DNA nanostructure-based drug delivery system (DDS) has become an advanced therapeutic strategy for cancer because of its unsurpassed editability, intrinsic biodegradability, and tunable multifunctionality. An intelligent DNA nanosystem integrating targeting, immunostimulation, and chemotherapy was constructed based on unmethylated cytosine-phosphate-guanine oligonucleotides (CpG ODNs) DNA nanohydrogels (CpG-MUC1-hydrogel). By facile one-step self-assembly, the cross-shaped DNAs (C-DNAs) assembled from pH-responsive I-motif sequences and targeted MUC1 aptamer-immunoadjuvant CpG-fused sequences (CpG-MUC1) were integrated into DNA nanohydrogels with controllable size by the hybridization of DNA linkers. Subsequently, DOX was successively intercalated into the base pairs of CpG-MUC1-hydrogel, resulting in CpG-MUC1-hydrogel/Dox that would disassemble and release DOX and CpGs at acidic conditions. After MUC1-mediated internalization, CpG-MUC1-hydrogel/Dox dissociated in the endo/lysosomes and induced favorable apoptosis of tumor cells. Afterward, liberated CpGs triggered vast cytokine secretion from immune cells which elicited potent immune response against malignancy. Notably, CpG-MUC1-hydrogel induced an apoptosis effect on MCF-7 cells via significantly increasing the Bax/Bcl2 ratios and a higher level of tumor necrosis factor (TNF-α) on RAW264.7 cells than naked CpGs. Our results demonstrated that self-assembled CpG-MUC1-hydrogel represented an attractive DDS for precise delivery, potent immunostimulating activity, and considerable combination efficiency with few adverse effects, which is expected to make breakthroughs in clinical translation.

No abstract

It is significant to develop novel methods to diagnose the tumor status throughout chemotherapy. In the present work, we focused on identifying the elemental biomarkers of chemotherapy-treated and untreated tumor tissues by laser-induced breakdown spectroscopy (LIBS). The unsupervised algorithm as principal component analysis and three supervised algorithms including partial least squares discrimination analysis (PLS-DA), random forest (RF) as well as support vector machine (SVM) were used to develop efficient classification models. The average predictive accuracy was 90.74% via the PLS-DA, 88.89% via RF, and 83.33% via SVM, respectively. The results highlighted the spectral difference between chemotherapy-treated and untreated samples within the range of visible spectra between 300-700 nm. In the meantime, four major elements were found to contribute the classification over the following order: calcium > magnesium = copper > sodium. The results featured the importance of calcium on element-based therapeutic responsiveness biomarker monitoring via a new LIBS-based vision.

No abstract