TCM-HIN2Vec:A strategy for uncovering biological basis of heart qi deficiency pattern based on network embedding and transcriptomic experiment

Objective:To elucidate the biological basis of the heart qi deficiency(HQD)pattern,an in-depth understanding of which is essential for improving clinical herbal therapy.Methods: We predicted and characterized HQD pattern genes using the new strategy,TCM-HIN2Vec,which involves heterogeneous network embedding and transcriptomic experiments.First,a heterogeneous network of traditional Chinese medicine(TCM)patterns was constructed using public databases.Next,we predicted HQD pattern genes using a heterogeneous network-embedding algorithm.We then analyzed the functional characteristics of HQD pattern genes using gene enrichment analysis and examined gene expression levels using RNA-seq.Finally,we identified TCM herbs that demonstrated enriched interactions with HQD pattern genes via herbal enrichment analysis.Results: Our TCM-HIN2Vec strategy revealed that candidate genes associated with HQD pattern were significantly enriched in energy metabolism,signal transduction pathways,and immune processes.Moreover,we found that these candidate genes were significantly differentially expressed in the transcriptional profile of mice model with heart failure with a qi deficiency pattern.Furthermore,herbal enrichment analysis identified TCM herbs that demonstrated enriched interactions with the top 10 candidate genes and could potentially serve as drug candidates for treating HQD.Conclusion: Our results suggested that TCM-HIN2Vec is capable of not only accurately identifying HQD pattern genes,but also deciphering the basis of HQD pattern.Furthermore our finding indicated that TCM-HIN2Vec may be further expanded to develop other patterns,leading to a new approach aimed at elucidating general TCM patterns and developing precision medicine.

Biological basis of “depression with liver-qi stagnation and spleen deficiency syndrome”: A digital gene expression profiling study

Objective:To investigate the biological basis of“depression with liver-qi stagnation and spleen deficiency syndrome”.Methods:A digital gene expression profiling method was conducted to explore global changes in the mRNA transcriptome in a rat model of depression with liver-qi stagnation and spleen deficiency syndrome.Real-time quantitative polymerase chain reaction(q-PCR)was performed to verify the five genes most interest based on the Kyoto Encyclopedia of Genes and Genome(KEGG)analysis.Sini San,which disperses stagnated liver qi and strengthens the spleen,was administered to the model rats to observe whether it could reverse these genetic changes in the liver.Results:Forty-six differentially expressed genes were identified.Three of the five genes of most interestdHnf4a,Hnf4g and Cyp1a1dbased on KEGG analysis,were confirmed by realtime q-PCR.Sini San reduced the gene expression changes of Hnf4a,Hnf4g and Cyp1a1 in the rat model.Conclusions:Hnf4a,Hnf4g and Cyp1a1 are involved in“depression with liver-qi stagnation and spleen deficiency syndrome”.These findings indicate that depressed rats with liver-qi stagnation and spleen deficiency syndrome are at risk of liver diseases.Furthermore,our results will inform exploration of the etiology of depression and help in the development of effective therapeutic strategies.

Independent and reproducible hippocampal radiomic biomarkers for multisite Alzheimer’s disease:diagnosis,longitudinal progress and biological basis

Hippocampal morphological change is one of the main hallmarks of Alzheimer’s disease(AD).However,whether hippocampal radiomic features are robust as predictors of progression from mild cognitive impairment(MCI)to AD dementia and whether these features provide any neurobiological foundation remains unclear.The primary aim of this study was to verify whether hippocampal radiomic features can serve as robust magnetic resonance imaging(MRI)markers for AD.Multivariate classifier-based support vector machine(SVM)analysis provided individual-level predictions for distinguishing AD patients(n=261)from normal controls(NCs;n=231)with an accuracy of 88.21%and intersite crossvalidation.Further analyses of a large,independent the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset(n=1228)reinforced these findings.In MCI groups,a systemic analysis demonstrated that the identified features were significantly associated with clinical features(e.g.,apolipoprotein E(APOE)genotype,polygenic risk scores,cerebrospinal fluid(CSF)Ab,CSF Tau),and longitudinal changes in cognition ability;more importantly,the radiomic features had a consistently altered pattern with changes in the MMSE scores over 5 years of follow-up.These comprehensive results suggest that hippocampal radiomic features can serve as robust biomarkers for clinical application in AD/MCI,and further provide evidence for predicting whether an MCI subject would convert to AD based on the radiomics of the hippocampus.The results of this study are expected to have a substantial impact on the early diagnosis of AD/MCI.

Research advances in traditional Chinese medicine syndromes in cancer patients

Traditional Chinese medicine （TCM） syndrome, also known as TCM ZHENG or TCM pattern, is an integral and essential part of TCM theory that helps to guide the design of individualized treatments. ATCM syndrome, in essence, is a characteristic profile of all clinical manifestations in one patient that can be readily identified by a TCM practitioner. In this article, the authors reviewed the presentations of TCM syndromes in seven common malignancies （liver, lung, gastric, breast, colorectal, pancreatic and esophageal cancers）, the objectivity and the standardization of TCM syndrome differentiation, the evaluation of TCM syndrome modeling in cancer research, and syndrome differentiation-guided TCM treatment of cancers. A better understanding of TCM syndrome theory, as well as its potential biologica basis, may contribute greatly to the clinical TCM diagnosis and the treatment of cancer.