Mono‐chemotherapy has significant side effects and unsatisfactory efficacy, limiting its clinical application. Therefore, a combination of multiple treatments is becoming more common in oncotherapy. Chemotherapy combined with the induction of ferroptosis is a potential new oncotherapy. Furthermore, polymeric nanoparticles (NPs) can improve the antitumor efficacy and decrease the toxicity of drugs. Herein, a polymeric NP, mPEG‐b‐PPLGFc@Dox, is synthesized to decrease the toxicity of doxorubicin (Dox) and enhance the efficacy of chemotherapy by combining it with the induction of ferroptosis. First, mPEG‐b‐PPLGFc@Dox is oxidized by endogenous H2O2 and releases Dox, which leads to an increase of H2O2 by breaking the redox balance. The Fe(II) group of ferrocene converts H2O2 into ·OH, inducing subsequent ferroptosis. Furthermore, glutathione peroxidase 4, a biomarker of ferroptosis, is suppressed and the lipid peroxidation level is elevated in cells incubated with mPEG‐b‐PPLGFc@Dox compared to those treated with Dox alone, indicating ferroptosis induction by mPEG‐b‐PPLGFc@Dox. In vivo, the antitumor efficacy of mPEG‐b‐PPLGFc@Dox is higher than that of free Dox. Moreover, the loss of body weight in mice treated mPEG‐b‐PPLGFc@Dox is lower than in those treated with free Dox, indicating that mPEG‐b‐PPLGFc@Dox is less toxic than free Dox. In conclusion, mPEG‐b‐PPLGFc@Dox not only has higher antitumor efficacy but it reduces the damage to normal tissue.

BackgroundProstate cancer (PCa) is the most common malignant male neoplasm in the American male population. Our prior studies have demonstrated that protein phosphatase 1 regulatory subunit 12A (PPP1R12A) could be an efficient prognostic factor in patients with PCa, promoting further investigation. The present study attempted to construct a gene signature based on PPP1R12A and metabolism-related genes to predict the prognosis of PCa patients.MethodsThe mRNA expression profiles of 499 tumor and 52 normal tissues were extracted from The Cancer Genome Atlas (TCGA) database. We selected differentially expressed PPP1R12A-related genes among these mRNAs. Tandem affinity purification-mass spectrometry was used to identify the proteins that directly interact with PPP1R12A. Gene set enrichment analysis (GSEA) was used to extract metabolism-related genes. Univariate Cox regression analysis and a random survival forest algorithm were used to confirm optimal genes to build a prognostic risk model.ResultsWe identified a five-gene signature (PPP1R12A, PTGS2, GGCT, AOX1, and NT5E) that was associated with PPP1R12A and metabolism in PCa, which effectively predicted disease-free survival (DFS) and biochemical relapse-free survival (BRFS). Moreover, the signature was validated by two internal datasets from TCGA and one external dataset from the Gene Expression Omnibus (GEO).ConclusionThe five-gene signature is an effective potential factor to predict the prognosis of PCa, classifying PCa patients into high- and low-risk groups, which might provide potential novel treatment strategies for these patients.

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Prostate cancer (PCa) is a common type of cancer in men worldwide. Metabolic reprogramming is an important factor in its pathogenesis. Two-dimensional (2D) nanomaterials have tremendous potential for cancer treatment owing to their unique physicochemical properties. However, very few studies have focused on the metabolic reprogramming mechanisms of PCa using 2D nanomaterials. In this study, for the first time, 2D graphdiyne (GDY) was used as a template to immobilize copper (Cu) ions to form a novel nanocomposite (GDY-Cu). GDY provides a large π-conjugated architecture that spatiotemporally restricts Cu ions spatiotemporally to realize the functional moiety of Cu ions as tumor therapeutics. The GDY-Cu nanocomposite with a 2D morphological structure was characterized using a transmission electron microscopy and atomic force microscopy. The distribution of Cu loaded on the GDY was confirmed by high-resolution transmission electron microscopy with energy-dispersive x-ray spectroscopy analysis. In vitro and in vivo, GDY-Cu exhibits a good antitumor effect and is associated with specific metabolic reprogramming characteristics of PCa. In this study, the effect of GDY-Cu on the metabolic reprogramming of PCa cells was analyzed using untargeted metabolomics. Differences in metabolites in DU145 cells treated with GDY-Cu were analyzed by clustering and target analysis using bioinformatics methods. GDY-Cu inhibited the growth of PCa cells by decreasing the expression levels of acetyl-CoA carboxylase and cytoplasmic acetyl-CoA synthase, which inhibits the synthesis of related fatty acids and lipid metabolism. These results indicated that GDY-Cu inhibits the growth of PCa cells mainly via lipid metabolic pathways. At present, combinatory therapeutic modalities based on GDY and Cu are in their infancy. Further exploration is required to promote the development of 2D nanocomposite combinatory therapeutic modalities based on metabolic reprogramming.