Ovarian cancer (OC) is a common gynecological condition. Cancer stem cells (CSCs) are tumor cells with the potential to differentiate and self-renew. The aim of this study was to identify genes relevant to stem cells and oxidative stress (OS) in OC and to construct corresponding prognostic models. OS-related genes were obtained from GenBank. The mRNAsi-OS differentially expressed genes (DEGs) were filtered by overlapping OS-related genes, DEGs associated with mRNAsi, and DEGs in OC. Then, the Absolute Shrinkage and Selection Operator (LASSO) algorithm and univariate Cox regression were adopted to construct an OS-mRNAsi-related prognostic model. Subsequently, we validated the predictive value of the model using both the training and validation sets. The differences in immune infiltration and immunotherapy between the OS-CSC-related high- and low-risk subgroups were further explored. Finally, we analyzed the drug sensitivity between the 2 subgroups. A total of 5 prognostic genes (PLK2, CACNA1C, PENK, NR0B1, and HNF4A) related to CSC and OS were screened. The area under the curve (AUC) value of the prognostic model in predicting the 3-, 5-, and 7-year survival rate of patients with OC was >0.6, which revealed that the efficiency of the prognostic model was acceptable. The results of CIBERSORT demonstrated noticeable differences in the tumor microenvironment between the OS-CSC-related high- and low-risk subgroups. In addition, the risk score obtained based on OS and mRNAsi can be used to estimate the effectiveness of immunotherapy in patients with OC. Finally, the sensitivity of 5 common drugs (docetaxel, cisplatin, doxorubicin, mitomycin C, and paclitaxel) was evaluated using an OS-CSC-related prognostic model. In conclusion, an OS-CSC-related prognostic model based on 5 genes (PLK2, CACNA1C, PENK, NR0B1, and HNF4A) was constructed using bioinformatics analysis, which may provide new insights into the treatment and evaluation of OC.
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