Traditional Chinese medicine (TCM) not only maintains the health of Asian people but also provides a great resource of active natural products for modern drug development. Herein, we developed a Database of Constituents Absorbed into the Blood and Metabolites of TCM (DCABM-TCM), the first database systematically collecting blood constituents of TCM prescriptions and herbs, including prototypes and metabolites experimentally detected in the blood, together with the corresponding detailed detection conditions through manual literature mining. The DCABM-TCM has collected 1816 blood constituents with chemical structures of 192 prescriptions and 194 herbs and integrated their related annotations, including physicochemical, absorption, distribution, metabolism, excretion, and toxicity properties, and associated targets, pathways, and diseases. Furthermore, the DCABM-TCM supported two blood constituent-based analysis functions, the network pharmacology analysis for TCM molecular mechanism elucidation, and the target/pathway/disease-based screening of candidate blood constituents, herbs, or prescriptions for TCM-based drug discovery. The DCABM-TCM is freely accessible at . The DCABM-TCM will contribute to the elucidation of effective constituents and molecular mechanism of TCMs and the discovery of TCM-derived drug-like compounds that are both bioactive and bioavailable.

Due to cancer heterogeneity, only some patients can benefit from drug therapy. The personalized drug usage is important for improving the treatment response rate of cancer patients. The value of the transcriptome of patients has been recently demonstrated in guiding personalized drug use, and the Connectivity Map (CMAP) is a reliable computational approach for drug recommendation. However, there is still no personalized drug recommendation tool based on transcriptomic profiles of patients and CMAP. To fill this gap, here, we proposed such a feasible workflow and a user-friendly R package—Cancer-Personalized Drug Recommendation (CPDR). CPDR has three features. 1) It identifies the individual disease signature by using the patient subgroup with transcriptomic profiles similar to those of the input patient. 2) Transcriptomic profile purification is supported for the subgroup with high infiltration of non-cancerous cells. 3) It supports in silico drug efficacy assessment using drug sensitivity data on cancer cell lines. We demonstrated the workflow of CPDR with the aid of a colorectal cancer dataset from GEO and performed the in silico validation of drug efficacy. We further assessed the performance of CPDR by a pancreatic cancer dataset with clinical response to gemcitabine. The results showed that CPDR can recommend promising therapeutic agents for the individual patient. The CPDR R package is available at https://github.com/AllenSpike/CPDR.

The rapid production of high-throughput cancer omics data provides valuable data resources for revealing the pathogenesis, prognosis prediction and treatment strategies of cancers. However, the huge data scale brings great challenges to data analysis. Therefore, we applied the representation learning method to the joint analysis of biomedical network and omics data. According to the protein expression profile of patients with early-stage hepatocellular carcinoma, 15 dimensional embedding vectors of 101 samples were obtained. Unsupervised learning was then used to cluster the embedded vectors of the samples, and we found that the clustering of the embedded vectors of the samples was consistent with the clustering of the original data. Therefore, the spatial distribution of embedded vectors can maintain the similarity of samples. New pan-cancer subtypes were obtained by joint embedding the expression profile of pan-cancer proteomic and pathway network data. Nine hunded and forty four proteins such as KIF2C, AURKA, ATP1B1, BDH1 and C6ORF106 were found to be significantly related to these subtypes, and 143 biological pathways or processes such as p53 signaling pathway, nucleotide synthesis, immune diseases, metabolism, cholesterol synthesis and transportation were found to be significantly related to these subtypes. These results show that the representation learning system developed can realize the seamless connection between the omics data and the pathway network. Our method is expected to help mine the biological knowledge contained in the omics data and provide a new perspective for further explanation of the molecular mechanism.