Cancer classification of single-cell gene expression data by neural network.

MOTIVATION: Cancer classification based on gene expression profiles has provided insight on the causes of cancer and cancer treatment. Recently, machine learning-based approaches have been attempted in downstream cancer analysis to address the large differences in gene expression values, as determined by single-cell RNA sequencing (scRNA-seq). RESULTS: We designed cancer classifiers that can identify 21 types of cancers and normal tissues based on bulk RNA-seq as well as scRNA-seq data. Training was performed with 7398 cancer samples and 640 normal samples from 21 tumors and normal tissues in TCGA based on the 300 most significant genes expressed in each cancer. Then, we compared neural network (NN), support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF) methods. The NN performed consistently better than other methods. We further applied our approach to scRNA-seq transformed by kNN smoothing and found that our model successfully classified cancer types and normal samples. AVAILABILITY AND IMPLEMENTATION: Cancer classification by neural network. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Rehmannia glutinosa polysaccharide functions as a mucosal adjuvant to induce dendritic cell activation in mediastinal lymph node.

In our previous study, we showed that Rehmannia glutinosa polysaccharide (RGP) treatment induced activation of dendritic cells (DCs) in human and mouse subjects. In this study, we evaluated the effect of RGP as a mucosal adjuvant for inducting activation of DCs in the mediastinal lymph node (mLN) in the mouse. The C57BL/6 mice were intranasally (i.n.) treated with RGP and activation of DC in the mLN was analyzed. The treatment with RGP induced a substantial increase in the number of DCs in the mLN due to the up-regulation of C-C motif chemokine receptor 7 (CCR7) in the DCs. Moreover, the expression of co-stimulatory molecules in the mLN DCs and the concentration of pro-inflammatory cytokines in the lung were up-regulated by RGP treatment. Also, RGP treatment induced interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α) production in the mLN T cells. The combination treatment of RGP and ovalbumin (OVA) induced OVA-specific TCR transgenic I (OT-I) and OT-II cell proliferation in the mLN. Finally, the combination treatment of RGP and tyrosinase-related protein 2 (TRP2) peptide, a melanoma self-antigen, protected mice from melanoma challenge. Thus, these data demonstrated that RGP can be used as a mucosal adjuvant for inducing activation of immune responses in the lung.