Machine learning identifies SRD5A3 as a propionate-related prognostic biomarker in triple-negative breast cancer

机器学习发现SRD5A3是三阴性乳腺癌中与丙酸相关的预后生物标志物

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Abstract

The increased risk of recurrence and metastasis are obstacles to treating TNBC. Propionate-related genes play an important role in tumor development and immune cell infiltration. The study was to identify the association between propionate-related genes and the prognosis of TNBC patients. Propionate-related genes were collected and analyzed to establish propionate-related gene characteristics. Then, the survival analysis was performed, and the responses to immunotherapies were evaluated. Furthermore, the drug sensitivity of some traditional chemotherapeutic drugs was evaluated. Finally, the hub genes were discovered and validated by in vitro experiments. Based on the five-propionate-related gene signature, TNBC patients were divided into high and low-risk groups. In addition, DEGs between the different risk groups were enriched in the biological activities associated with immunity. TNBC patients in the high-risk groups were suggested to have worse responses to immunotherapies and a poorer prognosis. SRD5A3 was finally found to be a hub gene, and in vitro experiments revealed that silencing SRD5A3 inhibited tumor cell proliferation, invasion, and migration. The five-propionate-related risk model presented novel insights into the efficacy of immunotherapy. It was found that down-regulation of SRD5A3 inhibited the growth and invasion of tumor cells, thereby affecting the prognosis of TNBC.

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