Identification of a 3-miRNA Signature Associated With the Prediction of Prognosis in Nasopharyngeal Carcinoma

Nasopharyngeal carcinoma (NPC) is a malignant tumor caused by an infection of the epithelial cells of the nasopharynx, which is highly metastatic and aggressive. Due to the deep anatomical site and atypical early symptoms, the majority of NPC patients are diagnosed at terminal stages. There is growing evidence that microRNAs offer options for early detection, accurate diagnosis, and prediction of malignancy treatment response. Therefore, the purpose of this article was to identify microRNAs that predict the prognosis of patients with NPC by integrating biological information analysis. In this study, we utilized the GSE36682 dataset rooted in the Gene Expression Omnibus (GEO) data bank, including 62 cases of NPC tissues and six cases of non-cancerous tissues. The miRNAs were subjected to weighted gene co-expression network analysis, and hub miRNAs were screened for differentially upregulated miRNAs from modules highly correlated with tumor progression. We took a lot of time to calculate the risk scores of miRNA markers for 62 NPC patients, and incidentally combined the clinical survival information of patients to finally identify the three key miRNAs, and then divided the patients into low- and high-risk groups. Kaplan-Meier curve analysis revealed that the overall survival of patients in the high-risk group was obviously shorter than that of the low-risk group. Subsequently, the target genes of the three miRNAs were predicted and analyzed for functional enrichment. In summary, a prognostic predictive risk model based on three miRNA profiles may increase prognostic predictive value and provide reference information for the precise treatment of nasopharyngeal carcinoma.

Automated summary

This study aimed to predict the prognosis of patients with nasopharyngeal carcinoma (NPC) by analyzing microRNAs. The researchers utilized the GSE36682 dataset for weighted gene co-expression network analysis, and identified differentially upregulated miRNAs highly correlated with tumor progression. Risk scores were calculated for 62 NPC patients, and clinical survival information was incorporated to identify three key miRNAs and divide patients into low- and high-risk groups. Kaplan-Meier curve analysis showed that patients in the high-risk group had significantly shorter overall survival. Functional enrichment analysis of the target genes of the three miRNAs was also conducted. Overall, a prognostic predictive risk model based on three miRNA profiles may enhance prognostic value and provide reference information for the precise treatment of NPC.