Interaction of NKX3.1 and SELENOP genotype with prostate cancer recurrence

Loading of "cocktail siRNAs" into extracellular vesicles via TAT-DRBD peptide for the treatment of castration-resistant prostate cancer

Extracellular vesicles (EVs) are cell-derived, membranous nanoparticles that mediate intercellular communication by transferring biomolecules between cells. As natural vehicles, EVs may exhibit higher delivery efficiency, lower immunogenicity, and better compatibility than existing RNA carriers. A major limitation of their therapeutic use is the shortage of efficient, robust, and scalable methods to load siRNA of interest. Here, we report a novel strategy using polycationic membrane-penetrating peptide TAT to encapsulate siRNAs into EVs. Three TAT peptides were co-expressed with DRBD as 3TD fusion protein. The sequence-independent binding of DRBD facilitates multiplex genes targeting of mixed siRNAs. Functional assays for siRNA-mediated gene silencing of CRPC were performed after engineered EVs treatment. EVs were isolated using differential centrifugation from WPMY-1 cell culture medium. The increase of merged yellow fluorescence in the engineered EVs showed by TIRFM and the decrease in zeta potential absolute values certified the co-localization of siRNA with EVs, which indicated that siRNA had been successfully delivered into WPMY-1 EVs. qRT-PCR analysis revealed that the mRNA level of FLOH1, NKX3, and DHRS7 was dramatically decreased when cells were treated with engineered EVs loaded with siRNAs mixtures relative to the level of untreated cells. Western and flow cytometry results indicate that delivery of siRNA mixtures by engineered EVs can effectively downregulate AR expression and induce LNCaP-AI cell apoptosis. The uptake efficiency of the EVs and the significantly downregulated expression of three genes suggested the potential of TAT as efficient siRNA carriers by keeping the function of the cargoes.

Artificial Intelligence Analysis of Gene Expression Predicted the Overall Survival of Mantle Cell Lymphoma and a Large Pan-Cancer Series

Mantle cell lymphoma (MCL) is a subtype of mature B-cell non-Hodgkin lymphoma characterized by a poor prognosis. First, we analyzed a series of 123 cases (GSE93291). An algorithm using multilayer perceptron artificial neural network, radial basis function, gene set enrichment analysis (GSEA), and conventional statistics, correlated 20,862 genes with 28 MCL prognostic genes for dimensionality reduction, to predict the patients' overall survival and highlight new markers. As a result, 58 genes predicted survival with high accuracy (area under the curve = 0.9). Further reduction identified 10 genes: KIF18A, YBX3, PEMT, GCNA, and POGLUT3 that associated with a poor survival; and SELENOP, AMOTL2, IGFBP7, KCTD12, and ADGRG2 with a favorable survival. Correlation with the proliferation index (Ki67) was also made. Interestingly, these genes, which were related to cell cycle, apoptosis, and metabolism, also predicted the survival of diffuse large B-cell lymphoma (GSE10846, n = 414), and a pan-cancer series of The Cancer Genome Atlas (TCGA, n = 7289), which included the most relevant cancers (lung, breast, colorectal, prostate, stomach, liver, etcetera). Secondly, survival was predicted using 10 oncology panels (transcriptome, cancer progression and pathways, metabolic pathways, immuno-oncology, and host response), and TYMS was highlighted. Finally, using machine learning, C5 tree and Bayesian network had the highest accuracy for prediction and correlation with the LLMPP MCL35 proliferation assay and RGS1 was made. In conclusion, artificial intelligence analysis predicted the overall survival of MCL with high accuracy, and highlighted genes that predicted the survival of a large pan-cancer series.

Selenoproteins of the Human Prostate: Unusual Properties and Role in Cancer Etiology

The prostate is an important organ for the maintenance of sperm health with prostate cancer being a common disease for which there is a critical need to distinguish indolent from aggressive disease. Several selenium-containing proteins have been implicated in prostate cancer risk or outcome due to either enzyme function, the reduced levels of these proteins being associated with cancer recurrence after prostatectomy or their corresponding genes containing single-nucleotide polymorphisms associated with increased risk. Moreover, experimental data obtained from the manipulation of either cultured cells or animal models have indicated that some of these proteins are contributing mechanistically to prostate cancer incidence or progression. Among these are selenocysteine-containing proteins selenoprotein P (SELENOP), glutathione peroxidase (GPX1), and selenoprotein 15 (SELENOF); and the selenium-associated protein selenium-binding protein 1 (SBP1). Genotyping of some of the genes for these proteins has identified functional single-nucleotide polymorphisms that are associated with prostate cancer risk and the direct quantification of these proteins in human prostate tissues has not only revealed associations to clinical outcomes but have also identified unique properties that are different from what is observed in other tissue types. The location of GPX1 in the nucleus and SELENOF in the plasma membrane of prostate epithelial cells indicates that these proteins may have functions in normal prostate tissue that are distinct from that of the other tissue types.