mProX™ Human P2RX4 Stable Cell Line

Pang, Xiaoao. et al. "Coordination of Single-cell and Bulk RNA Sequencing to Construct a Cuproptosis-related Gene Prognostic Index for Endometrial Cancer Prognosis, Immune Microenvironment Infiltration, and Immunotherapy Treatment Options." Journal of Cancer, 2023.
This article examines the concept of procrastination in relation to academic performance. It explores the various factors that contribute to procrastination and its potential impact on students' academic success. The study also investigates the coping strategies employed by students to overcome procrastination. Results suggest that procrastination is a prevalent issue among students and can have significant consequences on their academic achievements. The findings highlight the importance of addressing procrastination in educational settings and suggest potential interventions to improve academic performance.
Pang, Xiaoao. et al. "Coordination of Single-cell and Bulk RNA Sequencing to Construct a Cuproptosis-related Gene Prognostic Index for Endometrial Cancer Prognosis, Immune Microenvironment Infiltration, and Immunotherapy Treatment Options." Journal of Cancer, 2023.
Pubmed: 37859813   DOI: 10.7150/jca.86325

Zhou, Chufan. et al. "The inflammatory response-related robust machine learning signature in endometrial cancer: Based on multi-cohort studies." The journal of gene medicine, 2023.
Uterine corpus endometrial carcinoma (UCEC) is a common cancer affecting women, characterized by abnormal cell growth in the inner lining of the uterus. Inflammation is a key factor in the progression and outcomes of UCEC, thus identifying inflammatory response-related subtypes is crucial for personalized treatment. This research identified significant differences in immune response among UCEC tumors based on molecular subtypes of inflammatory response-related genes. Subtype A showed a more favorable prognosis and was more responsive to immunotherapies. Further analysis revealed subtype-specific variations in immune response and sensitivity to chemotherapy. A 12-gene signature was also identified as a prognostic marker for UCEC patients. A validated machine learning-based signature also highlighted important clinical distinctions between low and high-risk groups. These differences included increased infiltration of certain immune cells in the low-risk group. Additionally, a correlation was found between higher risk scores and certain clinical characteristics. Knockdown of the P2RX4 gene was also observed to inhibit the proliferation of UCEC cells. In conclusion, these findings have the potential to improve targeted therapy and personalized medicine in UCEC patients through the use of this developed signature as a clinical prediction index.
Zhou, Chufan. et al. "The inflammatory response-related robust machine learning signature in endometrial cancer: Based on multi-cohort studies." The journal of gene medicine, 2023.
Pubmed: 37845174   DOI: 10.1002/jgm.3603