Existing traditional Chinese medicine (TCM)-related databases are still insufficient in data standardization, integrity and precision, and need to be updated urgently. Herein, an Encyclopedia of Traditional Chinese Medicine version 2.0 (ETCM v2.0, http://www.tcmip.cn/ETCM2/front/#/) was constructed as the latest curated database hosting 48,442 TCM formulas recorded by ancient Chinese medical books, 9872 Chinese patent drugs, 2079 Chinese medicinal materials and 38,298 ingredients. To facilitate the mechanistic research and new drug discovery, we improved the target identification method based on a two-dimensional ligand similarity search module, which provides the confirmed and/or potential targets of each ingredient, as well as their binding activities. Importantly, five TCM formulas/Chinese patent drugs/herbs/ingredients with the highest Jaccard similarity scores to the submitted drugs are offered in ETCM v2.0, which may be of significance to identify prescriptions/herbs/ingredients with similar clinical efficacy, to summarize the rules of prescription use, and to find alternative drugs for endangered Chinese medicinal materials. Moreover, ETCM v2.0 provides an enhanced JavaScript-based network visualization tool for creating, modifying and exploring multi-scale biological networks. ETCM v2.0 may be a major data warehouse for the quality marker identification of TCMs, the TCM-derived drug discovery and repurposing, and the pharmacological mechanism investigation of TCMs against various human diseases.

Gastric cancer (GC) is a heterogeneous disease and a leading cause of cancer-related deaths. Discovering robust, clinically relevant molecular classifications is critical for guiding personalized therapies for GC. Here, we propose a refined molecular classification scheme for GC using integrated optimal algorithms and multi-omics data. Based on the important features of mRNA, microRNA, and DNA methylation data selected by the multivariate Cox regression model, three subtypes linked to distinct clinical outcomes were identified by combining similarity network fusion and consensus clustering methods. Three subtypes were validated by an extreme gradient boosting machine learning prediction model with 125 differentially expressed genes in multiple independent cohorts. The molecular characteristics of mutation signatures, characteristic gene sets, driver genes, and chemotherapy sensitivity for each subtype were also identified: subtype 1 was associated with favorable prognosis and characterized by high ARID1A and PIK3CA mutations, subtype 2 was associated with a poor prognosis and harbored high recurrent TP53 mutations, and subtype 3 was associated with high CHD1, APOA1 mutations, and a poor prognosis. The proposed three-subtype scheme achieved a better clinical prediction performance (area under the curve value = 0.71) than The Cancer Genome Atlas classification, which may provide a practical subtyping framework to improve the treatment of GC.

Traditional Chinese medicine (TCM) has accumulated thousands years of knowledge in herbal therapy, but the use of herbal formulas is still characterized by reliance on personal experience. Due to the complex mechanism of herbal actions, it is challenging to discover effective herbal formulas for diseases by integrating the traditional experiences and modern pharmacological mechanisms of multi-target interactions. In this study, we propose a herbal formula prediction approach (TCMFP) combined therapy experience of TCM, artificial intelligence and network science algorithms to screen optimal herbal formula for diseases efficiently, which integrates a herb score (Hscore) based on the importance of network targets, a pair score (Pscore) based on empirical learning and herbal formula predictive score (FmapScore) based on intelligent optimization and genetic algorithm. The validity of Hscore, Pscore and FmapScore was verified by functional similarity and network topological evaluation. Moreover, TCMFP was used successfully to generate herbal formulae for three diseases, i.e. the Alzheimer’s disease, asthma and atherosclerosis. Functional enrichment and network analysis indicates the efficacy of targets for the predicted optimal herbal formula. The proposed TCMFP may provides a new strategy for the optimization of herbal formula, TCM herbs therapy and drug development.

Traditional Chinese medicine for transformation of gastric precancerous lesions to gastric cancer: A critical review

Danhong injection (DHI) has been widely prescribed to patients with acute ischemic stroke (AIS). However, due to methodological deficiencies, previous research has not yet provided rigorous evidence to support the use of DHI in the treatment of AIS. Therefore, we designed this multicenter, randomized, controlled, and double-blind trial to evaluate the efficacy and safety of DHI for AIS. It is a randomized, multicenter, double-blind, placebo-controlled, adaptive clinical trial. A total of 864 eligible patients will be randomized into either the DHI or placebo group in a 2:1 ratio. All patients will be given the standard medical care as recommended by guidelines. Participants will undergo a 2-week treatment regimen and 76-day follow-up period. The primary outcome is the proportion of patients with a favorable outcome, defined as a score of 0–1 on the modified Rankin scale at day 90. Secondary outcomes include a change in the total score of the Chinese medicine symptom scales of “Xueyu Zheng” (blood stasis syndrome), the proportion of patients with a Barthel Index score of ≥90, the proportion of patients with an improvement in NIHSS score of ≥4 or NIHSS score of 0–1, quality of life measured by the EQ-5D scale, etc. Safety outcomes such as global disability (mRS ≥3) at day 90 will also be assessed. The changes in mRNA and microRNA profiles in 96 patients selected from certain centers will also be assessed. As this is an adaptive design, two interim analyses are prospectively planned, which will be carried out after one-third and two-thirds of patients have completed the trial, respectively. Based on the results of the interim analyses, the Data Monitoring Committee (DMC) will decide how to modify the study. This trial will provide high-quality evidence for DHI in treatment of AIS. Clinical Trials.gov NCT01677208 (Date of registration 22 December 2012).

Cerebral ischemia-reperfusion injury may simultaneously result in functional variation of multiple genes/pathways. However, most prior time-sequence studies on its pathomechanism only focused on a single gene or pathway. Our study aimed to systematically analyze the time-dependent variation in the expression of multiple pathways and networks within 24 h after cerebral ischemia-reperfusion injury. By uploading 374 ischemia-related genes into the MetaCore software, the variation in the expression of multiple pathways and networks in 3 h, 12 h, and 24 h after cerebral ischemia-reperfusion injury had been analyzed. The conserved TNFR1-signaling pathway, among the top 10 pathways, was consistently enriched in 3 h, 12 h, and 24 h groups. Three overlapping pathways were found between 3 h and 12 h groups; 2 between 12 h and 24 h groups; and 1 between 3 h and 24 h groups. Five, 4, and 6 non-overlapping pathways were observed in 3 h, 12 h, and 24 h groups, respectively. Apart from pathways reported by earlier studies, we identified a novel pathway related to the time-dependent development of cerebral ischemia pathogenesis. The process of apoptosis stimulation by external signals, among the top 10 processes, was consistently enriched in 3 h, 12 h, and 24 h groups; 2, 1, and 2 processes overlapped between 3 h and 12 h groups, 12 h and 24 h groups, and 3 h and 24 h groups, respectively. Four, 5, and 5 non-overlapping processes were found in 3 h, 12 h and 24 h groups, respectively. The presence of apoptotic processes was observed in all the 3 groups; while anti-apoptotic processes only existed in 3 h and 12 h groups. Additionally, according to node degree, network comparison identified 1, 8,and 5 important genes or proteins (e.g. Pyk2, PKC, E2F1, and VEGF-A) in 3 h, 12 h, and 24 h groups, respectively. The Jaccard similarity index revealed a higher level of similarity between 12 h and 24 h groups than that between 3 h and 12 h groups. Time-dependent treatment can be utilized to reduce apoptosis, which may activate anti-apoptotic pathways within 12 h after cerebral ischemia-reperfusion injury. Pathway and network analyses may help identify novel pathways and genes implicated in disease pathogenesis.