The widespread use of high-throughput sequencing technologies has revolutionized the understanding of biology and cancer heterogeneity. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patients’ outcome and clinical response. However, an open-source R package covering state-of-the-art machine-learning algorithms for user-friendly access has yet to be developed. Thus, we proposed a flexible computational framework to construct a machine learning-based integration model with elegant performance (Mime). Mime streamlines the process of developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with prognosis. An in silico combined model based on de novo PIEZO1-associated signatures constructed by Mime demonstrated high accuracy in predicting the outcomes of patients compared with other published models. Furthermore, the PIEZO1-associated signatures could also precisely infer immunotherapy response by applying different algorithms in Mime. Finally, SDC1 selected from the PIEZO1-associated signatures demonstrated high potential as a glioma target. Taken together, our package provides a user-friendly solution for constructing machine learning-based integration models and will be greatly expanded to provide valuable insights into current fields. The Mime package is available on GitHub ( https://github.com/l-magnificence/Mime ).

UCSC Xena platform provides huge amounts of processed cancer omics data from large cancer research projects (e.g. TCGA, CCLE and PCAWG) or individual research groups and enables unprecedented research opportunities. However, a graphical user interface-based tool for interactively analyzing UCSC Xena data and generating elegant plots is still lacking, especially for cancer researchers and clinicians with limited programming experience. Here, we present UCSCXenaShiny, an R Shiny package for quickly searching, downloading, exploring, analyzing and visualizing data from UCSC Xena data hubs. This tool could effectively promote the practical use of public data, and can serve as an important complement to the current Xena genomics explorer. UCSCXenaShiny is an open source R package under GPLv3 license and it is freely available at https://github.com/openbiox/UCSCXenaShiny or https://cran.r-project.org/package=UCSCXenaShiny. The docker image is available at https://hub.docker.com/r/shixiangwang/ucscxenashiny. Supplementary data are available at Bioinformatics online.

Glioblastoma (GBM) is one of the most lethal central nervous systems (CNS) tumours in adults. As supplements to standard of care (SOC), various immunotherapies improve the therapeutic effect in other cancers. Among them, tumour vaccines can serve as complementary monotherapy or boost the clinical efficacy with other immunotherapies, such as immune checkpoint blockade (ICB) and chimeric antigen receptor T cells (CAR-T) therapy. Previous studies in GBM therapeutic vaccines have suggested that few neoantigens could be targeted in GBM due to low mutation burden, and single-peptide therapeutic vaccination had limited efficacy in tumour control as monotherapy. Combining diverse antigens, including neoantigens, tumour-associated antigens (TAAs), and pathogen-derived antigens, and optimizing vaccine design or vaccination strategy may help with clinical efficacy improvement. In this review, we discussed current GBM therapeutic vaccine platforms, evaluated and potential antigenic targets, current challenges, and perspective opportunities for efficacy improvement.

Berberine (BBR) shows very low plasma levels after oral administration due to its poor absorption by the gastrointestinal tract. We have previously demonstrated that BBR showed increased gastrointestinal absorption and enhanced antidiabetic effects in db/db mice after being entrapped into solid lipid nanoparticles (SLNs). However, whether BBR-loaded SLNs (BBR-SLNs) also have beneficial effects on hepatosteatosis is not clear. We investigated the effects of BBR-SLNs on lipid metabolism in the liver using histological staining and reverse transcription polymerase chain reaction analysis. The results showed that oral administration of BBR-SLNs inhibited the increase of body weight and decreased liver weight in parallel with the reduction of serum alanine transaminase and liver triglyceride levels in db/db mice. The maximum drug concentration in the liver was 20-fold higher than that in the blood. BBR-SLNs reduced fat accumulation and lipid droplet sizes significantly in the liver, as indicated by hematoxylin and eosin and Oil Red O staining. The expression of lipogenic genes, including fatty acid synthase (FAS), stearoyl-CoA desaturase (SCD1), and sterol regulatory element-binding protein 1c (SREBP1c) were downregulated, while lipolytic gene carnitine palmitoyltransferase-1 (CPT1) was upregulated in BBR-SLN-treated livers. In summary, we have uncovered an unexpected effect of BBR-SLNs on hepatosteatosis treatment through the inhibition of lipogenesis and the induction of lipolysis in the liver of db/db mice.

Cases with pituitary adenoma comprise 10–25% of intracranial neoplasm, being the third most common intracranial tumor, most of the adenomas are considered to be benign. About 35% of pituitary adenomas are invasive. This review summarized the known molecular basis of the invasiveness of pituitary adenomas. The study pointed out that hypoxia-inducible factor-1α, pituitary tumor transforming gene, vascular endothelial growth factor, fibroblast growth factor-2, and matrix metalloproteinases (MMPs, mainly MMP-2, and MMP-9) are core molecules responsible for the invasiveness of pituitary adenomas. The reason is that these molecules have the ability to directly or indirectly induce cell proliferation, epithelial-to-mesenchymal transition, angiogenesis, degradation, and remodeling of extracellular matrix. HIF-1α induced by hypoxia or apoplexy inside the adenoma might be the initiating factor of invasive transformation, followed with angiogenesis for overexpressed VEGF, EMT for overexpressed PTTG, degradation of ECM for overexpressed MMPs, creating a suitable microenvironment within the tumor. Together, they form a complex interactive network. More investigations are required to further elucidate the mechanisms underlying the invasiveness of pituitary adenomas.