Integrating single cell analysis and machine learning methods reveals stem cell-related gene S100A10 as an important target for prediction of liver cancer diagnosis and immunotherapy

We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu). Show more

Hepatocellular carcinoma appears frequently in patients with cirrhosis. Surveillance by biannual ultrasound is recommended for such patients because it allows diagnosis at an early stage, when effective therapies are feasible. The best candidates for resection are patients with a solitary tumour and preserved liver function. Liver transplantation benefits patients who are not good candidates for surgical resection, and the best candidates are those within Milan criteria (solitary tumour ≤5 cm or up to three nodules ≤3 cm). Image-guided ablation is the most frequently used therapeutic strategy, but its efficacy is limited by the size of the tumour and its localisation. Chemoembolisation has survival benefit in asymptomatic patients with multifocal disease without vascular invasion or extrahepatic spread. Finally, sorafenib, lenvatinib, which is non-inferior to sorafenib, and regorafenib increase survival and are the standard treatments in advanced hepatocellular carcinoma. This Seminar summarises the scientific evidence that supports the current recommendations for clinical practice, and discusses the areas in which more research is needed. Show more

Background Nonnegative Matrix Factorization (NMF) is an unsupervised learning technique that has been applied successfully in several fields, including signal processing, face recognition and text mining. Recent applications of NMF in bioinformatics have demonstrated its ability to extract meaningful information from high-dimensional data such as gene expression microarrays. Developments in NMF theory and applications have resulted in a variety of algorithms and methods. However, most NMF implementations have been on commercial platforms, while those that are freely available typically require programming skills. This limits their use by the wider research community. Results Our objective is to provide the bioinformatics community with an open-source, easy-to-use and unified interface to standard NMF algorithms, as well as with a simple framework to help implement and test new NMF methods. For that purpose, we have developed a package for the R/BioConductor platform. The package ports public code to R, and is structured to enable users to easily modify and/or add algorithms. It includes a number of published NMF algorithms and initialization methods and facilitates the combination of these to produce new NMF strategies. Commonly used benchmark data and visualization methods are provided to help in the comparison and interpretation of the results. Conclusions The NMF package helps realize the potential of Nonnegative Matrix Factorization, especially in bioinformatics, providing easy access to methods that have already yielded new insights in many applications. Documentation, source code and sample data are available from CRAN. Show more