卵巣がんの幹細胞および酸化ストレス関連遺伝子に基づく予後モデル

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.