AI Article Synopsis
- Ovarian cancer is a highly lethal gynecological cancer often diagnosed late, with metabolic disorders in lipid and arachidonic acid pathways linked to its progression.
- A robust 10-gene signature model has been developed to predict prognosis based on PUFA-related genes, showing strong associations with clinical outcomes across multiple datasets.
- High levels of the enzyme LTA4H correlate with poor prognosis and may affect the sensitivity to chemotherapy and immune cell behavior, suggesting new therapeutic targets in ovarian cancer treatment.
Article Abstract
Ovarian cancer is the most lethal and aggressive gynecological cancer with a high recurrence rate and is often diagnosed late. In ovarian cancer, multiple metabolic enzymes of lipid metabolism are abnormally expressed, resulting in metabolism disorder. As a characteristic pathway in polyunsaturated fatty acid (PUFA) metabolism, arachidonic acid (AA) metabolism is disturbed in ovarian cancer. Therefore, we established a 10-gene signature model to evaluate the prognostic risk of PUFA-related genes. This 10-gene signature has strong robustness and can play a stable predictive role in datasets of various platforms (TCGA, ICGC, and GSE17260). The high association between the risk subgroups and clinical characteristics indicated a good performance of the model. Our data further indicated that the high expression of LTA4H was positively correlated with poor prognosis in ovarian cancer. Deficiency of LTA4H enhanced sensitivity to Cisplatin and modified the characteristics of immune cell infiltration in ovarian cancer. Additionally, our results indicate that CCL5 was involved in the aberrant metabolism of the AA/LTA4H axis, which contributes to the reduction of tumor-infiltrating CD8 T cells and immune escape in ovarian cancer. These findings provide new insights into the prognosis and potential target of LTA4H/CCL5 in treating ovarian cancer.
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| http://dx.doi.org/10.1007/s11427-023-2444-5 | DOI Listing |
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