Abstract
This study aimed to construct a N1-methyladenosine (m1A)-related biomarker model for predicting the prognosis of ovarian cancer (OVCA). OVCA samples were clustered into two subtypes using the Non-Negative Matrix Factorization (NMF) algorithm, including TCGA (n = 374) as the training set and GSE26712 (n = 185) as the external validation set. Hub genes, which were screened to construct a risk model, and nomogram to predict the overall survival of OVCA were explored and validated through various bioinformatic analysis and quantitative real-time PCR. Following bootstrap correction, the C-index of nomogram was 0.62515, showing reliable performance. The functions of DEGs in the high- and low-risk groups were mainly enriched in immune response, immune regulation, and immune-related diseases. The immune cells relevant to the expression of hub genes were explored, for example, Natural Killer (NK) cells, T cells, activated dendritic cells (aDC). AADAC, CD38, CACNA1C, and ATP1A3 might be used as m1A-related biomarkers for OVCA, and the nomogram labeled with m1A for the first time had excellent performance for predicting overall survival in OVCA.
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