Near‐infrared (NIR) photothermal therapy plays a critical role in the cancer treatment and diagnosis as a promising carcinoma treatment modalities nowadays. However, development of clinical application has been greatly limited due to the inefficient drug release and low tumor accumulation. Herein, we designed a NIR‐light triggered indocyanine green (ICG)‐based PCL core/P(MEO 2 MA‐ b ‐HMAM) shell nanocomposites (PPH@ICG) and evaluated their therapeutic effects in vitro and in vivo. The anticancer drug 5‐fluorouracil (5Fu) and the photothermal agent ICG were loaded into a thermo‐sensitive micelle (PPH@5Fu@ICG) by self‐assembly. The nanoparticles formed were characterized using transmission electron microscopy, dynamic light scattering, and fluorescence spectra. The thermo‐sensitive copolymer (PPH@5Fu@ICG) showed a great temperature‐controlled drug release response with lower critical solution temperature. In vitro cellular uptake and TEM imaging proved that PPH@5Fu@ICG nanoparticles can home into the lysosomal compartments under NIR. Moreover, in gastric tumor‐bearing nude mice, PPH@5Fu@ICG + NIR group exhibited excellent improvement in antitumor efficacy based on the NIR‐triggered thermo‐chemotherapy synergy, both in vitro and in vivo. In summary, the proposed strategy of synergistic photo‐hyperthermia chemotherapy effectively reduced the 5Fu dose, toxic or side effect, which could serve as a secure and efficient approach for cancer theranostics.
Background: Cuproptosis has recently been considered a novel form of programmed cell death. To date, factors crucial to the regulation of this process remain unelucidated. Here, we aimed to identify long-chain non-coding RNAs (lncRNAs) associated with cuproptosis in order to predict the prognosis of patients with hepatocellular carcinoma (HCC). Methods: Using RNA sequence data from The Cancer Genome Atlas Live Hepatocellular Carcinoma (TCGA-LIHC), a co-expression network of cuproptosis-related mRNAs and lncRNAs was constructed. For HCC prognosis, we developed a cuproptosis-related lncRNA signature (CupRLSig) using univariate Cox, lasso, and multivariate Cox regression analyses. Kaplan-Meier analysis was used to compare overall survival among high- and low-risk groups stratified by median CupRLSig score. Furthermore, comparisons of functional annotation, immune infiltration, somatic mutation, TMB (tumor mutation burden), and pharmacologic options were made between high- and low-risk groups. Results: Our prognostic risk model was constructed using the cuproptosis-related PICSAR, FOXD2-AS1, and AP001065.1 lncRNAs. The CupRLSig high-risk group was associated with poor overall survival (hazard ratio = 1.162, 95% CI = 1.063-1.270; p < 0.001). Model accuracy was further supported by receiver operating characteristic and principal component analysis as well as internal validation cohorts. A prognostic nomogram developed considering CupRLSig data and a number of clinical characteristics were found to exhibit adequate performance in survival risk stratification. Mutation analysis revealed that high-risk combinations with high TMB carried worse prognoses. Finally, differences in immune checkpoint expression and responses to chemotherapy as well as in targeted therapy among CupRLSig stratified high- and low-risk groups were explored. Conclusions: The lncRNA signature constructed in this study is valuable in prognostic estimation in the setting of HCC.
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