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Background: Tumor mutation burden (TMB) is considered as a novel biomarker of response to immunotherapy and correlated with survival outcomes in various malignancies. Here, TMB-related genes (TRGs) expression signatures were constructed to investigate the association between TMB and prognosis in epithelial ovarian cancer (EOC), and the potential mechanism in immunoregulation was also explored. Methods: Based on somatic mutation data of 436 EOC samples from The Cancer Genome Atlas database, we examined the relationship between TMB level and overall survival (OS), as well as disease-free survival (DFS). Next, the TRGs signatures were constructed and validated. Differential abundance of immune cell infiltration, expression levels of immunomodulators and functional enrichment in high- and low-risk groups were also analyzed. Results: Higher TMB level revealed better OS and DFS, and correlated with earlier clinical stages in EOCs ( P = 2.796e-04). The OS-related prognostic model constructed based on seven TRGs ( B3GALT1, LIN7B, ANGPT2, D2HGDH, TAF13, PFDN4 and DNAJC19 ) significantly stratified EOC patients into high- and low-risk groups ( P &lt; 0.001). The AUC values of the seven-gene prognostic signature at 1 year, 3 years, and 5 years were 0.703, 0.758 and 0.777. While the DFS-related prognostic model was constructed based on the 4 TRGs ( LPIN3, PXYLP1, IGSF23 and B3GALT1 ), with AUCs of 0.617, 0.756, and 0.731, respectively. Functional analysis indicated that immune-related pathways were enriched in low-risk groups. When considering the infiltration patterns of immune cells, we found higher proportions of follicular helper T (Tfh) cell and M1 macrophage, while lower infiltration of M0 macrophage in low-risk groups ( P &lt; 0.05). Accordingly, TMB levels of low-risk patients were significantly higher both in OS and DFS model ( P &lt; 0.01). Conclusions: Our TRGs-based models are reliable predictive tools for OS and DFS. High TMB may confer with an immunogenic microenvironment and predict favorable outcomes in EOCs.

Multi‐omics analysis of tumor mutational burden combined with prognostic assessment in epithelial ovarian cancer based on TCGA database