ObjectiveTo screen the necrotic apoptosis-related genes for prognosis of patients with ovarian cancer by bioinformatics. MethodsNecrotic apoptosis-related genes, expressions of ovarian cancerous genes, and relevant clinical data of ovarian cancer were downloaded from the public databases. Differentially expressed necrotic apoptosis-related genes (differentially expressed genes) between ovarian cancerous tissues and normal ovarian tissues were screened by employing the RStudio 4.1 software, and the Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on differentially expressed genes. The multivariate COX regression model was used to screen necrotic apoptosis-related genes related to prognosis of patients with ovarian cancer. According to the results of multivariate COX regression, the prognostic risk model of ovarian cancer was established, and ovarian cancerous samples were assigned to high-risk group or low-risk group according to median risk score of this model. The survival status was compared between the high-risk group and the low-risk group by using the Kaplan-Meier method. The receiver operating characteristic (ROC) curve was employed to validate evaluation efficiency of prognostic risk model of ovarian cancer. The independent influencing factors for prognosis of ovarian cancer were analyzed by the COX regression model, and the correlation of independent prognostic influencing factors for ovarian cancer with total survival rate of patients with ovarian cancer was presented by the nomogram. Results(1) A total of 25 differentially expressed genes were determined in ovarian cancer, including 13 up-regulated genes and 12 down-regulated genes. (2) The results of GO analysis revealed that differentially expressed genes involved biological processes including regulating apoptosis signaling pathway, etc.,involved cellular compositions mainly including endometrium in late stage, etc.,and involved molecular functions mainly containing cytokines activities, etc. The results of KEGG pathway enrichment analysis indicated that differentially expressed genes were mainly enriched in signaling pathways of necrotic apoptosis signaling pathway, etc. (3) The results of multivariate COX regression model analysis revealed that signal transducer and activator of transcription 4 (STAT4) gene and calcium/calmodulin dependent protein kinase Ⅱ alpha (CAMK2A) gene correlated with prognosis of patients with ovarian cancer (P＜0.05), therein, STAT4 gene belonged to protective gene, and CAMK2A gene to risk gene. (4) The prognostic risk model of ovarian cancer was established based on STAT4 and CAMK2A genes. The results of survival analysis interpreted that the total survival of patients in the high-risk group was lower than that in the low-risk group (P＜0.05); moreover, areas under the curve of 1-, 2-, and 3-year survival state in patients with ovarian cancer evaluated by this model were 0.575, 0.616, and 0.593, respectively. (5) The results of COX regression analysis interpreted that the risk score of this model was the independent influencing factor for prognosis of patients with ovarian cancer (P＜0.05); in addition, the results of nomogram C-index line chart and nomogram calibration curve for predicting patients′ total survival established based on independent prognostic influencing factors of ovarian cancer indicated that the nomogram exerted relatively favorable accuracy.ConclusionSTAT4 and CAMK2A genes in necrotic apoptosis-related genes correlate with prognosis of patients with ovarian cancer, therein, STAT4 gene belongs to protective gene, and CAMK2A gene to risk gene. The prognostic risk model of ovarian cancer established based on STAT4 and CAMK2A genes exerts values on evaluating prognosis of patients with ovarian cancer to a certain extent.

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