子宫内膜癌中炎症反应相关稳健机器学习特征：基于多队列研究

Uterine corpus endometrial carcinoma (UCEC) is a prevalent form of cancer in women, affecting the inner lining of the uterus. Inflammation plays a crucial role in the progression and prognosis of cancer, making it important to identify inflammatory response-related subtypes in UCEC for targeted therapy and personalized medicine.

This study discovered significant variation in immune response within UCEC tumors based on molecular subtypes of inflammatory response-related genes. Subtype A showed a more favorable prognosis and better response to immunotherapies like anti-CTLA4 and anti-PDCD1 therapy.

Functional analysis revealed subtype-specific differences in immune response, with subtype A exhibiting higher expression of genes related to cytokine signaling pathways, NK cell-mediated cytotoxicity pathways and inflammatory processes. Subtype A also showed increased sensitivity to three chemotherapeutic agents. A 12-gene inflammatory response-related signature was found to have prognostic value for 1, 2 and 3 year survival in UCEC patients.

Additionally, a validated machine learning-based signature demonstrated significant differences in clinical traits between low-risk and high-risk cohorts. Elevated risk scores were associated with higher pathological grading, older age, advanced stage and immune subtype C2. Low-risk groups had higher infiltration of immune cell types such as CD8 + T cells and activated CD4 + cells.

However, the abundance of cytotoxic immune cells decreased with increasing risk scores.

Finally, PCR was applied to test the different expression in P2PX4. P2RX4 knockdown inhibited the proliferation and proliferation of the endometrial carcinoma Ishikawa cell line.

In conclusion, this developed signature can serve as a clinical prediction index and reveal distinct immune expression patterns. Ultimately, this study has the potential to enhance targeted therapy and personalized medicine for UCEC patients.