in TIME play active roles in tumor occurrence, development, invasion, metastasis, and recurrence and are key considerations influencing tumor prognosis [ 16 , 22 , 23 , 24 ]. Endometrial biopsy under hysteroscopy seems to be the best method for the diagnosis of benign EC.…”

Bioinformatics reveal elevated levels of Myosin Vb in uterine corpus endometrial carcinoma patients which correlates to increased cell metabolism and poor prognosis

“…Importantly, UCEC is currently the fourth most life-threatening cancer in females, after ovarian cancer and cervical cancer [4]. Fortunately, approximately 75% of UCEC patients are diagnosed early, resulting in a 5-year survival rate of more than 70% [5][6][7]. However, the prognosis of UCEC patients with grade III endometrioid cancer, serous carcinoma, and mixed adenocarcinoma is still poor with a 5-year survival rate of 17% [5,[8][9][10].…”

“…Fortunately, approximately 75% of UCEC patients are diagnosed early, resulting in a 5-year survival rate of more than 70% [5][6][7]. However, the prognosis of UCEC patients with grade III endometrioid cancer, serous carcinoma, and mixed adenocarcinoma is still poor with a 5-year survival rate of 17% [5,[8][9][10]. Additionally, the risk of metastasis and recurrence is also high in these patients.…”

“…Bioinformatics is a rapidly growing discipline that can be applied to large datasets and can uncover the biological significance of cancer-related data [5,12,13]. Analysis of high-throughput RNA-sequencing data of UCEC revealed that the molecular motor Myosin Vb (MYO5B) was elevated at the level of mRNA and protein in the beginning stages of UCEC.…”