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Updated: Jun 9, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
[Construction and validation of an immune prognostic risk model in oral squamous cell carcinoma]
Jiao Zhao1, Bai-Yan Sui, Xin Liu
1School of Stomatology, Weifang Medical University. Weifang 261053, Shandong Province, China.
Purpose:
To analyze the immune-related core genes differentially expressed in oral squamous cell carcinoma(OSCC) and construct an immune-related prognostic risk model for OSCC patients.
Methods:
Weighted gene co-expression network analysis of RNA sequencing data from OSCC patients in the Cancer Genome Atlas (TCGA) database was conducted to identify immune-related modules and core genes. Core genes associated with immune prognosis were screened using univariate Cox regression analysis and survival analysis to construct an immune-related prognostic risk model for OSCC. The prognostic risk model's predictive ability was evaluated using Kaplan-Meier analysis, receiver operating characteristic curves, and external datasets from GSE41613. The expression of 8 immune prognostic core genes in tumor samples from OSCC patients was detected by real-time quantitative PCR assay(RT-qPCR), and the correlation between risk score and depth of invasion was assessed by calculating risk scores for OSCC patients. Statistical analysis was performed with SPSS 21.0 software package.
Results:
Prognostic risk model for OSCC was successfully constructed based on 8 immune prognostic core genes(CSF2RA, CLEC4C, COL5A3, CTSG, EDNRA, GPC4, GUCY1A2, ANGPT2). The prognostic risk model demonstrated perfect predictive value validated using Kaplan-Meier analysis, receiver operating characteristic curve, and the GSE41613 dataset. The risk scores of OSCC patients calculated based on this model were positively correlated with the depth of invasion, indicating that the model have the ability to predict the potential risk of OSCC.
Conclusions:
An OSCC prognostic risk model is constructed based on the signatures of 8 immune prognostic core genes, which may effectively predict the prognosis of OSCC patients, providing an important reference for immune prevention of OSCC.
