The deubiquitinase enzyme RPN11 is involved in oncogenesis in various types of cancer. However, in breast cancer, the expression levels, prognostic relevance and biological function of RPN11 remains unclear. In the present study, RPN11 expression levels in breast cancer tissues and adjacent non‑tumor tissues were determined by reverse transcription‑quantitative polymerase chain reaction and immunohistochemical staining, and the association of RPN11 with clinicopathological features of breast cancer was evaluated. RPN11 expression was upregulated in breast cancer tissues compared with healthy tissues. Additionally, high expression levels of RPN11 may be an indicator of poor prognosis, as validated by a breast cancer cohort from the Gene Expression Omnibus database. Knockdown of RPN11 in MDA‑MB‑231 and T47D cells significantly reduced cell proliferation and enhanced G0/G1 arrest and apoptosis. Exogenous overexpression of RPN11 in MCF7 and Hs578T cells promoted cell growth and inhibited apoptosis. In addition, knockdown of RPN11 abrogated cell migration and reduced epithelial‑mesenchymal transition. In conclusion, these findings suggested that RPN11 may function as an oncogene and its upregulation in breast cancer suggests that it may be a therapeutic target.

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Lymph node metastasis is a major prognostic factor for patients with breast cancer.

Macrophages are key innate immune cells in the tumor microenvironment that regulate primary tumor growth, vascularization, metastatic spread and response to therapies. Macrophages can polarize into two different states (M1 and M2) with distinct phenotypes and functions. To investigate the known tumoricidal effects of M1 macrophages, we obtained RNA expression profiles and clinical data from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA). The proportions of immune cells in tumor samples were assessed using CIBERSORT, and weighted gene co-expression network analysis (WGCNA) was used to identify M1 macrophage-related modules. Univariate Cox analysis and LASSO-Cox regression analysis were performed, and four genes (SPP1, DHRS3, SLC11A1, and CFB) with significant differential expression were selected through GEPIA. These four genes can be considered hub genes. The four-gene risk-scoring model may be an independent prognostic factor for THCA patients. The validation cohort and the entire cohort confirmed the results. Univariate and multivariate Cox analysis was performed to identify independent prognostic factors for THCA. Finally, a prognostic nomogram was built based on the entire cohort, and the nomogram combining the risk score and clinical prognostic factors was superior to the nomogram with individual clinical prognostic factors in predicting overall survival. Time-dependent ROC curves and DCA confirmed that the combined nomogram is useful. Gene set enrichment analysis (GSEA) was used to elucidate the potential molecular functions of the high-risk group. Our study identified four genes associated with M1 macrophages and established a prognostic nomogram that predicts overall survival for patients with THCA, which may help determine clinical treatment options for different patients.