A risk prediction model mediated by genes of APOD/APOC1/SQLE associates with prognosis in cervical cancer

Abstract

AbstractCervical cancer is one of the most common gynecological malignancies. Due to the high heterogeneity of cervical cancer accelerating cancer progression, it is necessary to identify new prognostic markers and treatment regimens for cervical cancer to improve patients’ survival rates. We purpose to construct and verify a risk prediction model for cervical cancer patients. Based on the analysis of data from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA), differences of genes in normal and cancer samples were analyzed and then used analysis of WGCNA along with consistent clustering to construct single-factor + multi-factor risk models. After regression analysis, the target genes were obtained as prognostic genes and prognostic risk models were constructed, and the validity of the risk model was confirmed using the receiver operating characteristic curve (ROC) and Kaplan–Meier curve. Subsequently, the above model was verified on the GSE44001 data validation followed by independent prognostic analysis. Enrichment analysis was conducted by grouping the high and low risks of the model. In addition, differences in immune analysis (immune infiltration, immunotherapy), drug sensitivity, and other levels were counted by the high and low risks groups. In our study, three prognostic genes including APOD, APOC1, and SQLE were obtained, and a risk model was constructed along with validation based on the above-mentioned analysis. According to the model, immune correlation and immunotherapy analyses were carried out, which will provide a theoretical basis and reference value for the exploration and treatment of cervical cancer.