Transcriptomic analysis of human breast cancer cells reveals differentially expressed genes and related cellular functions and pathways in response to gold nanorods

Targeted gold nanoparticles for ovarian cancer (Review)

Among all malignant gynecological tumors, ovarian cancer (OC) has the highest mortality rate. OC is often diagnosed at advanced and incurable stages; however, early diagnosis can enable the use of optimized and personalized treatments. Intensive research into the synthesis and characterization of gold nanoparticles (AuNPs) has been performed with the aim of developing innovative materials for use in biological and photothermal therapies for OC. AuNPs can be chemically modified and functionalized by binding to a variety of organic compounds and biomolecules, such as peptides, antibodies and therapeutic agents, via simple synthetic processes. They are particularly suitable for use as carriers for drug delivery. In the present review, the synthesis and characteristics of AuNPs are summarized, and their potential in OC therapy are discussed.

Core-Satellite Nanomedicines for in Vivo Real-Time Monitoring of Enzyme-Activatable Drug Release by Fluorescence and Photoacoustic Dual-Modal Imaging

It remains an unresolved challenge to achieve spatial and temporal monitoring of drug release from nanomedicines (NMs) in vivo, which is of crucial importance in disease treatment. To tackle this issue, we constructed core-satellite ICG/DOX@Gel-CuS NMs, which consist of gelatin (Gel) nanoparticles (NPs) with payloads of near-infrared fluorochrome indocyanine green (ICG) and chemo-drug doxorubicin (DOX) and surrounding CuS NPs. The fluorescence of ICG was initially shielded by satellite CuS NPs within the intact ICG/DOX@Gel-CuS NMs and increased in proportion to the amount of DOX released from NMs in response to enzyme-activated NMs degradation. For more comprehensive understanding of the drug-release profile, a theoretical model derived from computer simulation was employed to reconstruct the enzyme-activatable drug release of the ICG/DOX@Gel-CuS NMs, which demonstrated the underlying kinetics functional relationship between the released DOX amount and recovered ICG fluorescence intensity. The kinetics of drug release in vivo was assessed by administrating ICG/DOX@Gel-CuS NMs both locally and systemically into MDA-MB-231 tumor-bearing mice. Upon accumulation of ICG/DOX@Gel-CuS NMs in the tumor, overexpressed enzymes triggered the degradation of the gelatin scaffold as well as the release of DOX and ICG, which can be visually depicted with the ICG fluorescence signal increasing only in the tumor area by fluorescence imaging. Additionally, the photoacoustic signal from CuS NPs was independent from the physical status of ICG/DOX@Gel-CuS NMs and hence was utilized for real-time NMs tracking. Thus, by taking advantage of the core-satellite architecture and NMs degradability in tumor site, the DOX release profile of ICG/DOX@Gel-CuS NMs was monitored by fluorescence and photoacoustic dual-modal imaging in a real-time noninvasive manner.

Antisense Oligonucleotide-Conjugated Nanostructure-Targeting lncRNA MALAT1 Inhibits Cancer Metastasis

Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), a long noncoding RNA (lncRNA) located in the cell nucleus, is a critical regulator of tumor cell migration. Antisense oligonucleotides (ASOs), which can downregulate the expression level of specific RNAs, have been used in clinical for disease treatment. Herein, we constructed MALAT1-specific ASO and nucleus-targeting TAT peptide cofunctionalized Au nanoparticles, namely, ASO-Au-TAT NPs, which stabilized the fragile ASOs, enhanced nuclear internalization, and exhibited good biocompatibility. After treatment with the ASO-Au-TAT NPs, A549 lung cancer cells showed a greatly reduced MALAT1 expression level and decreased migration ability  in vitro. Moreover, the ASO-Au-TAT NPs significantly reduced metastatic tumor nodule formation in vivo. Our results demonstrate that the ASO-Au-TAT nanostructures (NSs) have great potential for treatment of cancer metastasis.

A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT GENE IDENTIFICATION

Cancer detection from gene expression data continues to pose a challenge due to the high dimensionality and complexity of these data. After decades of research there is still uncertainty in the clinical diagnosis of cancer and the identification of tumor-specific markers. Here we present a deep learning approach to cancer detection, and to the identification of genes critical for the diagnosis of breast cancer. First, we used Stacked Denoising Autoencoder (SDAE) to deeply extract functional features from high dimensional gene expression profiles. Next, we evaluated the performance of the extracted representation through supervised classification models to verify the usefulness of the new features in cancer detection. Lastly, we identified a set of highly interactive genes by analyzing the SDAE connectivity matrices. Our results and analysis illustrate that these highly interactive genes could be useful cancer biomarkers for the detection of breast cancer that deserve further studies.