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Updated: Jul 11, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Identification of a prognostic model based on immune and hypoxia-related gene expressions in cervical cancer
Liqun Wang1, Caizhi Wang1, Yu He1
1Department of Gynecology, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China.
Background:
Tumour immune microenvironment (TIME) has long been a key direction of tumour research. Understanding the occurrence, metastasis and other processes of cervical cancer (CC) is of great significance in the diagnosis and prognosis of tumours.
Methods:
Here, this study applied the univariate Cox regression model to determine the prognostic association of immune and hypoxia signature genes in CC, and used Least Absolute Shrinkage and Selection Operator (LASSO) Cox method to build immune and hypoxia related risk score model to uncover the immune signature of the TIME of CC. Moreover, we used in vitro experiment to validate the expression level of signature genes. Notably, we assessed the predictive effect of anti-PD1/PDL1 immunotherapy using risk score model.
Results:
Through the LASSO Cox regression model, we obtained 12 characteristic genes associated with the prognosis of CC, and also associated with immunity and hypoxia. Interestingly, the high-risk group had the properties of high hypoxia and low immunity, while the low-risk group had the properties of low hypoxia and high immunity. In the low-risk group, patients lived longer and had a significant therapeutic advantage of anti-PD-1 immunotherapy.
Conclusions:
Established risk scores model can help predict response to anti-PD-1 immunotherapy of CC.
