No abstract

There are significant challenges in developing drug carriers for therapeutic perspective. We have investigated a novel nanocarrier system, based on combining functionalized magnetic nanocomposite with Metal–Organic Frameworks (MOFs). Magnetic nanoparticles modified using biocompatible copolymers may be suitable for delivering hydrophobic drugs, such as cisplatin. Furthermore, compared to polymeric nanocarriers, nanocomposite constructed from zeolitic imidazolate framework-8 (ZIF-8) have demonstrated better drug loading capacity, as well as excellent pH-triggered drug release. Cisplatin-encapsulated Fe3O4@SiO2-ZIF-8@N-Chit-FA has been evaluated to determine the antitumor effects of free cisplatin enhancement in cervical cancer cells. In order to increase the stability of the proposed nanocarrier in aqueous solutions, in addition to the density of functional groups, a nano-chitosan layer was coated on top of the magnetic nanocomposite. It was then added with cisplatin onto the surface of Fe3O4@SiO2-ZIF-8@N-Chit-FA to deliver anticancer treatment that could be targeted using a magnetic field. A mouse isograft model of TC1 cells was used to evaluate the in vivo tumor growth inhibition. In tumor-bearing mice, Fe3O4@SiO2-ZIF-8@N-Chit-FA-cisplatin was injected intraperitoneally, and the targeted delivery was amplified by an external magnet (10 mm by 10 mm, surface field strength 0.4 T) fixed over the tumor site. Based on in vivo results, cisplatin-Loaded Mesoporous Magnetic Nanobiocomposite inhibited the growth of cervical tumors (P < 0.001) through the induction of tumor necrosis (P < 0.05) when compared to cisplatin alone. With the application of an external magnetic field, the drug was demonstrated to be able to induce its effects on specific target areas. In summary, Fe3O4 @ SiO2-ZIF-8 @ N-Chit-FA nanocomposites have the potential to be implemented in targeted nanomedicine to deliver bio-functional molecules.

Gastric cancer is the high mortality rate cancers globally, and the current survival rate is 30% even with the use of combination therapies. Recently, mounting evidence indicates the potential role of miRNAs in the diagnosis and assessing the prognosis of cancers. In the state-of-art research in cancer, machine-learning (ML) has gained increasing attention to find clinically useful biomarkers. The present study aimed to identify potential diagnostic and prognostic miRNAs in GC with the application of ML. Using the TCGA database and ML algorithms such as Support Vector Machine (SVM), Random Forest, k-NN, etc., a panel of 29 was obtained. Among the ML algorithms, SVM was chosen (AUC:88.5%, Accuracy:93% in GC). To find common molecular mechanisms of the miRNAs, their common gene targets were predicted using online databases such as miRWalk, miRDB, and Targetscan. Functional and enrichment analyzes were performed using Gene Ontology (GO) and Kyoto Database of Genes and Genomes (KEGG), as well as identification of protein–protein interactions (PPI) using the STRING database. Pathway analysis of the target genes revealed the involvement of several cancer-related pathways including miRNA mediated inhibition of translation, regulation of gene expression by genetic imprinting, and the Wnt signaling pathway. Survival and ROC curve analysis showed that the expression levels of hsa-miR-21, hsa-miR-133a, hsa-miR-146b, and hsa-miR-29c were associated with higher mortality and potentially earlier detection of GC patients. A panel of dysregulated miRNAs that may serve as reliable biomarkers for gastric cancer were identified using machine learning, which represents a powerful tool in biomarker identification.

Mutations in the Wilm's tumor 1 (WT1) gene are associated with a wide spectrum of renal manifestations, ultimately leading to end-stage kidney failure. There is an inadequate understanding of the molecular functions of WT1 in renal development, and this has limited the potential for therapeutic interventions in WT1-related diseases. In this review, we discuss the existing data on the genetic and epigenetic abnormalities that have been described in WTs and their potential utility as biomarkers for risk stratification, prediction and prognosis in patients with WTs.

Background: Stomach adenocarcinoma (STAD) is common cancer with poor clinical outcomes globally. Due to a lack of early diagnostic markers of disease, the majority of patients are diagnosed at an advanced stage. Objective: The aim of the present study is to provide some new insights into the available biomarkers for patients with STAD using bioinformatics. Methods: RNA-Sequencing and other relevant data of patients with STAD from The Cancer Genome Atlas (TCGA) database were evaluated to identify differentially expressed genes (DEGs). Then, machine learning algorithms were undertaken to predict biomarkers. Additionally, Kaplan–Meier analysis was used to detect prognostic biomarkers. Furthermore, the Gene Ontology and Reactome pathways, protein-protein interactions (PPI), multiple sequence alignment, phylogenetic mapping, and correlation between clinical parameters were evaluated. Results: The results demonstrated 61 DEGs, and the key dysregulated genes associated with STAD are MTHFD1L (Methylenetetrahydrofolate dehydrogenase 1-like), ZWILCH (Zwilch Kinetochore Protein), RCC2 (Regulator of chromosome condensation 2), DPT (Dermatopontin), GCOM1 (GRINL1A complex locus 1), and CLEC3B (C-Type Lectin Domain Family 3 Member B). Moreover, the survival analysis reported ASPA (Aspartoacylase) as a prognostic marker. Conclusion: Our study provides a proof of concept of the potential value of ASPA as a prognostic factor in STAD, requiring further functional investigations to explore the value of emerging markers. other: n/a