The following is the summary of “A risk prediction model mediated by genes of APOD/APOC1/SQLE associates with prognosis in cervical cancer” published in the December 2022 issue of Women’s health by Zhang, et al.

One of the most prevalent cancers affecting women, cervical cancer is a serious health concern. New prognostic markers and treatment regimens for cervical cancer are needed to increase patient survival rates because of the disease’s high heterogeneity, which accelerates cancer growth. Their goal is to build and validate a model to predict the likelihood of a patient getting cervical cancer. Differences between normal and cancer samples were studied using data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), and then single-factor + multi-factor risk models were built using analysis of WGCNA and consistent clustering.

Receiver operating characteristic (ROC) and Kaplan-Meier curves were used to validate the risk model and confirm that the target genes acquired through regression analysis were indeed prognostic genes. Independent prognostic analysis and validation of the aforementioned model were then performed on the GSE44001 data. Together, the model’s high and low risks allowed for an enrichment analysis to be performed. The high-risk and low-risk groups were also compared with regard to drug sensitivity, immunological analysis (immune infiltration, immunotherapy), and other factors. 

In our work, we gathered data on three prognostic genes—APOD, APOC1, and SQLE—and used that information to build a risk model and test it for accuracy. The model was used to conduct immunological correlation and immunotherapy assessments, which will serve as a theoretical foundation and benchmark for future research into and treatment of cervical cancer.