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Published on: April 29, 2014
Development of a prognostic model for cervical cancer based on inflammation-related gene signatures and immune
Yu Wang1, Chao Qi1, Mingyu Hao1
1Department of Pathology, Taiyuan Maternal and Child Health Hospital Taiyuan 030021, Shanxi, China.
Aims:
To develop a prognostic model for cervical cancer by integrating inflammation-related gene signatures and clinical factors, aimed at improving patient outcome prediction and exploring the role of immune microenvironment regulation in tumor progression.
Methods:
This retrospective study analyzed 150 cervical cancer patients from Taiyuan Maternal and Child Health Hospital, diagnosed between January 2019 and January 2022. Patients were categorized into a poor prognosis (n=60) and a good prognosis group (n=90) based on prognosis. The study evaluated the prognostic value of inflammation-related gene expression on overall survival (OS) and progression-free survival (PFS). MMP-9, IL-6, and COX-2 expression levels were stratified as high or low based on median values.
Results:
Pro-inflammatory genes (TNF-alpha, IL-6, MMP-9, and COX-2) were significantly higher in the poor prognosis group, while IL-10 levels were lower (all P<0.001). Multivariate analysis identified independent risk factors for poor prognosis, including HPV infection status, MMP-9, IL-6, TNF-alpha, and lymph node involvement. A nomogram incorporating these factors demonstrated strong discrimination (AUC 0.83) and effective poor outcome prediction. Elevated levels of MMP-9, IL-6, and COX-2 correlated with poor OS and PFS, highlighting their potential as prognostic markers and therapeutic targets. These gene expression alterations were also associated with immune microenvironment dysregulation, suggesting their role in immune evasion and chronic inflammation in tumors.
Conclusion:
A prognostic model for cervical cancer was developed integrating inflammation-related gene signatures and clinical factors. Elevated MMP-9, IL-6, and COX-2 expression are linked to poor prognosis and immune microenvironment disruption, offering a promising approach for personalized treatment.
