Cuproptosis is a newfound cell death form that depends on copper (Cu) ionophores to transport Cu into cancer cells. Studies on the relationship have covered most common cancer types and analyzed the links between cuproptosis-related genes (CRGs) and various aspects of tumor characteristics. In this study, we evaluated the role of cuproptosis in lung adenocarcinoma (LUAD) and constructed the cuproptosis-related score (CuS) to predict aggressiveness and prognosis in LUAD, so as to achieve precise treatment for patients. CuS had a better predictive performance than cuproptosis genes, possibly due to the synergy of SLC family genes, and patients with a high CuS had a poor prognosis. Functional enrichment analysis revealed the correlation between CuS and immune and mitochondrial pathways in multiple datasets. Furthermore, we predicted six potential drugs targeting high-CuS patients, including AZD3759, which is a targeted drug for LUAD. In conclusion, cuproptosis is involved in LUAD aggressiveness, and CuS can accurately predict the prognosis of patients. These findings provide a basis for precise treatment of patients with high CuS in LUAD.
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http://dx.doi.org/10.3390/genes14051055 | DOI Listing |
Eur J Surg Oncol
December 2024
Department of Surgery, Tokyo Medical University, Japan.
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Univeristy of Alabama at Birmingham, Birmingham, Alabama, United States.
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In a recent study by He , the nomogram integrates postoperative serum tumor markers such as carbohydrate antigen 19-9 and carcinoembryonic antigen, thereby improving the accuracy of identifying high-risk patients compared to relying solely on preoperative markers, which has significant implications for customizing adjuvant therapy and potentially improving outcomes for this aggressive form of cancer. However, the study's single-center design and short follow-up period may limit the generalizability of its findings and potentially introduce reporting bias. Future studies could consider additional confounding factors, such as adjuvant chemotherapy and variations in surgical techniques, to improve the model's accuracy.
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