Xianzhen Zhang,1,2 Aihua Li,2,3 Wanqi Zhu,4 Qiufen Guo,5 Qian Wu,5 Hong Zhao,4 Yunbei Yu,1 Peng Xie,5 Xiaolin Li4 1Department of Oncology, L
Xianzhen Zhang,1,2 Aihua Li,2,3 Wanqi Zhu,4 Qiufen Guo,5 Qian Wu,5 Hong Zhao,4 Yunbei Yu,1 Peng Xie,5 Xiaolin Li4 1Department of Oncology, Liaocheng People’s Hospital, Liaocheng, Shandong, 252000, People’s Republic of China; 2Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250000, People’s Republic of China; 3Department of Obstetrics and Gynaecology, Liaocheng People’s Hospital, Liaocheng, Shandong, 252000, People’s Republic of China; 4Department of Radiation Oncology, Shandong First Medical University Affiliated Cancer Hospital (Shandong Academy of Medical Sciences), Jinan, Shandong, 250117, People’s Republic of China; 5Department of Gynecological Tumor Radiation Oncology, Shandong First Medical University Affiliated Cancer Hospital (Shandong Academy of Medical Sciences), Jinan, Shandong, 250117, People’s Republic of ChinaCorrespondence: Peng Xie, Email xiepeng100@126.comIntroduction: The aim of this study was to clarify the genome of ferroptosis in the genes involved in radiotherapy resistance and regulation of tumor immune microenvironment by multigene analysis of cervical cancer (CC) patients.Methods: Different radiation sensitivity samples from CC patients were collected for RNA sequencing. Differentially expressed genes (DEGs) between the RNA dataset and the GSE9750 dataset were considered as radiotherapy-DEGs. The intersection genes of radiotherapy-DEGs with ferroptosis-related genes (FRGs) and the intersection genes of radiotherapy-DEGs with immune-related genes (IRGs) were labeled as FRGs-IRGs-DEGs (FIGs). A risk model was established by prognostic genes selected from FIGs by univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) analysis. The results were further validated using samples from CC tissue samples.Results: The 329 DEGs related to CC radiotherapy were identified. LSAAO analysis was utilized to identify five prognostic genes (CALCRL, UCHL1, GNRH1, ACVRL1, and MUC1) from six candidate prognosis genes and construct a risk model. The risk model demonstrated favorable effectiveness in predicting outcomes at 1, 3, and 5 years, as evidenced by ROC curves. Univariate and multivariate Cox regression analysis demonstrated that CALCRL, GNRH1, and MUC1 were independent prognostic factors. The results of functional similarity analysis showed that CALCRL, UCHL1, ACVRL1 and MUC1 had high average functional similarity. The results of PCR and IHC showed the same trend with the results above.Discussion: A novel prognostic model related to ferroptosis and immune microenvironment in CC radiotherapy was developed and validated, providing valuable guidance for personalized anti-cancer therapy.Keywords: cervical cancer, radiotherapy, ferroptosis, risk model, single-cell RNA-seq analysis
